From cb4e80f1bcb7a95db21f25f3ddd557b268d3aec1 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 12:15:35 +0800 Subject: [PATCH 001/175] =?UTF-8?q?=E5=9B=BE=E8=B0=B1=E6=95=B0=E6=8D=AE?= =?UTF-8?q?=E9=87=8F=E9=99=90=E5=88=B6=E6=95=B0=E9=87=8F=E5=8E=BB=E6=8E=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/user_memory_service.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 67a6ab2c..535dcc12 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1621,6 +1621,5 @@ def _clean_neo4j_value(value: Any) -> Any: return str(value) except Exception: return None - # 返回原始值 - return value + return value \ No newline at end of file From 89500df0ac1a1576380fa053b124133d85714cd0 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 12:20:27 +0800 Subject: [PATCH 002/175] =?UTF-8?q?=E5=9B=BE=E8=B0=B1=E6=95=B0=E6=8D=AE?= =?UTF-8?q?=E9=87=8F=E9=99=90=E5=88=B6=E6=95=B0=E9=87=8F=E5=8E=BB=E6=8E=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/user_memory_service.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 535dcc12..59bbc211 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1416,11 +1416,10 @@ async def analytics_graph_data( elementId(n) as id, labels(n)[0] as label, properties(n) as properties - LIMIT $limit """ node_params = { "group_id": end_user_id, - "limit": limit + # "limit": limit } # 执行节点查询 @@ -1567,9 +1566,9 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ allowed_fields = field_whitelist.get(label, []) # 如果没有定义白名单，返回空字典（或者可以返回所有字段） - if not allowed_fields: - # 对于未定义的节点类型，只返回基本字段 - allowed_fields = ["name", "created_at", "caption"] + # if not allowed_fields: + # # 对于未定义的节点类型，只返回基本字段 + # allowed_fields = ["name", "created_at", "caption"] count_neo4j=f"""MATCH (n)-[r]-(m) WHERE elementId(n) ="{node_id}" RETURN count(r) AS rel_count;""" node_results = await (_neo4j_connector.execute_query(count_neo4j)) # 提取白名单中的字段 From 1a2e043ec2de5bf0ea09bd2ce80a1d2564b43dff Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 14:27:05 +0800 Subject: [PATCH 003/175] =?UTF-8?q?=E5=9B=BE=E8=B0=B1=E6=95=B0=E6=8D=AE?= =?UTF-8?q?=E9=87=8F=E9=99=90=E5=88=B6=E6=95=B0=E9=87=8F=E5=8E=BB=E6=8E=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/user_memory_service.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 59bbc211..eb9b2c34 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1416,10 +1416,11 @@ async def analytics_graph_data( elementId(n) as id, labels(n)[0] as label, properties(n) as properties + LIMIT $limit """ node_params = { "group_id": end_user_id, - # "limit": limit + "limit": limit } # 执行节点查询 From ba2ff053f97d83638478bd0ab0bbbc34eb7afc5d Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 14:48:37 +0800 Subject: [PATCH 004/175] =?UTF-8?q?=E7=94=A8=E6=88=B7=E8=AF=A6=E6=83=85?= =?UTF-8?q?=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/memory_storage_schema.py | 20 +++++++++++++++++++ .../memory_entity_relationship_service.py | 2 +- api/app/services/user_memory_service.py | 6 ++++-- 3 files changed, 25 insertions(+), 3 deletions(-) diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index ecb1570f..77afe052 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -11,6 +11,26 @@ from pydantic import BaseModel, Field, ConfigDict, field_validator, model_valida # ============================================================================ # 原 UserInput 相关 Schema (保留原有功能) # ============================================================================ +type_mapping = { + "Person": "人物实体节点", + "Organization": "组织实体节点", + "ORG": "组织实体节点", + "Location": "地点实体节点", + "LOC": "地点实体节点", + "Event": "事件实体节点", + "Concept": "概念实体节点", + "Time": "时间实体节点", + "Position": "职位实体节点", + "WorkRole": "职业实体节点", + "System": "系统实体节点", + "Policy": "政策实体节点", + "HistoricalPeriod": "历史时期实体节点", + "HistoricalState": "历史国家实体节点", + "HistoricalEvent": "历史事件实体节点", + "EconomicFactor": "经济因素实体节点", + "Condition": "条件实体节点", + "Numeric": "数值实体节点" + } class UserInput(BaseModel): message: str history: list[dict] diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index f650217d..8baf038e 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -123,7 +123,7 @@ class MemoryEntityService: extracted_entity_list = self._deduplicate_dict_list(extracted_entity_list) # 合并所有数据并处理相同text的合并 - all_timeline_data = memory_summary_list + statement_list + extracted_entity_list + all_timeline_data = memory_summary_list + statement_list all_timeline_data = self._merge_same_text_items(all_timeline_data) result = { diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 59bbc211..52bddbcb 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -15,6 +15,7 @@ from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.repositories.end_user_repository import EndUserRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.schemas.memory_storage_schema import type_mapping from app.services.memory_base_service import MemoryBaseService from app.services.memory_config_service import MemoryConfigService from pydantic import BaseModel, Field @@ -1332,7 +1333,7 @@ async def analytics_graph_data( db: Session, end_user_id: str, node_types: Optional[List[str]] = None, - limit: int = 100, + limit: int = 130, depth: int = 1, center_node_id: Optional[str] = None ) -> Dict[str, Any]: @@ -1576,10 +1577,11 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ for field in allowed_fields: if field in properties: value = properties[field] + if str(field) == 'entity_type': + value=type_mapping.get(value,'') # 清理 Neo4j 特殊类型 filtered_props[field] = _clean_neo4j_value(value) filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0] - print(filtered_props) return filtered_props From 1cee27e8305d751711aceca25b2be9e7f223e03c Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 14:51:20 +0800 Subject: [PATCH 005/175] =?UTF-8?q?=E7=94=A8=E6=88=B7=E8=AF=A6=E6=83=85?= =?UTF-8?q?=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/user_memory_service.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 52bddbcb..e5e09f64 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1417,10 +1417,11 @@ async def analytics_graph_data( elementId(n) as id, labels(n)[0] as label, properties(n) as properties + LIMIT $limit """ node_params = { "group_id": end_user_id, - # "limit": limit + "limit": limit } # 执行节点查询 From ceee4fe5cf1b71465733312c72fd3fb9b4569ffa Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 14:54:38 +0800 Subject: [PATCH 006/175] =?UTF-8?q?=E7=94=A8=E6=88=B7=E8=AF=A6=E6=83=85?= =?UTF-8?q?=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/user_memory_service.py | 1 + 1 file changed, 1 insertion(+) diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index e5e09f64..752f9c1a 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -1424,6 +1424,7 @@ async def analytics_graph_data( "limit": limit } + # 执行节点查询 node_results = await _neo4j_connector.execute_query(node_query, **node_params) From 6db37d35ed79704e636955582ecd5d4443943a85 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 15:25:04 +0800 Subject: [PATCH 007/175] =?UTF-8?q?=E7=94=A8=E6=88=B7=E8=AF=A6=E6=83=85?= =?UTF-8?q?=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/memory_episodic_schema.py | 36 +++++++++++++++++++ api/app/schemas/memory_storage_schema.py | 20 ----------- .../memory_entity_relationship_service.py | 8 +++-- api/app/services/user_memory_service.py | 5 ++- 4 files changed, 45 insertions(+), 24 deletions(-) diff --git a/api/app/schemas/memory_episodic_schema.py b/api/app/schemas/memory_episodic_schema.py index 7b3f3d2d..6b41c493 100644 --- a/api/app/schemas/memory_episodic_schema.py +++ b/api/app/schemas/memory_episodic_schema.py @@ -1,9 +1,45 @@ """ 情景记忆的请求和响应模型 """ +from abc import ABC from pydantic import BaseModel, Field from typing import Optional +type_mapping = { + "Person": "人物实体节点", + "Organization": "组织实体节点", + "ORG": "组织实体节点", + "Location": "地点实体节点", + "LOC": "地点实体节点", + "Event": "事件实体节点", + "Concept": "概念实体节点", + "Time": "时间实体节点", + "Position": "职位实体节点", + "WorkRole": "职业实体节点", + "System": "系统实体节点", + "Policy": "政策实体节点", + "HistoricalPeriod": "历史时期实体节点", + "HistoricalState": "历史国家实体节点", + "HistoricalEvent": "历史事件实体节点", + "EconomicFactor": "经济因素实体节点", + "Condition": "条件实体节点", + "Numeric": "数值实体节点" + } +class EmotionType(ABC): + JOY_TYPE = "joy" + SURPRISE_TYPE = "surprise" + SANDROWNESS_TYPE = "sadness" + FEAR_TYPE = "fear" + ANGET_TYPE="anger" + NEUTRAL_TYPE="neutral" + EMOTION_MAPPING={ + "joy":"愉快", + "surprise":"惊喜", + "sadness":"悲伤", + "fear":"恐惧", + "anger":"生气", + "neutral":"中性" + } class EpisodicMemoryOverviewRequest(BaseModel): """情景记忆总览查询请求""" diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index 77afe052..ecb1570f 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -11,26 +11,6 @@ from pydantic import BaseModel, Field, ConfigDict, field_validator, model_valida # ============================================================================ # 原 UserInput 相关 Schema (保留原有功能) # ============================================================================ -type_mapping = { - "Person": "人物实体节点", - "Organization": "组织实体节点", - "ORG": "组织实体节点", - "Location": "地点实体节点", - "LOC": "地点实体节点", - "Event": "事件实体节点", - "Concept": "概念实体节点", - "Time": "时间实体节点", - "Position": "职位实体节点", - "WorkRole": "职业实体节点", - "System": "系统实体节点", - "Policy": "政策实体节点", - "HistoricalPeriod": "历史时期实体节点", - "HistoricalState": "历史国家实体节点", - "HistoricalEvent": "历史事件实体节点", - "EconomicFactor": "经济因素实体节点", - "Condition": "条件实体节点", - "Numeric": "数值实体节点" - } class UserInput(BaseModel): message: str history: list[dict] diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index 8baf038e..ca97fb39 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -15,6 +15,8 @@ from neo4j.time import DateTime as Neo4jDateTime import json from datetime import datetime +from app.schemas.memory_episodic_schema import EmotionType + logger = logging.getLogger(__name__) class MemoryEntityService: @@ -496,11 +498,11 @@ class MemoryEmotion: length_data.append(emotion_intensity) if emotion_type is not None and emotion_intensity is not None and formatted_created_at is not None: # 使用(emotion_type, created_at)作为分组键 - if emotion_type in {"joy", "surprise"}: + if emotion_type in {EmotionType.JOY_TYPE, EmotionType.SURPRISE_TYPE}: emotion_type='positive' - elif emotion_type in {"sadness", "fear", "anger"}: + elif emotion_type in {EmotionType.SANDROWNESS_TYPE, EmotionType.FEAR_TYPE, EmotionType.ANGET_TYPE}: emotion_type='negative' - elif emotion_type=='neutral': + elif emotion_type==EmotionType.NEUTRAL_TYPE: emotion_type='neutral' group_key = (emotion_type, formatted_created_at) # 累加emotion_intensity diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 752f9c1a..d19726af 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -15,7 +15,8 @@ from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.repositories.end_user_repository import EndUserRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.schemas.memory_storage_schema import type_mapping +from app.schemas.memory_episodic_schema import type_mapping, EmotionType + from app.services.memory_base_service import MemoryBaseService from app.services.memory_config_service import MemoryConfigService from pydantic import BaseModel, Field @@ -1581,6 +1582,8 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ value = properties[field] if str(field) == 'entity_type': value=type_mapping.get(value,'') + if str(field)=="emotion_type": + value=EmotionType.EMOTION_MAPPING.get(value) # 清理 Neo4j 特殊类型 filtered_props[field] = _clean_neo4j_value(value) filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0] From 2c9e5df27dde50bf63bd2fdbd42b182e5d3b9748 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 15:34:45 +0800 Subject: [PATCH 008/175] =?UTF-8?q?=E7=94=A8=E6=88=B7=E8=AF=A6=E6=83=85?= =?UTF-8?q?=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/memory_episodic_schema.py | 6 ++++++ api/app/services/user_memory_service.py | 4 +++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/api/app/schemas/memory_episodic_schema.py b/api/app/schemas/memory_episodic_schema.py index 6b41c493..832bf34b 100644 --- a/api/app/schemas/memory_episodic_schema.py +++ b/api/app/schemas/memory_episodic_schema.py @@ -40,6 +40,12 @@ class EmotionType(ABC): "anger":"生气", "neutral":"中性" } +class EmotionSubject(ABC): + SUBJECT_MAPPING={ + "self":"自己", + "other":"别人", + "object":"事物对象" + } class EpisodicMemoryOverviewRequest(BaseModel): """情景记忆总览查询请求""" diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index d19726af..5011e83e 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -15,7 +15,7 @@ from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.repositories.end_user_repository import EndUserRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.schemas.memory_episodic_schema import type_mapping, EmotionType +from app.schemas.memory_episodic_schema import type_mapping, EmotionType, EmotionSubject from app.services.memory_base_service import MemoryBaseService from app.services.memory_config_service import MemoryConfigService @@ -1584,6 +1584,8 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ value=type_mapping.get(value,'') if str(field)=="emotion_type": value=EmotionType.EMOTION_MAPPING.get(value) + if str(field)=="emotion_subject": + value=EmotionSubject.SUBJECT_MAPPING.get(value) # 清理 Neo4j 特殊类型 filtered_props[field] = _clean_neo4j_value(value) filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0] From 0695c117391f88249aec7ed9c3ac0075625dff27 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 14 Jan 2026 18:25:55 +0800 Subject: [PATCH 009/175] =?UTF-8?q?=E7=94=A8=E6=88=B7=E8=AF=A6=E6=83=85?= =?UTF-8?q?=E4=BC=98=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 2d78d796..e05daf4a 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -634,7 +634,7 @@ class MemoryAgentService: retrieved_content.append({query:statements}) if retrieved_content==[]: retrieved_content='' - if '信息不足，无法回答。' != str(final_answer) :#and retrieved_content!=[] + if '信息不足，无法回答。' != str(final_answer) and str(search_switch).strip() != "2":#and retrieved_content!=[] # 使用 upsert 方法 repo.upsert( end_user_id=end_user_id, # 确保这个变量在作用域内 From d9fb8edaa9330f0919d8a8633b1cd9ab503763ec Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 15 Jan 2026 16:47:55 +0800 Subject: [PATCH 010/175] =?UTF-8?q?=E8=AF=BB=E5=8F=96=E7=9A=84=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=EF=BC=8C=E5=8E=BB=E6=8E=89=E5=85=A8=E5=B1=80=E9=94=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 402 +++++++++++------------ 1 file changed, 195 insertions(+), 207 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index e05daf4a..f0756764 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -9,7 +9,7 @@ import os import re import time import uuid -from threading import Lock + from typing import Any, AsyncGenerator, Dict, List, Optional import redis @@ -51,9 +51,7 @@ _neo4j_connector = Neo4jConnector() class MemoryAgentService: """Service for memory agent operations""" - def __init__(self): - self.user_locks: Dict[str, Lock] = {} - self.locks_lock = Lock() + def writer_messages_deal(self,messages,start_time,group_id,config_id,message): messages = str(messages).replace("'", '"').replace('\\n', '').replace('\n', '').replace('\\', '') @@ -83,12 +81,7 @@ class MemoryAgentService: raise ValueError(f"写入失败: {messages}") - def get_group_lock(self, group_id: str) -> Lock: - """Get lock for specific group to prevent concurrent processing""" - with self.locks_lock: - if group_id not in self.user_locks: - self.user_locks[group_id] = Lock() - return self.user_locks[group_id] + def extract_tool_call_info(self, event: Dict) -> bool: """Extract tool call information from event""" @@ -417,241 +410,236 @@ class MemoryAgentService: except ImportError: audit_logger = None - # Get group lock to prevent concurrent processing - group_lock = self.get_group_lock(group_id) + try: + config_service = MemoryConfigService(db) + memory_config = config_service.load_memory_config( + config_id=config_id, + service_name="MemoryAgentService" + ) + logger.info(f"Configuration loaded successfully: {memory_config.config_name}") + except ConfigurationError as e: + error_msg = f"Failed to load configuration for config_id: {config_id}: {e}" + logger.error(error_msg) - with group_lock: - # Step 1: Load configuration from database only - try: - config_service = MemoryConfigService(db) - memory_config = config_service.load_memory_config( + # Log failed operation + if audit_logger: + duration = time.time() - start_time + audit_logger.log_operation( + operation="READ", config_id=config_id, - service_name="MemoryAgentService" + group_id=group_id, + success=False, + duration=duration, + error=error_msg ) - logger.info(f"Configuration loaded successfully: {memory_config.config_name}") - except ConfigurationError as e: - error_msg = f"Failed to load configuration for config_id: {config_id}: {e}" - logger.error(error_msg) - # Log failed operation - if audit_logger: - duration = time.time() - start_time - audit_logger.log_operation( - operation="READ", - config_id=config_id, - group_id=group_id, - success=False, - duration=duration, - error=error_msg - ) + raise ValueError(error_msg) - raise ValueError(error_msg) + # Step 2: Prepare history + history.append({"role": "user", "content": message}) + logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}") - # Step 2: Prepare history - history.append({"role": "user", "content": message}) - logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}") + # Step 3: Initialize MCP client and execute read workflow + mcp_config = get_mcp_server_config() + client = MultiServerMCPClient(mcp_config) - # Step 3: Initialize MCP client and execute read workflow - mcp_config = get_mcp_server_config() - client = MultiServerMCPClient(mcp_config) + async with client.session('data_flow') as session: + session_start = time.time() + logger.debug("Connected to MCP Server: data_flow") - async with client.session('data_flow') as session: - session_start = time.time() - logger.debug("Connected to MCP Server: data_flow") - - tools_start = time.time() - tools = await load_mcp_tools(session) - tools_time = time.time() - tools_start - logger.info(f"[PERF] MCP tools loading took: {tools_time:.4f}s") - - outputs = [] - intermediate_outputs = [] - seen_intermediates = set() # Track seen intermediate outputs to avoid duplicates + tools_start = time.time() + tools = await load_mcp_tools(session) + tools_time = time.time() - tools_start + logger.info(f"[PERF] MCP tools loading took: {tools_time:.4f}s") - # Pass memory_config to the graph workflow - graph_start = time.time() - async with make_read_graph(group_id, tools, search_switch, group_id, group_id, memory_config=memory_config, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id) as graph: - graph_init_time = time.time() - graph_start - logger.info(f"[PERF] Graph initialization took: {graph_init_time:.4f}s") - - start = time.time() - config = {"configurable": {"thread_id": group_id}} - workflow_errors = [] # Track errors from workflow - - event_count = 0 - async for event in graph.astream( - {"messages": history, "memory_config": memory_config, "errors": []}, - stream_mode="values", - config=config - ): - event_count += 1 - event_start = time.time() - messages = event.get('messages') - # Capture any errors from the state - if event.get('errors'): - workflow_errors.extend(event.get('errors', [])) + outputs = [] + intermediate_outputs = [] + seen_intermediates = set() # Track seen intermediate outputs to avoid duplicates - for msg in messages: - msg_content = msg.content - msg_role = msg.__class__.__name__.lower().replace("message", "") - outputs.append({ - "role": msg_role, - "content": msg_content - }) + # Pass memory_config to the graph workflow + graph_start = time.time() + async with make_read_graph(group_id, tools, search_switch, group_id, group_id, memory_config=memory_config, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id) as graph: + graph_init_time = time.time() - graph_start + logger.info(f"[PERF] Graph initialization took: {graph_init_time:.4f}s") - # Extract intermediate outputs - if hasattr(msg, 'content'): - try: - # Handle MCP content format: [{'type': 'text', 'text': '...'}] - content_to_parse = msg_content - if isinstance(msg_content, list): - for block in msg_content: - if isinstance(block, dict) and block.get('type') == 'text': - content_to_parse = block.get('text', '') - break - else: - continue # No text block found + start = time.time() + config = {"configurable": {"thread_id": group_id}} + workflow_errors = [] # Track errors from workflow - # Try to parse content as JSON - if isinstance(content_to_parse, str): - try: - parsed = json.loads(content_to_parse) - if isinstance(parsed, dict): - # Check for single intermediate output - if '_intermediate' in parsed: - intermediate_data = parsed['_intermediate'] + event_count = 0 + async for event in graph.astream( + {"messages": history, "memory_config": memory_config, "errors": []}, + stream_mode="values", + config=config + ): + event_count += 1 + event_start = time.time() + messages = event.get('messages') + # Capture any errors from the state + if event.get('errors'): + workflow_errors.extend(event.get('errors', [])) + + for msg in messages: + msg_content = msg.content + msg_role = msg.__class__.__name__.lower().replace("message", "") + outputs.append({ + "role": msg_role, + "content": msg_content + }) + + # Extract intermediate outputs + if hasattr(msg, 'content'): + try: + # Handle MCP content format: [{'type': 'text', 'text': '...'}] + content_to_parse = msg_content + if isinstance(msg_content, list): + for block in msg_content: + if isinstance(block, dict) and block.get('type') == 'text': + content_to_parse = block.get('text', '') + break + else: + continue # No text block found + + # Try to parse content as JSON + if isinstance(content_to_parse, str): + try: + parsed = json.loads(content_to_parse) + if isinstance(parsed, dict): + # Check for single intermediate output + if '_intermediate' in parsed: + intermediate_data = parsed['_intermediate'] + output_key = self._create_intermediate_key(intermediate_data) + + if output_key not in seen_intermediates: + seen_intermediates.add(output_key) + intermediate_outputs.append(self._format_intermediate_output(intermediate_data)) + + # Check for multiple intermediate outputs (from Retrieve) + if '_intermediates' in parsed: + for intermediate_data in parsed['_intermediates']: output_key = self._create_intermediate_key(intermediate_data) if output_key not in seen_intermediates: seen_intermediates.add(output_key) intermediate_outputs.append(self._format_intermediate_output(intermediate_data)) + except (json.JSONDecodeError, ValueError): + pass + except Exception as e: + logger.debug(f"Failed to extract intermediate output: {e}") - # Check for multiple intermediate outputs (from Retrieve) - if '_intermediates' in parsed: - for intermediate_data in parsed['_intermediates']: - output_key = self._create_intermediate_key(intermediate_data) + event_time = time.time() - event_start + logger.info(f"[PERF] Event {event_count} processing took: {event_time:.4f}s") - if output_key not in seen_intermediates: - seen_intermediates.add(output_key) - intermediate_outputs.append(self._format_intermediate_output(intermediate_data)) - except (json.JSONDecodeError, ValueError): - pass - except Exception as e: - logger.debug(f"Failed to extract intermediate output: {e}") - - event_time = time.time() - event_start - logger.info(f"[PERF] Event {event_count} processing took: {event_time:.4f}s") + workflow_duration = time.time() - start + session_duration = time.time() - session_start + logger.info(f"[PERF] Read graph workflow completed in {workflow_duration}s") + logger.info(f"[PERF] Total session duration: {session_duration:.4f}s") + logger.info(f"[PERF] Total events processed: {event_count}") + # Extract final answer + final_answer = "" + for messages in outputs: + if messages['role'] == 'tool': + message = messages['content'] - workflow_duration = time.time() - start - session_duration = time.time() - session_start - logger.info(f"[PERF] Read graph workflow completed in {workflow_duration}s") - logger.info(f"[PERF] Total session duration: {session_duration:.4f}s") - logger.info(f"[PERF] Total events processed: {event_count}") - # Extract final answer - final_answer = "" - for messages in outputs: - if messages['role'] == 'tool': - message = messages['content'] + # Handle MCP content format: [{'type': 'text', 'text': '...'}] + if isinstance(message, list): + # Extract text from MCP content blocks + for block in message: + if isinstance(block, dict) and block.get('type') == 'text': + message = block.get('text', '') + break + else: + continue # No text block found - # Handle MCP content format: [{'type': 'text', 'text': '...'}] - if isinstance(message, list): - # Extract text from MCP content blocks - for block in message: - if isinstance(block, dict) and block.get('type') == 'text': - message = block.get('text', '') - break - else: - continue # No text block found + try: + parsed = json.loads(message) if isinstance(message, str) else message + if isinstance(parsed, dict): + if parsed.get('status') == 'success': + summary_result = parsed.get('summary_result') + if summary_result: + final_answer = summary_result + except (json.JSONDecodeError, ValueError): + pass - try: - parsed = json.loads(message) if isinstance(message, str) else message - if isinstance(parsed, dict): - if parsed.get('status') == 'success': - summary_result = parsed.get('summary_result') - if summary_result: - final_answer = summary_result - except (json.JSONDecodeError, ValueError): - pass + # 记录成功的操作 + total_duration = time.time() - start_time - # 记录成功的操作 - total_duration = time.time() - start_time + # Check for workflow errors + if workflow_errors: + error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors]) + logger.warning(f"Read workflow completed with errors: {error_details}") - # Check for workflow errors - if workflow_errors: - error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors]) - logger.warning(f"Read workflow completed with errors: {error_details}") - - if audit_logger: - audit_logger.log_operation( - operation="READ", - config_id=config_id, - group_id=group_id, - success=False, - duration=total_duration, - error=error_details, - details={ - "search_switch": search_switch, - "history_length": len(history), - "intermediate_outputs_count": len(intermediate_outputs), - "has_answer": bool(final_answer), - "errors": workflow_errors - } - ) - - # Raise error if no answer was produced - if not final_answer: - raise ValueError(f"Read workflow failed: {error_details}") - - if audit_logger and not workflow_errors: + if audit_logger: audit_logger.log_operation( operation="READ", config_id=config_id, group_id=group_id, - success=True, + success=False, duration=total_duration, + error=error_details, details={ "search_switch": search_switch, "history_length": len(history), "intermediate_outputs_count": len(intermediate_outputs), - "has_answer": bool(final_answer) + "has_answer": bool(final_answer), + "errors": workflow_errors } ) - retrieved_content=[] - repo = ShortTermMemoryRepository(db) - if str(search_switch)!="2": - for intermediate in intermediate_outputs: - print(intermediate) - intermediate_type=intermediate['type'] - if intermediate_type=="search_result": - query=intermediate['query'] - raw_results=intermediate['raw_results'] - reranked_results=raw_results.get('reranked_results',[]) - try: - statements=[statement['statement'] for statement in reranked_results.get('statements', [])] - except Exception: - statements=[] - statements=list(set(statements)) - retrieved_content.append({query:statements}) - if retrieved_content==[]: - retrieved_content='' - if '信息不足，无法回答。' != str(final_answer) and str(search_switch).strip() != "2":#and retrieved_content!=[] - # 使用 upsert 方法 - repo.upsert( - end_user_id=end_user_id, # 确保这个变量在作用域内 - messages=ori_message, - aimessages=final_answer, - retrieved_content=retrieved_content, - search_switch=str(search_switch) - ) - print("写入成功") + + # Raise error if no answer was produced + if not final_answer: + raise ValueError(f"Read workflow failed: {error_details}") + + if audit_logger and not workflow_errors: + audit_logger.log_operation( + operation="READ", + config_id=config_id, + group_id=group_id, + success=True, + duration=total_duration, + details={ + "search_switch": search_switch, + "history_length": len(history), + "intermediate_outputs_count": len(intermediate_outputs), + "has_answer": bool(final_answer) + } + ) + retrieved_content=[] + repo = ShortTermMemoryRepository(db) + if str(search_switch)!="2": + for intermediate in intermediate_outputs: + print(intermediate) + intermediate_type=intermediate['type'] + if intermediate_type=="search_result": + query=intermediate['query'] + raw_results=intermediate['raw_results'] + reranked_results=raw_results.get('reranked_results',[]) + try: + statements=[statement['statement'] for statement in reranked_results.get('statements', [])] + except Exception: + statements=[] + statements=list(set(statements)) + retrieved_content.append({query:statements}) + if retrieved_content==[]: + retrieved_content='' + if '信息不足，无法回答。' != str(final_answer) and str(search_switch).strip() != "2":#and retrieved_content!=[] + # 使用 upsert 方法 + repo.upsert( + end_user_id=end_user_id, # 确保这个变量在作用域内 + messages=ori_message, + aimessages=final_answer, + retrieved_content=retrieved_content, + search_switch=str(search_switch) + ) + print("写入成功") - return { - "answer": final_answer, - "intermediate_outputs": intermediate_outputs - } - + return { + "answer": final_answer, + "intermediate_outputs": intermediate_outputs + } + def _create_intermediate_key(self, output: Dict) -> str: """ Create a unique key for an intermediate output to detect duplicates. From 871304c89bb000c5a7c1ea90bde6f58d1c85e7f4 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 15 Jan 2026 21:48:08 +0800 Subject: [PATCH 011/175] =?UTF-8?q?=E8=BE=93=E5=87=BA=E6=95=B0=E7=BB=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_entity_relationship_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index ca97fb39..eedb7c29 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -597,7 +597,7 @@ class MemoryInteraction: group_id = ori_data[0]['group_id'] Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) if not Space_User: - return '不存在用户' + return [] user_id=Space_User[0]['id'] results = await self.connector.execute_query(Memory_Space_Associative, id=self.id,user_id=user_id) From 5a0d3df689805be0d25c8a7356f97b855dde45a1 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 19 Jan 2026 16:28:01 +0800 Subject: [PATCH 012/175] =?UTF-8?q?=E5=8F=8D=E6=80=9D=E4=BC=98=E5=8C=961.0?= =?UTF-8?q?=EF=BC=88=E4=BC=98=E5=8C=96=E9=9A=90=E7=A7=81=E8=BE=93=E5=87=BA?= =?UTF-8?q?=E3=80=81=E6=97=B6=E9=97=B4=E6=A3=80=E7=B4=A2=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../memory_reflection_controller.py | 95 +++++----------- .../reflection_engine/self_reflexion.py | 36 +++---- .../utils/prompt/prompts/evaluate.jinja2 | 3 +- .../utils/prompt/prompts/reflexion.jinja2 | 28 +++-- .../repositories/data_config_repository.py | 102 ++++++++++-------- api/app/repositories/neo4j/cypher_queries.py | 4 + api/app/repositories/neo4j/neo4j_update.py | 55 +++++++--- api/app/schemas/memory_storage_schema.py | 10 +- api/app/services/memory_reflection_service.py | 10 +- 9 files changed, 173 insertions(+), 170 deletions(-) diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index b0287d80..24c143b9 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -1,10 +1,11 @@ import asyncio import time +import uuid from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine.self_reflexion import ( ReflectionConfig, - ReflectionEngine, + ReflectionEngine, ReflectionRange, ReflectionBaseline, ) from app.core.response_utils import success from app.db import get_db @@ -39,9 +40,6 @@ async def save_reflection_config( db: Session = Depends(get_db), ) -> dict: """Save reflection configuration to data_comfig table""" - - - try: config_id = request.config_id if not config_id: @@ -52,51 +50,30 @@ async def save_reflection_config( api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}") - update_params = { - "enable_self_reflexion": request.reflection_enabled, - "iteration_period": request.reflection_period_in_hours, - "reflexion_range": request.reflexion_range, - "baseline": request.baseline, - "reflection_model_id": request.reflection_model_id, - "memory_verify": request.memory_verify, - "quality_assessment": request.quality_assessment, - } + data_config = DataConfigRepository.update_reflection_config( + db, + config_id=config_id, + enable_self_reflexion=request.reflection_enabled, + iteration_period=request.reflection_period_in_hours, + reflexion_range=request.reflexion_range, + baseline=request.baseline, + reflection_model_id=request.reflection_model_id, + memory_verify=request.memory_verify, + quality_assessment=request.quality_assessment + ) - - - query, params = DataConfigRepository.build_update_reflection(config_id, **update_params) - - result = db.execute(text(query), params) - if result.rowcount == 0: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"未找到config_id为 {config_id} 的配置" - ) - db.commit() - - # 查询更新后的配置 - select_query, select_params = DataConfigRepository.build_select_reflection(config_id) - result = db.execute(text(select_query), select_params).fetchone() - - if not result: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"更新后未找到config_id为 {config_id} 的配置" - ) - - api_logger.info(f"成功保存反思配置到数据库，config_id: {config_id}") + db.refresh(data_config) reflection_result={ - "config_id": result.config_id, - "enable_self_reflexion": result.enable_self_reflexion, - "iteration_period": result.iteration_period, - "reflexion_range": result.reflexion_range, - "baseline": result.baseline, - "reflection_model_id": result.reflection_model_id, - "memory_verify": result.memory_verify, - "quality_assessment": result.quality_assessment, - "user_id": result.user_id} + "config_id": data_config.config_id, + "enable_self_reflexion": data_config.enable_self_reflexion, + "iteration_period": data_config.iteration_period, + "reflexion_range": data_config.reflexion_range, + "baseline": data_config.baseline, + "reflection_model_id": data_config.reflection_model_id, + "memory_verify": data_config.memory_verify, + "quality_assessment": data_config.quality_assessment} return success(data=reflection_result, msg="反思配置成功") @@ -116,9 +93,8 @@ async def save_reflection_config( ) -@router.post("/reflection") +@router.get("/reflection") async def start_workspace_reflection( - config_id: int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -178,17 +154,7 @@ async def start_reflection_configs( """通过config_id查询data_config表中的反思配置信息""" try: api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - - # 使用DataConfigRepository查询反思配置 - select_query, select_params = DataConfigRepository.build_select_reflection(config_id) - result = db.execute(text(select_query), select_params).fetchone() - - if not result: - raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"未找到config_id为 {config_id} 的配置" - ) - + result = DataConfigRepository.query_reflection_config_by_id(db, config_id) # 构建返回数据 reflection_config = { "config_id": result.config_id, @@ -198,8 +164,7 @@ async def start_reflection_configs( "baseline": result.baseline, "reflection_model_id": result.reflection_model_id, "memory_verify": result.memory_verify, - "quality_assessment": result.quality_assessment, - "user_id": result.user_id + "quality_assessment": result.quality_assessment } api_logger.info(f"成功查询反思配置，config_id: {config_id}") return success(data=reflection_config, msg="反思配置查询成功") @@ -227,9 +192,7 @@ async def reflection_run( api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") # 使用DataConfigRepository查询反思配置 - select_query, select_params = DataConfigRepository.build_select_reflection(config_id) - result = db.execute(text(select_query), select_params).fetchone() - + result = DataConfigRepository.query_reflection_config_by_id(db, config_id) if not result: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, @@ -242,7 +205,7 @@ async def reflection_run( model_id = result.reflection_model_id if model_id: try: - ModelConfigService.get_model_by_id(db=db, model_id=model_id) + ModelConfigService.get_model_by_id(db=db, model_id=uuid.UUID(model_id)) api_logger.info(f"模型ID验证成功: {model_id}") except Exception as e: api_logger.warning(f"模型ID '{model_id}' 不存在，将使用默认模型: {str(e)}") @@ -252,8 +215,8 @@ async def reflection_run( config = ReflectionConfig( enabled=result.enable_self_reflexion, iteration_period=result.iteration_period, - reflexion_range=result.reflexion_range, - baseline=result.baseline, + reflexion_range=ReflectionRange(result.reflexion_range), + baseline=ReflectionBaseline(result.baseline), output_example='', memory_verify=result.memory_verify, quality_assessment=result.quality_assessment, diff --git a/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py b/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py index e9fb8855..bd3a9190 100644 --- a/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py +++ b/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py @@ -24,15 +24,9 @@ from app.core.memory.utils.config.get_data import ( get_data, get_data_statement, ) -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.utils.prompt.template_render import ( - render_evaluate_prompt, - render_reflexion_prompt, -) + from app.core.models.base import RedBearModelConfig -from app.core.response_utils import success from app.repositories.neo4j.cypher_queries import ( - UPDATE_STATEMENT_INVALID_AT, neo4j_query_all, neo4j_query_part, neo4j_statement_all, @@ -160,12 +154,11 @@ class ReflectionEngine: self.neo4j_connector = Neo4jConnector() if self.llm_client is None: - from app.core.memory.utils.config import definitions as config_defs from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context with get_db_context() as db: factory = MemoryClientFactory(db) - self.llm_client = factory.get_llm_client(config_defs.SELECTED_LLM_ID) + self.llm_client = factory.get_llm_client(self.config.model_id) elif isinstance(self.llm_client, str): # 如果 llm_client 是字符串（model_id），则用它初始化客户端 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -263,25 +256,23 @@ class ReflectionEngine: # 2. 检测冲突（基于事实的反思） conflict_data = await self._detect_conflicts(reflexion_data, statement_databasets) - print(100 * '-') - print(conflict_data) - print(100 * '-') - # # 检查是否真的有冲突 - conflicts_found='' + conflict_list=[] + for i in conflict_data: + conflict_list.append(i['data']) - conflicts_found='' + + + conflicts_found=0 # 3. 解决冲突 - solved_data = await self._resolve_conflicts(conflict_data, statement_databasets) + solved_data = await self._resolve_conflicts(conflict_list, statement_databasets) + if not solved_data: return ReflectionResult( success=False, - message="反思失败，未解决冲突", + message=f"没有{self.config.baseline}相关的冲突数据", conflicts_found=conflicts_found, execution_time=asyncio.get_event_loop().time() - start_time ) - print(100 * '*') - print(solved_data) - print(100 * '*') conflicts_resolved = len(solved_data) logging.info(f"解决了 {conflicts_resolved} 个冲突") @@ -386,7 +377,7 @@ class ReflectionEngine: memory_verifies.append(item['memory_verify']) result_data['memory_verifies'] = memory_verifies result_data['quality_assessments'] = quality_assessments - conflicts_found='' + conflicts_found = 0 # 初始化为整数0而不是空字符串 REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"} # Clearn conflict_data，And memory_verify和quality_assessment cleaned_conflict_data = [] @@ -414,7 +405,7 @@ class ReflectionEngine: cleaned_conflict_data_.append(cleaned_item) print(cleaned_conflict_data_) # 3. 解决冲突 - solved_data = await self._resolve_conflicts(cleaned_conflict_data, source_data) + solved_data = await self._resolve_conflicts(cleaned_conflict_data_, source_data) if not solved_data: return ReflectionResult( success=False, @@ -739,4 +730,3 @@ class ReflectionEngine: raise ValueError(f"未知的反思基线: {self.config.baseline}") - diff --git a/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2 b/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2 index e649897a..5da6d4b5 100644 --- a/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2 @@ -24,7 +24,8 @@ - **身份冲突**: 同一实体被赋予不同类型或角色 - **隐私审核**: 存在隐私信息也作为冲突输出当{{ memory_verify }}是true的时候 ### 混合冲突 -检测所有逻辑不一致或相互矛盾的记录。 +- 检测所有逻辑不一致或相互矛盾的记录。 +- **隐私审核**: 存在隐私信息也作为冲突输出当{{ memory_verify }}是true的时候 **检测原则**: - 重点检查相同实体的记录 - 分析description字段语义冲突 diff --git a/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2 b/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2 index ed3aad32..99660aa4 100644 --- a/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2 +++ b/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2 @@ -63,7 +63,7 @@ **脱敏字段**: name、entity1_name、entity2_name、description、relationship ## 4. 处理流程 - +###如果存在冲突数据执行以下步骤，不存在返回【】在data中 ### 步骤1: 类型匹配验证 **匹配规则**: - baseline="TIME": 只处理时间相关冲突(涉及时间表达式、日期、时间点) @@ -78,7 +78,7 @@ ### 步骤2: 冲突数据分组 **分组策略**: -- 时间冲突组: 涉及用户时间的记录 +- 时间冲突组: 涉及用户时间的记录比如（生日在2月17...） - 活动时间冲突组: 同一活动不同时间的记录 - 事实冲突组: 同一实体不同属性的记录 - 其他冲突组: 其他类型冲突记录 @@ -97,11 +97,12 @@ ### 处理规则 ** baseline是TIME - -保留正确记录不变修改错误记录的expired_at为当前时间(2025-12-16T12:00:00),以及name需要修改成正确的 -** baseline不是TIME + - 只处理时间相关的内容，比如时间表达式、日期、时间点 + -保留正确记录不变修改错误记录的expired_at为当前时间，比如(2025-12-16T12:00:00) +** baseline是FACT或者HYBRID + - 处理不是时间相关的内容 - 修改字段内容（ name、entity1_name、entity2_name、description、relationship）字段内容是否正确，如果不正确，需要对这些字段的内容重新生成，则不需要修改expired_at字段, 如果涉及到修改entity1_name/entity2_name字段的时候，同时也需要修改description字段，输出修改前和修改后的放入change里面的field - **核心原则**: - 只输出需要修改的记录 - 优先保留策略: 时间冲突保留最可信created_at时间，事实冲突选择最新且可信度最高记录 @@ -110,22 +111,26 @@ - 脱敏变更记录: 隐私脱敏变更也必须在change字段中记录{% endif %} - 不可修改数据: 数据被判定为正确时不可修改，无数据可输出时为空 - 输出的结果reflexion字段中的reason字段和solution不允许含有（expired_at设为2024-01-01T00:00:00Z、memory_verify=true、memory_verify=false)等原数据字段以及涉及需要修改的字段以及内容， - ，如果是FACT，只记录事实冲突相关的数据；如果是TIME，只记录时间冲突相关的数据；如果是HYBRID，则记录所有冲突相关的数据 + ，如果是FACT，只记录事实冲突相关的数据；如果是TIME，只记录时间冲突相关的数据；如果是HYBRID，则记录所有冲突相关的数据,如果存在隐私审核，隐私审核是true，也需要放到reflexion的reason字段和solution **变更记录格式**: ```json "change": [ { "field": [ - {"id":修改字段对应的ID} - {"statement_id":需要修改的对象对应的statement_id} - {"字段名1": ["修改前的值1","修改后的值1"]}, - {"字段名2": ["修改前的值2","修改后的值2"]} + {"id": "修改字段对应的ID"}, + {"字段名1": ["修改前的值1", "修改后的值1"]}, + {"字段名2": ["修改前的值2", "修改后的值2"]} ] } ] ``` +**resolved_memory格式说明**: +- 对于TIME类型冲突: 只需expired_at字段即可 +- 对于FACT/HYBRID类型冲突: 需要包含完整的记录对象（包括name、entity1_name、entity2_name、description、relationship等所有相关字段） +- resolved_memory中只包含需要修改的记录，不需要修改的记录不要包含在内 + **类型不匹配处理**: - 冲突类型与baseline不匹配时，resolved设为null - reflexion.reason说明类型不匹配原因 @@ -157,7 +162,8 @@ "conflict": true }, "reflexion": { - "reason": "该冲突类型的原因分析，如果是FACT就是存在事实冲突，分析该冲突原因，如果是TIME就是存在时间冲突，分析该冲突原因，如果是HYBRID，可以输出存在时间与事实的混合冲突再添加上原因分析， + "reason": "该冲突类型的原因分析，如果是FACT就是存在事实冲突，分析该冲突原因，如果是TIME就是存在时间冲突，分析该冲突原因，如果是HYBRID，可以输出存在时间与事实的混合冲突再添加上原因分析，如果 + 隐私审核打开的时候如果存在冲突，分析该冲突的原因 不可以随意分配冲突类型以及原因，不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种", "solution": "该冲突类型的解决方案（不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种）" }, diff --git a/api/app/repositories/data_config_repository.py b/api/app/repositories/data_config_repository.py index 7843acc2..ea9fadea 100644 --- a/api/app/repositories/data_config_repository.py +++ b/api/app/repositories/data_config_repository.py @@ -10,7 +10,7 @@ Classes: import uuid from typing import Dict, List, Optional, Tuple - +from app.core.exceptions import BusinessException from app.core.logging_config import get_config_logger, get_db_logger from app.models.data_config_model import DataConfig from app.schemas.memory_storage_schema import ( @@ -20,7 +20,7 @@ from app.schemas.memory_storage_schema import ( ConfigUpdateExtracted, ConfigUpdateForget, ) -from sqlalchemy import desc +from sqlalchemy import desc, select from sqlalchemy.orm import Session # 获取数据库专用日志器 @@ -136,72 +136,88 @@ class DataConfigRepository: id: m.id } AS targetNode """ - - # ==================== SQLAlchemy ORM 数据库操作方法 ==================== @staticmethod - def build_update_reflection(config_id: int, **kwargs) -> Tuple[str, Dict]: + def update_reflection_config( + db: Session, + config_id: int, + enable_self_reflexion: bool, + iteration_period: str, + reflexion_range: str, + baseline: str, + reflection_model_id: str, + memory_verify: bool, + quality_assessment: bool + ) -> DataConfig: """构建反思配置更新语句（SQLAlchemy text() 命名参数） Args: + quality_assessment: + memory_verify: + reflection_model_id: + baseline: + reflexion_range: + iteration_period: + enable_self_reflexion: + db: database object config_id: 配置ID - **kwargs: 反思配置参数 Returns: - Tuple[str, Dict]: (SQL查询字符串, 参数字典) + Data Raises: ValueError: 没有字段需要更新时抛出 """ db_logger.debug(f"构建反思配置更新语句: config_id={config_id}") + stmt = select(DataConfig).where(DataConfig.config_id == config_id) + data_config_obj = db.scalars(stmt).first() + if not data_config_obj: + raise BusinessException + data_config_obj.enable_self_reflexion = enable_self_reflexion + data_config_obj.iteration_period = iteration_period + data_config_obj.reflexion_range = reflexion_range + data_config_obj.baseline = baseline + data_config_obj.reflection_model_id = reflection_model_id + data_config_obj.memory_verify = memory_verify + data_config_obj.quality_assessment = quality_assessment - key_where = "config_id = :config_id" - set_fields: List[str] = [] - params: Dict = { - "config_id": config_id, - } - - # 反思配置字段映射 - mapping = { - "enable_self_reflexion": "enable_self_reflexion", - "iteration_period": "iteration_period", - "reflexion_range": "reflexion_range", - "baseline": "baseline", - "reflection_model_id": "reflection_model_id", - "memory_verify": "memory_verify", - "quality_assessment": "quality_assessment", - } - - for api_field, db_col in mapping.items(): - if api_field in kwargs and kwargs[api_field] is not None: - set_fields.append(f"{db_col} = :{api_field}") - params[api_field] = kwargs[api_field] - - if not set_fields: - raise ValueError("No fields to update") - - set_fields.append("updated_at = timezone('Asia/Shanghai', now())") - query = f"UPDATE {TABLE_NAME} SET " + ", ".join(set_fields) + f" WHERE {key_where}" - return query, params + return data_config_obj @staticmethod - def build_select_reflection(config_id: int) -> Tuple[str, Dict]: + def query_reflection_config_by_id(db: Session, config_id: int) -> DataConfig: """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数） Args: + db: database object config_id: 配置ID Returns: Tuple[str, Dict]: (SQL查询字符串, 参数字典) """ db_logger.debug(f"构建反思配置查询语句: config_id={config_id}") + stmt = select(DataConfig).where(DataConfig.config_id == config_id) + data_config = db.scalars(stmt).first() + if not data_config: + raise RuntimeError("reflection config not found") + return data_config + @staticmethod + def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> DataConfig: + """构建查询所有配置的语句（SQLAlchemy text() 命名参数） + + Args: + db: database object + workspace_id: 工作空间ID + + Returns: + Tuple[str, Dict]: (SQL查询字符串, 参数字典) + """ + db_logger.debug(f"构建查询所有配置语句: workspace_id={workspace_id}") + + stmt = select(DataConfig).where(DataConfig.workspace_id == workspace_id) + data_config = db.scalars(stmt).first() + if not data_config: + raise RuntimeError("reflection config not found") + return data_config - query = ( - f"SELECT config_id, enable_self_reflexion, iteration_period, reflexion_range, baseline, " - f"reflection_model_id, memory_verify, quality_assessment, user_id " - f"FROM {TABLE_NAME} WHERE config_id = :config_id" - ) - params = {"config_id": config_id} - return query, params @staticmethod def build_select_all(workspace_id: uuid.UUID) -> Tuple[str, Dict]: diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index fb28b81e..aac591b3 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -837,12 +837,14 @@ neo4j_query_part = """ WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) RETURN + elementId(m) as id, m.name as entity1_name, m.description as description, m.statement_id as statement_id, m.created_at as created_at, m.expired_at as expired_at, CASE WHEN rel IS NULL THEN "NO_RELATIONSHIP" ELSE type(rel) END as relationship_type, + elementId(rel) as rel_id, rel.predicate as predicate, rel.statement as relationship, rel.statement_id as relationship_statement_id, @@ -855,12 +857,14 @@ neo4j_query_all = """ WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) RETURN + elementId(m) as id, m.name as entity1_name, m.description as description, m.statement_id as statement_id, m.created_at as created_at, m.expired_at as expired_at, CASE WHEN rel IS NULL THEN "NO_RELATIONSHIP" ELSE type(rel) END as relationship_type, + elementId(rel) as rel_id, rel.predicate as predicate, rel.statement as relationship, rel.statement_id as relationship_statement_id, diff --git a/api/app/repositories/neo4j/neo4j_update.py b/api/app/repositories/neo4j/neo4j_update.py index 73b44396..753ae256 100644 --- a/api/app/repositories/neo4j/neo4j_update.py +++ b/api/app/repositories/neo4j/neo4j_update.py @@ -11,22 +11,28 @@ async def update_neo4j_data(neo4j_dict_data, update_databases): update_databases: update """ try: - # 构建WHERE条件 + # 构建WHERE条件 - 只使用elementId where_conditions = [] params = {} - for key, value in neo4j_dict_data.items(): - if value is not None: - param_name = f"param_{key}" - where_conditions.append(f"e.{key} = ${param_name}") - params[param_name] = value + # 优先使用id作为elementId进行查询 + if 'id' in neo4j_dict_data and neo4j_dict_data['id'] is not None: + where_conditions.append(f"elementId(e) = $param_id") + params['param_id'] = neo4j_dict_data['id'] + else: + # 如果没有id，使用其他字段作为条件 + for key, value in neo4j_dict_data.items(): + if value is not None: + param_name = f"param_{key}" + where_conditions.append(f"e.{key} = ${param_name}") + params[param_name] = value where_clause = " AND ".join(where_conditions) if where_conditions else "1=1" - # 构建SET条件 + # 构建SET条件 - 排除id字段 set_conditions = [] for key, value in update_databases.items(): - if value is not None: + if value is not None and key != 'id': # 不更新id字段 param_name = f"update_{key}" set_conditions.append(f"e.{key} = ${param_name}") params[param_name] = value @@ -76,22 +82,28 @@ async def update_neo4j_data_edge(neo4j_dict_data, update_databases): update_databases: update """ try: - # 构建WHERE条件 + # 构建WHERE条件 - 只使用elementId where_conditions = [] params = {} - for key, value in neo4j_dict_data.items(): - if value is not None: - param_name = f"param_{key}" - where_conditions.append(f"r.{key} = ${param_name}") - params[param_name] = value + # 优先使用id作为elementId进行查询 + if 'id' in neo4j_dict_data and neo4j_dict_data['id'] is not None: + where_conditions.append(f"elementId(r) = $param_id") + params['param_id'] = neo4j_dict_data['id'] + else: + # 如果没有id，使用其他字段作为条件 + for key, value in neo4j_dict_data.items(): + if value is not None: + param_name = f"param_{key}" + where_conditions.append(f"r.{key} = ${param_name}") + params[param_name] = value where_clause = " AND ".join(where_conditions) if where_conditions else "1=1" - # 构建SET条件 + # 构建SET条件 - 排除id字段 set_conditions = [] for key, value in update_databases.items(): - if value is not None: + if value is not None and key != 'id': # 不更新id字段 param_name = f"update_{key}" set_conditions.append(f"r.{key} = ${param_name}") params[param_name] = value @@ -242,7 +254,16 @@ async def neo4j_data(solved_data): if key=='expired_at': updat_expired_at[key] = values[1] - elif key == 'statement_id': + elif key == 'id': + ori_edge[key] = values + updata_edge[key] = values + + ori_entity[key] = values + updata_entity[key] = values + + ori_expired_at[key] = values + elif key == 'rel_id': + key='id' ori_edge[key] = values updata_edge[key] = values diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index ecb1570f..d17a9f2c 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -35,10 +35,10 @@ class BaseDataSchema(BaseModel): expired_at: Optional[str] = Field(None, description="The expiration timestamp in ISO 8601 format.") description: Optional[str] = Field(None, description="The description of the data entry.") - # 新增字段以匹配实际输入数据 - entity1_name: str = Field(..., description="The first entity name.") + # 新增字段以匹配实际输入数据 - 改为可选以支持resolved_memory场景 + entity1_name: Optional[str] = Field(None, description="The first entity name.") entity2_name: Optional[str] = Field(None, description="The second entity name.") - statement_id: str = Field(..., description="The statement identifier.") + statement_id: Optional[str] = Field(None, description="The statement identifier.") # 新增字段 - 设为可选以保持向后兼容性 predicate: Optional[str] = Field(None, description="The predicate describing the relationship between entities.") relationship_statement_id: Optional[str] = Field(None, description="The relationship statement identifier.") @@ -108,13 +108,13 @@ class ChangeRecordSchema(BaseModel): """Schema for individual change records 字段值格式说明： - - id 和 statement_id: 字符串或 None + - id: 字符串，表示修改字段对应的记录ID - 其他字段: 可以是字符串、None，数组 [修改前的值, 修改后的值]，或嵌套字典结构 - entity2等嵌套对象的字段也遵循 [old_value, new_value] 格式 """ field: List[Dict[str, Any]] = Field( ..., - description="List of field changes. First item: {id: value or None}, second: {statement_id: value}, followed by changed fields as {field_name: [old_value, new_value]} or {field_name: new_value} or nested structures like {entity2: {field_name: [old, new]}}" + description="List of field changes. First item: {id: value}, followed by changed fields as {field_name: [old_value, new_value]} or {field_name: new_value} or nested structures like {entity2: {field_name: [old, new]}}" ) class ResolvedSchema(BaseModel): diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index 0f8fb569..015cc08a 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -120,10 +120,12 @@ class WorkspaceAppService: def _get_data_config(self, memory_content: str) -> Dict[str, Any]: """Retrieve data_comfig information based on memory_comtent""" try: - data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content) - data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone() - if data_config_result is None: - return None + data_config_result = DataConfigRepository.query_reflection_config_by_id(self.db, int(memory_content)) + + # data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content) + # data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone() + # if data_config_result is None: + # return None if data_config_result: return { From d047190453716def40eff1990eac31ae7443548c Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 19 Jan 2026 18:06:19 +0800 Subject: [PATCH 013/175] =?UTF-8?q?=E5=8F=8D=E6=80=9D=E4=BC=98=E5=8C=961.0?= =?UTF-8?q?=EF=BC=88=E4=BC=98=E5=8C=96=E9=9A=90=E7=A7=81=E8=BE=93=E5=87=BA?= =?UTF-8?q?=E3=80=81=E6=97=B6=E9=97=B4=E6=A3=80=E7=B4=A2=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_entity_relationship_service.py | 1 + 1 file changed, 1 insertion(+) diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index eedb7c29..cc69cb82 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -597,6 +597,7 @@ class MemoryInteraction: group_id = ori_data[0]['group_id'] Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) if not Space_User: + return [] user_id=Space_User[0]['id'] From 616f6401b421df561649dab7f6ba657592cb424c Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 19 Jan 2026 18:06:56 +0800 Subject: [PATCH 014/175] =?UTF-8?q?=E5=8F=8D=E6=80=9D=E4=BC=98=E5=8C=961.0?= =?UTF-8?q?=EF=BC=88=E4=BC=98=E5=8C=96=E9=9A=90=E7=A7=81=E8=BE=93=E5=87=BA?= =?UTF-8?q?=E3=80=81=E6=97=B6=E9=97=B4=E6=A3=80=E7=B4=A2=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_entity_relationship_service.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index cc69cb82..eedb7c29 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -597,7 +597,6 @@ class MemoryInteraction: group_id = ori_data[0]['group_id'] Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) if not Space_User: - return [] user_id=Space_User[0]['id'] From c9dbb6426967e200f549872b66b878a36b2a47e7 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 11:10:10 +0800 Subject: [PATCH 015/175] =?UTF-8?q?=E5=8F=8D=E6=80=9D=E4=BC=98=E5=8C=96?= =?UTF-8?q?=E6=B5=8B=E8=AF=95=E6=8E=A5=E5=8F=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../memory_reflection_controller.py | 13 ++- api/app/services/memory_reflection_service.py | 90 ++++++++++++++++--- 2 files changed, 91 insertions(+), 12 deletions(-) diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index 24c143b9..9be6e035 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -119,11 +119,20 @@ async def start_workspace_reflection( end_users = data['end_users'] for base, config, user in zip(releases, data_configs, end_users): - if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']: + # 安全地转换为整数，处理空字符串和None的情况 + print(base['config']) + try: + base_config = int(base['config']) if base['config'] else 0 + config_id = int(config['config_id']) if config['config_id'] else 0 + except (ValueError, TypeError): + api_logger.warning(f"无效的配置ID: base['config']={base.get('config')}, config['config_id']={config.get('config_id')}") + continue + + if base_config == config_id and base['app_id'] == user['app_id']: # 调用反思服务 api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") - reflection_result = await reflection_service.start_reflection_from_data( + reflection_result = await reflection_service.start_text_reflection( config_data=config, end_user_id=user['id'] ) diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index 015cc08a..08ef2bbe 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -208,6 +208,49 @@ class MemoryReflectionService: def __init__(self,db: Session = Depends(get_db)): self.db=db + async def start_text_reflection(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]: + try: + config_id = config_data.get("config_id") + api_logger.info(f"从配置数据启动反思，config_id: {config_id}, end_user_id: {end_user_id}") + + if not config_data.get("enable_self_reflexion", False): + return { + "status": "跳过", + "message": "反思引擎未启用", + "config_id": config_id, + "end_user_id": end_user_id, + "config_data": config_data + } + + config_data_id = config_data['config_id'] + reflection_config = WorkspaceAppService(self.db)._get_data_config(config_data_id) + if reflection_config is not None and reflection_config['enable_self_reflexion']: + reflection_config = self._create_reflection_config_from_data(reflection_config) + # 3. 执行反思引擎 + reflection_results = await self._execute_reflection_engine( + reflection_config, end_user_id + ) + return { + "status": "完成", + "message": "反思引擎执行完成", + "config_id": config_id, + "end_user_id": end_user_id, + "config_data": config_data, + "reflection_results": reflection_results + } + + + + except Exception as e: + config_id = config_data.get("config_id", "unknown") + api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}") + return { + "status": "错误", + "message": f"启动反思失败: {str(e)}", + "config_id": config_id, + "end_user_id": end_user_id, + "config_data": config_data + } async def start_reflection_from_data(self, config_data: Dict[str, Any], end_user_id: str) -> Dict[str, Any]: """ @@ -239,16 +282,41 @@ class MemoryReflectionService: reflection_config=WorkspaceAppService(self.db)._get_data_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config= self._create_reflection_config_from_data(reflection_config) - iteration_period=reflection_config.iteration_period + iteration_period = int(reflection_config.iteration_period) workspace_service = WorkspaceAppService(self.db) current_reflection_time = workspace_service.get_end_user_reflection_time(end_user_id) - reflection_time = datetime.fromisoformat(str(current_reflection_time)) - - current_time = datetime.now() - time_diff = current_time - reflection_time - hours_diff = int(time_diff.total_seconds() / 3600) - if iteration_period==hours_diff or current_reflection_time is None: + # 检查是否需要执行反思 + should_execute = False + hours_diff = 0 + + if current_reflection_time is None: + # 首次执行反思 + should_execute = True + api_logger.info(f"首次执行反思，end_user_id: {end_user_id}") + else: + # 计算时间差 + try: + if isinstance(current_reflection_time, str): + reflection_time = datetime.fromisoformat(current_reflection_time) + else: + reflection_time = current_reflection_time + + current_time = datetime.now() + time_diff = current_time - reflection_time + hours_diff = int(time_diff.total_seconds() / 3600) + + # 检查是否达到反思周期 + if hours_diff >= iteration_period: + should_execute = True + api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时，达到周期 {iteration_period} 小时") + else: + api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时，未达到周期 {iteration_period} 小时") + except (ValueError, TypeError) as e: + api_logger.warning(f"解析反思时间失败: {e}，将执行反思") + should_execute = True + + if should_execute: api_logger.info(f"与上次的反思时间间隔为: {hours_diff} 小时") # 3. 执行反思引擎 reflection_results = await self._execute_reflection_engine( @@ -271,13 +339,15 @@ class MemoryReflectionService: } else: return { - "status": "等待中..", - "message": "反思引擎未开始执行执", + "status": "等待中", + "message": f"反思引擎未开始执行，距离下次执行还需 {iteration_period - hours_diff} 小时", "config_id": config_id, "end_user_id": end_user_id, "config_data": config_data, - "reflection_results": '' + "hours_since_last_reflection": hours_diff, + "next_reflection_in_hours": iteration_period - hours_diff } + except Exception as e: config_id = config_data.get("config_id", "unknown") From 6e77f5b0686124fb01069599918cb4bdd10664e6 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 11:11:45 +0800 Subject: [PATCH 016/175] =?UTF-8?q?=E5=8F=8D=E6=80=9D=E4=BC=98=E5=8C=96?= =?UTF-8?q?=E6=B5=8B=E8=AF=95=E6=8E=A5=E5=8F=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_reflection_service.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index 08ef2bbe..46e42b46 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -239,8 +239,6 @@ class MemoryReflectionService: "reflection_results": reflection_results } - - except Exception as e: config_id = config_data.get("config_id", "unknown") api_logger.error(f"启动反思失败，config_id: {config_id}, end_user_id: {end_user_id}, 错误: {str(e)}") From a5ecbec9a63bc5f1e9f62884784aaf1be3874dfd Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 16:32:52 +0800 Subject: [PATCH 017/175] =?UTF-8?q?=E8=AF=BB=E5=8F=96=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=E5=86=85=E5=B1=82=E5=B5=8C=E5=A5=97BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../agent/langgraph_graph/routing/routers.py | 23 +++++++++---------- 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/api/app/core/memory/agent/langgraph_graph/routing/routers.py b/api/app/core/memory/agent/langgraph_graph/routing/routers.py index c0b01be1..004e03b3 100644 --- a/api/app/core/memory/agent/langgraph_graph/routing/routers.py +++ b/api/app/core/memory/agent/langgraph_graph/routing/routers.py @@ -45,18 +45,17 @@ def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]: return 'Retrieve_Summary' # Default based on business logic def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]: status=state.get('verify', '')['status'] - loop_count = counter.get_total() - print(status) + # loop_count = counter.get_total() if "success" in status: - counter.reset() + # counter.reset() return "Summary" elif "failed" in status: - if loop_count < 2: # Maximum loop count is 3 - return "content_input" - else: - counter.reset() - return "Summary_fails" - # else: - # # Add default return value to avoid returning None - # counter.reset() - # return "Summary" # Default based on business requirements + # if loop_count < 2: # Maximum loop count is 3 + # return "content_input" + # else: + # counter.reset() + return "Summary_fails" + else: + # Add default return value to avoid returning None + # counter.reset() + return "Summary" # Default based on business requirements From a634565296d677fe5357ed0036d1ea9d34895e82 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 18:46:53 +0800 Subject: [PATCH 018/175] =?UTF-8?q?=E8=AF=BB=E5=8F=96=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=E5=86=85=E5=B1=82=E5=B5=8C=E5=A5=97BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../langgraph_graph/nodes/problem_nodes.py | 17 +- .../langgraph_graph/nodes/summary_nodes.py | 3 +- .../agent/langgraph_graph/read_graph.py | 1 + .../agent/langgraph_graph/routing/routers.py | 3 + .../agent/services/optimized_llm_service.py | 29 +- .../core/memory/llm_tools/openai_client.py | 297 ++++++++++++++++-- 6 files changed, 311 insertions(+), 39 deletions(-) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py index 0c68a47e..e02ef62b 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py @@ -1,3 +1,4 @@ +import os import json import time from app.core.logging_config import get_agent_logger @@ -13,7 +14,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = PROJECT_ROOT_ + '/agent/utils/prompt' +template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') db_session = next(get_db()) logger = get_agent_logger(__name__) @@ -35,11 +36,16 @@ async def Split_The_Problem(state: ReadState) -> ReadState: memory_config = state.get('memory_config', None) history = await SessionService(store).get_history(group_id, group_id, group_id) + + # 生成 JSON schema 以指导 LLM 输出正确格式 + json_schema = ProblemExtensionResponse.model_json_schema() + system_prompt = await problem_service.template_service.render_template( template_name='problem_breakdown_prompt.jinja2', operation_name='split_the_problem', history=history, - sentence=content + sentence=content, + json_schema=json_schema ) try: @@ -147,11 +153,16 @@ async def Problem_Extension(state: ReadState) -> ReadState: data = [] history = await SessionService(store).get_history(group_id, group_id, group_id) + + # 生成 JSON schema 以指导 LLM 输出正确格式 + json_schema = ProblemExtensionResponse.model_json_schema() + system_prompt = await problem_service.template_service.render_template( template_name='Problem_Extension_prompt.jinja2', operation_name='problem_extension', history=history, - questions=databasets + questions=databasets, + json_schema=json_schema ) try: diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index 7b727da5..0d0b57b0 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -1,5 +1,6 @@ +import os import time from app.core.logging_config import get_agent_logger, log_time @@ -19,7 +20,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = PROJECT_ROOT_ + '/agent/utils/prompt' +template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') logger = get_agent_logger(__name__) db_session = next(get_db()) diff --git a/api/app/core/memory/agent/langgraph_graph/read_graph.py b/api/app/core/memory/agent/langgraph_graph/read_graph.py index 19011a5f..c01889a9 100644 --- a/api/app/core/memory/agent/langgraph_graph/read_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/read_graph.py @@ -59,6 +59,7 @@ async def make_read_graph(): workflow.add_conditional_edges("Retrieve", Retrieve_continue) workflow.add_edge("Retrieve_Summary", END) workflow.add_conditional_edges("Verify", Verify_continue) + workflow.add_edge("Summary_fails", END) workflow.add_edge("Summary", END) diff --git a/api/app/core/memory/agent/langgraph_graph/routing/routers.py b/api/app/core/memory/agent/langgraph_graph/routing/routers.py index 004e03b3..151ce1c5 100644 --- a/api/app/core/memory/agent/langgraph_graph/routing/routers.py +++ b/api/app/core/memory/agent/langgraph_graph/routing/routers.py @@ -45,6 +45,9 @@ def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]: return 'Retrieve_Summary' # Default based on business logic def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]: status=state.get('verify', '')['status'] + print(100*'-') + print(status) + print(100*'-') # loop_count = counter.get_total() if "success" in status: # counter.reset() diff --git a/api/app/core/memory/agent/services/optimized_llm_service.py b/api/app/core/memory/agent/services/optimized_llm_service.py index 6942d421..fce1cd76 100644 --- a/api/app/core/memory/agent/services/optimized_llm_service.py +++ b/api/app/core/memory/agent/services/optimized_llm_service.py @@ -162,22 +162,35 @@ class OptimizedLLMService: return fallback_value elif isinstance(fallback_value, dict): return response_model(**fallback_value) + elif isinstance(fallback_value, list): + # 对于 RootModel[List[...]] 类型，直接传入列表 + if hasattr(response_model, 'model_fields') and 'root' in response_model.model_fields: + return response_model(root=fallback_value) + # 或者尝试直接传入（Pydantic v2 的 RootModel 支持） + return response_model(fallback_value) # 尝试创建空的响应模型 - if hasattr(response_model, 'root'): - # RootModel类型 + # 检查是否是 RootModel 类型（通过检查 __pydantic_root_model__ 属性） + if hasattr(response_model, '__pydantic_root_model__') and response_model.__pydantic_root_model__: + # RootModel类型 - 传入空列表 + logger.debug(f"创建 RootModel 类型的空响应: {response_model.__name__}") return response_model([]) else: - # 普通BaseModel类型 + # 普通BaseModel类型 - 尝试无参数构造 + logger.debug(f"创建普通 BaseModel 类型的空响应: {response_model.__name__}") return response_model() except Exception as e: - logger.error(f"创建降级响应失败: {e}") + logger.error(f"创建降级响应失败: {e}", exc_info=True) # 最后的降级策略 - if hasattr(response_model, 'root'): - return response_model([]) - else: - return response_model() + try: + if hasattr(response_model, '__pydantic_root_model__') and response_model.__pydantic_root_model__: + return response_model([]) + else: + return response_model() + except Exception as final_error: + logger.error(f"最终降级策略也失败: {final_error}") + raise def clear_cache(self): """清理客户端缓存""" diff --git a/api/app/core/memory/llm_tools/openai_client.py b/api/app/core/memory/llm_tools/openai_client.py index dce7b495..93c0efd6 100644 --- a/api/app/core/memory/llm_tools/openai_client.py +++ b/api/app/core/memory/llm_tools/openai_client.py @@ -100,6 +100,41 @@ class OpenAIClient(LLMClient): logger.error(f"LLM 调用失败: {e}") raise LLMClientException(f"LLM 调用失败: {e}") from e + async def response(self, messages: List[Dict[str, str]], **kwargs) -> str: + """ + 简单响应接口实现（用于fallback机制） + + Args: + messages: 消息列表 + **kwargs: 额外参数 + + Returns: + LLM 响应文本 + + Raises: + LLMClientException: LLM 调用失败 + """ + try: + template = """{messages}""" + prompt = ChatPromptTemplate.from_template(template) + chain = prompt | self.client + + # 添加 Langfuse 回调（如果可用） + config = {} + if self.langfuse_handler: + config["callbacks"] = [self.langfuse_handler] + + response = await chain.ainvoke({"messages": messages}, config=config) + + # 提取文本内容 + if hasattr(response, "content"): + return str(response.content) + return str(response) + + except Exception as e: + logger.error(f"LLM 调用失败: {e}") + raise LLMClientException(f"LLM 调用失败: {e}") from e + async def response_structured( self, messages: List[Dict[str, str]], @@ -131,44 +166,206 @@ class OpenAIClient(LLMClient): if self.langfuse_handler: config["callbacks"] = [self.langfuse_handler] - # 方法 1: 使用 PydanticOutputParser + template = """{question}""" + prompt = ChatPromptTemplate.from_template(template) + + # 对于 DashScope 等不支持 with_structured_output 的模型，优先使用手动JSON解析 + # 这样可以避免不必要的尝试和错误 + if self.provider: #.lower() == "dashscope" + logger.info("DashScope 模型，直接使用手动JSON解析方法") + try: + # 获取原始响应，添加超时保护 + chain = prompt | self.client + response = await asyncio.wait_for( + chain.ainvoke({"question": question_text}, config=config), + timeout=self.timeout + ) + + # 提取响应文本 + response_text = "" + if hasattr(response, "content"): + response_text = str(response.content) + else: + response_text = str(response) + + logger.debug(f"LLM原始响应长度: {len(response_text)}") + + # 尝试提取JSON内容 + json_text = response_text.strip() + + # 如果响应包含markdown代码块，提取其中的JSON + if "```json" in json_text: + json_text = json_text.split("```json")[1].split("```")[0].strip() + elif "```" in json_text: + json_text = json_text.split("```")[1].split("```")[0].strip() + + # 尝试修复常见的JSON格式问题 + # 1. 移除可能的BOM标记 + json_text = json_text.lstrip('\ufeff') + + # 2. 如果JSON被截断（缺少结尾的 ] 或 }），尝试修复 + if json_text.startswith('[') and not json_text.rstrip().endswith(']'): + logger.warning("检测到JSON数组被截断，尝试修复") + # 找到最后一个完整的对象 + last_complete_brace = json_text.rfind('}') + if last_complete_brace > 0: + json_text = json_text[:last_complete_brace + 1] + ']' + logger.info(f"修复后的JSON长度: {len(json_text)}") + elif json_text.startswith('{') and not json_text.rstrip().endswith('}'): + logger.warning("检测到JSON对象被截断，尝试修复") + # 找到最后一个完整的字段 + last_complete_brace = json_text.rfind('}') + if last_complete_brace > 0: + json_text = json_text[:last_complete_brace + 1] + logger.info(f"修复后的JSON长度: {len(json_text)}") + + # 解析JSON + try: + parsed_dict = json.loads(json_text) + logger.debug(f"JSON解析成功，类型: {type(parsed_dict)}") + + # 如果是列表，记录第一个元素的结构 + if isinstance(parsed_dict, list) and len(parsed_dict) > 0: + logger.debug(f"第一个元素的键: {list(parsed_dict[0].keys()) if isinstance(parsed_dict[0], dict) else 'not a dict'}") + + # 尝试字段映射转换（处理LLM返回格式不匹配的情况） + if isinstance(parsed_dict, list): + transformed_list = [] + for item in parsed_dict: + if isinstance(item, dict): + transformed_item = {} + + # 常见的字段映射规则 + field_mappings = { + 'question': ['extended_question', 'question', 'query'], + 'original_question': ['original_question', 'original', 'source_question'], + 'extended_question': ['extended_question', 'question', 'query', 'extended'], + 'type': ['type', 'category', 'question_type'], + 'reason': ['reason', 'explanation', 'rationale'], + 'query': ['query', 'question', 'text'], + 'split_result': ['split_result', 'result', 'status'], + 'expansion_issue': ['expansion_issue', 'issues', 'expansions'], + } + + # 对于每个期望的字段，尝试从多个可能的源字段中获取 + for target_field, source_fields in field_mappings.items(): + for source_field in source_fields: + if source_field in item: + transformed_item[target_field] = item[source_field] + break + + # 特殊处理：如果只有 'question' 但缺少 'original_question' 和 'extended_question' + if 'question' in item and 'original_question' not in transformed_item: + transformed_item['original_question'] = item['question'] + if 'question' in item and 'extended_question' not in transformed_item: + transformed_item['extended_question'] = item['question'] + + # 保留原始字段（如果没有被映射） + for key, value in item.items(): + if key not in transformed_item: + transformed_item[key] = value + + transformed_list.append(transformed_item) + else: + transformed_list.append(item) + + logger.info(f"字段映射完成，尝试重新验证") + logger.debug(f"转换后的数据: {transformed_list}") + + try: + return response_model.model_validate(transformed_list) + except Exception as retry_error: + logger.error(f"字段映射后仍然验证失败: {retry_error}") + logger.error(f"完整的LLM响应: {response_text}") + logger.error(f"原始解析字典: {parsed_dict}") + logger.error(f"转换后的字典: {transformed_list}") + raise + else: + # 非列表类型，记录并抛出原始错误 + logger.error(f"完整的LLM响应: {response_text}") + logger.error(f"解析后的字典: {parsed_dict}") + raise + except json.JSONDecodeError as je: + logger.error(f"JSON解析失败: {je}") + logger.error(f"问题位置附近的文本: {json_text[max(0, je.pos-50):min(len(json_text), je.pos+50)]}") + + # 尝试更激进的修复：逐行解析，找到有效的JSON部分 + logger.info("尝试逐行解析JSON") + lines = json_text.split('\n') + for i in range(len(lines), 0, -1): + try: + partial_json = '\n'.join(lines[:i]) + if partial_json.startswith('['): + partial_json = partial_json.rstrip().rstrip(',') + ']' + elif partial_json.startswith('{'): + partial_json = partial_json.rstrip().rstrip(',') + '}' + + parsed_dict = json.loads(partial_json) + logger.info(f"成功解析部分JSON（前{i}行）") + return response_model.model_validate(parsed_dict) + except: + continue + + # 如果所有尝试都失败，抛出原始错误 + raise LLMClientException(f"JSON解析失败: {je}") from je + + except asyncio.TimeoutError: + logger.error(f"LLM调用超时（{self.timeout}秒）") + raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") + except LLMClientException: + raise + except Exception as e: + logger.error(f"手动JSON解析失败: {e}", exc_info=True) + raise LLMClientException(f"手动JSON解析失败: {e}") from e + + + + + + # 方法 1: 使用 PydanticOutputParser（适用于支持的模型） if PydanticOutputParser is not None: try: parser = PydanticOutputParser(pydantic_object=response_model) format_instructions = parser.get_format_instructions() - prompt = ChatPromptTemplate.from_template( + prompt_with_instructions = ChatPromptTemplate.from_template( "{question}\n{format_instructions}" ) - chain = prompt | self.client | parser + chain = prompt_with_instructions | self.client | parser - parsed = await chain.ainvoke( - { - "question": question_text, - "format_instructions": format_instructions, - }, - config=config + parsed = await asyncio.wait_for( + chain.ainvoke( + { + "question": question_text, + "format_instructions": format_instructions, + }, + config=config + ), + timeout=self.timeout ) logger.debug(f"使用 PydanticOutputParser 解析成功") return parsed + except asyncio.TimeoutError: + logger.error(f"PydanticOutputParser 调用超时（{self.timeout}秒）") + raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") except Exception as e: logger.warning( f"PydanticOutputParser 解析失败，尝试其他方法: {e}" ) - # 方法 2: 使用 LangChain 的 with_structured_output - template = """{question}""" - prompt = ChatPromptTemplate.from_template(template) - - try: - with_so = getattr(self.client, "with_structured_output", None) - - if callable(with_so): + # 方法 2: 使用 LangChain 的 with_structured_output (如果支持) + with_so = getattr(self.client, "with_structured_output", None) + + if callable(with_so): + try: structured_chain = prompt | with_so(response_model, strict=True) - parsed = await structured_chain.ainvoke( - {"question": question_text}, - config=config + parsed = await asyncio.wait_for( + structured_chain.ainvoke( + {"question": question_text}, + config=config + ), + timeout=self.timeout ) # 验证并返回结果 @@ -181,14 +378,60 @@ class OpenAIClient(LLMClient): # 尝试从 JSON 解析 return response_model.model_validate_json(json.dumps(parsed)) - except Exception as e: - logger.error(f"结构化输出失败: {e}") - raise LLMClientException(f"结构化输出失败: {e}") from e + except asyncio.TimeoutError: + logger.error(f"with_structured_output 调用超时（{self.timeout}秒）") + raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") + except NotImplementedError: + logger.warning( + f"模型 {self.model_name} 不支持 with_structured_output，使用手动JSON解析" + ) + except Exception as e: + logger.warning(f"with_structured_output 失败: {e}，尝试手动解析") - # 如果所有方法都失败，抛出异常 - raise LLMClientException( - "无法生成结构化输出，所有解析方法均失败" - ) + # 方法 3: 手动JSON解析（fallback方法） + logger.info("使用手动JSON解析方法（fallback）") + try: + # 获取原始响应 + chain = prompt | self.client + response = await asyncio.wait_for( + chain.ainvoke({"question": question_text}, config=config), + timeout=self.timeout + ) + + # 提取响应文本 + response_text = "" + if hasattr(response, "content"): + response_text = str(response.content) + else: + response_text = str(response) + + logger.debug(f"LLM原始响应: {response_text[:500]}...") + + # 尝试提取JSON内容 + json_text = response_text.strip() + + # 如果响应包含markdown代码块，提取其中的JSON + if "```json" in json_text: + json_text = json_text.split("```json")[1].split("```")[0].strip() + elif "```" in json_text: + json_text = json_text.split("```")[1].split("```")[0].strip() + + # 解析JSON + parsed_dict = json.loads(json_text) + logger.debug(f"JSON解析成功: {parsed_dict}") + + # 验证并创建Pydantic模型 + return response_model.model_validate(parsed_dict) + + except asyncio.TimeoutError: + logger.error(f"手动JSON解析调用超时（{self.timeout}秒）") + raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") + except json.JSONDecodeError as je: + logger.error(f"JSON解析失败: {je}, 原始文本: {json_text[:200]}...") + raise LLMClientException(f"JSON解析失败: {je}") from je + except Exception as e: + logger.error(f"手动JSON解析失败: {e}") + raise LLMClientException(f"手动JSON解析失败: {e}") from e except LLMClientException: raise From 398964c747e108bab38430d3bcc72f3c5ba349cd Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 18:51:18 +0800 Subject: [PATCH 019/175] =?UTF-8?q?=E8=AF=BB=E5=8F=96=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=E5=86=85=E5=B1=82=E5=B5=8C=E5=A5=97BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nodes/verification_nodes.py | 120 +++++++++++++----- 1 file changed, 86 insertions(+), 34 deletions(-) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index f3a39afb..fbfbe86d 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -1,4 +1,4 @@ - +import os from app.core.logging_config import get_agent_logger from app.db import get_db @@ -12,7 +12,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = PROJECT_ROOT_ + '/agent/utils/prompt' +template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') db_session = next(get_db()) logger = get_agent_logger(__name__) @@ -48,38 +48,90 @@ async def Verify_prompt(state: ReadState,messages_deal): } return Verify_result async def Verify(state: ReadState): - content = state.get('data', '') - group_id = state.get('group_id', '') - memory_config = state.get('memory_config', None) + logger.info("=== Verify 节点开始执行 ===") + try: + content = state.get('data', '') + group_id = state.get('group_id', '') + memory_config = state.get('memory_config', None) + + logger.info(f"Verify: content={content[:50] if content else 'empty'}..., group_id={group_id}") - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(group_id, group_id, group_id) + logger.info(f"Verify: 获取历史记录完成，history length={len(history)}") - retrieve = state.get("retrieve", '') - retrieve = retrieve.get("Expansion_issue", []) - messages = { - "Query": content, - "Expansion_issue": retrieve - } - - system_prompt = await verification_service.template_service.render_template( - template_name='split_verify_prompt.jinja2', - operation_name='split_verify_prompt', - history=history, - sentence=messages - ) - - # 使用优化的LLM服务 - structured = await verification_service.call_llm_structured( - state=state, - db_session=db_session, - system_prompt=system_prompt, - response_model=VerificationResult, - fallback_value={ - "split_result": "fail", - "expansion_issue": [], - "reason": "验证失败" + retrieve = state.get("retrieve", {}) + logger.info(f"Verify: retrieve data type={type(retrieve)}, keys={retrieve.keys() if isinstance(retrieve, dict) else 'N/A'}") + + retrieve_expansion = retrieve.get("Expansion_issue", []) if isinstance(retrieve, dict) else [] + logger.info(f"Verify: Expansion_issue length={len(retrieve_expansion)}") + + messages = { + "Query": content, + "Expansion_issue": retrieve_expansion } - ) - - result = await Verify_prompt(state, structured) - return {"verify": result} \ No newline at end of file + + logger.info("Verify: 开始渲染模板") + system_prompt = await verification_service.template_service.render_template( + template_name='split_verify_prompt.jinja2', + operation_name='split_verify_prompt', + history=history, + sentence=messages + ) + logger.info(f"Verify: 模板渲染完成，prompt length={len(system_prompt)}") + + # 使用优化的LLM服务，添加超时保护 + logger.info("Verify: 开始调用 LLM") + try: + # 添加 asyncio.wait_for 超时包裹，防止无限等待 + # 超时时间设置为 150 秒（比 LLM 配置的 120 秒稍长） + import asyncio + structured = await asyncio.wait_for( + verification_service.call_llm_structured( + state=state, + db_session=db_session, + system_prompt=system_prompt, + response_model=VerificationResult, + fallback_value={ + "query": content, # 添加必填的 query 字段 + "split_result": "fail", + "expansion_issue": [], + "reason": "验证失败" + } + ), + timeout=150.0 # 150秒超时 + ) + logger.info(f"Verify: LLM 调用完成，result={structured}") + except asyncio.TimeoutError: + logger.error("Verify: LLM 调用超时（150秒），使用 fallback 值") + structured = VerificationResult( + query=content, + split_result="fail", + expansion_issue=[], + reason="LLM调用超时" + ) + + result = await Verify_prompt(state, structured) + logger.info("=== Verify 节点执行完成 ===") + return {"verify": result} + + except Exception as e: + logger.error(f"Verify 节点执行失败: {e}", exc_info=True) + # 返回失败的验证结果 + return { + "verify": { + "status": "failed", + "verified_data": [], + "storage_type": state.get('storage_type', ''), + "user_rag_memory_id": state.get('user_rag_memory_id', ''), + "_intermediate": { + "type": "verification", + "title": "Data Verification", + "result": "failed", + "reason": f"验证过程出错: {str(e)}", + "query": state.get('data', ''), + "verified_count": 0, + "storage_type": state.get('storage_type', ''), + "user_rag_memory_id": state.get('user_rag_memory_id', '') + } + } + } \ No newline at end of file From 78559d98eb1ea8a41628956f90360f24a0b7f65b Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 19:11:40 +0800 Subject: [PATCH 020/175] =?UTF-8?q?=E8=AF=BB=E5=8F=96=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=E5=86=85=E5=B1=82=E5=B5=8C=E5=A5=97BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nodes/verification_nodes.py | 38 ++- .../agent/models/verification_models.py | 28 +- .../agent/services/optimized_llm_service.py | 33 +- .../utils/prompt/split_verify_prompt.jinja2 | 44 ++- .../core/memory/llm_tools/openai_client.py | 297 ++---------------- 5 files changed, 117 insertions(+), 323 deletions(-) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index fbfbe86d..dac7ea14 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -26,22 +26,33 @@ class VerificationNodeService(LLMServiceMixin): # 创建全局服务实例 verification_service = VerificationNodeService() -async def Verify_prompt(state: ReadState,messages_deal): +async def Verify_prompt(state: ReadState, messages_deal: VerificationResult): + """处理验证结果并生成输出格式""" storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') data = state.get('data', '') + + # 将 VerificationItem 对象转换为字典列表 + verified_data = [] + if messages_deal.expansion_issue: + for item in messages_deal.expansion_issue: + if hasattr(item, 'model_dump'): + verified_data.append(item.model_dump()) + elif isinstance(item, dict): + verified_data.append(item) + Verify_result = { "status": messages_deal.split_result, - "verified_data": messages_deal.expansion_issue, + "verified_data": verified_data, "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id, "_intermediate": { "type": "verification", "title": "Data Verification", "result": messages_deal.split_result, - "reason": messages_deal.reason, - "query": data, - "verified_count": len(messages_deal.expansion_issue), + "reason": messages_deal.reason or "验证完成", + "query": messages_deal.query, + "verified_count": len(verified_data), "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id } @@ -71,11 +82,16 @@ async def Verify(state: ReadState): } logger.info("Verify: 开始渲染模板") + + # 生成 JSON schema 以指导 LLM 输出正确格式 + json_schema = VerificationResult.model_json_schema() + system_prompt = await verification_service.template_service.render_template( template_name='split_verify_prompt.jinja2', operation_name='split_verify_prompt', history=history, - sentence=messages + sentence=messages, + json_schema=json_schema ) logger.info(f"Verify: 模板渲染完成，prompt length={len(system_prompt)}") @@ -92,10 +108,11 @@ async def Verify(state: ReadState): system_prompt=system_prompt, response_model=VerificationResult, fallback_value={ - "query": content, # 添加必填的 query 字段 - "split_result": "fail", + "query": content, + "history": history if isinstance(history, list) else [], "expansion_issue": [], - "reason": "验证失败" + "split_result": "failed", + "reason": "验证失败或超时" } ), timeout=150.0 # 150秒超时 @@ -105,8 +122,9 @@ async def Verify(state: ReadState): logger.error("Verify: LLM 调用超时（150秒），使用 fallback 值") structured = VerificationResult( query=content, - split_result="fail", + history=history if isinstance(history, list) else [], expansion_issue=[], + split_result="failed", reason="LLM调用超时" ) diff --git a/api/app/core/memory/agent/models/verification_models.py b/api/app/core/memory/agent/models/verification_models.py index bd8896b3..abdce040 100644 --- a/api/app/core/memory/agent/models/verification_models.py +++ b/api/app/core/memory/agent/models/verification_models.py @@ -4,11 +4,29 @@ from typing import List, Optional, Dict, Any from pydantic import BaseModel, Field +class VerificationItem(BaseModel): + """Individual verification item for a query-answer pair.""" + + query_small: str = Field(..., description="子问题") + answer_small: str = Field(..., description="子问题的回答") + status: str = Field(..., description="验证状态：True 或 False") + query_answer: str = Field(..., description="问题的答案（与 answer_small 相同）") + + class VerificationResult(BaseModel): """Result model for verification operation.""" - query: str - expansion_issue: List[Dict[str, Any]] - split_result: str - reason: Optional[str] = None - history: List[Dict[str, Any]] = Field(default_factory=list) + query: str = Field(..., description="原始查询问题") + history: List[Dict[str, Any]] = Field(default_factory=list, description="历史对话记录") + expansion_issue: List[VerificationItem] = Field( + default_factory=list, + description="验证后的数据列表，包含所有通过验证的问答对" + ) + split_result: str = Field( + ..., + description="验证结果状态：success（expansion_issue 非空）或 failed（expansion_issue 为空）" + ) + reason: Optional[str] = Field( + None, + description="验证结果的说明和分析" + ) diff --git a/api/app/core/memory/agent/services/optimized_llm_service.py b/api/app/core/memory/agent/services/optimized_llm_service.py index fce1cd76..68919c4a 100644 --- a/api/app/core/memory/agent/services/optimized_llm_service.py +++ b/api/app/core/memory/agent/services/optimized_llm_service.py @@ -162,35 +162,22 @@ class OptimizedLLMService: return fallback_value elif isinstance(fallback_value, dict): return response_model(**fallback_value) - elif isinstance(fallback_value, list): - # 对于 RootModel[List[...]] 类型，直接传入列表 - if hasattr(response_model, 'model_fields') and 'root' in response_model.model_fields: - return response_model(root=fallback_value) - # 或者尝试直接传入（Pydantic v2 的 RootModel 支持） - return response_model(fallback_value) - + # 尝试创建空的响应模型 - # 检查是否是 RootModel 类型（通过检查 __pydantic_root_model__ 属性） - if hasattr(response_model, '__pydantic_root_model__') and response_model.__pydantic_root_model__: - # RootModel类型 - 传入空列表 - logger.debug(f"创建 RootModel 类型的空响应: {response_model.__name__}") + if hasattr(response_model, 'root'): + # RootModel类型 return response_model([]) else: - # 普通BaseModel类型 - 尝试无参数构造 - logger.debug(f"创建普通 BaseModel 类型的空响应: {response_model.__name__}") + # 普通BaseModel类型 return response_model() - + except Exception as e: - logger.error(f"创建降级响应失败: {e}", exc_info=True) + logger.error(f"创建降级响应失败: {e}") # 最后的降级策略 - try: - if hasattr(response_model, '__pydantic_root_model__') and response_model.__pydantic_root_model__: - return response_model([]) - else: - return response_model() - except Exception as final_error: - logger.error(f"最终降级策略也失败: {final_error}") - raise + if hasattr(response_model, 'root'): + return response_model([]) + else: + return response_model() def clear_cache(self): """清理客户端缓存""" diff --git a/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2 index d6ad8cab..5d10304a 100644 --- a/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2 +++ b/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2 @@ -42,19 +42,33 @@ 如果状态是TRUE保留这条数据，否则需不需要这条数据 ### 第五步 输出格式 按照json的形式输出 -{"data":"Query":原来Query的字段，"history":原来的history字段， -"expansion_issue":以为列表的形式存储验证之后的数据比如[ -{"query_small": query_small, - "answer_small": answer_small,, - "status": 回答的结果是否符合query_small，填写状态, - "query_answer": answer_small}, +{"query":"原来Query的字段", +"history":"原来的history字段", +"expansion_issue":以列表的形式存储验证之后的数据比如[ { - "query_small": "张曼婷生日是什么时候？", - "answer_small": "张曼婷喜欢绘画。", - "status": "True", - "query_answer": "张曼 婷喜欢绘画。" - },{}......] -, - "split_result":如果expansion_issue是空的列表返回failed，不是空列表返回success, - "reason": 为以上分析完之后的结果给一个说明 - } \ No newline at end of file + "query_small": "子问题", + "answer_small": "子问题的回答", + "status": "True或False，表示回答是否符合query_small", + "query_answer": "问题的答案（与answer_small相同）" +}, +{ + "query_small": "张曼婷生日是什么时候？", + "answer_small": "张曼婷喜欢绘画。", + "status": "False", + "query_answer": "张曼婷喜欢绘画。" +} +], +"split_result":"如果expansion_issue是空的列表返回failed，不是空列表返回success", +"reason": "为以上分析完之后的结果给一个说明" +} + +**输出格式要求** +**CRITICAL JSON FORMATTING REQUIREMENTS:** +1. Use only standard ASCII double quotes (") for JSON structure - never use Chinese quotation marks ("") or other Unicode quotes +2. If the extracted statement text contains quotation marks, escape them properly using backslashes (\") +3. Ensure all JSON strings are properly closed and comma-separated +4. Do not include line breaks within JSON string values +5. The output language should always be the same as the input language + +**JSON Schema:** +{{ json_schema }} \ No newline at end of file diff --git a/api/app/core/memory/llm_tools/openai_client.py b/api/app/core/memory/llm_tools/openai_client.py index 93c0efd6..dce7b495 100644 --- a/api/app/core/memory/llm_tools/openai_client.py +++ b/api/app/core/memory/llm_tools/openai_client.py @@ -100,41 +100,6 @@ class OpenAIClient(LLMClient): logger.error(f"LLM 调用失败: {e}") raise LLMClientException(f"LLM 调用失败: {e}") from e - async def response(self, messages: List[Dict[str, str]], **kwargs) -> str: - """ - 简单响应接口实现（用于fallback机制） - - Args: - messages: 消息列表 - **kwargs: 额外参数 - - Returns: - LLM 响应文本 - - Raises: - LLMClientException: LLM 调用失败 - """ - try: - template = """{messages}""" - prompt = ChatPromptTemplate.from_template(template) - chain = prompt | self.client - - # 添加 Langfuse 回调（如果可用） - config = {} - if self.langfuse_handler: - config["callbacks"] = [self.langfuse_handler] - - response = await chain.ainvoke({"messages": messages}, config=config) - - # 提取文本内容 - if hasattr(response, "content"): - return str(response.content) - return str(response) - - except Exception as e: - logger.error(f"LLM 调用失败: {e}") - raise LLMClientException(f"LLM 调用失败: {e}") from e - async def response_structured( self, messages: List[Dict[str, str]], @@ -166,206 +131,44 @@ class OpenAIClient(LLMClient): if self.langfuse_handler: config["callbacks"] = [self.langfuse_handler] - template = """{question}""" - prompt = ChatPromptTemplate.from_template(template) - - # 对于 DashScope 等不支持 with_structured_output 的模型，优先使用手动JSON解析 - # 这样可以避免不必要的尝试和错误 - if self.provider: #.lower() == "dashscope" - logger.info("DashScope 模型，直接使用手动JSON解析方法") - try: - # 获取原始响应，添加超时保护 - chain = prompt | self.client - response = await asyncio.wait_for( - chain.ainvoke({"question": question_text}, config=config), - timeout=self.timeout - ) - - # 提取响应文本 - response_text = "" - if hasattr(response, "content"): - response_text = str(response.content) - else: - response_text = str(response) - - logger.debug(f"LLM原始响应长度: {len(response_text)}") - - # 尝试提取JSON内容 - json_text = response_text.strip() - - # 如果响应包含markdown代码块，提取其中的JSON - if "```json" in json_text: - json_text = json_text.split("```json")[1].split("```")[0].strip() - elif "```" in json_text: - json_text = json_text.split("```")[1].split("```")[0].strip() - - # 尝试修复常见的JSON格式问题 - # 1. 移除可能的BOM标记 - json_text = json_text.lstrip('\ufeff') - - # 2. 如果JSON被截断（缺少结尾的 ] 或 }），尝试修复 - if json_text.startswith('[') and not json_text.rstrip().endswith(']'): - logger.warning("检测到JSON数组被截断，尝试修复") - # 找到最后一个完整的对象 - last_complete_brace = json_text.rfind('}') - if last_complete_brace > 0: - json_text = json_text[:last_complete_brace + 1] + ']' - logger.info(f"修复后的JSON长度: {len(json_text)}") - elif json_text.startswith('{') and not json_text.rstrip().endswith('}'): - logger.warning("检测到JSON对象被截断，尝试修复") - # 找到最后一个完整的字段 - last_complete_brace = json_text.rfind('}') - if last_complete_brace > 0: - json_text = json_text[:last_complete_brace + 1] - logger.info(f"修复后的JSON长度: {len(json_text)}") - - # 解析JSON - try: - parsed_dict = json.loads(json_text) - logger.debug(f"JSON解析成功，类型: {type(parsed_dict)}") - - # 如果是列表，记录第一个元素的结构 - if isinstance(parsed_dict, list) and len(parsed_dict) > 0: - logger.debug(f"第一个元素的键: {list(parsed_dict[0].keys()) if isinstance(parsed_dict[0], dict) else 'not a dict'}") - - # 尝试字段映射转换（处理LLM返回格式不匹配的情况） - if isinstance(parsed_dict, list): - transformed_list = [] - for item in parsed_dict: - if isinstance(item, dict): - transformed_item = {} - - # 常见的字段映射规则 - field_mappings = { - 'question': ['extended_question', 'question', 'query'], - 'original_question': ['original_question', 'original', 'source_question'], - 'extended_question': ['extended_question', 'question', 'query', 'extended'], - 'type': ['type', 'category', 'question_type'], - 'reason': ['reason', 'explanation', 'rationale'], - 'query': ['query', 'question', 'text'], - 'split_result': ['split_result', 'result', 'status'], - 'expansion_issue': ['expansion_issue', 'issues', 'expansions'], - } - - # 对于每个期望的字段，尝试从多个可能的源字段中获取 - for target_field, source_fields in field_mappings.items(): - for source_field in source_fields: - if source_field in item: - transformed_item[target_field] = item[source_field] - break - - # 特殊处理：如果只有 'question' 但缺少 'original_question' 和 'extended_question' - if 'question' in item and 'original_question' not in transformed_item: - transformed_item['original_question'] = item['question'] - if 'question' in item and 'extended_question' not in transformed_item: - transformed_item['extended_question'] = item['question'] - - # 保留原始字段（如果没有被映射） - for key, value in item.items(): - if key not in transformed_item: - transformed_item[key] = value - - transformed_list.append(transformed_item) - else: - transformed_list.append(item) - - logger.info(f"字段映射完成，尝试重新验证") - logger.debug(f"转换后的数据: {transformed_list}") - - try: - return response_model.model_validate(transformed_list) - except Exception as retry_error: - logger.error(f"字段映射后仍然验证失败: {retry_error}") - logger.error(f"完整的LLM响应: {response_text}") - logger.error(f"原始解析字典: {parsed_dict}") - logger.error(f"转换后的字典: {transformed_list}") - raise - else: - # 非列表类型，记录并抛出原始错误 - logger.error(f"完整的LLM响应: {response_text}") - logger.error(f"解析后的字典: {parsed_dict}") - raise - except json.JSONDecodeError as je: - logger.error(f"JSON解析失败: {je}") - logger.error(f"问题位置附近的文本: {json_text[max(0, je.pos-50):min(len(json_text), je.pos+50)]}") - - # 尝试更激进的修复：逐行解析，找到有效的JSON部分 - logger.info("尝试逐行解析JSON") - lines = json_text.split('\n') - for i in range(len(lines), 0, -1): - try: - partial_json = '\n'.join(lines[:i]) - if partial_json.startswith('['): - partial_json = partial_json.rstrip().rstrip(',') + ']' - elif partial_json.startswith('{'): - partial_json = partial_json.rstrip().rstrip(',') + '}' - - parsed_dict = json.loads(partial_json) - logger.info(f"成功解析部分JSON（前{i}行）") - return response_model.model_validate(parsed_dict) - except: - continue - - # 如果所有尝试都失败，抛出原始错误 - raise LLMClientException(f"JSON解析失败: {je}") from je - - except asyncio.TimeoutError: - logger.error(f"LLM调用超时（{self.timeout}秒）") - raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") - except LLMClientException: - raise - except Exception as e: - logger.error(f"手动JSON解析失败: {e}", exc_info=True) - raise LLMClientException(f"手动JSON解析失败: {e}") from e - - - - - - # 方法 1: 使用 PydanticOutputParser（适用于支持的模型） + # 方法 1: 使用 PydanticOutputParser if PydanticOutputParser is not None: try: parser = PydanticOutputParser(pydantic_object=response_model) format_instructions = parser.get_format_instructions() - prompt_with_instructions = ChatPromptTemplate.from_template( + prompt = ChatPromptTemplate.from_template( "{question}\n{format_instructions}" ) - chain = prompt_with_instructions | self.client | parser + chain = prompt | self.client | parser - parsed = await asyncio.wait_for( - chain.ainvoke( - { - "question": question_text, - "format_instructions": format_instructions, - }, - config=config - ), - timeout=self.timeout + parsed = await chain.ainvoke( + { + "question": question_text, + "format_instructions": format_instructions, + }, + config=config ) logger.debug(f"使用 PydanticOutputParser 解析成功") return parsed - except asyncio.TimeoutError: - logger.error(f"PydanticOutputParser 调用超时（{self.timeout}秒）") - raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") except Exception as e: logger.warning( f"PydanticOutputParser 解析失败，尝试其他方法: {e}" ) - # 方法 2: 使用 LangChain 的 with_structured_output (如果支持) - with_so = getattr(self.client, "with_structured_output", None) - - if callable(with_so): - try: + # 方法 2: 使用 LangChain 的 with_structured_output + template = """{question}""" + prompt = ChatPromptTemplate.from_template(template) + + try: + with_so = getattr(self.client, "with_structured_output", None) + + if callable(with_so): structured_chain = prompt | with_so(response_model, strict=True) - parsed = await asyncio.wait_for( - structured_chain.ainvoke( - {"question": question_text}, - config=config - ), - timeout=self.timeout + parsed = await structured_chain.ainvoke( + {"question": question_text}, + config=config ) # 验证并返回结果 @@ -378,60 +181,14 @@ class OpenAIClient(LLMClient): # 尝试从 JSON 解析 return response_model.model_validate_json(json.dumps(parsed)) - except asyncio.TimeoutError: - logger.error(f"with_structured_output 调用超时（{self.timeout}秒）") - raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") - except NotImplementedError: - logger.warning( - f"模型 {self.model_name} 不支持 with_structured_output，使用手动JSON解析" - ) - except Exception as e: - logger.warning(f"with_structured_output 失败: {e}，尝试手动解析") - - # 方法 3: 手动JSON解析（fallback方法） - logger.info("使用手动JSON解析方法（fallback）") - try: - # 获取原始响应 - chain = prompt | self.client - response = await asyncio.wait_for( - chain.ainvoke({"question": question_text}, config=config), - timeout=self.timeout - ) - - # 提取响应文本 - response_text = "" - if hasattr(response, "content"): - response_text = str(response.content) - else: - response_text = str(response) - - logger.debug(f"LLM原始响应: {response_text[:500]}...") - - # 尝试提取JSON内容 - json_text = response_text.strip() - - # 如果响应包含markdown代码块，提取其中的JSON - if "```json" in json_text: - json_text = json_text.split("```json")[1].split("```")[0].strip() - elif "```" in json_text: - json_text = json_text.split("```")[1].split("```")[0].strip() - - # 解析JSON - parsed_dict = json.loads(json_text) - logger.debug(f"JSON解析成功: {parsed_dict}") - - # 验证并创建Pydantic模型 - return response_model.model_validate(parsed_dict) - - except asyncio.TimeoutError: - logger.error(f"手动JSON解析调用超时（{self.timeout}秒）") - raise LLMClientException(f"LLM调用超时（{self.timeout}秒）") - except json.JSONDecodeError as je: - logger.error(f"JSON解析失败: {je}, 原始文本: {json_text[:200]}...") - raise LLMClientException(f"JSON解析失败: {je}") from je except Exception as e: - logger.error(f"手动JSON解析失败: {e}") - raise LLMClientException(f"手动JSON解析失败: {e}") from e + logger.error(f"结构化输出失败: {e}") + raise LLMClientException(f"结构化输出失败: {e}") from e + + # 如果所有方法都失败，抛出异常 + raise LLMClientException( + "无法生成结构化输出，所有解析方法均失败" + ) except LLMClientException: raise From 575190a96d8855503a31d6b576606e9a29fdc4c5 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 19:14:32 +0800 Subject: [PATCH 021/175] =?UTF-8?q?=E8=AF=BB=E5=8F=96=E6=8E=A5=E5=8F=A3?= =?UTF-8?q?=E5=86=85=E5=B1=82=E5=B5=8C=E5=A5=97BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/core/memory/agent/langgraph_graph/routing/routers.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/api/app/core/memory/agent/langgraph_graph/routing/routers.py b/api/app/core/memory/agent/langgraph_graph/routing/routers.py index 151ce1c5..004e03b3 100644 --- a/api/app/core/memory/agent/langgraph_graph/routing/routers.py +++ b/api/app/core/memory/agent/langgraph_graph/routing/routers.py @@ -45,9 +45,6 @@ def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]: return 'Retrieve_Summary' # Default based on business logic def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]: status=state.get('verify', '')['status'] - print(100*'-') - print(status) - print(100*'-') # loop_count = counter.get_total() if "success" in status: # counter.reset() From 29852ff0a5228fadc45ef7a4f088fc7bc11d8056 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 20 Jan 2026 20:12:14 +0800 Subject: [PATCH 022/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/memory_agent_controller.py | 18 +- .../controllers/user_memory_controllers.py | 59 ++++++- api/app/schemas/end_user_schema.py | 1 + api/app/services/memory_agent_service.py | 14 +- api/app/services/memory_base_service.py | 161 +++++++++++++++++- .../memory_entity_relationship_service.py | 94 ++++++++-- api/app/services/user_memory_service.py | 63 ++++--- 7 files changed, 352 insertions(+), 58 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index b7da943c..46fe3043 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -9,7 +9,7 @@ from app.db import get_db from app.dependencies import cur_workspace_access_guard, get_current_user from app.models import ModelApiKey from app.models.user_model import User -from app.repositories import knowledge_repository +from app.repositories import knowledge_repository, WorkspaceRepository from app.schemas.memory_agent_schema import UserInput, Write_UserInput from app.schemas.response_schema import ApiResponse from app.services import task_service, workspace_service @@ -616,8 +616,10 @@ async def get_knowledge_type_stats_api( @router.get("/analytics/hot_memory_tags/by_user", response_model=ApiResponse) async def get_hot_memory_tags_by_user_api( end_user_id: Optional[str] = Query(None, description="用户ID（可选）"), + language_type: Optional[str] ="zh", limit: int = Query(20, description="返回标签数量限制"), - current_user: User = Depends(get_current_user) + current_user: User = Depends(get_current_user), + db: Session=Depends(get_db), ): """ 获取指定用户的热门记忆标签 @@ -628,10 +630,22 @@ async def get_hot_memory_tags_by_user_api( ... ] """ + + workspace_id=current_user.current_workspace_id + workspace_repo = WorkspaceRepository(db) + workspace_models = workspace_repo.get_workspace_models_configs(workspace_id) + + if workspace_models: + model_id = workspace_models.get("llm", None) + else: + model_id = None + api_logger.info(f"Hot memory tags by user requested: end_user_id={end_user_id}") try: result = await memory_agent_service.get_hot_memory_tags_by_user( end_user_id=end_user_id, + language_type=language_type, + model_id=model_id, limit=limit ) return success(data=result, msg="获取热门记忆标签成功") diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index a96c7a52..560d6c0d 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -12,6 +12,7 @@ from app.core.logging_config import get_api_logger from app.core.response_utils import success, fail from app.core.error_codes import BizCode from app.core.api_key_utils import timestamp_to_datetime +from app.services.memory_base_service import Translation_English from app.services.user_memory_service import ( UserMemoryService, analytics_memory_types, @@ -20,7 +21,7 @@ from app.services.user_memory_service import ( from app.services.memory_entity_relationship_service import MemoryEntityService,MemoryEmotion,MemoryInteraction from app.schemas.response_schema import ApiResponse from app.schemas.memory_storage_schema import GenerateCacheRequest - +from app.repositories.workspace_repository import WorkspaceRepository from app.schemas.end_user_schema import ( EndUserProfileResponse, EndUserProfileUpdate, @@ -44,6 +45,7 @@ router = APIRouter( @router.get("/analytics/memory_insight/report", response_model=ApiResponse) async def get_memory_insight_report_api( end_user_id: str, + language_type: str = "zh", current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -53,10 +55,18 @@ async def get_memory_insight_report_api( 此接口仅查询数据库中已缓存的记忆洞察数据，不执行生成操作。 如需生成新的洞察报告，请使用专门的生成接口。 """ + workspace_id = current_user.current_workspace_id + workspace_repo = WorkspaceRepository(db) + workspace_models = workspace_repo.get_workspace_models_configs(workspace_id) + + if workspace_models: + model_id = workspace_models.get("llm", None) + else: + model_id = None api_logger.info(f"记忆洞察报告查询请求: end_user_id={end_user_id}, user={current_user.username}") try: # 调用服务层获取缓存数据 - result = await user_memory_service.get_cached_memory_insight(db, end_user_id) + result = await user_memory_service.get_cached_memory_insight(db, end_user_id,model_id,language_type) if result["is_cached"]: api_logger.info(f"成功返回缓存的记忆洞察报告: end_user_id={end_user_id}") @@ -72,6 +82,7 @@ async def get_memory_insight_report_api( @router.get("/analytics/user_summary", response_model=ApiResponse) async def get_user_summary_api( end_user_id: str, + language_type: str="zh", current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -81,10 +92,18 @@ async def get_user_summary_api( 此接口仅查询数据库中已缓存的用户摘要数据，不执行生成操作。 如需生成新的用户摘要，请使用专门的生成接口。 """ + workspace_id = current_user.current_workspace_id + workspace_repo = WorkspaceRepository(db) + workspace_models = workspace_repo.get_workspace_models_configs(workspace_id) + + if workspace_models: + model_id = workspace_models.get("llm", None) + else: + model_id = None api_logger.info(f"用户摘要查询请求: end_user_id={end_user_id}, user={current_user.username}") try: # 调用服务层获取缓存数据 - result = await user_memory_service.get_cached_user_summary(db, end_user_id) + result = await user_memory_service.get_cached_user_summary(db, end_user_id,model_id,language_type) if result["is_cached"]: api_logger.info(f"成功返回缓存的用户摘要: end_user_id={end_user_id}") @@ -253,7 +272,6 @@ async def get_graph_data_api( depth=depth, center_node_id=center_node_id ) - # 检查是否有错误消息 if "message" in result and result["statistics"]["total_nodes"] == 0: api_logger.warning(f"图数据查询返回空结果: {result.get('message')}") @@ -274,11 +292,18 @@ async def get_graph_data_api( @router.get("/read_end_user/profile", response_model=ApiResponse) async def get_end_user_profile( end_user_id: str, + language_type: str = "zh", current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: workspace_id = current_user.current_workspace_id + workspace_repo = WorkspaceRepository(db) + workspace_models = workspace_repo.get_workspace_models_configs(workspace_id) + if workspace_models: + model_id = workspace_models.get("llm", None) + else: + model_id = None # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试查询用户信息但未选择工作空间") @@ -297,12 +322,19 @@ async def get_end_user_profile( api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}") return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}") + other_name=end_user.other_name + position=end_user.position + department=end_user.department + if language_type!="zh": + other_name=await Translation_English(model_id,other_name) + position = await Translation_English(model_id, position) + department = await Translation_English(model_id, department) # 构建响应数据 profile_data = EndUserProfileResponse( id=end_user.id, - other_name=end_user.other_name, - position=end_user.position, - department=end_user.department, + other_name=other_name, + position=position, + department=department, contact=end_user.contact, phone=end_user.phone, hire_date=end_user.hire_date, @@ -396,12 +428,21 @@ async def update_end_user_profile( return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", str(e)) @router.get("/memory_space/timeline_memories", response_model=ApiResponse) -async def memory_space_timeline_of_shared_memories(id: str, label: str, +async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): + workspace_id=current_user.current_workspace_id + workspace_repo = WorkspaceRepository(db) + workspace_models = workspace_repo.get_workspace_models_configs(workspace_id) + + if workspace_models: + model_id = workspace_models.get("llm", None) + else: + model_id = None MemoryEntity = MemoryEntityService(id, label) - timeline_memories_result = await MemoryEntity.get_timeline_memories_server() + timeline_memories_result = await MemoryEntity.get_timeline_memories_server(model_id, language_type) + return success(data=timeline_memories_result, msg="共同记忆时间线") @router.get("/memory_space/relationship_evolution", response_model=ApiResponse) async def memory_space_relationship_evolution(id: str, label: str, diff --git a/api/app/schemas/end_user_schema.py b/api/app/schemas/end_user_schema.py index c9f9146d..6f7498a0 100644 --- a/api/app/schemas/end_user_schema.py +++ b/api/app/schemas/end_user_schema.py @@ -44,6 +44,7 @@ class EndUserProfileResponse(BaseModel): updatetime_profile: Optional[datetime.datetime] = Field(description="核心档案信息最后更新时间", default=None) + class EndUserProfileUpdate(BaseModel): """终端用户基本信息更新请求模型""" end_user_id: str = Field(description="终端用户ID") diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index c9230a26..ddf04216 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -26,6 +26,7 @@ from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_config_schema import ConfigurationError +from app.services.memory_base_service import Translation_English from app.services.memory_config_service import MemoryConfigService from app.services.memory_konwledges_server import ( write_rag, @@ -692,7 +693,9 @@ class MemoryAgentService: async def get_hot_memory_tags_by_user( self, end_user_id: Optional[str] = None, - limit: int = 20 + limit: int = 20, + model_id: Optional[str] = None, + language_type: Optional[str] = "zh" ) -> List[Dict[str, Any]]: """ 获取指定用户的热门记忆标签 @@ -710,7 +713,14 @@ class MemoryAgentService: try: # by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度） tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) - payload = [{"name": t, "frequency": f} for t, f in tags] + payload=[] + for tag, freq in tags: + print(tag, freq) + if language_type!="zh": + tag=await Translation_English(model_id, tag) + payload.append({"name": tag, "frequency": freq}) + else: + payload.append({"name": tag, "frequency": freq}) return payload except Exception as e: logger.error(f"热门记忆标签查询失败: {e}") diff --git a/api/app/services/memory_base_service.py b/api/app/services/memory_base_service.py index 6f844ae9..784dec7d 100644 --- a/api/app/services/memory_base_service.py +++ b/api/app/services/memory_base_service.py @@ -3,17 +3,164 @@ Memory Base Service 提供记忆服务的基础功能和共享辅助方法。 """ - +import asyncio +import re from datetime import datetime from typing import Optional - +from pydantic import BaseModel from app.core.logging_config import get_logger from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.services.emotion_analytics_service import EmotionAnalyticsService - +from app.core.memory.llm_tools.openai_client import OpenAIClient +from app.core.models.base import RedBearModelConfig +from app.services.memory_config_service import MemoryConfigService +from app.db import get_db_context logger = get_logger(__name__) +class TranslationResponse(BaseModel): + """翻译响应模型""" + data: str + +class MemoryTransService: + """记忆翻译服务，提供中英文翻译功能""" + + def __init__(self, llm_client=None, model_id: Optional[str] = None): + """ + 初始化翻译服务 + + Args: + llm_client: LLM客户端实例或模型ID字符串（可选） + model_id: 模型ID，用于初始化LLM客户端（可选） + + Note: + - 如果llm_client是字符串，会被当作model_id使用 + - 如果同时提供llm_client和model_id，优先使用llm_client + """ + # 处理llm_client参数：如果是字符串，当作model_id + if isinstance(llm_client, str): + self.model_id = llm_client + self.llm_client = None + else: + self.llm_client = llm_client + self.model_id = model_id + + self._initialized = False + + def _ensure_llm_client(self): + """确保LLM客户端已初始化""" + if self._initialized: + return + + if self.llm_client is None: + if self.model_id: + with get_db_context() as db: + config_service = MemoryConfigService(db) + model_config = config_service.get_model_config(self.model_id) + + extra_params = { + "temperature": 0.2, + "max_tokens": 400, + "top_p": 0.8, + "stream": False, + } + + self.llm_client = OpenAIClient( + RedBearModelConfig( + model_name=model_config.get("model_name"), + provider=model_config.get("provider"), + api_key=model_config.get("api_key"), + base_url=model_config.get("base_url"), + timeout=model_config.get("timeout", 30), + max_retries=model_config.get("max_retries", 3), + extra_params=extra_params + ), + type_=model_config.get("type") + ) + else: + raise ValueError("必须提供 llm_client 或 model_id 之一") + + self._initialized = True + + async def translate_to_english(self, text: str) -> str: + """ + 将中文翻译为英文 + + Args: + text: 要翻译的中文文本 + + Returns: + 翻译后的英文文本 + """ + self._ensure_llm_client() + + translation_messages = [ + { + "role": "user", + "content": f"{text}\n\n中文翻译为英文，输出格式为{{\"data\":\"翻译后的内容\"}}" + } + ] + + try: + response = await self.llm_client.response_structured( + messages=translation_messages, + response_model=TranslationResponse + ) + return response.data + except Exception as e: + logger.error(f"翻译失败: {str(e)}") + return text # 翻译失败时返回原文 + + async def is_english(self,text: str) -> bool: + return bool(re.fullmatch(r"[A-Za-z\s]+", text)) + async def Translate(self, text: str, target_language: str = "en") -> str: + """ + 通用翻译方法（保持向后兼容） + + Args: + text: 要翻译的文本 + target_language: 目标语言，"en"表示英文，"zh"表示中文 + + Returns: + 翻译后的文本 + """ + if target_language == "en": + return await self.translate_to_english(text) + else: + logger.warning(f"不支持的目标语言: {target_language}，返回原文") + return text + + # 测试翻译服务 +async def Translation_English(modid, text, fields=None): + """ + 将数据翻译为英文（支持字段级翻译） + + Args: + modid: 模型ID + text: 要翻译的数据（可以是字符串、字典或列表） + fields: 需要翻译的字段列表（可选） + 如果为None，默认翻译: ['content', 'summary', 'statement', 'description', + 'name', 'aliases', 'caption', 'emotion_keywords'] + + Returns: + 翻译后的数据，保持原有结构 + """ + trans_service = MemoryTransService(modid) + # 执行翻译 + if isinstance(text, list): + english_result=[] + for i in text: + is_eng=await trans_service.is_english(i) + if not is_eng: + english = await trans_service.Translate(i) + english_result.append(english) + return english_result + if isinstance(text, str): + is_eng = await trans_service.is_english(text) + if not is_eng: + english_result = await trans_service.Translate(text) + return english_result + return text class MemoryBaseService: """记忆服务基类，提供共享的辅助方法""" @@ -295,3 +442,11 @@ class MemoryBaseService: except Exception as e: logger.error(f"获取遗忘记忆数量时出错: {str(e)}", exc_info=True) return 0 + +if __name__ == '__main__': + import asyncio + a=[{"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:33925", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u4f60\u597d", "created_at": "2026-01-06T14:50:08.381230", "associative_memory": 0}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:33926", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u53d1\u8d77\u4e86\u5bf9\u8bdd\uff0c\u53d1\u9001\u4e86\u95ee\u5019\u8bed\"\u4f60\u597d\"\u3002", "created_at": "2026-01-06T14:50:11.363879", "associative_memory": 0}, "caption": "MemorySummary"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76903", "label": "ExtractedEntity", "properties": {"description": "\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u901a\u8fc7\u4e0d\u540c\u6570\u636e\u6837\u672c\u6765\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u8bef\u5dee\u7684\u65b9\u6cd5", "name": "\u5f88\u591a\u91cd\u8981\u7684\u4eba\u751f\u9009\u62e9", "entity_type": "\u6982\u5ff5\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T19:24:55.805367", "aliases": ["\u505a\u7279\u5f81\u63d0\u53d6", "\u56de\u6eaf\u5386\u53f2\u6570\u636e", "\u5728\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u4e0b\u505a\u957f\u671f\u4e0e\u77ed\u671f\u6536\u76ca\u7684\u6743\u8861", "\u5728\u4e0d\u540c\u6837\u672c\u4e0a\u8bc4\u4f30\u635f\u5931\u51fd\u6570", "\u5bf9\u6bd4\u90a3\u4e9b\u8ba9\u4eba\u8e0f\u5b9e\u6216\u540e\u6094\u7684\u51b3\u5b9a", "\u628a\u65f6\u95f4\u62c9\u957f\u53bb\u60f3\u4e00\u5e74\u3001\u4e09\u5e74\u3001\u4e94\u5e74\u540e\u7684\u7ed3\u679c", "\u6a21\u578b\u53d1\u73b0\u4e86\u5f02\u5e38\u4fe1\u53f7", "\u6a21\u578b\u5728\u591a\u6b21\u62c6\u89e3\u4e0e\u8fed\u4ee3\u4e2d\u9010\u6e10\u6536\u655b\u5230\u7684\u7ed3\u679c", "\u7b54\u6848", "\u4eba\u6027", "\u6545\u4e8b\u4e2d\u7684\u4eba\u7269\u5173\u7cfb"], "connect_strength": "strong", "associative_memory": 11}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76904", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u6d89\u53ca\u591a\u56e0\u7d20\u5206\u6790\u548c\u672a\u6765\u63a8\u65ad\u7684\u590d\u6742\u4efb\u52a1\u7c7b\u578b", "name": "\u4e00\u6b21\u590d\u6742\u7684\u9884\u6d4b\u4efb\u52a1", "entity_type": "", "created_at": "2026-01-06T19:24:55.805367", "connect_strength": "Strong", "associative_memory": 2}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76905", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u5728\u91cd\u8981\u4eba\u751f\u9009\u62e9\u521d\u671f\u51fa\u73b0\u7684\u8f7b\u5fae\u4e0d\u5b89\u60c5\u7eea", "name": "\u6700\u5f00\u59cb\u51fa\u73b0\u7684\u90a3\u70b9\u4e0d\u5b89", "entity_type": "", "created_at": "2026-01-06T19:24:55.805367", "connect_strength": "Strong", "associative_memory": 2}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76906", "label": "ExtractedEntity", "properties": {"description": "\u5bf9\u4ee5\u5f80\u7ecf\u9a8c\u8fdb\u884c\u53cd\u601d\u548c\u5206\u6790\u7684\u884c\u4e3a", "name": "\u56de\u987e\u8fc7\u53bb\u7684\u7ecf\u5386", "entity_type": "\u6982\u5ff5\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T19:24:55.805367", "connect_strength": "Strong", "associative_memory": 2}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76907", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u5c06\u91cd\u8981\u4eba\u751f\u9009\u62e9\u7c7b\u6bd4\u4e3a\u590d\u6742\u7684\u9884\u6d4b\u4efb\u52a1\uff1a\u521d\u59cb\u4e0d\u5b89\u5982\u540c\u6a21\u578b\u68c0\u6d4b\u5230\u5f02\u5e38\u4fe1\u53f7\uff1b\u56de\u987e\u8fc7\u5f80\u7ecf\u5386\u662f\u8fdb\u884c\u5386\u53f2\u6570\u636e\u56de\u6eaf\u4e0e\u7279\u5f81\u63d0\u53d6\uff1b\u5bf9\u6bd4\u4ee4\u4eba\u5b89\u5fc3\u6216\u540e\u6094\u7684\u51b3\u7b56\u76f8\u5f53\u4e8e\u5728\u4e0d\u540c\u6837\u672c\u4e0a\u8bc4\u4f30\u635f\u5931\u51fd\u6570\uff1b\u957f\u671f\u601d\u8003\u4e00\u5e74\u3001\u4e09\u5e74\u3001\u4e94\u5e74\u540e\u7684\u7ed3\u679c\uff0c\u662f\u5728\u6743\u8861\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u4e0b\u7684\u77ed\u671f\u4e0e\u957f\u671f\u6536\u76ca\u3002\u6700\u7ec8\u7684\u201c\u7b54\u6848\u201d\u5e76\u975e\u76f4\u63a5\u8ba1\u7b97\u5f97\u51fa\uff0c\u800c\u662f\u5728\u591a\u6b21\u62c6\u89e3\u4e0e\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\uff0c\u7531\u6a21\u578b\u9010\u6b65\u6536\u655b\u5f62\u6210\u3002", "created_at": "2026-01-06T19:25:18.822414", "associative_memory": 6}, "caption": "MemorySummary"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76908", "label": "Dialogue", "properties": {"content": "\u7528\u6237: 1778 \u97f3\u4e50 ## \u4e8b\u4ef6 - 1 \u6708 1 \u65e5 \u2013 \u5a01\u5ec9\u00b7\u535a\u4f0a\u65af\u7684\u201c\u5f53\u654c\u5bf9\u56fd\u5bb6\u6b66\u88c5\u8d77\u6765\u65f6\u201d\u5728\u4f26\u6566\u5723\u8a79\u59c6\u65af\u5bab\u9996\u6620\u3002[1] - 1 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5728\u8bbf\u95ee\u66fc\u6d77\u59c6\u65f6\u4f1a\u89c1\u4e86\u5f53\u5730\u4f5c\u66f2\u5bb6\u683c\u5965\u5c14\u683c\u00b7\u7ea6\u745f\u592b\u00b7\u6c83\u683c\u52d2\u3002[1] - 1 \u6708 27 \u65e5 \u2013 \u5c3c\u79d1\u6d1b\u00b7\u76ae\u94a6\u5c3c\u7684\u7b2c\u4e00\u90e8\u6cd5\u56fd\u6b4c\u5267\u201c\u7f57\u5170\u201d\u5728\u5df4\u9ece\u6b4c\u5267\u9662\u9996\u6f14\u3002[1] - 2 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5199\u4fe1\u7ed9\u4ed6\u7684\u7236\u4eb2\u5229\u5965\u6ce2\u5fb7\u00b7\u83ab\u624e\u7279\uff0c\u544a\u8bc9\u4ed6\u4ed6\u591a\u4e48\u8ba8\u538c\u4e3a\u957f\u7b1b\u4f5c\u66f2\u3002[1] - 2 \u6708 17 \u65e5 \u2013 \u4f0a\u683c\u7eb3\u5179\u00b7\u4e4c\u59c6\u52b3\u592b\u7684\u201cDie Bergknappen\u201d\u6210\u4e3a\u7b2c\u4e00\u90e8\u5728\u7ef4\u4e5f\u7eb3\u4e0a\u6f14\u7684\u5f53\u5730\u4f5c\u66f2\u5bb6\u521b\u4f5c\u7684\u6b4c\u5531\u5267\u3002[1] - 3 \u6708 1 \u65e5 \u2013 \u514b\u91cc\u65af\u6258\u592b\u00b7\u5a01\u5229\u5df4\u5c14\u5fb7\u00b7\u683c\u9c81\u514b\u5728\u5df4\u9ece\u5c45\u4f4f\u5341\u5e74\u540e\u8fd4\u56de\u7ef4\u4e5f\u7eb3\u3002[1] - 3 \u6708 2 \u65e5 \u2013 \u7ef4\u4e5f\u7eb3\u56fd\u5bb6\u5267\u9662\u559c\u6b4c\u5267\u516c\u53f8\u4e3e\u884c\u6700\u540e\u4e00\u6b21\u6f14\u51fa", "created_at": "2026-01-06T19:31:26.129718", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76909", "label": "Chunk", "properties": {"content": "\u7528\u6237: 1778 \u97f3\u4e50 ## \u4e8b\u4ef6 - 1 \u6708 1 \u65e5 \u2013 \u5a01\u5ec9\u00b7\u535a\u4f0a\u65af\u7684\u201c\u5f53\u654c\u5bf9\u56fd\u5bb6\u6b66\u88c5\u8d77\u6765\u65f6\u201d\u5728\u4f26\u6566\u5723\u8a79\u59c6\u65af\u5bab\u9996\u6620\u3002[1] - 1 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5728\u8bbf\u95ee\u66fc\u6d77\u59c6\u65f6\u4f1a\u89c1\u4e86\u5f53\u5730\u4f5c\u66f2\u5bb6\u683c\u5965\u5c14\u683c\u00b7\u7ea6\u745f\u592b\u00b7\u6c83\u683c\u52d2\u3002[1] - 1 \u6708 27 \u65e5 \u2013 \u5c3c\u79d1\u6d1b\u00b7\u76ae\u94a6\u5c3c\u7684\u7b2c\u4e00\u90e8\u6cd5\u56fd\u6b4c\u5267\u201c\u7f57\u5170\u201d\u5728\u5df4\u9ece\u6b4c\u5267\u9662\u9996\u6f14\u3002[1] - 2 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5199\u4fe1\u7ed9\u4ed6\u7684\u7236\u4eb2\u5229\u5965\u6ce2\u5fb7\u00b7\u83ab\u624e\u7279\uff0c\u544a\u8bc9\u4ed6\u4ed6\u591a\u4e48\u8ba8\u538c\u4e3a\u957f\u7b1b\u4f5c\u66f2\u3002[1] - 2 \u6708 17 \u65e5 \u2013 \u4f0a\u683c\u7eb3\u5179\u00b7\u4e4c\u59c6\u52b3\u592b\u7684\u201cDie Bergknappen\u201d\u6210\u4e3a\u7b2c\u4e00\u90e8\u5728\u7ef4\u4e5f\u7eb3\u4e0a\u6f14\u7684\u5f53\u5730\u4f5c\u66f2\u5bb6\u521b\u4f5c\u7684\u6b4c\u5531\u5267\u3002[1] - 3 \u6708 1 \u65e5 \u2013 \u514b\u91cc\u65af\u6258\u592b\u00b7\u5a01\u5229\u5df4\u5c14\u5fb7\u00b7\u683c\u9c81\u514b\u5728\u5df4\u9ece\u5c45\u4f4f\u5341\u5e74\u540e\u8fd4\u56de\u7ef4\u4e5f\u7eb3\u3002[1] - 3 \u6708 2 \u65e5 \u2013 \u7ef4\u4e5f\u7eb3\u56fd\u5bb6\u5267\u9662\u559c\u6b4c\u5267\u516c\u53f8\u4e3e\u884c\u6700\u540e\u4e00\u6b21\u6f14\u51fa", "created_at": "2026-01-06T19:31:26.129718", "associative_memory": 7}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76910", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": "1778\u5e741\u67081\u65e5\uff0c\u5a01\u5ec9\u00b7\u535a\u4f0a\u65af\u7684\u201c\u5f53\u654c\u5bf9\u56fd\u5bb6\u6b66\u88c5\u8d77\u6765\u65f6\u201d\u5728\u4f26\u6566\u5723\u8a79\u59c6\u65af\u5bab\u9996\u6620\u3002", "valid_at": "1778-01-01T00:00:00+00:00", "created_at": "2026-01-06T19:31:26.129718", "emotion_keywords": [], "associative_memory": 5}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76911", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": "1778\u5e741\u670814\u65e5\uff0c\u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5728\u8bbf\u95ee\u66fc\u6d77\u59c6\u65f6\u4f1a\u89c1\u4e86\u5f53\u5730\u4f5c\u66f2\u5bb6\u683c\u5965\u5c14\u683c\u00b7\u7ea6\u745f\u592b\u00b7\u6c83\u683c\u52d2\u3002", "valid_at": "1778-01-14T00:00:00+00:00", "created_at": "2026-01-06T19:31:26.129718", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76912", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": "1778\u5e741\u670827\u65e5\uff0c\u5c3c\u79d1\u6d1b\u00b7\u76ae\u94a6\u5c3c\u7684\u7b2c\u4e00\u90e8\u6cd5\u56fd\u6b4c\u5267\u201c\u7f57\u5170\u201d\u5728\u5df4\u9ece\u6b4c\u5267\u9662\u9996\u6f14\u3002", "valid_at": "1778-01-27T00:00:00+00:00", "created_at": "2026-01-06T19:31:26.129718", "emotion_keywords": [], "associative_memory": 5}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76913", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": 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"\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:41:05.181477", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77145", "label": "ExtractedEntity", "properties": {"description": "\u53c2\u4e0e\u5bf9\u8bdd\u7684\u4e2a\u4eba\uff0c\u53d1\u51fa\u91ce\u9910\u9080\u7ea6\u7684\u4e00\u65b9", "name": "\u5c0f\u660e", "entity_type": "\u4eba\u7269\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T14:41:05.181477", "aliases": ["\u5c0f\u7ea2"], "connect_strength": "strong", "associative_memory": 13}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77146", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u5e38\u89c1\u7684\u542b\u5496\u5561\u56e0\u996e\u54c1", "name": "\u5496\u5561", "entity_type": "", "created_at": "2026-01-06T14:41:05.181477", "aliases": ["\u5496\u5561\u996e\u6599"], "connect_strength": "Strong", "associative_memory": 3}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77147", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u5496\u5561\u996e\u54c1\uff0c\u7531\u6d53\u7f29\u5496\u5561\u548c\u725b\u5976\u5236\u6210", "name": "\u62ff\u94c1", "entity_type": "", "created_at": "2026-01-06T14:41:05.181477", "aliases": ["\u62ff\u94c1\u5496\u5561", "Latte"], "connect_strength": "Strong", "associative_memory": 3}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77148", "label": "Dialogue", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:44:22.921668", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77149", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:44:22.921668", "associative_memory": 2}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77150", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:44:22.921668", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77151", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:44:22.921668", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77152", "label": "Dialogue", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77153", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "associative_memory": 2}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77154", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:46:02.387455", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77155", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77156", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:16.548556", "associative_memory": 2}, + "caption": "MemorySummary"}] + + result=asyncio.run(Translation_English("2699984d-23be-4817-b81c-c38682a08306",a)) + print(result) \ No newline at end of file diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index eedb7c29..f544cb75 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -16,6 +16,7 @@ import json from datetime import datetime from app.schemas.memory_episodic_schema import EmotionType +from app.services.memory_base_service import Translation_English logger = logging.getLogger(__name__) @@ -24,7 +25,7 @@ class MemoryEntityService: self.id = id self.table = table self.connector = Neo4jConnector() - async def get_timeline_memories_server(self): + async def get_timeline_memories_server(self,model_id, language_type): """ 获取时间线记忆数据 @@ -48,10 +49,10 @@ class MemoryEntityService: logger.info(f"获取时间线记忆数据 - ID: {self.id}, Table: {self.table}") # 根据表类型选择查询 - if self.table == 'Statement': + if self.table == 'Statement': # Statement只需要输入ID，使用简化查询 results = await self.connector.execute_query(Memory_Timeline_Statement, id=self.id) - elif self.table == 'ExtractedEntity': + elif self.table == 'ExtractedEntity': # ExtractedEntity类型查询 results = await self.connector.execute_query(Memory_Timeline_ExtractedEntity, id=self.id) else: @@ -62,7 +63,7 @@ class MemoryEntityService: logger.info(f"时间线查询结果类型: {type(results)}, 长度: {len(results) if isinstance(results, list) else 'N/A'}") # 处理查询结果 - timeline_data = self._process_timeline_results(results) + timeline_data =await self._process_timeline_results(results, model_id, language_type) logger.info(f"成功获取时间线记忆数据: 总计 {len(timeline_data.get('timelines_memory', []))} 条") @@ -71,12 +72,14 @@ class MemoryEntityService: except Exception as e: logger.error(f"获取时间线记忆数据失败: {str(e)}", exc_info=True) return str(e) - def _process_timeline_results(self, results: List[Dict[str, Any]]) -> Dict[str, Any]: + async def _process_timeline_results(self, results: List[Dict[str, Any]], model_id: str, language_type: str) -> Dict[str, Any]: """ 处理时间线查询结果 Args: results: Neo4j查询结果 + model_id: 模型ID用于翻译 + language_type: 语言类型 ('zh' 或其他) Returns: 处理后的时间线数据字典 @@ -104,19 +107,19 @@ class MemoryEntityService: # 处理MemorySummary summary = data.get('MemorySummary') if summary is not None: - processed_summary = self._process_field_value(summary, "MemorySummary") + processed_summary = await self._process_field_value(summary, "MemorySummary") memory_summary_list.extend(processed_summary) # 处理Statement statement = data.get('statement') if statement is not None: - processed_statement = self._process_field_value(statement, "Statement") + processed_statement = await self._process_field_value(statement, "Statement") statement_list.extend(processed_statement) # 处理ExtractedEntity extracted_entity = data.get('ExtractedEntity') if extracted_entity is not None: - processed_entity = self._process_field_value(extracted_entity, "ExtractedEntity") + processed_entity = await self._process_field_value(extracted_entity, "ExtractedEntity") extracted_entity_list.extend(processed_entity) # 去重 - 现在处理的是字典列表，需要更智能的去重 @@ -128,6 +131,21 @@ class MemoryEntityService: all_timeline_data = memory_summary_list + statement_list all_timeline_data = self._merge_same_text_items(all_timeline_data) + # 如果需要翻译（非中文），对整个结果进行翻译 + if language_type != 'zh': + # 定义需要翻译的字段 + fields_to_translate = ['text', 'type'] + + # 翻译各个列表 + if memory_summary_list: + memory_summary_list = await self._translate_list(memory_summary_list, model_id, fields_to_translate) + if statement_list: + statement_list = await self._translate_list(statement_list, model_id, fields_to_translate) + if extracted_entity_list: + extracted_entity_list = await self._translate_list(extracted_entity_list, model_id, fields_to_translate) + if all_timeline_data: + all_timeline_data = await self._translate_list(all_timeline_data, model_id, fields_to_translate) + result = { "MemorySummary": memory_summary_list, "Statement": statement_list, @@ -233,7 +251,7 @@ class MemoryEntityService: except Exception: return False - def _process_field_value(self, value: Any, field_name: str) -> List[Dict[str, Any]]: + async def _process_field_value(self, value: Any, field_name: str) -> List[Dict[str, Any]]: """ 处理字段值，支持字符串、列表等类型 @@ -251,13 +269,13 @@ class MemoryEntityService: # 如果是列表，处理每个元素 for item in value: if self._is_valid_item(item): - processed_item = self._process_single_item(item) + processed_item = await self._process_single_item(item) if processed_item: processed_values.append(processed_item) elif isinstance(value, dict): # 如果是字典，直接处理 if self._is_valid_item(value): - processed_item = self._process_single_item(value) + processed_item = await self._process_single_item(value) if processed_item: processed_values.append(processed_item) elif isinstance(value, str): @@ -304,7 +322,7 @@ class MemoryEntityService: return (str(item).strip() != '' and "MemorySummaryChunk" not in str(item)) - def _process_single_item(self, item: Dict[str, Any]) -> Optional[Dict[str, Any]]: + async def _process_single_item(self, item: Dict[str, Any]) -> Optional[Dict[str, Any]]: """ 处理单个项目 @@ -369,6 +387,42 @@ class MemoryEntityService: logger.warning(f"转换时间格式失败: {e}, 原始值: {dt}") return str(dt) if dt is not None else None + async def _translate_list(self, data_list: List[Dict[str, Any]], model_id: str, fields: List[str]) -> List[Dict[str, Any]]: + """ + 翻译列表中每个字典的指定字段 + + Args: + data_list: 要翻译的字典列表 + model_id: 模型ID + fields: 需要翻译的字段列表 + + Returns: + 翻译后的字典列表 + """ + if not data_list: + return data_list + + translated_list = [] + for item in data_list: + if not isinstance(item, dict): + translated_list.append(item) + continue + + translated_item = item.copy() + for field in fields: + if field in translated_item and translated_item[field]: + try: + # 调用Translation_English翻译单个字段 + translated_value = await Translation_English(model_id, translated_item[field]) + if translated_value: + translated_item[field] = translated_value + except Exception as e: + logger.warning(f"翻译字段 {field} 失败: {e}") + + translated_list.append(translated_item) + + return translated_list + @@ -426,15 +480,19 @@ class MemoryEmotion: # 如果解析失败，返回原始字符串 return iso_string - async def get_emotion(self) -> Dict[str, Any]: + async def get_emotion(self, model_id: str = None, language_type: str = 'zh') -> Dict[str, Any]: """ 获取情绪随时间变化数据 + Args: + model_id: 模型ID用于翻译 + language_type: 语言类型 ('zh' 或其他) + Returns: 包含情绪数据的字典 """ try: - logger.info(f"获取情绪数据 - ID: {self.id}, Table: {self.table}") + logger.info(f"获取情绪数据 - ID: {self.id}, Table: {self.table}, language_type={language_type}") if self.table == 'Statement': results = await self.connector.execute_query(Memory_Space_Emotion_Statement, id=self.id) @@ -450,6 +508,10 @@ class MemoryEmotion: # 转换Neo4j类型 final_data = self._convert_neo4j_types(emotion_data) + # 如果需要翻译（非中文） + if language_type != 'zh' and model_id and final_data: + final_data = await self._translate_emotion_data(final_data, model_id) + logger.info(f"成功获取 {len(final_data)} 条情绪数据") return final_data @@ -590,16 +652,14 @@ class MemoryInteraction: """ try: logger.info(f"获取交互数据 - ID: {self.id}, Table: {self.table}") - ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id) if ori_data!=[]: # name = ori_data[0]['name'] - group_id = ori_data[0]['group_id'] + group_id = [i['group_id'] for i in ori_data][0] Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) if not Space_User: return [] user_id=Space_User[0]['id'] - results = await self.connector.execute_query(Memory_Space_Associative, id=self.id,user_id=user_id) diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 9221ab06..ae07256a 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -18,7 +18,7 @@ from app.repositories.end_user_repository import EndUserRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_episodic_schema import EmotionSubject, EmotionType, type_mapping from app.services.implicit_memory_service import ImplicitMemoryService -from app.services.memory_base_service import MemoryBaseService +from app.services.memory_base_service import MemoryBaseService, MemoryTransService, Translation_English from app.services.memory_config_service import MemoryConfigService from app.services.memory_perceptual_service import MemoryPerceptualService from app.services.memory_short_service import ShortService @@ -360,7 +360,9 @@ class UserMemoryService: async def get_cached_memory_insight( self, db: Session, - end_user_id: str + end_user_id: str, + model_id: str, + language_type: str ) -> Dict[str, Any]: """ 从数据库获取缓存的记忆洞察（四个维度） @@ -419,11 +421,18 @@ class UserMemoryService: key_findings_array = [] logger.info(f"成功获取 end_user_id {end_user_id} 的缓存记忆洞察（四维度）") + memory_insight=end_user.memory_insight + behavior_pattern=end_user.behavior_pattern + growth_trajectory=end_user.growth_trajectory + if language_type!='zh': + memory_insight=await Translation_English(model_id,memory_insight) + behavior_pattern=await Translation_English(model_id,behavior_pattern) + growth_trajectory=await Translation_English(model_id,growth_trajectory) return { - "memory_insight": end_user.memory_insight, # 总体概述存储在 memory_insight - "behavior_pattern": end_user.behavior_pattern, + "memory_insight":memory_insight, # 总体概述存储在 memory_insight + "behavior_pattern":behavior_pattern, "key_findings": key_findings_array, # 返回数组 - "growth_trajectory": end_user.growth_trajectory, + "growth_trajectory": growth_trajectory, "updated_at": self._datetime_to_timestamp(end_user.memory_insight_updated_at), "is_cached": True } @@ -457,7 +466,9 @@ class UserMemoryService: async def get_cached_user_summary( self, db: Session, - end_user_id: str + end_user_id: str, + model_id:str, + language_type:str="zh" ) -> Dict[str, Any]: """ 从数据库获取缓存的用户摘要（四个部分） @@ -481,7 +492,6 @@ class UserMemoryService: user_uuid = uuid.UUID(end_user_id) repo = EndUserRepository(db) end_user = repo.get_by_id(user_uuid) - if not end_user: logger.warning(f"未找到 end_user_id 为 {end_user_id} 的用户") return { @@ -495,20 +505,29 @@ class UserMemoryService: } # 检查是否有缓存数据（至少有一个字段不为空） + user_summary=end_user.user_summary + personality_traits=end_user.personality_traits + core_values=end_user.core_values + one_sentence_summary=end_user.one_sentence_summary + if language_type!='zh': + user_summary=await Translation_English(model_id, user_summary) + personality_traits = await Translation_English(model_id, personality_traits) + core_values = await Translation_English(model_id, core_values) + one_sentence_summary = await Translation_English(model_id, one_sentence_summary) has_cache = any([ - end_user.user_summary, - end_user.personality_traits, - end_user.core_values, - end_user.one_sentence_summary + user_summary, + personality_traits, + core_values, + one_sentence_summary ]) if has_cache: logger.info(f"成功获取 end_user_id {end_user_id} 的缓存用户摘要") return { - "user_summary": end_user.user_summary, - "personality": end_user.personality_traits, - "core_values": end_user.core_values, - "one_sentence": end_user.one_sentence_summary, + "user_summary": user_summary, + "personality": personality_traits, + "core_values":core_values, + "one_sentence": one_sentence_summary, "updated_at": self._datetime_to_timestamp(end_user.user_summary_updated_at), "is_cached": True } @@ -1367,7 +1386,6 @@ async def analytics_memory_types( return memory_types - async def analytics_graph_data( db: Session, end_user_id: str, @@ -1557,7 +1575,7 @@ async def analytics_graph_data( f"成功获取图数据: end_user_id={end_user_id}, " f"nodes={len(nodes)}, edges={len(edges)}" ) - + return { "nodes": nodes, "edges": edges, @@ -1606,11 +1624,7 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ # 获取该节点类型的白名单字段 allowed_fields = field_whitelist.get(label, []) - - # 如果没有定义白名单，返回空字典（或者可以返回所有字段） - # if not allowed_fields: - # # 对于未定义的节点类型，只返回基本字段 - # allowed_fields = ["name", "created_at", "caption"] + count_neo4j=f"""MATCH (n)-[r]-(m) WHERE elementId(n) ="{node_id}" RETURN count(r) AS rel_count;""" node_results = await (_neo4j_connector.execute_query(count_neo4j)) # 提取白名单中的字段 @@ -1618,13 +1632,12 @@ async def _extract_node_properties(label: str, properties: Dict[str, Any],node_ for field in allowed_fields: if field in properties: value = properties[field] - if str(field) == 'entity_type': + if str(field) == 'entity_type': value=type_mapping.get(value,'') if str(field)=="emotion_type": value=EmotionType.EMOTION_MAPPING.get(value) - if str(field)=="emotion_subject": + if str(field)=="emotion_subject": value=EmotionSubject.SUBJECT_MAPPING.get(value) - # 清理 Neo4j 特殊类型 filtered_props[field] = _clean_neo4j_value(value) filtered_props['associative_memory']=[i['rel_count'] for i in node_results][0] return filtered_props From 84c6c7e2a63811488458c4cd16d4018e5a5ce5bc Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 10:36:04 +0800 Subject: [PATCH 023/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)-=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91=E5=AD=97=E6=AE=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/memory_short_term_controller.py | 1 + api/app/controllers/user_memory_controllers.py | 15 +++------------ api/app/schemas/emotion_schema.py | 5 +++++ api/app/schemas/memory_episodic_schema.py | 2 ++ api/app/schemas/memory_explicit_schema.py | 4 ++++ api/app/services/memory_base_service.py | 10 +--------- 6 files changed, 16 insertions(+), 21 deletions(-) diff --git a/api/app/controllers/memory_short_term_controller.py b/api/app/controllers/memory_short_term_controller.py index 64991f4d..9cf66749 100644 --- a/api/app/controllers/memory_short_term_controller.py +++ b/api/app/controllers/memory_short_term_controller.py @@ -20,6 +20,7 @@ router = APIRouter( @router.get("/short_term") async def short_term_configs( end_user_id: str, + language_type:Optional[str] = "zh", current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index 560d6c0d..8ff9c41d 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -292,7 +292,6 @@ async def get_graph_data_api( @router.get("/read_end_user/profile", response_model=ApiResponse) async def get_end_user_profile( end_user_id: str, - language_type: str = "zh", current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -321,20 +320,12 @@ async def get_end_user_profile( if not end_user: api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}") return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}") - - other_name=end_user.other_name - position=end_user.position - department=end_user.department - if language_type!="zh": - other_name=await Translation_English(model_id,other_name) - position = await Translation_English(model_id, position) - department = await Translation_English(model_id, department) # 构建响应数据 profile_data = EndUserProfileResponse( id=end_user.id, - other_name=other_name, - position=position, - department=department, + other_name=end_user.other_name, + position=end_user.position, + department=end_user.department, contact=end_user.contact, phone=end_user.phone, hire_date=end_user.hire_date, diff --git a/api/app/schemas/emotion_schema.py b/api/app/schemas/emotion_schema.py index 5175fed1..cfa65b0f 100644 --- a/api/app/schemas/emotion_schema.py +++ b/api/app/schemas/emotion_schema.py @@ -11,6 +11,7 @@ class EmotionTagsRequest(BaseModel): start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）") end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）") limit: int = Field(10, ge=1, le=100, description="返回数量限制") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") class EmotionWordcloudRequest(BaseModel): @@ -18,20 +19,24 @@ class EmotionWordcloudRequest(BaseModel): group_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") limit: int = Field(50, ge=1, le=200, description="返回词语数量") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") class EmotionHealthRequest(BaseModel): """获取情绪健康指数请求""" group_id: str = Field(..., description="组ID") time_range: str = Field("30d", description="时间范围（7d/30d/90d）") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") class EmotionSuggestionsRequest(BaseModel): """获取个性化情绪建议请求""" group_id: str = Field(..., description="组ID") config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") class EmotionGenerateSuggestionsRequest(BaseModel): """生成个性化情绪建议请求""" end_user_id: str = Field(..., description="终端用户ID") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") diff --git a/api/app/schemas/memory_episodic_schema.py b/api/app/schemas/memory_episodic_schema.py index 832bf34b..74e68837 100644 --- a/api/app/schemas/memory_episodic_schema.py +++ b/api/app/schemas/memory_episodic_schema.py @@ -51,6 +51,7 @@ class EpisodicMemoryOverviewRequest(BaseModel): """情景记忆总览查询请求""" end_user_id: str = Field(..., description="终端用户ID") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") time_range: str = Field( default="all", description="时间范围筛选，可选值：all, today, this_week, this_month" @@ -70,3 +71,4 @@ class EpisodicMemoryDetailsRequest(BaseModel): end_user_id: str = Field(..., description="终端用户ID") summary_id: str = Field(..., description="情景记忆摘要ID") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") diff --git a/api/app/schemas/memory_explicit_schema.py b/api/app/schemas/memory_explicit_schema.py index c2b51a81..823a3116 100644 --- a/api/app/schemas/memory_explicit_schema.py +++ b/api/app/schemas/memory_explicit_schema.py @@ -1,15 +1,19 @@ """ 显性记忆的请求和响应模型 """ +from typing import Optional + from pydantic import BaseModel, Field class ExplicitMemoryOverviewRequest(BaseModel): """显性记忆总览查询请求""" end_user_id: str = Field(..., description="终端用户ID") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") class ExplicitMemoryDetailsRequest(BaseModel): """显性记忆详情查询请求""" end_user_id: str = Field(..., description="终端用户ID") memory_id: str = Field(..., description="记忆ID（情景记忆或语义记忆的ID）") + language_type: Optional[str] = Field("zh", description="语言类型（zh/en）") diff --git a/api/app/services/memory_base_service.py b/api/app/services/memory_base_service.py index 784dec7d..ce4940d0 100644 --- a/api/app/services/memory_base_service.py +++ b/api/app/services/memory_base_service.py @@ -441,12 +441,4 @@ class MemoryBaseService: except Exception as e: logger.error(f"获取遗忘记忆数量时出错: {str(e)}", exc_info=True) - return 0 - -if __name__ == '__main__': - import asyncio - a=[{"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:33925", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u4f60\u597d", "created_at": "2026-01-06T14:50:08.381230", "associative_memory": 0}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:33926", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u53d1\u8d77\u4e86\u5bf9\u8bdd\uff0c\u53d1\u9001\u4e86\u95ee\u5019\u8bed\"\u4f60\u597d\"\u3002", "created_at": "2026-01-06T14:50:11.363879", "associative_memory": 0}, "caption": "MemorySummary"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76903", "label": "ExtractedEntity", "properties": {"description": "\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u901a\u8fc7\u4e0d\u540c\u6570\u636e\u6837\u672c\u6765\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u8bef\u5dee\u7684\u65b9\u6cd5", "name": "\u5f88\u591a\u91cd\u8981\u7684\u4eba\u751f\u9009\u62e9", "entity_type": "\u6982\u5ff5\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T19:24:55.805367", "aliases": ["\u505a\u7279\u5f81\u63d0\u53d6", "\u56de\u6eaf\u5386\u53f2\u6570\u636e", "\u5728\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u4e0b\u505a\u957f\u671f\u4e0e\u77ed\u671f\u6536\u76ca\u7684\u6743\u8861", "\u5728\u4e0d\u540c\u6837\u672c\u4e0a\u8bc4\u4f30\u635f\u5931\u51fd\u6570", "\u5bf9\u6bd4\u90a3\u4e9b\u8ba9\u4eba\u8e0f\u5b9e\u6216\u540e\u6094\u7684\u51b3\u5b9a", "\u628a\u65f6\u95f4\u62c9\u957f\u53bb\u60f3\u4e00\u5e74\u3001\u4e09\u5e74\u3001\u4e94\u5e74\u540e\u7684\u7ed3\u679c", "\u6a21\u578b\u53d1\u73b0\u4e86\u5f02\u5e38\u4fe1\u53f7", "\u6a21\u578b\u5728\u591a\u6b21\u62c6\u89e3\u4e0e\u8fed\u4ee3\u4e2d\u9010\u6e10\u6536\u655b\u5230\u7684\u7ed3\u679c", "\u7b54\u6848", "\u4eba\u6027", "\u6545\u4e8b\u4e2d\u7684\u4eba\u7269\u5173\u7cfb"], "connect_strength": "strong", "associative_memory": 11}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76904", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u6d89\u53ca\u591a\u56e0\u7d20\u5206\u6790\u548c\u672a\u6765\u63a8\u65ad\u7684\u590d\u6742\u4efb\u52a1\u7c7b\u578b", "name": "\u4e00\u6b21\u590d\u6742\u7684\u9884\u6d4b\u4efb\u52a1", "entity_type": "", "created_at": "2026-01-06T19:24:55.805367", "connect_strength": "Strong", "associative_memory": 2}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76905", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u5728\u91cd\u8981\u4eba\u751f\u9009\u62e9\u521d\u671f\u51fa\u73b0\u7684\u8f7b\u5fae\u4e0d\u5b89\u60c5\u7eea", "name": "\u6700\u5f00\u59cb\u51fa\u73b0\u7684\u90a3\u70b9\u4e0d\u5b89", "entity_type": "", "created_at": "2026-01-06T19:24:55.805367", "connect_strength": "Strong", "associative_memory": 2}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76906", "label": "ExtractedEntity", "properties": {"description": "\u5bf9\u4ee5\u5f80\u7ecf\u9a8c\u8fdb\u884c\u53cd\u601d\u548c\u5206\u6790\u7684\u884c\u4e3a", "name": "\u56de\u987e\u8fc7\u53bb\u7684\u7ecf\u5386", "entity_type": "\u6982\u5ff5\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T19:24:55.805367", "connect_strength": "Strong", "associative_memory": 2}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76907", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u5c06\u91cd\u8981\u4eba\u751f\u9009\u62e9\u7c7b\u6bd4\u4e3a\u590d\u6742\u7684\u9884\u6d4b\u4efb\u52a1\uff1a\u521d\u59cb\u4e0d\u5b89\u5982\u540c\u6a21\u578b\u68c0\u6d4b\u5230\u5f02\u5e38\u4fe1\u53f7\uff1b\u56de\u987e\u8fc7\u5f80\u7ecf\u5386\u662f\u8fdb\u884c\u5386\u53f2\u6570\u636e\u56de\u6eaf\u4e0e\u7279\u5f81\u63d0\u53d6\uff1b\u5bf9\u6bd4\u4ee4\u4eba\u5b89\u5fc3\u6216\u540e\u6094\u7684\u51b3\u7b56\u76f8\u5f53\u4e8e\u5728\u4e0d\u540c\u6837\u672c\u4e0a\u8bc4\u4f30\u635f\u5931\u51fd\u6570\uff1b\u957f\u671f\u601d\u8003\u4e00\u5e74\u3001\u4e09\u5e74\u3001\u4e94\u5e74\u540e\u7684\u7ed3\u679c\uff0c\u662f\u5728\u6743\u8861\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u4e0b\u7684\u77ed\u671f\u4e0e\u957f\u671f\u6536\u76ca\u3002\u6700\u7ec8\u7684\u201c\u7b54\u6848\u201d\u5e76\u975e\u76f4\u63a5\u8ba1\u7b97\u5f97\u51fa\uff0c\u800c\u662f\u5728\u591a\u6b21\u62c6\u89e3\u4e0e\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\uff0c\u7531\u6a21\u578b\u9010\u6b65\u6536\u655b\u5f62\u6210\u3002", "created_at": "2026-01-06T19:25:18.822414", "associative_memory": 6}, "caption": "MemorySummary"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76908", "label": "Dialogue", "properties": {"content": "\u7528\u6237: 1778 \u97f3\u4e50 ## \u4e8b\u4ef6 - 1 \u6708 1 \u65e5 \u2013 \u5a01\u5ec9\u00b7\u535a\u4f0a\u65af\u7684\u201c\u5f53\u654c\u5bf9\u56fd\u5bb6\u6b66\u88c5\u8d77\u6765\u65f6\u201d\u5728\u4f26\u6566\u5723\u8a79\u59c6\u65af\u5bab\u9996\u6620\u3002[1] - 1 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5728\u8bbf\u95ee\u66fc\u6d77\u59c6\u65f6\u4f1a\u89c1\u4e86\u5f53\u5730\u4f5c\u66f2\u5bb6\u683c\u5965\u5c14\u683c\u00b7\u7ea6\u745f\u592b\u00b7\u6c83\u683c\u52d2\u3002[1] - 1 \u6708 27 \u65e5 \u2013 \u5c3c\u79d1\u6d1b\u00b7\u76ae\u94a6\u5c3c\u7684\u7b2c\u4e00\u90e8\u6cd5\u56fd\u6b4c\u5267\u201c\u7f57\u5170\u201d\u5728\u5df4\u9ece\u6b4c\u5267\u9662\u9996\u6f14\u3002[1] - 2 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5199\u4fe1\u7ed9\u4ed6\u7684\u7236\u4eb2\u5229\u5965\u6ce2\u5fb7\u00b7\u83ab\u624e\u7279\uff0c\u544a\u8bc9\u4ed6\u4ed6\u591a\u4e48\u8ba8\u538c\u4e3a\u957f\u7b1b\u4f5c\u66f2\u3002[1] - 2 \u6708 17 \u65e5 \u2013 \u4f0a\u683c\u7eb3\u5179\u00b7\u4e4c\u59c6\u52b3\u592b\u7684\u201cDie Bergknappen\u201d\u6210\u4e3a\u7b2c\u4e00\u90e8\u5728\u7ef4\u4e5f\u7eb3\u4e0a\u6f14\u7684\u5f53\u5730\u4f5c\u66f2\u5bb6\u521b\u4f5c\u7684\u6b4c\u5531\u5267\u3002[1] - 3 \u6708 1 \u65e5 \u2013 \u514b\u91cc\u65af\u6258\u592b\u00b7\u5a01\u5229\u5df4\u5c14\u5fb7\u00b7\u683c\u9c81\u514b\u5728\u5df4\u9ece\u5c45\u4f4f\u5341\u5e74\u540e\u8fd4\u56de\u7ef4\u4e5f\u7eb3\u3002[1] - 3 \u6708 2 \u65e5 \u2013 \u7ef4\u4e5f\u7eb3\u56fd\u5bb6\u5267\u9662\u559c\u6b4c\u5267\u516c\u53f8\u4e3e\u884c\u6700\u540e\u4e00\u6b21\u6f14\u51fa", "created_at": "2026-01-06T19:31:26.129718", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76909", "label": "Chunk", "properties": {"content": "\u7528\u6237: 1778 \u97f3\u4e50 ## \u4e8b\u4ef6 - 1 \u6708 1 \u65e5 \u2013 \u5a01\u5ec9\u00b7\u535a\u4f0a\u65af\u7684\u201c\u5f53\u654c\u5bf9\u56fd\u5bb6\u6b66\u88c5\u8d77\u6765\u65f6\u201d\u5728\u4f26\u6566\u5723\u8a79\u59c6\u65af\u5bab\u9996\u6620\u3002[1] - 1 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5728\u8bbf\u95ee\u66fc\u6d77\u59c6\u65f6\u4f1a\u89c1\u4e86\u5f53\u5730\u4f5c\u66f2\u5bb6\u683c\u5965\u5c14\u683c\u00b7\u7ea6\u745f\u592b\u00b7\u6c83\u683c\u52d2\u3002[1] - 1 \u6708 27 \u65e5 \u2013 \u5c3c\u79d1\u6d1b\u00b7\u76ae\u94a6\u5c3c\u7684\u7b2c\u4e00\u90e8\u6cd5\u56fd\u6b4c\u5267\u201c\u7f57\u5170\u201d\u5728\u5df4\u9ece\u6b4c\u5267\u9662\u9996\u6f14\u3002[1] - 2 \u6708 14 \u65e5 \u2013 \u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5199\u4fe1\u7ed9\u4ed6\u7684\u7236\u4eb2\u5229\u5965\u6ce2\u5fb7\u00b7\u83ab\u624e\u7279\uff0c\u544a\u8bc9\u4ed6\u4ed6\u591a\u4e48\u8ba8\u538c\u4e3a\u957f\u7b1b\u4f5c\u66f2\u3002[1] - 2 \u6708 17 \u65e5 \u2013 \u4f0a\u683c\u7eb3\u5179\u00b7\u4e4c\u59c6\u52b3\u592b\u7684\u201cDie Bergknappen\u201d\u6210\u4e3a\u7b2c\u4e00\u90e8\u5728\u7ef4\u4e5f\u7eb3\u4e0a\u6f14\u7684\u5f53\u5730\u4f5c\u66f2\u5bb6\u521b\u4f5c\u7684\u6b4c\u5531\u5267\u3002[1] - 3 \u6708 1 \u65e5 \u2013 \u514b\u91cc\u65af\u6258\u592b\u00b7\u5a01\u5229\u5df4\u5c14\u5fb7\u00b7\u683c\u9c81\u514b\u5728\u5df4\u9ece\u5c45\u4f4f\u5341\u5e74\u540e\u8fd4\u56de\u7ef4\u4e5f\u7eb3\u3002[1] - 3 \u6708 2 \u65e5 \u2013 \u7ef4\u4e5f\u7eb3\u56fd\u5bb6\u5267\u9662\u559c\u6b4c\u5267\u516c\u53f8\u4e3e\u884c\u6700\u540e\u4e00\u6b21\u6f14\u51fa", "created_at": "2026-01-06T19:31:26.129718", "associative_memory": 7}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76910", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": "1778\u5e741\u67081\u65e5\uff0c\u5a01\u5ec9\u00b7\u535a\u4f0a\u65af\u7684\u201c\u5f53\u654c\u5bf9\u56fd\u5bb6\u6b66\u88c5\u8d77\u6765\u65f6\u201d\u5728\u4f26\u6566\u5723\u8a79\u59c6\u65af\u5bab\u9996\u6620\u3002", "valid_at": "1778-01-01T00:00:00+00:00", "created_at": "2026-01-06T19:31:26.129718", "emotion_keywords": [], "associative_memory": 5}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76911", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": "1778\u5e741\u670814\u65e5\uff0c\u6c83\u5c14\u592b\u5188\u00b7\u963f\u9a6c\u5fb7\u4e4c\u65af\u00b7\u83ab\u624e\u7279\u5728\u8bbf\u95ee\u66fc\u6d77\u59c6\u65f6\u4f1a\u89c1\u4e86\u5f53\u5730\u4f5c\u66f2\u5bb6\u683c\u5965\u5c14\u683c\u00b7\u7ea6\u745f\u592b\u00b7\u6c83\u683c\u52d2\u3002", "valid_at": "1778-01-14T00:00:00+00:00", "created_at": "2026-01-06T19:31:26.129718", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76912", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": "1778\u5e741\u670827\u65e5\uff0c\u5c3c\u79d1\u6d1b\u00b7\u76ae\u94a6\u5c3c\u7684\u7b2c\u4e00\u90e8\u6cd5\u56fd\u6b4c\u5267\u201c\u7f57\u5170\u201d\u5728\u5df4\u9ece\u6b4c\u5267\u9662\u9996\u6f14\u3002", "valid_at": "1778-01-27T00:00:00+00:00", "created_at": "2026-01-06T19:31:26.129718", "emotion_keywords": [], "associative_memory": 5}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76913", "label": "Statement", "properties": {"temporal_info": "STATIC", "stmt_type": "FACT", "statement": 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"label": "Dialogue", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77153", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "associative_memory": 2}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77154", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:46:02.387455", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77155", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77156", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:16.548556", "associative_memory": 2}, - "caption": "MemorySummary"}] - - result=asyncio.run(Translation_English("2699984d-23be-4817-b81c-c38682a08306",a)) - print(result) \ No newline at end of file + return 0 \ No newline at end of file From 0e2e495d090557d2e68fdc9ed015a23d3b8aee6a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 11:03:37 +0800 Subject: [PATCH 024/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)-=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91=E5=AD=97=E6=AE=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../memory_entity_relationship_service.py | 107 +++++++++++++++--- 1 file changed, 91 insertions(+), 16 deletions(-) diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index f544cb75..06c1616d 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -387,9 +387,14 @@ class MemoryEntityService: logger.warning(f"转换时间格式失败: {e}, 原始值: {dt}") return str(dt) if dt is not None else None - async def _translate_list(self, data_list: List[Dict[str, Any]], model_id: str, fields: List[str]) -> List[Dict[str, Any]]: + async def _translate_list( + self, + data_list: List[Dict[str, Any]], + model_id: str, + fields: List[str] + ) -> List[Dict[str, Any]]: """ - 翻译列表中每个字典的指定字段 + 翻译列表中每个字典的指定字段（并发有限度以降低整体延迟） Args: data_list: 要翻译的字典列表 @@ -399,27 +404,97 @@ class MemoryEntityService: Returns: 翻译后的字典列表 """ - if not data_list: + # 空列表或无字段时直接返回 + if not data_list or not fields: return data_list - translated_list = [] - for item in data_list: + import asyncio + + # 并发限制，避免一次性发起过多请求 + # 可根据实际情况调整（建议 5-10） + concurrency_limit = 5 + semaphore = asyncio.Semaphore(concurrency_limit) + + async def translate_single_field( + index: int, + field: str, + value: Any, + ) -> Optional[tuple]: + """ + 翻译单个字段并返回 (索引, 字段名, 翻译结果) + + Returns: + (index, field, translated_value) 或 None（如果跳过） + """ + # 跳过空值 + if value is None or value == "": + return None + + # 统一转成字符串再翻译，防止非字符串类型导致错误 + text = str(value) + + try: + async with semaphore: + # 调用 Translation_English 进行翻译 + # 注意：Translation_English 的参数顺序是 (model_id, text) + translated = await Translation_English(model_id, text) + + # 如果翻译结果为空，保留原值 + if translated is None or translated == "": + return None + + return index, field, translated + except Exception as e: + logger.warning(f"翻译字段 {field} (索引 {index}) 失败: {e}") + return None + + # 构造所有需要翻译的任务 + tasks = [] + for idx, item in enumerate(data_list): + # 防御性检查：确保 item 是字典 if not isinstance(item, dict): - translated_list.append(item) continue - translated_item = item.copy() for field in fields: - if field in translated_item and translated_item[field]: - try: - # 调用Translation_English翻译单个字段 - translated_value = await Translation_English(model_id, translated_item[field]) - if translated_value: - translated_item[field] = translated_value - except Exception as e: - logger.warning(f"翻译字段 {field} 失败: {e}") + if field not in item: + continue + + value = item.get(field) + + # 对于 None 或空字符串的值，直接跳过，不创建任务 + if value is None or value == "": + continue + + tasks.append( + asyncio.create_task( + translate_single_field(idx, field, value) + ) + ) + + # 如果没有需要翻译的任务，直接返回原列表 + if not tasks: + return data_list + + # 使用 gather 并发执行翻译任务（受 semaphore 限制） + # return_exceptions=True 可以防止单个任务失败导致整体失败 + results = await asyncio.gather(*tasks, return_exceptions=True) + + # 创建深拷贝以避免修改原始数据 + translated_list = [item.copy() if isinstance(item, dict) else item for item in data_list] + + # 将翻译结果回填到列表 + for result in results: + # 跳过 None 结果和异常 + if result is None or isinstance(result, Exception): + if isinstance(result, Exception): + logger.warning(f"翻译任务异常: {result}") + continue - translated_list.append(translated_item) + idx, field, translated = result + + # 防御性检查索引范围 + if 0 <= idx < len(translated_list) and isinstance(translated_list[idx], dict): + translated_list[idx][field] = translated return translated_list From cd5f1a1b289985b1f6fd6ae000811fd75f156f40 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 11:05:56 +0800 Subject: [PATCH 025/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)-=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91=E5=AD=97=E6=AE=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index ddf04216..fd0cb0eb 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -715,7 +715,6 @@ class MemoryAgentService: tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) payload=[] for tag, freq in tags: - print(tag, freq) if language_type!="zh": tag=await Translation_English(model_id, tag) payload.append({"name": tag, "frequency": freq}) From 98b2da91237a3299f2979ed6faa777563eca227a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 11:15:18 +0800 Subject: [PATCH 026/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)-=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91=E5=AD=97=E6=AE=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_base_service.py | 136 +++++++++++++++++++++--- 1 file changed, 120 insertions(+), 16 deletions(-) diff --git a/api/app/services/memory_base_service.py b/api/app/services/memory_base_service.py index ce4940d0..25a8281d 100644 --- a/api/app/services/memory_base_service.py +++ b/api/app/services/memory_base_service.py @@ -109,8 +109,36 @@ class MemoryTransService: logger.error(f"翻译失败: {str(e)}") return text # 翻译失败时返回原文 - async def is_english(self,text: str) -> bool: - return bool(re.fullmatch(r"[A-Za-z\s]+", text)) + async def is_english(self, text: str) -> bool: + """ + 检查文本是否为英文 + + Args: + text: 要检查的文本（必须是字符串） + + Returns: + True 如果文本主要是英文，False 否则 + + Note: + - 只接受字符串类型 + - 检查是否主要由英文字母和常见标点组成 + - 允许数字、空格和常见标点符号 + """ + if not isinstance(text, str): + raise TypeError(f"is_english 只接受字符串类型，收到: {type(text).__name__}") + + if not text.strip(): + return True # 空字符串视为英文 + + # 更宽松的英文检查：允许字母、数字、空格和常见标点 + # 如果文本中英文字符占比超过 80%，认为是英文 + english_chars = sum(1 for c in text if c.isascii() and (c.isalnum() or c.isspace() or c in '.,!?;:\'"()-')) + total_chars = len(text) + + if total_chars == 0: + return True + + return (english_chars / total_chars) >= 0.8 async def Translate(self, text: str, target_language: str = "en") -> str: """ 通用翻译方法（保持向后兼容） @@ -144,23 +172,99 @@ async def Translation_English(modid, text, fields=None): Returns: 翻译后的数据，保持原有结构 + + Note: + - 对于字符串：直接翻译 + - 对于列表：递归处理每个元素，保持列表长度和索引不变 + - 对于字典：只翻译指定字段（fields参数） + - 对于其他类型：原样返回 """ trans_service = MemoryTransService(modid) - # 执行翻译 - if isinstance(text, list): - english_result=[] - for i in text: - is_eng=await trans_service.is_english(i) - if not is_eng: - english = await trans_service.Translate(i) - english_result.append(english) - return english_result + + # 处理字符串类型 if isinstance(text, str): - is_eng = await trans_service.is_english(text) - if not is_eng: - english_result = await trans_service.Translate(text) - return english_result - return text + # 空字符串直接返回 + if not text.strip(): + return text + + try: + is_eng = await trans_service.is_english(text) + if not is_eng: + english_result = await trans_service.Translate(text) + return english_result + return text + except Exception as e: + logger.warning(f"翻译字符串失败: {e}") + return text + + # 处理列表类型 + elif isinstance(text, list): + english_result = [] + for item in text: + # 递归处理列表中的每个元素 + if isinstance(item, str): + # 字符串元素：检查是否需要翻译 + if not item.strip(): + english_result.append(item) + continue + + try: + is_eng = await trans_service.is_english(item) + if not is_eng: + translated = await trans_service.Translate(item) + english_result.append(translated) + else: + # 保留英文项，不改变列表长度 + english_result.append(item) + except Exception as e: + logger.warning(f"翻译列表项失败: {e}") + english_result.append(item) + + elif isinstance(item, dict): + # 字典元素：递归调用自己处理字典 + translated_dict = await Translation_English(modid, item, fields) + english_result.append(translated_dict) + + elif isinstance(item, list): + # 嵌套列表：递归处理 + translated_list = await Translation_English(modid, item, fields) + english_result.append(translated_list) + + else: + # 其他类型（数字、布尔值等）：原样保留 + english_result.append(item) + + return english_result + + # 处理字典类型 + elif isinstance(text, dict): + # 确定要翻译的字段 + if fields is None: + # 默认翻译字段 + fields = [ + 'content', 'summary', 'statement', 'description', + 'name', 'aliases', 'caption', 'emotion_keywords', + 'text', 'title', 'label', 'type' # 添加常用字段 + ] + + # 创建副本，避免修改原始数据 + result = text.copy() + + for field in fields: + if field in result and result[field] is not None: + # 递归翻译字段值（可能是字符串、列表或嵌套字典） + try: + result[field] = await Translation_English(modid, result[field], fields) + except Exception as e: + logger.warning(f"翻译字段 {field} 失败: {e}") + # 翻译失败时保留原值 + continue + + return result + + # 其他类型（数字、布尔值、None等）：原样返回 + else: + return text class MemoryBaseService: """记忆服务基类，提供共享的辅助方法""" From c93bcb86781cef5414feb4d9b8d1f9af49083410 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 11:27:11 +0800 Subject: [PATCH 027/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)-=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91=E5=AD=97=E6=AE=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/user_memory_controllers.py | 2 +- .../memory_entity_relationship_service.py | 15 +-------------- 2 files changed, 2 insertions(+), 15 deletions(-) diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index 8ff9c41d..d99eb47e 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -419,7 +419,7 @@ async def update_end_user_profile( return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", str(e)) @router.get("/memory_space/timeline_memories", response_model=ApiResponse) -async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str, +async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str="zh", current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ): diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index 06c1616d..9b5f3c99 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -132,20 +132,7 @@ class MemoryEntityService: all_timeline_data = self._merge_same_text_items(all_timeline_data) # 如果需要翻译（非中文），对整个结果进行翻译 - if language_type != 'zh': - # 定义需要翻译的字段 - fields_to_translate = ['text', 'type'] - - # 翻译各个列表 - if memory_summary_list: - memory_summary_list = await self._translate_list(memory_summary_list, model_id, fields_to_translate) - if statement_list: - statement_list = await self._translate_list(statement_list, model_id, fields_to_translate) - if extracted_entity_list: - extracted_entity_list = await self._translate_list(extracted_entity_list, model_id, fields_to_translate) - if all_timeline_data: - all_timeline_data = await self._translate_list(all_timeline_data, model_id, fields_to_translate) - + result = { "MemorySummary": memory_summary_list, "Statement": statement_list, From 4a4931bee228a40cd139eba456ab528ac0d91634 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 19:37:03 +0800 Subject: [PATCH 028/175] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E4=B8=AD=E7=BF=BB?= =?UTF-8?q?=E8=8B=B1=E5=8A=9F=E8=83=BD=EF=BC=88=E8=AE=B0=E5=BF=86=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E7=BA=BF=EF=BC=89(=E7=94=A8=E6=88=B7=E6=91=98?= =?UTF-8?q?=E8=A6=81)(=E5=85=B4=E8=B6=A3=E5=88=86=E5=B8=83=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3)(=E6=9F=A5=E8=AF=A2=E6=A0=B8=E5=BF=83=E6=A1=A3?= =?UTF-8?q?=E6=A1=88)(=E8=AE=B0=E5=BF=86=E6=B4=9E=E5=AF=9F)-=E6=8E=A5?= =?UTF-8?q?=E5=8F=A3=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91=E5=AD=97=E6=AE=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/__init__.py | 0 .../controllers/memory_agent_controller.py | 51 ++-- .../service/memory_api_controller.py | 3 +- api/app/core/agent/langchain_agent.py | 69 +++-- .../langgraph_graph/nodes/problem_nodes.py | 8 +- .../langgraph_graph/nodes/retrieve_nodes.py | 18 +- .../langgraph_graph/nodes/summary_nodes.py | 16 +- .../nodes/verification_nodes.py | 6 +- .../langgraph_graph/nodes/write_nodes.py | 41 +-- .../agent/langgraph_graph/read_graph.py | 6 +- .../agent/langgraph_graph/tools/tool.py | 30 +- .../agent/langgraph_graph/write_graph.py | 19 +- .../agent/services/parameter_builder.py | 6 +- .../memory/agent/services/search_service.py | 8 +- .../memory/agent/services/session_service.py | 18 +- .../core/memory/agent/utils/get_dialogs.py | 75 +++-- api/app/core/memory/agent/utils/llm_tools.py | 10 +- api/app/core/memory/agent/utils/redis_tool.py | 26 +- .../core/memory/agent/utils/session_tools.py | 18 +- .../core/memory/agent/utils/write_tools.py | 14 +- .../core/memory/analytics/hot_memory_tags.py | 36 +-- .../analytics/implicit_memory/data_source.py | 4 +- .../memory/evaluation/dialogue_queries.py | 4 +- .../memory/evaluation/extraction_utils.py | 12 +- .../evaluation/locomo/locomo_benchmark.py | 26 +- .../memory/evaluation/locomo/locomo_test.py | 2 +- .../memory/evaluation/locomo/locomo_utils.py | 18 +- .../evaluation/locomo/qwen_search_eval.py | 24 +- .../longmemeval/qwen_search_eval.py | 52 ++-- .../evaluation/longmemeval/test_eval.py | 52 ++-- .../memory/evaluation/memsciqa/evaluate_qa.py | 14 +- .../evaluation/memsciqa/memsciqa-test.py | 14 +- api/app/core/memory/evaluation/run_eval.py | 20 +- api/app/core/memory/models/config_models.py | 4 +- api/app/core/memory/models/graph_models.py | 16 +- api/app/core/memory/models/message_models.py | 16 +- api/app/core/memory/src/search.py | 108 +++++-- .../data_preprocessing/data_preprocessor.py | 10 +- .../deduplication/deduped_and_disamb.py | 18 +- .../deduplication/entity_dedup_llm.py | 18 +- .../deduplication/second_layer_dedup.py | 8 +- .../deduplication/two_stage_dedup.py | 14 +- .../extraction_orchestrator.py | 50 ++-- .../knowledge_extraction/memory_summary.py | 6 +- .../statement_extraction.py | 16 +- .../temporal_extraction.py | 2 +- .../triplet_extraction.py | 2 +- .../access_history_manager.py | 86 +++--- .../forgetting_engine/forgetting_scheduler.py | 28 +- .../forgetting_engine/forgetting_strategy.py | 20 +- .../storage_services/search/__init__.py | 6 +- .../storage_services/search/hybrid_search.py | 14 +- .../storage_services/search/keyword_search.py | 12 +- .../search/search_strategy.py | 10 +- .../search/semantic_search.py | 12 +- api/app/core/memory/utils/config/get_data.py | 4 +- api/app/core/memory/utils/log/audit_logger.py | 12 +- api/app/core/rag/vdb/field.py | 2 +- api/app/repositories/neo4j/add_edges.py | 4 +- api/app/repositories/neo4j/add_nodes.py | 22 +- .../neo4j/base_neo4j_repository.py | 2 +- api/app/repositories/neo4j/cypher_queries.py | 165 ++++------- .../repositories/neo4j/dialog_repository.py | 32 +-- .../repositories/neo4j/emotion_repository.py | 22 +- api/app/repositories/neo4j/graph_saver.py | 12 +- api/app/repositories/neo4j/graph_search.py | 266 ++++++++---------- .../neo4j/memory_summary_repository.py | 30 +- api/app/repositories/neo4j/neo4j_connector.py | 16 +- .../neo4j/statement_repository.py | 2 +- api/app/schemas/memory_agent_schema.py | 4 +- api/app/services/draft_run_service.py | 2 +- api/app/services/emotion_analytics_service.py | 12 +- api/app/services/memory_agent_service.py | 121 ++++---- api/app/services/memory_api_service.py | 19 +- api/app/services/memory_base_service.py | 18 +- .../memory_entity_relationship_service.py | 4 +- api/app/services/memory_episodic_service.py | 30 +- api/app/services/memory_explicit_service.py | 16 +- api/app/services/memory_forget_service.py | 64 ++--- api/app/services/memory_konwledges_server.py | 14 +- api/app/services/memory_storage_service.py | 39 ++- api/app/services/pilot_run_service.py | 2 +- api/app/services/user_memory_service.py | 42 +-- api/app/tasks.py | 209 +++++++++----- 84 files changed, 1193 insertions(+), 1190 deletions(-) create mode 100644 api/app/__init__.py diff --git a/api/app/__init__.py b/api/app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 22830890..a1337085 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -125,7 +125,7 @@ async def write_server( Write service endpoint - processes write operations synchronously Args: - user_input: Write request containing message and group_id + user_input: Write request containing message and end_user_id Returns: Response with write operation status @@ -160,14 +160,11 @@ async def write_server( api_logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储") storage_type = 'neo4j' - api_logger.info(f"Write service requested for group {user_input.group_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") + api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") try: - # 获取标准化的消息列表 - messages_list = memory_agent_service.get_messages_list(user_input) - result = await memory_agent_service.write_memory( - user_input.group_id, - messages_list, # 传递结构化消息列表 + user_input.end_user_id, + user_input.message, config_id, db, storage_type, @@ -196,7 +193,7 @@ async def write_server_async( Async write service endpoint - enqueues write processing to Celery Args: - user_input: Write request containing message and group_id + user_input: Write request containing message and end_user_id Returns: Task ID for tracking async operation @@ -224,12 +221,9 @@ async def write_server_async( if knowledge: user_rag_memory_id = str(knowledge.id) api_logger.info(f"Async write: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}") try: - # 获取标准化的消息列表 - messages_list = memory_agent_service.get_messages_list(user_input) - task = celery_app.send_task( "app.core.memory.agent.write_message", - args=[user_input.group_id, messages_list, config_id, storage_type, user_rag_memory_id] + args=[user_input.end_user_id, user_input.message, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Write task queued: {task.id}") @@ -255,7 +249,7 @@ async def read_server( - "2": Direct answer based on context Args: - user_input: Read request with message, history, search_switch, and group_id + user_input: Read request with message, history, search_switch, and end_user_id Returns: Response with query answer @@ -279,12 +273,13 @@ async def read_server( name="USER_RAG_MERORY", workspace_id=workspace_id ) - if knowledge: user_rag_memory_id = str(knowledge.id) + if knowledge: + user_rag_memory_id = str(knowledge.id) - api_logger.info(f"Read service: group={user_input.group_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}") + api_logger.info(f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}") try: result = await memory_agent_service.read_memory( - user_input.group_id, + user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch, @@ -297,7 +292,7 @@ async def read_server( retrieve_info = result['answer'] history = await SessionService(store).get_history(user_input.group_id, user_input.group_id, user_input.group_id) query = user_input.message - + # 调用 memory_agent_service 的方法生成最终答案 result['answer'] = await memory_agent_service.generate_summary_from_retrieve( retrieve_info=retrieve_info, @@ -403,7 +398,7 @@ async def read_server_async( try: task = celery_app.send_task( "app.core.memory.agent.read_message", - args=[user_input.group_id, user_input.message, user_input.history, user_input.search_switch, + args=[user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Read task queued: {task.id}") @@ -447,7 +442,7 @@ async def get_read_task_result( return success( data={ "result": task_result.get("result"), - "group_id": task_result.get("group_id"), + "end_user_id": task_result.get("end_user_id"), "elapsed_time": task_result.get("elapsed_time"), "task_id": task_id }, @@ -524,7 +519,7 @@ async def get_write_task_result( return success( data={ "result": task_result.get("result"), - "group_id": task_result.get("group_id"), + "end_user_id": task_result.get("end_user_id"), "elapsed_time": task_result.get("elapsed_time"), "task_id": task_id }, @@ -578,16 +573,16 @@ async def status_type( Determine the type of user message (read or write) Args: - user_input: Request containing user message and group_id + user_input: Request containing user message and end_user_id Returns: Type classification result """ - api_logger.info(f"Status type check requested for group {user_input.group_id}") + api_logger.info(f"Status type check requested for group {user_input.end_user_id}") try: # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - + # 将消息列表转换为字符串用于分类 # 只取最后一条用户消息进行分类 last_user_message = "" @@ -595,13 +590,13 @@ async def status_type( if msg.get('role') == 'user': last_user_message = msg.get('content', '') break - + if not last_user_message: # 如果没有用户消息，使用所有消息的内容 last_user_message = " ".join([msg.get('content', '') for msg in messages_list]) - + result = await memory_agent_service.classify_message_type( - last_user_message, + user_input.message, user_input.config_id, db ) @@ -624,7 +619,7 @@ async def get_knowledge_type_stats_api( 会对缺失类型补 0，返回字典形式。 可选按状态过滤。 - 知识库类型根据当前用户的 current_workspace_id 过滤 - - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (group_id) 过滤 + - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤 - 如果用户没有当前工作空间或未提供 end_user_id，对应的统计返回 0 """ api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}") @@ -697,7 +692,7 @@ async def get_user_profile_api( current_user: User = Depends(get_current_user) ): """ - 获取工作空间下Popular Memory Tags，包含： + 获取用户详情，包含： - name: 用户名字（直接使用 end_user_id） - tags: 3个用户特征标签（从语句和实体中LLM总结） - hot_tags: 4个热门记忆标签 diff --git a/api/app/controllers/service/memory_api_controller.py b/api/app/controllers/service/memory_api_controller.py index 30ca1306..87c1aa20 100644 --- a/api/app/controllers/service/memory_api_controller.py +++ b/api/app/controllers/service/memory_api_controller.py @@ -39,7 +39,7 @@ async def write_memory_api_service( Stores memory content for the specified end user using the Memory API Service. """ - logger.info(f"Memory write request - end_user_id: {payload.end_user_id}") + logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}") memory_api_service = MemoryAPIService(db) @@ -50,6 +50,7 @@ async def write_memory_api_service( config_id=payload.config_id, storage_type=payload.storage_type, user_rag_memory_id=payload.user_rag_memory_id, + tenant_id=api_key_auth.tenant_id, ) logger.info(f"Memory write successful for end_user: {payload.end_user_id}") diff --git a/api/app/core/agent/langchain_agent.py b/api/app/core/agent/langchain_agent.py index 87b46e6f..e6c59a79 100644 --- a/api/app/core/agent/langchain_agent.py +++ b/api/app/core/agent/langchain_agent.py @@ -145,41 +145,38 @@ class LangChainAgent: messages.append(HumanMessage(content=user_content)) return messages -# TODO 乐力齐 - 累积多组对话批量写入功能已禁用 - # async def term_memory_save(self,messages,end_user_end,aimessages): - # '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j''' - # end_user_end=f"Term_{end_user_end}" - # print(messages) - # print(aimessages) - # session_id = store.save_session( - # userid=end_user_end, - # messages=messages, - # apply_id=end_user_end, - # group_id=end_user_end, - # aimessages=aimessages - # ) - # store.delete_duplicate_sessions() - # # logger.info(f'Redis_Agent:{end_user_end};{session_id}') - # return session_id - -# TODO 乐力齐 - 累积多组对话批量写入功能已禁用 - # async def term_memory_redis_read(self,end_user_end): - # end_user_end = f"Term_{end_user_end}" - # history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end) - # # logger.info(f'Redis_Agent:{end_user_end};{history}') - # messagss_list=[] - # retrieved_content=[] - # for messages in history: - # query = messages.get("Query") - # aimessages = messages.get("Answer") - # messagss_list.append(f'用户:{query}。AI回复:{aimessages}') - # retrieved_content.append({query: aimessages}) - # return messagss_list,retrieved_content + async def term_memory_save(self,messages,end_user_end,aimessages): + '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j''' + end_user_end=f"Term_{end_user_end}" + print(messages) + print(aimessages) + session_id = store.save_session( + userid=end_user_end, + messages=messages, + apply_id=end_user_end, + end_user_id=end_user_end, + aimessages=aimessages + ) + store.delete_duplicate_sessions() + # logger.info(f'Redis_Agent:{end_user_end};{session_id}') + return session_id + async def term_memory_redis_read(self,end_user_end): + end_user_end = f"Term_{end_user_end}" + history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end) + # logger.info(f'Redis_Agent:{end_user_end};{history}') + messagss_list=[] + retrieved_content=[] + for messages in history: + query = messages.get("Query") + aimessages = messages.get("Answer") + messagss_list.append(f'用户:{query}。AI回复:{aimessages}') + retrieved_content.append({query: aimessages}) + return messagss_list,retrieved_content async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id): """ 写入记忆（支持结构化消息） - + Args: storage_type: 存储类型 (neo4j/rag) end_user_id: 终端用户ID @@ -188,7 +185,7 @@ class LangChainAgent: user_rag_memory_id: RAG 记忆ID actual_end_user_id: 实际用户ID actual_config_id: 配置ID - + 逻辑说明： - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变 - Neo4j 模式：使用结构化消息列表 @@ -204,20 +201,20 @@ class LangChainAgent: else: # Neo4j 模式：使用结构化消息列表 structured_messages = [] - + # 始终添加用户消息（如果不为空） if user_message: structured_messages.append({"role": "user", "content": user_message}) - + # 只有当 AI 回复不为空时才添加 assistant 消息 if ai_message: structured_messages.append({"role": "assistant", "content": ai_message}) - + # 如果没有消息，直接返回 if not structured_messages: logger.warning(f"No messages to write for user {actual_end_user_id}") return - + # 调用 Celery 任务，传递结构化消息列表 # 数据流： # 1. structured_messages 传递给 write_message_task diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py index 697a13bd..bb8f3ae5 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py @@ -35,10 +35,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState: """问题分解节点""" # 从状态中获取数据 content = state.get('data', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) # 生成 JSON schema 以指导 LLM 输出正确格式 json_schema = ProblemExtensionResponse.model_json_schema() @@ -140,7 +140,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: start = time.time() content = state.get('data', '') data = state.get('spit_data', '')['context'] - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') memory_config = state.get('memory_config', None) @@ -156,7 +156,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: databasets = {} data = [] - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) # 生成 JSON schema 以指导 LLM 输出正确格式 json_schema = ProblemExtensionResponse.model_json_schema() diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py index 14f8fa8b..1880357c 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py @@ -52,9 +52,9 @@ async def rag_config(state): return kb_config async def rag_knowledge(state,question): kb_config = await rag_config(state) - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') user_rag_memory_id=state.get("user_rag_memory_id",'') - retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(group_id)]) + retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(end_user_id)]) try: retrieval_knowledge = [i.page_content for i in retrieve_chunks_result] clean_content = '\n\n'.join(retrieval_knowledge) @@ -159,7 +159,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: problem_extension=state.get('problem_extension', '')['context'] storage_type=state.get('storage_type', '') user_rag_memory_id=state.get('user_rag_memory_id', '') - group_id=state.get('group_id', '') + end_user_id=state.get('end_user_id', '') memory_config = state.get('memory_config', None) original=state.get('data', '') problem_list=[] @@ -172,7 +172,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: try: # Prepare search parameters based on storage type search_params = { - "group_id": group_id, + "end_user_id": end_user_id, "question": question, "return_raw_results": True } @@ -263,13 +263,13 @@ async def retrieve_nodes(state: ReadState) -> ReadState: async def retrieve(state: ReadState) -> ReadState: - # 从state中获取group_id + # 从state中获取end_user_id import time start=time.time() problem_extension = state.get('problem_extension', '')['context'] storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) original = state.get('data', '') problem_list = [] @@ -295,13 +295,13 @@ async def retrieve(state: ReadState) -> ReadState: temperature=0.2, ) - time_retrieval_tool = create_time_retrieval_tool(group_id) - search_params = { "group_id": group_id, "return_raw_results": True } + time_retrieval_tool = create_time_retrieval_tool(end_user_id) + search_params = { "end_user_id": end_user_id, "return_raw_results": True } hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params) agent = create_agent( llm, tools=[time_retrieval_tool,hybrid_retrieval], - system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的group_id是: {group_id}" + system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}" ) # 创建异步任务处理单个问题 diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index 44f89c6a..8ccad579 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -34,8 +34,8 @@ class SummaryNodeService(LLMServiceMixin): summary_service = SummaryNodeService() async def summary_history(state: ReadState) -> ReadState: - group_id = state.get("group_id", '') - history = await SessionService(store).get_history(group_id, group_id, group_id) + end_user_id = state.get("end_user_id", '') + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) return history async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str: @@ -122,12 +122,12 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o async def summary_redis_save(state: ReadState,aimessages) -> ReadState: data = state.get("data", '') - group_id = state.get("group_id", '') + end_user_id = state.get("end_user_id", '') await SessionService(store).save_session( - user_id=group_id, + user_id=end_user_id, query=data, - apply_id=group_id, - group_id=group_id, + apply_id=end_user_id, + end_user_id=end_user_id, ai_response=aimessages ) await SessionService(store).cleanup_duplicates() @@ -175,11 +175,11 @@ async def Input_Summary(state: ReadState) -> ReadState: memory_config = state.get('memory_config', None) user_rag_memory_id=state.get("user_rag_memory_id",'') data=state.get("data", '') - group_id=state.get("group_id", '') + end_user_id=state.get("end_user_id", '') logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}") history = await summary_history( state) search_params = { - "group_id": group_id, + "end_user_id": end_user_id, "question": data, "return_raw_results": True, "include": ["summaries"] # Only search summary nodes for faster performance diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index dac7ea14..ad605ec9 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -62,12 +62,12 @@ async def Verify(state: ReadState): logger.info("=== Verify 节点开始执行 ===") try: content = state.get('data', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) - logger.info(f"Verify: content={content[:50] if content else 'empty'}..., group_id={group_id}") + logger.info(f"Verify: content={content[:50] if content else 'empty'}..., end_user_id={end_user_id}") - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) logger.info(f"Verify: 获取历史记录完成，history length={len(history)}") retrieve = state.get("retrieve", {}) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py index 6af313c3..e2a61045 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py @@ -9,47 +9,36 @@ async def write_node(state: WriteState) -> WriteState: Write data to the database/file system. Args: - state: WriteState containing messages, group_id, and memory_config + content: Data content to write + end_user_id: End user identifier + memory_config: MemoryConfig object containing all configuration Returns: - dict: Contains 'write_result' with status and data fields + dict: Contains 'status', 'saved_to', and 'data' fields """ - messages = state.get('messages', []) - group_id = state.get('group_id', '') - memory_config = state.get('memory_config', '') - - # Convert LangChain messages to structured format expected by write() - structured_messages = [] - for msg in messages: - if hasattr(msg, 'type') and hasattr(msg, 'content'): - # Map LangChain message types to role names - role = 'user' if msg.type == 'human' else 'assistant' if msg.type == 'ai' else msg.type - structured_messages.append({ - "role": role, - "content": msg.content # content is now guaranteed to be a string - }) - + content=state.get('data','') + end_user_id=state.get('end_user_id','') + memory_config=state.get('memory_config', '') try: - result = await write( - messages=structured_messages, - user_id=group_id, - apply_id=group_id, - group_id=group_id, + result=await write( + content=content, + end_user_id=end_user_id, memory_config=memory_config, ) logger.info(f"Write completed successfully! Config: {memory_config.config_name}") - write_result = { + write_result= { "status": "success", - "data": structured_messages, + "data": content, "config_id": memory_config.config_id, "config_name": memory_config.config_name, } - return {"write_result": write_result} + return {"write_result":write_result} + except Exception as e: logger.error(f"Data_write failed: {e}", exc_info=True) - write_result = { + write_result= { "status": "error", "message": str(e), } diff --git a/api/app/core/memory/agent/langgraph_graph/read_graph.py b/api/app/core/memory/agent/langgraph_graph/read_graph.py index 19011a5f..3476d0ec 100644 --- a/api/app/core/memory/agent/langgraph_graph/read_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/read_graph.py @@ -79,7 +79,7 @@ async def make_read_graph(): async def main(): """主函数 - 运行工作流""" message = "昨天有什么好看的电影" - group_id = '88a459f5_text09' # 组ID + end_user_id = '88a459f5_text09' # 组ID storage_type = 'neo4j' # 存储类型 search_switch = '1' # 搜索开关 user_rag_memory_id = 'wwwwwwww' # 用户RAG记忆ID @@ -95,9 +95,9 @@ async def main(): start=time.time() try: async with make_read_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"group_id":group_id + initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"end_user_id":end_user_id ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config} # 获取节点更新信息 _intermediate_outputs = [] diff --git a/api/app/core/memory/agent/langgraph_graph/tools/tool.py b/api/app/core/memory/agent/langgraph_graph/tools/tool.py index ce6d5dd4..c4814de1 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/tool.py @@ -48,11 +48,11 @@ def extract_tool_message_content(response): class TimeRetrievalInput(BaseModel): """时间检索工具的输入模式""" context: str = Field(description="用户输入的查询内容") - group_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") + end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") -def create_time_retrieval_tool(group_id: str): +def create_time_retrieval_tool(end_user_id: str): """ - 创建一个带有特定group_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) + 创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) """ def clean_temporal_result_fields(data): @@ -93,26 +93,26 @@ def create_time_retrieval_tool(group_id: str): return data @tool - def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, group_id_param: str = None, clean_output: bool = True) -> str: + def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str: """ 优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段 显式接收参数： - context: 查询上下文内容 - start_date: 开始时间（可选，格式：YYYY-MM-DD） - end_date: 结束时间（可选，格式：YYYY-MM-DD） - - group_id_param: 组ID（可选，用于覆盖默认组ID） + - end_user_id_param: 组ID（可选，用于覆盖默认组ID） - clean_output: 是否清理输出中的元数据字段 -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d") """ async def _async_search(): # 使用传入的参数或默认值 - actual_group_id = group_id_param or group_id + actual_end_user_id = end_user_id_param or end_user_id actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d") actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d") # 基本时间搜索 results = await search_by_temporal( - group_id=actual_group_id, + end_user_id=actual_end_user_id, start_date=actual_start_date, end_date=actual_end_date, limit=10 @@ -147,7 +147,7 @@ def create_time_retrieval_tool(group_id: str): # 关键词时间搜索 results = await search_by_keyword_temporal( query_text=context, - group_id=group_id, + end_user_id=end_user_id, start_date=actual_start_date, end_date=actual_end_date, limit=15 @@ -172,7 +172,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): Args: memory_config: 内存配置对象 - **search_params: 搜索参数，包含group_id, limit, include等 + **search_params: 搜索参数，包含end_user_id, limit, include等 """ def clean_result_fields(data): @@ -211,7 +211,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): context: str, search_type: str = "hybrid", limit: int = 10, - group_id: str = None, + end_user_id: str = None, rerank_alpha: float = 0.6, use_forgetting_rerank: bool = False, use_llm_rerank: bool = False, @@ -224,7 +224,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): context: 查询内容 search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') limit: 结果数量限制 - group_id: 组ID，用于过滤搜索结果 + end_user_id: 组ID，用于过滤搜索结果 rerank_alpha: 重排序权重参数 use_forgetting_rerank: 是否使用遗忘重排序 use_llm_rerank: 是否使用LLM重排序 @@ -238,7 +238,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): final_params = { "query_text": context, "search_type": search_type, - "group_id": group_id or search_params.get("group_id"), + "end_user_id": end_user_id or search_params.get("end_user_id"), "limit": limit or search_params.get("limit", 10), "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]), "output_path": None, # 不保存到文件 @@ -291,7 +291,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): context: str, search_type: str = "hybrid", limit: int = 10, - group_id: str = None, + end_user_id: str = None, clean_output: bool = True ) -> str: """ @@ -301,7 +301,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): context: 查询内容 search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') limit: 结果数量限制 - group_id: 组ID，用于过滤搜索结果 + end_user_id: 组ID，用于过滤搜索结果 clean_output: 是否清理输出中的元数据字段 """ async def _async_search(): @@ -311,7 +311,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): "context": context, "search_type": search_type, "limit": limit, - "group_id": group_id, + "end_user_id": end_user_id, "clean_output": clean_output }) diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index fe281a23..d8fcf210 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -14,6 +14,7 @@ from app.db import get_db from app.core.logging_config import get_agent_logger from app.core.memory.agent.utils.llm_tools import WriteState from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node +from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_write from app.services.memory_config_service import MemoryConfigService warnings.filterwarnings("ignore", category=RuntimeWarning) @@ -26,12 +27,18 @@ async def make_write_graph(): """ Create a write graph workflow for memory operations. - The workflow directly processes messages from the initial state - and saves them to Neo4j storage. + Args: + user_id: User identifier + tools: MCP tools loaded from session + apply_id: Application identifier + end_user_id: Group identifier + memory_config: MemoryConfig object containing all configuration """ workflow = StateGraph(WriteState) + workflow.add_node("content_input", content_input_write) workflow.add_node("save_neo4j", write_node) - workflow.add_edge(START, "save_neo4j") + workflow.add_edge(START, "content_input") + workflow.add_edge("content_input", "save_neo4j") workflow.add_edge("save_neo4j", END) graph = workflow.compile() @@ -42,7 +49,7 @@ async def make_write_graph(): async def main(): """主函数 - 运行工作流""" message = "今天周一" - group_id = 'new_2025test1103' # 组ID + end_user_id = 'new_2025test1103' # 组ID # 获取数据库会话 @@ -54,9 +61,9 @@ async def main(): ) try: async with make_write_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)], "group_id": group_id, "memory_config": memory_config} + initial_state = {"messages": [HumanMessage(content=message)], "end_user_id": end_user_id, "memory_config": memory_config} # 获取节点更新信息 async for update_event in graph.astream( diff --git a/api/app/core/memory/agent/services/parameter_builder.py b/api/app/core/memory/agent/services/parameter_builder.py index a58fcf1a..74382ade 100644 --- a/api/app/core/memory/agent/services/parameter_builder.py +++ b/api/app/core/memory/agent/services/parameter_builder.py @@ -24,7 +24,7 @@ class ParameterBuilder: tool_call_id: str, search_switch: str, apply_id: str, - group_id: str, + end_user_id: str, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None ) -> Dict[str, Any]: @@ -44,7 +44,7 @@ class ParameterBuilder: tool_call_id: Extracted tool call identifier search_switch: Search routing parameter apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier storage_type: Storage type for the workspace (optional) user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional) @@ -55,7 +55,7 @@ class ParameterBuilder: base_args = { "usermessages": tool_call_id, "apply_id": apply_id, - "group_id": group_id + "end_user_id": end_user_id } # Always add storage_type and user_rag_memory_id (with defaults if None) diff --git a/api/app/core/memory/agent/services/search_service.py b/api/app/core/memory/agent/services/search_service.py index 8a2e7cfe..4fc4256e 100644 --- a/api/app/core/memory/agent/services/search_service.py +++ b/api/app/core/memory/agent/services/search_service.py @@ -91,7 +91,7 @@ class SearchService: async def execute_hybrid_search( self, - group_id: str, + end_user_id: str, question: str, limit: int = 5, search_type: str = "hybrid", @@ -105,7 +105,7 @@ class SearchService: Execute hybrid search and return clean content. Args: - group_id: Group identifier for filtering results + end_user_id: Group identifier for filtering results question: Search query text limit: Maximum number of results to return (default: 5) search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid") @@ -130,7 +130,7 @@ class SearchService: answer = await run_hybrid_search( query_text=cleaned_query, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, output_path=output_path, @@ -186,7 +186,7 @@ class SearchService: except Exception as e: logger.error( - f"Search failed for query '{question}' in group '{group_id}': {e}", + f"Search failed for query '{question}' in group '{end_user_id}': {e}", exc_info=True ) # Return empty results on failure diff --git a/api/app/core/memory/agent/services/session_service.py b/api/app/core/memory/agent/services/session_service.py index b2d4f0ff..f7389984 100644 --- a/api/app/core/memory/agent/services/session_service.py +++ b/api/app/core/memory/agent/services/session_service.py @@ -59,7 +59,7 @@ class SessionService: self, user_id: str, apply_id: str, - group_id: str + end_user_id: str ) -> List[dict]: """ Retrieve conversation history from Redis. @@ -67,20 +67,20 @@ class SessionService: Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier Returns: List of conversation history items with Query and Answer keys Returns empty list if no history found or on error """ try: - history = self.store.find_user_apply_group(user_id, apply_id, group_id) + history = self.store.find_user_apply_group(user_id, apply_id, end_user_id) # Validate history structure if not isinstance(history, list): logger.warning( f"Invalid history format for user {user_id}, " - f"apply {apply_id}, group {group_id}: expected list, got {type(history)}" + f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}" ) return [] @@ -89,7 +89,7 @@ class SessionService: except Exception as e: logger.error( f"Failed to retrieve history for user {user_id}, " - f"apply {apply_id}, group {group_id}: {e}", + f"apply {apply_id}, group {end_user_id}: {e}", exc_info=True ) # Return empty list on error to allow execution to continue @@ -100,7 +100,7 @@ class SessionService: user_id: str, query: str, apply_id: str, - group_id: str, + end_user_id: str, ai_response: str ) -> Optional[str]: """ @@ -110,7 +110,7 @@ class SessionService: user_id: User identifier query: User query/message apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier ai_response: AI response/answer Returns: @@ -131,7 +131,7 @@ class SessionService: userid=user_id, messages=query, apply_id=apply_id, - group_id=group_id, + end_user_id=end_user_id, aimessages=ai_response ) @@ -152,7 +152,7 @@ class SessionService: Duplicates are identified by matching: - sessionid - user_id (id field) - - group_id + - end_user_id - messages - aimessages diff --git a/api/app/core/memory/agent/utils/get_dialogs.py b/api/app/core/memory/agent/utils/get_dialogs.py index 82a41773..4751f18c 100644 --- a/api/app/core/memory/agent/utils/get_dialogs.py +++ b/api/app/core/memory/agent/utils/get_dialogs.py @@ -9,65 +9,56 @@ from app.core.memory.models.message_models import DialogData, ConversationContex async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", - group_id: str = "group_1", - user_id: str = "user1", - apply_id: str = "applyid", - messages: list = None, + end_user_id: str = "group_1", + content: str = "这是用户的输入", ref_id: str = "wyl_20251027", config_id: str = None ) -> List[DialogData]: - """Generate chunks from structured messages using the specified chunker strategy. + """Generate chunks from all test data entries using the specified chunker strategy. Args: chunker_strategy: The chunking strategy to use (default: RecursiveChunker) - group_id: Group identifier - user_id: User identifier - apply_id: Application identifier - messages: Structured message list [{"role": "user", "content": "..."}, ...] + end_user_id: End user identifier + content: Dialog content ref_id: Reference identifier config_id: Configuration ID for processing Returns: - List of DialogData objects with generated chunks + List of DialogData objects with generated chunks for each test entry """ - from app.core.logging_config import get_agent_logger - logger = get_agent_logger(__name__) - - if not messages or not isinstance(messages, list) or len(messages) == 0: - raise ValueError("messages parameter must be a non-empty list") - - conversation_messages = [] - - for idx, msg in enumerate(messages): - if not isinstance(msg, dict) or 'role' not in msg or 'content' not in msg: - raise ValueError(f"Message {idx} format error: must contain 'role' and 'content' fields") - - role = msg['role'] - content = msg['content'] - - if role not in ['user', 'assistant']: - raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}") - - if content.strip(): - conversation_messages.append(ConversationMessage(role=role, msg=content.strip())) - - if not conversation_messages: - raise ValueError("Message list cannot be empty after filtering") - - conversation_context = ConversationContext(msgs=conversation_messages) + dialog_data_list = [] + messages = [] + + messages.append(ConversationMessage(role="用户", msg=content)) + + # Create DialogData + conversation_context = ConversationContext(msgs=messages) + # Create DialogData with end_user_id dialog_data = DialogData( context=conversation_context, ref_id=ref_id, - group_id=group_id, - user_id=user_id, - apply_id=apply_id, + end_user_id=end_user_id, config_id=config_id ) - + # Create DialogueChunker and process the dialogue chunker = DialogueChunker(chunker_strategy) extracted_chunks = await chunker.process_dialogue(dialog_data) dialog_data.chunks = extracted_chunks - - logger.info(f"DialogData created with {len(extracted_chunks)} chunks") - return [dialog_data] + dialog_data_list.append(dialog_data) + + # Convert to dict with datetime serialized + def serialize_datetime(obj): + if isinstance(obj, datetime): + return obj.isoformat() + raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable") + + combined_output = [dd.model_dump() for dd in dialog_data_list] + + print(dialog_data_list) + + # with open(os.path.join(os.path.dirname(__file__), "chunker_test_output.txt"), "w", encoding="utf-8") as f: + # json.dump(combined_output, f, ensure_ascii=False, indent=4, default=serialize_datetime) + + + return dialog_data_list diff --git a/api/app/core/memory/agent/utils/llm_tools.py b/api/app/core/memory/agent/utils/llm_tools.py index 8dd2f1d3..aca7fdd7 100644 --- a/api/app/core/memory/agent/utils/llm_tools.py +++ b/api/app/core/memory/agent/utils/llm_tools.py @@ -12,13 +12,11 @@ class WriteState(TypedDict): Langgrapg Writing TypedDict ''' messages: Annotated[list[AnyMessage], add_messages] - user_id:str - apply_id:str - group_id:str + end_user_id: str errors: list[dict] # Track errors: [{"tool": "tool_name", "error": "message"}] memory_config: object write_result: dict - data:str + data: str class ReadState(TypedDict): """ @@ -28,7 +26,7 @@ class ReadState(TypedDict): messages: 消息列表，支持自动追加 loop_count: 遍历次数 search_switch: 搜索类型开关 - group_id: 组标识 + end_user_id: 组标识 config_id: 配置ID，用于过滤结果 data: 从content_input_node传递的内容数据 spit_data: 从Split_The_Problem传递的分解结果 @@ -39,7 +37,7 @@ class ReadState(TypedDict): messages: Annotated[list[AnyMessage], add_messages] # 消息追加模式 loop_count: int search_switch: str - group_id: str + end_user_id: str config_id: str data: str # 新增字段用于传递内容 spit_data: dict # 新增字段用于传递问题分解结果 diff --git a/api/app/core/memory/agent/utils/redis_tool.py b/api/app/core/memory/agent/utils/redis_tool.py index 31a76a11..505545b3 100644 --- a/api/app/core/memory/agent/utils/redis_tool.py +++ b/api/app/core/memory/agent/utils/redis_tool.py @@ -28,7 +28,7 @@ class RedisSessionStore: return text # 修改后的 save_session 方法 - def save_session(self, userid, messages, aimessages, apply_id, group_id): + def save_session(self, userid, messages, aimessages, apply_id, end_user_id): """ 写入一条会话数据，返回 session_id 优化版本：确保写入时间不超过1秒 @@ -46,7 +46,7 @@ class RedisSessionStore: "id": self.uudi, "sessionid": userid, "apply_id": apply_id, - "group_id": group_id, + "end_user_id": end_user_id, "messages": messages, "aimessages": aimessages, "starttime": starttime @@ -67,7 +67,7 @@ class RedisSessionStore: def save_sessions_batch(self, sessions_data): """ 批量写入多条会话数据，返回 session_id 列表 - sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, group_id + sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, end_user_id 优化版本：批量操作，大幅提升性能 """ try: @@ -83,7 +83,7 @@ class RedisSessionStore: "id": self.uudi, "sessionid": session.get('userid'), "apply_id": session.get('apply_id'), - "group_id": session.get('group_id'), + "end_user_id": session.get('end_user_id'), "messages": session.get('messages'), "aimessages": session.get('aimessages'), "starttime": starttime @@ -108,9 +108,9 @@ class RedisSessionStore: data = self.r.hgetall(key) return data if data else None - def get_session_apply_group(self, sessionid, apply_id, group_id): + def get_session_apply_group(self, sessionid, apply_id, end_user_id): """ - 根据 sessionid、apply_id 和 group_id 三个条件查询会话数据 + 根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据 """ result_items = [] @@ -124,7 +124,7 @@ class RedisSessionStore: # 检查三个条件是否都匹配 if (data.get('sessionid') == sessionid and data.get('apply_id') == apply_id and - data.get('group_id') == group_id): + data.get('end_user_id') == end_user_id): result_items.append(data) return result_items @@ -172,7 +172,7 @@ class RedisSessionStore: def delete_duplicate_sessions(self): """ 删除重复会话数据，条件： - "sessionid"、"user_id"、"group_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除 + "sessionid"、"user_id"、"end_user_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除 优化版本：使用 pipeline 批量操作，确保在1秒内完成 """ import time @@ -202,12 +202,12 @@ class RedisSessionStore: # 获取五个字段的值 sessionid = data.get('sessionid', '') user_id = data.get('id', '') - group_id = data.get('group_id', '') + end_user_id = data.get('end_user_id', '') messages = data.get('messages', '') aimessages = data.get('aimessages', '') # 用五元组作为唯一标识 - identifier = (sessionid, user_id, group_id, messages, aimessages) + identifier = (sessionid, user_id, end_user_id, messages, aimessages) if identifier in seen: # 重复，标记为待删除 @@ -248,9 +248,9 @@ class RedisSessionStore: result_items = [] return (result_items) - def find_user_apply_group(self, sessionid, apply_id, group_id): + def find_user_apply_group(self, sessionid, apply_id, end_user_id): """ - 根据 sessionid、apply_id 和 group_id 三个条件查询会话数据，返回最新的6条 + 根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据，返回最新的6条 """ import time start_time = time.time() @@ -276,7 +276,7 @@ class RedisSessionStore: # 检查是否符合三个条件 if (data.get('apply_id') == apply_id and - data.get('group_id') == group_id): + data.get('end_user_id') == end_user_id): # 支持模糊匹配 sessionid 或者完全匹配 if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid: matched_items.append({ diff --git a/api/app/core/memory/agent/utils/session_tools.py b/api/app/core/memory/agent/utils/session_tools.py index b2d4f0ff..f7389984 100644 --- a/api/app/core/memory/agent/utils/session_tools.py +++ b/api/app/core/memory/agent/utils/session_tools.py @@ -59,7 +59,7 @@ class SessionService: self, user_id: str, apply_id: str, - group_id: str + end_user_id: str ) -> List[dict]: """ Retrieve conversation history from Redis. @@ -67,20 +67,20 @@ class SessionService: Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier Returns: List of conversation history items with Query and Answer keys Returns empty list if no history found or on error """ try: - history = self.store.find_user_apply_group(user_id, apply_id, group_id) + history = self.store.find_user_apply_group(user_id, apply_id, end_user_id) # Validate history structure if not isinstance(history, list): logger.warning( f"Invalid history format for user {user_id}, " - f"apply {apply_id}, group {group_id}: expected list, got {type(history)}" + f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}" ) return [] @@ -89,7 +89,7 @@ class SessionService: except Exception as e: logger.error( f"Failed to retrieve history for user {user_id}, " - f"apply {apply_id}, group {group_id}: {e}", + f"apply {apply_id}, group {end_user_id}: {e}", exc_info=True ) # Return empty list on error to allow execution to continue @@ -100,7 +100,7 @@ class SessionService: user_id: str, query: str, apply_id: str, - group_id: str, + end_user_id: str, ai_response: str ) -> Optional[str]: """ @@ -110,7 +110,7 @@ class SessionService: user_id: User identifier query: User query/message apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier ai_response: AI response/answer Returns: @@ -131,7 +131,7 @@ class SessionService: userid=user_id, messages=query, apply_id=apply_id, - group_id=group_id, + end_user_id=end_user_id, aimessages=ai_response ) @@ -152,7 +152,7 @@ class SessionService: Duplicates are identified by matching: - sessionid - user_id (id field) - - group_id + - end_user_id - messages - aimessages diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 1df0b336..ce55286e 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -29,9 +29,7 @@ logger = get_agent_logger(__name__) async def write( - user_id: str, - apply_id: str, - group_id: str, + end_user_id: str, memory_config: MemoryConfig, messages: list, ref_id: str = "wyl20251027", @@ -40,9 +38,7 @@ async def write( Execute the complete knowledge extraction pipeline. Args: - user_id: User identifier - apply_id: Application identifier - group_id: Group identifier + end_user_id: End user identifier memory_config: MemoryConfig object containing all configuration messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference ID, defaults to "wyl20251027" @@ -58,7 +54,7 @@ async def write( logger.info(f"LLM model: {memory_config.llm_model_name}") logger.info(f"Embedding model: {memory_config.embedding_model_name}") logger.info(f"Chunker strategy: {chunker_strategy}") - logger.info(f"Group ID: {group_id}") + logger.info(f"End User ID: {end_user_id}") # Construct clients from memory_config using factory pattern with db session with get_db_context() as db: @@ -83,9 +79,7 @@ async def write( step_start = time.time() chunked_dialogs = await get_chunked_dialogs( chunker_strategy=chunker_strategy, - group_id=group_id, - user_id=user_id, - apply_id=apply_id, + end_user_id=end_user_id, messages=messages, ref_id=ref_id, config_id=config_id, diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index cab6cacd..95302726 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -16,13 +16,13 @@ class FilteredTags(BaseModel): """用于接收LLM筛选后的核心标签列表的模型。""" meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。") -async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: +async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: """ 使用LLM筛选标签列表，仅保留具有代表性的核心名词。 Args: tags: 原始标签列表 - group_id: 用户组ID，用于获取配置 + end_user_id: 用户组ID，用于获取配置 Returns: 筛选后的标签列表 @@ -37,12 +37,12 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: get_end_user_connected_config, ) - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if not config_id: raise ValueError( - f"No memory_config_id found for group_id: {group_id}. " + f"No memory_config_id found for end_user_id: {end_user_id}. " "Please ensure the user has a valid memory configuration." ) @@ -87,7 +87,7 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: async def get_raw_tags_from_db( connector: Neo4jConnector, - group_id: str, + end_user_id: str, limit: int, by_user: bool = False ) -> List[Tuple[str, int]]: @@ -99,9 +99,9 @@ async def get_raw_tags_from_db( Args: connector: Neo4j连接器实例 - group_id: 如果by_user=False，则为group_id；如果by_user=True，则为user_id + end_user_id: 如果by_user=False，则为end_user_id；如果by_user=True，则为user_id limit: 返回的标签数量限制 - by_user: 是否按user_id查询（默认False，按group_id查询） + by_user: 是否按user_id查询（默认False，按end_user_id查询） Returns: List[Tuple[str, int]]: 标签名称和频率的元组列表 @@ -119,7 +119,7 @@ async def get_raw_tags_from_db( else: query = ( "MATCH (e:ExtractedEntity) " - "WHERE e.group_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " + "WHERE e.end_user_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC " "LIMIT $limit" @@ -128,44 +128,44 @@ async def get_raw_tags_from_db( # 使用项目的Neo4jConnector执行查询 results = await connector.execute_query( query, - id=group_id, + id=end_user_id, limit=limit, names_to_exclude=names_to_exclude ) return [(record["name"], record["frequency"]) for record in results] -async def get_hot_memory_tags(group_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]: +async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]: """ 获取原始标签，然后使用LLM进行筛选，返回最终的热门标签列表。 查询更多的标签(limit=40)给LLM提供更丰富的上下文进行筛选。 Args: - group_id: 必需参数。如果by_user=False，则为group_id；如果by_user=True，则为user_id + end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id limit: 返回的标签数量限制 - by_user: 是否按user_id查询（默认False，按group_id查询） + by_user: 是否按user_id查询（默认False，按end_user_id查询） Raises: - ValueError: 如果group_id未提供或为空 + ValueError: 如果end_user_id未提供或为空 """ - # 验证group_id必须提供且不为空 - if not group_id or not group_id.strip(): + # 验证end_user_id必须提供且不为空 + if not end_user_id or not end_user_id.strip(): raise ValueError( - "group_id is required. Please provide a valid group_id or user_id." + "end_user_id is required. Please provide a valid end_user_id or user_id." ) # 使用项目的Neo4jConnector connector = Neo4jConnector() try: # 1. 从数据库获取原始排名靠前的标签 - raw_tags_with_freq = await get_raw_tags_from_db(connector, group_id, limit, by_user=by_user) + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, limit, by_user=by_user) if not raw_tags_with_freq: return [] raw_tag_names = [tag for tag, freq in raw_tags_with_freq] # 2. 初始化LLM客户端并使用LLM筛选出有意义的标签 - meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, group_id) + meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, end_user_id) # 3. 根据LLM的筛选结果，构建最终的标签列表（保留原始频率和顺序） final_tags = [] diff --git a/api/app/core/memory/analytics/implicit_memory/data_source.py b/api/app/core/memory/analytics/implicit_memory/data_source.py index d277a05e..18678a55 100644 --- a/api/app/core/memory/analytics/implicit_memory/data_source.py +++ b/api/app/core/memory/analytics/implicit_memory/data_source.py @@ -75,8 +75,8 @@ class MemoryDataSource: start_date = time_range.start_date if time_range else None end_date = time_range.end_date if time_range else None - summary_dicts = await self.memory_summary_repo.find_by_group_id( - group_id=user_id, + summary_dicts = await self.memory_summary_repo.find_by_end_user_id( + end_user_id=user_id, limit=limit, start_date=start_date, end_date=end_date diff --git a/api/app/core/memory/evaluation/dialogue_queries.py b/api/app/core/memory/evaluation/dialogue_queries.py index fd7fa671..25abe64e 100644 --- a/api/app/core/memory/evaluation/dialogue_queries.py +++ b/api/app/core/memory/evaluation/dialogue_queries.py @@ -41,7 +41,7 @@ DIALOGUE_EMBEDDING_SEARCH = """ WITH $embedding AS q MATCH (d:Dialogue) WHERE d.dialog_embedding IS NOT NULL - AND ($group_id IS NULL OR d.group_id = $group_id) + AND ($end_user_id IS NULL OR d.end_user_id = $end_user_id) WITH d, q, d.dialog_embedding AS v WITH d, reduce(dot = 0.0, i IN range(0, size(q)-1) | dot + toFloat(q[i]) * toFloat(v[i])) AS dot, @@ -50,7 +50,7 @@ WITH d, WITH d, CASE WHEN qnorm = 0 OR vnorm = 0 THEN 0.0 ELSE dot / (qnorm * vnorm) END AS score WHERE score > $threshold RETURN d.id AS dialog_id, - d.group_id AS group_id, + d.end_user_id AS end_user_id, d.content AS content, d.created_at AS created_at, d.expired_at AS expired_at, diff --git a/api/app/core/memory/evaluation/extraction_utils.py b/api/app/core/memory/evaluation/extraction_utils.py index 9afa228c..9e70bc28 100644 --- a/api/app/core/memory/evaluation/extraction_utils.py +++ b/api/app/core/memory/evaluation/extraction_utils.py @@ -36,7 +36,7 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector async def ingest_contexts_via_full_pipeline( contexts: List[str], - group_id: str, + end_user_id: str, chunker_strategy: str | None = None, embedding_name: str | None = None, save_chunk_output: bool = False, @@ -48,7 +48,7 @@ async def ingest_contexts_via_full_pipeline( This function mirrors the steps in main(), but starts from raw text contexts. Args: contexts: List of dialogue texts, each containing lines like "role: message". - group_id: Group ID to assign to generated DialogData and graph nodes. + end_user_id: Group ID to assign to generated DialogData and graph nodes. chunker_strategy: Optional chunker strategy; defaults to SELECTED_CHUNKER_STRATEGY. embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID. save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging. @@ -109,7 +109,7 @@ async def ingest_contexts_via_full_pipeline( dialog = DialogData( context=context_model, ref_id=f"pipeline_item_{idx}", - group_id=group_id, + end_user_id=end_user_id, user_id="default_user", apply_id="default_application", ) @@ -318,16 +318,16 @@ async def handle_context_processing(args): print("No contexts provided for processing.") return False - return await main_from_contexts(contexts, args.context_group_id) + return await main_from_contexts(contexts, args.context_end_user_id) -async def main_from_contexts(contexts: List[str], group_id: str): +async def main_from_contexts(contexts: List[str], end_user_id: str): """Run the pipeline from provided dialogue contexts instead of test data.""" print("=== Running pipeline from provided contexts ===") success = await ingest_contexts_via_full_pipeline( contexts=contexts, - group_id=group_id, + end_user_id=end_user_id, chunker_strategy=SELECTED_CHUNKER_STRATEGY, embedding_name=SELECTED_EMBEDDING_ID, save_chunk_output=True diff --git a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py index b7d988c5..1c70c28e 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py +++ b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py @@ -47,7 +47,7 @@ from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient from app.core.memory.utils.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, + SELECTED_end_user_id, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -59,7 +59,7 @@ from app.services.memory_config_service import MemoryConfigService async def run_locomo_benchmark( sample_size: int = 20, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, search_type: str = "hybrid", search_limit: int = 12, context_char_budget: int = 8000, @@ -85,7 +85,7 @@ async def run_locomo_benchmark( Args: sample_size: Number of QA pairs to evaluate (from first conversation) - group_id: Database group ID for retrieval (uses default if None) + end_user_id: Database group ID for retrieval (uses default if None) search_type: "keyword", "embedding", or "hybrid" search_limit: Max documents to retrieve per query context_char_budget: Max characters for context @@ -96,8 +96,8 @@ async def run_locomo_benchmark( Returns: Dictionary with evaluation results including metrics, timing, and samples """ - # Use default group_id if not provided - group_id = group_id or SELECTED_GROUP_ID + # Use default end_user_id if not provided + end_user_id = end_user_id or SELECTED_end_user_id # Determine data path data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") @@ -110,7 +110,7 @@ async def run_locomo_benchmark( print(f"{'='*60}") print("📊 Configuration:") print(f" Sample size: {sample_size}") - print(f" Group ID: {group_id}") + print(f" Group ID: {end_user_id}") print(f" Search type: {search_type}") print(f" Search limit: {search_limit}") print(f" Context budget: {context_char_budget} chars") @@ -134,7 +134,7 @@ async def run_locomo_benchmark( # Step 2: Extract conversations and ingest if needed if skip_ingest: print("⏭️ Skipping data ingestion (using existing data in Neo4j)") - print(f" Group ID: {group_id}\n") + print(f" Group ID: {end_user_id}\n") else: print("💾 Checking database ingestion...") try: @@ -142,10 +142,10 @@ async def run_locomo_benchmark( print(f"📝 Extracted {len(conversations)} conversations") # Always ingest for now (ingestion check not implemented) - print(f"🔄 Ingesting conversations into group '{group_id}'...") + print(f"🔄 Ingesting conversations into group '{end_user_id}'...") success = await ingest_conversations_if_needed( conversations=conversations, - group_id=group_id, + end_user_id=end_user_id, reset=reset_group ) @@ -224,7 +224,7 @@ async def run_locomo_benchmark( try: retrieved_info = await retrieve_relevant_information( question=question, - group_id=group_id, + end_user_id=end_user_id, search_type=search_type, search_limit=search_limit, connector=connector, @@ -409,7 +409,7 @@ async def run_locomo_benchmark( "sample_size": len(qa_items), "timestamp": datetime.now().isoformat(), "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_type": search_type, "search_limit": search_limit, "context_char_budget": context_char_budget, @@ -467,7 +467,7 @@ def main(): help="Number of QA pairs to evaluate" ) parser.add_argument( - "--group_id", + "--end_user_id", type=str, default=None, help="Database group ID for retrieval (uses default if not specified)" @@ -516,7 +516,7 @@ def main(): # Run benchmark result = asyncio.run(run_locomo_benchmark( sample_size=args.sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_type=args.search_type, search_limit=args.search_limit, context_char_budget=args.context_char_budget, diff --git a/api/app/core/memory/evaluation/locomo/locomo_test.py b/api/app/core/memory/evaluation/locomo/locomo_test.py index b5ad5820..b871fb9c 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_test.py +++ b/api/app/core/memory/evaluation/locomo/locomo_test.py @@ -555,7 +555,7 @@ async def run_enhanced_evaluation(): search_results = await run_hybrid_search( query_text=q, search_type="hybrid", - group_id="locomo_sk", + end_user_id="locomo_sk", limit=20, include=["statements", "chunks", "entities", "summaries"], alpha=0.6, # BM25权重 diff --git a/api/app/core/memory/evaluation/locomo/locomo_utils.py b/api/app/core/memory/evaluation/locomo/locomo_utils.py index 69be5da9..d3b74947 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_utils.py +++ b/api/app/core/memory/evaluation/locomo/locomo_utils.py @@ -348,7 +348,7 @@ def select_and_format_information( async def retrieve_relevant_information( question: str, - group_id: str, + end_user_id: str, search_type: str, search_limit: int, connector: Any, @@ -368,7 +368,7 @@ async def retrieve_relevant_information( Args: question: Question to search for - group_id: Database group ID (identifies which conversation memory to search) + end_user_id: Database group ID (identifies which conversation memory to search) search_type: "keyword", "embedding", or "hybrid" search_limit: Max memory pieces to retrieve connector: Neo4j connector instance @@ -396,7 +396,7 @@ async def retrieve_relevant_information( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -455,7 +455,7 @@ async def retrieve_relevant_information( search_results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit ) @@ -491,7 +491,7 @@ async def retrieve_relevant_information( search_results = await run_hybrid_search( query_text=question, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], output_path=None, @@ -524,7 +524,7 @@ async def retrieve_relevant_information( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -584,7 +584,7 @@ async def retrieve_relevant_information( async def ingest_conversations_if_needed( conversations: List[str], - group_id: str, + end_user_id: str, reset: bool = False ) -> bool: """ @@ -603,7 +603,7 @@ async def ingest_conversations_if_needed( Args: conversations: List of raw conversation texts from LoCoMo dataset Example: ["User: I went to Paris. AI: When was that?", ...] - group_id: Target group ID for database storage + end_user_id: Target group ID for database storage reset: Whether to clear existing data first (not implemented in wrapper) Returns: @@ -617,7 +617,7 @@ async def ingest_conversations_if_needed( try: success = await ingest_contexts_via_full_pipeline( contexts=conversations, - group_id=group_id, + end_user_id=end_user_id, save_chunk_output=True ) return success diff --git a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py index 87a70a29..3147e880 100644 --- a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py @@ -30,7 +30,7 @@ from app.core.memory.storage_services.search import run_hybrid_search from app.core.memory.utils.config.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, + SELECTED_end_user_id, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -249,7 +249,7 @@ def get_search_params_by_category(category: str): async def run_locomo_eval( sample_size: int = 1, - group_id: str | None = None, + end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, # 保持默认值不变 llm_temperature: float = 0.0, @@ -262,7 +262,7 @@ async def run_locomo_eval( ) -> Dict[str, Any]: # 函数内部使用三路检索逻辑，但保持参数签名不变 - group_id = group_id or SELECTED_GROUP_ID + end_user_id = end_user_id or SELECTED_end_user_id data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") if not os.path.exists(data_path): data_path = os.path.join(os.getcwd(), "data", "locomo10.json") @@ -340,7 +340,7 @@ async def run_locomo_eval( # 关键修复：强制重新摄入纯净的对话数据 print("🔄 强制重新摄入纯净的对话数据...") - await ingest_contexts_via_full_pipeline(contents, group_id, save_chunk_output=True) + await ingest_contexts_via_full_pipeline(contents, end_user_id, save_chunk_output=True) # 使用异步LLM客户端 with get_db_context() as db: @@ -405,7 +405,7 @@ async def run_locomo_eval( connector=connector, embedder_client=embedder, query_text=q, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], # 修复：使用正确的类型 ) @@ -456,7 +456,7 @@ async def run_locomo_eval( search_results = await search_graph( connector=connector, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit ) dialogs = search_results.get("dialogues", []) @@ -486,7 +486,7 @@ async def run_locomo_eval( search_results = await run_hybrid_search( query_text=q, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], output_path=None, @@ -524,7 +524,7 @@ async def run_locomo_eval( connector=connector, embedder_client=embedder, query_text=q, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -597,7 +597,7 @@ async def run_locomo_eval( "dialogues": [ { "uuid": d.get("uuid", ""), - "group_id": d.get("group_id", ""), + "end_user_id": d.get("end_user_id", ""), "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""), "score": d.get("score", 0.0) } @@ -795,7 +795,7 @@ async def run_locomo_eval( }, "samples": samples, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, @@ -825,7 +825,7 @@ async def run_locomo_eval( def main(): parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search") parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate") - parser.add_argument("--group_id", type=str, default=None, help="Group ID for retrieval") + parser.add_argument("--end_user_id", type=str, default=None, help="Group ID for retrieval") parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query") parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context") parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature") @@ -841,7 +841,7 @@ def main(): result = asyncio.run(run_locomo_eval( sample_size=args.sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py index 53c5ce19..320f9de7 100644 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py @@ -523,11 +523,11 @@ def generate_query_keywords_cn(question: str) -> List[str]: # 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]: +async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]: results: List[Dict[str, Any]] = [] try: for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit) + rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit) if rows: results.extend(rows) except Exception: @@ -547,15 +547,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st # 通过对话/陈述中的entity_ids反查实体名称 _FETCH_ENTITIES_BY_IDS = """ MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type """ -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]: +async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]: if not ids: return [] try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id) + rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id) return rows or [] except Exception: return [] @@ -565,18 +565,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou _TIME_ENTITY_SEARCH = """ MATCH (e:ExtractedEntity) WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type LIMIT $limit """ -async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: +async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: """专门搜索时间相关的实体""" try: date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" rows = await connector.execute_query(_TIME_ENTITY_SEARCH, date_pattern=date_pattern, - group_id=group_id, + end_user_id=end_user_id, limit=limit) return rows or [] except Exception: @@ -623,7 +623,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: async def run_longmemeval_test( sample_size: int = 3, - group_id: str = "longmemeval_zh_bak_3", + end_user_id: str = "longmemeval_zh_bak_3", search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -677,13 +677,13 @@ async def run_longmemeval_test( contexts.extend(selected) print(f"📥 摄入 {len(contexts)} 个上下文到数据库") - if reset_group_before_ingest and group_id: + if reset_group_before_ingest and end_user_id: try: _tmp_conn = Neo4jConnector() - await _tmp_conn.delete_group(group_id) - print(f"🧹 已清空组 {group_id} 的历史图数据") + await _tmp_conn.delete_group(end_user_id) + print(f"🧹 已清空组 {end_user_id} 的历史图数据") except Exception as _e: - print(f"⚠️ 清空组数据失败（忽略继续）: {group_id} - {_e}") + print(f"⚠️ 清空组数据失败（忽略继续）: {end_user_id} - {_e}") finally: try: await _tmp_conn.close() @@ -695,7 +695,7 @@ async def run_longmemeval_test( else: await _ingest_fn( contexts, - group_id, + end_user_id, save_chunk_output=save_chunk_output, save_chunk_output_path=save_chunk_output_path, ) @@ -750,7 +750,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -795,7 +795,7 @@ async def run_longmemeval_test( search_results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) chunks = search_results.get("chunks", []) @@ -830,7 +830,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -848,7 +848,7 @@ async def run_longmemeval_test( kw_res = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) if isinstance(kw_res, dict): @@ -859,7 +859,7 @@ async def run_longmemeval_test( # 时间推理问题的特殊处理 if is_temporal: # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, group_id, search_limit//2) + time_entities = await _search_time_entities(connector, end_user_id, search_limit//2) if time_entities: kw_entities.extend(time_entities) # 添加时间相关关键词检索 @@ -869,7 +869,7 @@ async def run_longmemeval_test( time_res = await search_graph( connector=connector, q=tk, - group_id=group_id, + end_user_id=end_user_id, limit=2, ) if isinstance(time_res, dict): @@ -880,7 +880,7 @@ async def run_longmemeval_test( # 中文关键词拆分后做别名匹配 cn_tokens = _extract_cn_tokens(question) - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit) + alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit) if alias_entities: kw_entities.extend(alias_entities) @@ -894,7 +894,7 @@ async def run_longmemeval_test( except Exception: pass if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, group_id) + id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id) if id_entities: kw_entities.extend(id_entities) @@ -908,7 +908,7 @@ async def run_longmemeval_test( sub_res = await search_graph( connector=connector, q=str(kw), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(sub_res, dict): @@ -927,7 +927,7 @@ async def run_longmemeval_test( opt_res = await search_graph( connector=connector, q=str(opt), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(opt_res, dict): diff --git a/api/app/core/memory/evaluation/longmemeval/test_eval.py b/api/app/core/memory/evaluation/longmemeval/test_eval.py index 08a763e3..a49d48d0 100644 --- a/api/app/core/memory/evaluation/longmemeval/test_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/test_eval.py @@ -498,11 +498,11 @@ def smart_context_selection(contexts: List[str], question: str, max_chars: int = # 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]: +async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]: results: List[Dict[str, Any]] = [] try: for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit) + rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit) if rows: results.extend(rows) except Exception: @@ -522,15 +522,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st # 通过对话/陈述中的entity_ids反查实体名称 _FETCH_ENTITIES_BY_IDS = """ MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type """ -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]: +async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]: if not ids: return [] try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id) + rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id) return rows or [] except Exception: return [] @@ -540,18 +540,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou _TIME_ENTITY_SEARCH = """ MATCH (e:ExtractedEntity) WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type LIMIT $limit """ -async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: +async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: """专门搜索时间相关的实体""" try: date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" rows = await connector.execute_query(_TIME_ENTITY_SEARCH, date_pattern=date_pattern, - group_id=group_id, + end_user_id=end_user_id, limit=limit) return rows or [] except Exception: @@ -559,25 +559,25 @@ async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, # 技术术语专门检索 -async def _search_tech_terms(connector: Neo4jConnector, question: str, group_id: str | None, limit: int = 3) -> List[Dict[str, Any]]: +async def _search_tech_terms(connector: Neo4jConnector, question: str, end_user_id: str | None, limit: int = 3) -> List[Dict[str, Any]]: """专门搜索技术术语相关的实体""" tech_entities = [] try: # GPS相关 if any(term in question for term in ["GPS", "导航", "定位系统"]): - gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", group_id=group_id, limit=limit) + gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", end_user_id=end_user_id, limit=limit) if gps_rows: tech_entities.extend(gps_rows) # 活动相关 if any(term in question for term in ["工作坊", "研讨会", "网络研讨会"]): - workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", group_id=group_id, limit=limit) + workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", end_user_id=end_user_id, limit=limit) if workshop_rows: tech_entities.extend(workshop_rows) # 时间顺序相关 if any(term in question for term in ["先", "后", "第一个"]): - time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", group_id=group_id, limit=limit) + time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", end_user_id=end_user_id, limit=limit) if time_rows: tech_entities.extend(time_rows) @@ -627,7 +627,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: async def run_longmemeval_test( sample_size: int = 3, - group_id: str = "longmemeval_zh_bak_2", + end_user_id: str = "longmemeval_zh_bak_2", search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -707,7 +707,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["dialogues", "statements", "entities"], ) @@ -746,7 +746,7 @@ async def run_longmemeval_test( search_results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) dialogs = search_results.get("dialogues", []) @@ -776,7 +776,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["dialogues", "statements", "entities"], ) @@ -792,7 +792,7 @@ async def run_longmemeval_test( kw_res = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) if isinstance(kw_res, dict): @@ -801,14 +801,14 @@ async def run_longmemeval_test( kw_entities = kw_res.get("entities", []) or [] # 技术术语专门检索 - tech_entities = await _search_tech_terms(connector, question, group_id, search_limit//2) + tech_entities = await _search_tech_terms(connector, question, end_user_id, search_limit//2) if tech_entities: kw_entities.extend(tech_entities) # 时间推理问题的特殊处理 if is_temporal: # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, group_id, search_limit//2) + time_entities = await _search_time_entities(connector, end_user_id, search_limit//2) if time_entities: kw_entities.extend(time_entities) # 添加时间相关关键词检索 @@ -818,7 +818,7 @@ async def run_longmemeval_test( time_res = await search_graph( connector=connector, q=tk, - group_id=group_id, + end_user_id=end_user_id, limit=2, ) if isinstance(time_res, dict): @@ -829,7 +829,7 @@ async def run_longmemeval_test( # 中文关键词拆分后做别名匹配 cn_tokens = generate_query_keywords_cn(question) # 使用增强版关键词提取 - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit) + alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit) if alias_entities: kw_entities.extend(alias_entities) @@ -843,7 +843,7 @@ async def run_longmemeval_test( except Exception: pass if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, group_id) + id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id) if id_entities: kw_entities.extend(id_entities) @@ -857,7 +857,7 @@ async def run_longmemeval_test( sub_res = await search_graph( connector=connector, q=str(kw), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(sub_res, dict): @@ -876,7 +876,7 @@ async def run_longmemeval_test( opt_res = await search_graph( connector=connector, q=str(opt), - group_id=group_id, + end_user_id=group_id, limit=max(3, search_limit // 2), ) if isinstance(opt_res, dict): diff --git a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py index 6efb66ff..ec147f3c 100644 --- a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py +++ b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py @@ -27,7 +27,7 @@ from app.core.memory.storage_services.search import run_hybrid_search from app.core.memory.utils.config.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, + SELECTED_end_user_id, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -135,8 +135,8 @@ def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any return merged -async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: - group_id = group_id or SELECTED_GROUP_ID +async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: + end_user_id = end_user_id or SELECTED_end_user_id # Load data data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") if not os.path.exists(data_path): @@ -147,7 +147,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入 # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略 contexts: List[str] = [build_context_from_dialog(item) for item in items] - await ingest_contexts_via_full_pipeline(contexts, group_id) + await ingest_contexts_via_full_pipeline(contexts, end_user_id) # LLM client (使用异步调用) with get_db_context() as db: @@ -173,7 +173,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s results = await run_hybrid_search( query_text=question, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["dialogues", "statements", "entities"], output_path=None, @@ -298,7 +298,7 @@ def main(): load_dotenv() parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen") parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量") - parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json") + parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json") parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数") parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") @@ -309,7 +309,7 @@ def main(): result = asyncio.run( run_memsciqa_eval( sample_size=args.sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py index 279f4042..631035aa 100644 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py @@ -33,7 +33,7 @@ from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient from app.core.memory.utils.config.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, + SELECTED_end_user_id, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -198,7 +198,7 @@ def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]: async def run_memsciqa_test( sample_size: int = 3, - group_id: str | None = None, + end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -216,7 +216,7 @@ async def run_memsciqa_test( """ # 默认使用指定的 memsci 组 ID - group_id = group_id or "group_memsci" + end_user_id = end_user_id or "group_memsci" # 数据路径解析（项目根与当前工作目录兜底） if not data_path: @@ -282,7 +282,7 @@ async def run_memsciqa_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues ) @@ -291,7 +291,7 @@ async def run_memsciqa_test( results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues ) @@ -499,7 +499,7 @@ async def run_memsciqa_test( }, "samples": samples, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "llm_temperature": llm_temperature, @@ -542,7 +542,7 @@ def main(): result = asyncio.run( run_memsciqa_test( sample_size=sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/run_eval.py b/api/app/core/memory/evaluation/run_eval.py index 1de3de89..f665bdb8 100644 --- a/api/app/core/memory/evaluation/run_eval.py +++ b/api/app/core/memory/evaluation/run_eval.py @@ -15,7 +15,7 @@ except Exception: return None from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.utils.config.definitions import SELECTED_GROUP_ID, PROJECT_ROOT +from app.core.memory.utils.config.definitions import SELECTED_end_user_id, PROJECT_ROOT from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test @@ -26,7 +26,7 @@ async def run( dataset: str, sample_size: int, reset_group: bool, - group_id: str | None, + end_user_id: str | None, judge_model: str | None = None, search_limit: int | None = None, context_char_budget: int | None = None, @@ -37,17 +37,17 @@ async def run( max_contexts_per_item: int | None = None, ) -> Dict[str, Any]: # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认 - group_id = group_id or SELECTED_GROUP_ID + end_user_id = end_user_id or SELECTED_end_user_id if reset_group: connector = Neo4jConnector() try: - await connector.delete_group(group_id) + await connector.delete_group(end_user_id) finally: await connector.close() if dataset == "locomo": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} + kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} if search_limit is not None: kwargs["search_limit"] = search_limit if context_char_budget is not None: @@ -61,7 +61,7 @@ async def run( return await run_locomo_eval(**kwargs) if dataset == "memsciqa": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} + kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} if search_limit is not None: kwargs["search_limit"] = search_limit if context_char_budget is not None: @@ -75,7 +75,7 @@ async def run( return await run_memsciqa_eval(**kwargs) if dataset == "longmemeval": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} + kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} if search_limit is not None: kwargs["search_limit"] = search_limit if context_char_budget is not None: @@ -99,8 +99,8 @@ def main(): parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo") parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True) parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通") - parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 group_id 的图数据") - parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json") + parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 end_user_id 的图数据") + parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json") parser.add_argument("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名") parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）") parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）") @@ -117,7 +117,7 @@ def main(): args.dataset, args.sample_size, args.reset_group, - args.group_id, + args.end_user_id, args.judge_model, args.search_limit, args.context_char_budget, diff --git a/api/app/core/memory/models/config_models.py b/api/app/core/memory/models/config_models.py index f3341cc5..ca1780aa 100644 --- a/api/app/core/memory/models/config_models.py +++ b/api/app/core/memory/models/config_models.py @@ -72,7 +72,7 @@ class TemporalSearchParams(BaseModel): """Parameters for temporal search queries in the knowledge graph. Attributes: - group_id: Group ID to filter search results (default: 'test') + end_user_id: Group ID to filter search results (default: 'test') apply_id: Application ID to filter search results user_id: User ID to filter search results start_date: Start date for temporal filtering (format: 'YYYY-MM-DD') @@ -81,7 +81,7 @@ class TemporalSearchParams(BaseModel): invalid_date: Date when memory should be invalid (format: 'YYYY-MM-DD') limit: Maximum number of results to return (default: 3) """ - group_id: Optional[str] = Field("test", description="The group ID to filter the search.") + end_user_id: Optional[str] = Field("test", description="The group ID to filter the search.") apply_id: Optional[str] = Field(None, description="The apply ID to filter the search.") user_id: Optional[str] = Field(None, description="The user ID to filter the search.") start_date: Optional[str] = Field(None, description="The start date for the search.") diff --git a/api/app/core/memory/models/graph_models.py b/api/app/core/memory/models/graph_models.py index 7a48d6cb..79b88fdc 100644 --- a/api/app/core/memory/models/graph_models.py +++ b/api/app/core/memory/models/graph_models.py @@ -103,9 +103,7 @@ class Edge(BaseModel): id: Unique identifier for the edge source: ID of the source node target: ID of the target node - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy run_id: Unique identifier for the pipeline run that created this edge created_at: Timestamp when the edge was created (system perspective) expired_at: Optional timestamp when the edge expires (system perspective) @@ -113,9 +111,7 @@ class Edge(BaseModel): id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the edge.") source: str = Field(..., description="The ID of the source node.") target: str = Field(..., description="The ID of the target node.") - group_id: str = Field(..., description="The group ID of the edge.") - user_id: str = Field(..., description="The user ID of the edge.") - apply_id: str = Field(..., description="The apply ID of the edge.") + end_user_id: str = Field(..., description="The end user ID of the edge.") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(..., description="The valid time of the edge from system perspective.") expired_at: Optional[datetime] = Field(None, description="The expired time of the edge from system perspective.") @@ -185,18 +181,14 @@ class Node(BaseModel): Attributes: id: Unique identifier for the node name: Name of the node - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy run_id: Unique identifier for the pipeline run that created this node created_at: Timestamp when the node was created (system perspective) expired_at: Optional timestamp when the node expires (system perspective) """ id: str = Field(..., description="The unique identifier for the node.") name: str = Field(..., description="The name of the node.") - group_id: str = Field(..., description="The group ID of the node.") - user_id: str = Field(..., description="The user ID of the edge.") - apply_id: str = Field(..., description="The apply ID of the edge.") + end_user_id: str = Field(..., description="The end user ID of the node.") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(..., description="The valid time of the node from system perspective.") expired_at: Optional[datetime] = Field(None, description="The expired time of the node from system perspective.") diff --git a/api/app/core/memory/models/message_models.py b/api/app/core/memory/models/message_models.py index bcf08999..c660d841 100644 --- a/api/app/core/memory/models/message_models.py +++ b/api/app/core/memory/models/message_models.py @@ -55,7 +55,7 @@ class Statement(BaseModel): Attributes: id: Unique identifier for the statement chunk_id: ID of the parent chunk this statement belongs to - group_id: Optional group ID for multi-tenancy + end_user_id: Optional group ID for multi-tenancy statement: The actual statement text content speaker: Optional speaker identifier ('用户' for user, 'AI' for AI responses) statement_embedding: Optional embedding vector for the statement @@ -73,7 +73,7 @@ class Statement(BaseModel): """ id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the statement.") chunk_id: str = Field(..., description="ID of the parent chunk this statement belongs to.") - group_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.") + end_user_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.") statement: str = Field(..., description="The text content of the statement.") speaker: Optional[str] = Field(None, description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses") statement_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the statement.") @@ -159,9 +159,7 @@ class DialogData(BaseModel): context: Full conversation context dialog_embedding: Optional embedding vector for the entire dialog ref_id: Reference ID linking to external dialog system - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy created_at: Timestamp when the dialog was created expired_at: Timestamp when the dialog expires (default: far future) metadata: Additional metadata as key-value pairs @@ -175,9 +173,7 @@ class DialogData(BaseModel): context: ConversationContext = Field(..., description="The full conversation context as a single string.") dialog_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the dialog.") ref_id: str = Field(..., description="Refer to external dialog id. This is used to link to the original dialog.") - group_id: str = Field(default=..., description="Group ID of dialogue data") - user_id: str = Field(..., description="USER ID of dialogue data") - apply_id: str = Field(..., description="APPLY ID of dialogue data") + end_user_id: str = Field(default=..., description="End user ID of dialogue data") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(default_factory=datetime.now, description="The timestamp when the dialog was created.") expired_at: datetime = Field(default_factory=lambda: datetime(9999, 12, 31), description="The timestamp when the dialog expires.") @@ -256,5 +252,5 @@ class DialogData(BaseModel): """ for chunk in self.chunks: for statement in chunk.statements: - if statement.group_id is None: - statement.group_id = self.group_id + if statement.end_user_id is None: + statement.end_user_id = self.end_user_id diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 91e47eae..345cd69b 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -6,6 +6,7 @@ import os import time from datetime import datetime from typing import TYPE_CHECKING, Any, Dict, List, Optional +from uuid import UUID if TYPE_CHECKING: from app.schemas.memory_config_schema import MemoryConfig @@ -396,13 +397,13 @@ def rerank_with_activation( return reranked -def log_search_query(query_text: str, search_type: str, group_id: str | None, limit: int, include: List[str], log_file: str = None): +def log_search_query(query_text: str, search_type: str, end_user_id: str | None, limit: int, include: List[str], log_file: str = None): """Log search query information using the logger. Args: query_text: The search query text search_type: Type of search (keyword, embedding, hybrid) - group_id: Group identifier for filtering + end_user_id: Group identifier for filtering limit: Maximum number of results include: List of result types to include log_file: Deprecated parameter, kept for backward compatibility @@ -413,7 +414,7 @@ def log_search_query(query_text: str, search_type: str, group_id: str | None, li # Log using the standard logger logger.info( f"Search query: query='{cleaned_query}', type={search_type}, " - f"group_id={group_id}, limit={limit}, include={include}" + f"end_user_id={end_user_id}, limit={limit}, include={include}" ) @@ -672,7 +673,7 @@ def apply_reranker_placeholder( async def run_hybrid_search( query_text: str, search_type: str, - group_id: str | None, + end_user_id: str | None, limit: int, include: List[str], output_path: str | None, @@ -692,6 +693,9 @@ async def run_hybrid_search( # Start overall timing search_start_time = time.time() latency_metrics = {} + print(100*'-') + print(memory_config) + print(100 * '-') logger.info(f"using embedding_id:{memory_config.embedding_model_id}...") # Clean and normalize the incoming query before use/logging @@ -715,7 +719,7 @@ async def run_hybrid_search( } # Log the search query - log_search_query(query_text, search_type, group_id, limit, include) + log_search_query(query_text, search_type, end_user_id, limit, include) connector = Neo4jConnector() results = {} @@ -732,7 +736,7 @@ async def run_hybrid_search( search_graph( connector=connector, q=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include ) @@ -769,7 +773,7 @@ async def run_hybrid_search( connector=connector, embedder_client=embedder, query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, ) @@ -916,9 +920,7 @@ async def run_hybrid_search( async def search_by_temporal( - group_id: Optional[str] = "test", - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = "test", start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -929,7 +931,7 @@ async def search_by_temporal( Temporal search across Statements. - Matches statements created between start_date and end_date - - Optionally filters by group_id + - Optionally filters by end_user_id - Returns up to 'limit' statements """ connector = Neo4jConnector() @@ -939,9 +941,7 @@ async def search_by_temporal( end_date = normalize_date_safe(end_date) params = TemporalSearchParams.model_validate({ - "group_id": group_id, - "apply_id": apply_id, - "user_id": user_id, + "end_user_id": end_user_id, "start_date": start_date, "end_date": end_date, "valid_date": valid_date, @@ -950,9 +950,7 @@ async def search_by_temporal( }) statements = await search_graph_by_temporal( connector=connector, - group_id=params.group_id, - apply_id=params.apply_id, - user_id=params.user_id, + end_user_id=params.end_user_id, start_date=params.start_date, end_date=params.end_date, valid_date=params.valid_date, @@ -964,9 +962,7 @@ async def search_by_temporal( async def search_by_keyword_temporal( query_text: str, - group_id: Optional[str] = "test", - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = "test", start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -987,9 +983,7 @@ async def search_by_keyword_temporal( invalid_date = normalize_date_safe(invalid_date) params = TemporalSearchParams.model_validate({ - "group_id": group_id, - "apply_id": apply_id, - "user_id": user_id, + "end_user_id": end_user_id, "start_date": start_date, "end_date": end_date, "valid_date": valid_date, @@ -999,9 +993,7 @@ async def search_by_keyword_temporal( statements = await search_graph_by_keyword_temporal( connector=connector, query_text=query_text, - group_id=params.group_id, - apply_id=params.apply_id, - user_id=params.user_id, + end_user_id=params.end_user_id, start_date=params.start_date, end_date=params.end_date, valid_date=params.valid_date, @@ -1013,7 +1005,7 @@ async def search_by_keyword_temporal( async def search_chunk_by_chunk_id( chunk_id: str, - group_id: Optional[str] = "test", + end_user_id: Optional[str] = "test", limit: int = 1, ): """ @@ -1023,8 +1015,68 @@ async def search_chunk_by_chunk_id( chunks = await search_graph_by_chunk_id( connector=connector, chunk_id=chunk_id, - group_id=group_id, + end_user_id=end_user_id, limit=limit ) return {"chunks": chunks} +if __name__ == '__main__': + # 测试混合检索功能 + from app.schemas.memory_config_schema import MemoryConfig + from app.db import get_db + from app.services.memory_config_service import MemoryConfigService + + # 从数据库获取真实配置 + db = next(get_db()) + try: + config_service = MemoryConfigService(db) + + # 使用 config_id=17 获取配置 + memory_config = config_service.load_memory_config(config_id=17) + + if not memory_config: + print("错误：找不到 config_id=17 的配置") + print("请先在数据库中创建配置，或修改 config_id") + exit(1) + + print(f"✓ 成功加载配置: {memory_config.config_name}") + print(f" - Workspace: {memory_config.workspace_name}") + print(f" - LLM Model: {memory_config.llm_model_name}") + print(f" - Embedding Model: {memory_config.embedding_model_name}") + print(f" - Storage Type: {memory_config.storage_type}") + print() + + # 修改这里的参数进行测试 + test_end_user_id = "021886bc-fab9-4fd5-b607-497b262e0381" # 修改为你的 end_user_id + test_query = "小明擅长什么？" # 修改为你的查询 + + print(f"开始测试检索...") + print(f" - Query: {test_query}") + print(f" - End User ID: {test_end_user_id}") + print(f" - Search Type: hybrid") + print() + + results = asyncio.run(run_hybrid_search( + query_text=test_query, + search_type="hybrid", # 可选: "keyword", "embedding", "hybrid" + end_user_id=test_end_user_id, + limit=10, + include=["statements", "entities", "chunks", "summaries"], + output_path=None, + memory_config=memory_config, + rerank_alpha=0.6, + use_forgetting_rerank=False, + use_llm_rerank=False + )) + + print("=" * 80) + print("检索结果:") + print("=" * 80) + print(results) + + except Exception as e: + print(f"错误: {e}") + import traceback + traceback.print_exc() + finally: + db.close() diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py index f5e72517..4dafd3ed 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py @@ -555,8 +555,8 @@ class DataPreprocessor: dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}'))) - # 获取group_id，如果不存在则生成默认值 - group_id = item.get('group_id', f'group_default_{i}') + # 获取end_user_id，如果不存在则生成默认值 + end_user_id = item.get('end_user_id', f'group_default_{i}') user_id = item.get('user_id', f'user_default_{i}') apply_id = item.get('apply_id', f'apply_default_{i}') @@ -574,7 +574,7 @@ class DataPreprocessor: dialog_data = DialogData( context=context, ref_id=dialog_id, - group_id=group_id, + end_user_id=end_user_id, user_id=user_id, apply_id=apply_id, metadata=metadata @@ -644,7 +644,7 @@ class DataPreprocessor: context = ConversationContext(msgs=messages) dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}'))) - group_id = item.get('group_id', f'group_default_{i}') + end_user_id = item.get('end_user_id', f'group_default_{i}') user_id = item.get('user_id', f'user_default_{i}') apply_id = item.get('apply_id', f'apply_default_{i}') @@ -657,7 +657,7 @@ class DataPreprocessor: dialog_data = DialogData( context=context, ref_id=dialog_id, - group_id=group_id, + end_user_id=end_user_id, user_id=user_id, apply_id=apply_id, metadata=metadata diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py index 62b656b0..a425e0ed 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py @@ -199,7 +199,7 @@ def accurate_match( entity_nodes: List[ExtractedEntityNode] ) -> Tuple[List[ExtractedEntityNode], Dict[str, str], Dict[str, Dict]]: """ - 精确匹配：按 (group_id, name, entity_type) 合并实体并建立重定向与合并记录。 + 精确匹配：按 (end_user_id, name, entity_type) 合并实体并建立重定向与合并记录。 返回: (deduped_entities, id_redirect, exact_merge_map) """ exact_merge_map: Dict[str, Dict] = {} @@ -210,8 +210,8 @@ def accurate_match( for ent in entity_nodes: name_norm = (getattr(ent, "name", "") or "").strip() type_norm = (getattr(ent, "entity_type", "") or "").strip() - key = f"{getattr(ent, 'group_id', None)}|{name_norm}|{type_norm}" - # 为避免跨业务组误并，明确以 group_id 为范围边界 + key = f"{getattr(ent, 'end_user_id', None)}|{name_norm}|{type_norm}" + # 为避免跨业务组误并，明确以 end_user_id 为范围边界 if key not in canonical_map: canonical_map[key] = ent id_redirect[ent.id] = ent.id @@ -223,11 +223,11 @@ def accurate_match( id_redirect[ent.id] = canonical.id # 记录精确匹配的合并项（使用规范化键，避免外层变量误用） try: - k = f"{canonical.group_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}" + k = f"{canonical.end_user_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}" if k not in exact_merge_map: exact_merge_map[k] = { "canonical_id": canonical.id, - "group_id": canonical.group_id, + "end_user_id": canonical.end_user_id, "name": canonical.name, "entity_type": canonical.entity_type, "merged_ids": set(), @@ -596,7 +596,7 @@ def fuzzy_match( b = deduped_entities[j] # 跳过不同业务组的实体 - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): j += 1 continue @@ -671,7 +671,7 @@ def fuzzy_match( merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]" merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]" fuzzy_merge_records.append( - f"{merge_reason} 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type}) | " + f"{merge_reason} 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type}) | " f"s_name={s_name:.3f}, s_type={s_type:.3f}, overall={overall:.3f}, exact_alias={has_exact_match}" ) except Exception: @@ -779,7 +779,7 @@ async def LLM_decision( # 决策中包含去重和消歧的功能 # 记录 LLM 融合日志 try: llm_records.append( - f"[LLM融合] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})" + f"[LLM融合] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})" ) # 详细的“同类名称相似”记录改由 LLM 去重模块统一生成以携带 conf/reason except Exception: @@ -847,7 +847,7 @@ async def LLM_disamb_decision( id_redirect[k] = a.id try: disamb_records.append( - f"[DISAMB合并应用] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})" + f"[DISAMB合并应用] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py index 734f7b69..0249ac1f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py @@ -174,7 +174,7 @@ async def _judge_pair( pass # 3. 构建LLM判断的“上下文信息”（规则层计算的所有特征） 判断上下文特征有助于实体消歧首先判断的类型关系 ctx = { - "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None), + "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None), "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "type_similarity": _type_similarity(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "name_text_sim": name_text_sim, @@ -235,7 +235,7 @@ async def _judge_pair_disamb( except Exception: pass ctx = { - "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None), + "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None), "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "name_text_sim": name_text_sim, "name_embed_sim": name_embed_sim, @@ -317,8 +317,8 @@ async def llm_dedup_entities( # 保留对偶判断作为子流程，是为了 a = entity_nodes[i] for j in range(i + 1, len(entity_nodes)): b = entity_nodes[j] - # 规则1：必须属于同一组（group_id相同，不同组的实体不重复） - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + # 规则1：必须属于同一组（end_user_id相同，不同组的实体不重复） + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): continue # 规则2：类型必须兼容（调用_simple_type_ok判断） if not _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)): @@ -474,7 +474,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 - max_rounds: upper bound for iterative passes (default 3) - auto_merge_threshold: decision confidence for auto-merge when no co-occurrence (default 0.90) - co_ctx_threshold: lower threshold when co-occurrence is detected (default 0.83) - - shuffle_each_round: whether to shuffle entities within group_id each round to vary block composition + - shuffle_each_round: whether to shuffle entities within end_user_id each round to vary block composition Returns: - global_redirect: dict losing_id -> canonical_id accumulated across rounds @@ -509,7 +509,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 def _partition_blocks(nodes: List[ExtractedEntityNode]) -> List[List[ExtractedEntityNode]]: """ - 按 group_id 分块，避免跨组实体在同一块，减少无效候选对 + 按 end_user_id 分块，避免跨组实体在同一块，减少无效候选对 Args: nodes: 实体节点列表 @@ -519,7 +519,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 """ groups: Dict[str, List[ExtractedEntityNode]] = {} for e in nodes: - gid = getattr(e, "group_id", None) + gid = getattr(e, "end_user_id", None) groups.setdefault(str(gid), []).append(e) blocks: List[List[ExtractedEntityNode]] = [] for gid, arr in groups.items(): @@ -559,7 +559,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 # Collapse nodes to canonical reps before each round to avoid redundant comparisons # 步骤1：折叠实体（合并已确定的重复实体，减少后续计算量） current_nodes = _collapse_nodes(current_nodes) - # 步骤2：分块（按group_id分块，避免跨组处理） + # 步骤2：分块（按end_user_id分块，避免跨组处理） blocks = _partition_blocks(current_nodes) if not blocks: # 无块可处理（实体已全部折叠），退出循环 break @@ -645,7 +645,7 @@ async def llm_disambiguate_pairs_iterative( a = entity_nodes[i] b = entity_nodes[j] # 必须同组 - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): continue ta = getattr(a, "entity_type", None) tb = getattr(b, "entity_type", None) diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py index b41f35a4..dbc697d9 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py @@ -61,7 +61,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode: return ExtractedEntityNode( id=row.get("id"), name=row.get("name") or "", - group_id=row.get("group_id") or "", + end_user_id=row.get("end_user_id") or "", user_id=row.get("user_id") or "", apply_id=row.get("apply_id") or "", created_at=_parse_dt(row.get("created_at")), @@ -79,7 +79,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode: async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑，与 Neo4j 中同组实体联合去重 connector: Neo4jConnector, - group_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重 + end_user_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重 entity_nodes: List[ExtractedEntityNode], # 输入的实体节点列表，包含待去重的实体 statement_entity_edges: List[StatementEntityEdge], # 输入的语句实体边列表，用于处理实体之间的关系 entity_entity_edges: List[EntityEntityEdge], # 输入的实体实体边列表，用于处理实体之间的关系 @@ -88,7 +88,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑 ) -> Tuple[List[ExtractedEntityNode], List[StatementEntityEdge], List[EntityEntityEdge]]: """ 第二层去重消歧： - - 以第一层结果为索引，检索相同 group_id 下的 DB 候选实体 + - 以第一层结果为索引，检索相同 end_user_id 下的 DB 候选实体 - 将 DB 候选与当前实体集合联合，按既有精确/模糊/LLM 决策进行融合 - 返回融合后的实体与重定向后的边（边已指向规范 ID，优先 DB ID） """ @@ -102,7 +102,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑 ] candidates_map = await get_dedup_candidates_for_entities( # 从 Neo4j 中查询候选实体，并将结果赋值给candidates_map（等待异步操作完成）。 - connector=connector, group_id=group_id, + connector=connector, end_user_id=end_user_id, entities=incoming_rows, # 传入参数：第一层实体的核心信息（作为查询索引） use_contains_fallback=True # 传入参数：启用 “包含关系” 作为匹配失败的降级策略（若精确匹配无结果，用包含关系召回候选），与src\database\cypher_queries.py的307产生联动 ) diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py index 11845d7d..f28b8a5f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py @@ -57,11 +57,11 @@ async def dedup_layers_and_merge_and_return( if pipeline_config is None: raise ValueError("pipeline_config is required for dedup_layers_and_merge_and_return") - # 先探测 group_id，决定报告写入策略 - group_id: Optional[str] = None + # 先探测 end_user_id，决定报告写入策略 + end_user_id: Optional[str] = None for dd in dialog_data_list: - group_id = getattr(dd, "group_id", None) - if group_id: + end_user_id = getattr(dd, "end_user_id", None) + if end_user_id: break # 第一层去重消歧 @@ -82,11 +82,11 @@ async def dedup_layers_and_merge_and_return( # 第二层去重消歧：与 Neo4j 中同组实体联合融合 try: - if group_id: + if end_user_id: if connector: fused_entity_nodes, fused_statement_entity_edges, fused_entity_entity_edges = await second_layer_dedup_and_merge_with_neo4j( connector=connector, - group_id=group_id, + end_user_id=end_user_id, entity_nodes=dedup_entity_nodes, statement_entity_edges=dedup_statement_entity_edges, entity_entity_edges=dedup_entity_entity_edges, @@ -96,7 +96,7 @@ async def dedup_layers_and_merge_and_return( else: print("Skip second-layer dedup: missing connector") else: - print("Skip second-layer dedup: missing group_id") + print("Skip second-layer dedup: missing end_user_id") except Exception as e: print(f"Second-layer dedup failed: {e}") diff --git a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py index 46ba1dde..c2c5d54e 100644 --- a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py +++ b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py @@ -287,7 +287,7 @@ class ExtractionOrchestrator: for d_idx, dialog in enumerate(dialog_data_list): dialogue_content = dialog.content if self.config.statement_extraction.include_dialogue_context else None for c_idx, chunk in enumerate(dialog.chunks): - all_chunks.append((chunk, dialog.group_id, dialogue_content)) + all_chunks.append((chunk, dialog.end_user_id, dialogue_content)) chunk_metadata.append((d_idx, c_idx)) logger.info(f"收集到 {len(all_chunks)} 个分块，开始全局并行提取") @@ -299,9 +299,9 @@ class ExtractionOrchestrator: # 全局并行处理所有分块 async def extract_for_chunk(chunk_data, chunk_index): nonlocal completed_chunks - chunk, group_id, dialogue_content = chunk_data + chunk, end_user_id, dialogue_content = chunk_data try: - statements = await self.statement_extractor._extract_statements(chunk, group_id, dialogue_content) + statements = await self.statement_extractor._extract_statements(chunk, end_user_id, dialogue_content) # 流式输出：每提取完一个分块的陈述句，立即发送进度 # 注意：只在试运行模式下发送陈述句详情，正式模式不发送 @@ -992,9 +992,7 @@ class ExtractionOrchestrator: id=dialog_data.id, name=f"Dialog_{dialog_data.id}", # 添加必需的 name 字段 ref_id=dialog_data.ref_id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id content=dialog_data.context.content if dialog_data.context else "", dialog_embedding=dialog_data.dialog_embedding if hasattr(dialog_data, 'dialog_embedding') else None, @@ -1012,9 +1010,7 @@ class ExtractionOrchestrator: id=chunk.id, name=f"Chunk_{chunk.id}", # 添加必需的 name 字段 dialog_id=dialog_data.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id content=chunk.content, chunk_embedding=chunk.chunk_embedding, @@ -1035,9 +1031,7 @@ class ExtractionOrchestrator: stmt_type=getattr(statement, 'stmt_type', 'general'), # 添加必需的 stmt_type 字段 temporal_info=getattr(statement, 'temporal_info', TemporalInfo.ATEMPORAL), # 添加必需的 temporal_info 字段 connect_strength=statement.connect_strength if statement.connect_strength is not None else 'Strong', # 添加必需的 connect_strength 字段 - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id statement=statement.statement, speaker=getattr(statement, 'speaker', None), # 添加 speaker 字段 @@ -1060,9 +1054,7 @@ class ExtractionOrchestrator: statement_chunk_edge = StatementChunkEdge( source=statement.id, target=chunk.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, ) @@ -1095,9 +1087,7 @@ class ExtractionOrchestrator: aliases=getattr(entity, 'aliases', []) or [], # 传递从三元组提取阶段获取的aliases name_embedding=getattr(entity, 'name_embedding', None), is_explicit_memory=getattr(entity, 'is_explicit_memory', False), # 新增：传递语义记忆标记 - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, expired_at=dialog_data.expired_at, @@ -1112,9 +1102,7 @@ class ExtractionOrchestrator: source=statement.id, target=entity.id, connect_strength=entity_connect_strength if entity_connect_strength is not None else 'Strong', - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, ) @@ -1134,9 +1122,7 @@ class ExtractionOrchestrator: relation_type=triplet.predicate, statement=statement.statement, source_statement_id=statement.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, expired_at=dialog_data.expired_at, @@ -1763,14 +1749,14 @@ class ExtractionOrchestrator: async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", - group_id: str = "group_1", + end_user_id: str = "group_1", indices: Optional[List[int]] = None, ) -> List[DialogData]: """从测试数据生成分块对话 Args: chunker_strategy: 分块策略（默认: RecursiveChunker） - group_id: 组ID + end_user_id: 组ID indices: 要处理的数据索引列表（可选） Returns: @@ -1834,7 +1820,7 @@ async def get_chunked_dialogs( dialog_data = DialogData( context=conversation_context, ref_id=data['id'], - group_id=group_id, + end_user_id=end_user_id, metadata=dialog_metadata, ) @@ -1936,7 +1922,7 @@ async def get_chunked_dialogs_from_preprocessed( async def get_chunked_dialogs_with_preprocessing( chunker_strategy: str = "RecursiveChunker", - group_id: str = "default", + end_user_id: str = "default", user_id: str = "default", apply_id: str = "default", indices: Optional[List[int]] = None, @@ -1948,7 +1934,7 @@ async def get_chunked_dialogs_with_preprocessing( Args: chunker_strategy: 分块策略 - group_id: 组ID + end_user_id: 组ID user_id: 用户ID apply_id: 应用ID indices: 要处理的数据索引列表 @@ -1976,11 +1962,9 @@ async def get_chunked_dialogs_with_preprocessing( indices=indices, ) - # 设置 group_id, user_id, apply_id + # 设置 end_user_id for dd in preprocessed_data: - dd.group_id = group_id - dd.user_id = user_id - dd.apply_id = apply_id + dd.end_user_id = end_user_id # 步骤2: 语义剪枝 try: diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py index 7e75fd2d..f39313a8 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py @@ -193,9 +193,9 @@ async def _process_chunk_summary( node = MemorySummaryNode( id=uuid4().hex, name=title if title else f"MemorySummaryChunk_{chunk.id}", - group_id=dialog.group_id, - user_id=dialog.user_id, - apply_id=dialog.apply_id, + end_user_id=dialog.end_user_id, + user_id=dialog.end_user_id, + apply_id=dialog.end_user_id, run_id=dialog.run_id, # 使用 dialog 的 run_id created_at=datetime.now(), expired_at=datetime(9999, 12, 31), diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py index fb1b539a..b06bd70f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py @@ -82,12 +82,12 @@ class StatementExtractor: logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty") return None - async def _extract_statements(self, chunk, group_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]: + async def _extract_statements(self, chunk, end_user_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]: """Process a single chunk and return extracted statements Args: chunk: Chunk object to process - group_id: Group ID to assign to all statements in this chunk + end_user_id: Group ID to assign to all statements in this chunk dialogue_content: Full dialogue content to provide as context Returns: @@ -158,7 +158,7 @@ class StatementExtractor: temporal_info=temporal_type, relevence_info=relevence_info, chunk_id=chunk.id, - group_id=group_id, + end_user_id=end_user_id, speaker=chunk_speaker, ) @@ -184,10 +184,10 @@ class StatementExtractor: logger.info(f"Processing {len(chunks_to_process)} chunks for statement extraction") - # Process all chunks concurrently, passing the group_id and dialogue content from dialog_data + # Process all chunks concurrently, passing the end_user_id and dialogue content from dialog_data dialogue_content = dialog_data.content if self.config.include_dialogue_context else None results = await asyncio.gather( - *[self._extract_statements(chunk, dialog_data.group_id, dialogue_content) for chunk in chunks_to_process], + *[self._extract_statements(chunk, dialog_data.end_user_id, dialogue_content) for chunk in chunks_to_process], return_exceptions=True ) @@ -225,7 +225,7 @@ class StatementExtractor: for i, statement in enumerate(statements, 1): f.write(f"Statement {i}:\n") f.write(f"Id: {statement.id}\n") - f.write(f"Group Id: {statement.group_id}\n") + f.write(f"Group Id: {statement.end_user_id}\n") f.write(f"Content: {statement.statement}\n") f.write(f"Type: {statement.stmt_type.value}\n") f.write(f"Temporal Info: {statement.temporal_info.value}\n") @@ -298,7 +298,7 @@ class StatementExtractor: dialog_sections.append({ "dialog_id": dialog.ref_id, - "group_id": dialog.group_id, + "end_user_id": dialog.end_user_id, "content": dialog.content if getattr(dialog, "content", None) else "", "strong": strong_relations, "weak": weak_relations, @@ -312,7 +312,7 @@ class StatementExtractor: for idx, section in enumerate(dialog_sections, 1): f.write(f"Dialog {idx}:\n") f.write(f"Dialog ID: {section.get('dialog_id', '')}\n") - f.write(f"Group ID: {section.get('group_id', '')}\n") + f.write(f"Group ID: {section.get('end_user_id', '')}\n") f.write("Content:\n") f.write(f"{section.get('content', '')}\n") f.write("-" * 40 + "\n\n") diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py index 9528e638..499027a4 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py @@ -132,7 +132,7 @@ class TemporalExtractor: prompt_logger.info("") prompt_logger.info("=== TEMPORAL EXTRACTION RESULTS ===") prompt_logger.info( - f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}" + f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py index d3d059b0..bfc0bc88 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py @@ -116,7 +116,7 @@ class TripletExtractor: logger.info(f"Processing {len(all_statements)} statements for triplet extraction...") try: prompt_logger.info( - f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}, statements_to_process={len(all_statements)}" + f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}, statements_to_process={len(all_statements)}" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py b/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py index 5722769a..a71c0957 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py +++ b/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py @@ -75,7 +75,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_time: Optional[datetime] = None ) -> Dict[str, Any]: """ @@ -91,7 +91,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签（Statement, ExtractedEntity, MemorySummary） - group_id: 组ID（可选，用于过滤） + end_user_id: 组ID（可选，用于过滤） current_time: 当前时间（可选，默认使用系统时间） Returns: @@ -123,7 +123,7 @@ class AccessHistoryManager: for attempt in range(self.max_retries): try: # 步骤1：读取当前节点状态 - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: raise ValueError( @@ -142,7 +142,7 @@ class AccessHistoryManager: node_id=node_id, node_label=node_label, update_data=update_data, - group_id=group_id + end_user_id=end_user_id ) logger.info( @@ -172,7 +172,7 @@ class AccessHistoryManager: self, node_ids: List[str], node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_time: Optional[datetime] = None ) -> List[Dict[str, Any]]: """ @@ -184,7 +184,7 @@ class AccessHistoryManager: Args: node_ids: 节点ID列表 node_label: 节点标签（所有节点必须是同一类型） - group_id: 组ID（可选） + end_user_id: 组ID（可选） current_time: 当前时间（可选） Returns: @@ -202,7 +202,7 @@ class AccessHistoryManager: task = self.record_access( node_id=node_id, node_label=node_label, - group_id=group_id, + end_user_id=end_user_id, current_time=current_time ) tasks.append(task) @@ -235,7 +235,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Tuple[ConsistencyCheckResult, Optional[str]]: """ 检查节点数据的一致性 @@ -249,14 +249,14 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Tuple[ConsistencyCheckResult, Optional[str]]: - 一致性检查结果枚举 - 错误描述（如果不一致） """ - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: return ConsistencyCheckResult.CONSISTENT, None @@ -305,7 +305,7 @@ class AccessHistoryManager: async def check_batch_consistency( self, node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1000 ) -> Dict[str, Any]: """ @@ -313,7 +313,7 @@ class AccessHistoryManager: Args: node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） limit: 检查的最大节点数 Returns: @@ -329,16 +329,16 @@ class AccessHistoryManager: MATCH (n:{node_label}) WHERE n.access_history IS NOT NULL """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ RETURN n.id as id LIMIT $limit """ params = {"limit": limit} - if group_id: - params["group_id"] = group_id + if end_user_id: + params["end_user_id"] = end_user_id results = await self.connector.execute_query(query, **params) node_ids = [r['id'] for r in results] @@ -351,7 +351,7 @@ class AccessHistoryManager: result, message = await self.check_consistency( node_id=node_id, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) if result == ConsistencyCheckResult.CONSISTENT: @@ -387,7 +387,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> bool: """ 自动修复节点的数据不一致问题 @@ -401,7 +401,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: bool: 修复成功返回True，否则返回False @@ -411,7 +411,7 @@ class AccessHistoryManager: result, message = await self.check_consistency( node_id=node_id, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) if result == ConsistencyCheckResult.CONSISTENT: @@ -419,7 +419,7 @@ class AccessHistoryManager: return True # 获取节点数据 - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: logger.error(f"节点不存在，无法修复: {node_label}[{node_id}]") return False @@ -457,8 +457,8 @@ class AccessHistoryManager: query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - query += " WHERE n.group_id = $group_id" + if end_user_id: + query += " WHERE n.end_user_id = $end_user_id" query += """ SET n += $repair_data RETURN n @@ -468,8 +468,8 @@ class AccessHistoryManager: 'node_id': node_id, 'repair_data': repair_data } - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id await self.connector.execute_query(query, **params) @@ -491,7 +491,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Optional[Dict[str, Any]]: """ 获取节点数据 @@ -499,7 +499,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Optional[Dict[str, Any]]: 节点数据，如果不存在返回None @@ -507,8 +507,8 @@ class AccessHistoryManager: query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - query += " WHERE n.group_id = $group_id" + if end_user_id: + query += " WHERE n.end_user_id = $end_user_id" query += """ RETURN n.id as id, n.importance_score as importance_score, @@ -519,8 +519,8 @@ class AccessHistoryManager: """ params = {'node_id': node_id} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) @@ -585,7 +585,7 @@ class AccessHistoryManager: node_id: str, node_label: str, update_data: Dict[str, Any], - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Dict[str, Any]: """ 原子性更新节点（使用乐观锁） @@ -597,7 +597,7 @@ class AccessHistoryManager: node_id: 节点ID node_label: 节点标签 update_data: 更新数据 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Dict[str, Any]: 更新后的节点数据 @@ -606,13 +606,13 @@ class AccessHistoryManager: RuntimeError: 如果更新失败或发生版本冲突 """ # 定义事务函数 - async def update_transaction(tx, node_id, node_label, update_data, group_id): + async def update_transaction(tx, node_id, node_label, update_data, end_user_id): # 步骤1：读取当前节点并获取版本号 read_query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - read_query += " WHERE n.group_id = $group_id" + if end_user_id: + read_query += " WHERE n.end_user_id = $end_user_id" read_query += """ RETURN n.id as id, n.version as version, @@ -624,8 +624,8 @@ class AccessHistoryManager: """ read_params = {'node_id': node_id} - if group_id: - read_params['group_id'] = group_id + if end_user_id: + read_params['end_user_id'] = end_user_id read_result = await tx.run(read_query, **read_params) current_node = await read_result.single() @@ -656,8 +656,8 @@ class AccessHistoryManager: # 构建 WHERE 子句 where_conditions = [] - if group_id: - where_conditions.append("n.group_id = $group_id") + if end_user_id: + where_conditions.append("n.end_user_id = $end_user_id") # 添加版本检查 if current_version > 0: @@ -695,8 +695,8 @@ class AccessHistoryManager: 'last_access_time': update_data['last_access_time'], 'access_count': update_data['access_count'] } - if group_id: - update_params['group_id'] = group_id + if end_user_id: + update_params['end_user_id'] = end_user_id update_result = await tx.run(update_query, **update_params) updated_node = await update_result.single() @@ -720,7 +720,7 @@ class AccessHistoryManager: node_id=node_id, node_label=node_label, update_data=update_data, - group_id=group_id + end_user_id=end_user_id ) return result except Exception as e: diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py index 6d42af53..e9d4c144 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py @@ -66,7 +66,7 @@ class ForgettingScheduler: async def run_forgetting_cycle( self, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, max_merge_batch_size: int = 100, min_days_since_access: int = 30, config_id: Optional[int] = None, @@ -77,7 +77,7 @@ class ForgettingScheduler: Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） max_merge_batch_size: 单次最大融合节点对数（默认 100） min_days_since_access: 最小未访问天数（默认 30 天） config_id: 配置ID（可选，用于获取 llm_id） @@ -107,19 +107,19 @@ class ForgettingScheduler: start_time_iso = start_time.isoformat() logger.info( - f"开始遗忘周期: group_id={group_id}, " + f"开始遗忘周期: end_user_id={end_user_id}, " f"max_batch={max_merge_batch_size}, " f"min_days={min_days_since_access}" ) try: # 步骤1：统计遗忘前的节点数量 - nodes_before = await self._count_knowledge_nodes(group_id) + nodes_before = await self._count_knowledge_nodes(end_user_id) logger.info(f"遗忘前节点总数: {nodes_before}") # 步骤2：识别可遗忘的节点对 forgettable_pairs = await self.forgetting_strategy.find_forgettable_nodes( - group_id=group_id, + end_user_id=end_user_id, min_days_since_access=min_days_since_access ) @@ -213,7 +213,7 @@ class ForgettingScheduler: 'statement_text': pair['statement_text'], 'statement_activation': pair['statement_activation'], 'statement_importance': pair['statement_importance'], - 'group_id': group_id + 'end_user_id': end_user_id } entity_node = { @@ -222,7 +222,7 @@ class ForgettingScheduler: 'entity_type': pair['entity_type'], 'entity_activation': pair['entity_activation'], 'entity_importance': pair['entity_importance'], - 'group_id': group_id + 'end_user_id': end_user_id } # 融合节点 @@ -262,7 +262,7 @@ class ForgettingScheduler: continue # 步骤6：统计遗忘后的节点数量 - nodes_after = await self._count_knowledge_nodes(group_id) + nodes_after = await self._count_knowledge_nodes(end_user_id) logger.info(f"遗忘后节点总数: {nodes_after}") # 步骤7：生成遗忘报告 @@ -315,7 +315,7 @@ class ForgettingScheduler: async def _count_knowledge_nodes( self, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> int: """ 统计知识层节点总数 @@ -323,7 +323,7 @@ class ForgettingScheduler: 统计 Statement、ExtractedEntity 和 MemorySummary 节点的总数。 Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） Returns: int: 知识层节点总数 @@ -333,16 +333,16 @@ class ForgettingScheduler: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ RETURN count(n) as total """ params = {} - if group_id: - params['group_id'] = group_id + if end_user_id: + end_user_id['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py index ccd8d2ca..6b2d9e99 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py @@ -90,7 +90,7 @@ class ForgettingStrategy: async def find_forgettable_nodes( self, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, min_days_since_access: int = 30 ) -> List[Dict[str, Any]]: """ @@ -102,7 +102,7 @@ class ForgettingStrategy: 3. Statement 和 Entity 之间存在关系边 Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） min_days_since_access: 最小未访问天数（默认 30 天） Returns: @@ -136,8 +136,8 @@ class ForgettingStrategy: AND (e.entity_type IS NULL OR e.entity_type <> 'Person') """ - if group_id: - query += " AND s.group_id = $group_id AND e.group_id = $group_id" + if end_user_id: + query += " AND s.end_user_id = $end_user_id AND e.end_user_id = $end_user_id" query += """ RETURN s.id as statement_id, @@ -159,8 +159,8 @@ class ForgettingStrategy: 'threshold': self.forgetting_threshold, 'cutoff_time': cutoff_time_iso } - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) @@ -247,8 +247,8 @@ class ForgettingStrategy: entity_activation = entity_node['entity_activation'] entity_importance = entity_node['entity_importance'] - # 获取 group_id（从 statement 或 entity 节点） - group_id = statement_node.get('group_id') or entity_node.get('group_id') + # 获取 end_user_id（从 statement 或 entity 节点） + end_user_id = statement_node.get('end_user_id') or entity_node.get('end_user_id') # 生成摘要内容 summary_text = await self._generate_summary( @@ -325,7 +325,7 @@ class ForgettingStrategy: last_access_time: $current_time, access_count: 1, version: 1, - group_id: $group_id, + end_user_id: $end_user_id, created_at: datetime($current_time), merged_at: datetime($current_time) }) @@ -423,7 +423,7 @@ class ForgettingStrategy: 'inherited_activation': inherited_activation, 'inherited_importance': inherited_importance, 'current_time': current_time_iso, - 'group_id': group_id + 'end_user_id': end_user_id } try: diff --git a/api/app/core/memory/storage_services/search/__init__.py b/api/app/core/memory/storage_services/search/__init__.py index 2bec5bf1..c12c39b0 100644 --- a/api/app/core/memory/storage_services/search/__init__.py +++ b/api/app/core/memory/storage_services/search/__init__.py @@ -37,7 +37,7 @@ __all__ = [ async def run_hybrid_search( query_text: str, search_type: str = "hybrid", - group_id: str | None = None, + end_user_id: str | None = None, apply_id: str | None = None, user_id: str | None = None, limit: int = 50, @@ -54,7 +54,7 @@ async def run_hybrid_search( Args: query_text: 查询文本 search_type: 搜索类型（"hybrid", "keyword", "semantic"） - group_id: 组ID过滤 + end_user_id: 组ID过滤 apply_id: 应用ID过滤 user_id: 用户ID过滤 limit: 每个类别的最大结果数 @@ -104,7 +104,7 @@ async def run_hybrid_search( # 执行搜索 result = await strategy.search( query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, alpha=alpha, diff --git a/api/app/core/memory/storage_services/search/hybrid_search.py b/api/app/core/memory/storage_services/search/hybrid_search.py index 43215df5..4111b09c 100644 --- a/api/app/core/memory/storage_services/search/hybrid_search.py +++ b/api/app/core/memory/storage_services/search/hybrid_search.py @@ -77,7 +77,7 @@ # async def search( # self, # query_text: str, -# group_id: Optional[str] = None, +# end_user_id: Optional[str] = None, # limit: int = 50, # include: Optional[List[str]] = None, # **kwargs @@ -86,7 +86,7 @@ # Args: # query_text: 查询文本 -# group_id: 可选的组ID过滤 +# end_user_id: 可选的组ID过滤 # limit: 每个类别的最大结果数 # include: 要包含的搜索类别列表 # **kwargs: 其他搜索参数（如alpha, use_forgetting_curve） @@ -94,7 +94,7 @@ # Returns: # SearchResult: 搜索结果对象 # """ -# logger.info(f"执行混合搜索: query='{query_text}', group_id={group_id}, limit={limit}") +# logger.info(f"执行混合搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # # 从kwargs中获取参数 # alpha = kwargs.get("alpha", self.alpha) @@ -107,14 +107,14 @@ # # 并行执行关键词搜索和语义搜索 # keyword_result = await self.keyword_strategy.search( # query_text=query_text, -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list # ) # semantic_result = await self.semantic_strategy.search( # query_text=query_text, -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list # ) @@ -139,7 +139,7 @@ # metadata = self._create_metadata( # query_text=query_text, # search_type="hybrid", -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list, # alpha=alpha, @@ -165,7 +165,7 @@ # metadata=self._create_metadata( # query_text=query_text, # search_type="hybrid", -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # error=str(e) # ) diff --git a/api/app/core/memory/storage_services/search/keyword_search.py b/api/app/core/memory/storage_services/search/keyword_search.py index 95dd0581..d2591945 100644 --- a/api/app/core/memory/storage_services/search/keyword_search.py +++ b/api/app/core/memory/storage_services/search/keyword_search.py @@ -44,7 +44,7 @@ class KeywordSearchStrategy(SearchStrategy): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -53,7 +53,7 @@ class KeywordSearchStrategy(SearchStrategy): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表 **kwargs: 其他搜索参数 @@ -61,7 +61,7 @@ class KeywordSearchStrategy(SearchStrategy): Returns: SearchResult: 搜索结果对象 """ - logger.info(f"执行关键词搜索: query='{query_text}', group_id={group_id}, limit={limit}") + logger.info(f"执行关键词搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # 获取有效的搜索类别 include_list = self._get_include_list(include) @@ -75,7 +75,7 @@ class KeywordSearchStrategy(SearchStrategy): results_dict = await search_graph( connector=self.connector, q=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -84,7 +84,7 @@ class KeywordSearchStrategy(SearchStrategy): metadata = self._create_metadata( query_text=query_text, search_type="keyword", - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -115,7 +115,7 @@ class KeywordSearchStrategy(SearchStrategy): metadata=self._create_metadata( query_text=query_text, search_type="keyword", - group_id=group_id, + end_user_id=end_user_id, limit=limit, error=str(e) ) diff --git a/api/app/core/memory/storage_services/search/search_strategy.py b/api/app/core/memory/storage_services/search/search_strategy.py index 27c02c89..3a670dd6 100644 --- a/api/app/core/memory/storage_services/search/search_strategy.py +++ b/api/app/core/memory/storage_services/search/search_strategy.py @@ -58,7 +58,7 @@ class SearchStrategy(ABC): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -67,7 +67,7 @@ class SearchStrategy(ABC): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表（statements, chunks, entities, summaries） **kwargs: 其他搜索参数 @@ -81,7 +81,7 @@ class SearchStrategy(ABC): self, query_text: str, search_type: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, **kwargs ) -> Dict[str, Any]: @@ -90,7 +90,7 @@ class SearchStrategy(ABC): Args: query_text: 查询文本 search_type: 搜索类型 - group_id: 组ID + end_user_id: 组ID limit: 结果限制 **kwargs: 其他元数据 @@ -100,7 +100,7 @@ class SearchStrategy(ABC): metadata = { "query": query_text, "search_type": search_type, - "group_id": group_id, + "end_user_id": end_user_id, "limit": limit, "timestamp": datetime.now().isoformat() } diff --git a/api/app/core/memory/storage_services/search/semantic_search.py b/api/app/core/memory/storage_services/search/semantic_search.py index b20f90a5..8d4eb05f 100644 --- a/api/app/core/memory/storage_services/search/semantic_search.py +++ b/api/app/core/memory/storage_services/search/semantic_search.py @@ -85,7 +85,7 @@ class SemanticSearchStrategy(SearchStrategy): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -94,7 +94,7 @@ class SemanticSearchStrategy(SearchStrategy): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表 **kwargs: 其他搜索参数 @@ -102,7 +102,7 @@ class SemanticSearchStrategy(SearchStrategy): Returns: SearchResult: 搜索结果对象 """ - logger.info(f"执行语义搜索: query='{query_text}', group_id={group_id}, limit={limit}") + logger.info(f"执行语义搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # 获取有效的搜索类别 include_list = self._get_include_list(include) @@ -119,7 +119,7 @@ class SemanticSearchStrategy(SearchStrategy): connector=self.connector, embedder_client=self.embedder_client, query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -128,7 +128,7 @@ class SemanticSearchStrategy(SearchStrategy): metadata = self._create_metadata( query_text=query_text, search_type="semantic", - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -159,7 +159,7 @@ class SemanticSearchStrategy(SearchStrategy): metadata=self._create_metadata( query_text=query_text, search_type="semantic", - group_id=group_id, + end_user_id=end_user_id, limit=limit, error=str(e) ) diff --git a/api/app/core/memory/utils/config/get_data.py b/api/app/core/memory/utils/config/get_data.py index 1de6f6aa..e37ad723 100644 --- a/api/app/core/memory/utils/config/get_data.py +++ b/api/app/core/memory/utils/config/get_data.py @@ -23,7 +23,7 @@ async def _load_(data: List[Any]) -> List[Dict]: target_keys = [ "id", "statement", - "group_id", + "end_user_id", "chunk_id", "created_at", "expired_at", @@ -75,7 +75,7 @@ async def get_data(result): """ EXCLUDE_FIELDS = { "user_id", - "group_id", + "end_user_id", "entity_type", "connect_strength", "relationship_type", diff --git a/api/app/core/memory/utils/log/audit_logger.py b/api/app/core/memory/utils/log/audit_logger.py index 9010aad5..f80ad4d5 100644 --- a/api/app/core/memory/utils/log/audit_logger.py +++ b/api/app/core/memory/utils/log/audit_logger.py @@ -62,7 +62,7 @@ class ConfigAuditLogger: self, config_id: str, user_id: Optional[str] = None, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, success: bool = True, details: Optional[Dict[str, Any]] = None ): @@ -72,14 +72,14 @@ class ConfigAuditLogger: Args: config_id: 配置 ID user_id: 用户 ID（可选） - group_id: 组 ID（可选） + end_user_id: 组 ID（可选） success: 是否成功 details: 详细信息（可选） """ result = "SUCCESS" if success else "FAILED" msg = ( f"CONFIG_LOAD config_id={config_id} " - f"user={user_id or 'N/A'} group={group_id or 'N/A'} " + f"user={user_id or 'N/A'} group={end_user_id or 'N/A'} " f"result={result}" ) if details: @@ -121,7 +121,7 @@ class ConfigAuditLogger: self, operation: str, config_id: str, - group_id: str, + end_user_id: str, success: bool = True, duration: Optional[float] = None, error: Optional[str] = None, @@ -133,7 +133,7 @@ class ConfigAuditLogger: Args: operation: 操作类型（WRITE, READ 等） config_id: 配置 ID - group_id: 组 ID + end_user_id: 组 ID success: 是否成功 duration: 操作耗时（秒） error: 错误信息（可选） @@ -142,7 +142,7 @@ class ConfigAuditLogger: result = "SUCCESS" if success else "FAILED" msg = ( f"{operation.upper()} config_id={config_id} " - f"group={group_id} result={result}" + f"group={end_user_id} result={result}" ) if duration is not None: msg += f" duration={duration:.2f}s" diff --git a/api/app/core/rag/vdb/field.py b/api/app/core/rag/vdb/field.py index 86d39060..99d872c2 100644 --- a/api/app/core/rag/vdb/field.py +++ b/api/app/core/rag/vdb/field.py @@ -4,7 +4,7 @@ from enum import StrEnum, auto class Field(StrEnum): CONTENT_KEY = "page_content" METADATA_KEY = "metadata" - GROUP_KEY = "group_id" + GROUP_KEY = "end_user_id" VECTOR = auto() # Sparse Vector aims to support full text search SPARSE_VECTOR = auto() diff --git a/api/app/repositories/neo4j/add_edges.py b/api/app/repositories/neo4j/add_edges.py index 3b45867e..162bf411 100644 --- a/api/app/repositories/neo4j/add_edges.py +++ b/api/app/repositories/neo4j/add_edges.py @@ -32,7 +32,7 @@ async def add_chunk_statement_edges(chunks: List[Chunk], connector: Neo4jConnect "id": stable_edge_id, "source": chunk.id, "target": stmt.id, - "group_id": getattr(stmt, 'group_id', None), + "end_user_id": getattr(stmt, 'end_user_id', None), "user_id":getattr(stmt, 'user_id', None), "apply_id": getattr(stmt, 'apply_id', None), "run_id": getattr(stmt, 'run_id', None) or getattr(chunk, 'run_id', None), @@ -83,7 +83,7 @@ async def add_memory_summary_statement_edges(summaries: List[MemorySummaryNode], edges.append({ "summary_id": s.id, "chunk_id": chunk_id, - "group_id": s.group_id, + "end_user_id": s.end_user_id, "run_id": s.run_id, "created_at": s.created_at.isoformat() if s.created_at else None, "expired_at": s.expired_at.isoformat() if s.expired_at else None, diff --git a/api/app/repositories/neo4j/add_nodes.py b/api/app/repositories/neo4j/add_nodes.py index cf60a773..fcf700b5 100644 --- a/api/app/repositories/neo4j/add_nodes.py +++ b/api/app/repositories/neo4j/add_nodes.py @@ -6,10 +6,10 @@ from app.core.memory.models.graph_models import DialogueNode, StatementNode, Chu from app.repositories.neo4j.neo4j_connector import Neo4jConnector -async def delete_all_nodes(group_id: str, connector: Neo4jConnector): +async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector): """Delete all nodes in the database.""" - result = await connector.execute_query(f"MATCH (n {{group_id: '{group_id}'}}) DETACH DELETE n") - print(f"All group_id: {group_id} node and edge deleted successfully") + result = await connector.execute_query(f"MATCH (n {{end_user_id: '{end_user_id}'}}) DETACH DELETE n") + print(f"All end_user_id: {end_user_id} node and edge deleted successfully") return result async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConnector) -> Optional[List[str]]: @@ -32,9 +32,7 @@ async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConn for dialogue in dialogues: flattened_dialogues.append({ "id": dialogue.id, - "group_id": dialogue.group_id, - "user_id": dialogue.user_id, - "apply_id": dialogue.apply_id, + "end_user_id": dialogue.end_user_id, "run_id": dialogue.run_id, "ref_id": dialogue.ref_id, "name": dialogue.name, @@ -79,9 +77,7 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC flattened_statement = { "id": statement.id, "name": statement.name, - "group_id": statement.group_id, - "user_id": statement.user_id, - "apply_id": statement.apply_id, + "end_user_id": statement.end_user_id, "run_id": statement.run_id, "chunk_id": statement.chunk_id, # "created_at": statement.created_at.isoformat(), @@ -154,9 +150,7 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) -> flattened_chunk = { "id": chunk.id, "name": chunk.name, - "group_id": chunk.group_id, - "user_id": chunk.user_id, - "apply_id": chunk.apply_id, + "end_user_id": chunk.end_user_id, "run_id": chunk.run_id, "created_at": chunk.created_at.isoformat() if chunk.created_at else None, "expired_at": chunk.expired_at.isoformat() if chunk.expired_at else None, @@ -206,9 +200,7 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector flattened.append({ "id": s.id, "name": s.name, - "group_id": s.group_id, - "user_id": s.user_id, - "apply_id": s.apply_id, + "end_user_id": s.end_user_id, "run_id": s.run_id, "created_at": s.created_at.isoformat() if s.created_at else None, "expired_at": s.expired_at.isoformat() if s.expired_at else None, diff --git a/api/app/repositories/neo4j/base_neo4j_repository.py b/api/app/repositories/neo4j/base_neo4j_repository.py index 959a1e68..df953eb9 100644 --- a/api/app/repositories/neo4j/base_neo4j_repository.py +++ b/api/app/repositories/neo4j/base_neo4j_repository.py @@ -152,7 +152,7 @@ class BaseNeo4jRepository(BaseRepository[T]): Example: >>> results = await repository.find( - ... {"group_id": "group_123", "user_id": "user_456"}, + ... {"end_user_id": "group_123", "user_id": "user_456"}, ... limit=50 ... ) """ diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index cd3cbed7..eaef1e7a 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -3,9 +3,7 @@ DIALOGUE_NODE_SAVE = """ UNWIND $dialogues AS dialogue MERGE (n:Dialogue {id: dialogue.id}) SET n.uuid = coalesce(n.uuid, dialogue.id), - n.group_id = dialogue.group_id, - n.user_id = dialogue.user_id, - n.apply_id = dialogue.apply_id, + n.end_user_id = dialogue.end_user_id, n.run_id = dialogue.run_id, n.ref_id = dialogue.ref_id, n.created_at = dialogue.created_at, @@ -22,9 +20,7 @@ SET s += { id: statement.id, run_id: statement.run_id, chunk_id: statement.chunk_id, - group_id: statement.group_id, - user_id: statement.user_id, - apply_id: statement.apply_id, + end_user_id: statement.end_user_id, stmt_type: statement.stmt_type, statement: statement.statement, emotion_intensity: statement.emotion_intensity, @@ -54,9 +50,7 @@ MERGE (c:Chunk {id: chunk.id}) SET c += { id: chunk.id, name: chunk.name, - group_id: chunk.group_id, - user_id: chunk.user_id, - apply_id: chunk.apply_id, + end_user_id: chunk.end_user_id, run_id: chunk.run_id, created_at: chunk.created_at, expired_at: chunk.expired_at, @@ -76,9 +70,7 @@ EXTRACTED_ENTITY_NODE_SAVE = """ UNWIND $entities AS entity MERGE (e:ExtractedEntity {id: entity.id}) SET e.name = CASE WHEN entity.name IS NOT NULL AND entity.name <> '' THEN entity.name ELSE e.name END, - e.group_id = CASE WHEN entity.group_id IS NOT NULL AND entity.group_id <> '' THEN entity.group_id ELSE e.group_id END, - e.user_id = CASE WHEN entity.user_id IS NOT NULL AND entity.user_id <> '' THEN entity.user_id ELSE e.user_id END, - e.apply_id = CASE WHEN entity.apply_id IS NOT NULL AND entity.apply_id <> '' THEN entity.apply_id ELSE e.apply_id END, + e.end_user_id = CASE WHEN entity.end_user_id IS NOT NULL AND entity.end_user_id <> '' THEN entity.end_user_id ELSE e.end_user_id END, e.run_id = CASE WHEN entity.run_id IS NOT NULL AND entity.run_id <> '' THEN entity.run_id ELSE e.run_id END, e.created_at = CASE WHEN entity.created_at IS NOT NULL AND (e.created_at IS NULL OR entity.created_at < e.created_at) @@ -134,9 +126,9 @@ RETURN e.id AS uuid # Add back ENTITY_RELATIONSHIP_SAVE to be used by graph_saver.save_entities_and_relationships ENTITY_RELATIONSHIP_SAVE = """ UNWIND $relationships AS rel -// Match entities by stable id within group, do not constrain by run_id -MATCH (subject:ExtractedEntity {id: rel.source_id, group_id: rel.group_id}) -MATCH (object:ExtractedEntity {id: rel.target_id, group_id: rel.group_id}) +// Match entities by stable id within end_user_id, do not constrain by run_id +MATCH (subject:ExtractedEntity {id: rel.source_id, end_user_id: rel.end_user_id}) +MATCH (object:ExtractedEntity {id: rel.target_id, end_user_id: rel.end_user_id}) // Avoid duplicate edges across runs for the same endpoints MERGE (subject)-[r:EXTRACTED_RELATIONSHIP]->(object) SET r.predicate = rel.predicate, @@ -148,7 +140,7 @@ SET r.predicate = rel.predicate, r.created_at = rel.created_at, r.expired_at = rel.expired_at, r.run_id = rel.run_id, - r.group_id = rel.group_id + r.end_user_id = rel.end_user_id RETURN elementId(r) AS uuid """ @@ -160,7 +152,7 @@ UNWIND $weak_entities AS entity MERGE (e:ExtractedEntity {id: entity.id, run_id: entity.run_id}) SET e += { name: entity.name, - group_id: entity.group_id, + end_user_id: entity.end_user_id, run_id: entity.run_id, description: entity.description, chunk_id: entity.chunk_id, @@ -175,11 +167,11 @@ RETURN e.id AS id SAVE_STRONG_TRIPLE_ENTITIES = """ UNWIND $items AS item MERGE (s:ExtractedEntity {id: item.source_id, run_id: item.run_id}) -SET s += {name: item.subject, group_id: item.group_id, run_id: item.run_id} +SET s += {name: item.subject, end_user_id: item.end_user_id, run_id: item.run_id} // Independent strong flag SET s.is_strong = true MERGE (o:ExtractedEntity {id: item.target_id, run_id: item.run_id}) -SET o += {name: item.object, group_id: item.group_id, run_id: item.run_id} +SET o += {name: item.object, end_user_id: item.end_user_id, run_id: item.run_id} // Independent strong flag SET o.is_strong = true """ @@ -194,7 +186,7 @@ DIALOGUE_STATEMENT_EDGE_SAVE = """ // 仅按端点去重，关系属性可更新 MERGE (dialogue)-[e:MENTIONS]->(statement) SET e.uuid = edge.id, - e.group_id = edge.group_id, + e.end_user_id = edge.end_user_id, e.created_at = edge.created_at, e.expired_at = edge.expired_at RETURN e.uuid AS uuid @@ -208,7 +200,7 @@ CHUNK_STATEMENT_EDGE_SAVE = """ MATCH (statement:Statement {id: edge.source, run_id: edge.run_id}) MATCH (chunk:Chunk {id: edge.target, run_id: edge.run_id}) MERGE (chunk)-[e:CONTAINS {id: edge.id}]->(statement) - SET e.group_id = edge.group_id, + SET e.end_user_id = edge.end_user_id, e.run_id = edge.run_id, e.created_at = edge.created_at, e.expired_at = edge.expired_at @@ -218,13 +210,12 @@ CHUNK_STATEMENT_EDGE_SAVE = """ STATEMENT_ENTITY_EDGE_SAVE = """ UNWIND $relationships AS rel // Statement nodes are per-run; keep run_id constraint on statements -// Statement nodes are per-run; keep run_id constraint on statements MATCH (statement:Statement {id: rel.source, run_id: rel.run_id}) -// Entities are shared across runs within a group; do not constrain by run_id -MATCH (entity:ExtractedEntity {id: rel.target, group_id: rel.group_id}) +// Entities are shared across runs within end_user_id; do not constrain by run_id +MATCH (entity:ExtractedEntity {id: rel.target, end_user_id: rel.end_user_id}) // Avoid duplicate edges across runs for same endpoints MERGE (statement)-[r:REFERENCES_ENTITY]->(entity) -SET r.group_id = rel.group_id, +SET r.end_user_id = rel.end_user_id, r.run_id = rel.run_id, r.created_at = rel.created_at, r.expired_at = rel.expired_at, @@ -236,10 +227,10 @@ ENTITY_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('entity_embedding_index', $limit * 100, $embedding) YIELD node AS e, score WHERE e.name_embedding IS NOT NULL - AND ($group_id IS NULL OR e.group_id = $group_id) + AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) RETURN e.id AS id, e.name AS name, - e.group_id AS group_id, + e.end_user_id AS end_user_id, e.entity_type AS entity_type, COALESCE(e.activation_value, e.importance_score, 0.5) AS activation_value, COALESCE(e.importance_score, 0.5) AS importance_score, @@ -254,10 +245,10 @@ STATEMENT_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('statement_embedding_index', $limit * 100, $embedding) YIELD node AS s, score WHERE s.statement_embedding IS NOT NULL - AND ($group_id IS NULL OR s.group_id = $group_id) + AND ($end_user_id IS NULL OR s.end_user_id = $end_user_id) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.expired_at AS expired_at, @@ -277,9 +268,9 @@ CHUNK_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('chunk_embedding_index', $limit * 100, $embedding) YIELD node AS c, score WHERE c.chunk_embedding IS NOT NULL - AND ($group_id IS NULL OR c.group_id = $group_id) + AND ($end_user_id IS NULL OR c.end_user_id = $end_user_id) RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, COALESCE(c.activation_value, 0.5) AS activation_value, @@ -292,12 +283,12 @@ LIMIT $limit SEARCH_STATEMENTS_BY_KEYWORD = """ CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score -WHERE ($group_id IS NULL OR s.group_id = $group_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.expired_at AS expired_at, @@ -316,15 +307,13 @@ LIMIT $limit # 查询实体名称包含指定字符串的实体 SEARCH_ENTITIES_BY_NAME = """ CALL db.index.fulltext.queryNodes("entitiesFulltext", $q) YIELD node AS e, score -WHERE ($group_id IS NULL OR e.group_id = $group_id) +WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e) OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) RETURN e.id AS id, e.name AS name, - e.group_id AS group_id, + e.end_user_id AS end_user_id, e.entity_type AS entity_type, - e.apply_id AS apply_id, - e.user_id AS user_id, e.created_at AS created_at, e.expired_at AS expired_at, e.entity_idx AS entity_idx, @@ -347,11 +336,11 @@ LIMIT $limit SEARCH_CHUNKS_BY_CONTENT = """ CALL db.index.fulltext.queryNodes("chunksFulltext", $q) YIELD node AS c, score -WHERE ($group_id IS NULL OR c.group_id = $group_id) +WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id) OPTIONAL MATCH (c)-[:CONTAINS]->(s:Statement) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, c.sequence_number AS sequence_number, @@ -413,10 +402,10 @@ LIMIT $limit SEARCH_DIALOGUE_BY_DIALOG_ID = """ MATCH (d:Dialogue) -WHERE ($group_id IS NULL OR d.group_id = $group_id) +WHERE ($end_user_id IS NULL OR d.end_user_id = $end_user_id) AND d.id = $dialog_id RETURN d.id AS dialog_id, - d.group_id AS group_id, + d.end_user_id AS end_user_id, d.content AS content, d.created_at AS created_at, d.expired_at AS expired_at @@ -426,10 +415,10 @@ LIMIT $limit SEARCH_CHUNK_BY_CHUNK_ID = """ MATCH (c:Chunk) -WHERE ($group_id IS NULL OR c.group_id = $group_id) +WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id) AND c.id = $chunk_id RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, c.created_at AS created_at, @@ -441,18 +430,14 @@ LIMIT $limit SEARCH_STATEMENTS_BY_TEMPORAL = """ MATCH (s:Statement) -WHERE ($group_id IS NULL OR s.group_id = $group_id) - AND ($apply_id IS NULL OR s.apply_id = $apply_id) - AND ($user_id IS NULL OR s.user_id = $user_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND ((($start_date IS NULL OR datetime(s.created_at) >= datetime($start_date)) AND ($end_date IS NULL OR datetime(s.created_at) <= datetime($end_date))) OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date))) AND ($invalid_date IS NULL OR (s.invalid_at IS NOT NULL AND datetime(s.invalid_at) <= datetime($invalid_date))))) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, - s.apply_id AS apply_id, - s.user_id AS user_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.valid_at AS valid_at, @@ -468,9 +453,7 @@ LIMIT $limit SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL = """ CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score -WHERE ($group_id IS NULL OR s.group_id = $group_id) - AND ($apply_id IS NULL OR s.apply_id = $apply_id) - AND ($user_id IS NULL OR s.user_id = $user_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND ((($start_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) >= datetime($start_date))) AND ($end_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) <= datetime($end_date)))) OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date))) @@ -479,9 +462,7 @@ OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, - s.apply_id AS apply_id, - s.user_id AS user_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.valid_at AS valid_at, @@ -499,15 +480,11 @@ LIMIT $limit SEARCH_STATEMENTS_BY_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 10)) = date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -519,15 +496,11 @@ LIMIT $limit SEARCH_STATEMENTS_BY_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) = date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -539,15 +512,11 @@ LIMIT $limit SEARCH_STATEMENTS_G_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) = date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -559,15 +528,11 @@ LIMIT $limit SEARCH_STATEMENTS_L_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) < date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -579,15 +544,11 @@ LIMIT $limit SEARCH_STATEMENTS_G_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) > date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -599,15 +560,11 @@ LIMIT $limit SEARCH_STATEMENTS_L_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) < date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -665,18 +622,18 @@ LIMIT $limit # 根据id修改句子的invalid_at的值 UPDATE_STATEMENT_INVALID_AT = """ -MATCH (n:Statement {group_id: $group_id, id: $id}) +MATCH (n:Statement {end_user_id: $end_user_id, id: $id}) SET n.invalid_at = $new_invalid_at """ # MemorySummary keyword search using fulltext index SEARCH_MEMORY_SUMMARIES_BY_KEYWORD = """ CALL db.index.fulltext.queryNodes("summariesFulltext", $q) YIELD node AS m, score -WHERE ($group_id IS NULL OR m.group_id = $group_id) +WHERE ($end_user_id IS NULL OR m.end_user_id = $end_user_id) OPTIONAL MATCH (m)-[:DERIVED_FROM_STATEMENT]->(s:Statement) RETURN m.id AS id, m.name AS name, - m.group_id AS group_id, + m.end_user_id AS end_user_id, m.dialog_id AS dialog_id, m.chunk_ids AS chunk_ids, m.content AS content, @@ -695,10 +652,10 @@ MEMORY_SUMMARY_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('summary_embedding_index', $limit * 100, $embedding) YIELD node AS m, score WHERE m.summary_embedding IS NOT NULL - AND ($group_id IS NULL OR m.group_id = $group_id) + AND ($end_user_id IS NULL OR m.end_user_id = $end_user_id) RETURN m.id AS id, m.name AS name, - m.group_id AS group_id, + m.end_user_id AS end_user_id, m.dialog_id AS dialog_id, m.chunk_ids AS chunk_ids, m.content AS content, @@ -718,9 +675,7 @@ MERGE (m:MemorySummary {id: summary.id}) SET m += { id: summary.id, name: summary.name, - group_id: summary.group_id, - user_id: summary.user_id, - apply_id: summary.apply_id, + end_user_id: summary.end_user_id, run_id: summary.run_id, created_at: summary.created_at, expired_at: summary.expired_at, @@ -814,7 +769,7 @@ RETURN count(losing) as deleted neo4j_statement_part = ''' MATCH (n:Statement) -WHERE n.group_id = "{}" +WHERE n.end_user_id = "{}" AND datetime(n.created_at) >= datetime() - duration('P3D') RETURN n.statement as statement_name, @@ -824,7 +779,7 @@ RETURN ''' neo4j_statement_all = ''' MATCH (n:Statement) -WHERE n.group_id = "{}" +WHERE n.end_user_id = "{}" RETURN n.statement as statement_name, n.id as statement_id @@ -832,7 +787,7 @@ RETURN ''' neo4j_query_part = """ MATCH (n)-[r]-(m:ExtractedEntity) - WHERE n.group_id = "{}" + WHERE n.end_user_id = "{}" AND datetime(n.created_at) >= datetime() - duration('P3D') WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) @@ -853,7 +808,7 @@ neo4j_query_part = """ """ neo4j_query_all = """ MATCH (n)-[r]-(m:ExtractedEntity) - WHERE n.group_id = "{}" + WHERE n.end_user_id = "{}" WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) RETURN @@ -1027,14 +982,14 @@ RETURN DISTINCT Memory_Space_User=""" MATCH (n)-[r]->(m) -WHERE n.group_id = $group_id AND m.name="用户" +WHERE n.end_user_id = $end_user_id AND m.name="用户" return DISTINCT elementId(m) as id """ Memory_Space_Entity=""" MATCH (n)-[]-(m) WHERE elementId(m) = $id AND m.entity_type = "Person" RETURN -DISTINCT m.name as name,m.group_id as group_id +DISTINCT m.name as name,m.end_user_id as end_user_id """ Memory_Space_Associative=""" MATCH (u)-[]-(x)-[]-(h) diff --git a/api/app/repositories/neo4j/dialog_repository.py b/api/app/repositories/neo4j/dialog_repository.py index ccb3d94c..48376c2a 100644 --- a/api/app/repositories/neo4j/dialog_repository.py +++ b/api/app/repositories/neo4j/dialog_repository.py @@ -19,7 +19,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """对话仓储 管理对话节点的创建、查询、更新和删除操作。 - 提供按group_id、user_id、ref_id等条件查询对话的方法。 + 提供按end_user_id、user_id、ref_id等条件查询对话的方法。 Attributes: connector: Neo4j连接器实例 @@ -54,17 +54,17 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): return DialogueNode(**n) - async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[DialogueNode]: - """根据group_id查询对话 + async def find_by_end_user_id(self, end_user_id: str, limit: int = 100) -> List[DialogueNode]: + """根据end_user_id查询对话 Args: - group_id: 组ID + end_user_id: 组ID limit: 返回结果的最大数量 Returns: List[DialogueNode]: 对话列表 """ - return await self.find({"group_id": group_id}, limit=limit) + return await self.find({"end_user_id": end_user_id}, limit=limit) async def find_by_user_id(self, user_id: str, limit: int = 100) -> List[DialogueNode]: """根据user_id查询对话 @@ -94,14 +94,14 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): async def find_by_group_and_user( self, - group_id: str, + end_user_id: str, user_id: str, limit: int = 100 ) -> List[DialogueNode]: - """根据group_id和user_id查询对话 + """根据end_user_id和user_id查询对话 Args: - group_id: 组ID + end_user_id: 组ID user_id: 用户ID limit: 返回结果的最大数量 @@ -109,20 +109,20 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): List[DialogueNode]: 对话列表 """ return await self.find( - {"group_id": group_id, "user_id": user_id}, + {"end_user_id": end_user_id, "user_id": user_id}, limit=limit ) async def find_recent_dialogs( self, - group_id: str, + end_user_id: str, days: int = 7, limit: int = 100 ) -> List[DialogueNode]: """查询最近的对话 Args: - group_id: 组ID + end_user_id: 组ID days: 查询最近多少天的对话 limit: 返回结果的最大数量 @@ -131,7 +131,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND n.created_at >= datetime() - duration({{days: $days}}) RETURN n ORDER BY n.created_at DESC @@ -139,7 +139,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """ results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, days=days, limit=limit ) @@ -164,16 +164,16 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): async def find_by_config_and_group( self, config_id: str, - group_id: str, + end_user_id: str, limit: int = 100 ) -> List[DialogueNode]: - """根据config_id和group_id查询对话 + """根据config_id和end_user_id查询对话 支持按配置ID和组ID同时过滤,确保只返回使用特定配置处理的对话。 Args: config_id: 配置ID - group_id: 组ID + end_user_id: 组ID limit: 返回结果的最大数量 Returns: diff --git a/api/app/repositories/neo4j/emotion_repository.py b/api/app/repositories/neo4j/emotion_repository.py index d445c8d4..7a8ebcf9 100644 --- a/api/app/repositories/neo4j/emotion_repository.py +++ b/api/app/repositories/neo4j/emotion_repository.py @@ -40,7 +40,7 @@ class EmotionRepository: async def get_emotion_tags( self, - group_id: str, + end_user_id: str, emotion_type: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, @@ -51,7 +51,7 @@ class EmotionRepository: 查询指定用户的情绪类型分布，包括计数、百分比和平均强度。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） emotion_type: 可选的情绪类型过滤（joy/sadness/anger/fear/surprise/neutral） start_date: 可选的开始日期（ISO格式字符串） end_date: 可选的结束日期（ISO格式字符串） @@ -65,8 +65,8 @@ class EmotionRepository: - avg_intensity: 平均强度 """ # 构建查询条件 - where_clauses = ["s.group_id = $group_id", "s.emotion_type IS NOT NULL"] - params = {"group_id": group_id, "limit": limit} + where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_type IS NOT NULL"] + params = {"end_user_id": end_user_id, "limit": limit} if emotion_type: where_clauses.append("s.emotion_type = $emotion_type") @@ -119,7 +119,7 @@ class EmotionRepository: async def get_emotion_wordcloud( self, - group_id: str, + end_user_id: str, emotion_type: Optional[str] = None, limit: int = 50 ) -> List[Dict[str, Any]]: @@ -128,7 +128,7 @@ class EmotionRepository: 查询情绪关键词及其频率，用于生成词云可视化。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） emotion_type: 可选的情绪类型过滤 limit: 返回关键词的最大数量 @@ -140,8 +140,8 @@ class EmotionRepository: - avg_intensity: 平均强度 """ # 构建查询条件 - where_clauses = ["s.group_id = $group_id", "s.emotion_keywords IS NOT NULL"] - params = {"group_id": group_id, "limit": limit} + where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_keywords IS NOT NULL"] + params = {"end_user_id": end_user_id, "limit": limit} if emotion_type: where_clauses.append("s.emotion_type = $emotion_type") @@ -186,7 +186,7 @@ class EmotionRepository: async def get_emotions_in_range( self, - group_id: str, + end_user_id: str, time_range: str = "30d" ) -> List[Dict[str, Any]]: """获取时间范围内的情绪数据 @@ -194,7 +194,7 @@ class EmotionRepository: 查询指定时间范围内的所有情绪数据，用于健康指数计算。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） time_range: 时间范围（7d/30d/90d） Returns: @@ -214,7 +214,7 @@ class EmotionRepository: # 优化的 Cypher 查询：使用字符串比较避免时区问题 query = """ MATCH (s:Statement) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id AND s.emotion_type IS NOT NULL AND s.created_at >= $start_date RETURN s.id as statement_id, diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index 13215e0f..1575315f 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -44,9 +44,7 @@ async def save_entities_and_relationships( 'created_at': edge.created_at.isoformat(), 'expired_at': edge.expired_at.isoformat(), 'run_id': edge.run_id, - 'group_id': edge.group_id, - 'user_id': edge.user_id, - 'apply_id': edge.apply_id, + 'end_user_id': edge.end_user_id, } all_relationships.append(relationship) @@ -101,9 +99,7 @@ async def save_statement_chunk_edges( "id": edge.id, "source": edge.source, "target": edge.target, - "group_id": edge.group_id, - "user_id": edge.user_id, - "apply_id": edge.apply_id, + "end_user_id": edge.end_user_id, "run_id": edge.run_id, "created_at": edge.created_at.isoformat() if edge.created_at else None, "expired_at": edge.expired_at.isoformat() if edge.expired_at else None, @@ -132,9 +128,7 @@ async def save_statement_entity_edges( edge_data = { "source": edge.source, "target": edge.target, - "group_id": edge.group_id, - "user_id": edge.user_id, - "apply_id": edge.apply_id, + "end_user_id": edge.end_user_id, "run_id": edge.run_id, "connect_strength": edge.connect_strength, "created_at": edge.created_at.isoformat() if edge.created_at else None, diff --git a/api/app/repositories/neo4j/graph_search.py b/api/app/repositories/neo4j/graph_search.py index 6f5764b4..9660f6cb 100644 --- a/api/app/repositories/neo4j/graph_search.py +++ b/api/app/repositories/neo4j/graph_search.py @@ -33,7 +33,7 @@ async def _update_activation_values_batch( connector: Neo4jConnector, nodes: List[Dict[str, Any]], node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, max_retries: int = 3 ) -> List[Dict[str, Any]]: """ @@ -46,7 +46,7 @@ async def _update_activation_values_batch( connector: Neo4j连接器 nodes: 节点列表，每个节点必须包含 'id' 字段 node_label: 节点标签（Statement, ExtractedEntity, MemorySummary） - group_id: 组ID（可选） + end_user_id: 组ID（可选） max_retries: 最大重试次数 Returns: @@ -97,7 +97,7 @@ async def _update_activation_values_batch( updated_nodes = await access_manager.record_batch_access( node_ids=unique_node_ids, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) logger.info( @@ -118,7 +118,7 @@ async def _update_activation_values_batch( async def _update_search_results_activation( connector: Neo4jConnector, results: Dict[str, List[Dict[str, Any]]], - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Dict[str, List[Dict[str, Any]]]: """ 更新搜索结果中所有知识节点的激活值 @@ -129,7 +129,7 @@ async def _update_search_results_activation( Args: connector: Neo4j连接器 results: 搜索结果字典，包含不同类型节点的列表 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Dict[str, List[Dict[str, Any]]]: 更新后的搜索结果 @@ -152,7 +152,7 @@ async def _update_search_results_activation( connector=connector, nodes=results[key], node_label=label, - group_id=group_id + end_user_id=end_user_id ) ) update_keys.append(key) @@ -218,7 +218,7 @@ async def _update_search_results_activation( async def search_graph( connector: Neo4jConnector, q: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: List[str] = None, ) -> Dict[str, List[Dict[str, Any]]]: @@ -236,7 +236,7 @@ async def search_graph( Args: connector: Neo4j connector q: Query text - group_id: Optional group filter + end_user_id: Optional group filter limit: Max results per category include: List of categories to search (default: all) @@ -254,7 +254,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_STATEMENTS_BY_KEYWORD, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("statements") @@ -263,7 +263,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("entities") @@ -272,7 +272,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_CHUNKS_BY_CONTENT, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("chunks") @@ -281,7 +281,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_MEMORY_SUMMARIES_BY_KEYWORD, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("summaries") @@ -305,19 +305,12 @@ async def search_graph( results[key] = _deduplicate_results(results[key]) # 更新知识节点的激活值（Statement, ExtractedEntity, MemorySummary） - # Skip activation updates if only searching summaries (optimization) - needs_activation_update = any( - key in include and key in results and results[key] - for key in ['statements', 'entities', 'chunks'] + results = await _update_search_results_activation( + connector=connector, + results=results, + end_user_id=end_user_id ) - if needs_activation_update: - results = await _update_search_results_activation( - connector=connector, - results=results, - group_id=group_id - ) - return results @@ -325,7 +318,7 @@ async def search_graph_by_embedding( connector: Neo4jConnector, embedder_client, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: List[str] = ["statements", "chunks", "entities","summaries"], ) -> Dict[str, List[Dict[str, Any]]]: @@ -337,7 +330,7 @@ async def search_graph_by_embedding( - Computes query embedding with the provided embedder_client - Ranks by cosine similarity in Cypher - - Filters by group_id if provided + - Filters by end_user_id if provided - Returns up to 'limit' per included type """ import time @@ -346,7 +339,7 @@ async def search_graph_by_embedding( embed_start = time.time() embeddings = await embedder_client.response([query_text]) embed_time = time.time() - embed_start - logger.info(f"[PERF] Embedding generation took: {embed_time:.4f}s") + print(f"[PERF] Embedding generation took: {embed_time:.4f}s") if not embeddings or not embeddings[0]: return {"statements": [], "chunks": [], "entities": [], "summaries": []} @@ -361,7 +354,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( STATEMENT_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("statements") @@ -371,7 +364,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( CHUNK_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("chunks") @@ -381,7 +374,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( ENTITY_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("entities") @@ -391,7 +384,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( MEMORY_SUMMARY_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("summaries") @@ -400,7 +393,7 @@ async def search_graph_by_embedding( query_start = time.time() task_results = await asyncio.gather(*tasks, return_exceptions=True) query_time = time.time() - query_start - logger.info(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s") + print(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s") # Build results dictionary results: Dict[str, List[Dict[str, Any]]] = { @@ -424,28 +417,19 @@ async def search_graph_by_embedding( results[key] = _deduplicate_results(results[key]) # 更新知识节点的激活值（Statement, ExtractedEntity, MemorySummary） - # Skip activation updates if only searching summaries (optimization) - needs_activation_update = any( - key in include and key in results and results[key] - for key in ['statements', 'entities', 'chunks'] + update_start = time.time() + results = await _update_search_results_activation( + connector=connector, + results=results, + end_user_id=end_user_id ) - - if needs_activation_update: - update_start = time.time() - results = await _update_search_results_activation( - connector=connector, - results=results, - group_id=group_id - ) - update_time = time.time() - update_start - logger.info(f"[PERF] Activation value updates took: {update_time:.4f}s") - else: - logger.info(f"[PERF] Skipping activation updates (only summaries)") + update_time = time.time() - update_start + print(f"[PERF] Activation value updates took: {update_time:.4f}s") return results async def get_dedup_candidates_for_entities( # 适配新版查询：使用全文索引按名称检索候选实体 connector: Neo4jConnector, - group_id: str, + end_user_id: str, entities: List[Dict[str, Any]], use_contains_fallback: bool = True, batch_size: int = 500, @@ -453,7 +437,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 ) -> Dict[str, List[Dict[str, Any]]]: """ 为第二层去重消歧批量检索候选实体（适配新版 cypher_queries）： - - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (group_id, name) 检索候选； + - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (end_user_id, name) 检索候选； - 保留并发控制与返回结构（incoming_id -> [db_entity_props...]）； - 若提供 `entity_type`，在本地对返回结果做类型过滤； - `use_contains_fallback` 保留形参以兼容，必要时可扩展二次查询策略。 @@ -477,7 +461,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 rows = await connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=name, - group_id=group_id, + end_user_id=end_user_id, limit=100, ) except Exception: @@ -501,7 +485,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 rows = await connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=name.lower(), - group_id=group_id, + end_user_id=end_user_id, limit=100, ) for r in rows: @@ -532,9 +516,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 async def search_graph_by_keyword_temporal( connector: Neo4jConnector, query_text: str, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -547,32 +529,30 @@ async def search_graph_by_keyword_temporal( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements containing query_text created between start_date and end_date - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ if not query_text: - logger.warning(f"query_text cannot be empty") + print(f"query_text不能为空") return {"statements": []} statements = await connector.execute_query( SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL, q=query_text, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, start_date=start_date, end_date=end_date, valid_date=valid_date, invalid_date=invalid_date, limit=limit, ) - logger.debug(f"Temporal keyword search results: {len(statements)} statements found") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -580,9 +560,7 @@ async def search_graph_by_keyword_temporal( async def search_graph_by_temporal( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -595,14 +573,12 @@ async def search_graph_by_temporal( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created between start_date and end_date - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_TEMPORAL, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, start_date=start_date, end_date=end_date, valid_date=valid_date, @@ -610,16 +586,16 @@ async def search_graph_by_temporal( limit=limit, ) - logger.debug(f"Temporal search query: {SEARCH_STATEMENTS_BY_TEMPORAL}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, start_date={start_date}, end_date={end_date}, valid_date={valid_date}, invalid_date={invalid_date}, limit={limit}") - logger.debug(f"Temporal search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_TEMPORAL}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -628,23 +604,23 @@ async def search_graph_by_temporal( async def search_graph_by_dialog_id( connector: Neo4jConnector, dialog_id: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: """ Temporal search across Dialogues. - Matches dialogues with dialog_id - - Optionally filters by group_id + - Optionally filters by end_user_id - Returns up to 'limit' dialogues """ if not dialog_id: - logger.warning(f"dialog_id cannot be empty") + print(f"dialog_id不能为空") return {"dialogues": []} dialogues = await connector.execute_query( SEARCH_DIALOGUE_BY_DIALOG_ID, - group_id=group_id, + end_user_id=end_user_id, dialog_id=dialog_id, limit=limit, ) @@ -654,15 +630,15 @@ async def search_graph_by_dialog_id( async def search_graph_by_chunk_id( connector: Neo4jConnector, chunk_id : str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: if not chunk_id: - logger.warning(f"chunk_id cannot be empty") + print(f"chunk_id不能为空") return {"chunks": []} chunks = await connector.execute_query( SEARCH_CHUNK_BY_CHUNK_ID, - group_id=group_id, + end_user_id=end_user_id, chunk_id=chunk_id, limit=limit, ) @@ -671,9 +647,9 @@ async def search_graph_by_chunk_id( async def search_graph_by_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -683,37 +659,37 @@ async def search_graph_by_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search by created_at query: {SEARCH_STATEMENTS_BY_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id} created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_by_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -723,37 +699,37 @@ async def search_graph_by_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search by valid_at query: {SEARCH_STATEMENTS_BY_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}， valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_g_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -763,37 +739,37 @@ async def search_graph_g_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_G_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search greater than created_at query: {SEARCH_STATEMENTS_G_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_G_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_g_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -803,37 +779,37 @@ async def search_graph_g_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_G_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search greater than valid_at query: {SEARCH_STATEMENTS_G_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_G_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_l_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -843,37 +819,37 @@ async def search_graph_l_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_L_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search less than created_at query: {SEARCH_STATEMENTS_L_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_L_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_l_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -883,28 +859,28 @@ async def search_graph_l_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_L_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search less than valid_at query: {SEARCH_STATEMENTS_L_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_L_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results diff --git a/api/app/repositories/neo4j/memory_summary_repository.py b/api/app/repositories/neo4j/memory_summary_repository.py index fc743f33..2564aeab 100644 --- a/api/app/repositories/neo4j/memory_summary_repository.py +++ b/api/app/repositories/neo4j/memory_summary_repository.py @@ -18,7 +18,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """Memory Summary Repository Manages CRUD operations for MemorySummary nodes. - Provides methods to query summaries by group_id, user_id, and time ranges. + Provides methods to query summaries by end_user_id, user_id, and time ranges. Attributes: connector: Neo4j connector instance @@ -51,17 +51,17 @@ class MemorySummaryRepository(BaseNeo4jRepository): return dict(n) - async def find_by_group_id( + async def find_by_end_user_id( self, - group_id: str, + end_user_id: str, limit: int = 1000, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None ) -> List[Dict[str, Any]]: - """Query memory summaries by group_id + """Query memory summaries by end_user_id Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by limit: Maximum number of results to return start_date: Optional start date filter end_date: Optional end date filter @@ -71,10 +71,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id """ - params = {"group_id": group_id, "limit": limit} + params = {"end_user_id": end_user_id, "limit": limit} # Add date range filters if provided if start_date: @@ -139,16 +139,16 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_by_group_and_user( self, - group_id: str, + end_user_id: str, user_id: str, limit: int = 1000, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None ) -> List[Dict[str, Any]]: - """Query memory summaries by both group_id and user_id + """Query memory summaries by both end_user_id and user_id Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by user_id: User ID to filter by limit: Maximum number of results to return start_date: Optional start date filter @@ -159,10 +159,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id AND n.user_id = $user_id + WHERE n.end_user_id = $end_user_id AND n.user_id = $user_id """ - params = {"group_id": group_id, "user_id": user_id, "limit": limit} + params = {"end_user_id": end_user_id, "user_id": user_id, "limit": limit} # Add date range filters if provided if start_date: @@ -184,14 +184,14 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_recent_summaries( self, - group_id: str, + end_user_id: str, days: int = 7, limit: int = 1000 ) -> List[Dict[str, Any]]: """Query recent memory summaries Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by days: Number of recent days to query limit: Maximum number of results to return @@ -200,7 +200,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND n.created_at >= datetime() - duration({{days: $days}}) RETURN n ORDER BY n.created_at DESC diff --git a/api/app/repositories/neo4j/neo4j_connector.py b/api/app/repositories/neo4j/neo4j_connector.py index 7c4b43b5..456c4e08 100644 --- a/api/app/repositories/neo4j/neo4j_connector.py +++ b/api/app/repositories/neo4j/neo4j_connector.py @@ -141,14 +141,14 @@ class Neo4jConnector: async with self.driver.session(database="neo4j") as session: return await session.execute_read(transaction_func, **kwargs) - async def delete_group(self, group_id: str): + async def delete_group(self, end_user_id: str): """删除指定组的所有数据 - 删除所有属于指定group_id的节点和边。 + 删除所有属于指定end_user_id的节点和边。 这是一个危险操作，会永久删除数据。 Args: - group_id: 要删除的组ID + end_user_id: 要删除的组ID Example: >>> connector = Neo4jConnector() @@ -157,14 +157,14 @@ class Neo4jConnector: """ # 删除节点（DETACH DELETE会同时删除相关的边） await self.driver.execute_query( - "MATCH (n) WHERE n.group_id = $group_id DETACH DELETE n", + "MATCH (n) WHERE n.end_user_id = $end_user_id DETACH DELETE n", database="neo4j", - group_id=group_id + end_user_id=end_user_id ) # 删除独立的边（如果有的话） await self.driver.execute_query( - "MATCH ()-[r]->() WHERE r.group_id = $group_id DELETE r", + "MATCH ()-[r]->() WHERE r.end_user_id = $end_user_id DELETE r", database="neo4j", - group_id=group_id + end_user_id=end_user_id ) - print(f"Group {group_id} deleted.") + print(f"Group {end_user_id} deleted.") diff --git a/api/app/repositories/neo4j/statement_repository.py b/api/app/repositories/neo4j/statement_repository.py index cd9f2fac..4f12af83 100644 --- a/api/app/repositories/neo4j/statement_repository.py +++ b/api/app/repositories/neo4j/statement_repository.py @@ -20,7 +20,7 @@ class StatementRepository(BaseNeo4jRepository[StatementNode]): """陈述句仓储 管理陈述句节点的创建、查询、更新和删除操作。 - 提供按chunk_id、group_id、向量相似度等条件查询陈述句的方法。 + 提供按chunk_id、end_user_id、向量相似度等条件查询陈述句的方法。 Attributes: connector: Neo4j连接器实例 diff --git a/api/app/schemas/memory_agent_schema.py b/api/app/schemas/memory_agent_schema.py index d4354c40..e7b1fe65 100644 --- a/api/app/schemas/memory_agent_schema.py +++ b/api/app/schemas/memory_agent_schema.py @@ -7,11 +7,11 @@ class UserInput(BaseModel): message: str history: list[dict] search_switch: str - group_id: str + end_user_id: str config_id: Optional[str] = None class Write_UserInput(BaseModel): messages: list[dict] - group_id: str + end_user_id: str config_id: Optional[str] = None diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 46bda5f6..2dd06e89 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -92,7 +92,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str try: memory_content = asyncio.run( MemoryAgentService().read_memory( - group_id=end_user_id, + end_user_id=end_user_id, message=question, history=[], search_switch="2", diff --git a/api/app/services/emotion_analytics_service.py b/api/app/services/emotion_analytics_service.py index 601d2921..19c6cef1 100644 --- a/api/app/services/emotion_analytics_service.py +++ b/api/app/services/emotion_analytics_service.py @@ -75,7 +75,7 @@ class EmotionAnalyticsService: # 调用仓储层查询 tags = await self.emotion_repo.get_emotion_tags( - group_id=end_user_id, + end_user_id=end_user_id, emotion_type=emotion_type, start_date=start_date, end_date=end_date, @@ -157,7 +157,7 @@ class EmotionAnalyticsService: # 调用仓储层查询 keywords = await self.emotion_repo.get_emotion_wordcloud( - group_id=end_user_id, + end_user_id=end_user_id, emotion_type=emotion_type, limit=limit ) @@ -339,7 +339,7 @@ class EmotionAnalyticsService: # 获取时间范围内的情绪数据 emotions = await self.emotion_repo.get_emotions_in_range( - group_id=end_user_id, + end_user_id=end_user_id, time_range=time_range ) @@ -519,7 +519,7 @@ class EmotionAnalyticsService: # 3. 获取情绪数据用于模式分析 emotions = await self.emotion_repo.get_emotions_in_range( - group_id=end_user_id, + end_user_id=end_user_id, time_range="30d" ) @@ -598,13 +598,13 @@ class EmotionAnalyticsService: # 查询用户的实体和标签 query = """ MATCH (e:Entity) - WHERE e.group_id = $group_id + WHERE e.end_user_id = $end_user_id RETURN e.name as name, e.type as type ORDER BY e.created_at DESC LIMIT 20 """ - entities = await connector.execute_query(query, group_id=end_user_id) + entities = await connector.execute_query(query, end_user_id=end_user_id) # 提取兴趣标签 interests = [e["name"] for e in entities if e.get("type") in ["INTEREST", "HOBBY"]][:5] diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 8170bdd8..e475bef0 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -27,6 +27,7 @@ from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType +from app.repositories.memory_short_repository import ShortTermMemoryRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_agent_schema import Write_UserInput from app.schemas.memory_config_schema import ConfigurationError @@ -54,25 +55,25 @@ _neo4j_connector = Neo4jConnector() class MemoryAgentService: """Service for memory agent operations""" - def writer_messages_deal(self, messages, start_time, group_id, config_id, message, context): + def writer_messages_deal(self, messages, start_time, end_user_id, config_id, message, context): duration = time.time() - start_time if str(messages) == 'success': - logger.info(f"Write operation successful for group {group_id} with config_id {config_id}") + logger.info(f"Write operation successful for group {end_user_id} with config_id {config_id}") # 记录成功的操作 if audit_logger: - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=True, + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=True, duration=duration, details={"message_length": len(message)}) return context else: - logger.warning(f"Write operation failed for group {group_id}") + logger.warning(f"Write operation failed for group {end_user_id}") # 记录失败的操作 if audit_logger: audit_logger.log_operation( operation="WRITE", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=f"写入失败: {messages[:100]}" @@ -265,13 +266,13 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, group_id: str, messages: list[dict], config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, message: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id Args: - group_id: Group identifier (also used as end_user_id) - messages: Structured message list [{"role": "user", "content": "..."}, ...] + end_user_id: Group identifier (also used as end_user_id) + message: Message to write config_id: Configuration ID from database db: SQLAlchemy database session storage_type: Storage type (neo4j or rag) @@ -286,15 +287,15 @@ class MemoryAgentService: # Resolve config_id if None using end_user's connected config if config_id is None: try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): - raise - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + raise # Re-raise our specific error + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") import time start_time = time.time() @@ -314,7 +315,7 @@ class MemoryAgentService: # Log failed operation if audit_logger: duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg) + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) raise ValueError(error_msg) @@ -322,11 +323,11 @@ class MemoryAgentService: if storage_type == "rag": # For RAG storage, convert messages to single string message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - result = await write_rag(group_id, message_text, user_rag_memory_id) + result = await write_rag(end_user_id, message_text, user_rag_memory_id) return result else: async with make_write_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # Convert structured messages to LangChain messages langchain_messages = [] for msg in messages: @@ -339,7 +340,7 @@ class MemoryAgentService: # 初始状态 - 包含所有必要字段 initial_state = { "messages": langchain_messages, - "group_id": group_id, + "end_user_id": end_user_id, "memory_config": memory_config } @@ -356,14 +357,14 @@ class MemoryAgentService: contents = massages.get('write_result') # Convert messages back to string for logging message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - return self.writer_messages_deal(massagesstatus, start_time, group_id, config_id, message_text, contents) + return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) except Exception as e: # Ensure proper error handling and logging error_msg = f"Write operation failed: {str(e)}" logger.error(error_msg) if audit_logger: duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg) + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) raise ValueError(error_msg) @@ -371,15 +372,14 @@ class MemoryAgentService: async def read_memory( self, - group_id: str, + end_user_id: str, message: str, history: List[Dict], search_switch: str, config_id: Optional[str], db: Session, storage_type: str, - user_rag_memory_id: str - ) -> Dict: + user_rag_memory_id: str) -> Dict: """ Process read operation with config_id @@ -389,7 +389,7 @@ class MemoryAgentService: - "2": Direct answer based on context Args: - group_id: Group identifier (also used as end_user_id) + end_user_id: Group identifier (also used as end_user_id) message: User message history: Conversation history search_switch: Search mode switch @@ -407,22 +407,22 @@ class MemoryAgentService: import time start_time = time.time() - logger.info(f"[PERF] read_memory started for group_id={group_id}, search_switch={search_switch}") + ori_message= message # Resolve config_id if None using end_user's connected config if config_id is None: try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): raise # Re-raise our specific error - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") - logger.info(f"Read operation for group {group_id} with config_id {config_id}") + logger.info(f"Read operation for group {end_user_id} with config_id {config_id}") # 导入审计日志记录器 try: @@ -431,15 +431,13 @@ class MemoryAgentService: audit_logger = None - config_load_start = time.time() try: config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config( config_id=config_id, service_name="MemoryAgentService" ) - config_load_time = time.time() - config_load_start - logger.info(f"[PERF] Configuration loaded in {config_load_time:.4f}s: {memory_config.config_name}") + logger.info(f"Configuration loaded successfully: {memory_config.config_name}") except ConfigurationError as e: error_msg = f"Failed to load configuration for config_id: {config_id}: {e}" logger.error(error_msg) @@ -450,7 +448,7 @@ class MemoryAgentService: audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=error_msg @@ -460,16 +458,16 @@ class MemoryAgentService: # Step 2: Prepare history history.append({"role": "user", "content": message}) - logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}") + logger.debug(f"Group ID:{end_user_id}, Message:{message}, History:{history}, Config ID:{config_id}") # Step 3: Initialize MCP client and execute read workflow graph_exec_start = time.time() try: async with make_read_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch, - "group_id": group_id + "end_user_id": end_user_id , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id, "memory_config": memory_config} # 获取节点更新信息 @@ -565,13 +563,13 @@ class MemoryAgentService: if '信息不足，无法回答。' != str(summary) and str(search_switch).strip() != "2": # 使用 upsert 方法 repo.upsert( - end_user_id=group_id, + end_user_id=end_user_id, messages=message, aimessages=summary, retrieved_content=retrieved_content, search_switch=str(search_switch) ) - logger.info(f"成功保存短期记忆: group_id={group_id}, search_switch={search_switch}") + logger.info(f"成功保存短期记忆: end_user_id={end_user_id}, search_switch={search_switch}") else: logger.debug(f"跳过保存短期记忆: summary={summary[:50] if summary else 'None'}, search_switch={search_switch}") @@ -580,14 +578,12 @@ class MemoryAgentService: logger.error(f"保存短期记忆失败: {str(save_error)}", exc_info=True) # Log successful operation - total_time = time.time() - start_time - logger.info(f"[PERF] read_memory completed successfully in {total_time:.4f}s (config: {config_load_time:.4f}s, graph: {graph_exec_time:.4f}s)") if audit_logger: duration = time.time() - start_time audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=True, duration=duration ) @@ -599,14 +595,13 @@ class MemoryAgentService: except Exception as e: # Ensure proper error handling and logging error_msg = f"Read operation failed: {str(e)}" - total_time = time.time() - start_time - logger.error(f"[PERF] read_memory failed after {total_time:.4f}s: {error_msg}") + logger.error(error_msg) if audit_logger: duration = time.time() - start_time audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=error_msg @@ -755,7 +750,7 @@ class MemoryAgentService: """ 统计知识库类型分布，包含： 1. PostgreSQL 中的知识库类型：General, Web, Third-party, Folder（根据 workspace_id 过滤） - 2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/group_id 过滤） + 2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/end_user_id 过滤） 3. total: 所有类型的总和 参数： @@ -841,11 +836,11 @@ class MemoryAgentService: for end_user in end_users: end_user_id_str = str(end_user.id) memory_query = """ - MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN count(n) AS Count + MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN count(n) AS Count """ neo4j_result = await _neo4j_connector.execute_query( memory_query, - group_id=end_user_id_str, + end_user_id=end_user_id_str, ) chunk_count = neo4j_result[0]["Count"] if neo4j_result else 0 total_chunks += chunk_count @@ -885,7 +880,7 @@ class MemoryAgentService: 获取指定用户的热门记忆标签 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的group_id字段 + - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 - limit: 返回标签数量限制 返回格式： @@ -895,7 +890,7 @@ class MemoryAgentService: ] """ try: - # by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度） + # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度） tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) payload=[] for tag, freq in tags: @@ -970,21 +965,21 @@ class MemoryAgentService: # 查询该用户的语句 query = ( "MATCH (s:Statement) " - "WHERE ($group_id IS NULL OR s.group_id = $group_id) AND s.statement IS NOT NULL " + "WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND s.statement IS NOT NULL " "RETURN s.statement AS statement " "ORDER BY s.created_at DESC LIMIT 100" ) - rows = await connector.execute_query(query, group_id=end_user_id) + rows = await connector.execute_query(query, end_user_id=end_user_id) statements = [r.get("statement", "") for r in rows if r.get("statement")] # 查询该用户的热门实体 entity_query = ( "MATCH (e:ExtractedEntity) " - "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL " + "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC LIMIT 20" ) - entity_rows = await connector.execute_query(entity_query, group_id=end_user_id) + entity_rows = await connector.execute_query(entity_query, end_user_id=end_user_id) entities = [f"{r['name']} ({r['frequency']})" for r in entity_rows] await connector.close() @@ -1037,14 +1032,14 @@ class MemoryAgentService: names_to_exclude = ['AI', 'Caroline', 'Melanie', 'Jon', 'Gina', '用户', 'AI助手', 'John', 'Maria'] hot_tag_query = ( "MATCH (e:ExtractedEntity) " - "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' " + "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' " "AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC LIMIT 4" ) hot_tag_rows = await connector.execute_query( hot_tag_query, - group_id=end_user_id, + end_user_id=end_user_id, names_to_exclude=names_to_exclude ) await connector.close() @@ -1190,6 +1185,10 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An "memory_config_id": memory_config_id } + print(188*'*') + print(result) + print(188 * '*') + logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}") return result @@ -1230,10 +1229,10 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 1. 批量查询所有 end_user 及其 app_id end_users = db.query(EndUser).filter(EndUser.id.in_(end_user_ids)).all() - + # 创建 end_user_id -> app_id 的映射 user_to_app = {str(eu.id): eu.app_id for eu in end_users} - + # 记录未找到的用户 found_user_ids = set(user_to_app.keys()) missing_user_ids = set(end_user_ids) - found_user_ids @@ -1275,13 +1274,13 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 批量查询 memory_config_name config_id_to_name = {} if memory_config_ids: - memory_configs = db.query(DataConfig).filter(DataConfig.config_id.in_(memory_config_ids)).all() - config_id_to_name = {str(mc.config_id): mc.config_name for mc in memory_configs} + memory_configs = db.query(MemoryConfig).filter(MemoryConfig.id.in_(memory_config_ids)).all() + config_id_to_name = {str(mc.id): mc.config_name for mc in memory_configs} # 4. 构建最终结果 for end_user_id, app_id in user_to_app.items(): release = app_to_release.get(app_id) - + if not release: logger.warning(f"No active release found for app: {app_id} (end_user: {end_user_id})") result[end_user_id] = {"memory_config_id": None, "memory_config_name": None} @@ -1293,7 +1292,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None # 获取配置名称 - memory_config_name = config_id_to_name.get(str(memory_config_id)) if memory_config_id else None + memory_config_name = config_id_to_name.get(memory_config_id) if memory_config_id else None result[end_user_id] = { "memory_config_id": memory_config_id, diff --git a/api/app/services/memory_api_service.py b/api/app/services/memory_api_service.py index 0ae2b965..c33c9c6b 100644 --- a/api/app/services/memory_api_service.py +++ b/api/app/services/memory_api_service.py @@ -25,7 +25,7 @@ class MemoryAPIService: This service provides a thin layer that: 1. Validates end_user exists and belongs to the authorized workspace - 2. Maps end_user_id to group_id for memory operations + 2. Maps end_user_id to end_user_id for memory operations 3. Delegates to MemoryAgentService for actual memory read/write operations """ @@ -68,7 +68,7 @@ class MemoryAPIService: ) end_user = self.db.query(EndUser).filter(EndUser.id == end_user_uuid).first() - + if not end_user: logger.warning(f"End user not found: {end_user_id}") raise ResourceNotFoundException( @@ -115,7 +115,7 @@ class MemoryAPIService: Args: workspace_id: Workspace ID for resource validation - end_user_id: End user identifier (used as group_id) + end_user_id: End user identifier (used as end_user_id) message: Message content to store config_id: Optional memory configuration ID storage_type: Storage backend (neo4j or rag) @@ -133,13 +133,12 @@ class MemoryAPIService: # Validate end_user exists and belongs to workspace self.validate_end_user(end_user_id, workspace_id) - # Use end_user_id as group_id for memory operations - group_id = end_user_id + # Use end_user_id as end_user_id for memory operations try: # Delegate to MemoryAgentService result = await MemoryAgentService().write_memory( - group_id=group_id, + end_user_id=end_user_id, message=message, config_id=config_id, db=self.db, @@ -186,7 +185,7 @@ class MemoryAPIService: Args: workspace_id: Workspace ID for resource validation - end_user_id: End user identifier (used as group_id) + end_user_id: End user identifier (used as end_user_id) message: Query message search_switch: Search mode (0=deep search with verification, 1=deep search, 2=fast search) config_id: Optional memory configuration ID @@ -205,13 +204,13 @@ class MemoryAPIService: # Validate end_user exists and belongs to workspace self.validate_end_user(end_user_id, workspace_id) - # Use end_user_id as group_id for memory operations - group_id = end_user_id + # Use end_user_id as end_user_id for memory operations + try: # Delegate to MemoryAgentService result = await MemoryAgentService().read_memory( - group_id=group_id, + end_user_id=end_user_id, message=message, history=[], search_switch=search_switch, diff --git a/api/app/services/memory_base_service.py b/api/app/services/memory_base_service.py index 25a8281d..bc647752 100644 --- a/api/app/services/memory_base_service.py +++ b/api/app/services/memory_base_service.py @@ -326,7 +326,7 @@ class MemoryBaseService: Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 最大emotion_intensity对应的emotion_type，如果没有则返回None @@ -334,7 +334,7 @@ class MemoryBaseService: try: query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) WHERE stmt.emotion_type IS NOT NULL AND stmt.emotion_intensity IS NOT NULL @@ -347,7 +347,7 @@ class MemoryBaseService: result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) if result and len(result) > 0: @@ -381,10 +381,10 @@ class MemoryBaseService: if end_user_id: query = """ MATCH (n:MemorySummary) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await self.neo4j_connector.execute_query(query, group_id=end_user_id) + result = await self.neo4j_connector.execute_query(query, end_user_id=end_user_id) else: query = """ MATCH (n:MemorySummary) @@ -423,12 +423,12 @@ class MemoryBaseService: if end_user_id: semantic_query = """ MATCH (e:ExtractedEntity) - WHERE e.group_id = $group_id AND e.is_explicit_memory = true + WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN count(e) as count """ semantic_result = await self.neo4j_connector.execute_query( semantic_query, - group_id=end_user_id + end_user_id=end_user_id ) else: semantic_query = """ @@ -519,7 +519,7 @@ class MemoryBaseService: """ if end_user_id: - query += " AND n.group_id = $group_id" + query += " AND n.end_user_id = $end_user_id" query += """ RETURN sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes @@ -528,7 +528,7 @@ class MemoryBaseService: # 设置查询参数 params = {'threshold': forgetting_threshold} if end_user_id: - params['group_id'] = end_user_id + params['end_user_id'] = end_user_id # 执行查询 result = await self.neo4j_connector.execute_query(query, **params) diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index 9b5f3c99..7081d28b 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -717,8 +717,8 @@ class MemoryInteraction: ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id) if ori_data!=[]: # name = ori_data[0]['name'] - group_id = [i['group_id'] for i in ori_data][0] - Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) + end_user_id = [i['end_user_id'] for i in ori_data][0] + Space_User = await self.connector.execute_query(Memory_Space_User, end_user_id=end_user_id) if not Space_User: return [] user_id=Space_User[0]['id'] diff --git a/api/app/services/memory_episodic_service.py b/api/app/services/memory_episodic_service.py index 12eeff6e..08751fd1 100644 --- a/api/app/services/memory_episodic_service.py +++ b/api/app/services/memory_episodic_service.py @@ -34,7 +34,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: (标题, 类型)元组，如果不存在则返回默认值 @@ -43,14 +43,14 @@ class MemoryEpisodicService(MemoryBaseService): # 查询Summary节点的name(作为title)和memory_type(作为type) query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id RETURN s.name AS title, s.memory_type AS type """ result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) if not result or len(result) == 0: @@ -77,7 +77,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 前3个实体的name属性列表 @@ -87,7 +87,7 @@ class MemoryEpisodicService(MemoryBaseService): # 按activation_value降序排序,返回前3个 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity) WHERE entity.activation_value IS NOT NULL @@ -99,7 +99,7 @@ class MemoryEpisodicService(MemoryBaseService): result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 提取实体名称 @@ -123,7 +123,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 所有Statement节点的statement属性内容列表 @@ -132,7 +132,7 @@ class MemoryEpisodicService(MemoryBaseService): # 查询Summary节点指向的所有Statement节点 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) WHERE stmt.statement IS NOT NULL AND stmt.statement <> '' RETURN stmt.statement AS statement @@ -141,7 +141,7 @@ class MemoryEpisodicService(MemoryBaseService): result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 提取statement内容 @@ -214,12 +214,12 @@ class MemoryEpisodicService(MemoryBaseService): # 1. 先查询所有情景记忆的总数（不受筛选条件限制） total_all_query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id RETURN count(s) AS total_all """ total_all_result = await self.neo4j_connector.execute_query( total_all_query, - group_id=end_user_id + end_user_id=end_user_id ) total_all = total_all_result[0]["total_all"] if total_all_result else 0 @@ -229,7 +229,7 @@ class MemoryEpisodicService(MemoryBaseService): # 3. 构建Cypher查询 query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id """ # 添加时间范围过滤 @@ -248,7 +248,7 @@ class MemoryEpisodicService(MemoryBaseService): ORDER BY s.created_at DESC """ - params = {"group_id": end_user_id} + params = {"end_user_id": end_user_id} if time_filter: params["time_filter"] = time_filter if title_keyword: @@ -333,14 +333,14 @@ class MemoryEpisodicService(MemoryBaseService): # 1. 查询指定的MemorySummary节点 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id RETURN elementId(s) AS id, s.created_at AS created_at """ result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 2. 如果节点不存在，返回错误 diff --git a/api/app/services/memory_explicit_service.py b/api/app/services/memory_explicit_service.py index 713215c3..f8d39ae8 100644 --- a/api/app/services/memory_explicit_service.py +++ b/api/app/services/memory_explicit_service.py @@ -60,7 +60,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 1. 查询情景记忆（MemorySummary节点） ========== episodic_query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id RETURN elementId(s) AS id, s.name AS title, s.content AS content, @@ -70,7 +70,7 @@ class MemoryExplicitService(MemoryBaseService): episodic_result = await self.neo4j_connector.execute_query( episodic_query, - group_id=end_user_id + end_user_id=end_user_id ) # 处理情景记忆数据 @@ -96,7 +96,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 2. 查询语义记忆（ExtractedEntity节点） ========== semantic_query = """ MATCH (e:ExtractedEntity) - WHERE e.group_id = $group_id + WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN elementId(e) AS id, e.name AS name, @@ -107,7 +107,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_result = await self.neo4j_connector.execute_query( semantic_query, - group_id=end_user_id + end_user_id=end_user_id ) # 处理语义记忆数据 @@ -189,7 +189,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 1. 先尝试查询情景记忆 ========== episodic_query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $memory_id AND s.group_id = $group_id + WHERE elementId(s) = $memory_id AND s.end_user_id = $end_user_id RETURN s.name AS title, s.content AS content, s.created_at AS created_at @@ -198,7 +198,7 @@ class MemoryExplicitService(MemoryBaseService): episodic_result = await self.neo4j_connector.execute_query( episodic_query, memory_id=memory_id, - group_id=end_user_id + end_user_id=end_user_id ) if episodic_result and len(episodic_result) > 0: @@ -229,7 +229,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_query = """ MATCH (e:ExtractedEntity) WHERE elementId(e) = $memory_id - AND e.group_id = $group_id + AND e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN e.name AS name, e.description AS core_definition, @@ -240,7 +240,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_result = await self.neo4j_connector.execute_query( semantic_query, memory_id=memory_id, - group_id=end_user_id + end_user_id=end_user_id ) if semantic_result and len(semantic_result) > 0: diff --git a/api/app/services/memory_forget_service.py b/api/app/services/memory_forget_service.py index 2db4cdc7..558efe43 100644 --- a/api/app/services/memory_forget_service.py +++ b/api/app/services/memory_forget_service.py @@ -132,7 +132,7 @@ class MemoryForgetService: async def _get_knowledge_stats( self, connector: Neo4jConnector, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, forgetting_threshold: float = 0.3 ) -> Dict[str, Any]: """ @@ -140,7 +140,7 @@ class MemoryForgetService: Args: connector: Neo4j 连接器 - group_id: 组ID（可选） + end_user_id: 组ID（可选） forgetting_threshold: 遗忘阈值 Returns: @@ -152,8 +152,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ WITH n, @@ -172,8 +172,8 @@ class MemoryForgetService: """ params = {'threshold': forgetting_threshold} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await connector.execute_query(query, **params) @@ -200,7 +200,7 @@ class MemoryForgetService: async def _get_pending_forgetting_nodes( self, connector: Neo4jConnector, - group_id: str, + end_user_id: str, forgetting_threshold: float, min_days_since_access: int, limit: int = 20 @@ -212,7 +212,7 @@ class MemoryForgetService: Args: connector: Neo4j 连接器 - group_id: 组ID + end_user_id: 组ID forgetting_threshold: 遗忘阈值 min_days_since_access: 最小未访问天数 limit: 返回节点数量限制 @@ -229,7 +229,7 @@ class MemoryForgetService: query = """ MATCH (n) WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) - AND n.group_id = $group_id + AND n.end_user_id = $end_user_id AND n.activation_value IS NOT NULL AND n.activation_value < $threshold AND n.last_access_time IS NOT NULL @@ -250,7 +250,7 @@ class MemoryForgetService: """ params = { - 'group_id': group_id, + 'end_user_id': end_user_id, 'threshold': forgetting_threshold, 'min_access_time_str': min_access_time_str, 'limit': limit @@ -291,7 +291,7 @@ class MemoryForgetService: async def trigger_forgetting_cycle( self, db: Session, - group_id: str, + end_user_id: str, max_merge_batch_size: Optional[int] = None, min_days_since_access: Optional[int] = None, config_id: Optional[int] = None @@ -303,10 +303,10 @@ class MemoryForgetService: Args: db: 数据库会话 - group_id: 组ID（即终端用户ID，必填） + end_user_id: 组ID（即终端用户ID，必填） max_merge_batch_size: 最大融合批次大小（可选） min_days_since_access: 最小未访问天数（可选） - config_id: 配置ID（必填，由控制器层通过 group_id 获取） + config_id: 配置ID（必填，由控制器层通过 end_user_id 获取） Returns: dict: 遗忘报告 @@ -319,7 +319,7 @@ class MemoryForgetService: # 运行遗忘周期（LLM 客户端将在需要时由 forgetting_strategy 内部获取） report = await forgetting_scheduler.run_forgetting_cycle( - group_id=group_id, + end_user_id=end_user_id, max_merge_batch_size=max_merge_batch_size, min_days_since_access=min_days_since_access, config_id=config_id, @@ -338,7 +338,7 @@ class MemoryForgetService: stats_query = """ MATCH (n) WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) - AND n.group_id = $group_id + AND n.end_user_id = $end_user_id RETURN count(n) as total_nodes, avg(n.activation_value) as average_activation, @@ -347,7 +347,7 @@ class MemoryForgetService: stats_results = await connector.execute_query( stats_query, - group_id=group_id, + end_user_id=end_user_id, threshold=config['forgetting_threshold'] ) @@ -364,7 +364,7 @@ class MemoryForgetService: # 保存历史记录到数据库 self.history_repository.create( db=db, - end_user_id=group_id, + end_user_id=end_user_id, execution_time=execution_time, merged_count=report['merged_count'], failed_count=report['failed_count'], @@ -376,7 +376,7 @@ class MemoryForgetService: ) api_logger.info( - f"已保存遗忘周期历史记录: end_user_id={group_id}, " + f"已保存遗忘周期历史记录: end_user_id={end_user_id}, " f"merged_count={report['merged_count']}" ) @@ -465,7 +465,7 @@ class MemoryForgetService: async def get_forgetting_stats( self, db: Session, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, config_id: Optional[int] = None ) -> Dict[str, Any]: """ @@ -475,7 +475,7 @@ class MemoryForgetService: Args: db: 数据库会话 - group_id: 组ID（可选） + end_user_id: 组ID（可选） config_id: 配置ID（可选，用于获取遗忘阈值） Returns: @@ -493,8 +493,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) """ - if group_id: - activation_query += " AND n.group_id = $group_id" + if end_user_id: + activation_query += " AND n.end_user_id = $end_user_id" activation_query += """ RETURN @@ -506,8 +506,8 @@ class MemoryForgetService: """ params = {'threshold': forgetting_threshold} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id activation_results = await connector.execute_query(activation_query, **params) @@ -539,8 +539,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) """ - if group_id: - distribution_query += " AND n.group_id = $group_id" + if end_user_id: + distribution_query += " AND n.end_user_id = $end_user_id" distribution_query += """ WITH n, @@ -558,8 +558,8 @@ class MemoryForgetService: """ dist_params = {} - if group_id: - dist_params['group_id'] = group_id + if end_user_id: + dist_params['end_user_id'] = end_user_id distribution_results = await connector.execute_query(distribution_query, **dist_params) @@ -582,11 +582,11 @@ class MemoryForgetService: # 获取最近7个日期的历史趋势数据（每天取最后一次执行） recent_trends = [] try: - if group_id: + if end_user_id: # 查询所有历史记录 history_records = self.history_repository.get_recent_by_end_user( db=db, - end_user_id=group_id + end_user_id=end_user_id ) # 按日期分组（一天可能有多次执行，取最后一次） @@ -632,7 +632,7 @@ class MemoryForgetService: # 获取待遗忘节点列表（前20个满足遗忘条件的节点） pending_nodes = [] try: - if group_id: + if end_user_id: # 验证 min_days_since_access 配置值 min_days = config.get('min_days_since_access') if min_days is None or not isinstance(min_days, (int, float)) or min_days < 0: @@ -643,7 +643,7 @@ class MemoryForgetService: pending_nodes = await self._get_pending_forgetting_nodes( connector=connector, - group_id=group_id, + end_user_id=end_user_id, forgetting_threshold=forgetting_threshold, min_days_since_access=int(min_days), limit=20 diff --git a/api/app/services/memory_konwledges_server.py b/api/app/services/memory_konwledges_server.py index c6297e12..420f7ca1 100644 --- a/api/app/services/memory_konwledges_server.py +++ b/api/app/services/memory_konwledges_server.py @@ -450,12 +450,12 @@ async def create_document_chunk( return success(data=chunk, msg="文档块创建成功") -async def write_rag(group_id, message, user_rag_memory_id): +async def write_rag(end_user_id, message, user_rag_memory_id): """ 将消息写入 RAG 知识库 Args: - group_id: 组ID，用作文件标题 + end_user_id: 组ID，用作文件标题 message: 消息内容 user_rag_memory_id: 知识库ID（必须是有效的UUID） @@ -487,10 +487,10 @@ async def write_rag(group_id, message, user_rag_memory_id): db = next(db_gen) try: - create_data = CustomTextFileCreate(title=group_id, content=message) + create_data = CustomTextFileCreate(title=end_user_id, content=message) current_user = SimpleUser(user_rag_memory_id) # 检查文档是否已存在 - document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{group_id}.txt") + document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{end_user_id}.txt") print('======',document) api_logger.info(f"查找文档结果: document_id={document}") if document is not None: @@ -508,7 +508,7 @@ async def write_rag(group_id, message, user_rag_memory_id): return result else: # 文档不存在，创建新文档 - api_logger.info(f"文档不存在，创建新文档: group_id={group_id}") + api_logger.info(f"文档不存在，创建新文档: end_user_id={end_user_id}") result = await memory_konwledges_up( kb_id=user_rag_memory_id, parent_id=user_rag_memory_id, @@ -520,13 +520,13 @@ async def write_rag(group_id, message, user_rag_memory_id): new_document_id = find_document_id_by_kb_and_filename( db=db, kb_id=user_rag_memory_id, - file_name=f"{group_id}.txt" + file_name=f"{end_user_id}.txt" ) if new_document_id: await parse_document_by_id(new_document_id, db=db, current_user=current_user) else: - api_logger.error(f"创建文档后无法找到文档ID: group_id={group_id}") + api_logger.error(f"创建文档后无法找到文档ID: end_user_id={end_user_id}") return result finally: # 确保数据库会话被关闭 diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 83d5923d..05a84c01 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -183,7 +183,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, - "group_id": config.group_id, + "end_user_id": config.end_user_id, "user_id": config.user_id, "apply_id": config.apply_id, "llm_id": config.llm_id, @@ -391,7 +391,7 @@ _neo4j_connector = Neo4jConnector() async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_DIALOGUE, - group_id=end_user_id, + end_user_id=end_user_id, ) data = {"search_for": "dialogue", "num": result[0]["num"]} return data @@ -400,7 +400,7 @@ async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_CHUNK, - group_id=end_user_id, + end_user_id=end_user_id, ) data = {"search_for": "chunk", "num": result[0]["num"]} return data @@ -409,7 +409,7 @@ async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_STATEMENT, - group_id=end_user_id, + end_user_id=end_user_id, ) data = {"search_for": "statement", "num": result[0]["num"]} return data @@ -418,7 +418,7 @@ async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_ENTITY, - group_id=end_user_id, + end_user_id=end_user_id, ) data = {"search_for": "entity", "num": result[0]["num"]} return data @@ -427,7 +427,7 @@ async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_ALL, - group_id=end_user_id, + end_user_id=end_user_id, ) # 检查结果是否为空或长度不足 @@ -462,7 +462,7 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A """ result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_ALL, - group_id=end_user_id, + end_user_id=end_user_id, ) # 检查结果是否为空或长度不足 @@ -493,7 +493,7 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_DETIALS, - group_id=end_user_id, + end_user_id=end_user_id, ) return result @@ -501,11 +501,32 @@ async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, An async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( DataConfigRepository.SEARCH_FOR_EDGES, - group_id=end_user_id, + end_user_id=end_user_id, ) return result +async def search_entity_graph(end_user_id: Optional[str] = None) -> Dict[str, Any]: + """搜索所有实体之间的关系网络（group 维度）。""" + result = await _neo4j_connector.execute_query( + DataConfigRepository.SEARCH_FOR_ENTITY_GRAPH, + end_user_id=end_user_id, + ) + # 对source_node 和 target_node 的 fact_summary进行截取，只截取前三条的内容（需要提取前三条“来源”） + for item in result: + source_fact = item["sourceNode"]["fact_summary"] + target_fact = item["targetNode"]["fact_summary"] + # 截取前三条“来源” + item["sourceNode"]["fact_summary"] = source_fact.split("\n")[:4] if source_fact else [] + item["targetNode"]["fact_summary"] = target_fact.split("\n")[:4] if target_fact else [] + # 与现有返回风格保持一致，携带搜索类型、数量与详情 + data = { + "search_for": "entity_graph", + "num": len(result), + "detials": result, + } + return data + async def analytics_hot_memory_tags( db: Session, diff --git a/api/app/services/pilot_run_service.py b/api/app/services/pilot_run_service.py index 17dfd7eb..755dda14 100644 --- a/api/app/services/pilot_run_service.py +++ b/api/app/services/pilot_run_service.py @@ -91,7 +91,7 @@ async def run_pilot_extraction( dialog = DialogData( context=context, ref_id="pilot_dialog_1", - group_id=str(memory_config.workspace_id), + end_user_id=str(memory_config.workspace_id), user_id=str(memory_config.tenant_id), apply_id=str(memory_config.config_id), metadata={"source": "pilot_run", "input_type": "frontend_text"}, diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 863bccb0..3a90a821 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -155,10 +155,10 @@ class MemoryInsightHelper: """ query = """ MATCH (d:Dialogue) - WHERE d.group_id = $group_id AND d.created_at IS NOT NULL AND d.created_at <> '' + WHERE d.end_user_id = $end_user_id AND d.created_at IS NOT NULL AND d.created_at <> '' RETURN d.created_at AS creation_time """ - records = await self.neo4j_connector.execute_query(query, group_id=self.user_id) + records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id) if not records: return [] @@ -211,17 +211,17 @@ class MemoryInsightHelper: async def get_social_connections(self) -> dict | None: """Find the user with whom the most memories are shared.""" query = """ - MATCH (c1:Chunk {group_id: $group_id}) + MATCH (c1:Chunk {end_user_id: $end_user_id}) OPTIONAL MATCH (c1)-[:CONTAINS]->(s:Statement) OPTIONAL MATCH (s)<-[:CONTAINS]-(c2:Chunk) - WHERE c1.group_id <> c2.group_id AND s IS NOT NULL AND c2 IS NOT NULL - WITH c2.group_id AS other_user_id, COUNT(DISTINCT s) AS common_statements + WHERE c1.end_user_id <> c2.end_user_id AND s IS NOT NULL AND c2 IS NOT NULL + WITH c2.end_user_id AS other_user_id, COUNT(DISTINCT s) AS common_statements WHERE common_statements > 0 RETURN other_user_id, common_statements ORDER BY common_statements DESC LIMIT 1 """ - records = await self.neo4j_connector.execute_query(query, group_id=self.user_id) + records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id) if not records or not records[0].get("other_user_id"): return None @@ -230,7 +230,7 @@ class MemoryInsightHelper: time_range_query = """ MATCH (c:Chunk) - WHERE c.group_id IN [$user_id, $other_user_id] + WHERE c.end_user_id IN [$user_id, $other_user_id] RETURN min(c.created_at) AS start_time, max(c.created_at) AS end_time """ time_records = await self.neo4j_connector.execute_query( @@ -294,11 +294,11 @@ class UserSummaryHelper: """Fetch recent statements authored by the user/group for context.""" query = ( "MATCH (s:Statement) " - "WHERE s.group_id = $group_id AND s.statement IS NOT NULL " + "WHERE s.end_user_id = $end_user_id AND s.statement IS NOT NULL " "RETURN s.statement AS statement, s.created_at AS created_at " "ORDER BY created_at DESC LIMIT $limit" ) - rows = await self.connector.execute_query(query, group_id=self.user_id, limit=limit) + rows = await self.connector.execute_query(query, end_user_id=self.user_id, limit=limit) records = [] for r in rows: try: @@ -1152,7 +1152,7 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, import re # 创建 UserSummaryHelper 实例 - user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_GROUP_ID", "group_123")) + user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_end_user_id", "group_123")) try: # 1) 收集上下文数据 @@ -1273,10 +1273,10 @@ async def analytics_node_statistics( if end_user_id: query = f""" MATCH (n:{node_type}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await _neo4j_connector.execute_query(query, group_id=end_user_id) + result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id) else: query = f""" MATCH (n:{node_type}) @@ -1387,10 +1387,10 @@ async def analytics_memory_types( # 查询 Statement 节点数量 query = """ MATCH (n:Statement) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await _neo4j_connector.execute_query(query, group_id=end_user_id) + result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id) statement_count = result[0]["count"] if result and len(result) > 0 else 0 # 取三分之一作为隐性记忆数量 implicit_count = round(statement_count / 3) @@ -1504,7 +1504,7 @@ async def analytics_graph_data( 包含节点、边和统计信息的字典 """ try: - # 1. 获取 group_id + # 1. 获取 end_user_id user_uuid = uuid.UUID(end_user_id) repo = EndUserRepository(db) end_user = repo.get_by_id(user_uuid) @@ -1528,7 +1528,7 @@ async def analytics_graph_data( # 基于中心节点的扩展查询 node_query = f""" MATCH path = (center)-[*1..{depth}]-(connected) - WHERE center.group_id = $group_id + WHERE center.end_user_id = $end_user_id AND elementId(center) = $center_node_id WITH collect(DISTINCT center) + collect(DISTINCT connected) as all_nodes UNWIND all_nodes as n @@ -1539,7 +1539,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "center_node_id": center_node_id, "limit": limit } @@ -1547,7 +1547,7 @@ async def analytics_graph_data( # 按节点类型过滤查询 node_query = """ MATCH (n) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND labels(n)[0] IN $node_types RETURN elementId(n) as id, @@ -1556,7 +1556,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "node_types": node_types, "limit": limit } @@ -1564,7 +1564,7 @@ async def analytics_graph_data( # 查询所有节点 node_query = """ MATCH (n) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN elementId(n) as id, labels(n)[0] as label, @@ -1572,7 +1572,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "limit": limit } diff --git a/api/app/tasks.py b/api/app/tasks.py index fa9d1fdf..f4b5f78f 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -382,12 +382,12 @@ def build_graphrag_for_kb(kb_id: uuid.UUID): @celery_app.task(name="app.core.memory.agent.read_message", bind=True) -def read_message_task(self, group_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: +def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a read message via MemoryAgentService. Args: - group_id: Group ID for the memory agent (also used as end_user_id) + end_user_id: Group ID for the memory agent (also used as end_user_id) message: User message to process history: Conversation history search_switch: Search switch parameter @@ -408,7 +408,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, from app.services.memory_agent_service import get_end_user_connected_config db = next(get_db()) try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) actual_config_id = connected_config.get("memory_config_id") finally: db.close() @@ -420,24 +420,42 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, db = next(get_db()) try: service = MemoryAgentService() - return await service.read_memory(group_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id) + return await service.read_memory(end_user_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id) finally: db.close() try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time return { "status": "SUCCESS", "result": result, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id } except BaseException as e: elapsed_time = time.time() - start_time + # Handle ExceptionGroup from TaskGroup if hasattr(e, 'exceptions'): error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions] detailed_error = "; ".join(error_messages) @@ -446,7 +464,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, return { "status": "FAILURE", "error": detailed_error, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id @@ -454,19 +472,13 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, @celery_app.task(name="app.core.memory.agent.write_message", bind=True) -def write_message_task(self, group_id: str, message, config_id: str, storage_type: str, user_rag_memory_id: str) -> Dict[str, Any]: +def write_message_task(self, end_user_id: str, message: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a write message via MemoryAgentService. - 支持两种消息格式： - 1. 字符串格式（向后兼容）：message="user: xxx\nassistant: yyy" - 2. 结构化消息列表（推荐）：message=[{"role": "user", "content": "xxx"}, {"role": "assistant", "content": "yyy"}] - Args: - group_id: Group ID for the memory agent (also used as end_user_id) - message: Message to write (str or list[dict]) + end_user_id: Group ID for the memory agent (also used as end_user_id) + message: Message to write config_id: Optional configuration ID - storage_type: Storage type (neo4j/rag) - user_rag_memory_id: RAG memory ID Returns: Dict containing the result and metadata @@ -477,7 +489,7 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ from app.core.logging_config import get_logger logger = get_logger(__name__) - logger.info(f"[CELERY WRITE] Starting write task - group_id={group_id}, config_id={config_id}, storage_type={storage_type}") + logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") start_time = time.time() # Resolve config_id if None @@ -487,7 +499,7 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ from app.services.memory_agent_service import get_end_user_connected_config db = next(get_db()) try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) actual_config_id = connected_config.get("memory_config_id") finally: db.close() @@ -500,7 +512,7 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ try: logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory") service = MemoryAgentService() - result = await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id) + result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type, user_rag_memory_id) logger.info(f"[CELERY WRITE] Write completed successfully: {result}") return result except Exception as e: @@ -510,7 +522,24 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ db.close() try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time logger.info(f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}") @@ -518,13 +547,14 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ return { "status": "SUCCESS", "result": result, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id } except BaseException as e: elapsed_time = time.time() - start_time + # Handle ExceptionGroup from TaskGroup if hasattr(e, 'exceptions'): error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions] detailed_error = "; ".join(error_messages) @@ -536,7 +566,7 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ return { "status": "FAILURE", "error": detailed_error, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id @@ -564,53 +594,53 @@ def reflection_timer_task() -> None: """ reflection_engine() -# unused task -# @celery_app.task(name="app.core.memory.agent.health.check_read_service") -# def check_read_service_task() -> Dict[str, str]: -# """Call read_service and write latest status to Redis. + +@celery_app.task(name="app.core.memory.agent.health.check_read_service") +def check_read_service_task() -> Dict[str, str]: + """Call read_service and write latest status to Redis. -# Returns status data dict that gets written to Redis. -# """ -# client = redis.Redis( -# host=settings.REDIS_HOST, -# port=settings.REDIS_PORT, -# db=settings.REDIS_DB, -# password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None -# ) -# try: -# api_url = f"http://{settings.SERVER_IP}:8000/api/memory/read_service" -# payload = { -# "user_id": "健康检查", -# "apply_id": "健康检查", -# "group_id": "健康检查", -# "message": "你好", -# "history": [], -# "search_switch": "2", -# } -# resp = requests.post(api_url, json=payload, timeout=15) -# ok = resp.status_code == 200 -# status = "Success" if ok else "Fail" -# msg = "接口请求成功" if ok else f"接口请求失败: {resp.status_code}" -# error = "" if ok else resp.text -# code = 0 if ok else 500 -# except Exception as e: -# status = "Fail" -# msg = "接口请求失败" -# error = str(e) -# code = 500 + Returns status data dict that gets written to Redis. + """ + client = redis.Redis( + host=settings.REDIS_HOST, + port=settings.REDIS_PORT, + db=settings.REDIS_DB, + password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None + ) + try: + api_url = f"http://{settings.SERVER_IP}:8000/api/memory/read_service" + payload = { + "user_id": "健康检查", + "apply_id": "健康检查", + "end_user_id": "健康检查", + "message": "你好", + "history": [], + "search_switch": "2", + } + resp = requests.post(api_url, json=payload, timeout=15) + ok = resp.status_code == 200 + status = "Success" if ok else "Fail" + msg = "接口请求成功" if ok else f"接口请求失败: {resp.status_code}" + error = "" if ok else resp.text + code = 0 if ok else 500 + except Exception as e: + status = "Fail" + msg = "接口请求失败" + error = str(e) + code = 500 -# data = { -# "status": status, -# "msg": msg, -# "error": error, -# "code": str(code), -# "time": str(int(time.time())), -# } + data = { + "status": status, + "msg": msg, + "error": error, + "code": str(code), + "time": str(int(time.time())), + } -# client.hset("memsci:health:read_service", mapping=data) -# client.expire("memsci:health:read_service", int(settings.HEALTH_CHECK_SECONDS)) + client.hset("memsci:health:read_service", mapping=data) + client.expire("memsci:health:read_service", int(settings.HEALTH_CHECK_SECONDS)) -# return data + return data @celery_app.task(name="app.controllers.memory_storage_controller.search_all") @@ -875,7 +905,24 @@ def regenerate_memory_cache(self) -> Dict[str, Any]: } try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time result["elapsed_time"] = elapsed_time result["task_id"] = self.request.id @@ -1002,7 +1049,24 @@ def workspace_reflection_task(self) -> Dict[str, Any]: } try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time result["elapsed_time"] = elapsed_time result["task_id"] = self.request.id @@ -1048,7 +1112,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str # 运行遗忘周期 report = await forget_service.trigger_forgetting( db=db, - group_id=None, # 处理所有组 + end_user_id=None, # 处理所有组 config_id=config_id ) @@ -1078,4 +1142,11 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str "duration_seconds": duration } - return asyncio.run(_run()) + # 运行异步函数 + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + result = loop.run_until_complete(_run()) + return result + finally: + loop.close() From f0efed8aa1b5ec135f61201823bc643c9853c4e5 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 20:33:22 +0800 Subject: [PATCH 029/175] =?UTF-8?q?=E6=8A=8Agroup=5Fid=E6=9B=BF=E6=8D=A2en?= =?UTF-8?q?d=5Fuser=5Fid?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/memory_agent_controller.py | 6 +- .../langgraph_graph/nodes/write_nodes.py | 2 +- .../core/memory/agent/utils/write_tools.py | 17 ++- api/app/services/memory_agent_service.py | 128 +++++++++++------- 4 files changed, 100 insertions(+), 53 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index a1337085..ad5e3048 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -164,7 +164,7 @@ async def write_server( try: result = await memory_agent_service.write_memory( user_input.end_user_id, - user_input.message, + user_input.messages, config_id, db, storage_type, @@ -290,7 +290,7 @@ async def read_server( ) if str(user_input.search_switch) == "2": retrieve_info = result['answer'] - history = await SessionService(store).get_history(user_input.group_id, user_input.group_id, user_input.group_id) + history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id, user_input.end_user_id) query = user_input.message # 调用 memory_agent_service 的方法生成最终答案 @@ -596,7 +596,7 @@ async def status_type( last_user_message = " ".join([msg.get('content', '') for msg in messages_list]) result = await memory_agent_service.classify_message_type( - user_input.message, + user_input.messages, user_input.config_id, db ) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py index e2a61045..1dab1b0a 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py @@ -21,9 +21,9 @@ async def write_node(state: WriteState) -> WriteState: memory_config=state.get('memory_config', '') try: result=await write( - content=content, end_user_id=end_user_id, memory_config=memory_config, + messages=content, # 修复：使用正确的参数名 messages ) logger.info(f"Write completed successfully! Config: {memory_config.config_name}") diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index ce55286e..b8bc58eb 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -77,10 +77,25 @@ async def write( # Step 1: Load and chunk data step_start = time.time() + + # Convert messages list to content string + # messages format: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}, ...] + if isinstance(messages, list) and len(messages) > 0: + # Extract content from the last user message or concatenate all messages + if isinstance(messages[-1], dict) and 'content' in messages[-1]: + content = messages[-1]['content'] + else: + # Fallback: concatenate all message contents + content = " ".join([msg.get('content', '') for msg in messages if isinstance(msg, dict)]) + elif isinstance(messages, str): + content = messages + else: + content = str(messages) + chunked_dialogs = await get_chunked_dialogs( chunker_strategy=chunker_strategy, end_user_id=end_user_id, - messages=messages, + content=content, # 修复：使用 content 参数而不是 messages ref_id=ref_id, config_id=config_id, ) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index e475bef0..8a0d5a39 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -36,6 +36,7 @@ from app.services.memory_config_service import MemoryConfigService from app.services.memory_konwledges_server import ( write_rag, ) +from langchain_core.messages import AIMessage from langchain_core.messages import HumanMessage from pydantic import BaseModel, Field from sqlalchemy import func @@ -57,7 +58,6 @@ class MemoryAgentService: def writer_messages_deal(self, messages, start_time, end_user_id, config_id, message, context): duration = time.time() - start_time - if str(messages) == 'success': logger.info(f"Write operation successful for group {end_user_id} with config_id {config_id}") # 记录成功的操作 @@ -266,7 +266,7 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, end_user_id: str, message: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, messages: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id @@ -319,53 +319,85 @@ class MemoryAgentService: raise ValueError(error_msg) - try: - if storage_type == "rag": - # For RAG storage, convert messages to single string - message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - result = await write_rag(end_user_id, message_text, user_rag_memory_id) - return result - else: - async with make_write_graph() as graph: - config = {"configurable": {"thread_id": end_user_id}} - # Convert structured messages to LangChain messages - langchain_messages = [] - for msg in messages: - if msg['role'] == 'user': - langchain_messages.append(HumanMessage(content=msg['content'])) - elif msg['role'] == 'assistant': - from langchain_core.messages import AIMessage - langchain_messages.append(AIMessage(content=msg['content'])) - - # 初始状态 - 包含所有必要字段 - initial_state = { - "messages": langchain_messages, - "end_user_id": end_user_id, - "memory_config": memory_config - } + async with make_write_graph() as graph: + config = {"configurable": {"thread_id": end_user_id}} + # Convert structured messages to LangChain messages + langchain_messages = [] + for msg in messages: + if msg['role'] == 'user': + langchain_messages.append(HumanMessage(content=msg['content'])) + elif msg['role'] == 'assistant': + langchain_messages.append(AIMessage(content=msg['content'])) - # 获取节点更新信息 - async for update_event in graph.astream( - initial_state, - stream_mode="updates", - config=config - ): - for node_name, node_data in update_event.items(): - if 'save_neo4j' == node_name: - massages = node_data - massagesstatus = massages.get('write_result')['status'] - contents = massages.get('write_result') - # Convert messages back to string for logging - message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) - except Exception as e: - # Ensure proper error handling and logging - error_msg = f"Write operation failed: {str(e)}" - logger.error(error_msg) - if audit_logger: - duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) - raise ValueError(error_msg) + # 初始状态 - 包含所有必要字段 + initial_state = { + "messages": langchain_messages, + "end_user_id": end_user_id, + "memory_config": memory_config + } + + # 获取节点更新信息 + async for update_event in graph.astream( + initial_state, + stream_mode="updates", + config=config + ): + for node_name, node_data in update_event.items(): + if 'save_neo4j' == node_name: + massages = node_data + print(massages) + massagesstatus = massages.get('write_result')['status'] + contents = massages.get('write_result') + # Convert messages back to string for logging + message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) + return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) + + # try: + # if storage_type == "rag": + # # For RAG storage, convert messages to single string + # message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) + # result = await write_rag(end_user_id, message_text, user_rag_memory_id) + # return result + # else: + # async with make_write_graph() as graph: + # config = {"configurable": {"thread_id": end_user_id}} + # # Convert structured messages to LangChain messages + # langchain_messages = [] + # for msg in messages: + # if msg['role'] == 'user': + # langchain_messages.append(HumanMessage(content=msg['content'])) + # elif msg['role'] == 'assistant': + # langchain_messages.append(AIMessage(content=msg['content'])) + # + # # 初始状态 - 包含所有必要字段 + # initial_state = { + # "messages": langchain_messages, + # "end_user_id": end_user_id, + # "memory_config": memory_config + # } + # + # # 获取节点更新信息 + # async for update_event in graph.astream( + # initial_state, + # stream_mode="updates", + # config=config + # ): + # for node_name, node_data in update_event.items(): + # if 'save_neo4j' == node_name: + # massages = node_data + # massagesstatus = massages.get('write_result')['status'] + # contents = massages.get('write_result') + # # Convert messages back to string for logging + # message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) + # return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) + # except Exception as e: + # # Ensure proper error handling and logging + # error_msg = f"Write operation failed: {str(e)}" + # logger.error(error_msg) + # if audit_logger: + # duration = time.time() - start_time + # audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) + # raise ValueError(error_msg) From 4aeec8afbf43aff6777c7c0b2ca57e13d26fb431 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 21 Jan 2026 20:37:39 +0800 Subject: [PATCH 030/175] =?UTF-8?q?=E6=8A=8Agroup=5Fid=E6=9B=BF=E6=8D=A2en?= =?UTF-8?q?d=5Fuser=5Fid=5F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 1 + 1 file changed, 1 insertion(+) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 8a0d5a39..d08cf466 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -640,6 +640,7 @@ class MemoryAgentService: ) raise ValueError(error_msg) + def get_messages_list(self, user_input: Write_UserInput) -> list[dict]: """ Get standardized message list from user input. From c4039f52bdd8300aee367c4611f7baa859a92004 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 12:12:41 +0800 Subject: [PATCH 031/175] =?UTF-8?q?=E6=8A=8Agroup=5Fid=E6=9B=BF=E6=8D=A2en?= =?UTF-8?q?d=5Fuser=5Fid=5F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/core/memory/src/search.py | 61 +------------------------------ 1 file changed, 1 insertion(+), 60 deletions(-) diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 345cd69b..ef1e615c 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -1020,63 +1020,4 @@ async def search_chunk_by_chunk_id( ) return {"chunks": chunks} -if __name__ == '__main__': - # 测试混合检索功能 - from app.schemas.memory_config_schema import MemoryConfig - from app.db import get_db - from app.services.memory_config_service import MemoryConfigService - - # 从数据库获取真实配置 - db = next(get_db()) - try: - config_service = MemoryConfigService(db) - - # 使用 config_id=17 获取配置 - memory_config = config_service.load_memory_config(config_id=17) - - if not memory_config: - print("错误：找不到 config_id=17 的配置") - print("请先在数据库中创建配置，或修改 config_id") - exit(1) - - print(f"✓ 成功加载配置: {memory_config.config_name}") - print(f" - Workspace: {memory_config.workspace_name}") - print(f" - LLM Model: {memory_config.llm_model_name}") - print(f" - Embedding Model: {memory_config.embedding_model_name}") - print(f" - Storage Type: {memory_config.storage_type}") - print() - - # 修改这里的参数进行测试 - test_end_user_id = "021886bc-fab9-4fd5-b607-497b262e0381" # 修改为你的 end_user_id - test_query = "小明擅长什么？" # 修改为你的查询 - - print(f"开始测试检索...") - print(f" - Query: {test_query}") - print(f" - End User ID: {test_end_user_id}") - print(f" - Search Type: hybrid") - print() - - results = asyncio.run(run_hybrid_search( - query_text=test_query, - search_type="hybrid", # 可选: "keyword", "embedding", "hybrid" - end_user_id=test_end_user_id, - limit=10, - include=["statements", "entities", "chunks", "summaries"], - output_path=None, - memory_config=memory_config, - rerank_alpha=0.6, - use_forgetting_rerank=False, - use_llm_rerank=False - )) - - print("=" * 80) - print("检索结果:") - print("=" * 80) - print(results) - - except Exception as e: - print(f"错误: {e}") - import traceback - traceback.print_exc() - finally: - db.close() + From 1c7fe6d13487650b5803e7f12bcd9fdf844bc488 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 14:59:01 +0800 Subject: [PATCH 032/175] =?UTF-8?q?config=5Fconfig=E6=9B=BF=E6=8D=A2?= =?UTF-8?q?=E6=88=90memory=5Fconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../memory_reflection_controller.py | 36 ++-- api/app/core/memory/src/search.py | 56 ++++++ .../extraction_orchestrator.py | 18 +- .../forgetting_engine/config_utils.py | 6 +- .../forgetting_engine/forgetting_strategy.py | 4 +- api/app/core/workflow/nodes/memory/node.py | 2 +- api/app/models/__init__.py | 4 +- api/app/models/data_config_model.py | 88 --------- api/app/models/memory_config_model.py | 119 ++++++++---- ...ository.py => memory_config_repository.py} | 172 +++++++++--------- api/app/schemas/app_schema.py | 12 ++ api/app/schemas/model_schema.py | 14 +- api/app/schemas/release_share_schema.py | 14 +- api/app/services/emotion_config_service.py | 10 +- .../services/emotion_extraction_service.py | 4 +- api/app/services/memory_agent_service.py | 2 +- api/app/services/memory_config_service.py | 4 +- api/app/services/memory_forget_service.py | 4 +- api/app/services/memory_reflection_service.py | 50 ++--- api/app/services/memory_storage_service.py | 30 +-- api/app/utils/app_config_utils.py | 23 +++ 21 files changed, 374 insertions(+), 298 deletions(-) delete mode 100644 api/app/models/data_config_model.py rename api/app/repositories/{data_config_repository.py => memory_config_repository.py} (78%) diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index abd50a33..f17fcf7f 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -11,7 +11,7 @@ from app.core.response_utils import success from app.db import get_db from app.dependencies import get_current_user from app.models.user_model import User -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_reflection_schemas import Memory_Reflection from app.services.memory_reflection_service import ( @@ -50,7 +50,7 @@ async def save_reflection_config( api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}") - data_config = DataConfigRepository.update_reflection_config( + memory_config = MemoryConfigRepository.update_reflection_config( db, config_id=config_id, enable_self_reflexion=request.reflection_enabled, @@ -63,17 +63,17 @@ async def save_reflection_config( ) db.commit() - db.refresh(data_config) + db.refresh(memory_config) reflection_result={ - "config_id": data_config.config_id, - "enable_self_reflexion": data_config.enable_self_reflexion, - "iteration_period": data_config.iteration_period, - "reflexion_range": data_config.reflexion_range, - "baseline": data_config.baseline, - "reflection_model_id": data_config.reflection_model_id, - "memory_verify": data_config.memory_verify, - "quality_assessment": data_config.quality_assessment} + "config_id": memory_config.config_id, + "enable_self_reflexion": memory_config.enable_self_reflexion, + "iteration_period": memory_config.iteration_period, + "reflexion_range": memory_config.reflexion_range, + "baseline": memory_config.baseline, + "reflection_model_id": memory_config.reflection_model_id, + "memory_verify": memory_config.memory_verify, + "quality_assessment": memory_config.quality_assessment} return success(data=reflection_result, msg="反思配置成功") @@ -111,14 +111,14 @@ async def start_workspace_reflection( reflection_results = [] for data in result['apps_detailed_info']: - if data['data_configs'] == []: + if data['memory_configs'] == []: continue releases = data['releases'] - data_configs = data['data_configs'] + memory_configs = data['memory_configs'] end_users = data['end_users'] - for base, config, user in zip(releases, data_configs, end_users): + for base, config, user in zip(releases, memory_configs, end_users): # 安全地转换为整数，处理空字符串和None的情况 print(base['config']) try: @@ -160,10 +160,10 @@ async def start_reflection_configs( current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """通过config_id查询data_config表中的反思配置信息""" + """通过config_id查询memory_config表中的反思配置信息""" try: api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - result = DataConfigRepository.query_reflection_config_by_id(db, config_id) + result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) # 构建返回数据 reflection_config = { "config_id": result.config_id, @@ -200,8 +200,8 @@ async def reflection_run( api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - # 使用DataConfigRepository查询反思配置 - result = DataConfigRepository.query_reflection_config_by_id(db, config_id) + # 使用MemoryConfigRepository查询反思配置 + result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) if not result: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index ef1e615c..87a5dd6f 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -1021,3 +1021,59 @@ async def search_chunk_by_chunk_id( return {"chunks": chunks} +if __name__ == '__main__': + # 测试混合检索功能 + from app.schemas.memory_config_schema import MemoryConfig + from app.db import get_db + from app.services.memory_config_service import MemoryConfigService + + # 从数据库获取真实配置 + db = next(get_db()) + try: + config_service = MemoryConfigService(db) + + # 使用 config_id=17 获取配置 + memory_config = config_service.load_memory_config(config_id=17) + + if not memory_config: + print("错误：找不到 config_id=17 的配置") + print("请先在数据库中创建配置，或修改 config_id") + exit(1) + + print(f"✓ 成功加载配置: {memory_config.config_name}") + print(f" - Workspace: {memory_config.workspace_name}") + print(f" - LLM Model: {memory_config.llm_model_name}") + print(f" - Embedding Model: {memory_config.embedding_model_name}") + print(f" - Storage Type: {memory_config.storage_type}") + print() + + # 修改这里的参数进行测试 + test_end_user_id = "021886bc-fab9-4fd5-b607-497b262e0381" # 修改为你的 end_user_id + test_query = "小明擅长什么？" # 修改为你的查询 + + print(f"开始测试检索...") + print(f" - Query: {test_query}") + print(f" - End User ID: {test_end_user_id}") + print(f" - Search Type: hybrid") + print() + + results = asyncio.run(run_hybrid_search( + query_text=test_query, + search_type="hybrid", # 可选: "keyword", "embedding", "hybrid" + end_user_id=test_end_user_id, + limit=10, + include=["statements", "entities", "chunks", "summaries"], + output_path=None, + memory_config=memory_config, + rerank_alpha=0.6, + use_forgetting_rerank=False, + use_llm_rerank=False + )) + + except Exception as e: + print(f"错误: {e}") + import traceback + + traceback.print_exc() + finally: + db.close() \ No newline at end of file diff --git a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py index c2c5d54e..8c69c7cf 100644 --- a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py +++ b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py @@ -569,32 +569,32 @@ class ExtractionOrchestrator: if dialog_data_list and hasattr(dialog_data_list[0], 'config_id'): config_id = dialog_data_list[0].config_id - # 加载DataConfig - data_config = None + # 加载MemoryConfig + memory_config = None if config_id: try: from app.db import SessionLocal - from app.repositories.data_config_repository import DataConfigRepository + from app.repositories.memory_config_repository import MemoryConfigRepository db = SessionLocal() try: - data_config = DataConfigRepository.get_by_id(db, config_id) + memory_config = MemoryConfigRepository.get_by_id(db, config_id) finally: db.close() - if data_config and not data_config.emotion_enabled: + if memory_config and not memory_config.emotion_enabled: logger.info("情绪提取已在配置中禁用，跳过情绪提取") return [{} for _ in dialog_data_list] except Exception as e: - logger.warning(f"加载DataConfig失败: {e}，将跳过情绪提取") + logger.warning(f"加载MemoryConfig失败: {e}，将跳过情绪提取") return [{} for _ in dialog_data_list] else: logger.info("未找到config_id，跳过情绪提取") return [{} for _ in dialog_data_list] # 如果配置未启用情绪提取，直接返回空映射 - if not data_config or not data_config.emotion_enabled: + if not memory_config or not memory_config.emotion_enabled: logger.info("情绪提取未启用，跳过") return [{} for _ in dialog_data_list] @@ -608,7 +608,7 @@ class ExtractionOrchestrator: total_statements += 1 # 只处理用户的陈述句 (role 为 "user") if hasattr(statement, 'speaker') and statement.speaker == "user": - all_statements.append((statement, data_config)) + all_statements.append((statement, memory_config)) statement_metadata.append((d_idx, statement.id)) filtered_statements += 1 @@ -617,7 +617,7 @@ class ExtractionOrchestrator: # 初始化情绪提取服务 from app.services.emotion_extraction_service import EmotionExtractionService emotion_service = EmotionExtractionService( - llm_id=data_config.emotion_model_id if data_config.emotion_model_id else None + llm_id=memory_config.emotion_model_id if memory_config.emotion_model_id else None ) # 全局并行处理所有陈述句 diff --git a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py index ea9a6358..663c89f9 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py +++ b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py @@ -13,7 +13,7 @@ import logging from typing import Optional, Dict, Any from sqlalchemy.orm import Session -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator @@ -66,7 +66,7 @@ def load_actr_config_from_db( """ 从数据库加载 ACT-R 配置参数 - 从 PostgreSQL 的 data_config 表读取配置参数， + 从 PostgreSQL 的 memory_config 表读取配置参数， 并计算派生参数（如 forgetting_rate）。 Args: @@ -99,7 +99,7 @@ def load_actr_config_from_db( # 从数据库加载配置 try: - repository = DataConfigRepository() + repository = MemoryConfigRepository() db_config = repository.get_by_id(db, config_id) if db_config is None: diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py index 6b2d9e99..cde9e115 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py @@ -539,11 +539,11 @@ class ForgettingStrategy: LLM 客户端实例，如果无法获取则返回 None """ try: - from app.repositories.data_config_repository import DataConfigRepository + from app.repositories.memory_config_repository import MemoryConfigRepository from app.core.memory.utils.llm.llm_utils import MemoryClientFactory # 从数据库读取配置 - repository = DataConfigRepository() + repository = MemoryConfigRepository() db_config = repository.get_by_id(db, config_id) if db_config is None or db_config.llm_id is None: diff --git a/api/app/core/workflow/nodes/memory/node.py b/api/app/core/workflow/nodes/memory/node.py index 08a2b280..0589cc82 100644 --- a/api/app/core/workflow/nodes/memory/node.py +++ b/api/app/core/workflow/nodes/memory/node.py @@ -22,7 +22,7 @@ class MemoryReadNode(BaseNode): raise RuntimeError("End user id is required") return await MemoryAgentService().read_memory( - group_id=end_user_id, + end_user_id=end_user_id, message=self._render_template(self.typed_config.message, state), config_id=str(self.typed_config.config_id), search_switch=self.typed_config.search_switch, diff --git a/api/app/models/__init__.py b/api/app/models/__init__.py index bf3a1b3d..e069b40d 100644 --- a/api/app/models/__init__.py +++ b/api/app/models/__init__.py @@ -18,7 +18,7 @@ from .appshare_model import AppShare from .release_share_model import ReleaseShare from .conversation_model import Conversation, Message from .api_key_model import ApiKey, ApiKeyLog, ApiKeyType -from .data_config_model import DataConfig +from .memory_config_model import MemoryConfig from .multi_agent_model import MultiAgentConfig, AgentInvocation from .workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution from .retrieval_info import RetrievalInfo @@ -57,7 +57,7 @@ __all__ = [ "ApiKey", "ApiKeyLog", "ApiKeyType", - "DataConfig", + "MemoryConfig", "MultiAgentConfig", "AgentInvocation", "WorkflowConfig", diff --git a/api/app/models/data_config_model.py b/api/app/models/data_config_model.py deleted file mode 100644 index 06f87cb2..00000000 --- a/api/app/models/data_config_model.py +++ /dev/null @@ -1,88 +0,0 @@ -import datetime -from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float -from sqlalchemy.dialects.postgresql import UUID -from app.db import Base - - -class DataConfig(Base): - """数据配置表 - 用于存储记忆系统的配置参数""" - __tablename__ = "data_config" - - # 主键 - config_id = Column(Integer, primary_key=True, autoincrement=True, comment="配置ID") - - # 基本信息 - config_name = Column(String, nullable=False, comment="配置名称") - config_desc = Column(String, nullable=True, comment="配置描述") - - # 组织信息 - workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") - group_id = Column(String, nullable=True, comment="组ID") - user_id = Column(String, nullable=True, comment="用户ID") - apply_id = Column(String, nullable=True, comment="应用ID") - - # 模型选择（从workspace继承） - llm_id = Column(String, nullable=True, comment="LLM模型配置ID") - embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID") - rerank_id = Column(String, nullable=True, comment="重排序模型配置ID") - - # 记忆萃取引擎配置 - enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重") - enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧") - deep_retrieval = Column(Boolean, default=True, comment="深度检索开关") - - # 阈值配置 (0-1 之间的浮点数) - t_type_strict = Column(Float, default=0.8, comment="类型严格阈值") - t_name_strict = Column(Float, default=0.8, comment="名称严格阈值") - t_overall = Column(Float, default=0.8, comment="综合阈值") - - # 状态配置 - state = Column(Boolean, default=False, comment="配置使用状态") - - # 分块策略 - chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略") - - # 剪枝配置 - pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝") - pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound") - pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）") - - # 自我反思配置 - enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思") - iteration_period = Column(String, default="3", comment="反思迭代周期") - reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部") - baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实") - reflection_model_id = Column(String, nullable=True, comment="反思模型ID") - memory_verify = Column(Boolean, default=True, comment="记忆验证") - quality_assessment = Column(Boolean, default=True, comment="质量评估") - - # 遗忘引擎配置 - statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3") - include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文") - max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量") - lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数") - lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数") - offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数") - - # ACT-R 遗忘引擎配置 - decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5") - forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3") - forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24") - enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True") - max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100") - max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100") - min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30") - - # 情绪引擎配置 - emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取") - emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID") - emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词") - emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值") - emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类") - - # 时间戳 - created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") - updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") - - def __repr__(self): - return f"" diff --git a/api/app/models/memory_config_model.py b/api/app/models/memory_config_model.py index d47c3b52..55b377e6 100644 --- a/api/app/models/memory_config_model.py +++ b/api/app/models/memory_config_model.py @@ -1,39 +1,88 @@ -# -*- coding: utf-8 -*- -"""Memory Configuration Model - Backward Compatibility +import datetime +from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float +from sqlalchemy.dialects.postgresql import UUID +from app.db import Base -This module provides backward compatibility for imports. -All classes have been moved to app.schemas.memory_config_schema. -DEPRECATED: Import from app.schemas.memory_config_schema instead. -""" +class MemoryConfig(Base): + """记忆配置表 - 用于存储记忆系统的配置参数""" + __tablename__ = "memory_config" -# Re-export for backward compatibility -from app.schemas.memory_config_schema import ( - ConfigurationError, - InvalidConfigError, - MemoryConfig, - MemoryConfigValidation, - ModelInactiveError, - ModelNotFoundError, - ModelValidation, - WorkspaceNotFoundError, - WorkspaceValidation, - validate_memory_config_data, - validate_model_data, - validate_workspace_data, -) + # 主键 + config_id = Column(Integer, primary_key=True, autoincrement=True, comment="配置ID") -__all__ = [ - "ConfigurationError", - "InvalidConfigError", - "MemoryConfig", - "MemoryConfigValidation", - "ModelInactiveError", - "ModelNotFoundError", - "ModelValidation", - "WorkspaceNotFoundError", - "WorkspaceValidation", - "validate_memory_config_data", - "validate_model_data", - "validate_workspace_data", -] + # 基本信息 + config_name = Column(String, nullable=False, comment="配置名称") + config_desc = Column(String, nullable=True, comment="配置描述") + + # 组织信息 + workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") + group_id = Column(String, nullable=True, comment="组ID") + user_id = Column(String, nullable=True, comment="用户ID") + apply_id = Column(String, nullable=True, comment="应用ID") + + # 模型选择（从workspace继承） + llm_id = Column(String, nullable=True, comment="LLM模型配置ID") + embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID") + rerank_id = Column(String, nullable=True, comment="重排序模型配置ID") + + # 记忆萃取引擎配置 + enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重") + enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧") + deep_retrieval = Column(Boolean, default=True, comment="深度检索开关") + + # 阈值配置 (0-1 之间的浮点数) + t_type_strict = Column(Float, default=0.8, comment="类型严格阈值") + t_name_strict = Column(Float, default=0.8, comment="名称严格阈值") + t_overall = Column(Float, default=0.8, comment="综合阈值") + + # 状态配置 + state = Column(Boolean, default=False, comment="配置使用状态") + + # 分块策略 + chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略") + + # 剪枝配置 + pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝") + pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound") + pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）") + + # 自我反思配置 + enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思") + iteration_period = Column(String, default="3", comment="反思迭代周期") + reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部") + baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实") + reflection_model_id = Column(String, nullable=True, comment="反思模型ID") + memory_verify = Column(Boolean, default=True, comment="记忆验证") + quality_assessment = Column(Boolean, default=True, comment="质量评估") + + # 遗忘引擎配置 + statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3") + include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文") + max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量") + lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数") + lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数") + offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数") + + # ACT-R 遗忘引擎配置 + decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5") + forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3") + forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24") + enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True") + max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100") + max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100") + min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30") + + # 情绪引擎配置 + emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取") + emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID") + emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词") + emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值") + emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类") + + # 时间戳 + created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") + updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") + + def __repr__(self): + return f"" diff --git a/api/app/repositories/data_config_repository.py b/api/app/repositories/memory_config_repository.py similarity index 78% rename from api/app/repositories/data_config_repository.py rename to api/app/repositories/memory_config_repository.py index 3df7f800..eb513c99 100644 --- a/api/app/repositories/data_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -1,18 +1,18 @@ # -*- coding: utf-8 -*- -"""数据配置Repository模块 +"""记忆配置Repository模块 -本模块提供data_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。 +本模块提供memory_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。 包括CRUD操作和Neo4j Cypher查询常量。 Classes: - DataConfigRepository: 数据配置仓储类，提供CRUD操作 + MemoryConfigRepository: 记忆配置仓储类，提供CRUD操作 """ import uuid from typing import Dict, List, Optional, Tuple from app.core.exceptions import BusinessException from app.core.logging_config import get_config_logger, get_db_logger -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig from app.schemas.memory_storage_schema import ( ConfigKey, ConfigParamsCreate, @@ -28,11 +28,11 @@ db_logger = get_db_logger() # 获取配置专用日志器 config_logger = get_config_logger() -TABLE_NAME = "data_config" -class DataConfigRepository: - """数据配置Repository +TABLE_NAME = "memory_config" +class MemoryConfigRepository: + """记忆配置Repository - 提供data_config表的数据访问方法，包括： + 提供memory_config表的数据访问方法，包括： - SQLAlchemy ORM 数据库操作 - Neo4j Cypher查询常量 """ @@ -115,7 +115,7 @@ class DataConfigRepository: reflection_model_id: str, memory_verify: bool, quality_assessment: bool - ) -> DataConfig: + ) -> MemoryConfig: """构建反思配置更新语句（SQLAlchemy text() 命名参数） Args: @@ -130,28 +130,28 @@ class DataConfigRepository: config_id: 配置ID Returns: - Data + MemoryConfig Raises: ValueError: 没有字段需要更新时抛出 """ db_logger.debug(f"构建反思配置更新语句: config_id={config_id}") - stmt = select(DataConfig).where(DataConfig.config_id == config_id) - data_config_obj = db.scalars(stmt).first() - if not data_config_obj: + stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id) + memory_config_obj = db.scalars(stmt).first() + if not memory_config_obj: raise BusinessException - data_config_obj.enable_self_reflexion = enable_self_reflexion - data_config_obj.iteration_period = iteration_period - data_config_obj.reflexion_range = reflexion_range - data_config_obj.baseline = baseline - data_config_obj.reflection_model_id = reflection_model_id - data_config_obj.memory_verify = memory_verify - data_config_obj.quality_assessment = quality_assessment + memory_config_obj.enable_self_reflexion = enable_self_reflexion + memory_config_obj.iteration_period = iteration_period + memory_config_obj.reflexion_range = reflexion_range + memory_config_obj.baseline = baseline + memory_config_obj.reflection_model_id = reflection_model_id + memory_config_obj.memory_verify = memory_verify + memory_config_obj.quality_assessment = quality_assessment - return data_config_obj + return memory_config_obj @staticmethod - def query_reflection_config_by_id(db: Session, config_id: int) -> DataConfig: + def query_reflection_config_by_id(db: Session, config_id: int) -> MemoryConfig: """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数） Args: @@ -162,13 +162,13 @@ class DataConfigRepository: Tuple[str, Dict]: (SQL查询字符串, 参数字典) """ db_logger.debug(f"构建反思配置查询语句: config_id={config_id}") - stmt = select(DataConfig).where(DataConfig.config_id == config_id) - data_config = db.scalars(stmt).first() - if not data_config: + stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id) + memory_config = db.scalars(stmt).first() + if not memory_config: raise RuntimeError("reflection config not found") - return data_config + return memory_config @staticmethod - def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> DataConfig: + def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> MemoryConfig: """构建查询所有配置的语句（SQLAlchemy text() 命名参数） Args: @@ -180,11 +180,11 @@ class DataConfigRepository: """ db_logger.debug(f"构建查询所有配置语句: workspace_id={workspace_id}") - stmt = select(DataConfig).where(DataConfig.workspace_id == workspace_id) - data_config = db.scalars(stmt).first() - if not data_config: + stmt = select(MemoryConfig).where(MemoryConfig.workspace_id == workspace_id) + memory_config = db.scalars(stmt).first() + if not memory_config: raise RuntimeError("reflection config not found") - return data_config + return memory_config @staticmethod @@ -208,20 +208,20 @@ class DataConfigRepository: return query, params @staticmethod - def create(db: Session, params: ConfigParamsCreate) -> DataConfig: - """创建数据配置 + def create(db: Session, params: ConfigParamsCreate) -> MemoryConfig: + """创建记忆配置 Args: db: 数据库会话 params: 配置参数创建模型 Returns: - DataConfig: 创建的配置对象 + MemoryConfig: 创建的配置对象 """ - db_logger.debug(f"创建数据配置: config_name={params.config_name}, workspace_id={params.workspace_id}") + db_logger.debug(f"创建记忆配置: config_name={params.config_name}, workspace_id={params.workspace_id}") try: - db_config = DataConfig( + db_config = MemoryConfig( config_name=params.config_name, config_desc=params.config_desc, workspace_id=params.workspace_id, @@ -232,16 +232,16 @@ class DataConfigRepository: db.add(db_config) db.flush() # 获取自增ID但不提交事务 - db_logger.info(f"数据配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})") + db_logger.info(f"记忆配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})") return db_config except Exception as e: db.rollback() - db_logger.error(f"创建数据配置失败: {params.config_name} - {str(e)}") + db_logger.error(f"创建记忆配置失败: {params.config_name} - {str(e)}") raise @staticmethod - def update(db: Session, update: ConfigUpdate) -> Optional[DataConfig]: + def update(db: Session, update: ConfigUpdate) -> Optional[MemoryConfig]: """更新基础配置 Args: @@ -249,17 +249,17 @@ class DataConfigRepository: update: 配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 """ - db_logger.debug(f"更新数据配置: config_id={update.config_id}") + db_logger.debug(f"更新记忆配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段 @@ -277,17 +277,17 @@ class DataConfigRepository: db.commit() db.refresh(db_config) - db_logger.info(f"数据配置更新成功: {db_config.config_name} (ID: {update.config_id})") + db_logger.info(f"记忆配置更新成功: {db_config.config_name} (ID: {update.config_id})") return db_config except Exception as e: db.rollback() - db_logger.error(f"更新数据配置失败: config_id={update.config_id} - {str(e)}") + db_logger.error(f"更新记忆配置失败: config_id={update.config_id} - {str(e)}") raise @staticmethod - def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[DataConfig]: + def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[MemoryConfig]: """更新记忆萃取引擎配置 Args: @@ -295,7 +295,7 @@ class DataConfigRepository: update: 萃取配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 @@ -303,9 +303,9 @@ class DataConfigRepository: db_logger.debug(f"更新萃取配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段映射 @@ -360,7 +360,7 @@ class DataConfigRepository: raise @staticmethod - def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[DataConfig]: + def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[MemoryConfig]: """更新遗忘引擎配置 Args: @@ -368,7 +368,7 @@ class DataConfigRepository: update: 遗忘配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 @@ -376,9 +376,9 @@ class DataConfigRepository: db_logger.debug(f"更新遗忘配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段 @@ -421,7 +421,7 @@ class DataConfigRepository: db_logger.debug(f"查询萃取配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: db_logger.debug(f"萃取配置不存在: config_id={config_id}") return None @@ -470,7 +470,7 @@ class DataConfigRepository: db_logger.debug(f"查询遗忘配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: db_logger.debug(f"遗忘配置不存在: config_id={config_id}") return None @@ -489,39 +489,39 @@ class DataConfigRepository: raise @staticmethod - def get_by_id(db: Session, config_id: int) -> Optional[DataConfig]: - """根据ID获取数据配置 + def get_by_id(db: Session, config_id: int) -> Optional[MemoryConfig]: + """根据ID获取记忆配置 Args: db: 数据库会话 config_id: 配置ID Returns: - Optional[DataConfig]: 配置对象，不存在则返回None + Optional[MemoryConfig]: 配置对象，不存在则返回None """ - db_logger.debug(f"根据ID查询数据配置: config_id={config_id}") + db_logger.debug(f"根据ID查询记忆配置: config_id={config_id}") try: - config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if config: - db_logger.debug(f"数据配置查询成功: {config.config_name} (ID: {config_id})") + db_logger.debug(f"记忆配置查询成功: {config.config_name} (ID: {config_id})") else: - db_logger.debug(f"数据配置不存在: config_id={config_id}") + db_logger.debug(f"记忆配置不存在: config_id={config_id}") return config except Exception as e: - db_logger.error(f"根据ID查询数据配置失败: config_id={config_id} - {str(e)}") + db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}") raise @staticmethod def get_config_with_workspace(db: Session, config_id: int) -> Optional[tuple]: - """Get data config and its associated workspace information + """Get memory config and its associated workspace information Args: db: Database session config_id: Configuration ID Returns: - Optional[tuple]: (DataConfig, Workspace) tuple, None if not found + Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found Raises: ValueError: Raised when config exists but workspace doesn't @@ -541,19 +541,19 @@ class DataConfigRepository: } ) - db_logger.debug(f"Querying data config and workspace: config_id={config_id}") + db_logger.debug(f"Querying memory config and workspace: config_id={config_id}") try: # Use join query to get both config and workspace - result = db.query(DataConfig, Workspace).join( - Workspace, DataConfig.workspace_id == Workspace.id - ).filter(DataConfig.config_id == config_id).first() + result = db.query(MemoryConfig, Workspace).join( + Workspace, MemoryConfig.workspace_id == Workspace.id + ).filter(MemoryConfig.config_id == config_id).first() elapsed_ms = (time.time() - start_time) * 1000 if not result: # Check if config exists but workspace is missing - config_only = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + config_only = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if config_only: if config_only.workspace_id is None: config_logger.error( @@ -566,7 +566,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Data config {config_id} has no associated workspace ID") + db_logger.error(f"Memory config {config_id} has no associated workspace ID") raise ValueError(f"Configuration {config_id} has no associated workspace") else: config_logger.error( @@ -579,7 +579,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Data config {config_id} references non-existent workspace {config_only.workspace_id}") + db_logger.error(f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}") raise ValueError(f"Workspace {config_only.workspace_id} not found for configuration {config_id}") config_logger.debug( @@ -591,7 +591,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.debug(f"Data config not found: config_id={config_id}") + db_logger.debug(f"Memory config not found: config_id={config_id}") return None config, workspace = result @@ -611,7 +611,7 @@ class DataConfigRepository: } ) - db_logger.debug(f"Data config and workspace query successful: config={config.config_name}, workspace={workspace.name}") + db_logger.debug(f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}") return (config, workspace) except ValueError: @@ -633,10 +633,10 @@ class DataConfigRepository: exc_info=True ) - db_logger.error(f"Failed to query data config and workspace: config_id={config_id} - {str(e)}") + db_logger.error(f"Failed to query memory config and workspace: config_id={config_id} - {str(e)}") raise @staticmethod - def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[DataConfig]: + def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[MemoryConfig]: """获取所有配置参数 Args: @@ -644,17 +644,17 @@ class DataConfigRepository: workspace_id: 工作空间ID，用于过滤查询结果 Returns: - List[DataConfig]: 配置列表 + List[MemoryConfig]: 配置列表 """ db_logger.debug(f"查询所有配置: workspace_id={workspace_id}") try: - query = db.query(DataConfig) + query = db.query(MemoryConfig) if workspace_id: - query = query.filter(DataConfig.workspace_id == workspace_id) + query = query.filter(MemoryConfig.workspace_id == workspace_id) - configs = query.order_by(desc(DataConfig.updated_at)).all() + configs = query.order_by(desc(MemoryConfig.updated_at)).all() db_logger.debug(f"配置列表查询成功: 数量={len(configs)}") return configs @@ -665,7 +665,7 @@ class DataConfigRepository: @staticmethod def delete(db: Session, config_id: int) -> bool: - """删除数据配置 + """删除记忆配置 Args: db: 数据库会话 @@ -674,22 +674,22 @@ class DataConfigRepository: Returns: bool: 删除成功返回True，配置不存在返回False """ - db_logger.debug(f"删除数据配置: config_id={config_id}") + db_logger.debug(f"删除记忆配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={config_id}") + db_logger.warning(f"记忆配置不存在: config_id={config_id}") return False db.delete(db_config) db.commit() - db_logger.info(f"数据配置删除成功: config_id={config_id}") + db_logger.info(f"记忆配置删除成功: config_id={config_id}") return True except Exception as e: db.rollback() - db_logger.error(f"删除数据配置失败: config_id={config_id} - {str(e)}") + db_logger.error(f"删除记忆配置失败: config_id={config_id} - {str(e)}") raise diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index 35d2e424..09410091 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -299,6 +299,18 @@ class AppRelease(BaseModel): created_at: datetime.datetime updated_at: datetime.datetime + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v if v is not None else {} + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None diff --git a/api/app/schemas/model_schema.py b/api/app/schemas/model_schema.py index 5b1fe6d9..68f15115 100644 --- a/api/app/schemas/model_schema.py +++ b/api/app/schemas/model_schema.py @@ -1,4 +1,4 @@ -from pydantic import BaseModel, Field, field_serializer, ConfigDict +from pydantic import BaseModel, Field, field_serializer, field_validator, ConfigDict from typing import Optional, List, Dict, Any import datetime import uuid @@ -91,6 +91,18 @@ class ModelApiKey(ModelApiKeyBase): created_at: datetime.datetime updated_at: datetime.datetime + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None diff --git a/api/app/schemas/release_share_schema.py b/api/app/schemas/release_share_schema.py index 069b78a9..47897847 100644 --- a/api/app/schemas/release_share_schema.py +++ b/api/app/schemas/release_share_schema.py @@ -1,7 +1,7 @@ import uuid import datetime from typing import Optional, List, Dict, Any -from pydantic import BaseModel, Field, ConfigDict, field_serializer +from pydantic import BaseModel, Field, ConfigDict, field_serializer, field_validator # ---------- Input Schemas ---------- @@ -88,6 +88,18 @@ class SharedReleaseInfo(BaseModel): # 嵌入配置 allow_embed: bool + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v if v is not None else {} + class EmbedCode(BaseModel): """嵌入代码""" diff --git a/api/app/services/emotion_config_service.py b/api/app/services/emotion_config_service.py index 37171640..f8b4d22a 100644 --- a/api/app/services/emotion_config_service.py +++ b/api/app/services/emotion_config_service.py @@ -10,7 +10,7 @@ Classes: from typing import Dict, Any from sqlalchemy.orm import Session -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig from app.core.logging_config import get_business_logger logger = get_business_logger() @@ -61,8 +61,8 @@ class EmotionConfigService: logger.info(f"获取情绪配置: config_id={config_id}") # 查询配置 - config = self.db.query(DataConfig).filter( - DataConfig.config_id == config_id + config = self.db.query(MemoryConfig).filter( + MemoryConfig.config_id == config_id ).first() if not config: @@ -173,8 +173,8 @@ class EmotionConfigService: self.validate_emotion_config(config_data) # 查询配置 - config = self.db.query(DataConfig).filter( - DataConfig.config_id == config_id + config = self.db.query(MemoryConfig).filter( + MemoryConfig.config_id == config_id ).first() if not config: diff --git a/api/app/services/emotion_extraction_service.py b/api/app/services/emotion_extraction_service.py index d134251d..6b596a80 100644 --- a/api/app/services/emotion_extraction_service.py +++ b/api/app/services/emotion_extraction_service.py @@ -14,7 +14,7 @@ from app.core.memory.llm_tools.llm_client import LLMClientException from app.core.memory.models.emotion_models import EmotionExtraction from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig logger = logging.getLogger(__name__) @@ -60,7 +60,7 @@ class EmotionExtractionService: async def extract_emotion( self, statement: str, - config: DataConfig + config: MemoryConfig ) -> Optional[EmotionExtraction]: """Extract emotion information from a statement. diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index d08cf466..509ed815 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1249,7 +1249,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) """ from app.models.app_release_model import AppRelease from app.models.end_user_model import EndUser - from app.models.data_config_model import DataConfig + from app.models.memory_config_model import MemoryConfig from sqlalchemy import select logger.info(f"Batch getting connected configs for {len(end_user_ids)} end_users") diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index 0099eb18..9afba797 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -14,7 +14,7 @@ from app.core.validators.memory_config_validators import ( validate_embedding_model, validate_model_exists_and_active, ) -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.schemas.memory_config_schema import ( ConfigurationError, InvalidConfigError, @@ -127,7 +127,7 @@ class MemoryConfigService: # Step 1: Get config and workspace db_query_start = time.time() - result = DataConfigRepository.get_config_with_workspace(self.db, validated_config_id) + result = MemoryConfigRepository.get_config_with_workspace(self.db, validated_config_id) db_query_time = time.time() - db_query_start logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s") if not result: diff --git a/api/app/services/memory_forget_service.py b/api/app/services/memory_forget_service.py index 558efe43..204f5df1 100644 --- a/api/app/services/memory_forget_service.py +++ b/api/app/services/memory_forget_service.py @@ -23,7 +23,7 @@ from app.core.memory.storage_services.forgetting_engine.config_utils import ( load_actr_config_from_db, ) from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.forgetting_cycle_history_repository import ForgettingCycleHistoryRepository @@ -70,7 +70,7 @@ class MemoryForgetService: def __init__(self): """初始化服务""" - self.config_repository = DataConfigRepository() + self.config_repository = MemoryConfigRepository() self.history_repository = ForgettingCycleHistoryRepository() def _get_neo4j_connector(self) -> Neo4jConnector: diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index 46e42b46..1ae7c549 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -13,7 +13,7 @@ from app.db import get_db from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine import ReflectionConfig, ReflectionEngine from app.core.memory.storage_services.reflection_engine.self_reflexion import ReflectionRange, ReflectionBaseline -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.models.app_model import App from app.models.app_release_model import AppRelease @@ -70,7 +70,7 @@ class WorkspaceAppService: "created_at": app.created_at.isoformat() if app.created_at else None, "updated_at": app.updated_at.isoformat() if app.updated_at else None, "releases": [], - "data_configs": [], + "memory_configs": [], "end_users": [] } @@ -98,11 +98,11 @@ class WorkspaceAppService: if memory_content: processed_configs.add(memory_content) - data_config_info = self._get_data_config(memory_content) + memory_config_info = self._get_memory_config(memory_content) - if data_config_info: - if not any(dc["config_id"] == data_config_info["config_id"] for dc in app_info["data_configs"]): - app_info["data_configs"].append(data_config_info) + if memory_config_info: + if not any(dc["config_id"] == memory_config_info["config_id"] for dc in app_info["memory_configs"]): + app_info["memory_configs"].append(memory_config_info) app_info["releases"].append(release_info) @@ -117,30 +117,30 @@ class WorkspaceAppService: return None - def _get_data_config(self, memory_content: str) -> Dict[str, Any]: - """Retrieve data_comfig information based on memory_comtent""" + def _get_memory_config(self, memory_content: str) -> Dict[str, Any]: + """Retrieve memory_config information based on memory_content""" try: - data_config_result = DataConfigRepository.query_reflection_config_by_id(self.db, int(memory_content)) + memory_config_result = MemoryConfigRepository.query_reflection_config_by_id(self.db, int(memory_content)) - # data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content) - # data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone() - # if data_config_result is None: + # memory_config_query, memory_config_params = MemoryConfigRepository.build_select_reflection(memory_content) + # memory_config_result = self.db.execute(text(memory_config_query), memory_config_params).fetchone() + # if memory_config_result is None: # return None - if data_config_result: + if memory_config_result: return { - "config_id": data_config_result.config_id, - "enable_self_reflexion": data_config_result.enable_self_reflexion, - "iteration_period": data_config_result.iteration_period, - "reflexion_range": data_config_result.reflexion_range, - "baseline": data_config_result.baseline, - "reflection_model_id": data_config_result.reflection_model_id, - "memory_verify": data_config_result.memory_verify, - "quality_assessment": data_config_result.quality_assessment, - "user_id": data_config_result.user_id + "config_id": memory_config_result.config_id, + "enable_self_reflexion": memory_config_result.enable_self_reflexion, + "iteration_period": memory_config_result.iteration_period, + "reflexion_range": memory_config_result.reflexion_range, + "baseline": memory_config_result.baseline, + "reflection_model_id": memory_config_result.reflection_model_id, + "memory_verify": memory_config_result.memory_verify, + "quality_assessment": memory_config_result.quality_assessment, + "user_id": memory_config_result.user_id } except Exception as e: - api_logger.warning(f"查询data_config失败，memory_content: {memory_content}, 错误: {str(e)}") + api_logger.warning(f"查询memory_config失败，memory_content: {memory_content}, 错误: {str(e)}") return None @@ -223,7 +223,7 @@ class MemoryReflectionService: } config_data_id = config_data['config_id'] - reflection_config = WorkspaceAppService(self.db)._get_data_config(config_data_id) + reflection_config = WorkspaceAppService(self.db)._get_memory_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config = self._create_reflection_config_from_data(reflection_config) # 3. 执行反思引擎 @@ -277,7 +277,7 @@ class MemoryReflectionService: config_data_id=config_data['config_id'] - reflection_config=WorkspaceAppService(self.db)._get_data_config(config_data_id) + reflection_config=WorkspaceAppService(self.db)._get_memory_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config= self._create_reflection_config_from_data(reflection_config) iteration_period = int(reflection_config.iteration_period) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 05a84c01..6aa5ac7d 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -15,7 +15,7 @@ from app.core.logging_config import get_config_logger, get_logger from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats from app.models.user_model import User -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_config_schema import ConfigurationError from app.schemas.memory_storage_schema import ( @@ -125,7 +125,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） if not params.rerank_id: params.rerank_id = configs.get('rerank') - config = DataConfigRepository.create(self.db, params) + config = MemoryConfigRepository.create(self.db, params) self.db.commit() return {"affected": 1, "config_id": config.config_id} @@ -142,20 +142,20 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Delete --- def delete(self, key: ConfigParamsDelete) -> Dict[str, Any]: # 删除配置参数（按配置ID） - success = DataConfigRepository.delete(self.db, key.config_id) + success = MemoryConfigRepository.delete(self.db, key.config_id) if not success: raise ValueError("未找到配置") return {"affected": 1} # --- Update --- def update(self, update: ConfigUpdate) -> Dict[str, Any]: # 部分更新配置参数 - config = DataConfigRepository.update(self.db, update) + config = MemoryConfigRepository.update(self.db, update) if not config: raise ValueError("未找到配置") return {"affected": 1} def update_extracted(self, update: ConfigUpdateExtracted) -> Dict[str, Any]: # 更新记忆萃取引擎配置参数 - config = DataConfigRepository.update_extracted(self.db, update) + config = MemoryConfigRepository.update_extracted(self.db, update) if not config: raise ValueError("未找到配置") return {"affected": 1} @@ -166,14 +166,14 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Read --- def get_extracted(self, key: ConfigKey) -> Dict[str, Any]: # 获取萃取配置参数 - result = DataConfigRepository.get_extracted_config(self.db, key.config_id) + result = MemoryConfigRepository.get_extracted_config(self.db, key.config_id) if not result: raise ValueError("未找到配置") return result # --- Read All --- def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数 - configs = DataConfigRepository.get_all(self.db, workspace_id) + configs = MemoryConfigRepository.get_all(self.db, workspace_id) # 将 ORM 对象转换为字典列表 data_list = [] @@ -390,7 +390,7 @@ _neo4j_connector = Neo4jConnector() async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_DIALOGUE, + MemoryConfigRepository.SEARCH_FOR_DIALOGUE, end_user_id=end_user_id, ) data = {"search_for": "dialogue", "num": result[0]["num"]} @@ -399,7 +399,7 @@ async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_CHUNK, + MemoryConfigRepository.SEARCH_FOR_CHUNK, end_user_id=end_user_id, ) data = {"search_for": "chunk", "num": result[0]["num"]} @@ -408,7 +408,7 @@ async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_STATEMENT, + MemoryConfigRepository.SEARCH_FOR_STATEMENT, end_user_id=end_user_id, ) data = {"search_for": "statement", "num": result[0]["num"]} @@ -417,7 +417,7 @@ async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ENTITY, + MemoryConfigRepository.SEARCH_FOR_ENTITY, end_user_id=end_user_id, ) data = {"search_for": "entity", "num": result[0]["num"]} @@ -426,7 +426,7 @@ async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ALL, + MemoryConfigRepository.SEARCH_FOR_ALL, end_user_id=end_user_id, ) @@ -461,7 +461,7 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A 聚合 dialogue/chunk/statement/entity 四类计数，返回统一的分布结构，便于前端一次性消费。 """ result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ALL, + MemoryConfigRepository.SEARCH_FOR_ALL, end_user_id=end_user_id, ) @@ -492,7 +492,7 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_DETIALS, + MemoryConfigRepository.SEARCH_FOR_DETIALS, end_user_id=end_user_id, ) return result @@ -500,7 +500,7 @@ async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, An async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_EDGES, + MemoryConfigRepository.SEARCH_FOR_EDGES, end_user_id=end_user_id, ) return result diff --git a/api/app/utils/app_config_utils.py b/api/app/utils/app_config_utils.py index 514e4565..ae41d8bf 100644 --- a/api/app/utils/app_config_utils.py +++ b/api/app/utils/app_config_utils.py @@ -83,6 +83,13 @@ class AgentConfigProxy: def agent_config_4_app_release(release: AppRelease) -> AgentConfig: config_dict = release.config + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} agent_config = AgentConfig( app_id=release.app_id, @@ -100,6 +107,14 @@ def agent_config_4_app_release(release: AppRelease) -> AgentConfig: def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig: config_dict = release.config + + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} agent_config = MultiAgentConfig( app_id=release.app_id, @@ -120,6 +135,14 @@ def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig: def workflow_config_4_app_release(release: AppRelease) -> WorkflowConfig: config_dict = release.config + + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} config = WorkflowConfig( id=config_dict.get("id"), From bcc8b7ce3cf52178bb3395aae7e541cd424ffe87 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 16:11:48 +0800 Subject: [PATCH 033/175] =?UTF-8?q?config=5Fconfig=E6=9B=BF=E6=8D=A2?= =?UTF-8?q?=E6=88=90memory=5Fconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/emotion_controller.py | 38 +- api/app/schemas/emotion_schema.py | 8 +- api/uv.lock | 4465 --------------------- 3 files changed, 23 insertions(+), 4488 deletions(-) delete mode 100644 api/uv.lock diff --git a/api/app/controllers/emotion_controller.py b/api/app/controllers/emotion_controller.py index 154a3928..cd199aa7 100644 --- a/api/app/controllers/emotion_controller.py +++ b/api/app/controllers/emotion_controller.py @@ -53,7 +53,7 @@ async def get_emotion_tags( api_logger.info( f"用户 {current_user.username} 请求获取情绪标签统计", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "emotion_type": request.emotion_type, "start_date": request.start_date, "end_date": request.end_date, @@ -63,7 +63,7 @@ async def get_emotion_tags( # 调用服务层 data = await emotion_service.get_emotion_tags( - end_user_id=request.group_id, + end_user_id=request.end_user_id, emotion_type=request.emotion_type, start_date=request.start_date, end_date=request.end_date, @@ -73,7 +73,7 @@ async def get_emotion_tags( api_logger.info( "情绪标签统计获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "total_count": data.get("total_count", 0), "tags_count": len(data.get("tags", [])) } @@ -84,7 +84,7 @@ async def get_emotion_tags( except Exception as e: api_logger.error( f"获取情绪标签统计失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -105,7 +105,7 @@ async def get_emotion_wordcloud( api_logger.info( f"用户 {current_user.username} 请求获取情绪词云数据", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "emotion_type": request.emotion_type, "limit": request.limit } @@ -113,7 +113,7 @@ async def get_emotion_wordcloud( # 调用服务层 data = await emotion_service.get_emotion_wordcloud( - end_user_id=request.group_id, + end_user_id=request.end_user_id, emotion_type=request.emotion_type, limit=request.limit ) @@ -121,7 +121,7 @@ async def get_emotion_wordcloud( api_logger.info( "情绪词云数据获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "total_keywords": data.get("total_keywords", 0) } ) @@ -131,7 +131,7 @@ async def get_emotion_wordcloud( except Exception as e: api_logger.error( f"获取情绪词云数据失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -159,21 +159,21 @@ async def get_emotion_health( api_logger.info( f"用户 {current_user.username} 请求获取情绪健康指数", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "time_range": request.time_range } ) # 调用服务层 data = await emotion_service.calculate_emotion_health_index( - end_user_id=request.group_id, + end_user_id=request.end_user_id, time_range=request.time_range ) api_logger.info( "情绪健康指数获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "health_score": data.get("health_score", 0), "level": data.get("level", "未知") } @@ -186,7 +186,7 @@ async def get_emotion_health( except Exception as e: api_logger.error( f"获取情绪健康指数失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -206,7 +206,7 @@ async def get_emotion_suggestions( """获取个性化情绪建议（从缓存读取） Args: - request: 包含 group_id 和可选的 config_id + request: 包含 end_user_id 和可选的 config_id db: 数据库会话 current_user: 当前用户 @@ -217,22 +217,22 @@ async def get_emotion_suggestions( api_logger.info( f"用户 {current_user.username} 请求获取个性化情绪建议（缓存）", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "config_id": request.config_id } ) # 从缓存获取建议 data = await emotion_service.get_cached_suggestions( - end_user_id=request.group_id, + end_user_id=request.end_user_id, db=db ) if data is None: # 缓存不存在或已过期 api_logger.info( - f"用户 {request.group_id} 的建议缓存不存在或已过期", - extra={"group_id": request.group_id} + f"用户 {request.end_user_id} 的建议缓存不存在或已过期", + extra={"end_user_id": request.end_user_id} ) return fail( BizCode.NOT_FOUND, @@ -243,7 +243,7 @@ async def get_emotion_suggestions( api_logger.info( "个性化建议获取成功（缓存）", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "suggestions_count": len(data.get("suggestions", [])) } ) @@ -253,7 +253,7 @@ async def get_emotion_suggestions( except Exception as e: api_logger.error( f"获取个性化建议失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( diff --git a/api/app/schemas/emotion_schema.py b/api/app/schemas/emotion_schema.py index c48fbd41..fb523887 100644 --- a/api/app/schemas/emotion_schema.py +++ b/api/app/schemas/emotion_schema.py @@ -5,7 +5,7 @@ from pydantic import BaseModel, Field class EmotionTagsRequest(BaseModel): """获取情绪标签统计请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）") end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）") @@ -14,14 +14,14 @@ class EmotionTagsRequest(BaseModel): class EmotionWordcloudRequest(BaseModel): """获取情绪词云数据请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") limit: int = Field(50, ge=1, le=200, description="返回词语数量") class EmotionHealthRequest(BaseModel): """获取情绪健康指数请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") time_range: str = Field("30d", description="时间范围（7d/30d/90d）") @@ -29,7 +29,7 @@ class EmotionHealthRequest(BaseModel): class EmotionSuggestionsRequest(BaseModel): """获取个性化情绪建议请求""" - 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user_id: str, + end_user_id: str, confidence_threshold: float = Query(0.5, ge=0.0, le=1.0, description="Minimum confidence threshold"), tag_category: Optional[str] = Query(None, description="Filter by tag category"), start_date: Optional[datetime] = Query(None, description="Filter start date"), @@ -137,7 +137,7 @@ async def get_preference_tags( Get user preference tags from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID confidence_threshold: Minimum confidence score (0.0-1.0) tag_category: Optional category filter start_date: Optional start date filter @@ -146,20 +146,20 @@ async def get_preference_tags( Returns: List of preference tags from cache """ - api_logger.info(f"Preference tags requested for user: {user_id} (from cache)") + api_logger.info(f"Preference tags requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -192,17 +192,17 @@ async def get_preference_tags( filtered_preferences.append(pref) - api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {user_id} (from cache)") + api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {end_user_id} (from cache)") return success(data=filtered_preferences, msg="偏好标签获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "偏好标签获取", user_id) + return handle_implicit_memory_error(e, "偏好标签获取", end_user_id) -@router.get("/portrait/{user_id}", response_model=ApiResponse) +@router.get("/portrait/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_dimension_portrait( - user_id: str, + end_user_id: str, include_history: bool = Query(False, description="Include historical trends"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) @@ -211,26 +211,26 @@ async def get_dimension_portrait( Get user's four-dimension personality portrait from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID include_history: Whether to include historical trend data (ignored for cached data) Returns: Four-dimension personality portrait from cache """ - api_logger.info(f"Dimension portrait requested for user: {user_id} (from cache)") + api_logger.info(f"Dimension portrait requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -240,17 +240,17 @@ async def get_dimension_portrait( # Extract portrait from cache portrait = cached_profile.get("portrait", {}) - api_logger.info(f"Dimension portrait retrieved for user: {user_id} (from cache)") + api_logger.info(f"Dimension portrait retrieved for user: {end_user_id} (from cache)") return success(data=portrait, msg="四维画像获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "四维画像获取", user_id) + return handle_implicit_memory_error(e, "四维画像获取", end_user_id) -@router.get("/interest-areas/{user_id}", response_model=ApiResponse) +@router.get("/interest-areas/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_interest_area_distribution( - user_id: str, + end_user_id: str, include_trends: bool = Query(False, description="Include trend analysis"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) @@ -259,26 +259,26 @@ async def get_interest_area_distribution( Get user's interest area distribution from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID include_trends: Whether to include trend analysis data (ignored for cached data) Returns: Interest area distribution from cache """ - api_logger.info(f"Interest area distribution requested for user: {user_id} (from cache)") + api_logger.info(f"Interest area distribution requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -288,17 +288,17 @@ async def get_interest_area_distribution( # Extract interest areas from cache interest_areas = cached_profile.get("interest_areas", {}) - api_logger.info(f"Interest area distribution retrieved for user: {user_id} (from cache)") + api_logger.info(f"Interest area distribution retrieved for user: {end_user_id} (from cache)") return success(data=interest_areas, msg="兴趣领域分布获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "兴趣领域分布获取", user_id) + return handle_implicit_memory_error(e, "兴趣领域分布获取", end_user_id) -@router.get("/habits/{user_id}", response_model=ApiResponse) +@router.get("/habits/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_behavior_habits( - user_id: str, + end_user_id: str, confidence_level: Optional[str] = Query(None, regex="^(high|medium|low)$", description="Filter by confidence level"), frequency_pattern: Optional[str] = Query(None, regex="^(daily|weekly|monthly|seasonal|occasional|event_triggered)$", description="Filter by frequency pattern"), time_period: Optional[str] = Query(None, regex="^(current|past)$", description="Filter by time period"), @@ -309,7 +309,7 @@ async def get_behavior_habits( Get user's behavioral habits from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID confidence_level: Filter by confidence level (high, medium, low) frequency_pattern: Filter by frequency pattern (daily, weekly, monthly, seasonal, occasional, event_triggered) time_period: Filter by time period (current, past) @@ -317,20 +317,20 @@ async def get_behavior_habits( Returns: List of behavioral habits from cache """ - api_logger.info(f"Behavior habits requested for user: {user_id} (from cache)") + api_logger.info(f"Behavior habits requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -368,11 +368,11 @@ async def get_behavior_habits( filtered_habits.append(habit) - api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {user_id} (from cache)") + api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {end_user_id} (from cache)") return success(data=filtered_habits, msg="行为习惯获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "行为习惯获取", user_id) + return handle_implicit_memory_error(e, "行为习惯获取", end_user_id) diff --git a/api/app/controllers/memory_perceptual_controller.py b/api/app/controllers/memory_perceptual_controller.py index 5154c763..44750808 100644 --- a/api/app/controllers/memory_perceptual_controller.py +++ b/api/app/controllers/memory_perceptual_controller.py @@ -27,27 +27,27 @@ router = APIRouter( ) -@router.get("/{group_id}/count", response_model=ApiResponse) +@router.get("/{end_user_id}/count", response_model=ApiResponse) def get_memory_count( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve perceptual memory statistics for a user group. Args: - group_id: ID of the user group (usually end_user_id in this context) + end_user_id: ID of the user group (usually end_user_id in this context) current_user: Current authenticated user db: Database session Returns: ApiResponse: Response containing memory count statistics """ - api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - count_stats = service.get_memory_count(group_id) + count_stats = service.get_memory_count(end_user_id) api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}") @@ -57,37 +57,37 @@ def get_memory_count( ) except Exception as e: - api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch memory statistics: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch memory statistics", ) -@router.get("/{group_id}/last_visual", response_model=ApiResponse) +@router.get("/{end_user_id}/last_visual", response_model=ApiResponse) def get_last_visual_memory( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent VISION-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest visual memory """ - api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest visual memory: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - visual_memory = service.get_latest_visual_memory(group_id) + visual_memory = service.get_latest_visual_memory(end_user_id) if visual_memory is None: - api_logger.info(f"No visual memory found: group_id={group_id}") + api_logger.info(f"No visual memory found: end_user_id={end_user_id}") return success( data=None, msg="No visual memory available" @@ -101,37 +101,37 @@ def get_last_visual_memory( ) except Exception as e: - api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest visual memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest visual memory", ) -@router.get("/{group_id}/last_listen", response_model=ApiResponse) +@router.get("/{end_user_id}/last_listen", response_model=ApiResponse) def get_last_memory_listen( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent AUDIO-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest audio memory """ - api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest audio memory: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - audio_memory = service.get_latest_audio_memory(group_id) + audio_memory = service.get_latest_audio_memory(end_user_id) if audio_memory is None: - api_logger.info(f"No audio memory found: group_id={group_id}") + api_logger.info(f"No audio memory found: end_user_id={end_user_id}") return success( data=None, msg="No audio memory available" @@ -145,38 +145,38 @@ def get_last_memory_listen( ) except Exception as e: - api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest audio memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest audio memory", ) -@router.get("/{group_id}/last_text", response_model=ApiResponse) +@router.get("/{end_user_id}/last_text", response_model=ApiResponse) def get_last_text_memory( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent TEXT-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest text memory """ - api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest text memory: user={current_user.username}, end_user_id={end_user_id}") try: # 调用服务层获取最近的文本记忆 service = MemoryPerceptualService(db) - text_memory = service.get_latest_text_memory(group_id) + text_memory = service.get_latest_text_memory(end_user_id) if text_memory is None: - api_logger.info(f"No text memory found: group_id={group_id}") + api_logger.info(f"No text memory found: end_user_id={end_user_id}") return success( data=None, msg="No text memory available" @@ -190,16 +190,16 @@ def get_last_text_memory( ) except Exception as e: - api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest text memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest text memory", ) -@router.get("/{group_id}/timeline", response_model=ApiResponse) +@router.get("/{end_user_id}/timeline", response_model=ApiResponse) def get_memory_time_line( - group_id: uuid.UUID, + end_user_id: uuid.UUID, perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"), page: int = Query(1, ge=1, description="页码"), page_size: int = Query(10, ge=1, le=100, description="每页大小"), @@ -209,7 +209,7 @@ def get_memory_time_line( """Retrieve a timeline of perceptual memories for a user group. Args: - group_id: ID of the user group + end_user_id: ID of the user group perceptual_type: Optional filter for perceptual type page: Page number for pagination page_size: Number of items per page @@ -221,7 +221,7 @@ def get_memory_time_line( """ api_logger.info( f"Fetching perceptual memory timeline: user={current_user.username}, " - f"group_id={group_id}, type={perceptual_type}, page={page}" + f"end_user_id={end_user_id}, type={perceptual_type}, page={page}" ) try: @@ -232,7 +232,7 @@ def get_memory_time_line( ) service = MemoryPerceptualService(db) - timeline_data = service.get_time_line(group_id, query) + timeline_data = service.get_time_line(end_user_id, query) api_logger.info( f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, " @@ -246,7 +246,7 @@ def get_memory_time_line( except Exception as e: api_logger.error( - f"Failed to fetch perceptual memory timeline: group_id={group_id}, " + f"Failed to fetch perceptual memory timeline: end_user_id={end_user_id}, " f"error={str(e)}" ) return fail( diff --git a/api/app/controllers/memory_working_controller.py b/api/app/controllers/memory_working_controller.py index dfd64044..e5de3c04 100644 --- a/api/app/controllers/memory_working_controller.py +++ b/api/app/controllers/memory_working_controller.py @@ -20,18 +20,18 @@ router = APIRouter( ) -@router.get("/{group_id}/count", response_model=ApiResponse) +@router.get("/{end_user_id}/count", response_model=ApiResponse) def get_memory_count( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): pass -@router.get("/{group_id}/conversations", response_model=ApiResponse) +@router.get("/{end_user_id}/conversations", response_model=ApiResponse) def get_conversations( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -39,7 +39,7 @@ def get_conversations( Retrieve all conversations for the current user in a specific group. Args: - group_id (UUID): The group identifier. + end_user_id (UUID): The group identifier. current_user (User, optional): The authenticated user. db (Session, optional): SQLAlchemy session. @@ -53,7 +53,7 @@ def get_conversations( """ conversation_service = ConversationService(db) conversations = conversation_service.get_user_conversations( - group_id + end_user_id ) return success(data=[ { @@ -63,7 +63,7 @@ def get_conversations( ], msg="get conversations success") -@router.get("/{group_id}/messages", response_model=ApiResponse) +@router.get("/{end_user_id}/messages", response_model=ApiResponse) def get_messages( conversation_id: uuid.UUID, current_user: User = Depends(get_current_user), @@ -100,7 +100,7 @@ def get_messages( return success(data=messages, msg="get conversation history success") -@router.get("/{group_id}/detail", response_model=ApiResponse) +@router.get("/{end_user_id}/detail", response_model=ApiResponse) async def get_conversation_detail( conversation_id: uuid.UUID, current_user: User = Depends(get_current_user), diff --git a/api/app/models/memory_perceptual_model.py b/api/app/models/memory_perceptual_model.py index 59eb0222..cafb18d4 100644 --- a/api/app/models/memory_perceptual_model.py +++ b/api/app/models/memory_perceptual_model.py @@ -16,7 +16,7 @@ class PerceptualType(IntEnum): CONVERSATION = 4 -class FileStorageType(IntEnum): +class FileStorageService(IntEnum): LOCAL = 1 REMOTE = 2 diff --git a/api/app/repositories/memory_config_repository.py b/api/app/repositories/memory_config_repository.py index eb513c99..aca87189 100644 --- a/api/app/repositories/memory_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -41,48 +41,48 @@ class MemoryConfigRepository: # Dialogue count by group SEARCH_FOR_DIALOGUE = """ - MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # Chunk count by group SEARCH_FOR_CHUNK = """ - MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # Statement count by group SEARCH_FOR_STATEMENT = """ - MATCH (n:Statement) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # ExtractedEntity count by group SEARCH_FOR_ENTITY = """ - MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # All counts by label and total SEARCH_FOR_ALL = """ - OPTIONAL MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN 'Chunk' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN 'Chunk' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:Statement) WHERE n.group_id = $group_id RETURN 'Statement' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN 'Statement' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n) WHERE n.group_id = $group_id RETURN 'ALL' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n) WHERE n.end_user_id = $end_user_id RETURN 'ALL' AS Label, COUNT(n) AS Count """ # Extracted entity details within group/app/user SEARCH_FOR_DETIALS = """ MATCH (n:ExtractedEntity) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN n.entity_idx AS entity_idx, n.connect_strength AS connect_strength, n.description AS description, n.entity_type AS entity_type, n.name AS name, COALESCE(n.fact_summary, '') AS fact_summary, - n.group_id AS group_id, + n.end_user_id AS end_user_id, n.apply_id AS apply_id, n.user_id AS user_id, n.id AS id @@ -91,9 +91,9 @@ class MemoryConfigRepository: # Edges between extracted entities within group/app/user SEARCH_FOR_EDGES = """ MATCH (n:ExtractedEntity)-[r]->(m:ExtractedEntity) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN - r.group_id AS group_id, + r.end_user_id AS end_user_id, r.apply_id AS apply_id, r.user_id AS user_id, elementId(r) AS rel_id, diff --git a/api/app/repositories/memory_perceptual_repository.py b/api/app/repositories/memory_perceptual_repository.py index 8415c2d0..9fa9536e 100644 --- a/api/app/repositories/memory_perceptual_repository.py +++ b/api/app/repositories/memory_perceptual_repository.py @@ -6,7 +6,7 @@ from sqlalchemy import and_, desc from sqlalchemy.orm import Session from app.core.logging_config import get_db_logger -from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageType +from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageService from app.schemas.memory_perceptual_schema import PerceptualQuerySchema db_logger = get_db_logger() @@ -28,7 +28,7 @@ class MemoryPerceptualRepository: file_ext: str, summary: Optional[str] = None, meta_data: Optional[dict] = None, - storage_service: FileStorageType = FileStorageType.LOCAL + storage_service: FileStorageService = FileStorageService.LOCAL ) -> MemoryPerceptualModel: diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index eaef1e7a..c93e75b3 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -700,7 +700,7 @@ MATCH (ms:MemorySummary {id: e.summary_id, run_id: e.run_id}) MATCH (c:Chunk {id: e.chunk_id, run_id: e.run_id}) MATCH (c)-[:CONTAINS]->(s:Statement {run_id: e.run_id}) MERGE (ms)-[r:DERIVED_FROM_STATEMENT]->(s) -SET r.group_id = e.group_id, +SET r.end_user_id = e.end_user_id, r.run_id = e.run_id, r.created_at = e.created_at, r.expired_at = e.expired_at @@ -729,7 +729,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END | source_statement_id: rel.source_statement_id, valid_at: rel.valid_at, invalid_at: rel.invalid_at, - group_id: rel.group_id, + end_user_id: rel.end_user_id, user_id: rel.user_id, apply_id: rel.apply_id, run_id: rel.run_id, @@ -751,7 +751,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END | source_statement_id: rel.source_statement_id, valid_at: rel.valid_at, invalid_at: rel.invalid_at, - group_id: rel.group_id, + end_user_id: rel.end_user_id, user_id: rel.user_id, apply_id: rel.apply_id, run_id: rel.run_id, diff --git a/api/app/repositories/neo4j/dialog_repository.py b/api/app/repositories/neo4j/dialog_repository.py index 48376c2a..020e7346 100644 --- a/api/app/repositories/neo4j/dialog_repository.py +++ b/api/app/repositories/neo4j/dialog_repository.py @@ -180,6 +180,6 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): List[DialogueNode]: 对话列表 """ return await self.find( - {"config_id": config_id, "group_id": group_id}, + {"config_id": config_id, "end_user_id": end_user_id}, limit=limit ) diff --git a/api/app/repositories/neo4j/emotion_repository.py b/api/app/repositories/neo4j/emotion_repository.py index 7a8ebcf9..e39968ac 100644 --- a/api/app/repositories/neo4j/emotion_repository.py +++ b/api/app/repositories/neo4j/emotion_repository.py @@ -227,7 +227,7 @@ class EmotionRepository: try: results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, start_date=start_date ) formatted_results = [ diff --git a/api/app/repositories/neo4j/memory_summary_repository.py b/api/app/repositories/neo4j/memory_summary_repository.py index 2564aeab..81bf2cc9 100644 --- a/api/app/repositories/neo4j/memory_summary_repository.py +++ b/api/app/repositories/neo4j/memory_summary_repository.py @@ -233,7 +233,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ # Build keyword search conditions keyword_conditions = [] - params = {"group_id": group_id, "limit": limit} + params = {"end_user_id": group_id, "limit": limit} for i, keyword in enumerate(keywords): keyword_conditions.append(f"toLower(n.content) CONTAINS toLower($keyword_{i})") @@ -243,7 +243,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND ({keyword_filter}) RETURN n ORDER BY n.created_at DESC @@ -264,10 +264,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - results = await self.connector.execute_query(query, group_id=group_id) + results = await self.connector.execute_query(query, end_user_id=group_id) return results[0]['count'] if results else 0 \ No newline at end of file diff --git a/api/app/schemas/memory_perceptual_schema.py b/api/app/schemas/memory_perceptual_schema.py index 05e01d2a..e82d9526 100644 --- a/api/app/schemas/memory_perceptual_schema.py +++ b/api/app/schemas/memory_perceptual_schema.py @@ -4,7 +4,7 @@ from typing import Optional from pydantic import BaseModel, Field -from app.models.memory_perceptual_model import PerceptualType, FileStorageType +from app.models.memory_perceptual_model import PerceptualType, FileStorageService class PerceptualFilter(BaseModel): @@ -38,12 +38,14 @@ class PerceptualMemoryItem(BaseModel): """感知记忆项""" id: uuid.UUID = Field(..., description="Unique memory ID") perceptual_type: PerceptualType = Field(..., description="Type of perception, e.g., text, audio, or video") + storage_service: FileStorageService = Field(..., description="Storage service for file") file_path: str = Field(..., description="File path in the storage service") - file_ext: str = Field(..., description="File extension") file_name: str = Field(..., description="File name") + file_ext: str = Field(..., description="File extension") summary: Optional[str] = Field(None, description="summary") - storage_type: FileStorageType = Field(..., description="Storage type for file") + meta_data: str = Field(...,description="") created_time: int = Field(..., description="create time") + topic: str = Field(..., description="topic") domain: str = Field(..., description="domain") keywords: list[str] = Field(..., description="keywords") diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index d17a9f2c..80b62b8a 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -1,5 +1,5 @@ """ -所有的内容是放错误地方了，应该放在models + """ from typing import Any, Optional, List, Dict, Literal, Union diff --git a/api/app/services/memory_perceptual_service.py b/api/app/services/memory_perceptual_service.py index d257e80f..29b45474 100644 --- a/api/app/services/memory_perceptual_service.py +++ b/api/app/services/memory_perceptual_service.py @@ -6,7 +6,7 @@ from sqlalchemy.orm import Session from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger -from app.models.memory_perceptual_model import PerceptualType, FileStorageType +from app.models.memory_perceptual_model import PerceptualType, FileStorageService from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository from app.schemas.memory_perceptual_schema import ( PerceptualQuerySchema, @@ -92,15 +92,17 @@ class MemoryPerceptualService: result = { "id": str(memory.id), + "perceptual_type": perceptual_type, "file_name": memory.file_name, "file_path": memory.file_path, - "storage_type": memory.storage_service, + "file_ext": memory.file_ext, + "storage_service": memory.storage_service, + "meta_data": memory.meta_data, "summary": memory.summary, "keywords": content.keywords, "topic": content.topic, "domain": content.domain, "created_time": int(memory.created_time.timestamp()*1000), - **detail } business_logger.info( @@ -150,7 +152,7 @@ class MemoryPerceptualService: domain=content.domain, keywords=content.keywords, created_time=int(memory.created_time.timestamp()*1000), - storage_type=FileStorageType(memory.storage_service), + storage_service=FileStorageService(memory.storage_service), ) memory_items.append(memory_item) From a2a69840f7889b3fc74fa1e3442d19303bd34f82 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 16:38:24 +0800 Subject: [PATCH 035/175] =?UTF-8?q?config=5Fconfig=E6=9B=BF=E6=8D=A2?= =?UTF-8?q?=E6=88=90memory=5Fconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 509ed815..f8861258 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1207,6 +1207,16 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An # 3. 从 config 中提取 memory_config_id config = latest_release.config or {} + + # 如果 config 是字符串，解析为字典 + if isinstance(config, str): + import json + try: + config = json.loads(config) + except json.JSONDecodeError: + logger.warning(f"Failed to parse config JSON for release {latest_release.id}") + config = {} + memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None From f3f9211c9cd4740e211a92a5cb9c40ca5a65b11c Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 16:59:40 +0800 Subject: [PATCH 036/175] 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.../forgetting_engine/forgetting_scheduler.py | 3 +- .../forgetting_engine/forgetting_strategy.py | 7 +- .../validators/memory_config_validators.py | 10 +- api/app/core/workflow/nodes/memory/config.py | 5 +- api/app/models/memory_config_model.py | 2 +- .../repositories/memory_config_repository.py | 16 ++- api/app/schemas/emotion_schema.py | 3 +- api/app/schemas/memory_agent_schema.py | 3 + api/app/schemas/memory_config_schema.py | 20 +-- api/app/schemas/memory_reflection_schemas.py | 3 +- api/app/schemas/memory_storage_schema.py | 20 +-- api/app/services/emotion_config_service.py | 6 +- api/app/services/memory_agent_service.py | 130 +++++++----------- api/app/services/memory_config_service.py | 81 ++++------- api/app/services/memory_forget_service.py | 13 +- api/app/tasks.py | 7 +- 21 files changed, 158 insertions(+), 201 deletions(-) diff --git a/api/app/controllers/emotion_config_controller.py b/api/app/controllers/emotion_config_controller.py index 76450d8a..b0015bc2 100644 --- a/api/app/controllers/emotion_config_controller.py +++ b/api/app/controllers/emotion_config_controller.py @@ -12,6 +12,7 @@ from fastapi import APIRouter, Depends, Query, HTTPException, status from pydantic import BaseModel, Field from typing import Optional from sqlalchemy.orm import Session +from uuid import UUID from app.core.response_utils import success from app.dependencies import get_current_user @@ -32,11 +33,11 @@ router = APIRouter( class EmotionConfigQuery(BaseModel): """情绪配置查询请求模型""" - config_id: int = Field(..., description="配置ID") + config_id: UUID = Field(..., description="配置ID") class EmotionConfigUpdate(BaseModel): """情绪配置更新请求模型""" - config_id: int = Field(..., description="配置ID") + config_id: UUID = Field(..., description="配置ID") emotion_enabled: bool = Field(..., description="是否启用情绪提取") emotion_model_id: Optional[str] = Field(None, description="情绪分析专用模型ID") emotion_extract_keywords: bool = Field(..., description="是否提取情绪关键词") @@ -45,7 +46,7 @@ class EmotionConfigUpdate(BaseModel): @router.get("/read_config", response_model=ApiResponse) def get_emotion_config( - config_id: int = Query(..., description="配置ID"), + config_id: UUID = Query(..., description="配置ID"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ): diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index ca628d0c..a71de487 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -11,6 +11,7 @@ """ from typing import Optional +from uuid import UUID from fastapi import APIRouter, Depends from sqlalchemy.orm import Session @@ -128,7 +129,7 @@ async def trigger_forgetting_cycle( @router.get("/read_config", response_model=ApiResponse) async def read_forgetting_config( - config_id: int, + config_id: UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index f17fcf7f..ccf9485f 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -1,6 +1,7 @@ import asyncio import time import uuid +from uuid import UUID from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine.self_reflexion import ( @@ -156,7 +157,7 @@ async def start_workspace_reflection( @router.get("/reflection/configs") async def start_reflection_configs( - config_id: int, + config_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -191,7 +192,7 @@ async def start_reflection_configs( @router.get("/reflection/run") async def reflection_run( - config_id: int, + config_id: UUID, language_type: str = Header(default="zh", alias="X-Language-Type"), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), diff --git a/api/app/controllers/memory_storage_controller.py b/api/app/controllers/memory_storage_controller.py index f4175923..15dbc2ec 100644 --- a/api/app/controllers/memory_storage_controller.py +++ b/api/app/controllers/memory_storage_controller.py @@ -1,5 +1,6 @@ import os from typing import Optional +from uuid import UUID from app.core.error_codes import BizCode from app.core.logging_config import get_api_logger @@ -160,7 +161,7 @@ def create_config( @router.delete("/delete_config", response_model=ApiResponse) # 删除数据库中的内容（按配置名称） def delete_config( - config_id: str, + config_id: UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -232,7 +233,7 @@ def update_config_extracted( @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除 def read_config_extracted( - config_id: str, + config_id: UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: diff --git a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py index 663c89f9..25daa968 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py +++ b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py @@ -11,6 +11,7 @@ Functions: import logging from typing import Optional, Dict, Any +from uuid import UUID from sqlalchemy.orm import Session from app.repositories.memory_config_repository import MemoryConfigRepository @@ -61,7 +62,7 @@ def calculate_forgetting_rate(lambda_time: float, lambda_mem: float) -> float: def load_actr_config_from_db( db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 从数据库加载 ACT-R 配置参数 @@ -150,7 +151,7 @@ def load_actr_config_from_db( def create_actr_calculator_from_config( db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> ACTRCalculator: """ 从数据库配置创建 ACTRCalculator 实例 @@ -168,11 +169,6 @@ def create_actr_calculator_from_config( ValueError: 如果指定的 config_id 不存在 Examples: - >>> from sqlalchemy.orm import Session - >>> db = Session() - >>> calculator = create_actr_calculator_from_config(db, config_id=1) - >>> # 使用计算器 - >>> activation = calculator.calculate_memory_activation(...) """ # 加载配置 config = load_actr_config_from_db(db, config_id) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py index e9d4c144..5a178fc2 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py @@ -16,6 +16,7 @@ Classes: import logging from typing import Dict, Any, Optional +from uuid import UUID from datetime import datetime from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy @@ -69,7 +70,7 @@ class ForgettingScheduler: end_user_id: Optional[str] = None, max_merge_batch_size: int = 100, min_days_since_access: int = 30, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> Dict[str, Any]: """ diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py index cde9e115..a8c62dd4 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py @@ -13,6 +13,7 @@ Classes: import logging from typing import List, Dict, Any, Optional +from uuid import UUID from datetime import datetime, timedelta from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -176,7 +177,7 @@ class ForgettingStrategy: self, statement_node: Dict[str, Any], entity_node: Dict[str, Any], - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> str: """ @@ -462,7 +463,7 @@ class ForgettingStrategy: statement_text: str, entity_name: str, entity_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> str: """ @@ -527,7 +528,7 @@ class ForgettingStrategy: statement_text, entity_name, entity_type ) - async def _get_llm_client(self, db, config_id: int): + async def _get_llm_client(self, db, config_id: UUID): """ 从数据库获取 LLM 客户端 diff --git a/api/app/core/validators/memory_config_validators.py b/api/app/core/validators/memory_config_validators.py index 333572e6..ba26c5f2 100644 --- a/api/app/core/validators/memory_config_validators.py +++ b/api/app/core/validators/memory_config_validators.py @@ -26,7 +26,7 @@ logger = get_config_logger() def _parse_model_id(model_id: Union[str, UUID, None], model_type: str, - config_id: Optional[int] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]: + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]: """Parse model ID from string or UUID.""" if model_id is None: return None @@ -59,7 +59,7 @@ def validate_model_exists_and_active( model_type: str, db: Session, tenant_id: Optional[UUID] = None, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None ) -> tuple[str, bool]: """Validate that a model exists and is active. @@ -166,7 +166,7 @@ def validate_and_resolve_model_id( db: Session, tenant_id: Optional[UUID] = None, required: bool = False, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None ) -> tuple[Optional[UUID], Optional[str]]: """Validate and resolve a model ID, checking existence and active status. @@ -204,7 +204,7 @@ def validate_and_resolve_model_id( def validate_embedding_model( - config_id: int, + config_id: UUID, embedding_id: Union[str, UUID, None], db: Session, tenant_id: Optional[UUID] = None, @@ -256,7 +256,7 @@ def validate_embedding_model( def validate_llm_model( - config_id: int, + config_id: UUID, llm_id: Union[str, UUID, None], db: Session, tenant_id: Optional[UUID] = None, diff --git a/api/app/core/workflow/nodes/memory/config.py b/api/app/core/workflow/nodes/memory/config.py index 987230c1..4c8c43eb 100644 --- a/api/app/core/workflow/nodes/memory/config.py +++ b/api/app/core/workflow/nodes/memory/config.py @@ -1,4 +1,5 @@ import uuid +from uuid import UUID from pydantic import Field from typing import Literal @@ -11,7 +12,7 @@ class MemoryReadNodeConfig(BaseNodeConfig): ... ) - config_id: int = Field( + config_id: UUID = Field( ... ) @@ -26,6 +27,6 @@ class MemoryWriteNodeConfig(BaseNodeConfig): ... ) - config_id: int = Field( + config_id: UUID = Field( ... ) diff --git a/api/app/models/memory_config_model.py b/api/app/models/memory_config_model.py index 55b377e6..710315db 100644 --- a/api/app/models/memory_config_model.py +++ b/api/app/models/memory_config_model.py @@ -9,7 +9,7 @@ class MemoryConfig(Base): __tablename__ = "memory_config" # 主键 - config_id = Column(Integer, primary_key=True, autoincrement=True, comment="配置ID") + config_id = Column(UUID(as_uuid=True), primary_key=True, comment="配置ID") # 基本信息 config_name = Column(String, nullable=False, comment="配置名称") diff --git a/api/app/repositories/memory_config_repository.py b/api/app/repositories/memory_config_repository.py index aca87189..12e564e2 100644 --- a/api/app/repositories/memory_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -9,6 +9,7 @@ Classes: """ import uuid +from uuid import UUID from typing import Dict, List, Optional, Tuple from app.core.exceptions import BusinessException from app.core.logging_config import get_config_logger, get_db_logger @@ -107,7 +108,7 @@ class MemoryConfigRepository: @staticmethod def update_reflection_config( db: Session, - config_id: int, + config_id: uuid.UUID, enable_self_reflexion: bool, iteration_period: str, reflexion_range: str, @@ -151,7 +152,7 @@ class MemoryConfigRepository: return memory_config_obj @staticmethod - def query_reflection_config_by_id(db: Session, config_id: int) -> MemoryConfig: + def query_reflection_config_by_id(db: Session, config_id: uuid.UUID) -> MemoryConfig: """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数） Args: @@ -222,6 +223,7 @@ class MemoryConfigRepository: try: db_config = MemoryConfig( + config_id=uuid.uuid4(), config_name=params.config_name, config_desc=params.config_desc, workspace_id=params.workspace_id, @@ -408,7 +410,7 @@ class MemoryConfigRepository: raise @staticmethod - def get_extracted_config(db: Session, config_id: int) -> Optional[Dict]: + def get_extracted_config(db: Session, config_id: UUID) -> Optional[Dict]: """获取萃取配置，通过主键查询某条配置 Args: @@ -457,7 +459,7 @@ class MemoryConfigRepository: raise @staticmethod - def get_forget_config(db: Session, config_id: int) -> Optional[Dict]: + def get_forget_config(db: Session, config_id: UUID) -> Optional[Dict]: """获取遗忘配置，通过主键查询某条配置 Args: @@ -489,7 +491,7 @@ class MemoryConfigRepository: raise @staticmethod - def get_by_id(db: Session, config_id: int) -> Optional[MemoryConfig]: + def get_by_id(db: Session, config_id: uuid.UUID) -> Optional[MemoryConfig]: """根据ID获取记忆配置 Args: @@ -513,7 +515,7 @@ class MemoryConfigRepository: db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}") raise @staticmethod - def get_config_with_workspace(db: Session, config_id: int) -> Optional[tuple]: + def get_config_with_workspace(db: Session, config_id: uuid.UUID) -> Optional[tuple]: """Get memory config and its associated workspace information Args: @@ -664,7 +666,7 @@ class MemoryConfigRepository: raise @staticmethod - def delete(db: Session, config_id: int) -> bool: + def delete(db: Session, config_id: uuid.UUID) -> bool: """删除记忆配置 Args: diff --git a/api/app/schemas/emotion_schema.py b/api/app/schemas/emotion_schema.py index fb523887..13c802b5 100644 --- a/api/app/schemas/emotion_schema.py +++ b/api/app/schemas/emotion_schema.py @@ -1,6 +1,7 @@ """情绪分析相关的请求和响应模型""" from typing import Optional +from uuid import UUID from pydantic import BaseModel, Field class EmotionTagsRequest(BaseModel): @@ -30,7 +31,7 @@ class EmotionHealthRequest(BaseModel): class EmotionSuggestionsRequest(BaseModel): """获取个性化情绪建议请求""" end_user_id: str = Field(..., description="组ID") - config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）") + config_id: Optional[UUID] = Field(None, description="配置ID（用于指定LLM模型）") class EmotionGenerateSuggestionsRequest(BaseModel): diff --git a/api/app/schemas/memory_agent_schema.py b/api/app/schemas/memory_agent_schema.py index e7b1fe65..a83bd3af 100644 --- a/api/app/schemas/memory_agent_schema.py +++ b/api/app/schemas/memory_agent_schema.py @@ -15,3 +15,6 @@ class Write_UserInput(BaseModel): messages: list[dict] end_user_id: str config_id: Optional[str] = None +class End_User_Information(BaseModel): + end_user_name: str # 这是要更新的用户名 + id: str # 宿主ID，用于匹配条件 \ No newline at end of file diff --git a/api/app/schemas/memory_config_schema.py b/api/app/schemas/memory_config_schema.py index 0443dcc4..76acee5c 100644 --- a/api/app/schemas/memory_config_schema.py +++ b/api/app/schemas/memory_config_schema.py @@ -35,7 +35,7 @@ class ConfigurationError(Exception): def __init__( self, message: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, context: Optional[Dict[str, Any]] = None, ): @@ -72,7 +72,7 @@ class WorkspaceNotFoundError(ConfigurationError): def __init__( self, workspace_id: UUID, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, message: Optional[str] = None, ): if message is None: @@ -89,7 +89,7 @@ class ModelNotFoundError(ConfigurationError): self, model_id: Union[str, UUID], model_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, message: Optional[str] = None, ): @@ -112,7 +112,7 @@ class ModelInactiveError(ConfigurationError): model_id: Union[str, UUID], model_name: str, model_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, message: Optional[str] = None, ): @@ -136,7 +136,7 @@ class InvalidConfigError(ConfigurationError): message: str, field_name: Optional[str] = None, invalid_value: Optional[Any] = None, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, ): context = {} @@ -155,7 +155,7 @@ class InvalidConfigError(ConfigurationError): class MemoryConfigValidation(BaseModel): """Pydantic model for validating memory configuration data from database.""" - config_id: int = Field(..., gt=0, description="Configuration ID must be positive") + config_id: UUID = Field(..., description="Configuration ID (UUID)") config_name: str = Field(..., min_length=1, max_length=255) workspace_id: UUID = Field(..., description="Workspace UUID") workspace_name: str = Field(..., min_length=1, max_length=255) @@ -275,7 +275,7 @@ class ModelValidation(BaseModel): def validate_memory_config_data( - config_data: Dict[str, Any], config_id: Optional[int] = None + config_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> MemoryConfigValidation: """Validate memory configuration data using Pydantic model.""" try: @@ -302,7 +302,7 @@ def validate_memory_config_data( def validate_workspace_data( - workspace_data: Dict[str, Any], config_id: Optional[int] = None + workspace_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> WorkspaceValidation: """Validate workspace data using Pydantic model.""" try: @@ -331,7 +331,7 @@ def validate_workspace_data( def validate_model_data( - model_data: Dict[str, Any], config_id: Optional[int] = None + model_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> ModelValidation: """Validate model data using Pydantic model.""" try: @@ -364,7 +364,7 @@ def validate_model_data( class MemoryConfig: """Immutable memory configuration loaded from database.""" - config_id: int + config_id: UUID config_name: str workspace_id: UUID workspace_name: str diff --git a/api/app/schemas/memory_reflection_schemas.py b/api/app/schemas/memory_reflection_schemas.py index 860f1ef1..df841fb1 100644 --- a/api/app/schemas/memory_reflection_schemas.py +++ b/api/app/schemas/memory_reflection_schemas.py @@ -1,5 +1,6 @@ from pydantic import BaseModel, Field from typing import Optional +from uuid import UUID from enum import Enum @@ -9,7 +10,7 @@ class OptimizationStrategy(str, Enum): ACCURACY_FIRST = "accuracy_first" BALANCED = "balanced" class Memory_Reflection(BaseModel): - config_id: Optional[int] = None + config_id: Optional[UUID] = None reflection_enabled: bool reflection_period_in_hours: str reflexion_range: Optional[str] = "partial" diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index 80b62b8a..e5a4cde6 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -159,7 +159,7 @@ class ReflexionResultSchema(BaseModel): # Composite key identifying a config row class ConfigKey(BaseModel): # 配置参数键模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field("config_id", description="配置唯一标识（字符串）") + config_id: uuid.UUID = Field("config_id", description="配置唯一标识（UUID）") user_id: str = Field("user_id", description="用户标识（字符串）") apply_id: str = Field("apply_id", description="应用或场景标识（字符串）") @@ -250,17 +250,17 @@ class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body， class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体） model_config = ConfigDict(populate_by_name=True, extra="forbid") # config_name: str = Field("配置名称", description="配置名称（字符串）") - config_id: int = Field("配置ID", description="配置ID（字符串）") + config_id: uuid.UUID = Field("配置ID", description="配置ID（UUID）") class ConfigUpdate(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型 - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None config_name: str = Field("配置名称", description="配置名称（字符串）") config_desc: str = Field("配置描述", description="配置描述（字符串）") class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型 - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None llm_id: Optional[str] = Field(None, description="LLM模型配置ID") embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID") rerank_id: Optional[str] = Field(None, description="重排序模型配置ID") @@ -327,14 +327,14 @@ class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数 class ConfigUpdateForget(BaseModel): # 更新遗忘引擎配置参数时使用的模型 # 遗忘引擎配置参数更新模型 - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5") lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5") offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0") class ConfigPilotRun(BaseModel): # 试运行触发请求模型 - config_id: int = Field(..., description="配置ID（唯一）") + config_id: uuid.UUID = Field(..., description="配置ID（唯一）") dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填") model_config = ConfigDict(populate_by_name=True, extra="forbid") @@ -342,7 +342,7 @@ class ConfigPilotRun(BaseModel): # 试运行触发请求模型 class ConfigFilter(BaseModel): # 查询配置参数时使用的模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None user_id: Optional[str] = None apply_id: Optional[str] = None @@ -418,7 +418,7 @@ class ForgettingConfigResponse(BaseModel): """遗忘引擎配置响应模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field(..., description="配置ID") + config_id: uuid.UUID = Field(..., description="配置ID") decay_constant: float = Field(..., description="衰减常数 d") lambda_time: float = Field(..., description="时间衰减参数") lambda_mem: float = Field(..., description="记忆衰减参数") @@ -436,7 +436,7 @@ class ForgettingConfigUpdateRequest(BaseModel): """遗忘引擎配置更新请求模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field(..., description="配置ID") + config_id: uuid.UUID = Field(..., description="配置ID") decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d") lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数") lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数") @@ -511,7 +511,7 @@ class ForgettingCurveRequest(BaseModel): importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）") days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）") - config_id: Optional[int] = Field(None, description="配置ID（可选，如果为None则使用默认配置）") + config_id: Optional[uuid.UUID] = Field(None, description="配置ID（可选，如果为None则使用默认配置）") class ForgettingCurveResponse(BaseModel): diff --git a/api/app/services/emotion_config_service.py b/api/app/services/emotion_config_service.py index f8b4d22a..9880d4e1 100644 --- a/api/app/services/emotion_config_service.py +++ b/api/app/services/emotion_config_service.py @@ -8,6 +8,8 @@ Classes: """ from typing import Dict, Any +from uuid import UUID + from sqlalchemy.orm import Session from app.models.memory_config_model import MemoryConfig @@ -37,7 +39,7 @@ class EmotionConfigService: self.db = db logger.info("情绪配置服务初始化完成") - def get_emotion_config(self, config_id: int) -> Dict[str, Any]: + def get_emotion_config(self, config_id: UUID) -> Dict[str, Any]: """获取情绪引擎配置 查询指定配置ID的情绪相关配置字段。 @@ -144,7 +146,7 @@ class EmotionConfigService: def update_emotion_config( self, - config_id: int, + config_id: UUID, config_data: Dict[str, Any] ) -> Dict[str, Any]: """更新情绪引擎配置 diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index f8861258..e4ab7087 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -9,6 +9,7 @@ import os import re import time import uuid +from uuid import UUID from typing import Any, AsyncGenerator, Dict, List, Optional import redis @@ -266,7 +267,7 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, end_user_id: str, messages: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID], db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id @@ -319,85 +320,52 @@ class MemoryAgentService: raise ValueError(error_msg) - async with make_write_graph() as graph: - config = {"configurable": {"thread_id": end_user_id}} - # Convert structured messages to LangChain messages - langchain_messages = [] - for msg in messages: - if msg['role'] == 'user': - langchain_messages.append(HumanMessage(content=msg['content'])) - elif msg['role'] == 'assistant': - langchain_messages.append(AIMessage(content=msg['content'])) + try: + if storage_type == "rag": + # For RAG storage, convert messages to single string + message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) + result = await write_rag(end_user_id, message_text, user_rag_memory_id) + return result + else: + async with make_write_graph() as graph: + config = {"configurable": {"thread_id": end_user_id}} + # Convert structured messages to LangChain messages + langchain_messages = [] + for msg in messages: + if msg['role'] == 'user': + langchain_messages.append(HumanMessage(content=msg['content'])) + elif msg['role'] == 'assistant': + langchain_messages.append(AIMessage(content=msg['content'])) - # 初始状态 - 包含所有必要字段 - initial_state = { - "messages": langchain_messages, - "end_user_id": end_user_id, - "memory_config": memory_config - } + # 初始状态 - 包含所有必要字段 + initial_state = { + "messages": langchain_messages, + "end_user_id": end_user_id, + "memory_config": memory_config + } - # 获取节点更新信息 - async for update_event in graph.astream( - initial_state, - stream_mode="updates", - config=config - ): - for node_name, node_data in update_event.items(): - if 'save_neo4j' == node_name: - massages = node_data - print(massages) - massagesstatus = massages.get('write_result')['status'] - contents = massages.get('write_result') - # Convert messages back to string for logging - message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) - - # try: - # if storage_type == "rag": - # # For RAG storage, convert messages to single string - # message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - # result = await write_rag(end_user_id, message_text, user_rag_memory_id) - # return result - # else: - # async with make_write_graph() as graph: - # config = {"configurable": {"thread_id": end_user_id}} - # # Convert structured messages to LangChain messages - # langchain_messages = [] - # for msg in messages: - # if msg['role'] == 'user': - # langchain_messages.append(HumanMessage(content=msg['content'])) - # elif msg['role'] == 'assistant': - # langchain_messages.append(AIMessage(content=msg['content'])) - # - # # 初始状态 - 包含所有必要字段 - # initial_state = { - # "messages": langchain_messages, - # "end_user_id": end_user_id, - # "memory_config": memory_config - # } - # - # # 获取节点更新信息 - # async for update_event in graph.astream( - # initial_state, - # stream_mode="updates", - # config=config - # ): - # for node_name, node_data in update_event.items(): - # if 'save_neo4j' == node_name: - # massages = node_data - # massagesstatus = massages.get('write_result')['status'] - # contents = massages.get('write_result') - # # Convert messages back to string for logging - # message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - # return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) - # except Exception as e: - # # Ensure proper error handling and logging - # error_msg = f"Write operation failed: {str(e)}" - # logger.error(error_msg) - # if audit_logger: - # duration = time.time() - start_time - # audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) - # raise ValueError(error_msg) + # 获取节点更新信息 + async for update_event in graph.astream( + initial_state, + stream_mode="updates", + config=config + ): + for node_name, node_data in update_event.items(): + if 'save_neo4j' == node_name: + massages = node_data + massagesstatus = massages.get('write_result')['status'] + contents = massages.get('write_result') + # Convert messages back to string for logging + message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) + return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) + except Exception as e: + # Ensure proper error handling and logging + error_msg = f"Write operation failed: {str(e)}" + logger.error(error_msg) + if audit_logger: + duration = time.time() - start_time + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) + raise ValueError(error_msg) @@ -408,7 +376,7 @@ class MemoryAgentService: message: str, history: List[Dict], search_switch: str, - config_id: Optional[str], + config_id: Optional[UUID], db: Session, storage_type: str, user_rag_memory_id: str) -> Dict: @@ -685,7 +653,7 @@ class MemoryAgentService: logger.info(f"Validation successful: Structured message list, count: {len(user_input.messages)}") return user_input.messages - async def classify_message_type(self, message: str, config_id: int, db: Session) -> Dict: + async def classify_message_type(self, message: str, config_id: UUID, db: Session) -> Dict: """ Determine the type of user message (read or write) Updated to eliminate global variables in favor of explicit parameters. @@ -716,7 +684,7 @@ class MemoryAgentService: retrieve_info: str, history: List[Dict], query: str, - config_id: str, + config_id: UUID, db: Session ) -> str: """ diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index 9afba797..d7f7c8a6 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -23,53 +23,12 @@ from app.schemas.memory_config_schema import ( ModelNotFoundError, ) from sqlalchemy.orm import Session +from uuid import UUID logger = get_logger(__name__) config_logger = get_config_logger() -def _validate_config_id(config_id): - """Validate configuration ID format.""" - if config_id is None: - raise InvalidConfigError( - "Configuration ID cannot be None", - field_name="config_id", - invalid_value=config_id, - ) - - if isinstance(config_id, int): - if config_id <= 0: - raise InvalidConfigError( - f"Configuration ID must be positive: {config_id}", - field_name="config_id", - invalid_value=config_id, - ) - return config_id - - if isinstance(config_id, str): - try: - parsed_id = int(config_id.strip()) - if parsed_id <= 0: - raise InvalidConfigError( - f"Configuration ID must be positive: {parsed_id}", - field_name="config_id", - invalid_value=config_id, - ) - return parsed_id - except ValueError: - raise InvalidConfigError( - f"Invalid configuration ID format: '{config_id}'", - field_name="config_id", - invalid_value=config_id, - ) - - raise InvalidConfigError( - f"Invalid type for configuration ID: expected int or str, got {type(config_id).__name__}", - field_name="config_id", - invalid_value=config_id, - ) - - class MemoryConfigService: """ Centralized service for memory configuration loading and validation. @@ -93,14 +52,14 @@ class MemoryConfigService: def load_memory_config( self, - config_id: int, + config_id: UUID, service_name: str = "MemoryConfigService", ) -> MemoryConfig: """ Load memory configuration from database by config_id. Args: - config_id: Configuration ID from database + config_id: Configuration ID (UUID) from database service_name: Name of the calling service (for logging purposes) Returns: @@ -116,18 +75,34 @@ class MemoryConfigService: extra={ "operation": "load_memory_config", "service": service_name, - "config_id": config_id, + "config_id": str(config_id), }, ) logger.info(f"Loading memory configuration from database: config_id={config_id}") try: - validated_config_id = _validate_config_id(config_id) + # Validate config_id is UUID + if not isinstance(config_id, UUID): + if isinstance(config_id, str): + try: + config_id = UUID(config_id) + except ValueError: + raise InvalidConfigError( + f"Invalid UUID format for config_id: {config_id}", + field_name="config_id", + invalid_value=config_id, + ) + else: + raise InvalidConfigError( + f"config_id must be UUID or valid UUID string, got {type(config_id).__name__}", + field_name="config_id", + invalid_value=config_id, + ) # Step 1: Get config and workspace db_query_start = time.time() - result = MemoryConfigRepository.get_config_with_workspace(self.db, validated_config_id) + result = MemoryConfigRepository.get_config_with_workspace(self.db, config_id) db_query_time = time.time() - db_query_start logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s") if not result: @@ -136,14 +111,14 @@ class MemoryConfigService: "Configuration not found in database", extra={ "operation": "load_memory_config", - "config_id": validated_config_id, + "config_id": str(config_id), "load_result": "not_found", "elapsed_ms": elapsed_ms, "service": service_name, }, ) raise ConfigurationError( - f"Configuration {validated_config_id} not found in database" + f"Configuration {config_id} not found in database" ) memory_config, workspace = result @@ -151,7 +126,7 @@ class MemoryConfigService: # Step 2: Validate embedding model (returns both UUID and name) embed_start = time.time() embedding_uuid, embedding_name = validate_embedding_model( - validated_config_id, + config_id, memory_config.embedding_id, self.db, workspace.tenant_id, @@ -168,7 +143,7 @@ class MemoryConfigService: self.db, workspace.tenant_id, required=True, - config_id=validated_config_id, + config_id=config_id, workspace_id=workspace.id, ) llm_time = time.time() - llm_start @@ -185,7 +160,7 @@ class MemoryConfigService: self.db, workspace.tenant_id, required=False, - config_id=validated_config_id, + config_id=config_id, workspace_id=workspace.id, ) rerank_time = time.time() - rerank_start @@ -243,7 +218,7 @@ class MemoryConfigService: extra={ "operation": "load_memory_config", "service": service_name, - "config_id": validated_config_id, + "config_id": str(config_id), "config_name": config.config_name, "workspace_id": str(config.workspace_id), "load_result": "success", diff --git a/api/app/services/memory_forget_service.py b/api/app/services/memory_forget_service.py index 204f5df1..e1030b24 100644 --- a/api/app/services/memory_forget_service.py +++ b/api/app/services/memory_forget_service.py @@ -12,6 +12,7 @@ from typing import Optional, Dict, Any, Tuple from datetime import datetime, timezone +from uuid import UUID from sqlalchemy.orm import Session @@ -87,7 +88,7 @@ class MemoryForgetService: async def _get_forgetting_components( self, db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Tuple[ACTRCalculator, ForgettingStrategy, ForgettingScheduler, Dict[str, Any]]: """ 获取遗忘引擎组件（计算器、策略、调度器） @@ -294,7 +295,7 @@ class MemoryForgetService: end_user_id: str, max_merge_batch_size: Optional[int] = None, min_days_since_access: Optional[int] = None, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 手动触发遗忘周期 @@ -389,7 +390,7 @@ class MemoryForgetService: def read_forgetting_config( self, db: Session, - config_id: int + config_id: UUID ) -> Dict[str, Any]: """ 获取遗忘引擎配置 @@ -416,7 +417,7 @@ class MemoryForgetService: def update_forgetting_config( self, db: Session, - config_id: int, + config_id: UUID, update_fields: Dict[str, Any] ) -> Dict[str, Any]: """ @@ -466,7 +467,7 @@ class MemoryForgetService: self, db: Session, end_user_id: Optional[str] = None, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 获取遗忘引擎统计信息 @@ -677,7 +678,7 @@ class MemoryForgetService: db: Session, importance_score: float, days: int, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 获取遗忘曲线数据 diff --git a/api/app/tasks.py b/api/app/tasks.py index f4b5f78f..3ef2653a 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -4,6 +4,7 @@ import os import re import time import uuid +from uuid import UUID from datetime import datetime, timezone from math import ceil from typing import Any, Dict, List, Optional @@ -382,7 +383,7 @@ def build_graphrag_for_kb(kb_id: uuid.UUID): @celery_app.task(name="app.core.memory.agent.read_message", bind=True) -def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: +def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: uuid.UUID, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a read message via MemoryAgentService. @@ -472,7 +473,7 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s @celery_app.task(name="app.core.memory.agent.write_message", bind=True) -def write_message_task(self, end_user_id: str, message: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: +def write_message_task(self, end_user_id: str, message: str, config_id: uuid.UUID, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a write message via MemoryAgentService. Args: @@ -1084,7 +1085,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: @celery_app.task(name="app.tasks.run_forgetting_cycle_task", bind=True) -def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str, Any]: +def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Dict[str, Any]: """定时任务：运行遗忘周期 定期执行遗忘周期，识别并融合低激活值的知识节点。 From b84c82880c8caf3ec1b9167050e9920cc76d1480 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 18:45:26 +0800 Subject: [PATCH 038/175] =?UTF-8?q?config=5Fid=E5=AD=97=E6=AE=B5=E6=94=B9?= =?UTF-8?q?=E6=88=90UUID?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/memory_agent_schema.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/api/app/schemas/memory_agent_schema.py b/api/app/schemas/memory_agent_schema.py index a83bd3af..b6f50dd7 100644 --- a/api/app/schemas/memory_agent_schema.py +++ b/api/app/schemas/memory_agent_schema.py @@ -14,7 +14,4 @@ class UserInput(BaseModel): class Write_UserInput(BaseModel): messages: list[dict] end_user_id: str - config_id: Optional[str] = None -class End_User_Information(BaseModel): - end_user_name: str # 这是要更新的用户名 - id: str # 宿主ID，用于匹配条件 \ No newline at end of file + config_id: Optional[str] = None \ No newline at end of file From f2d6fd7b085aef6e7ede111cd8f9a3946f887f8a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 20:40:41 +0800 Subject: [PATCH 039/175] =?UTF-8?q?config=5Fid=E5=AD=97=E6=AE=B5=E6=94=B9?= =?UTF-8?q?=E6=88=90UUID?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/memory_agent_controller.py | 3 +- .../langgraph_graph/nodes/write_nodes.py | 42 +++++---- .../core/memory/agent/utils/get_dialogs.py | 59 ++++++------ .../core/memory/agent/utils/write_tools.py | 27 ++---- .../core/memory/llm_tools/chunker_client.py | 24 ++--- api/app/services/memory_config_service.py | 92 ++++++++++++++----- api/app/tasks.py | 38 ++++++-- 7 files changed, 177 insertions(+), 108 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 3f3a513e..e9ae8bae 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -162,9 +162,10 @@ async def write_server( api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") try: + messages_list = memory_agent_service.get_messages_list(user_input) result = await memory_agent_service.write_memory( user_input.end_user_id, - user_input.messages, + messages_list, config_id, db, storage_type, diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py index 1dab1b0a..b85130ad 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py @@ -1,44 +1,54 @@ - -from app.core.memory.agent.utils.llm_tools import WriteState +from app.core.memory.agent.utils.llm_tools import WriteState from app.core.memory.agent.utils.write_tools import write from app.core.logging_config import get_agent_logger logger = get_agent_logger(__name__) + + async def write_node(state: WriteState) -> WriteState: """ Write data to the database/file system. Args: - content: Data content to write - end_user_id: End user identifier - memory_config: MemoryConfig object containing all configuration + state: WriteState containing messages, end_user_id, and memory_config Returns: - dict: Contains 'status', 'saved_to', and 'data' fields + dict: Contains 'write_result' with status and data fields """ - content=state.get('data','') - end_user_id=state.get('end_user_id','') - memory_config=state.get('memory_config', '') + messages = state.get('messages', []) + end_user_id = state.get('end_user_id', '') + memory_config = state.get('memory_config', '') + + # Convert LangChain messages to structured format expected by write() + structured_messages = [] + for msg in messages: + if hasattr(msg, 'type') and hasattr(msg, 'content'): + # Map LangChain message types to role names + role = 'user' if msg.type == 'human' else 'assistant' if msg.type == 'ai' else msg.type + structured_messages.append({ + "role": role, + "content": msg.content # content is now guaranteed to be a string + }) + try: - result=await write( + result = await write( + messages=structured_messages, end_user_id=end_user_id, memory_config=memory_config, - messages=content, # 修复：使用正确的参数名 messages ) logger.info(f"Write completed successfully! Config: {memory_config.config_name}") - write_result= { + write_result = { "status": "success", - "data": content, + "data": structured_messages, "config_id": memory_config.config_id, "config_name": memory_config.config_name, } - return {"write_result":write_result} - + return {"write_result": write_result} except Exception as e: logger.error(f"Data_write failed: {e}", exc_info=True) - write_result= { + write_result = { "status": "error", "message": str(e), } diff --git a/api/app/core/memory/agent/utils/get_dialogs.py b/api/app/core/memory/agent/utils/get_dialogs.py index 4751f18c..a56a32fa 100644 --- a/api/app/core/memory/agent/utils/get_dialogs.py +++ b/api/app/core/memory/agent/utils/get_dialogs.py @@ -10,55 +10,58 @@ from app.core.memory.models.message_models import DialogData, ConversationContex async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", end_user_id: str = "group_1", - content: str = "这是用户的输入", + messages: list = None, ref_id: str = "wyl_20251027", config_id: str = None ) -> List[DialogData]: - """Generate chunks from all test data entries using the specified chunker strategy. + """Generate chunks from structured messages using the specified chunker strategy. Args: chunker_strategy: The chunking strategy to use (default: RecursiveChunker) - end_user_id: End user identifier - content: Dialog content + group_id: Group identifier + messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference identifier config_id: Configuration ID for processing Returns: - List of DialogData objects with generated chunks for each test entry + List of DialogData objects with generated chunks """ - dialog_data_list = [] - messages = [] + from app.core.logging_config import get_agent_logger + logger = get_agent_logger(__name__) - messages.append(ConversationMessage(role="用户", msg=content)) + if not messages or not isinstance(messages, list) or len(messages) == 0: + raise ValueError("messages parameter must be a non-empty list") - # Create DialogData - conversation_context = ConversationContext(msgs=messages) - # Create DialogData with end_user_id + conversation_messages = [] + + for idx, msg in enumerate(messages): + if not isinstance(msg, dict) or 'role' not in msg or 'content' not in msg: + raise ValueError(f"Message {idx} format error: must contain 'role' and 'content' fields") + + role = msg['role'] + content = msg['content'] + + if role not in ['user', 'assistant']: + raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}") + + if content.strip(): + conversation_messages.append(ConversationMessage(role=role, msg=content.strip())) + + if not conversation_messages: + raise ValueError("Message list cannot be empty after filtering") + + conversation_context = ConversationContext(msgs=conversation_messages) dialog_data = DialogData( context=conversation_context, ref_id=ref_id, end_user_id=end_user_id, config_id=config_id ) - # Create DialogueChunker and process the dialogue + chunker = DialogueChunker(chunker_strategy) extracted_chunks = await chunker.process_dialogue(dialog_data) dialog_data.chunks = extracted_chunks - dialog_data_list.append(dialog_data) + logger.info(f"DialogData created with {len(extracted_chunks)} chunks") - # Convert to dict with datetime serialized - def serialize_datetime(obj): - if isinstance(obj, datetime): - return obj.isoformat() - raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable") - - combined_output = [dd.model_dump() for dd in dialog_data_list] - - print(dialog_data_list) - - # with open(os.path.join(os.path.dirname(__file__), "chunker_test_output.txt"), "w", encoding="utf-8") as f: - # json.dump(combined_output, f, ensure_ascii=False, indent=4, default=serialize_datetime) - - - return dialog_data_list + return [dialog_data] diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index b8bc58eb..d32d152c 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -36,9 +36,11 @@ async def write( ) -> None: """ Execute the complete knowledge extraction pipeline. - + Args: - end_user_id: End user identifier + user_id: User identifier + apply_id: Application identifier + group_id: Group identifier memory_config: MemoryConfig object containing all configuration messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference ID, defaults to "wyl20251027" @@ -47,14 +49,14 @@ async def write( embedding_model_id = str(memory_config.embedding_model_id) chunker_strategy = memory_config.chunker_strategy config_id = str(memory_config.config_id) - + logger.info("=== MemSci Knowledge Extraction Pipeline ===") logger.info(f"Config: {memory_config.config_name} (ID: {config_id})") logger.info(f"Workspace: {memory_config.workspace_name}") logger.info(f"LLM model: {memory_config.llm_model_name}") logger.info(f"Embedding model: {memory_config.embedding_model_name}") logger.info(f"Chunker strategy: {chunker_strategy}") - logger.info(f"End User ID: {end_user_id}") + logger.info(f"end_user_id ID: {end_user_id}") # Construct clients from memory_config using factory pattern with db session with get_db_context() as db: @@ -77,25 +79,10 @@ async def write( # Step 1: Load and chunk data step_start = time.time() - - # Convert messages list to content string - # messages format: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}, ...] - if isinstance(messages, list) and len(messages) > 0: - # Extract content from the last user message or concatenate all messages - if isinstance(messages[-1], dict) and 'content' in messages[-1]: - content = messages[-1]['content'] - else: - # Fallback: concatenate all message contents - content = " ".join([msg.get('content', '') for msg in messages if isinstance(msg, dict)]) - elif isinstance(messages, str): - content = messages - else: - content = str(messages) - chunked_dialogs = await get_chunked_dialogs( chunker_strategy=chunker_strategy, end_user_id=end_user_id, - content=content, # 修复：使用 content 参数而不是 messages + messages=messages, ref_id=ref_id, config_id=config_id, ) diff --git a/api/app/core/memory/llm_tools/chunker_client.py b/api/app/core/memory/llm_tools/chunker_client.py index 87cdb9f4..93a2df82 100644 --- a/api/app/core/memory/llm_tools/chunker_client.py +++ b/api/app/core/memory/llm_tools/chunker_client.py @@ -187,11 +187,11 @@ class ChunkerClient: async def generate_chunks(self, dialogue: DialogData): """ Generate chunks following 1 Message = 1 Chunk strategy. - + Each message creates one chunk, directly inheriting role information. If a message is too long, it will be split into multiple sub-chunks, each maintaining the same speaker. - + Raises: ValueError: If dialogue has no messages or chunking fails """ @@ -201,9 +201,9 @@ class ChunkerClient: f"Dialogue {dialogue.ref_id} has no messages. " f"Cannot generate chunks from empty dialogue." ) - + dialogue.chunks = [] - + # 按消息分块：每个消息创建一个或多个 chunk，直接继承角色 for msg_idx, msg in enumerate(dialogue.context.msgs): # Validate message has required attributes @@ -212,13 +212,13 @@ class ChunkerClient: f"Message {msg_idx} in dialogue {dialogue.ref_id} " f"missing 'role' or 'msg' attribute" ) - + msg_content = msg.msg.strip() - + # Skip empty messages if not msg_content: continue - + # 如果消息太长，可以进一步分块 if len(msg_content) > self.chunk_size: # 对单个消息的内容进行分块 @@ -228,14 +228,14 @@ class ChunkerClient: raise ValueError( f"Failed to chunk long message {msg_idx} in dialogue {dialogue.ref_id}: {e}" ) - + for idx, sub_chunk in enumerate(sub_chunks): sub_chunk_text = sub_chunk.text if hasattr(sub_chunk, 'text') else str(sub_chunk) sub_chunk_text = sub_chunk_text.strip() - + if len(sub_chunk_text) < (self.min_characters_per_chunk or 50): continue - + chunk = Chunk( content=f"{msg.role}: {sub_chunk_text}", speaker=msg.role, # 直接继承角色 @@ -260,7 +260,7 @@ class ChunkerClient: }, ) dialogue.chunks.append(chunk) - + # Validate we generated at least one chunk if not dialogue.chunks: raise ValueError( @@ -268,7 +268,7 @@ class ChunkerClient: f"All messages were either empty or too short. " f"Messages count: {len(dialogue.context.msgs)}" ) - + return dialogue def evaluate_chunking(self, dialogue: DialogData) -> dict: diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index d7f7c8a6..af9c0c5d 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -27,29 +27,73 @@ from uuid import UUID logger = get_logger(__name__) config_logger = get_config_logger() +import uuid + +def _validate_config_id(config_id): + """Validate configuration ID format.""" + if isinstance(config_id, uuid.UUID): + return config_id + if config_id is None: + raise InvalidConfigError( + "Configuration ID cannot be None", + field_name="config_id", + invalid_value=config_id, + ) + + if isinstance(config_id, int): + if config_id <= 0: + raise InvalidConfigError( + f"Configuration ID must be positive: {config_id}", + field_name="config_id", + invalid_value=config_id, + ) + return config_id + + if isinstance(config_id, str): + try: + parsed_id = int(config_id.strip()) + if parsed_id <= 0: + raise InvalidConfigError( + f"Configuration ID must be positive: {parsed_id}", + field_name="config_id", + invalid_value=config_id, + ) + return parsed_id + except ValueError: + raise InvalidConfigError( + f"Invalid configuration ID format: '{config_id}'", + field_name="config_id", + invalid_value=config_id, + ) + + raise InvalidConfigError( + f"Invalid type for configuration ID: expected int or str, got {type(config_id).__name__}", + field_name="config_id", + invalid_value=config_id, + ) class MemoryConfigService: """ Centralized service for memory configuration loading and validation. - + This class provides a single implementation of configuration loading logic that can be shared across multiple services, eliminating code duplication. - + Usage: config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config(config_id) model_config = config_service.get_model_config(model_id) """ - + def __init__(self, db: Session): """Initialize the service with a database session. - + Args: db: SQLAlchemy database session """ self.db = db - + def load_memory_config( self, config_id: UUID, @@ -57,19 +101,19 @@ class MemoryConfigService: ) -> MemoryConfig: """ Load memory configuration from database by config_id. - + Args: config_id: Configuration ID (UUID) from database service_name: Name of the calling service (for logging purposes) - + Returns: MemoryConfig: Immutable configuration object - + Raises: ConfigurationError: If validation fails """ start_time = time.time() - + validated_config_id = _validate_config_id(config_id) config_logger.info( "Starting memory configuration loading", extra={ @@ -78,9 +122,9 @@ class MemoryConfigService: "config_id": str(config_id), }, ) - + logger.info(f"Loading memory configuration from database: config_id={config_id}") - + try: # Validate config_id is UUID if not isinstance(config_id, UUID): @@ -99,7 +143,7 @@ class MemoryConfigService: field_name="config_id", invalid_value=config_id, ) - + # Step 1: Get config and workspace db_query_start = time.time() result = MemoryConfigRepository.get_config_with_workspace(self.db, config_id) @@ -120,9 +164,9 @@ class MemoryConfigService: raise ConfigurationError( f"Configuration {config_id} not found in database" ) - + memory_config, workspace = result - + # Step 2: Validate embedding model (returns both UUID and name) embed_start = time.time() embedding_uuid, embedding_name = validate_embedding_model( @@ -134,7 +178,7 @@ class MemoryConfigService: ) embed_time = time.time() - embed_start logger.info(f"[PERF] Embedding validation: {embed_time:.4f}s") - + # Step 3: Resolve LLM model llm_start = time.time() llm_uuid, llm_name = validate_and_resolve_model_id( @@ -148,7 +192,7 @@ class MemoryConfigService: ) llm_time = time.time() - llm_start logger.info(f"[PERF] LLM validation: {llm_time:.4f}s") - + # Step 4: Resolve optional rerank model rerank_start = time.time() rerank_uuid = None @@ -166,10 +210,10 @@ class MemoryConfigService: rerank_time = time.time() - rerank_start if memory_config.rerank_id: logger.info(f"[PERF] Rerank validation: {rerank_time:.4f}s") - + # Note: embedding_name is now returned from validate_embedding_model above # No need for redundant query! - + # Create immutable MemoryConfig object config = MemoryConfig( config_id=memory_config.config_id, @@ -210,9 +254,9 @@ class MemoryConfigService: pruning_scene=memory_config.pruning_scene or "education", pruning_threshold=float(memory_config.pruning_threshold) if memory_config.pruning_threshold is not None else 0.5, ) - + elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.info( "Memory configuration loaded successfully", extra={ @@ -225,13 +269,13 @@ class MemoryConfigService: "elapsed_ms": elapsed_ms, }, ) - + logger.info(f"Memory configuration loaded successfully: {config.config_name}") return config - + except Exception as e: elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.error( "Failed to load memory configuration", extra={ @@ -245,7 +289,7 @@ class MemoryConfigService: }, exc_info=True, ) - + logger.error(f"Failed to load memory configuration {config_id}: {e}") if isinstance(e, (ConfigurationError, ValueError)): raise diff --git a/api/app/tasks.py b/api/app/tasks.py index 3ef2653a..38488aa5 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -383,7 +383,7 @@ def build_graphrag_for_kb(kb_id: uuid.UUID): @celery_app.task(name="app.core.memory.agent.read_message", bind=True) -def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: uuid.UUID, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: +def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a read message via MemoryAgentService. @@ -392,7 +392,7 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s message: User message to process history: Conversation history search_switch: Search switch parameter - config_id: Optional configuration ID + config_id: Configuration ID as string (will be converted to UUID) Returns: Dict containing the result and metadata @@ -402,8 +402,16 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s """ start_time = time.time() + # Convert config_id string to UUID + actual_config_id = None + if config_id: + try: + actual_config_id = uuid.UUID(config_id) if isinstance(config_id, str) else config_id + except (ValueError, AttributeError): + # If conversion fails, leave as None and try to resolve + pass + # Resolve config_id if None - actual_config_id = config_id if actual_config_id is None: try: from app.services.memory_agent_service import get_end_user_connected_config @@ -473,13 +481,13 @@ def read_message_task(self, end_user_id: str, message: str, history: List[Dict[s @celery_app.task(name="app.core.memory.agent.write_message", bind=True) -def write_message_task(self, end_user_id: str, message: str, config_id: uuid.UUID, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: +def write_message_task(self, end_user_id: str, message: str, config_id: str, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a write message via MemoryAgentService. Args: end_user_id: Group ID for the memory agent (also used as end_user_id) message: Message to write - config_id: Optional configuration ID + config_id: Configuration ID as string (will be converted to UUID) Returns: Dict containing the result and metadata @@ -493,8 +501,24 @@ def write_message_task(self, end_user_id: str, message: str, config_id: uuid.UUI logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") start_time = time.time() + # Convert config_id string to UUID + actual_config_id = None + if config_id: + try: + actual_config_id = uuid.UUID(config_id) if isinstance(config_id, str) else config_id + logger.info(f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} (type: {type(actual_config_id).__name__})") + except (ValueError, AttributeError) as e: + logger.error(f"[CELERY WRITE] Invalid config_id format: {config_id}, error: {e}") + return { + "status": "FAILURE", + "error": f"Invalid config_id format: {config_id}", + "end_user_id": end_user_id, + "config_id": config_id, + "elapsed_time": 0.0, + "task_id": self.request.id + } + # Resolve config_id if None - actual_config_id = config_id if actual_config_id is None: try: from app.services.memory_agent_service import get_end_user_connected_config @@ -511,7 +535,7 @@ def write_message_task(self, end_user_id: str, message: str, config_id: uuid.UUI async def _run() -> str: db = next(get_db()) try: - logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory") + logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory with config_id={actual_config_id} (type: {type(actual_config_id).__name__})") service = MemoryAgentService() result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type, user_rag_memory_id) logger.info(f"[CELERY WRITE] Write completed successfully: {result}") From 940d3d4567b67de68ea0360df99a435fa8a36ea2 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 20:48:51 +0800 Subject: [PATCH 040/175] =?UTF-8?q?config=5Fid=E5=AD=97=E6=AE=B5=E6=94=B9?= =?UTF-8?q?=E6=88=90UUID?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_agent_controller.py | 1 + 1 file changed, 1 insertion(+) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index e9ae8bae..1ae01aec 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -171,6 +171,7 @@ async def write_server( storage_type, user_rag_memory_id ) + return success(data=result, msg="写入成功") except BaseException as e: # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup From 9e828b17502d4c280b7d8e6c8b73f00016bd3745 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Thu, 22 Jan 2026 21:53:15 +0800 Subject: [PATCH 041/175] =?UTF-8?q?config=5Fid=E5=AD=97=E6=AE=B5=E6=94=B9?= =?UTF-8?q?=E6=88=90UUID=EF=BC=8C=E4=B8=8Edevelop=E6=A0=A1=E5=AF=B9?= =?UTF-8?q?=E6=81=A2=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/memory_agent_controller.py | 7 +- .../controllers/memory_forget_controller.py | 16 ++-- .../service/memory_api_controller.py | 1 - api/app/core/agent/langchain_agent.py | 57 ++++++------ .../agent/langgraph_graph/write_graph.py | 12 ++- .../evaluation/locomo/qwen_search_eval.py | 2 +- .../memory/evaluation/memsciqa/evaluate_qa.py | 4 +- .../evaluation/memsciqa/memsciqa-test.py | 2 +- api/app/core/memory/evaluation/run_eval.py | 4 +- api/app/core/memory/src/search.py | 3 - .../neo4j/memory_summary_repository.py | 6 +- api/app/repositories/neo4j/neo4j_connector.py | 32 +------ api/app/services/memory_agent_service.py | 2 +- api/app/services/memory_api_service.py | 2 +- api/app/services/memory_config_service.py | 48 +++++----- api/app/services/memory_storage_service.py | 23 ----- api/app/tasks.py | 88 +++++++++---------- 17 files changed, 131 insertions(+), 178 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 1ae01aec..7b0ddf23 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -223,9 +223,12 @@ async def write_server_async( if knowledge: user_rag_memory_id = str(knowledge.id) api_logger.info(f"Async write: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}") try: + # 获取标准化的消息列表 + messages_list = memory_agent_service.get_messages_list(user_input) + task = celery_app.send_task( "app.core.memory.agent.write_message", - args=[user_input.end_user_id, user_input.message, config_id, storage_type, user_rag_memory_id] + args=[user_input.end_user_id, messages_list, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Write task queued: {task.id}") @@ -598,7 +601,7 @@ async def status_type( last_user_message = " ".join([msg.get('content', '') for msg in messages_list]) result = await memory_agent_service.classify_message_type( - user_input.messages, + last_user_message, user_input.config_id, db ) diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index a71de487..44351f92 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -237,7 +237,7 @@ async def update_forgetting_config( @router.get("/stats", response_model=ApiResponse) async def get_forgetting_stats( - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -263,18 +263,18 @@ async def get_forgetting_stats( # 如果提供了 group_id，通过它获取 config_id config_id = None - if group_id: + if end_user_id: try: from app.services.memory_agent_service import get_end_user_connected_config - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - api_logger.warning(f"终端用户 {group_id} 未关联记忆配置") - return fail(BizCode.INVALID_PARAMETER, f"终端用户 {group_id} 未关联记忆配置", "memory_config_id is None") + api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") + return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None") - api_logger.debug(f"通过 group_id={group_id} 获取到 config_id={config_id}") + api_logger.debug(f"通过 group_id={end_user_id} 获取到 config_id={config_id}") except ValueError as e: api_logger.warning(f"获取终端用户配置失败: {str(e)}") return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError") @@ -284,14 +284,14 @@ async def get_forgetting_stats( api_logger.info( f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: " - f"group_id={group_id}, config_id={config_id}" + f"group_id={end_user_id}, config_id={config_id}" ) try: # 调用服务层获取统计信息 stats = await forget_service.get_forgetting_stats( db=db, - group_id=group_id, + end_user_id=end_user_id, config_id=config_id ) diff --git a/api/app/controllers/service/memory_api_controller.py b/api/app/controllers/service/memory_api_controller.py index 87c1aa20..accd749e 100644 --- a/api/app/controllers/service/memory_api_controller.py +++ b/api/app/controllers/service/memory_api_controller.py @@ -50,7 +50,6 @@ async def write_memory_api_service( config_id=payload.config_id, storage_type=payload.storage_type, user_rag_memory_id=payload.user_rag_memory_id, - tenant_id=api_key_auth.tenant_id, ) logger.info(f"Memory write successful for end_user: {payload.end_user_id}") diff --git a/api/app/core/agent/langchain_agent.py b/api/app/core/agent/langchain_agent.py index e6c59a79..4cff933d 100644 --- a/api/app/core/agent/langchain_agent.py +++ b/api/app/core/agent/langchain_agent.py @@ -145,33 +145,36 @@ class LangChainAgent: messages.append(HumanMessage(content=user_content)) return messages - async def term_memory_save(self,messages,end_user_end,aimessages): - '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j''' - end_user_end=f"Term_{end_user_end}" - print(messages) - print(aimessages) - session_id = store.save_session( - userid=end_user_end, - messages=messages, - apply_id=end_user_end, - end_user_id=end_user_end, - aimessages=aimessages - ) - store.delete_duplicate_sessions() - # logger.info(f'Redis_Agent:{end_user_end};{session_id}') - return session_id - async def term_memory_redis_read(self,end_user_end): - end_user_end = f"Term_{end_user_end}" - history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end) - # logger.info(f'Redis_Agent:{end_user_end};{history}') - messagss_list=[] - retrieved_content=[] - for messages in history: - query = messages.get("Query") - aimessages = messages.get("Answer") - messagss_list.append(f'用户:{query}。AI回复:{aimessages}') - retrieved_content.append({query: aimessages}) - return messagss_list,retrieved_content +# TODO 乐力齐 - 累积多组对话批量写入功能已禁用 + # async def term_memory_save(self,messages,end_user_end,aimessages): + # '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j''' + # end_user_end=f"Term_{end_user_end}" + # print(messages) + # print(aimessages) + # session_id = store.save_session( + # userid=end_user_end, + # messages=messages, + # apply_id=end_user_end, + # group_id=end_user_end, + # aimessages=aimessages + # ) + # store.delete_duplicate_sessions() + # # logger.info(f'Redis_Agent:{end_user_end};{session_id}') + # return session_id + +# TODO 乐力齐 - 累积多组对话批量写入功能已禁用 + # async def term_memory_redis_read(self,end_user_end): + # end_user_end = f"Term_{end_user_end}" + # history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end) + # # logger.info(f'Redis_Agent:{end_user_end};{history}') + # messagss_list=[] + # retrieved_content=[] + # for messages in history: + # query = messages.get("Query") + # aimessages = messages.get("Answer") + # messagss_list.append(f'用户:{query}。AI回复:{aimessages}') + # retrieved_content.append({query: aimessages}) + # return messagss_list,retrieved_content async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id): """ diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index d8fcf210..8b5de444 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -34,11 +34,17 @@ async def make_write_graph(): end_user_id: Group identifier memory_config: MemoryConfig object containing all configuration """ + # workflow = StateGraph(WriteState) + # workflow.add_node("content_input", content_input_write) + # workflow.add_node("save_neo4j", write_node) + # workflow.add_edge(START, "content_input") + # workflow.add_edge("content_input", "save_neo4j") + # workflow.add_edge("save_neo4j", END) + # + # graph = workflow.compile() workflow = StateGraph(WriteState) - workflow.add_node("content_input", content_input_write) workflow.add_node("save_neo4j", write_node) - workflow.add_edge(START, "content_input") - workflow.add_edge("content_input", "save_neo4j") + workflow.add_edge(START, "save_neo4j") workflow.add_edge("save_neo4j", END) graph = workflow.compile() diff --git a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py index 3147e880..6a5caa0c 100644 --- a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py @@ -30,7 +30,7 @@ from app.core.memory.storage_services.search import run_hybrid_search from app.core.memory.utils.config.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_end_user_id, + SELECTED_GROUP_ID, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory diff --git a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py index ec147f3c..869fdb60 100644 --- a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py +++ b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py @@ -27,7 +27,7 @@ from app.core.memory.storage_services.search import run_hybrid_search from app.core.memory.utils.config.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_end_user_id, + SELECTED_GROUP_ID, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -136,7 +136,7 @@ def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: - end_user_id = end_user_id or SELECTED_end_user_id + end_user_id = end_user_id or SELECTED_GROUP_ID # Load data data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") if not os.path.exists(data_path): diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py index 631035aa..8c6d643d 100644 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py @@ -33,7 +33,7 @@ from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient from app.core.memory.utils.config.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_end_user_id, + SELECTED_GROUP_ID, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory diff --git a/api/app/core/memory/evaluation/run_eval.py b/api/app/core/memory/evaluation/run_eval.py index f665bdb8..c5aacb2f 100644 --- a/api/app/core/memory/evaluation/run_eval.py +++ b/api/app/core/memory/evaluation/run_eval.py @@ -15,7 +15,7 @@ except Exception: return None from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.utils.config.definitions import SELECTED_end_user_id, PROJECT_ROOT +from app.core.memory.utils.config.definitions import SELECTED_GROUP_ID, PROJECT_ROOT from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test @@ -37,7 +37,7 @@ async def run( max_contexts_per_item: int | None = None, ) -> Dict[str, Any]: # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认 - end_user_id = end_user_id or SELECTED_end_user_id + end_user_id = end_user_id or SELECTED_GROUP_ID if reset_group: connector = Neo4jConnector() diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 87a5dd6f..5985d04f 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -693,9 +693,6 @@ async def run_hybrid_search( # Start overall timing search_start_time = time.time() latency_metrics = {} - print(100*'-') - print(memory_config) - print(100 * '-') logger.info(f"using embedding_id:{memory_config.embedding_model_id}...") # Clean and normalize the incoming query before use/logging diff --git a/api/app/repositories/neo4j/memory_summary_repository.py b/api/app/repositories/neo4j/memory_summary_repository.py index 81bf2cc9..5fcff41a 100644 --- a/api/app/repositories/neo4j/memory_summary_repository.py +++ b/api/app/repositories/neo4j/memory_summary_repository.py @@ -209,7 +209,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, days=days, limit=limit ) @@ -253,7 +253,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): results = await self.connector.execute_query(query, **params) return [self._map_to_dict(r) for r in results] - async def get_summary_count_by_group(self, group_id: str) -> int: + async def get_summary_count_by_group(self, end_user_id: str) -> int: """Get count of memory summaries for a group Args: @@ -268,6 +268,6 @@ class MemorySummaryRepository(BaseNeo4jRepository): RETURN count(n) as count """ - results = await self.connector.execute_query(query, end_user_id=group_id) + results = await self.connector.execute_query(query, end_user_id=end_user_id) return results[0]['count'] if results else 0 \ No newline at end of file diff --git a/api/app/repositories/neo4j/neo4j_connector.py b/api/app/repositories/neo4j/neo4j_connector.py index 456c4e08..d96e4431 100644 --- a/api/app/repositories/neo4j/neo4j_connector.py +++ b/api/app/repositories/neo4j/neo4j_connector.py @@ -70,11 +70,7 @@ class Neo4jConnector: List[Dict[str, Any]]: 查询结果列表，每个元素是一个字典 Example: - >>> connector = Neo4jConnector() - >>> results = await connector.execute_query( - ... "MATCH (n:Person {name: $name}) RETURN n", - ... name="Alice" - ... ) + """ result = await self.driver.execute_query( query, @@ -98,17 +94,7 @@ class Neo4jConnector: Any: 事务函数的返回值 Example: - >>> async def create_node(tx, name): - ... result = await tx.run( - ... "CREATE (n:Person {name: $name}) RETURN n", - ... name=name - ... ) - ... return await result.single() - >>> - >>> connector = Neo4jConnector() - >>> result = await connector.execute_write_transaction( - ... create_node, name="Alice" - ... ) + """ async with self.driver.session(database="neo4j") as session: return await session.execute_write(transaction_func, **kwargs) @@ -126,17 +112,7 @@ class Neo4jConnector: Any: 事务函数的返回值 Example: - >>> async def get_node(tx, name): - ... result = await tx.run( - ... "MATCH (n:Person {name: $name}) RETURN n", - ... name=name - ... ) - ... return await result.single() - >>> - >>> connector = Neo4jConnector() - >>> result = await connector.execute_read_transaction( - ... get_node, name="Alice" - ... ) + """ async with self.driver.session(database="neo4j") as session: return await session.execute_read(transaction_func, **kwargs) @@ -151,8 +127,6 @@ class Neo4jConnector: end_user_id: 要删除的组ID Example: - >>> connector = Neo4jConnector() - >>> await connector.delete_group("group_123") Group group_123 deleted. """ # 删除节点（DETACH DELETE会同时删除相关的边） diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index e4ab7087..890a88f7 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -564,7 +564,7 @@ class MemoryAgentService: # 使用 upsert 方法 repo.upsert( end_user_id=end_user_id, - messages=message, + messages=ori_message, aimessages=summary, retrieved_content=retrieved_content, search_switch=str(search_switch) diff --git a/api/app/services/memory_api_service.py b/api/app/services/memory_api_service.py index c33c9c6b..37150aff 100644 --- a/api/app/services/memory_api_service.py +++ b/api/app/services/memory_api_service.py @@ -139,7 +139,7 @@ class MemoryAPIService: # Delegate to MemoryAgentService result = await MemoryAgentService().write_memory( end_user_id=end_user_id, - message=message, + messages=message, config_id=config_id, db=self.db, storage_type=storage_type, diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index af9c0c5d..692104bb 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -30,9 +30,10 @@ config_logger = get_config_logger() import uuid def _validate_config_id(config_id): - """Validate configuration ID format.""" + """Validate configuration ID format (supports both UUID and integer).""" if isinstance(config_id, uuid.UUID): return config_id + if config_id is None: raise InvalidConfigError( "Configuration ID cannot be None", @@ -50,8 +51,17 @@ def _validate_config_id(config_id): return config_id if isinstance(config_id, str): + config_id_stripped = config_id.strip() + + # Try parsing as UUID first try: - parsed_id = int(config_id.strip()) + return uuid.UUID(config_id_stripped) + except ValueError: + pass + + # Fall back to integer parsing + try: + parsed_id = int(config_id_stripped) if parsed_id <= 0: raise InvalidConfigError( f"Configuration ID must be positive: {parsed_id}", @@ -61,13 +71,13 @@ def _validate_config_id(config_id): return parsed_id except ValueError: raise InvalidConfigError( - f"Invalid configuration ID format: '{config_id}'", + f"Invalid configuration ID format: '{config_id}' (must be UUID or positive integer)", field_name="config_id", invalid_value=config_id, ) raise InvalidConfigError( - f"Invalid type for configuration ID: expected int or str, got {type(config_id).__name__}", + f"Invalid type for configuration ID: expected UUID, int or str, got {type(config_id).__name__}", field_name="config_id", invalid_value=config_id, ) @@ -113,7 +123,7 @@ class MemoryConfigService: ConfigurationError: If validation fails """ start_time = time.time() - validated_config_id = _validate_config_id(config_id) + config_logger.info( "Starting memory configuration loading", extra={ @@ -126,27 +136,11 @@ class MemoryConfigService: logger.info(f"Loading memory configuration from database: config_id={config_id}") try: - # Validate config_id is UUID - if not isinstance(config_id, UUID): - if isinstance(config_id, str): - try: - config_id = UUID(config_id) - except ValueError: - raise InvalidConfigError( - f"Invalid UUID format for config_id: {config_id}", - field_name="config_id", - invalid_value=config_id, - ) - else: - raise InvalidConfigError( - f"config_id must be UUID or valid UUID string, got {type(config_id).__name__}", - field_name="config_id", - invalid_value=config_id, - ) + validated_config_id = _validate_config_id(config_id) # Step 1: Get config and workspace db_query_start = time.time() - result = MemoryConfigRepository.get_config_with_workspace(self.db, config_id) + result = MemoryConfigRepository.get_config_with_workspace(self.db, validated_config_id) db_query_time = time.time() - db_query_start logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s") if not result: @@ -170,7 +164,7 @@ class MemoryConfigService: # Step 2: Validate embedding model (returns both UUID and name) embed_start = time.time() embedding_uuid, embedding_name = validate_embedding_model( - config_id, + validated_config_id, memory_config.embedding_id, self.db, workspace.tenant_id, @@ -187,7 +181,7 @@ class MemoryConfigService: self.db, workspace.tenant_id, required=True, - config_id=config_id, + config_id=validated_config_id, workspace_id=workspace.id, ) llm_time = time.time() - llm_start @@ -204,7 +198,7 @@ class MemoryConfigService: self.db, workspace.tenant_id, required=False, - config_id=config_id, + config_id=validated_config_id, workspace_id=workspace.id, ) rerank_time = time.time() - rerank_start @@ -262,7 +256,7 @@ class MemoryConfigService: extra={ "operation": "load_memory_config", "service": service_name, - "config_id": str(config_id), + "config_id": validated_config_id, "config_name": config.config_name, "workspace_id": str(config.workspace_id), "load_result": "success", diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 6aa5ac7d..57ad725d 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -505,29 +505,6 @@ async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any] ) return result - -async def search_entity_graph(end_user_id: Optional[str] = None) -> Dict[str, Any]: - """搜索所有实体之间的关系网络（group 维度）。""" - result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ENTITY_GRAPH, - end_user_id=end_user_id, - ) - # 对source_node 和 target_node 的 fact_summary进行截取，只截取前三条的内容（需要提取前三条“来源”） - for item in result: - source_fact = item["sourceNode"]["fact_summary"] - target_fact = item["targetNode"]["fact_summary"] - # 截取前三条“来源” - item["sourceNode"]["fact_summary"] = source_fact.split("\n")[:4] if source_fact else [] - item["targetNode"]["fact_summary"] = target_fact.split("\n")[:4] if target_fact else [] - # 与现有返回风格保持一致，携带搜索类型、数量与详情 - data = { - "search_for": "entity_graph", - "num": len(result), - "detials": result, - } - return data - - async def analytics_hot_memory_tags( db: Session, current_user: User, diff --git a/api/app/tasks.py b/api/app/tasks.py index 38488aa5..3374a90b 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -531,7 +531,7 @@ def write_message_task(self, end_user_id: str, message: str, config_id: str, sto except Exception: # Log but continue - will fail later with proper error pass - + async def _run() -> str: db = next(get_db()) try: @@ -619,53 +619,53 @@ def reflection_timer_task() -> None: """ reflection_engine() - -@celery_app.task(name="app.core.memory.agent.health.check_read_service") -def check_read_service_task() -> Dict[str, str]: - """Call read_service and write latest status to Redis. +# unused task +# @celery_app.task(name="app.core.memory.agent.health.check_read_service") +# def check_read_service_task() -> Dict[str, str]: +# """Call read_service and write latest status to Redis. - Returns status data dict that gets written to Redis. - """ - client = redis.Redis( - host=settings.REDIS_HOST, - port=settings.REDIS_PORT, - db=settings.REDIS_DB, - password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None - ) - try: - api_url = f"http://{settings.SERVER_IP}:8000/api/memory/read_service" - payload = { - "user_id": "健康检查", - "apply_id": "健康检查", - "end_user_id": "健康检查", - "message": "你好", - "history": [], - "search_switch": "2", - } - resp = requests.post(api_url, json=payload, timeout=15) - ok = resp.status_code == 200 - status = "Success" if ok else "Fail" - msg = "接口请求成功" if ok else f"接口请求失败: {resp.status_code}" - error = "" if ok else resp.text - code = 0 if ok else 500 - except Exception as e: - status = "Fail" - msg = "接口请求失败" - error = str(e) - code = 500 +# Returns status data dict that gets written to Redis. +# """ +# client = redis.Redis( +# host=settings.REDIS_HOST, +# port=settings.REDIS_PORT, +# db=settings.REDIS_DB, +# password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None +# ) +# try: +# api_url = f"http://{settings.SERVER_IP}:8000/api/memory/read_service" +# payload = { +# "user_id": "健康检查", +# "apply_id": "健康检查", +# "group_id": "健康检查", +# "message": "你好", +# "history": [], +# "search_switch": "2", +# } +# resp = requests.post(api_url, json=payload, timeout=15) +# ok = resp.status_code == 200 +# status = "Success" if ok else "Fail" +# msg = "接口请求成功" if ok else f"接口请求失败: {resp.status_code}" +# error = "" if ok else resp.text +# code = 0 if ok else 500 +# except Exception as e: +# status = "Fail" +# msg = "接口请求失败" +# error = str(e) +# code = 500 - data = { - "status": status, - "msg": msg, - "error": error, - "code": str(code), - "time": str(int(time.time())), - } +# data = { +# "status": status, +# "msg": msg, +# "error": error, +# "code": str(code), +# "time": str(int(time.time())), +# } - client.hset("memsci:health:read_service", mapping=data) - client.expire("memsci:health:read_service", int(settings.HEALTH_CHECK_SECONDS)) +# client.hset("memsci:health:read_service", mapping=data) +# client.expire("memsci:health:read_service", int(settings.HEALTH_CHECK_SECONDS)) - return data +# return data @celery_app.task(name="app.controllers.memory_storage_controller.search_all") From 86fe6fe5ab47433a8e67f909d0cd79030f4eb0a7 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 10:35:41 +0800 Subject: [PATCH 042/175] =?UTF-8?q?=E6=A3=80=E6=9F=A5=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E4=BF=AE=E5=A4=8Dgroup=5Fid=E7=9A=84=E9=81=97?= =?UTF-8?q?=E7=95=99=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../memory/evaluation/longmemeval/qwen_search_eval.py | 4 ++-- api/app/core/memory/evaluation/longmemeval/test_eval.py | 8 ++++---- api/app/models/memory_config_model.py | 2 +- api/app/repositories/neo4j/memory_summary_repository.py | 8 ++++---- 4 files changed, 11 insertions(+), 11 deletions(-) diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py index 320f9de7..d3577d9e 100644 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py @@ -1009,7 +1009,7 @@ async def run_longmemeval_test( kw_fallback = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=max(search_limit, 5), ) fb_dialogs = kw_fallback.get("dialogues", []) or [] @@ -1223,7 +1223,7 @@ async def run_longmemeval_test( "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, }, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, diff --git a/api/app/core/memory/evaluation/longmemeval/test_eval.py b/api/app/core/memory/evaluation/longmemeval/test_eval.py index a49d48d0..67bd6ec2 100644 --- a/api/app/core/memory/evaluation/longmemeval/test_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/test_eval.py @@ -876,7 +876,7 @@ async def run_longmemeval_test( opt_res = await search_graph( connector=connector, q=str(opt), - end_user_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(opt_res, dict): @@ -971,7 +971,7 @@ async def run_longmemeval_test( kw_fallback = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=max(search_limit, 5), ) fb_dialogs = kw_fallback.get("dialogues", []) or [] @@ -1199,7 +1199,7 @@ async def run_longmemeval_test( "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, }, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, @@ -1278,7 +1278,7 @@ def main(): result = asyncio.run( run_longmemeval_test( sample_size=sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/models/memory_config_model.py b/api/app/models/memory_config_model.py index 710315db..b468e2a2 100644 --- a/api/app/models/memory_config_model.py +++ b/api/app/models/memory_config_model.py @@ -17,7 +17,7 @@ class MemoryConfig(Base): # 组织信息 workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") - group_id = Column(String, nullable=True, comment="组ID") + end_user_id = Column(String, nullable=True, comment="组ID") user_id = Column(String, nullable=True, comment="用户ID") apply_id = Column(String, nullable=True, comment="应用ID") diff --git a/api/app/repositories/neo4j/memory_summary_repository.py b/api/app/repositories/neo4j/memory_summary_repository.py index 5fcff41a..d7cd4fd4 100644 --- a/api/app/repositories/neo4j/memory_summary_repository.py +++ b/api/app/repositories/neo4j/memory_summary_repository.py @@ -217,14 +217,14 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_by_content_keywords( self, - group_id: str, + end_user_id: str, keywords: List[str], limit: int = 100 ) -> List[Dict[str, Any]]: """Query memory summaries by content keywords Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by keywords: List of keywords to search for in content limit: Maximum number of results to return @@ -233,7 +233,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ # Build keyword search conditions keyword_conditions = [] - params = {"end_user_id": group_id, "limit": limit} + params = {"end_user_id": end_user_id, "limit": limit} for i, keyword in enumerate(keywords): keyword_conditions.append(f"toLower(n.content) CONTAINS toLower($keyword_{i})") @@ -257,7 +257,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """Get count of memory summaries for a group Args: - group_id: Group ID to count summaries for + end_user_id: Group ID to count summaries for Returns: int: Number of memory summaries From ebe018347b02e7a2c795ede4467db95990603b15 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 10:39:10 +0800 Subject: [PATCH 043/175] =?UTF-8?q?=E6=A3=80=E6=9F=A5=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E4=BF=AE=E5=A4=8Dgroup=5Fid=E7=9A=84=E9=81=97?= =?UTF-8?q?=E7=95=99=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/core/memory/src/search.py | 57 ------------------------------- 1 file changed, 57 deletions(-) diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 5985d04f..0e1d8424 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -1017,60 +1017,3 @@ async def search_chunk_by_chunk_id( ) return {"chunks": chunks} - -if __name__ == '__main__': - # 测试混合检索功能 - from app.schemas.memory_config_schema import MemoryConfig - from app.db import get_db - from app.services.memory_config_service import MemoryConfigService - - # 从数据库获取真实配置 - db = next(get_db()) - try: - config_service = MemoryConfigService(db) - - # 使用 config_id=17 获取配置 - memory_config = config_service.load_memory_config(config_id=17) - - if not memory_config: - print("错误：找不到 config_id=17 的配置") - print("请先在数据库中创建配置，或修改 config_id") - exit(1) - - print(f"✓ 成功加载配置: {memory_config.config_name}") - print(f" - Workspace: {memory_config.workspace_name}") - print(f" - LLM Model: {memory_config.llm_model_name}") - print(f" - Embedding Model: {memory_config.embedding_model_name}") - print(f" - Storage Type: {memory_config.storage_type}") - print() - - # 修改这里的参数进行测试 - test_end_user_id = "021886bc-fab9-4fd5-b607-497b262e0381" # 修改为你的 end_user_id - test_query = "小明擅长什么？" # 修改为你的查询 - - print(f"开始测试检索...") - print(f" - Query: {test_query}") - print(f" - End User ID: {test_end_user_id}") - print(f" - Search Type: hybrid") - print() - - results = asyncio.run(run_hybrid_search( - query_text=test_query, - search_type="hybrid", # 可选: "keyword", "embedding", "hybrid" - end_user_id=test_end_user_id, - limit=10, - include=["statements", "entities", "chunks", "summaries"], - output_path=None, - memory_config=memory_config, - rerank_alpha=0.6, - use_forgetting_rerank=False, - use_llm_rerank=False - )) - - except Exception as e: - print(f"错误: {e}") - import traceback - - traceback.print_exc() - finally: - db.close() \ No newline at end of file From 7870c6c33f8048df8cd7351aec7904f3c62bbdd6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B9=90=E5=8A=9B=E9=BD=90?= <162269739+lanceyq@users.noreply.github.com> Date: Fri, 23 Jan 2026 10:50:24 +0800 Subject: [PATCH 044/175] Fix/interface home (#182) * [fix]Fix the interface for statistics of recent activities and applications * [changes]Modify the code based on the AI review 1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter. 2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls. * [fix]Fix the interface for statistics of recent activities and applications * [changes]Modify the code based on the AI review 1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter. 2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls. --- .../controllers/public_share_controller.py | 7 +- api/app/controllers/workflow_controller.py | 14 +-- api/app/core/memory/agent/utils/llm_tools.py | 3 +- .../core/memory/analytics/api_docs_parser.py | 3 +- .../memory/analytics/recent_activity_stats.py | 88 +++++++++---------- .../memory/evaluation/locomo/locomo_test.py | 5 +- .../longmemeval/qwen_search_eval.py | 5 +- .../evaluation/memsciqa/memsciqa-test.py | 5 +- api/app/repositories/app_repository.py | 10 ++- api/app/repositories/home_page_repository.py | 30 +++---- api/app/repositories/user_repository.py | 4 +- api/app/repositories/workflow_repository.py | 2 +- api/app/repositories/workspace_repository.py | 24 ++--- api/app/services/agent_registry.py | 4 +- api/app/services/app_service.py | 10 +-- api/app/services/draft_run_service.py | 2 +- api/app/services/memory_agent_service.py | 17 ++-- api/app/services/memory_api_service.py | 5 +- api/app/services/memory_reflection_service.py | 5 +- api/app/services/memory_storage_service.py | 3 +- api/app/services/multi_agent_orchestrator.py | 4 +- api/app/services/multi_agent_service.py | 4 +- api/app/services/shared_chat_service.py | 8 +- api/app/services/workflow_service.py | 5 +- api/app/tasks.py | 7 +- api/migrations/env.py | 3 +- 26 files changed, 148 insertions(+), 129 deletions(-) diff --git a/api/app/controllers/public_share_controller.py b/api/app/controllers/public_share_controller.py index 17ad70a7..6e2d383c 100644 --- a/api/app/controllers/public_share_controller.py +++ b/api/app/controllers/public_share_controller.py @@ -317,9 +317,12 @@ async def chat( appid = share.app_id """获取存储类型和工作空间的ID""" - # 直接通过 SQLAlchemy 查询 app + # 直接通过 SQLAlchemy 查询 app（仅查询未删除的应用） from app.models.app_model import App - app = db.query(App).filter(App.id == appid).first() + app = db.query(App).filter( + App.id == appid, + App.is_active.is_(True) + ).first() if not app: raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND) diff --git a/api/app/controllers/workflow_controller.py b/api/app/controllers/workflow_controller.py index c6d9ddab..8a15f717 100644 --- a/api/app/controllers/workflow_controller.py +++ b/api/app/controllers/workflow_controller.py @@ -54,7 +54,7 @@ async def create_workflow_config( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -214,7 +214,7 @@ async def delete_workflow_config( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -259,7 +259,7 @@ async def validate_workflow_config( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -329,7 +329,7 @@ async def get_workflow_executions( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -389,7 +389,7 @@ async def get_workflow_execution( app = db.query(App).filter( App.id == execution.app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -440,7 +440,7 @@ async def run_workflow( app = db.query(App).filter( App.id == app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: @@ -578,7 +578,7 @@ async def cancel_workflow_execution( app = db.query(App).filter( App.id == execution.app_id, App.workspace_id == current_user.current_workspace_id, - App.is_active == True + App.is_active.is_(True) ).first() if not app: diff --git a/api/app/core/memory/agent/utils/llm_tools.py b/api/app/core/memory/agent/utils/llm_tools.py index 8dd2f1d3..e73d5653 100644 --- a/api/app/core/memory/agent/utils/llm_tools.py +++ b/api/app/core/memory/agent/utils/llm_tools.py @@ -1,11 +1,12 @@ import os from collections import defaultdict +from pathlib import Path from typing import Annotated, TypedDict from langchain_core.messages import AnyMessage from langgraph.graph import add_messages -PROJECT_ROOT_ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3]) class WriteState(TypedDict): ''' diff --git a/api/app/core/memory/analytics/api_docs_parser.py b/api/app/core/memory/analytics/api_docs_parser.py index 94ed0f00..4a116520 100644 --- a/api/app/core/memory/analytics/api_docs_parser.py +++ b/api/app/core/memory/analytics/api_docs_parser.py @@ -139,7 +139,8 @@ def parse_api_docs(file_path: str) -> Dict[str, Any]: def get_default_docs_path() -> str: - project_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) return os.path.join(project_root, "src", "analytics", "API接口.md") diff --git a/api/app/core/memory/analytics/recent_activity_stats.py b/api/app/core/memory/analytics/recent_activity_stats.py index c41f4208..71f70c09 100644 --- a/api/app/core/memory/analytics/recent_activity_stats.py +++ b/api/app/core/memory/analytics/recent_activity_stats.py @@ -2,13 +2,16 @@ import os import re import glob import json +from pathlib import Path from typing import Tuple try: from app.core.memory.utils.config.definitions import PROJECT_ROOT except Exception: # Fallback: derive project root from this file location - PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + # 当前文件在 api/app/core/memory/analytics/recent_activity_stats.py + # 需要向上 5 级到达 api/ 目录 + PROJECT_ROOT = str(Path(__file__).resolve().parents[4]) def _get_latest_prompt_log_path() -> str | None: @@ -67,44 +70,43 @@ def parse_stats_from_log(log_path: str) -> dict: triplet_relations_count = 0 temporal_count = 0 - # Patterns + # 正则表达式模式 - 匹配当前日志格式 pat_chunk_render = re.compile(r"===\s*RENDERED\s*STATEMENT\s*EXTRACTION\s*PROMPT\s*===") - pat_triplet_start = re.compile(r"\[Triplet\].*statements_to_process\s*=\s*(\d+)") - pat_triplet_done = re.compile( - r"\[Triplet\].*completed,\s*total_triplets\s*=\s*(\d+),\s*total_entities\s*=\s*(\d+)" + pat_triplet_started = re.compile(r"\[Triplet\]\s+Started\s+-\s+statement_id=") + pat_triplet_completed = re.compile( + r"\[Triplet\]\s+Completed\s+-\s+statement_id=[^,]+,\s+triplets=(\d+),\s+entities=(\d+)" ) - pat_temporal_done = re.compile( - r"\[Temporal\].*completed,\s*extracted_valid_ranges\s*=\s*(\d+)" + pat_temporal_completed = re.compile( + r"\[Temporal\]\s+Completed\s+-\s+statement_id=[^,]+,\s+valid_ranges=(\d+)" ) with open(log_path, "r", encoding="utf-8", errors="ignore") as f: for line in f: - # Chunk prompts count (each chunk triggers one statement-extraction prompt render) + # 文本块数量（每个块触发一次陈述提取提示） if pat_chunk_render.search(line): chunk_count += 1 continue - m1 = pat_triplet_start.search(line) - if m1: + # 陈述数量（每个 Triplet Started 代表一个陈述被处理） + if pat_triplet_started.search(line): + statements_count += 1 + continue + + # 三元组完成：[Triplet] Completed - statement_id=xxx, triplets=X, entities=Y + m_triplet = pat_triplet_completed.search(line) + if m_triplet: try: - statements_count += int(m1.group(1)) + triplet_relations_count += int(m_triplet.group(1)) + triplet_entities_count += int(m_triplet.group(2)) except Exception: pass continue - m2 = pat_triplet_done.search(line) - if m2: + # 时间信息完成：[Temporal] Completed - statement_id=xxx, valid_ranges=X + m_temporal = pat_temporal_completed.search(line) + if m_temporal: try: - triplet_relations_count += int(m2.group(1)) - triplet_entities_count += int(m2.group(2)) - except Exception: - pass - continue - - m3 = pat_temporal_done.search(line) - if m3: - try: - temporal_count += int(m3.group(1)) + temporal_count += int(m_temporal.group(1)) except Exception: pass continue @@ -120,15 +122,20 @@ def parse_stats_from_log(log_path: str) -> dict: def get_recent_activity_stats() -> Tuple[dict, str]: - """Get aggregated stats from all prompt logs in logs/. + """Get stats from the latest prompt log file only. Returns (stats_dict, message). """ - all_logs = _get_all_prompt_logs() - # Fallback to recursive search if none found in logs/ - if not all_logs: + # 获取最新的日志文件 + latest_log = _get_latest_prompt_log_path() + + # 如果没有找到，尝试递归搜索 + if not latest_log: all_logs = _get_any_logs_recursive() - if not all_logs: + if all_logs: + latest_log = all_logs[-1] # 取最新的 + + if not latest_log: return ( { "chunk_count": 0, @@ -141,24 +148,13 @@ def get_recent_activity_stats() -> Tuple[dict, str]: "未找到日志文件，请确认已运行过提取流程。", ) - agg = { - "chunk_count": 0, - "statements_count": 0, - "triplet_entities_count": 0, - "triplet_relations_count": 0, - "temporal_count": 0, - } - for path in all_logs: - s = parse_stats_from_log(path) - agg["chunk_count"] += s.get("chunk_count", 0) - agg["statements_count"] += s.get("statements_count", 0) - agg["triplet_entities_count"] += s.get("triplet_entities_count", 0) - agg["triplet_relations_count"] += s.get("triplet_relations_count", 0) - agg["temporal_count"] += s.get("temporal_count", 0) - - # Attach a summary of files combined - agg["log_path"] = f"{len(all_logs)} 个日志文件，最新：{all_logs[-1]}" - return agg, "成功汇总 logs 目录中所有提示日志。" + # 只解析最新的日志文件 + stats = parse_stats_from_log(latest_log) + + # 添加日志文件路径信息 + stats["log_path"] = f"最新：{latest_log}" + + return stats, "成功读取最近一次记忆活动统计。" def _format_summary(stats: dict) -> str: diff --git a/api/app/core/memory/evaluation/locomo/locomo_test.py b/api/app/core/memory/evaluation/locomo/locomo_test.py index b5ad5820..affedd0f 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_test.py +++ b/api/app/core/memory/evaluation/locomo/locomo_test.py @@ -8,13 +8,14 @@ import sys import time from datetime import datetime, timedelta from typing import Any, Dict, List +from pathlib import Path from dotenv import load_dotenv # 1 # 添加项目根目录到路径 -current_dir = os.path.dirname(os.path.abspath(__file__)) -project_root = os.path.dirname(current_dir) +current_dir = Path(__file__).resolve().parent +project_root = str(current_dir.parent) if project_root not in sys.path: sys.path.insert(0, project_root) # 关键：将 src 目录置于最前，确保从当前仓库加载模块 diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py index 53c5ce19..292e7288 100644 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py @@ -16,9 +16,10 @@ except Exception: # 确保可以找到 src 及项目根路径 import sys +from pathlib import Path -_THIS_DIR = os.path.dirname(os.path.abspath(__file__)) -_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(_THIS_DIR))) +_THIS_DIR = Path(__file__).resolve().parent +_PROJECT_ROOT = str(_THIS_DIR.parents[2]) _SRC_DIR = os.path.join(_PROJECT_ROOT, "src") for _p in (_SRC_DIR, _PROJECT_ROOT): if _p not in sys.path: diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py index 279f4042..900cda9d 100644 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py @@ -15,9 +15,10 @@ except Exception: # 路径与模块导入保持与现有评估脚本一致 import sys +from pathlib import Path -_THIS_DIR = os.path.dirname(os.path.abspath(__file__)) -_PROJECT_ROOT = os.path.dirname(os.path.dirname(_THIS_DIR)) +_THIS_DIR = Path(__file__).resolve().parent +_PROJECT_ROOT = str(_THIS_DIR.parents[1]) _SRC_DIR = os.path.join(_PROJECT_ROOT, "src") for _p in (_SRC_DIR, _PROJECT_ROOT): if _p not in sys.path: diff --git a/api/app/repositories/app_repository.py b/api/app/repositories/app_repository.py index 11a2ea3e..0c7ba6a4 100644 --- a/api/app/repositories/app_repository.py +++ b/api/app/repositories/app_repository.py @@ -15,9 +15,13 @@ class AppRepository: self.db = db def get_apps_by_workspace_id(self, workspace_id: uuid.UUID) -> list[App]: - """根据工作空间ID查询应用""" + """根据工作空间ID查询应用（仅返回未删除的应用）""" try: - apps = self.db.query(App).filter(App.workspace_id == workspace_id).all() + apps = ( + self.db.query(App) + .filter(App.workspace_id == workspace_id, App.is_active.is_(True)) + .all() + ) db_logger.info(f"成功查询工作空间 {workspace_id} 下的 {len(apps)} 个应用") return apps except Exception as e: @@ -26,7 +30,7 @@ class AppRepository: def get_apps_by_id(self, app_id: uuid.UUID) -> App: try: - app = self.db.query(App).filter(App.id == app_id, App.is_active == True).first() + app = self.db.query(App).filter(App.id == app_id, App.is_active.is_(True)).first() return app except Exception as e: raise diff --git a/api/app/repositories/home_page_repository.py b/api/app/repositories/home_page_repository.py index 888071ac..bcb3b622 100644 --- a/api/app/repositories/home_page_repository.py +++ b/api/app/repositories/home_page_repository.py @@ -17,24 +17,24 @@ class HomePageRepository: """获取模型统计数据""" total_models = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True + ModelConfig.is_active.is_(True) ).count() total_llm = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True, + ModelConfig.is_active.is_(True), ModelConfig.type == "llm" ).count() total_embedding = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True, + ModelConfig.is_active.is_(True), ModelConfig.type == "embedding" ).count() new_models_this_week = db.query(ModelConfig).filter( ModelConfig.tenant_id == tenant_id, - ModelConfig.is_active == True, + ModelConfig.is_active.is_(True), ModelConfig.created_at >= week_start ).count() @@ -56,12 +56,12 @@ class HomePageRepository: """获取工作空间统计数据""" active_workspaces = db.query(Workspace).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).count() new_workspaces_this_week = db.query(Workspace).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True, + Workspace.is_active.is_(True), Workspace.created_at >= week_start ).count() @@ -83,7 +83,7 @@ class HomePageRepository: """获取用户统计数据""" workspace_ids = db.query(Workspace.id).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).subquery() total_users = db.query(EndUser).join( @@ -91,7 +91,7 @@ class HomePageRepository: EndUser.app_id == App.id ).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active" ).count() @@ -100,7 +100,7 @@ class HomePageRepository: EndUser.app_id == App.id ).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active", EndUser.created_at >= week_start ).count() @@ -123,18 +123,18 @@ class HomePageRepository: """获取应用统计数据""" workspace_ids = db.query(Workspace.id).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).subquery() running_apps = db.query(App).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active" ).count() new_apps_this_week = db.query(App).filter( App.workspace_id.in_(workspace_ids), - App.is_active == True, + App.is_active.is_(True), App.status == "active", App.created_at >= week_start ).count() @@ -158,7 +158,7 @@ class HomePageRepository: # 获取工作空间列表 workspaces = db.query(Workspace).filter( Workspace.tenant_id == tenant_id, - Workspace.is_active == True + Workspace.is_active.is_(True) ).all() workspace_ids = [ws.id for ws in workspaces] @@ -169,7 +169,7 @@ class HomePageRepository: func.count(App.id).label('count') ).filter( App.workspace_id.in_(workspace_ids), - App.is_active, + App.is_active.is_(True), App.status == "active" ).group_by(App.workspace_id).all() @@ -184,7 +184,7 @@ class HomePageRepository: EndUser.app_id == App.id ).filter( App.workspace_id.in_(workspace_ids), - App.is_active, + App.is_active.is_(True), App.status == "active" ).group_by(App.workspace_id).all() diff --git a/api/app/repositories/user_repository.py b/api/app/repositories/user_repository.py index a43c5869..b4c11aa4 100644 --- a/api/app/repositories/user_repository.py +++ b/api/app/repositories/user_repository.py @@ -68,7 +68,7 @@ class UserRepository: db_logger.debug("查询超级用户") try: - user = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active == True).filter(User.is_superuser == True).first() + user = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active.is_(True)).filter(User.is_superuser.is_(True)).first() if user: db_logger.debug(f"超级用户查询成功: {user.username}") else: @@ -82,7 +82,7 @@ class UserRepository: db_logger.debug("检查是否只有一个超级用户") try: - count = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active == True).filter(User.is_superuser == True).count() + count = self.db.query(User).options(joinedload(User.tenant)).filter(User.is_active.is_(True)).filter(User.is_superuser.is_(True)).count() return count == 1 except Exception as e: db_logger.error(f"检查超级用户数量失败: {str(e)}") diff --git a/api/app/repositories/workflow_repository.py b/api/app/repositories/workflow_repository.py index 04734640..b22673e6 100644 --- a/api/app/repositories/workflow_repository.py +++ b/api/app/repositories/workflow_repository.py @@ -33,7 +33,7 @@ class WorkflowConfigRepository: """ return self.db.query(WorkflowConfig).filter( WorkflowConfig.app_id == app_id, - WorkflowConfig.is_active == True + WorkflowConfig.is_active.is_(True) ).first() def create_or_update( diff --git a/api/app/repositories/workspace_repository.py b/api/app/repositories/workspace_repository.py index 106830be..70ed7521 100644 --- a/api/app/repositories/workspace_repository.py +++ b/api/app/repositories/workspace_repository.py @@ -103,7 +103,7 @@ class WorkspaceRepository: workspaces = ( self.db.query(Workspace) .filter(Workspace.tenant_id == user.tenant_id) - .filter(Workspace.is_active == True) + .filter(Workspace.is_active.is_(True)) .order_by(Workspace.updated_at.desc()) .all() ) @@ -115,7 +115,7 @@ class WorkspaceRepository: self.db.query(Workspace) .join(WorkspaceMember, Workspace.id == WorkspaceMember.workspace_id) .filter(WorkspaceMember.user_id == user_id) - .filter(Workspace.is_active == True) + .filter(Workspace.is_active.is_(True)) .order_by(Workspace.updated_at.desc()) .all() ) @@ -134,7 +134,7 @@ class WorkspaceRepository: workspaces = ( self.db.query(Workspace) .filter(Workspace.tenant_id == tenant_id) - .filter(Workspace.is_active == True) + .filter(Workspace.is_active.is_(True)) .all() ) db_logger.debug(f"租户工作空间查询成功: tenant_id={tenant_id}, 数量={len(workspaces)}") @@ -169,7 +169,7 @@ class WorkspaceRepository: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.user_id == user_id, WorkspaceMember.workspace_id == workspace_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if member: db_logger.debug(f"工作空间成员查询成功: user_id={user_id}, workspace_id={workspace_id}, role={member.role}") @@ -189,8 +189,8 @@ class WorkspaceRepository: .join(User, WorkspaceMember.user_id == User.id) .options(joinedload(WorkspaceMember.user), joinedload(WorkspaceMember.workspace)) .filter(WorkspaceMember.workspace_id == workspace_id) - .filter(WorkspaceMember.is_active == True) - .filter(User.is_active == True) + .filter(WorkspaceMember.is_active.is_(True)) + .filter(User.is_active.is_(True)) .all() ) db_logger.debug(f"成员列表查询成功: workspace_id={workspace_id}, 数量={len(members)}") @@ -208,8 +208,8 @@ class WorkspaceRepository: .join(User, WorkspaceMember.user_id == User.id) .options(joinedload(WorkspaceMember.user), joinedload(WorkspaceMember.workspace)) .filter(WorkspaceMember.id == member_id) - .filter(WorkspaceMember.is_active == True) - .filter(User.is_active == True) + .filter(WorkspaceMember.is_active.is_(True)) + .filter(User.is_active.is_(True)) .first() ) if member: @@ -226,7 +226,7 @@ class WorkspaceRepository: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.workspace_id == workspace_id, WorkspaceMember.user_id == user_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None @@ -243,7 +243,7 @@ class WorkspaceRepository: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.workspace_id == workspace_id, WorkspaceMember.user_id == user_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None @@ -259,7 +259,7 @@ class WorkspaceRepository: try: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.id == member_id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None @@ -275,7 +275,7 @@ class WorkspaceRepository: try: member = self.db.query(WorkspaceMember).filter( WorkspaceMember.id == id, - WorkspaceMember.is_active == True, + WorkspaceMember.is_active.is_(True), ).first() if not member: return None diff --git a/api/app/services/agent_registry.py b/api/app/services/agent_registry.py index 2b6d92e3..d221bbf5 100644 --- a/api/app/services/agent_registry.py +++ b/api/app/services/agent_registry.py @@ -55,8 +55,8 @@ class AgentRegistry: """ # 构建查询 stmt = select(AgentConfig).join(App).where( - AgentConfig.is_active == True, - App.is_active == True + AgentConfig.is_active.is_(True), + App.is_active.is_(True) ) # 工作空间过滤（同工作空间或公开） diff --git a/api/app/services/app_service.py b/api/app/services/app_service.py index 68acab1d..7ec4bc0e 100644 --- a/api/app/services/app_service.py +++ b/api/app/services/app_service.py @@ -758,7 +758,7 @@ class AppService: ) # 构建查询条件 - filters = [App.is_active == True] + filters = [App.is_active.is_(True)] if type: filters.append(App.type == type) if visibility: @@ -873,7 +873,7 @@ class AppService: self._validate_workspace_access(app, workspace_id) - stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active == True).order_by( + stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active.is_(True)).order_by( AgentConfig.updated_at.desc()) agent_cfg: Optional[AgentConfig] = self.db.scalars(stmt).first() now = datetime.datetime.now() @@ -1204,7 +1204,7 @@ class AppService: default_model_config_id = None if app.type == AppType.AGENT: - stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active == True).order_by( + stmt = select(AgentConfig).where(AgentConfig.app_id == app_id, AgentConfig.is_active.is_(True)).order_by( AgentConfig.updated_at.desc()) agent_cfg = self.db.scalars(stmt).first() if not agent_cfg: @@ -1226,7 +1226,7 @@ class AppService: select(MultiAgentConfig) .where( MultiAgentConfig.app_id == app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ) .order_by(MultiAgentConfig.updated_at.desc()) ) @@ -1380,7 +1380,7 @@ class AppService: stmt = ( select(AppRelease) - .where(AppRelease.app_id == app_id, AppRelease.is_active == True) + .where(AppRelease.app_id == app_id, AppRelease.is_active.is_(True)) .order_by(AppRelease.version.desc()) ) return list(self.db.scalars(stmt).all()) diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 46bda5f6..4f20f6d9 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -728,7 +728,7 @@ class DraftRunService: select(ModelApiKey) .where( ModelApiKey.model_config_id == model_config_id, - ModelApiKey.is_active == True + ModelApiKey.is_active.is_(True) ) .order_by(ModelApiKey.priority.desc()) .limit(1) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 8170bdd8..7c8ee9ac 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -173,10 +173,9 @@ class MemoryAgentService: """ logger.info("Reading log file") - - current_file = os.path.abspath(__file__) # app/services/memory_agent_service.py - app_dir = os.path.dirname(os.path.dirname(current_file)) # app directory - project_root = os.path.dirname(app_dir) # redbear-mem directory + # Get log file path - use project root directory + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) # api directory log_path = os.path.join(project_root, "logs", "agent_service.log") summer = '' @@ -215,9 +214,8 @@ class MemoryAgentService: logger.info("Starting log content streaming") # Get log file path - use project root directory - current_file = os.path.abspath(__file__) # app/services/memory_agent_service.py - app_dir = os.path.dirname(os.path.dirname(current_file)) # app directory - project_root = os.path.dirname(app_dir) # redbear-mem directory + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) # api directory log_path = os.path.join(project_root, "logs", "agent_service.log") # Check if file exists before starting stream @@ -1079,9 +1077,8 @@ class MemoryAgentService: logger.info("Starting log content streaming") # Get log file path - use project root directory - current_file = os.path.abspath(__file__) # app/services/memory_agent_service.py - app_dir = os.path.dirname(os.path.dirname(current_file)) # app directory - project_root = os.path.dirname(app_dir) # redbear-mem directory + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) # api directory log_path = os.path.join(project_root, "logs", "agent_service.log") # Check if file exists before starting stream diff --git a/api/app/services/memory_api_service.py b/api/app/services/memory_api_service.py index 0ae2b965..2d3d047e 100644 --- a/api/app/services/memory_api_service.py +++ b/api/app/services/memory_api_service.py @@ -77,7 +77,10 @@ class MemoryAPIService: ) # Verify end_user belongs to the workspace via App relationship - app = self.db.query(App).filter(App.id == end_user.app_id).first() + app = self.db.query(App).filter( + App.id == end_user.app_id, + App.is_active.is_(True) + ).first() if not app: logger.warning(f"App not found for end_user: {end_user_id}") diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index 46e42b46..af72e3cc 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -38,7 +38,10 @@ class WorkspaceAppService: Returns: Dictionary containing detailed application information """ - apps = self.db.query(App).filter(App.workspace_id == workspace_id).all() + apps = self.db.query(App).filter( + App.workspace_id == workspace_id, + App.is_active.is_(True) + ).all() app_ids = [str(app.id) for app in apps] apps_detailed_info = [] diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 83d5923d..48c3abf1 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -237,7 +237,8 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） ValueError: 当配置无效或参数缺失时 RuntimeError: 当管线执行失败时 """ - project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + from pathlib import Path + project_root = str(Path(__file__).resolve().parents[2]) try: # 发出初始进度事件 diff --git a/api/app/services/multi_agent_orchestrator.py b/api/app/services/multi_agent_orchestrator.py index 1972f344..4bcd28cd 100644 --- a/api/app/services/multi_agent_orchestrator.py +++ b/api/app/services/multi_agent_orchestrator.py @@ -2548,7 +2548,7 @@ class MultiAgentOrchestrator: # 获取 API Key 配置 api_key_config = self.db.query(ModelApiKey).filter( ModelApiKey.model_config_id == default_model_config_id, - ModelApiKey.is_active == True + ModelApiKey.is_active.is_(True) ).first() if not api_key_config: @@ -2705,7 +2705,7 @@ class MultiAgentOrchestrator: # 获取 API Key 配置 api_key_config = self.db.query(ModelApiKey).filter( ModelApiKey.model_config_id == default_model_config_id, - ModelApiKey.is_active == True + ModelApiKey.is_active.is_(True) ).first() if not api_key_config: diff --git a/api/app/services/multi_agent_service.py b/api/app/services/multi_agent_service.py index 1a08a5af..da984d16 100644 --- a/api/app/services/multi_agent_service.py +++ b/api/app/services/multi_agent_service.py @@ -74,7 +74,7 @@ class MultiAgentService: select(MultiAgentConfig) .where( MultiAgentConfig.app_id == app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ) .order_by(MultiAgentConfig.updated_at.desc()) ).first() @@ -144,7 +144,7 @@ class MultiAgentService: select(MultiAgentConfig) .where( MultiAgentConfig.app_id == app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ) .order_by(MultiAgentConfig.updated_at.desc()) ).first() diff --git a/api/app/services/shared_chat_service.py b/api/app/services/shared_chat_service.py index e5247e5e..5eee5edc 100644 --- a/api/app/services/shared_chat_service.py +++ b/api/app/services/shared_chat_service.py @@ -168,7 +168,7 @@ class SharedChatService: select(ModelApiKey) .where( ModelApiKey.model_config_id == model_config_id, - ModelApiKey.is_active == True + ModelApiKey.is_active.is_(True) ) .order_by(ModelApiKey.priority.desc()) .limit(1) @@ -362,7 +362,7 @@ class SharedChatService: select(ModelApiKey) .where( ModelApiKey.model_config_id == model_config_id, - ModelApiKey.is_active == True + ModelApiKey.is_active.is_(True) ) .order_by(ModelApiKey.priority.desc()) .limit(1) @@ -598,7 +598,7 @@ class SharedChatService: # 获取多 Agent 配置 multi_agent_config = self.db.query(MultiAgentConfig).filter( MultiAgentConfig.app_id == release.app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ).first() if not multi_agent_config: @@ -695,7 +695,7 @@ class SharedChatService: # 获取多 Agent 配置 multi_agent_config = self.db.query(MultiAgentConfig).filter( MultiAgentConfig.app_id == release.app_id, - MultiAgentConfig.is_active == True + MultiAgentConfig.is_active.is_(True) ).first() if not multi_agent_config: diff --git a/api/app/services/workflow_service.py b/api/app/services/workflow_service.py index b7d5df02..f9426c87 100644 --- a/api/app/services/workflow_service.py +++ b/api/app/services/workflow_service.py @@ -761,7 +761,10 @@ class WorkflowService: # 4. 获取工作空间 ID（从 app 获取） from app.models import App - app = self.db.query(App).filter(App.id == app_id).first() + app = self.db.query(App).filter( + App.id == app_id, + App.is_active.is_(True) + ).first() if not app: raise BusinessException( code=BizCode.NOT_FOUND, diff --git a/api/app/tasks.py b/api/app/tasks.py index fa9d1fdf..5f2b1ef5 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -635,8 +635,11 @@ def write_total_memory_task(workspace_id: str) -> Dict[str, Any]: try: workspace_uuid = uuid.UUID(workspace_id) - # 1. 查询当前workspace下的所有app - apps = db.query(App).filter(App.workspace_id == workspace_uuid).all() + # 1. 查询当前workspace下的所有app（仅未删除的） + apps = db.query(App).filter( + App.workspace_id == workspace_uuid, + App.is_active.is_(True) + ).all() if not apps: # 如果没有app，总量为0 diff --git a/api/migrations/env.py b/api/migrations/env.py index 95d74019..e4cd6dfb 100644 --- a/api/migrations/env.py +++ b/api/migrations/env.py @@ -46,7 +46,8 @@ def import_all_models_from_package(package_name: str): # Add the project root to sys.path if not already there # This is crucial for relative imports like 'app.db' to work - project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) + from pathlib import Path + project_root = str(Path(__file__).resolve().parent.parent) if project_root not in sys.path: sys.path.insert(0, project_root) From 6e18c92a130ceeb4c158bbeb9a267376d88ab74c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B9=90=E5=8A=9B=E9=BD=90?= <162269739+lanceyq@users.noreply.github.com> Date: Fri, 23 Jan 2026 12:21:28 +0800 Subject: [PATCH 045/175] Fix/optimize inerface (#183) * [changes]Optimize the time consumption of the "/end_users" interface * [fix]Optimize the time consumption of the "/hot_memory_tags" interface * [changes]Optimize the time consumption of the "/end_users" interface * [fix]Optimize the time consumption of the "/hot_memory_tags" interface * [changes]Improve the code based on AI review --- .../memory_dashboard_controller.py | 146 +++++++++++++----- .../controllers/memory_storage_controller.py | 82 +++++++++- api/app/services/memory_dashboard_service.py | 87 ++++++++++- api/app/services/memory_storage_service.py | 86 +++++++++-- 4 files changed, 340 insertions(+), 61 deletions(-) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index e03c1846..6181c319 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -49,63 +49,135 @@ async def get_workspace_end_users( current_user: User = Depends(get_current_user), ): """ - 获取工作空间的宿主列表 + 获取工作空间的宿主列表（高性能优化版本 v2） - 返回格式与原 memory_list 接口中的 end_users 字段相同， - 并包含每个用户的记忆配置信息（memory_config_id 和 memory_config_name） + 优化策略： + 1. 批量查询 end_users（一次查询而非循环） + 2. 并发查询所有用户的记忆数量（Neo4j） + 3. RAG 模式使用批量查询（一次 SQL） + 4. 只返回必要字段减少数据传输 + 5. 添加短期缓存减少重复查询 + 6. 并发执行配置查询和记忆数量查询 + + 返回格式： + { + "end_user": {"id": "uuid", "other_name": "名称"}, + "memory_num": {"total": 数量}, + "memory_config": {"memory_config_id": "id", "memory_config_name": "名称"} + } """ + import asyncio + import json + from app.aioRedis import aio_redis_get, aio_redis_set + workspace_id = current_user.current_workspace_id + + # 尝试从缓存获取（30秒缓存） + cache_key = f"end_users:workspace:{workspace_id}" + try: + cached_data = await aio_redis_get(cache_key) + if cached_data: + api_logger.info(f"从缓存获取宿主列表: workspace_id={workspace_id}") + return success(data=json.loads(cached_data), msg="宿主列表获取成功") + except Exception as e: + api_logger.warning(f"Redis 缓存读取失败: {str(e)}") + # 获取当前空间类型 current_workspace_type = memory_dashboard_service.get_current_workspace_type(db, workspace_id, current_user) api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表") + + # 获取 end_users（已优化为批量查询） end_users = memory_dashboard_service.get_workspace_end_users( db=db, workspace_id=workspace_id, current_user=current_user ) - # 批量获取所有用户的记忆配置信息（优化：一次查询而非 N 次） - end_user_ids = [str(user.id) for user in end_users] - memory_configs_map = {} - if end_user_ids: + if not end_users: + api_logger.info("工作空间下没有宿主") + # 缓存空结果，避免重复查询 try: - memory_configs_map = get_end_users_connected_configs_batch(end_user_ids, db) + await aio_redis_set(cache_key, json.dumps([]), expire=30) + except Exception as e: + api_logger.warning(f"Redis 缓存写入失败: {str(e)}") + return success(data=[], msg="宿主列表获取成功") + + end_user_ids = [str(user.id) for user in end_users] + + # 并发执行两个独立的查询任务 + async def get_memory_configs(): + """获取记忆配置（在线程池中执行同步查询）""" + try: + return await asyncio.to_thread( + get_end_users_connected_configs_batch, + end_user_ids, db + ) except Exception as e: api_logger.error(f"批量获取记忆配置失败: {str(e)}") - # 失败时使用空字典，不影响其他数据返回 + return {} + async def get_memory_nums(): + """获取记忆数量""" + if current_workspace_type == "rag": + # RAG 模式：批量查询 + try: + chunk_map = await asyncio.to_thread( + memory_dashboard_service.get_users_total_chunk_batch, + end_user_ids, db, current_user + ) + return {uid: {"total": count} for uid, count in chunk_map.items()} + except Exception as e: + api_logger.error(f"批量获取 RAG chunk 数量失败: {str(e)}") + return {uid: {"total": 0} for uid in end_user_ids} + + elif current_workspace_type == "neo4j": + # Neo4j 模式：并发查询（带并发限制） + # 使用信号量限制并发数，避免大量用户时压垮 Neo4j + MAX_CONCURRENT_QUERIES = 10 + semaphore = asyncio.Semaphore(MAX_CONCURRENT_QUERIES) + + async def get_neo4j_memory_num(end_user_id: str): + async with semaphore: + try: + return await memory_storage_service.search_all(end_user_id) + except Exception as e: + api_logger.error(f"获取用户 {end_user_id} Neo4j 记忆数量失败: {str(e)}") + return {"total": 0} + + memory_nums_list = await asyncio.gather(*[get_neo4j_memory_num(uid) for uid in end_user_ids]) + return {end_user_ids[i]: memory_nums_list[i] for i in range(len(end_user_ids))} + + return {uid: {"total": 0} for uid in end_user_ids} + + # 并发执行配置查询和记忆数量查询 + memory_configs_map, memory_nums_map = await asyncio.gather( + get_memory_configs(), + get_memory_nums() + ) + + # 构建结果（优化：使用列表推导式） result = [] for end_user in end_users: - memory_num = {} - if current_workspace_type == "neo4j": - # EndUser 是 Pydantic 模型，直接访问属性而不是使用 .get() - memory_num = await memory_storage_service.search_all(str(end_user.id)) - elif current_workspace_type == "rag": - memory_num = { - "total":memory_dashboard_service.get_current_user_total_chunk(str(end_user.id), db, current_user) - } - - # 从批量查询结果中获取配置信息 user_id = str(end_user.id) - memory_config_info = memory_configs_map.get(user_id, { - "memory_config_id": None, - "memory_config_name": None - }) - - # 只保留需要的字段，移除 error 字段（如果有） - memory_config = { - "memory_config_id": memory_config_info.get("memory_config_id"), - "memory_config_name": memory_config_info.get("memory_config_name") - } - - result.append( - { - 'end_user': end_user, - 'memory_num': memory_num, - 'memory_config': memory_config + config_info = memory_configs_map.get(user_id, {}) + result.append({ + 'end_user': { + 'id': user_id, + 'other_name': end_user.other_name + }, + 'memory_num': memory_nums_map.get(user_id, {"total": 0}), + 'memory_config': { + "memory_config_id": config_info.get("memory_config_id"), + "memory_config_name": config_info.get("memory_config_name") } - ) - + }) + + # 写入缓存（30秒过期） + try: + await aio_redis_set(cache_key, json.dumps(result), expire=30) + except Exception as e: + api_logger.warning(f"Redis 缓存写入失败: {str(e)}") + api_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return success(data=result, msg="宿主列表获取成功") diff --git a/api/app/controllers/memory_storage_controller.py b/api/app/controllers/memory_storage_controller.py index f4175923..3722be3d 100644 --- a/api/app/controllers/memory_storage_controller.py +++ b/api/app/controllers/memory_storage_controller.py @@ -420,15 +420,95 @@ async def get_hot_memory_tags_api( db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ) -> dict: - api_logger.info(f"Hot memory tags requested for current_user: {current_user.id}") + """ + 获取热门记忆标签（带Redis缓存） + + 缓存策略： + - 缓存键：workspace_id + limit + - 过期时间：5分钟（300秒） + - 缓存命中：~50ms + - 缓存未命中：~600-800ms（取决于LLM速度） + """ + workspace_id = current_user.current_workspace_id + + # 构建缓存键 + cache_key = f"hot_memory_tags:{workspace_id}:{limit}" + + api_logger.info(f"Hot memory tags requested for workspace: {workspace_id}, limit: {limit}") + try: + # 尝试从Redis缓存获取 + from app.aioRedis import aio_redis_get, aio_redis_set + import json + + cached_result = await aio_redis_get(cache_key) + if cached_result: + api_logger.info(f"Cache hit for key: {cache_key}") + try: + data = json.loads(cached_result) + return success(data=data, msg="查询成功（缓存）") + except json.JSONDecodeError: + api_logger.warning(f"Failed to parse cached data, will refresh") + + # 缓存未命中，执行查询 + api_logger.info(f"Cache miss for key: {cache_key}, executing query") result = await analytics_hot_memory_tags(db, current_user, limit) + + # 写入缓存（过期时间：5分钟） + # 注意：result是列表，需要转换为JSON字符串 + try: + cache_data = json.dumps(result, ensure_ascii=False) + await aio_redis_set(cache_key, cache_data, expire=300) + api_logger.info(f"Cached result for key: {cache_key}") + except Exception as cache_error: + # 缓存写入失败不影响主流程 + api_logger.warning(f"Failed to cache result: {str(cache_error)}") + return success(data=result, msg="查询成功") + except Exception as e: api_logger.error(f"Hot memory tags failed: {str(e)}") return fail(BizCode.INTERNAL_ERROR, "热门标签查询失败", str(e)) +@router.delete("/analytics/hot_memory_tags/cache", response_model=ApiResponse) +async def clear_hot_memory_tags_cache( + current_user: User = Depends(get_current_user), + ) -> dict: + """ + 清除热门标签缓存 + + 用于： + - 手动刷新数据 + - 调试和测试 + - 数据更新后立即生效 + """ + workspace_id = current_user.current_workspace_id + + api_logger.info(f"Clear hot memory tags cache requested for workspace: {workspace_id}") + + try: + from app.aioRedis import aio_redis_delete + + # 清除所有limit的缓存（常见的limit值） + cleared_count = 0 + for limit in [5, 10, 15, 20, 30, 50]: + cache_key = f"hot_memory_tags:{workspace_id}:{limit}" + result = await aio_redis_delete(cache_key) + if result: + cleared_count += 1 + api_logger.info(f"Cleared cache for key: {cache_key}") + + return success( + data={"cleared_count": cleared_count}, + msg=f"成功清除 {cleared_count} 个缓存" + ) + + except Exception as e: + api_logger.error(f"Clear cache failed: {str(e)}") + return fail(BizCode.INTERNAL_ERROR, "清除缓存失败", str(e)) + + @router.get("/analytics/recent_activity_stats", response_model=ApiResponse) async def get_recent_activity_stats_api( current_user: User = Depends(get_current_user), diff --git a/api/app/services/memory_dashboard_service.py b/api/app/services/memory_dashboard_service.py index a774647e..06a94060 100644 --- a/api/app/services/memory_dashboard_service.py +++ b/api/app/services/memory_dashboard_service.py @@ -53,18 +53,28 @@ def get_workspace_end_users( workspace_id: uuid.UUID, current_user: User ) -> List[EndUser]: - """获取工作空间的所有宿主""" + """获取工作空间的所有宿主（优化版本：减少数据库查询次数）""" business_logger.info(f"获取工作空间宿主列表: workspace_id={workspace_id}, 操作者: {current_user.username}") try: - # 查询应用（ORM）并转换为 Pydantic 模型 + # 查询应用（ORM） apps_orm = app_repository.get_apps_by_workspace_id(db, workspace_id) - apps = [AppSchema.model_validate(h) for h in apps_orm] - app_ids = [app.id for app in apps] - end_users = [] - for app_id in app_ids: - end_user_orm_list = end_user_repository.get_end_users_by_app_id(db, app_id) - end_users.extend([EndUserSchema.model_validate(h) for h in end_user_orm_list]) + + if not apps_orm: + business_logger.info("工作空间下没有应用") + return [] + + # 提取所有 app_id + app_ids = [app.id for app in apps_orm] + + # 批量查询所有 end_users（一次查询而非循环查询） + from app.models.end_user_model import EndUser as EndUserModel + end_users_orm = db.query(EndUserModel).filter( + EndUserModel.app_id.in_(app_ids) + ).all() + + # 转换为 Pydantic 模型（只在需要时转换） + end_users = [EndUserSchema.model_validate(eu) for eu in end_users_orm] business_logger.info(f"成功获取 {len(end_users)} 个宿主记录") return end_users @@ -414,6 +424,67 @@ def get_current_user_total_chunk( business_logger.error(f"获取用户总chunk数失败: end_user_id={end_user_id} - {str(e)}") raise + +def get_users_total_chunk_batch( + end_user_ids: List[str], + db: Session, + current_user: User +) -> dict: + """ + 批量获取多个用户的总chunk数（性能优化版本） + + Args: + end_user_ids: 用户ID列表 + db: 数据库会话 + current_user: 当前用户 + + Returns: + 字典，key为end_user_id，value为chunk总数 + 格式: {"user_id_1": 100, "user_id_2": 50, ...} + """ + business_logger.info(f"批量获取 {len(end_user_ids)} 个用户的总chunk数, 操作者: {current_user.username}") + + try: + from app.models.document_model import Document + from sqlalchemy import func, case + + if not end_user_ids: + return {} + + # 构造所有文件名 + file_names = [f"{user_id}.txt" for user_id in end_user_ids] + + # 一次查询获取所有用户的chunk总数 + # 使用 GROUP BY file_name 来分组统计 + results = db.query( + Document.file_name, + func.sum(Document.chunk_num).label('total_chunk') + ).filter( + Document.file_name.in_(file_names) + ).group_by( + Document.file_name + ).all() + + # 构建结果字典 + chunk_map = {} + for file_name, total_chunk in results: + # 从文件名中提取 end_user_id (去掉 .txt 后缀) + user_id = file_name.replace('.txt', '') + chunk_map[user_id] = int(total_chunk or 0) + + # 对于没有记录的用户，设置为0 + for user_id in end_user_ids: + if user_id not in chunk_map: + chunk_map[user_id] = 0 + + business_logger.info(f"成功批量获取 {len(chunk_map)} 个用户的总chunk数") + return chunk_map + + except Exception as e: + business_logger.error(f"批量获取用户总chunk数失败: {str(e)}") + raise + + def get_rag_content( end_user_id: str, limit: int, diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 48c3abf1..c276f337 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -12,7 +12,11 @@ from datetime import datetime from typing import Any, AsyncGenerator, Dict, List, Optional from app.core.logging_config import get_config_logger, get_logger -from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags +from app.core.memory.analytics.hot_memory_tags import ( + get_hot_memory_tags, + get_raw_tags_from_db, + filter_tags_with_llm, +) from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats from app.models.user_model import User from app.repositories.data_config_repository import DataConfigRepository @@ -515,27 +519,79 @@ async def analytics_hot_memory_tags( ) -> List[Dict[str, Any]]: """ 获取热门记忆标签，按数量排序并返回前N个 + + 优化策略： + 1. 先从所有用户收集原始标签（不调用LLM） + 2. 聚合并合并相同标签的频率 + 3. 排序后取前N个 + 4. 只调用一次LLM进行筛选 """ workspace_id = current_user.current_workspace_id # 获取更多标签供LLM筛选（获取limit*4个标签） raw_limit = limit * 4 from app.services.memory_dashboard_service import get_workspace_end_users - end_users = get_workspace_end_users(db, workspace_id, current_user) + # 使用 asyncio.to_thread 避免阻塞事件循环 + end_users = await asyncio.to_thread(get_workspace_end_users, db, workspace_id, current_user) - tags = [] - for end_user in end_users: - tag = await get_hot_memory_tags(str(end_user.id), limit=raw_limit) - if tag: - # 将每个用户的标签列表展平到总列表中 - tags.extend(tag) - - # 按频率降序排序（虽然数据库已经排序，但为了确保正确性再次排序） - sorted_tags = sorted(tags, key=lambda x: x[1], reverse=True) + if not end_users: + return [] - # 只返回前limit个 - top_tags = sorted_tags[:limit] - - return [{"name": t, "frequency": f} for t, f in top_tags] + # 步骤1: 收集所有用户的原始标签（不调用LLM） + connector = Neo4jConnector() + try: + all_raw_tags = [] + for end_user in end_users: + raw_tags = await get_raw_tags_from_db( + connector, + str(end_user.id), + limit=raw_limit, + by_user=False + ) + if raw_tags: + all_raw_tags.extend(raw_tags) + + if not all_raw_tags: + return [] + + # 步骤2: 聚合相同标签的频率 + tag_frequency_map = {} + for tag_name, frequency in all_raw_tags: + if tag_name in tag_frequency_map: + tag_frequency_map[tag_name] += frequency + else: + tag_frequency_map[tag_name] = frequency + + # 步骤3: 按频率降序排序，取前raw_limit个 + sorted_tags = sorted( + tag_frequency_map.items(), + key=lambda x: x[1], + reverse=True + )[:raw_limit] + + if not sorted_tags: + return [] + + # 步骤4: 只调用一次LLM进行筛选 + tag_names = [tag for tag, _ in sorted_tags] + + # 使用第一个用户的group_id来获取LLM配置 + # 因为同一工作空间下的用户应该使用相同的配置 + first_end_user_id = str(end_users[0].id) + filtered_tag_names = await filter_tags_with_llm(tag_names, first_end_user_id) + + # 步骤5: 根据LLM筛选结果构建最终列表（保留频率） + final_tags = [] + for tag, freq in sorted_tags: + if tag in filtered_tag_names: + final_tags.append((tag, freq)) + + # 步骤6: 只返回前limit个 + top_tags = final_tags[:limit] + + return [{"name": t, "frequency": f} for t, f in top_tags] + + finally: + await connector.close() async def analytics_recent_activity_stats() -> Dict[str, Any]: From 15f9c49418c93bd1521fd64baddc11f63b3ee6c9 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Fri, 23 Jan 2026 12:21:54 +0800 Subject: [PATCH 046/175] Fix/memory mcp2 1 (#184) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 优化快速检索的回复内容 * 优化快速检索的回复内容 --- .../controllers/memory_agent_controller.py | 2 + .../utils/prompt/direct_summary_prompt.jinja2 | 61 +++++++++++++++++++ api/app/services/memory_agent_service.py | 2 +- 3 files changed, 64 insertions(+), 1 deletion(-) create mode 100644 api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 78a5771f..8b5a55b9 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -306,6 +306,8 @@ async def read_server( config_id=config_id, db=db ) + if "信息不足，无法回答" in result['answer']: + result['answer']=retrieve_info return success(data=result, msg="回复对话消息成功") except BaseException as e: # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup diff --git a/api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 new file mode 100644 index 00000000..1e0690bf --- /dev/null +++ b/api/app/core/memory/agent/utils/prompt/direct_summary_prompt.jinja2 @@ -0,0 +1,61 @@ +# 角色 +你是一个智能问答助手，基于检索信息和历史对话回答用户问题。 +# 任务 +根据提供的上下文信息回答用户的问题。 +# 输入信息 +- 历史对话：{{history}} +- 检索信息：{{retrieve_info}} +# 用户问题 +{{query}} +# 回答指南 +## 1. 仔细阅读检索信息 +- 答案可能直接或间接地出现在检索信息中 +- 如果检索信息中提到"小曼会使用Python"，说明用户名是"小曼" +- 第三人称描述的偏好、行为通常指用户本人 + +## 2. 判断信息相关性 +**情况A：信息匹配问题** +- 直接回答，像自然对话一样 +- 例：检索到"小曼会使用Python" → 问"我叫什么" → 答"你叫小曼" + +**情况B：信息部分相关** +- 先回答已知部分，再自然地询问更多信息 +- 例：检索到"用户去过上海的面包店" → 问"我吃过哪家面包" → 答"我记得你去过上海的面包店，但具体是哪家我不太清楚，是哪家呢？" + +**情况C：信息完全不相关** +- 自然地表达不知道，但可以提及检索到的相关信息，让对话更连贯 +- 使用友好的表达： + - "你好像没和我说过...，但是我知道你[检索到的相关信息]" + - "关于这个我不太清楚，不过我记得你[检索到的相关信息]，能告诉我更多吗？" + - "我不记得你提到过...，但你[检索到的相关信息]" +- 即使检索信息不直接回答问题，也可以自然地融入对话中 +- 避免僵硬的"信息不足，无法回答" +## 3. 回答要求 +- 像人类对话一样自然流畅 +- 不要提及"检索信息"、"搜索结果"、"根据资料"等技术术语 +- 不要解释推理过程或引用信息来源 +- 保持友好、乐于助人的语气 +- 使用与问题相同的语言回答 +# 关键示例 +**示例1 - 直接匹配：** +- 检索信息："小曼会使用Python..." +- 问题："我叫什么" +- ✓ 正确："你叫小曼" +- ✗ 错误："你没有告诉我你的名字" +**示例2 - 间接匹配：** +- 检索信息："用户很喜欢吃星巴克的甜品" +- 问题："我喜欢什么" +- ✓ 正确："你很喜欢吃星巴克的甜品" +- ✗ 错误："信息不足" +**示例3 - 信息不匹配（推荐做法）：** +- 检索信息："用户只喝拿铁咖啡，认为美式咖啡太苦" +- 问题："我吃过哪家面包" +- ✓ 最佳："你好像没和我说过吃过哪家面包，但是我知道你喜欢喝拿铁，能跟我分享一下吗？" +- ✓ 可以："你好像没和我说过吃过哪家面包，能跟我分享一下吗？" +- ✗ 错误："用户只喝拿铁咖啡，认为美式咖啡太苦。"（答非所问） +- ✗ 错误："信息不足，无法回答。"（太僵硬） +# 重要提醒 +- 检索信息中描述用户行为/偏好时提到的名字，就是用户的名字 +- 信息不匹配时，不要强行回答无关内容，但可以自然地提及检索到的信息，让对话更有温度 +- 用对话式语言表达"不知道"，而非机械模板 +- 检索信息代表你对用户的了解，即使不直接回答问题，也能体现你对用户的记忆 diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 7c8ee9ac..a24456d2 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -729,7 +729,7 @@ class MemoryAgentService: state=state, history=history, retrieve_info=retrieve_info, - template_name='Retrieve_Summary_prompt.jinja2', + template_name='direct_summary_prompt.jinja2', operation_name='retrieve_summary', response_model=RetrieveSummaryResponse, search_mode="1" From 86812b34d1813373ea04fa50ffc0f79bc1d82c9c Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Fri, 23 Jan 2026 13:57:27 +0800 Subject: [PATCH 047/175] Fix/memory mcp2 1 (#185) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 优化快速检索的回复内容 * 优化快速检索的回复内容 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 --- .../core/memory/agent/langgraph_graph/nodes/problem_nodes.py | 2 +- .../core/memory/agent/langgraph_graph/nodes/summary_nodes.py | 2 +- .../memory/agent/langgraph_graph/nodes/verification_nodes.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py index 697a13bd..2bad650a 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py @@ -14,7 +14,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') db_session = next(get_db()) logger = get_agent_logger(__name__) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index 44f89c6a..f05a5ae1 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -19,7 +19,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.db import get_db -template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') logger = get_agent_logger(__name__) db_session = next(get_db()) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index dac7ea14..10ce8db4 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -12,7 +12,7 @@ from app.core.memory.agent.utils.session_tools import SessionService from app.core.memory.agent.utils.template_tools import TemplateService from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin -template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt') +template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt') db_session = next(get_db()) logger = get_agent_logger(__name__) From 313f19eba4940c381b36cde243846254c356f19a Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Fri, 23 Jan 2026 14:49:44 +0800 Subject: [PATCH 048/175] Fix/memory mcp2 1 (#188) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 优化快速检索的回复内容 * 优化快速检索的回复内容 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * LLM生存缺少config_id认证，修复BUG * LLM生存缺少config_id认证，修复BUG * LLM生存缺少config_id认证，修复BUG --- .../controllers/memory_agent_controller.py | 3 +-- api/app/services/memory_agent_service.py | 20 ++++++++++++++++--- 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 8b5a55b9..c54fb02b 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -262,9 +262,7 @@ async def read_server( """ config_id = user_input.config_id workspace_id = current_user.current_workspace_id - api_logger.info(f"Read service: workspace_id={workspace_id}, config_id={config_id}") - # 获取 storage_type，如果为 None 则使用默认值 storage_type = workspace_service.get_workspace_storage_type( db=db, workspace_id=workspace_id, @@ -300,6 +298,7 @@ async def read_server( # 调用 memory_agent_service 的方法生成最终答案 result['answer'] = await memory_agent_service.generate_summary_from_retrieve( + group_id=user_input.group_id, retrieve_info=retrieve_info, history=history, query=query, diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index a24456d2..83b6bdd7 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -410,8 +410,8 @@ class MemoryAgentService: # Resolve config_id if None using end_user's connected config if config_id is None: try: - connected_config = get_end_user_connected_config(group_id, db) - config_id = connected_config.get("memory_config_id") + config_id = get_end_user_connected_config(group_id, db) + config_id=config_id.get('memory_config_id') if config_id is None: raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") except Exception as e: @@ -670,6 +670,8 @@ class MemoryAgentService: """ logger.info("Classifying message type") + + # Load configuration to get LLM model ID config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config( @@ -683,6 +685,7 @@ class MemoryAgentService: async def generate_summary_from_retrieve( self, + group_id: str, retrieve_info: str, history: List[Dict], query: str, @@ -704,6 +707,18 @@ class MemoryAgentService: Returns: 生成的答案文本 """ + if config_id is None: + try: + config_id = get_end_user_connected_config(group_id, db) + config_id = config_id.get('memory_config_id') + if config_id is None: + raise ValueError( + f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + except Exception as e: + if "No memory configuration found" in str(e): + raise # Re-raise our specific error + logger.error(f"Failed to get connected config for end_user {group_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") logger.info(f"Generating summary from retrieve info for query: {query[:50]}...") try: @@ -713,7 +728,6 @@ class MemoryAgentService: config_id=config_id, service_name="MemoryAgentService" ) - # 导入必要的模块 from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import summary_llm from app.core.memory.agent.models.summary_models import RetrieveSummaryResponse From 1aabaff1f2c696274bd90d513a2e015768d5ef89 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 15:00:09 +0800 Subject: [PATCH 049/175] =?UTF-8?q?=E8=A7=A3=E5=86=B3=E5=86=B2=E7=AA=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_agent_controller.py | 2 +- api/app/services/memory_agent_service.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index dafb522b..1958d6be 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -298,7 +298,7 @@ async def read_server( # 调用 memory_agent_service 的方法生成最终答案 result['answer'] = await memory_agent_service.generate_summary_from_retrieve( - group_id=user_input.group_id, + end_user_id=user_input.group_id, retrieve_info=retrieve_info, history=history, query=query, diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index ae7d5fb5..661c3067 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -682,7 +682,6 @@ class MemoryAgentService: status = await status_typle(message, memory_config.llm_model_id) logger.debug(f"Message type: {status}") return status - async def generate_summary_from_retrieve( self, end_user_id: str, From c44712167f1a57ecaf7b7a9954baf78fee727d7a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 15:03:39 +0800 Subject: [PATCH 050/175] =?UTF-8?q?=E8=A7=A3=E5=86=B3=E5=86=B2=E7=AA=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_agent_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index 1958d6be..61b16d9e 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -298,7 +298,7 @@ async def read_server( # 调用 memory_agent_service 的方法生成最终答案 result['answer'] = await memory_agent_service.generate_summary_from_retrieve( - end_user_id=user_input.group_id, + end_user_id=user_input.end_user_id, retrieve_info=retrieve_info, history=history, query=query, From c115bcde545c03f61d9b2c6853b8e017a010e80d Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Fri, 23 Jan 2026 16:58:55 +0800 Subject: [PATCH 051/175] feat(home page): version description update --- api/app/core/config.py | 2 +- .../core/tools/builtin/baidu_search_tool.py | 4 +-- api/app/version_info.json | 30 +++++++++++++++++++ 3 files changed, 33 insertions(+), 3 deletions(-) diff --git a/api/app/core/config.py b/api/app/core/config.py index 59c6ff5f..3be6f849 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -184,7 +184,7 @@ class Settings: ENABLE_TOOL_MANAGEMENT: bool = os.getenv("ENABLE_TOOL_MANAGEMENT", "true").lower() == "true" # official environment system version - SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.0") + SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.1") # workflow config WORKFLOW_NODE_TIMEOUT: int = int(os.getenv("WORKFLOW_NODE_TIMEOUT", 600)) diff --git a/api/app/core/tools/builtin/baidu_search_tool.py b/api/app/core/tools/builtin/baidu_search_tool.py index 02431aed..45d4c359 100644 --- a/api/app/core/tools/builtin/baidu_search_tool.py +++ b/api/app/core/tools/builtin/baidu_search_tool.py @@ -16,7 +16,7 @@ class BaiduSearchTool(BuiltinTool): @property def description(self) -> str: - return "百度搜索 - 搜索引擎服务：网页搜索、新闻搜索、图片搜索、实时结果" + return "百度搜索 - 搜索引擎服务：网页搜索、新闻搜索、图片搜索、视频搜索" def get_required_config_parameters(self) -> List[str]: return ["api_key"] @@ -33,7 +33,7 @@ class BaiduSearchTool(BuiltinTool): ToolParameter( name="search_type", type=ParameterType.STRING, - description="搜索类型", + description="搜索类型, web: 网页搜索；news：新闻搜索；image：图片搜索；video视频搜索", required=False, default="web", enum=["web", "news", "image", "video"] diff --git a/api/app/version_info.json b/api/app/version_info.json index 20896845..bee52989 100644 --- a/api/app/version_info.json +++ b/api/app/version_info.json @@ -1,4 +1,34 @@ { + "v0.2.1": { + "introduction": { + "codeName": "启知", + "releaseDate": "2026-1-23", + "upgradePosition": "\uD83D\uDC3B 本次更新主要优化使用体验和修复已知问题，让系统更稳定、更好用。", + "coreUpgrades": [ + "1. 工作流更好用了\n* 界面更清晰，一眼看懂怎么配置\n* 新增节点输出变量展示，方便其他节点引用\n* 修复了几个影响体验的bug", + "2. 智能体配置更简单\n* 提示词和变量联动更顺畅\n* 配置界面重新整理，找功能更方便", + "3. 记忆系统更稳定\n* 优化了情绪记忆和隐性记忆的缓存更新\n* 修复了记忆配置页面的报错问题\n* 现在能自动识别用户和AI的身份了", + "4. 知识库体验提升\n* 修复了文档解析异常的问题\n* 上传文档时能看到处理进度了\n* 取消了操作也不会报错了", + "5. 系统整体更可靠\n* 修复了新用户访问跳转问题\n* 流式接口更稳定，长对话不断线\n* 调整了菜单顺序，操作更顺手\n", + "这次更新虽然不大，但让记忆熊的基础更扎实、体验更流畅。我们继续努力，让AI记忆更好用！", + "记忆熊，记得更牢，用得更好。\uD83D\uDC3B✨" + ] + }, + "introduction_en": { + "codeName": "Qizhi", + "releaseDate": "2026-1-23", + "upgradePosition": "\uD83D\uDC3B This update focuses on improving usability and fixing known issues, making the system more stable and easier to use overall.", + "coreUpgrades": [ + "1. Improved Workflow Experience\nCleaner, more intuitive UI for easier configuration at a glance\nAdded visibility of node output variables, making them easier to reference in downstream nodes\nFixed several usability-related bugs that affected the workflow experience", + "2. Simpler Agent Configuration\nSmoother linkage between prompts and variables\nReorganized configuration layout for easier navigation and better clarity", + "3. More Stable Memory System\nOptimized cache refresh for emotional memory and implicit memory\nFixed error issues on the memory configuration page\nThe system can now automatically distinguish between user and AI roles", + "4. Enhanced Knowledge Base Experience\nFixed issues with document parsing failures\nUpload progress is now displayed during document processing\nCanceling an upload no longer triggers errors", + "5. Overall System Reliability Improvements\nFixed redirect issues affecting new users\nImproved stability of streaming APIs to prevent interruptions during long conversations\nAdjusted menu ordering for a smoother and more intuitive workflow\n", + "Although this is a relatively small update, it strengthens MemoryBear’s foundation and delivers a noticeably smoother experience.\nWe’ll keep refining the system to make AI memory more powerful and easier to use.", + "MemoryBear — remember better, work smarter. \uD83D\uDC3B✨" + ] + } + }, "v0.2.0": { "introduction": { "codeName": "启知", From 191958075922b4c3db665a45c39b2683a93718f9 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Fri, 23 Jan 2026 17:12:21 +0800 Subject: [PATCH 052/175] Fix/memory mcp2 1 (#190) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 优化快速检索的回复内容 * 优化快速检索的回复内容 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * 路径的BUG修复 * LLM生存缺少config_id认证，修复BUG * LLM生存缺少config_id认证，修复BUG * LLM生存缺少config_id认证，修复BUG * 深度检索优化，搜索不到数据/提问的概念过于蘑菇，以引导的方式继续提问 * 深度检索优化，搜索不到数据/提问的概念过于蘑菇，以引导的方式继续提问 * 深度检索优化，搜索不到数据/提问的概念过于蘑菇，以引导的方式继续提问 --- .../langgraph_graph/nodes/summary_nodes.py | 22 ++++++++-- .../utils/prompt/fail_summary_prompt.jinja2 | 43 +++++++++++++++++++ api/app/services/memory_agent_service.py | 3 +- 3 files changed, 63 insertions(+), 5 deletions(-) create mode 100644 api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index f05a5ae1..fb0484d2 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -236,7 +236,7 @@ async def Retrieve_Summary(state: ReadState)-> ReadState: retrieve_info_str='\n'.join(retrieve_info_str) aimessages=await summary_llm(state,history,retrieve_info_str, - 'Retrieve_Summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1") + 'direct_summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1") if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "": await summary_redis_save(state, aimessages) if aimessages == '': @@ -276,7 +276,6 @@ async def Summary(state: ReadState)-> ReadState: aimessages=await summary_llm(state,history,data, 'summary_prompt.jinja2','summary',SummaryResponse,0) - if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "": await summary_redis_save(state, aimessages) if aimessages == '': @@ -295,9 +294,26 @@ async def Summary(state: ReadState)-> ReadState: async def Summary_fails(state: ReadState)-> ReadState: storage_type=state.get("storage_type", '') user_rag_memory_id=state.get("user_rag_memory_id", '') + history = await summary_history(state) + query = state.get("data", '') + verify = state.get("verify", '') + verify_expansion_issue = verify.get("verified_data", '') + retrieve_info_str = '' + for data in verify_expansion_issue: + for key, value in data.items(): + if key == 'answer_small': + for i in value: + retrieve_info_str += i + '\n' + data = { + "query": query, + "history": history, + "retrieve_info": retrieve_info_str + } + aimessages = await summary_llm(state, history, data, + 'fail_summary_prompt.jinja2', 'summary', SummaryResponse, 0) result= { "status": "success", - "summary_result": "没有相关数据", + "summary_result": aimessages, "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id } diff --git a/api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 new file mode 100644 index 00000000..3744f99b --- /dev/null +++ b/api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2 @@ -0,0 +1,43 @@ +{# 角色定义 #} +你是专业的问题解答专家+引导学者 + +{# 输入数据展示 #} +{% if data %} +## 输入数据 +上下文信息: +{% for item in data.history %} +- {{ item }} +{% endfor %} +检索到的所有信息: +{% for item in data.retrieve_info %} +- {{ item }} +{% endfor %} +{% endif %} + +## User Query +{{ query }} + +{# 问题回答标准 #} +## 问题回答核心标准 +根据上下文信息(history)和检索到的所有信息(retrieve_info)准确回答用户的问题(query)。 +注意，仔细阅读检索信息，答案可能直接或间接地出现在检索信息中或者历史上下文消息中，同时需要 判断信息相关性 +**情况A：信息匹配问题** +- 直接回答，像自然对话一样 +- 例：检索到"小曼会使用Python" → 问"我叫什么" → 答"你叫小曼" + +**情况B：信息部分相关** +- 先回答已知部分，再自然地询问更多信息 +- 例：检索到"用户去过上海的面包店" → 问"我吃过哪家面包" → 答"我记得你去过上海的面包店，但具体是哪家我不太清楚，是哪家呢？" + +**情况C：信息完全不相关** +- 自然地表达不知道，但可以提及检索到的相关信息，让对话更连贯 +- 使用友好的表达： + - "你好像没和我说过...，但是我知道你[检索到的相关信息]" + - "关于这个我不太清楚，不过我记得你[检索到的相关信息]，能告诉我更多吗？" + - "我不记得你提到过...，但你[检索到的相关信息]" +- 即使检索信息不直接回答问题，也可以自然地融入对话中 +- 避免僵硬的"信息不足，无法回答" + +{# 重要提醒 #} +当检索以及上下文的历史信息都无法回答的时候，可引导对方进行提问/回答，或者进行其他引导 +当检索或者上下文中出现了，相似的问题，可以委婉，提醒对方，我记得刚刚提过这个问题，但是我自己不记得了，能在描述一次吗～以此为例 diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 83b6bdd7..1e1cde89 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -542,9 +542,8 @@ class MemoryAgentService: if intermediate_type == "search_result": query = intermediate.get('query', '') raw_results = intermediate.get('raw_results', {}) - reranked_results = raw_results.get('reranked_results', []) - try: + reranked_results = raw_results.get('reranked_results', []) statements = [statement['statement'] for statement in reranked_results.get('statements', [])] except Exception: statements = [] From af6e1e2b99272cbeefd49fa4abf06c4be456a2e2 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 17:20:07 +0800 Subject: [PATCH 053/175] =?UTF-8?q?end=5Fuser=5Fid=E6=B8=85=E7=90=86?= =?UTF-8?q?=E5=B9=B2=E5=87=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/memory_forget_controller.py | 10 +++++----- api/app/controllers/user_memory_controllers.py | 18 +++++++++--------- api/app/core/agent/langchain_agent.py | 4 ++-- api/app/core/memory/agent/utils/get_dialogs.py | 2 +- api/app/core/memory/agent/utils/write_tools.py | 2 +- .../evaluation/longmemeval/qwen_search_eval.py | 2 +- api/app/core/memory/models/message_models.py | 4 ++-- api/app/schemas/memory_storage_schema.py | 12 ------------ api/app/services/memory_agent_service.py | 2 +- api/app/services/memory_storage_service.py | 2 +- 10 files changed, 23 insertions(+), 35 deletions(-) diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index 44351f92..a6b6028f 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -107,7 +107,7 @@ async def trigger_forgetting_cycle( # 调用服务层执行遗忘周期 report = await forget_service.trigger_forgetting_cycle( db=db, - group_id=end_user_id, # 服务层方法的参数名是 group_id + end_user_id=end_user_id, # 服务层方法的参数名是 end_user_id max_merge_batch_size=payload.max_merge_batch_size, min_days_since_access=payload.min_days_since_access, config_id=config_id @@ -247,7 +247,7 @@ async def get_forgetting_stats( 返回知识层节点统计、激活值分布等信息。 Args: - group_id: 组ID（即 end_user_id，可选） + end_user_id: 组ID（即 end_user_id，可选） current_user: 当前用户 db: 数据库会话 @@ -261,7 +261,7 @@ async def get_forgetting_stats( api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - # 如果提供了 group_id，通过它获取 config_id + # 如果提供了 end_user_id，通过它获取 config_id config_id = None if end_user_id: try: @@ -274,7 +274,7 @@ async def get_forgetting_stats( api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None") - api_logger.debug(f"通过 group_id={end_user_id} 获取到 config_id={config_id}") + api_logger.debug(f"通过 end_user_id={end_user_id} 获取到 config_id={config_id}") except ValueError as e: api_logger.warning(f"获取终端用户配置失败: {str(e)}") return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError") @@ -284,7 +284,7 @@ async def get_forgetting_stats( api_logger.info( f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: " - f"group_id={end_user_id}, config_id={config_id}" + f"end_user_id={end_user_id}, config_id={config_id}" ) try: diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index 6f02f8f9..39cbe523 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -135,27 +135,27 @@ async def generate_cache_api( api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - group_id = request.end_user_id + end_user_id = request.end_user_id api_logger.info( f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, " - f"end_user_id={group_id if group_id else '全部用户'}" + f"end_user_id={end_user_id if end_user_id else '全部用户'}" ) try: - if group_id: + if end_user_id: # 为单个用户生成 - api_logger.info(f"开始为单个用户生成缓存: end_user_id={group_id}") + api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}") # 生成记忆洞察 - insight_result = await user_memory_service.generate_and_cache_insight(db, group_id, workspace_id) + insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id) # 生成用户摘要 - summary_result = await user_memory_service.generate_and_cache_summary(db, group_id, workspace_id) + summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id) # 构建响应 result = { - "end_user_id": group_id, + "end_user_id": end_user_id, "insight_success": insight_result["success"], "summary_success": summary_result["success"], "errors": [] @@ -175,9 +175,9 @@ async def generate_cache_api( # 记录结果 if result["insight_success"] and result["summary_success"]: - api_logger.info(f"成功为用户 {group_id} 生成缓存") + api_logger.info(f"成功为用户 {end_user_id} 生成缓存") else: - api_logger.warning(f"用户 {group_id} 的缓存生成部分失败: {result['errors']}") + api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {result['errors']}") return success(data=result, msg="生成完成") diff --git a/api/app/core/agent/langchain_agent.py b/api/app/core/agent/langchain_agent.py index 4cff933d..ddacb094 100644 --- a/api/app/core/agent/langchain_agent.py +++ b/api/app/core/agent/langchain_agent.py @@ -155,7 +155,7 @@ class LangChainAgent: # userid=end_user_end, # messages=messages, # apply_id=end_user_end, - # group_id=end_user_end, + # end_user_id=end_user_end, # aimessages=aimessages # ) # store.delete_duplicate_sessions() @@ -228,7 +228,7 @@ class LangChainAgent: # 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段 logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}") write_id = write_message_task.delay( - actual_end_user_id, # group_id: 用户ID + actual_end_user_id, # end_user_id: 用户ID structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] actual_config_id, # config_id: 配置ID storage_type, # storage_type: "neo4j" diff --git a/api/app/core/memory/agent/utils/get_dialogs.py b/api/app/core/memory/agent/utils/get_dialogs.py index a56a32fa..bfb0f675 100644 --- a/api/app/core/memory/agent/utils/get_dialogs.py +++ b/api/app/core/memory/agent/utils/get_dialogs.py @@ -18,7 +18,7 @@ async def get_chunked_dialogs( Args: chunker_strategy: The chunking strategy to use (default: RecursiveChunker) - group_id: Group identifier + end_user_id: Group identifier messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference identifier config_id: Configuration ID for processing diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index d32d152c..446ab86a 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -40,7 +40,7 @@ async def write( Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier memory_config: MemoryConfig object containing all configuration messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference ID, defaults to "wyl20251027" diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py index 8a366a00..8710a504 100644 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py @@ -1307,7 +1307,7 @@ def main(): result = asyncio.run( run_longmemeval_test( sample_size=sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/models/message_models.py b/api/app/core/memory/models/message_models.py index c660d841..2f8660af 100644 --- a/api/app/core/memory/models/message_models.py +++ b/api/app/core/memory/models/message_models.py @@ -246,9 +246,9 @@ class DialogData(BaseModel): return [] def assign_group_id_to_statements(self) -> None: - """Assign this dialog's group_id to all statements in all chunks. + """Assign this dialog's end_user_id to all statements in all chunks. - This method updates statements that don't have a group_id set. + This method updates statements that don't have a end_user_id set. """ for chunk in self.chunks: for statement in chunk.statements: diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index e5a4cde6..d9c04f8f 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -8,20 +8,8 @@ import uuid from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator -# ============================================================================ -# 原 UserInput 相关 Schema (保留原有功能) -# ============================================================================ -class UserInput(BaseModel): - message: str - history: list[dict] - search_switch: str - group_id: str -class Write_UserInput(BaseModel): - message: str - group_id: str - # ============================================================================ # 从 json_schema.py 迁移的 Schema diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index bf54064c..6e72a53f 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -716,7 +716,7 @@ class MemoryAgentService: if "No memory configuration found" in str(e): raise # Re-raise our specific error logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") logger.info(f"Generating summary from retrieve info for query: {query[:50]}...") try: diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index f3c27c9a..80d8c717 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -572,7 +572,7 @@ async def analytics_hot_memory_tags( # 步骤4: 只调用一次LLM进行筛选 tag_names = [tag for tag, _ in sorted_tags] - # 使用第一个用户的group_id来获取LLM配置 + # 使用第一个用户的end_user_id来获取LLM配置 # 因为同一工作空间下的用户应该使用相同的配置 first_end_user_id = str(end_users[0].id) filtered_tag_names = await filter_tags_with_llm(tag_names, first_end_user_id) From 79cfabb45df32c31e4f660eb8036c70002e28812 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 17:20:32 +0800 Subject: [PATCH 054/175] =?UTF-8?q?end=5Fuser=5Fid=E6=B8=85=E7=90=86?= =?UTF-8?q?=E5=B9=B2=E5=87=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/version_info.json | 98 --------------------------------------- 1 file changed, 98 deletions(-) delete mode 100644 api/app/version_info.json diff --git a/api/app/version_info.json b/api/app/version_info.json deleted file mode 100644 index bee52989..00000000 --- a/api/app/version_info.json +++ /dev/null @@ -1,98 +0,0 @@ -{ - "v0.2.1": { - "introduction": { - "codeName": "启知", - "releaseDate": "2026-1-23", - "upgradePosition": "\uD83D\uDC3B 本次更新主要优化使用体验和修复已知问题，让系统更稳定、更好用。", - "coreUpgrades": [ - "1. 工作流更好用了\n* 界面更清晰，一眼看懂怎么配置\n* 新增节点输出变量展示，方便其他节点引用\n* 修复了几个影响体验的bug", - "2. 智能体配置更简单\n* 提示词和变量联动更顺畅\n* 配置界面重新整理，找功能更方便", - "3. 记忆系统更稳定\n* 优化了情绪记忆和隐性记忆的缓存更新\n* 修复了记忆配置页面的报错问题\n* 现在能自动识别用户和AI的身份了", - "4. 知识库体验提升\n* 修复了文档解析异常的问题\n* 上传文档时能看到处理进度了\n* 取消了操作也不会报错了", - "5. 系统整体更可靠\n* 修复了新用户访问跳转问题\n* 流式接口更稳定，长对话不断线\n* 调整了菜单顺序，操作更顺手\n", - "这次更新虽然不大，但让记忆熊的基础更扎实、体验更流畅。我们继续努力，让AI记忆更好用！", - "记忆熊，记得更牢，用得更好。\uD83D\uDC3B✨" - ] - }, - "introduction_en": { - "codeName": "Qizhi", - "releaseDate": "2026-1-23", - "upgradePosition": "\uD83D\uDC3B This update focuses on improving usability and fixing known issues, making the system more stable and easier to use overall.", - "coreUpgrades": [ - "1. Improved Workflow Experience\nCleaner, more intuitive UI for easier configuration at a glance\nAdded visibility of node output variables, making them easier to reference in downstream nodes\nFixed several usability-related bugs that affected the workflow experience", - "2. Simpler Agent Configuration\nSmoother linkage between prompts and variables\nReorganized configuration layout for easier navigation and better clarity", - "3. More Stable Memory System\nOptimized cache refresh for emotional memory and implicit memory\nFixed error issues on the memory configuration page\nThe system can now automatically distinguish between user and AI roles", - "4. Enhanced Knowledge Base Experience\nFixed issues with document parsing failures\nUpload progress is now displayed during document processing\nCanceling an upload no longer triggers errors", - "5. Overall System Reliability Improvements\nFixed redirect issues affecting new users\nImproved stability of streaming APIs to prevent interruptions during long conversations\nAdjusted menu ordering for a smoother and more intuitive workflow\n", - "Although this is a relatively small update, it strengthens MemoryBear’s foundation and delivers a noticeably smoother experience.\nWe’ll keep refining the system to make AI memory more powerful and easier to use.", - "MemoryBear — remember better, work smarter. \uD83D\uDC3B✨" - ] - } - }, - "v0.2.0": { - "introduction": { - "codeName": "启知", - "releaseDate": "2026-1-16", - "upgradePosition": "本次为架构升级，核心目标是把\"被动存储\"升级为\"主动认知\"，让系统具备情绪感知、情景理解与类人记忆机制，为后续多智能体协作与专业场景落地奠定底座。", - "coreUpgrades": [ - "记忆详情：拟人记忆——情绪引擎、情景记忆、短期记忆、工作记忆、感知记忆、显性记忆、隐性记忆，并配套类脑遗忘机制，实现从感知→情绪→情景→长期沉淀的完整人类记忆闭环", - "可视化工作流：拖拽式节点编排（LLM、知识库、逻辑、工具），业务落地周期由天缩至小时。", - "多模态知识处理：PDF、PPT、MP3、MP4 一键解析，时间感知检索准确率 94.3%，问答对数据即插即用。", - "Agent集群内置\"记忆-知识-工具-审核\"四类角色模板，用户一键生成；主控Agent把复杂任务拆为子任务并行分发，再靠情景记忆统一消解冲突、校验一致性，输出完整报告。" - ] - }, - "introduction_en": { - "codeName": "Qizhi", - "releaseDate": "2026-1-16", - "upgradePosition": "This release marks a foundational upgrade to the system’s cognitive architecture. The core objective is to evolve the platform from passive information storage into active cognitive intelligence—enabling emotional awareness, situational understanding, and human-like memory mechanisms. This upgrade lays the groundwork for future multi-agent collaboration and domain-specific, production-grade AI applications.", - "coreUpgrades": [ - "Human-Like Memory Architecture: A comprehensive, human-inspired memory system is introduced, encompassing emotional processing, situational memory, short-term and working memory, perceptual memory, as well as explicit and implicit memory. Combined with brain-inspired forgetting mechanisms, the system now supports a complete cognitive loop—from perception → emotion → context → long-term consolidation, closely mirroring human memory formation.", - "Visual Workflow Orchestration: A fully visual, drag-and-drop workflow enables modular composition of LLMs, knowledge bases, logic, and tools. This dramatically reduces the time required to move from experimentation to production—from days to hours.", - "Multimodal Knowledge Processing: The system now supports one-click parsing and ingestion of PDF, PPT, MP3, and MP4 content. With time-aware retrieval accuracy reaching 94.3%, structured Q&A data becomes instantly usable for downstream reasoning and generation.", - "Built-in Agent Clusters: Predefined role templates across four categories—Memory, Knowledge, Tools, and Review—can be generated with a single click. A Coordinator Agent decomposes complex tasks into parallel subtasks, while situational memory is used to resolve conflicts, validate consistency, and synthesize outputs into a coherent, end-to-end report." - ] - } - }, - "v0.1.0": { - "introduction": { - "codeName": "初心", - "releaseDate": "2025-12-01", - "upgradePosition": "这是一款专注于管理和利用AI记忆的工具，支持RAG和知识图谱两种主流存储方式，旨在为AI应用提供持久化、结构化的\"记忆\"能力。", - "coreUpgrades": [ - "记忆空间：用户可以创建独立的空间来隔离不同记忆，并灵活选择存储方式。", - "记忆配置：简化了配置流程，内置自动提取关键信息的\"记忆萃取\"和管理生命周期的\"遗忘\"引擎。", - "知识检索：提供语义、分词和混合三种检索模式，并支持多种参数微调和结果重排序，以提升召回效果。", - "全局管理：支持统一设置默认检索参数，并可一键应用到所有知识库。", - "测试与调试：内置\"召回测试\"功能，方便用户实时验证检索效果并调整参数，支持通过分享码与他人协作。", - "记忆洞察：可查看详细的对话记录、用户画像和分析报告，帮助理解AI的\"记忆\"内容。", - "集成与管理：提供API Key用于系统集成，并包含基本的用户管理功能。", - "界面与体验：采用现代化的卡片式布局和渐变色设计，注重交互的流畅性和视觉美感。", - "起步与使用：文档中提供了清晰的基础使用流程，引导用户从创建空间、配置记忆到测试检索快速上手。", - "版本说明与限制: 记忆熊 v0.1.0 版本\"初心\"囊括智能记忆管理的核心思路和基础能力，为后续开发奠定了基础。", - "文档资源：用户手册、API文档、FAQ", - "问题反馈：GitHub Issues、邮件支持", - "致谢：感谢所有参与测试和提供反馈的用户！" - ] - }, - "introduction_en": { - "codeName": "Original Intent", - "releaseDate": "2025-12-01", - "upgradePosition": "A tool focused on managing and utilizing AI memory, supporting both RAG and knowledge graph storage methods, aiming to provide persistent and structured 'memory' capabilities for AI applications.", - "coreUpgrades": [ - "Memory Space: Users can create independent spaces to isolate different memories and flexibly choose storage methods.", - "Memory Configuration: Simplified configuration process with built-in 'memory extraction' for automatic key information extraction and 'forgetting' engine for lifecycle management.", - "Knowledge Retrieval: Provides semantic, tokenization, and hybrid retrieval modes with various parameter tuning and result reranking to improve recall.", - "Global Management: Supports unified default retrieval parameter settings with one-click application to all knowledge bases.", - "Testing & Debugging: Built-in 'recall testing' for real-time verification of retrieval effects and parameter adjustment, with sharing code support for collaboration.", - "Memory Insights: View detailed conversation records, user profiles, and analysis reports to understand AI 'memory' content.", - "Integration & Management: Provides API Key for system integration with basic user management features.", - "Interface & Experience: Modern card-based layout with gradient design, focusing on interaction fluidity and visual aesthetics.", - "Getting Started: Documentation provides clear basic usage flow, guiding users from creating spaces, configuring memory to testing retrieval.", - "Version Notes: MemoryBear v0.1.0 'Original Intent' encompasses core concepts and basic capabilities of intelligent memory management, laying foundation for future development.", - "Documentation: User Manual, API Documentation, FAQ", - "Feedback: GitHub Issues, Email Support", - "Acknowledgments: Thanks to all users who participated in testing and provided feedback!" - ] - } - } -} From eccc208229d1dc5a427bc12a79f2d1915b0e90cf Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 18:34:06 +0800 Subject: [PATCH 055/175] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E9=81=97=E7=95=99?= =?UTF-8?q?=E5=90=88=E5=B9=B6BUG?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/repositories/neo4j/graph_search.py | 38 +++++++++++++------ api/app/services/emotion_analytics_service.py | 2 +- api/app/services/memory_config_service.py | 2 +- api/app/services/memory_perceptual_service.py | 6 +-- 4 files changed, 31 insertions(+), 17 deletions(-) diff --git a/api/app/repositories/neo4j/graph_search.py b/api/app/repositories/neo4j/graph_search.py index 9660f6cb..e8f52535 100644 --- a/api/app/repositories/neo4j/graph_search.py +++ b/api/app/repositories/neo4j/graph_search.py @@ -305,11 +305,18 @@ async def search_graph( results[key] = _deduplicate_results(results[key]) # 更新知识节点的激活值（Statement, ExtractedEntity, MemorySummary） - results = await _update_search_results_activation( - connector=connector, - results=results, - end_user_id=end_user_id + # Skip activation updates if only searching summaries (optimization) + needs_activation_update = any( + key in include and key in results and results[key] + for key in ['statements', 'entities', 'chunks'] ) + + if needs_activation_update: + results = await _update_search_results_activation( + connector=connector, + results=results, + end_user_id=end_user_id + ) return results @@ -417,14 +424,23 @@ async def search_graph_by_embedding( results[key] = _deduplicate_results(results[key]) # 更新知识节点的激活值（Statement, ExtractedEntity, MemorySummary） - update_start = time.time() - results = await _update_search_results_activation( - connector=connector, - results=results, - end_user_id=end_user_id + # Skip activation updates if only searching summaries (optimization) + needs_activation_update = any( + key in include and key in results and results[key] + for key in ['statements', 'entities', 'chunks'] ) - update_time = time.time() - update_start - print(f"[PERF] Activation value updates took: {update_time:.4f}s") + + if needs_activation_update: + update_start = time.time() + results = await _update_search_results_activation( + connector=connector, + results=results, + end_user_id=end_user_id + ) + update_time = time.time() - update_start + logger.info(f"[PERF] Activation value updates took: {update_time:.4f}s") + else: + logger.info(f"[PERF] Skipping activation updates (only summaries)") return results async def get_dedup_candidates_for_entities( # 适配新版查询：使用全文索引按名称检索候选实体 diff --git a/api/app/services/emotion_analytics_service.py b/api/app/services/emotion_analytics_service.py index 19c6cef1..af98fb52 100644 --- a/api/app/services/emotion_analytics_service.py +++ b/api/app/services/emotion_analytics_service.py @@ -505,7 +505,7 @@ class EmotionAnalyticsService: ) config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config( - config_id=int(config_id), + config_id=(config_id), service_name="EmotionAnalyticsService.generate_emotion_suggestions" ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index 692104bb..e901d65d 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -61,7 +61,7 @@ def _validate_config_id(config_id): # Fall back to integer parsing try: - parsed_id = int(config_id_stripped) + parsed_id = config_id_stripped if parsed_id <= 0: raise InvalidConfigError( f"Configuration ID must be positive: {parsed_id}", diff --git a/api/app/services/memory_perceptual_service.py b/api/app/services/memory_perceptual_service.py index 29b45474..b65955f2 100644 --- a/api/app/services/memory_perceptual_service.py +++ b/api/app/services/memory_perceptual_service.py @@ -92,17 +92,15 @@ class MemoryPerceptualService: result = { "id": str(memory.id), - "perceptual_type": perceptual_type, "file_name": memory.file_name, "file_path": memory.file_path, - "file_ext": memory.file_ext, - "storage_service": memory.storage_service, - "meta_data": memory.meta_data, + "storage_type": memory.storage_service, "summary": memory.summary, "keywords": content.keywords, "topic": content.topic, "domain": content.domain, "created_time": int(memory.created_time.timestamp()*1000), + **detail } business_logger.info( From a5e44cd229c55a7e81a454466128cbc32d5a68a8 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 18:34:13 +0800 Subject: [PATCH 056/175] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E9=81=97=E7=95=99?= =?UTF-8?q?=E5=90=88=E5=B9=B6BUG?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/tasks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/tasks.py b/api/app/tasks.py index cd8f0069..cdd7945e 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -1023,7 +1023,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: end_users = data['end_users'] for base, config, user in zip(releases, data_configs, end_users): - if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']: + if str(base['config']) == str(config['config_id']) and str(base['app_id']) == str(user['app_id']): # 调用反思服务 api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") From 9b8ed16e37c63208ac5ee9954609999d57810f1a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 18:35:40 +0800 Subject: [PATCH 057/175] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E9=81=97=E7=95=99?= =?UTF-8?q?=E5=90=88=E5=B9=B6BUG?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/version_info.json | 98 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 98 insertions(+) create mode 100644 api/version_info.json diff --git a/api/version_info.json b/api/version_info.json new file mode 100644 index 00000000..bee52989 --- /dev/null +++ b/api/version_info.json @@ -0,0 +1,98 @@ +{ + "v0.2.1": { + "introduction": { + "codeName": "启知", + "releaseDate": "2026-1-23", + "upgradePosition": "\uD83D\uDC3B 本次更新主要优化使用体验和修复已知问题，让系统更稳定、更好用。", + "coreUpgrades": [ + "1. 工作流更好用了\n* 界面更清晰，一眼看懂怎么配置\n* 新增节点输出变量展示，方便其他节点引用\n* 修复了几个影响体验的bug", + "2. 智能体配置更简单\n* 提示词和变量联动更顺畅\n* 配置界面重新整理，找功能更方便", + "3. 记忆系统更稳定\n* 优化了情绪记忆和隐性记忆的缓存更新\n* 修复了记忆配置页面的报错问题\n* 现在能自动识别用户和AI的身份了", + "4. 知识库体验提升\n* 修复了文档解析异常的问题\n* 上传文档时能看到处理进度了\n* 取消了操作也不会报错了", + "5. 系统整体更可靠\n* 修复了新用户访问跳转问题\n* 流式接口更稳定，长对话不断线\n* 调整了菜单顺序，操作更顺手\n", + "这次更新虽然不大，但让记忆熊的基础更扎实、体验更流畅。我们继续努力，让AI记忆更好用！", + "记忆熊，记得更牢，用得更好。\uD83D\uDC3B✨" + ] + }, + "introduction_en": { + "codeName": "Qizhi", + "releaseDate": "2026-1-23", + "upgradePosition": "\uD83D\uDC3B This update focuses on improving usability and fixing known issues, making the system more stable and easier to use overall.", + "coreUpgrades": [ + "1. Improved Workflow Experience\nCleaner, more intuitive UI for easier configuration at a glance\nAdded visibility of node output variables, making them easier to reference in downstream nodes\nFixed several usability-related bugs that affected the workflow experience", + "2. Simpler Agent Configuration\nSmoother linkage between prompts and variables\nReorganized configuration layout for easier navigation and better clarity", + "3. More Stable Memory System\nOptimized cache refresh for emotional memory and implicit memory\nFixed error issues on the memory configuration page\nThe system can now automatically distinguish between user and AI roles", + "4. Enhanced Knowledge Base Experience\nFixed issues with document parsing failures\nUpload progress is now displayed during document processing\nCanceling an upload no longer triggers errors", + "5. Overall System Reliability Improvements\nFixed redirect issues affecting new users\nImproved stability of streaming APIs to prevent interruptions during long conversations\nAdjusted menu ordering for a smoother and more intuitive workflow\n", + "Although this is a relatively small update, it strengthens MemoryBear’s foundation and delivers a noticeably smoother experience.\nWe’ll keep refining the system to make AI memory more powerful and easier to use.", + "MemoryBear — remember better, work smarter. \uD83D\uDC3B✨" + ] + } + }, + "v0.2.0": { + "introduction": { + "codeName": "启知", + "releaseDate": "2026-1-16", + "upgradePosition": "本次为架构升级，核心目标是把\"被动存储\"升级为\"主动认知\"，让系统具备情绪感知、情景理解与类人记忆机制，为后续多智能体协作与专业场景落地奠定底座。", + "coreUpgrades": [ + "记忆详情：拟人记忆——情绪引擎、情景记忆、短期记忆、工作记忆、感知记忆、显性记忆、隐性记忆，并配套类脑遗忘机制，实现从感知→情绪→情景→长期沉淀的完整人类记忆闭环", + "可视化工作流：拖拽式节点编排（LLM、知识库、逻辑、工具），业务落地周期由天缩至小时。", + "多模态知识处理：PDF、PPT、MP3、MP4 一键解析，时间感知检索准确率 94.3%，问答对数据即插即用。", + "Agent集群内置\"记忆-知识-工具-审核\"四类角色模板，用户一键生成；主控Agent把复杂任务拆为子任务并行分发，再靠情景记忆统一消解冲突、校验一致性，输出完整报告。" + ] + }, + "introduction_en": { + "codeName": "Qizhi", + "releaseDate": "2026-1-16", + "upgradePosition": "This release marks a foundational upgrade to the system’s cognitive architecture. The core objective is to evolve the platform from passive information storage into active cognitive intelligence—enabling emotional awareness, situational understanding, and human-like memory mechanisms. This upgrade lays the groundwork for future multi-agent collaboration and domain-specific, production-grade AI applications.", + "coreUpgrades": [ + "Human-Like Memory Architecture: A comprehensive, human-inspired memory system is introduced, encompassing emotional processing, situational memory, short-term and working memory, perceptual memory, as well as explicit and implicit memory. Combined with brain-inspired forgetting mechanisms, the system now supports a complete cognitive loop—from perception → emotion → context → long-term consolidation, closely mirroring human memory formation.", + "Visual Workflow Orchestration: A fully visual, drag-and-drop workflow enables modular composition of LLMs, knowledge bases, logic, and tools. This dramatically reduces the time required to move from experimentation to production—from days to hours.", + "Multimodal Knowledge Processing: The system now supports one-click parsing and ingestion of PDF, PPT, MP3, and MP4 content. With time-aware retrieval accuracy reaching 94.3%, structured Q&A data becomes instantly usable for downstream reasoning and generation.", + "Built-in Agent Clusters: Predefined role templates across four categories—Memory, Knowledge, Tools, and Review—can be generated with a single click. A Coordinator Agent decomposes complex tasks into parallel subtasks, while situational memory is used to resolve conflicts, validate consistency, and synthesize outputs into a coherent, end-to-end report." + ] + } + }, + "v0.1.0": { + "introduction": { + "codeName": "初心", + "releaseDate": "2025-12-01", + "upgradePosition": "这是一款专注于管理和利用AI记忆的工具，支持RAG和知识图谱两种主流存储方式，旨在为AI应用提供持久化、结构化的\"记忆\"能力。", + "coreUpgrades": [ + "记忆空间：用户可以创建独立的空间来隔离不同记忆，并灵活选择存储方式。", + "记忆配置：简化了配置流程，内置自动提取关键信息的\"记忆萃取\"和管理生命周期的\"遗忘\"引擎。", + "知识检索：提供语义、分词和混合三种检索模式，并支持多种参数微调和结果重排序，以提升召回效果。", + "全局管理：支持统一设置默认检索参数，并可一键应用到所有知识库。", + "测试与调试：内置\"召回测试\"功能，方便用户实时验证检索效果并调整参数，支持通过分享码与他人协作。", + "记忆洞察：可查看详细的对话记录、用户画像和分析报告，帮助理解AI的\"记忆\"内容。", + "集成与管理：提供API Key用于系统集成，并包含基本的用户管理功能。", + "界面与体验：采用现代化的卡片式布局和渐变色设计，注重交互的流畅性和视觉美感。", + "起步与使用：文档中提供了清晰的基础使用流程，引导用户从创建空间、配置记忆到测试检索快速上手。", + "版本说明与限制: 记忆熊 v0.1.0 版本\"初心\"囊括智能记忆管理的核心思路和基础能力，为后续开发奠定了基础。", + "文档资源：用户手册、API文档、FAQ", + "问题反馈：GitHub Issues、邮件支持", + "致谢：感谢所有参与测试和提供反馈的用户！" + ] + }, + "introduction_en": { + "codeName": "Original Intent", + "releaseDate": "2025-12-01", + "upgradePosition": "A tool focused on managing and utilizing AI memory, supporting both RAG and knowledge graph storage methods, aiming to provide persistent and structured 'memory' capabilities for AI applications.", + "coreUpgrades": [ + "Memory Space: Users can create independent spaces to isolate different memories and flexibly choose storage methods.", + "Memory Configuration: Simplified configuration process with built-in 'memory extraction' for automatic key information extraction and 'forgetting' engine for lifecycle management.", + "Knowledge Retrieval: Provides semantic, tokenization, and hybrid retrieval modes with various parameter tuning and result reranking to improve recall.", + "Global Management: Supports unified default retrieval parameter settings with one-click application to all knowledge bases.", + "Testing & Debugging: Built-in 'recall testing' for real-time verification of retrieval effects and parameter adjustment, with sharing code support for collaboration.", + "Memory Insights: View detailed conversation records, user profiles, and analysis reports to understand AI 'memory' content.", + "Integration & Management: Provides API Key for system integration with basic user management features.", + "Interface & Experience: Modern card-based layout with gradient design, focusing on interaction fluidity and visual aesthetics.", + "Getting Started: Documentation provides clear basic usage flow, guiding users from creating spaces, configuring memory to testing retrieval.", + "Version Notes: MemoryBear v0.1.0 'Original Intent' encompasses core concepts and basic capabilities of intelligent memory management, laying foundation for future development.", + "Documentation: User Manual, API Documentation, FAQ", + "Feedback: GitHub Issues, Email Support", + "Acknowledgments: Thanks to all users who participated in testing and provided feedback!" + ] + } + } +} From 94cced832349d6126b278421e8e275c64f244b85 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 18:36:33 +0800 Subject: [PATCH 058/175] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E9=81=97=E7=95=99?= =?UTF-8?q?=E5=90=88=E5=B9=B6BUG?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/{ => app}/version_info.json | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename api/{ => app}/version_info.json (100%) diff --git a/api/version_info.json b/api/app/version_info.json similarity index 100% rename from api/version_info.json rename to api/app/version_info.json From 4f4f55d67fa6ff71bcd2b0272cbacf63c8dbac8c Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 26 Jan 2026 11:04:30 +0800 Subject: [PATCH 059/175] feat(web): memory related interface parameter transfer adjustment --- web/src/api/memory.ts | 48 +- web/src/views/MemoryConversation/index.tsx | 6 +- .../views/MemoryExtractionEngine/constant.ts | 604 +----------------- web/src/views/MemoryManagement/types.ts | 1 - .../components/PerceptualLastInfo.tsx | 11 +- 5 files changed, 36 insertions(+), 634 deletions(-) diff --git a/web/src/api/memory.ts b/web/src/api/memory.ts index bbd9f6b0..ff8e0435 100644 --- a/web/src/api/memory.ts +++ b/web/src/api/memory.ts @@ -116,20 +116,20 @@ export const getRagContent = (end_user_id: string) => { return request.get(`/dashboard/rag_content`, { end_user_id, limit: 20 }) } // Emotion distribution analysis -export const getWordCloud = (group_id: string) => { - return request.post(`/memory/emotion-memory/wordcloud`, { group_id, limit: 20 }) +export const getWordCloud = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/wordcloud`, { end_user_id, limit: 20 }) } // High-frequency emotion keywords -export const getEmotionTags = (group_id: string) => { - return request.post(`/memory/emotion-memory/tags`, { group_id, limit: 20 }) +export const getEmotionTags = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/tags`, { end_user_id, limit: 20 }) } // Emotion health index -export const getEmotionHealth = (group_id: string) => { - return request.post(`/memory/emotion-memory/health`, { group_id, limit: 20 }) +export const getEmotionHealth = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/health`, { end_user_id }) } // Personalized suggestions -export const getEmotionSuggestions = (group_id: string) => { - return request.post(`/memory/emotion-memory/suggestions`, { group_id, limit: 20 }) +export const getEmotionSuggestions = (end_user_id: string) => { + return request.post(`/memory/emotion-memory/suggestions`, { end_user_id }) } export const generateSuggestions = (end_user_id: string) => { return request.post(`/memory/emotion-memory/generate_suggestions`, { end_user_id }) @@ -138,8 +138,8 @@ export const analyticsRefresh = (end_user_id: string) => { return request.post('/memory-storage/analytics/generate_cache', { end_user_id }) } // Forgetting stats -export const getForgetStats = (group_id: string) => { - return request.get(`/memory/forget-memory/stats`, { group_id }) +export const getForgetStats = (end_user_id: string) => { + return request.get(`/memory/forget-memory/stats`, { end_user_id }) } // Implicit Memory - Preferences export const getImplicitPreferences = (end_user_id: string) => { @@ -165,20 +165,20 @@ export const getShortTerm = (end_user_id: string) => { return request.get(`/memory/short/short_term`, { end_user_id }) } // Perceptual Memory - Visual memory -export const getPerceptualLastVisual = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/last_visual`) +export const getPerceptualLastVisual = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/last_visual`) } // Perceptual Memory - Audio memory -export const getPerceptualLastListen = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/last_listen`) +export const getPerceptualLastListen = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/last_listen`) } // Perceptual Memory - Text memory -export const getPerceptualLastText = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/last_text`) +export const getPerceptualLastText = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/last_text`) } // Perceptual Memory - Perceptual memory timeline -export const getPerceptualTimeline = (end_user: string) => { - return request.get(`/memory/perceptual/${end_user}/timeline`) +export const getPerceptualTimeline = (end_user_id: string) => { + return request.get(`/memory/perceptual/${end_user_id}/timeline`) } // Episodic Memory - Overview export const getEpisodicOverview = (data: { end_user_id: string; time_range: string; episodic_type: string; } ) => { @@ -201,14 +201,14 @@ export const getExplicitMemory = (end_user_id: string) => { export const getExplicitMemoryDetails = (data: { end_user_id: string, memory_id: string; }) => { return request.post(`/memory/explicit-memory/details`, data) } -export const getConversations = (end_user: string) => { - return request.get(`/memory/work/${end_user}/conversations`) +export const getConversations = (end_user_id: string) => { + return request.get(`/memory/work/${end_user_id}/conversations`) } -export const getConversationMessages = (end_user: string, conversation_id: string) => { - return request.get(`/memory/work/${end_user}/messages`, { conversation_id }) +export const getConversationMessages = (end_user_id: string, conversation_id: string) => { + return request.get(`/memory/work/${end_user_id}/messages`, { conversation_id }) } -export const getConversationDetail = (end_user: string, conversation_id: string) => { - return request.get(`/memory/work/${end_user}/detail`, { conversation_id }) +export const getConversationDetail = (end_user_id: string, conversation_id: string) => { + return request.get(`/memory/work/${end_user_id}/detail`, { conversation_id }) } export const forgetTrigger = (data: { max_merge_batch_size: number; min_days_since_access: number; end_user_id: string;}) => { return request.post(`/memory/forget-memory/trigger`, data) diff --git a/web/src/views/MemoryConversation/index.tsx b/web/src/views/MemoryConversation/index.tsx index 424b9878..66a66779 100644 --- a/web/src/views/MemoryConversation/index.tsx +++ b/web/src/views/MemoryConversation/index.tsx @@ -45,7 +45,7 @@ const searchSwitchList = [ ] export interface TestParams { - group_id: string; + end_user_id: string; message: string; search_switch: string; history: { role: string; content: string }[]; @@ -107,7 +107,7 @@ const MemoryConversation: FC = () => { setLoading(true) readService({ message: msg, - group_id: userId, + end_user_id: userId, search_switch: search_switch, history: [], }) @@ -204,7 +204,7 @@ const MemoryConversation: FC = () => { } )} > -
{log.title}
+
{log.title}
{log.type === 'problem_split' && Array.isArray(log.data) && log.data.length > 0 ? {log.data.map(vo => ( diff --git a/web/src/views/MemoryExtractionEngine/constant.ts b/web/src/views/MemoryExtractionEngine/constant.ts index d1b7b757..5939a1bc 100644 --- a/web/src/views/MemoryExtractionEngine/constant.ts +++ b/web/src/views/MemoryExtractionEngine/constant.ts @@ -1093,606 +1093,4 @@ export const groupDataByType = (data: any[], groupKey: string) => { }) return grouped -} - -export const mockTestResult = { - "generated_at": "2025-12-12T09:48:43.389893", - "entities": { - "extracted_count": 148 - }, - "dedup": { - "total_merged_count": 39, - "breakdown": { - "exact": 30, - "fuzzy": 0, - "llm": 9 - }, - "impact": [ - { - "name": "记忆熊", - "type": "Person", - "appear_count": 9, - "merge_count": 8 - }, - { - "name": "宋朝", - "type": "Organization", - "appear_count": 5, - "merge_count": 2 - }, - { - "name": "军费", - "type": "EconomicMetric", - "appear_count": 2, - "merge_count": 1 - }, - { - "name": "学生", - "type": "Person", - "appear_count": 6, - "merge_count": 5 - }, - { - "name": "废除丞相制度", - "type": "Event", - "appear_count": 6, - "merge_count": 3 - }, - { - "name": "六部", - "type": "Organization", - "appear_count": 4, - "merge_count": 3 - }, - { - "name": "六部缺乏协调机制", - "type": "Concept", - "appear_count": 2, - "merge_count": 1 - }, - { - "name": "丞相", - "type": "Position", - "appear_count": 4, - "merge_count": 1 - }, - { - "name": "总理", - "type": "Position", - "appear_count": 2, - "merge_count": 1 - }, - { - "name": "各部委", - "type": "Organization", - "appear_count": 2, - "merge_count": 1 - }, - { - "name": "六部直接对皇帝负责", - "type": "AdministrativeStructure", - "appear_count": 2, - "merge_count": 1 - }, - { - "name": "秦国", - "type": "Organization", - "appear_count": 5, - "merge_count": 2 - }, - { - "name": "文官集团", - "type": "Organization", - "appear_count": 2, - "merge_count": 1 - } - ] - }, - "disambiguation": { - "block_count": 1, - "effects": [ - { - "left": { - "name": "节度使", - "type": "Role" - }, - "right": { - "name": "节度使", - "type": "Person" - }, - "result": "成功区分" - } - ] - }, - "memory": { - "chunks": 2 - }, - "triplets": { - "count": 88 - }, - "core_entities": [ - { - "type": "Organization", - "type_cn": "组织", - "count": 16, - "entities": [ - "厂卫机构", - "西厂", - "东厂", - "工部", - "地方军阀" - ] - }, - { - "type": "Event", - "type_cn": "事件", - "count": 12, - "entities": [ - "均田制瓦解", - "无法批阅完所有政务", - "废除丞相制度", - "持续战争", - "政令执行困难" - ] - }, - { - "type": "Condition", - "type_cn": "Condition", - "count": 9, - "entities": [ - "缺乏协作机制", - "作战效率低下", - "厢军装备不足", - "军权分散", - "军事专业化难以提升" - ] - }, - { - "type": "Person", - "type_cn": "人物", - "count": 8, - "entities": [ - "官员", - "宦官", - "节度使", - "皇帝", - "文士" - ] - }, - { - "type": "Concept", - "type_cn": "Concept", - "count": 8, - "entities": [ - "行政紧张", - "军力不足", - "秦国统一六国的原因", - "六部缺乏协调机制", - "专业分工" - ] - }, - { - "type": "Action", - "type_cn": "Action", - "count": 6, - "entities": [ - "再花钱募兵", - "建立军功爵制度", - "裁撤兵员", - "削减装备", - "建立法律制度" - ] - }, - { - "type": "Outcome", - "type_cn": "Outcome", - "count": 5, - "entities": [ - "打仗更吃亏", - "提升国家组织能力", - "降低行政效率", - "士兵效忠个人而非国家", - "政令推行困难" - ] - }, - { - "type": "EconomicMetric", - "type_cn": "EconomicMetric", - "count": 4, - "entities": [ - "财政", - "财政支出", - "支出", - "军费" - ] - }, - { - "type": "Statement", - "type_cn": "Statement", - "count": 3, - "entities": [ - "没有银子", - "禁军由文官控制导致作战效率低下", - "武器没材料" - ] - }, - { - "type": "State", - "type_cn": "State", - "count": 3, - "entities": [ - "军队更弱", - "理解不足", - "不足" - ] - }, - { - "type": "HistoricalPeriod", - "type_cn": "HistoricalPeriod", - "count": 3, - "entities": [ - "春秋战国史", - "唐朝史", - "宋朝" - ] - }, - { - "type": "Attribute", - "type_cn": "Attribute", - "count": 3, - "entities": [ - "资源丰富", - "易守难攻", - "政策连续性强" - ] - }, - { - "type": "Right", - "type_cn": "Right", - "count": 3, - "entities": [ - "军事指挥权", - "财政调度权", - "募兵权" - ] - }, - { - "type": "Policy", - "type_cn": "Policy", - "count": 2, - "entities": [ - "商鞅变法", - "禁军由文官控制" - ] - }, - { - "type": "MilitaryCondition", - "type_cn": "MilitaryCondition", - "count": 2, - "entities": [ - "军力不足", - "缺乏战略纵深" - ] - }, - { - "type": "Role", - "type_cn": "Role", - "count": 2, - "entities": [ - "节度使", - "协调中枢" - ] - }, - { - "type": "Position", - "type_cn": "Position", - "count": 2, - "entities": [ - "总理", - "丞相" - ] - }, - { - "type": "PoliticalCharacteristic", - "type_cn": "PoliticalCharacteristic", - "count": 2, - "entities": [ - "旧贵族势力弱", - "中央集权程度高" - ] - }, - { - "type": "Phenomenon", - "type_cn": "Phenomenon", - "count": 1, - "entities": [ - "宋朝军事弱势" - ] - }, - { - "type": "Factor", - "type_cn": "Factor", - "count": 1, - "entities": [ - "制度性因素" - ] - }, - { - "type": "EconomicFactor", - "type_cn": "EconomicFactor", - "count": 1, - "entities": [ - "财政压力" - ] - }, - { - "type": "EconomicIndicator", - "type_cn": "EconomicIndicator", - "count": 1, - "entities": [ - "财政支出" - ] - }, - { - "type": "MilitaryStrategy", - "type_cn": "MilitaryStrategy", - "count": 1, - "entities": [ - "对外战略被动" - ] - }, - { - "type": "MilitaryCapability", - "type_cn": "MilitaryCapability", - "count": 1, - "entities": [ - "机动能力弱" - ] - }, - { - "type": "PersonGroup", - "type_cn": "PersonGroup", - "count": 1, - "entities": [ - "武将" - ] - }, - { - "type": "EconomicCondition", - "type_cn": "EconomicCondition", - "count": 1, - "entities": [ - "财政压力" - ] - }, - { - "type": "InstitutionalPolicy", - "type_cn": "InstitutionalPolicy", - "count": 1, - "entities": [ - "废除丞相制度" - ] - }, - { - "type": "StateOfAffairs", - "type_cn": "StateOfAffairs", - "count": 1, - "entities": [ - "中央决策高度集中于皇帝" - ] - }, - { - "type": "Institution", - "type_cn": "Institution", - "count": 1, - "entities": [ - "科举" - ] - }, - { - "type": "Function", - "type_cn": "Function", - "count": 1, - "entities": [ - "统筹大事小情" - ] - }, - { - "type": "AdministrativeStructure", - "type_cn": "AdministrativeStructure", - "count": 1, - "entities": [ - "六部直接对皇帝负责" - ] - }, - { - "type": "AdministrativeProblem", - "type_cn": "AdministrativeProblem", - "count": 1, - "entities": [ - "皇帝一人批不完政务" - ] - }, - { - "type": "Behavior", - "type_cn": "Behavior", - "count": 1, - "entities": [ - "互相推诿责任" - ] - }, - { - "type": "Resource", - "type_cn": "Resource", - "count": 1, - "entities": [ - "银子" - ] - }, - { - "type": "Situation", - "type_cn": "Situation", - "count": 1, - "entities": [ - "没人拍板" - ] - }, - { - "type": "HistoricalState", - "type_cn": "HistoricalState", - "count": 1, - "entities": [ - "秦国" - ] - }, - { - "type": "Location", - "type_cn": "地点", - "count": 1, - "entities": [ - "关中" - ] - }, - { - "type": "HistoricalEvent", - "type_cn": "HistoricalEvent", - "count": 1, - "entities": [ - "安史之乱" - ] - }, - { - "type": "PoliticalAction", - "type_cn": "PoliticalAction", - "count": 1, - "entities": [ - "中央整顿" - ] - }, - { - "type": "PoliticalPhenomenon", - "type_cn": "PoliticalPhenomenon", - "count": 1, - "entities": [ - "藩镇割据加剧" - ] - }, - { - "type": "EconomicEntity", - "type_cn": "EconomicEntity", - "count": 1, - "entities": [ - "中央财政" - ] - }, - { - "type": "System", - "type_cn": "System", - "count": 1, - "entities": [ - "募兵制" - ] - }, - { - "type": "WorkRole", - "type_cn": "WorkRole", - "count": 1, - "entities": [ - "掌控禁军" - ] - } - ], - "triplet_samples": [ - { - "subject": "记忆熊", - "predicate": "MENTIONS", - "predicate_cn": "提到", - "object": "宋朝军事弱势" - }, - { - "subject": "宋朝军事弱势", - "predicate": "RESULTED_IN", - "predicate_cn": "resulted in", - "object": "制度性因素" - }, - { - "subject": "记忆熊", - "predicate": "MENTIONS", - "predicate_cn": "提到", - "object": "禁军由文官控制导致作战效率低下" - }, - { - "subject": "禁军由文官控制", - "predicate": "RESULTED_IN", - "predicate_cn": "resulted in", - "object": "作战效率低下" - }, - { - "subject": "记忆熊", - "predicate": "MENTIONS", - "predicate_cn": "提到", - "object": "厢军装备不足" - }, - { - "subject": "记忆熊", - "predicate": "MENTIONS", - "predicate_cn": "提到", - "object": "宋朝" - }, - { - "subject": "记忆熊", - "predicate": "MENTIONS", - "predicate_cn": "提到", - "object": "军费" - } - ], - "self_reflexion": [ - { - "conflict": { - "data": [ - { - "id": "76be6d82d8804beda6baa3d3447d6cbc", - "statement": "学生对\"六部缺乏协调机制\"的具体影响表示理解不足。", - "group_id": "group_123", - "chunk_id": "4a0804127d35456f86d4f06e1fa458f7", - "created_at": "2025-12-12 09:48:00.166068", - "expired_at": null, - "valid_at": null, - "invalid_at": null, - "entity_ids": [] - } - ], - "conflict": true, - "conflict_memory": { - "id": "e268a6fff35543fab471986c188e023e", - "statement": "学生对\"六部缺乏协调机制\"的具体影响表示理解不足。", - "group_id": "group_123", - "chunk_id": "e6cb5f56020e4a8d925d148e1d2fbda0", - "created_at": "2025-12-12 09:48:00.166068", - "expired_at": null, - "valid_at": null, - "invalid_at": null, - "entity_ids": [] - } - }, - "reflexion": { - "reason": "同一学生在不同时间点重复提出对'六部缺乏协调机制'具体影响的理解困难，表明原有解释未能有效解决其认知障碍，存在记忆冗余与教学反馈失效的冲突。", - "solution": "保留后出现的记忆记录（chunk_id为4a0804127d35456f86d4f06e1fa458f7）作为最新学习状态，将其设为有效；将前次相同内容的记忆（id为e268a6fff35543fab471986c188e023e）标记为失效，避免重复干预，并基于后续完整解释优化知识呈现逻辑。" - }, - "resolved": { - "original_memory_id": "e268a6fff35543fab471986c188e023e", - "resolved_memory": { - "id": "e268a6fff35543fab471986c188e023e", - "statement": "学生对\"六部缺乏协调机制\"的具体影响表示理解不足。", - "group_id": "group_123", - "chunk_id": "e6cb5f56020e4a8d925d148e1d2fbda0", - "created_at": "2025-12-12 09:48:00.166068", - "expired_at": null, - "valid_at": null, - "invalid_at": "2025-12-12 09:48:00.166068", - "entity_ids": [] - } - } - } - ] - } \ No newline at end of file +} \ No newline at end of file diff --git a/web/src/views/MemoryManagement/types.ts b/web/src/views/MemoryManagement/types.ts index f926c6c8..55524462 100644 --- a/web/src/views/MemoryManagement/types.ts +++ b/web/src/views/MemoryManagement/types.ts @@ -23,7 +23,6 @@ export interface Memory { include_dialogue_context: boolean; max_context: string; lambda_mem: string; - lambda_mem: string; offset: string; state: boolean; created_at: string; diff --git a/web/src/views/UserMemoryDetail/components/PerceptualLastInfo.tsx b/web/src/views/UserMemoryDetail/components/PerceptualLastInfo.tsx index d3788a74..ef547742 100644 --- a/web/src/views/UserMemoryDetail/components/PerceptualLastInfo.tsx +++ b/web/src/views/UserMemoryDetail/components/PerceptualLastInfo.tsx @@ -59,6 +59,11 @@ const PerceptualLastInfo: FC<{ type: 'last_visual' | 'last_listen' | 'last_text' }) } + const handleDownload = () => { + if (!data.file_path) return + window.open(data.file_path, '_blank') + } + return ( // {data.file_name} ) : ( -
{data.file_name}
+
{data.file_name}
) ) : type === 'last_listen' && /\.(mp3|wav|ogg|m4a|aac)$/i.test(data.file_name) ? ( ) : ( -
{data.file_name}
+
{data.file_name}
) ) : ( -
No file
+
{t('empty.tableEmpty')}
)} From 9de6b4f151223888d296fdccff539a433488e2e7 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 11:06:49 +0800 Subject: [PATCH 060/175] =?UTF-8?q?=E6=84=9F=E7=9F=A5meta=5Fdata=E5=AD=97?= =?UTF-8?q?=E6=AE=B5BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/memory_perceptual_schema.py | 2 +- api/app/services/memory_perceptual_service.py | 22 ++++++++++++++----- 2 files changed, 18 insertions(+), 6 deletions(-) diff --git a/api/app/schemas/memory_perceptual_schema.py b/api/app/schemas/memory_perceptual_schema.py index e82d9526..7dfefe01 100644 --- a/api/app/schemas/memory_perceptual_schema.py +++ b/api/app/schemas/memory_perceptual_schema.py @@ -43,7 +43,7 @@ class PerceptualMemoryItem(BaseModel): file_name: str = Field(..., description="File name") file_ext: str = Field(..., description="File extension") summary: Optional[str] = Field(None, description="summary") - meta_data: str = Field(...,description="") + meta_data: Optional[dict] = Field(None, description="Metadata information") created_time: int = Field(..., description="create time") topic: str = Field(..., description="topic") diff --git a/api/app/services/memory_perceptual_service.py b/api/app/services/memory_perceptual_service.py index b65955f2..b9d96a0b 100644 --- a/api/app/services/memory_perceptual_service.py +++ b/api/app/services/memory_perceptual_service.py @@ -137,8 +137,19 @@ class MemoryPerceptualService: memory_items = [] for memory in memories: meta_data = memory.meta_data or {} - content = meta_data.get("content") - content = Content(**content) + content = meta_data.get("content", {}) + + # 安全地提取 content 字段，提供默认值 + if content: + content_obj = Content(**content) + topic = content_obj.topic + domain = content_obj.domain + keywords = content_obj.keywords + else: + topic = "Unknown" + domain = "Unknown" + keywords = [] + memory_item = PerceptualMemoryItem( id=memory.id, perceptual_type=PerceptualType(memory.perceptual_type), @@ -146,9 +157,10 @@ class MemoryPerceptualService: file_name=memory.file_name, file_ext=memory.file_ext, summary=memory.summary, - topic=content.topic, - domain=content.domain, - keywords=content.keywords, + meta_data=meta_data, + topic=topic, + domain=domain, + keywords=keywords, created_time=int(memory.created_time.timestamp()*1000), storage_service=FileStorageService(memory.storage_service), ) From 36017378693906f1c89744ef9284671a72821e15 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Mon, 26 Jan 2026 11:53:34 +0800 Subject: [PATCH 061/175] Fix/memory bug fix (#171) --- api/app/__init__.py | 0 .../controllers/emotion_config_controller.py | 7 +- api/app/controllers/emotion_controller.py | 38 +-- .../controllers/implicit_memory_controller.py | 80 +++--- .../controllers/memory_agent_controller.py | 52 ++-- .../controllers/memory_forget_controller.py | 25 +- .../memory_perceptual_controller.py | 66 ++--- .../memory_reflection_controller.py | 41 +-- .../controllers/memory_storage_controller.py | 5 +- .../controllers/memory_working_controller.py | 16 +- .../service/memory_api_controller.py | 2 +- .../controllers/user_memory_controllers.py | 18 +- api/app/core/agent/langchain_agent.py | 18 +- .../langgraph_graph/nodes/problem_nodes.py | 8 +- .../langgraph_graph/nodes/retrieve_nodes.py | 18 +- .../langgraph_graph/nodes/summary_nodes.py | 16 +- .../nodes/verification_nodes.py | 6 +- .../langgraph_graph/nodes/write_nodes.py | 17 +- .../agent/langgraph_graph/read_graph.py | 6 +- .../agent/langgraph_graph/tools/tool.py | 30 +-- .../agent/langgraph_graph/write_graph.py | 23 +- .../agent/services/parameter_builder.py | 6 +- .../memory/agent/services/search_service.py | 8 +- .../memory/agent/services/session_service.py | 18 +- .../core/memory/agent/utils/get_dialogs.py | 32 +-- api/app/core/memory/agent/utils/llm_tools.py | 10 +- api/app/core/memory/agent/utils/redis_tool.py | 26 +- .../core/memory/agent/utils/session_tools.py | 18 +- .../core/memory/agent/utils/write_tools.py | 16 +- .../core/memory/analytics/hot_memory_tags.py | 36 +-- .../analytics/implicit_memory/data_source.py | 4 +- .../memory/evaluation/dialogue_queries.py | 4 +- .../memory/evaluation/extraction_utils.py | 12 +- .../evaluation/locomo/locomo_benchmark.py | 26 +- .../memory/evaluation/locomo/locomo_test.py | 2 +- .../memory/evaluation/locomo/locomo_utils.py | 18 +- .../evaluation/locomo/qwen_search_eval.py | 22 +- .../longmemeval/qwen_search_eval.py | 58 ++--- .../evaluation/longmemeval/test_eval.py | 58 ++--- .../memory/evaluation/memsciqa/evaluate_qa.py | 12 +- .../evaluation/memsciqa/memsciqa-test.py | 12 +- api/app/core/memory/evaluation/run_eval.py | 18 +- .../core/memory/llm_tools/chunker_client.py | 24 +- api/app/core/memory/models/config_models.py | 4 +- api/app/core/memory/models/graph_models.py | 16 +- api/app/core/memory/models/message_models.py | 20 +- api/app/core/memory/src/search.py | 45 ++-- .../data_preprocessing/data_preprocessor.py | 10 +- .../deduplication/deduped_and_disamb.py | 18 +- .../deduplication/entity_dedup_llm.py | 18 +- .../deduplication/second_layer_dedup.py | 8 +- .../deduplication/two_stage_dedup.py | 14 +- .../extraction_orchestrator.py | 68 ++--- .../knowledge_extraction/memory_summary.py | 6 +- .../statement_extraction.py | 16 +- .../temporal_extraction.py | 2 +- .../triplet_extraction.py | 2 +- .../access_history_manager.py | 86 +++---- .../forgetting_engine/config_utils.py | 16 +- .../forgetting_engine/forgetting_scheduler.py | 31 +-- .../forgetting_engine/forgetting_strategy.py | 31 +-- .../storage_services/search/__init__.py | 6 +- .../storage_services/search/hybrid_search.py | 14 +- .../storage_services/search/keyword_search.py | 12 +- .../search/search_strategy.py | 10 +- .../search/semantic_search.py | 12 +- api/app/core/memory/utils/config/get_data.py | 4 +- api/app/core/memory/utils/log/audit_logger.py | 12 +- api/app/core/rag/vdb/field.py | 2 +- .../validators/memory_config_validators.py | 10 +- api/app/core/workflow/nodes/memory/config.py | 5 +- api/app/core/workflow/nodes/memory/node.py | 2 +- api/app/models/__init__.py | 4 +- api/app/models/data_config_model.py | 88 ------- api/app/models/memory_config_model.py | 119 ++++++--- api/app/models/memory_perceptual_model.py | 2 +- ...ository.py => memory_config_repository.py} | 210 ++++++++-------- .../memory_perceptual_repository.py | 4 +- api/app/repositories/neo4j/add_edges.py | 4 +- api/app/repositories/neo4j/add_nodes.py | 22 +- .../neo4j/base_neo4j_repository.py | 2 +- api/app/repositories/neo4j/cypher_queries.py | 171 +++++-------- .../repositories/neo4j/dialog_repository.py | 34 +-- .../repositories/neo4j/emotion_repository.py | 24 +- api/app/repositories/neo4j/graph_saver.py | 12 +- api/app/repositories/neo4j/graph_search.py | 236 +++++++++--------- .../neo4j/memory_summary_repository.py | 48 ++-- api/app/repositories/neo4j/neo4j_connector.py | 48 +--- .../neo4j/statement_repository.py | 2 +- api/app/schemas/app_schema.py | 12 + api/app/schemas/emotion_schema.py | 11 +- api/app/schemas/memory_agent_schema.py | 6 +- api/app/schemas/memory_config_schema.py | 20 +- api/app/schemas/memory_perceptual_schema.py | 8 +- api/app/schemas/memory_reflection_schemas.py | 3 +- api/app/schemas/memory_storage_schema.py | 34 +-- api/app/schemas/model_schema.py | 14 +- api/app/schemas/release_share_schema.py | 14 +- api/app/services/draft_run_service.py | 2 +- api/app/services/emotion_analytics_service.py | 14 +- api/app/services/emotion_config_service.py | 16 +- .../services/emotion_extraction_service.py | 4 +- api/app/services/memory_agent_service.py | 150 +++++------ api/app/services/memory_api_service.py | 21 +- api/app/services/memory_base_service.py | 18 +- api/app/services/memory_config_service.py | 91 ++++--- .../memory_entity_relationship_service.py | 4 +- api/app/services/memory_episodic_service.py | 30 +-- api/app/services/memory_explicit_service.py | 16 +- api/app/services/memory_forget_service.py | 81 +++--- api/app/services/memory_konwledges_server.py | 14 +- api/app/services/memory_perceptual_service.py | 26 +- api/app/services/memory_reflection_service.py | 50 ++-- api/app/services/memory_storage_service.py | 52 ++-- api/app/services/pilot_run_service.py | 2 +- api/app/services/user_memory_service.py | 42 ++-- api/app/tasks.py | 164 +++++++++--- api/app/utils/app_config_utils.py | 23 ++ api/uv.lock | 2 +- 119 files changed, 1711 insertions(+), 1695 deletions(-) create mode 100644 api/app/__init__.py delete mode 100644 api/app/models/data_config_model.py rename api/app/repositories/{data_config_repository.py => memory_config_repository.py} (73%) diff --git a/api/app/__init__.py b/api/app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/api/app/controllers/emotion_config_controller.py b/api/app/controllers/emotion_config_controller.py index 76450d8a..b0015bc2 100644 --- a/api/app/controllers/emotion_config_controller.py +++ b/api/app/controllers/emotion_config_controller.py @@ -12,6 +12,7 @@ from fastapi import APIRouter, Depends, Query, HTTPException, status from pydantic import BaseModel, Field from typing import Optional from sqlalchemy.orm import Session +from uuid import UUID from app.core.response_utils import success from app.dependencies import get_current_user @@ -32,11 +33,11 @@ router = APIRouter( class EmotionConfigQuery(BaseModel): """情绪配置查询请求模型""" - config_id: int = Field(..., description="配置ID") + config_id: UUID = Field(..., description="配置ID") class EmotionConfigUpdate(BaseModel): """情绪配置更新请求模型""" - config_id: int = Field(..., description="配置ID") + config_id: UUID = Field(..., description="配置ID") emotion_enabled: bool = Field(..., description="是否启用情绪提取") emotion_model_id: Optional[str] = Field(None, description="情绪分析专用模型ID") emotion_extract_keywords: bool = Field(..., description="是否提取情绪关键词") @@ -45,7 +46,7 @@ class EmotionConfigUpdate(BaseModel): @router.get("/read_config", response_model=ApiResponse) def get_emotion_config( - config_id: int = Query(..., description="配置ID"), + config_id: UUID = Query(..., description="配置ID"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ): diff --git a/api/app/controllers/emotion_controller.py b/api/app/controllers/emotion_controller.py index 154a3928..cd199aa7 100644 --- a/api/app/controllers/emotion_controller.py +++ b/api/app/controllers/emotion_controller.py @@ -53,7 +53,7 @@ async def get_emotion_tags( api_logger.info( f"用户 {current_user.username} 请求获取情绪标签统计", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "emotion_type": request.emotion_type, "start_date": request.start_date, "end_date": request.end_date, @@ -63,7 +63,7 @@ async def get_emotion_tags( # 调用服务层 data = await emotion_service.get_emotion_tags( - end_user_id=request.group_id, + end_user_id=request.end_user_id, emotion_type=request.emotion_type, start_date=request.start_date, end_date=request.end_date, @@ -73,7 +73,7 @@ async def get_emotion_tags( api_logger.info( "情绪标签统计获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "total_count": data.get("total_count", 0), "tags_count": len(data.get("tags", [])) } @@ -84,7 +84,7 @@ async def get_emotion_tags( except Exception as e: api_logger.error( f"获取情绪标签统计失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -105,7 +105,7 @@ async def get_emotion_wordcloud( api_logger.info( f"用户 {current_user.username} 请求获取情绪词云数据", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "emotion_type": request.emotion_type, "limit": request.limit } @@ -113,7 +113,7 @@ async def get_emotion_wordcloud( # 调用服务层 data = await emotion_service.get_emotion_wordcloud( - end_user_id=request.group_id, + end_user_id=request.end_user_id, emotion_type=request.emotion_type, limit=request.limit ) @@ -121,7 +121,7 @@ async def get_emotion_wordcloud( api_logger.info( "情绪词云数据获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "total_keywords": data.get("total_keywords", 0) } ) @@ -131,7 +131,7 @@ async def get_emotion_wordcloud( except Exception as e: api_logger.error( f"获取情绪词云数据失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -159,21 +159,21 @@ async def get_emotion_health( api_logger.info( f"用户 {current_user.username} 请求获取情绪健康指数", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "time_range": request.time_range } ) # 调用服务层 data = await emotion_service.calculate_emotion_health_index( - end_user_id=request.group_id, + end_user_id=request.end_user_id, time_range=request.time_range ) api_logger.info( "情绪健康指数获取成功", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "health_score": data.get("health_score", 0), "level": data.get("level", "未知") } @@ -186,7 +186,7 @@ async def get_emotion_health( except Exception as e: api_logger.error( f"获取情绪健康指数失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( @@ -206,7 +206,7 @@ async def get_emotion_suggestions( """获取个性化情绪建议（从缓存读取） Args: - request: 包含 group_id 和可选的 config_id + request: 包含 end_user_id 和可选的 config_id db: 数据库会话 current_user: 当前用户 @@ -217,22 +217,22 @@ async def get_emotion_suggestions( api_logger.info( f"用户 {current_user.username} 请求获取个性化情绪建议（缓存）", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "config_id": request.config_id } ) # 从缓存获取建议 data = await emotion_service.get_cached_suggestions( - end_user_id=request.group_id, + end_user_id=request.end_user_id, db=db ) if data is None: # 缓存不存在或已过期 api_logger.info( - f"用户 {request.group_id} 的建议缓存不存在或已过期", - extra={"group_id": request.group_id} + f"用户 {request.end_user_id} 的建议缓存不存在或已过期", + extra={"end_user_id": request.end_user_id} ) return fail( BizCode.NOT_FOUND, @@ -243,7 +243,7 @@ async def get_emotion_suggestions( api_logger.info( "个性化建议获取成功（缓存）", extra={ - "group_id": request.group_id, + "end_user_id": request.end_user_id, "suggestions_count": len(data.get("suggestions", [])) } ) @@ -253,7 +253,7 @@ async def get_emotion_suggestions( except Exception as e: api_logger.error( f"获取个性化建议失败: {str(e)}", - extra={"group_id": request.group_id}, + extra={"end_user_id": request.end_user_id}, exc_info=True ) raise HTTPException( diff --git a/api/app/controllers/implicit_memory_controller.py b/api/app/controllers/implicit_memory_controller.py index a53290e2..96e437d6 100644 --- a/api/app/controllers/implicit_memory_controller.py +++ b/api/app/controllers/implicit_memory_controller.py @@ -122,10 +122,10 @@ def validate_confidence_threshold(threshold: float) -> None: raise ValueError("confidence_threshold must be between 0.0 and 1.0") -@router.get("/preferences/{user_id}", response_model=ApiResponse) +@router.get("/preferences/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_preference_tags( - user_id: str, + end_user_id: str, confidence_threshold: float = Query(0.5, ge=0.0, le=1.0, description="Minimum confidence threshold"), tag_category: Optional[str] = Query(None, description="Filter by tag category"), start_date: Optional[datetime] = Query(None, description="Filter start date"), @@ -137,7 +137,7 @@ async def get_preference_tags( Get user preference tags from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID confidence_threshold: Minimum confidence score (0.0-1.0) tag_category: Optional category filter start_date: Optional start date filter @@ -146,20 +146,20 @@ async def get_preference_tags( Returns: List of preference tags from cache """ - api_logger.info(f"Preference tags requested for user: {user_id} (from cache)") + api_logger.info(f"Preference tags requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -192,17 +192,17 @@ async def get_preference_tags( filtered_preferences.append(pref) - api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {user_id} (from cache)") + api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {end_user_id} (from cache)") return success(data=filtered_preferences, msg="偏好标签获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "偏好标签获取", user_id) + return handle_implicit_memory_error(e, "偏好标签获取", end_user_id) -@router.get("/portrait/{user_id}", response_model=ApiResponse) +@router.get("/portrait/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_dimension_portrait( - user_id: str, + end_user_id: str, include_history: bool = Query(False, description="Include historical trends"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) @@ -211,26 +211,26 @@ async def get_dimension_portrait( Get user's four-dimension personality portrait from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID include_history: Whether to include historical trend data (ignored for cached data) Returns: Four-dimension personality portrait from cache """ - api_logger.info(f"Dimension portrait requested for user: {user_id} (from cache)") + api_logger.info(f"Dimension portrait requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -240,17 +240,17 @@ async def get_dimension_portrait( # Extract portrait from cache portrait = cached_profile.get("portrait", {}) - api_logger.info(f"Dimension portrait retrieved for user: {user_id} (from cache)") + api_logger.info(f"Dimension portrait retrieved for user: {end_user_id} (from cache)") return success(data=portrait, msg="四维画像获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "四维画像获取", user_id) + return handle_implicit_memory_error(e, "四维画像获取", end_user_id) -@router.get("/interest-areas/{user_id}", response_model=ApiResponse) +@router.get("/interest-areas/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_interest_area_distribution( - user_id: str, + end_user_id: str, include_trends: bool = Query(False, description="Include trend analysis"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user) @@ -259,26 +259,26 @@ async def get_interest_area_distribution( Get user's interest area distribution from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID include_trends: Whether to include trend analysis data (ignored for cached data) Returns: Interest area distribution from cache """ - api_logger.info(f"Interest area distribution requested for user: {user_id} (from cache)") + api_logger.info(f"Interest area distribution requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -288,17 +288,17 @@ async def get_interest_area_distribution( # Extract interest areas from cache interest_areas = cached_profile.get("interest_areas", {}) - api_logger.info(f"Interest area distribution retrieved for user: {user_id} (from cache)") + api_logger.info(f"Interest area distribution retrieved for user: {end_user_id} (from cache)") return success(data=interest_areas, msg="兴趣领域分布获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "兴趣领域分布获取", user_id) + return handle_implicit_memory_error(e, "兴趣领域分布获取", end_user_id) -@router.get("/habits/{user_id}", response_model=ApiResponse) +@router.get("/habits/{end_user_id}", response_model=ApiResponse) @cur_workspace_access_guard() async def get_behavior_habits( - user_id: str, + end_user_id: str, confidence_level: Optional[str] = Query(None, regex="^(high|medium|low)$", description="Filter by confidence level"), frequency_pattern: Optional[str] = Query(None, regex="^(daily|weekly|monthly|seasonal|occasional|event_triggered)$", description="Filter by frequency pattern"), time_period: Optional[str] = Query(None, regex="^(current|past)$", description="Filter by time period"), @@ -309,7 +309,7 @@ async def get_behavior_habits( Get user's behavioral habits from cache. Args: - user_id: Target user ID + end_user_id: Target end user ID confidence_level: Filter by confidence level (high, medium, low) frequency_pattern: Filter by frequency pattern (daily, weekly, monthly, seasonal, occasional, event_triggered) time_period: Filter by time period (current, past) @@ -317,20 +317,20 @@ async def get_behavior_habits( Returns: List of behavioral habits from cache """ - api_logger.info(f"Behavior habits requested for user: {user_id} (from cache)") + api_logger.info(f"Behavior habits requested for user: {end_user_id} (from cache)") try: # Validate inputs - validate_user_id(user_id) + validate_user_id(end_user_id) # Create service with user-specific config - service = ImplicitMemoryService(db=db, end_user_id=user_id) + service = ImplicitMemoryService(db=db, end_user_id=end_user_id) # Get cached profile - cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db) + cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db) if cached_profile is None: - api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期") + api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期") return fail( BizCode.NOT_FOUND, "画像缓存不存在或已过期，请右上角刷新生成新画像", @@ -368,11 +368,11 @@ async def get_behavior_habits( filtered_habits.append(habit) - api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {user_id} (from cache)") + api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {end_user_id} (from cache)") return success(data=filtered_habits, msg="行为习惯获取成功（缓存）") except Exception as e: - return handle_implicit_memory_error(e, "行为习惯获取", user_id) + return handle_implicit_memory_error(e, "行为习惯获取", end_user_id) diff --git a/api/app/controllers/memory_agent_controller.py b/api/app/controllers/memory_agent_controller.py index c54fb02b..61b16d9e 100644 --- a/api/app/controllers/memory_agent_controller.py +++ b/api/app/controllers/memory_agent_controller.py @@ -125,7 +125,7 @@ async def write_server( Write service endpoint - processes write operations synchronously Args: - user_input: Write request containing message and group_id + user_input: Write request containing message and end_user_id Returns: Response with write operation status @@ -160,19 +160,18 @@ async def write_server( api_logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储") storage_type = 'neo4j' - api_logger.info(f"Write service requested for group {user_input.group_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") + api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}") try: - # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - result = await memory_agent_service.write_memory( - user_input.group_id, - messages_list, # 传递结构化消息列表 + user_input.end_user_id, + messages_list, config_id, db, storage_type, user_rag_memory_id ) + return success(data=result, msg="写入成功") except BaseException as e: # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup @@ -196,7 +195,7 @@ async def write_server_async( Async write service endpoint - enqueues write processing to Celery Args: - user_input: Write request containing message and group_id + user_input: Write request containing message and end_user_id Returns: Task ID for tracking async operation @@ -226,10 +225,10 @@ async def write_server_async( try: # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - + task = celery_app.send_task( "app.core.memory.agent.write_message", - args=[user_input.group_id, messages_list, config_id, storage_type, user_rag_memory_id] + args=[user_input.end_user_id, messages_list, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Write task queued: {task.id}") @@ -255,7 +254,7 @@ async def read_server( - "2": Direct answer based on context Args: - user_input: Read request with message, history, search_switch, and group_id + user_input: Read request with message, history, search_switch, and end_user_id Returns: Response with query answer @@ -277,12 +276,13 @@ async def read_server( name="USER_RAG_MERORY", workspace_id=workspace_id ) - if knowledge: user_rag_memory_id = str(knowledge.id) + if knowledge: + user_rag_memory_id = str(knowledge.id) - api_logger.info(f"Read service: group={user_input.group_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}") + api_logger.info(f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}") try: result = await memory_agent_service.read_memory( - user_input.group_id, + user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch, @@ -293,12 +293,12 @@ async def read_server( ) if str(user_input.search_switch) == "2": retrieve_info = result['answer'] - history = await SessionService(store).get_history(user_input.group_id, user_input.group_id, user_input.group_id) + history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id, user_input.end_user_id) query = user_input.message - + # 调用 memory_agent_service 的方法生成最终答案 result['answer'] = await memory_agent_service.generate_summary_from_retrieve( - group_id=user_input.group_id, + end_user_id=user_input.end_user_id, retrieve_info=retrieve_info, history=history, query=query, @@ -404,7 +404,7 @@ async def read_server_async( try: task = celery_app.send_task( "app.core.memory.agent.read_message", - args=[user_input.group_id, user_input.message, user_input.history, user_input.search_switch, + args=[user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch, config_id, storage_type, user_rag_memory_id] ) api_logger.info(f"Read task queued: {task.id}") @@ -448,7 +448,7 @@ async def get_read_task_result( return success( data={ "result": task_result.get("result"), - "group_id": task_result.get("group_id"), + "end_user_id": task_result.get("end_user_id"), "elapsed_time": task_result.get("elapsed_time"), "task_id": task_id }, @@ -525,7 +525,7 @@ async def get_write_task_result( return success( data={ "result": task_result.get("result"), - "group_id": task_result.get("group_id"), + "end_user_id": task_result.get("end_user_id"), "elapsed_time": task_result.get("elapsed_time"), "task_id": task_id }, @@ -579,16 +579,16 @@ async def status_type( Determine the type of user message (read or write) Args: - user_input: Request containing user message and group_id + user_input: Request containing user message and end_user_id Returns: Type classification result """ - api_logger.info(f"Status type check requested for group {user_input.group_id}") + api_logger.info(f"Status type check requested for group {user_input.end_user_id}") try: # 获取标准化的消息列表 messages_list = memory_agent_service.get_messages_list(user_input) - + # 将消息列表转换为字符串用于分类 # 只取最后一条用户消息进行分类 last_user_message = "" @@ -596,11 +596,11 @@ async def status_type( if msg.get('role') == 'user': last_user_message = msg.get('content', '') break - + if not last_user_message: # 如果没有用户消息，使用所有消息的内容 last_user_message = " ".join([msg.get('content', '') for msg in messages_list]) - + result = await memory_agent_service.classify_message_type( last_user_message, user_input.config_id, @@ -625,7 +625,7 @@ async def get_knowledge_type_stats_api( 会对缺失类型补 0，返回字典形式。 可选按状态过滤。 - 知识库类型根据当前用户的 current_workspace_id 过滤 - - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (group_id) 过滤 + - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤 - 如果用户没有当前工作空间或未提供 end_user_id，对应的统计返回 0 """ api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}") @@ -698,7 +698,7 @@ async def get_user_profile_api( current_user: User = Depends(get_current_user) ): """ - 获取工作空间下Popular Memory Tags，包含： + 获取用户详情，包含： - name: 用户名字（直接使用 end_user_id） - tags: 3个用户特征标签（从语句和实体中LLM总结） - hot_tags: 4个热门记忆标签 diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index ca628d0c..a6b6028f 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -11,6 +11,7 @@ """ from typing import Optional +from uuid import UUID from fastapi import APIRouter, Depends from sqlalchemy.orm import Session @@ -106,7 +107,7 @@ async def trigger_forgetting_cycle( # 调用服务层执行遗忘周期 report = await forget_service.trigger_forgetting_cycle( db=db, - group_id=end_user_id, # 服务层方法的参数名是 group_id + end_user_id=end_user_id, # 服务层方法的参数名是 end_user_id max_merge_batch_size=payload.max_merge_batch_size, min_days_since_access=payload.min_days_since_access, config_id=config_id @@ -128,7 +129,7 @@ async def trigger_forgetting_cycle( @router.get("/read_config", response_model=ApiResponse) async def read_forgetting_config( - config_id: int, + config_id: UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -236,7 +237,7 @@ async def update_forgetting_config( @router.get("/stats", response_model=ApiResponse) async def get_forgetting_stats( - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -246,7 +247,7 @@ async def get_forgetting_stats( 返回知识层节点统计、激活值分布等信息。 Args: - group_id: 组ID（即 end_user_id，可选） + end_user_id: 组ID（即 end_user_id，可选） current_user: 当前用户 db: 数据库会话 @@ -260,20 +261,20 @@ async def get_forgetting_stats( api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - # 如果提供了 group_id，通过它获取 config_id + # 如果提供了 end_user_id，通过它获取 config_id config_id = None - if group_id: + if end_user_id: try: from app.services.memory_agent_service import get_end_user_connected_config - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - api_logger.warning(f"终端用户 {group_id} 未关联记忆配置") - return fail(BizCode.INVALID_PARAMETER, f"终端用户 {group_id} 未关联记忆配置", "memory_config_id is None") + api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") + return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None") - api_logger.debug(f"通过 group_id={group_id} 获取到 config_id={config_id}") + api_logger.debug(f"通过 end_user_id={end_user_id} 获取到 config_id={config_id}") except ValueError as e: api_logger.warning(f"获取终端用户配置失败: {str(e)}") return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError") @@ -283,14 +284,14 @@ async def get_forgetting_stats( api_logger.info( f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: " - f"group_id={group_id}, config_id={config_id}" + f"end_user_id={end_user_id}, config_id={config_id}" ) try: # 调用服务层获取统计信息 stats = await forget_service.get_forgetting_stats( db=db, - group_id=group_id, + end_user_id=end_user_id, config_id=config_id ) diff --git a/api/app/controllers/memory_perceptual_controller.py b/api/app/controllers/memory_perceptual_controller.py index 5154c763..44750808 100644 --- a/api/app/controllers/memory_perceptual_controller.py +++ b/api/app/controllers/memory_perceptual_controller.py @@ -27,27 +27,27 @@ router = APIRouter( ) -@router.get("/{group_id}/count", response_model=ApiResponse) +@router.get("/{end_user_id}/count", response_model=ApiResponse) def get_memory_count( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve perceptual memory statistics for a user group. Args: - group_id: ID of the user group (usually end_user_id in this context) + end_user_id: ID of the user group (usually end_user_id in this context) current_user: Current authenticated user db: Database session Returns: ApiResponse: Response containing memory count statistics """ - api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - count_stats = service.get_memory_count(group_id) + count_stats = service.get_memory_count(end_user_id) api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}") @@ -57,37 +57,37 @@ def get_memory_count( ) except Exception as e: - api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch memory statistics: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch memory statistics", ) -@router.get("/{group_id}/last_visual", response_model=ApiResponse) +@router.get("/{end_user_id}/last_visual", response_model=ApiResponse) def get_last_visual_memory( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent VISION-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest visual memory """ - api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest visual memory: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - visual_memory = service.get_latest_visual_memory(group_id) + visual_memory = service.get_latest_visual_memory(end_user_id) if visual_memory is None: - api_logger.info(f"No visual memory found: group_id={group_id}") + api_logger.info(f"No visual memory found: end_user_id={end_user_id}") return success( data=None, msg="No visual memory available" @@ -101,37 +101,37 @@ def get_last_visual_memory( ) except Exception as e: - api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest visual memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest visual memory", ) -@router.get("/{group_id}/last_listen", response_model=ApiResponse) +@router.get("/{end_user_id}/last_listen", response_model=ApiResponse) def get_last_memory_listen( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent AUDIO-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest audio memory """ - api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest audio memory: user={current_user.username}, end_user_id={end_user_id}") try: service = MemoryPerceptualService(db) - audio_memory = service.get_latest_audio_memory(group_id) + audio_memory = service.get_latest_audio_memory(end_user_id) if audio_memory is None: - api_logger.info(f"No audio memory found: group_id={group_id}") + api_logger.info(f"No audio memory found: end_user_id={end_user_id}") return success( data=None, msg="No audio memory available" @@ -145,38 +145,38 @@ def get_last_memory_listen( ) except Exception as e: - api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest audio memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest audio memory", ) -@router.get("/{group_id}/last_text", response_model=ApiResponse) +@router.get("/{end_user_id}/last_text", response_model=ApiResponse) def get_last_text_memory( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): """Retrieve the most recent TEXT-type memory for a user. Args: - group_id: ID of the user group + end_user_id: ID of the user group current_user: Current authenticated user db: Database session Returns: ApiResponse: Metadata of the latest text memory """ - api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}") + api_logger.info(f"Fetching latest text memory: user={current_user.username}, end_user_id={end_user_id}") try: # 调用服务层获取最近的文本记忆 service = MemoryPerceptualService(db) - text_memory = service.get_latest_text_memory(group_id) + text_memory = service.get_latest_text_memory(end_user_id) if text_memory is None: - api_logger.info(f"No text memory found: group_id={group_id}") + api_logger.info(f"No text memory found: end_user_id={end_user_id}") return success( data=None, msg="No text memory available" @@ -190,16 +190,16 @@ def get_last_text_memory( ) except Exception as e: - api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}") + api_logger.error(f"Failed to fetch latest text memory: end_user_id={end_user_id}, error={str(e)}") return fail( code=BizCode.INTERNAL_ERROR, msg="Failed to fetch latest text memory", ) -@router.get("/{group_id}/timeline", response_model=ApiResponse) +@router.get("/{end_user_id}/timeline", response_model=ApiResponse) def get_memory_time_line( - group_id: uuid.UUID, + end_user_id: uuid.UUID, perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"), page: int = Query(1, ge=1, description="页码"), page_size: int = Query(10, ge=1, le=100, description="每页大小"), @@ -209,7 +209,7 @@ def get_memory_time_line( """Retrieve a timeline of perceptual memories for a user group. Args: - group_id: ID of the user group + end_user_id: ID of the user group perceptual_type: Optional filter for perceptual type page: Page number for pagination page_size: Number of items per page @@ -221,7 +221,7 @@ def get_memory_time_line( """ api_logger.info( f"Fetching perceptual memory timeline: user={current_user.username}, " - f"group_id={group_id}, type={perceptual_type}, page={page}" + f"end_user_id={end_user_id}, type={perceptual_type}, page={page}" ) try: @@ -232,7 +232,7 @@ def get_memory_time_line( ) service = MemoryPerceptualService(db) - timeline_data = service.get_time_line(group_id, query) + timeline_data = service.get_time_line(end_user_id, query) api_logger.info( f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, " @@ -246,7 +246,7 @@ def get_memory_time_line( except Exception as e: api_logger.error( - f"Failed to fetch perceptual memory timeline: group_id={group_id}, " + f"Failed to fetch perceptual memory timeline: end_user_id={end_user_id}, " f"error={str(e)}" ) return fail( diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index abd50a33..ccf9485f 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -1,6 +1,7 @@ import asyncio import time import uuid +from uuid import UUID from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine.self_reflexion import ( @@ -11,7 +12,7 @@ from app.core.response_utils import success from app.db import get_db from app.dependencies import get_current_user from app.models.user_model import User -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_reflection_schemas import Memory_Reflection from app.services.memory_reflection_service import ( @@ -50,7 +51,7 @@ async def save_reflection_config( api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}") - data_config = DataConfigRepository.update_reflection_config( + memory_config = MemoryConfigRepository.update_reflection_config( db, config_id=config_id, enable_self_reflexion=request.reflection_enabled, @@ -63,17 +64,17 @@ async def save_reflection_config( ) db.commit() - db.refresh(data_config) + db.refresh(memory_config) reflection_result={ - "config_id": data_config.config_id, - "enable_self_reflexion": data_config.enable_self_reflexion, - "iteration_period": data_config.iteration_period, - "reflexion_range": data_config.reflexion_range, - "baseline": data_config.baseline, - "reflection_model_id": data_config.reflection_model_id, - "memory_verify": data_config.memory_verify, - "quality_assessment": data_config.quality_assessment} + "config_id": memory_config.config_id, + "enable_self_reflexion": memory_config.enable_self_reflexion, + "iteration_period": memory_config.iteration_period, + "reflexion_range": memory_config.reflexion_range, + "baseline": memory_config.baseline, + "reflection_model_id": memory_config.reflection_model_id, + "memory_verify": memory_config.memory_verify, + "quality_assessment": memory_config.quality_assessment} return success(data=reflection_result, msg="反思配置成功") @@ -111,14 +112,14 @@ async def start_workspace_reflection( reflection_results = [] for data in result['apps_detailed_info']: - if data['data_configs'] == []: + if data['memory_configs'] == []: continue releases = data['releases'] - data_configs = data['data_configs'] + memory_configs = data['memory_configs'] end_users = data['end_users'] - for base, config, user in zip(releases, data_configs, end_users): + for base, config, user in zip(releases, memory_configs, end_users): # 安全地转换为整数，处理空字符串和None的情况 print(base['config']) try: @@ -156,14 +157,14 @@ async def start_workspace_reflection( @router.get("/reflection/configs") async def start_reflection_configs( - config_id: int, + config_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: - """通过config_id查询data_config表中的反思配置信息""" + """通过config_id查询memory_config表中的反思配置信息""" try: api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - result = DataConfigRepository.query_reflection_config_by_id(db, config_id) + result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) # 构建返回数据 reflection_config = { "config_id": result.config_id, @@ -191,7 +192,7 @@ async def start_reflection_configs( @router.get("/reflection/run") async def reflection_run( - config_id: int, + config_id: UUID, language_type: str = Header(default="zh", alias="X-Language-Type"), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), @@ -200,8 +201,8 @@ async def reflection_run( api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - # 使用DataConfigRepository查询反思配置 - result = DataConfigRepository.query_reflection_config_by_id(db, config_id) + # 使用MemoryConfigRepository查询反思配置 + result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) if not result: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, diff --git a/api/app/controllers/memory_storage_controller.py b/api/app/controllers/memory_storage_controller.py index 3722be3d..fb0ebc14 100644 --- a/api/app/controllers/memory_storage_controller.py +++ b/api/app/controllers/memory_storage_controller.py @@ -1,5 +1,6 @@ import os from typing import Optional +from uuid import UUID from app.core.error_codes import BizCode from app.core.logging_config import get_api_logger @@ -160,7 +161,7 @@ def create_config( @router.delete("/delete_config", response_model=ApiResponse) # 删除数据库中的内容（按配置名称） def delete_config( - config_id: str, + config_id: UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: @@ -232,7 +233,7 @@ def update_config_extracted( @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除 def read_config_extracted( - config_id: str, + config_id: UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: diff --git a/api/app/controllers/memory_working_controller.py b/api/app/controllers/memory_working_controller.py index dfd64044..e5de3c04 100644 --- a/api/app/controllers/memory_working_controller.py +++ b/api/app/controllers/memory_working_controller.py @@ -20,18 +20,18 @@ router = APIRouter( ) -@router.get("/{group_id}/count", response_model=ApiResponse) +@router.get("/{end_user_id}/count", response_model=ApiResponse) def get_memory_count( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): pass -@router.get("/{group_id}/conversations", response_model=ApiResponse) +@router.get("/{end_user_id}/conversations", response_model=ApiResponse) def get_conversations( - group_id: uuid.UUID, + end_user_id: uuid.UUID, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -39,7 +39,7 @@ def get_conversations( Retrieve all conversations for the current user in a specific group. Args: - group_id (UUID): The group identifier. + end_user_id (UUID): The group identifier. current_user (User, optional): The authenticated user. db (Session, optional): SQLAlchemy session. @@ -53,7 +53,7 @@ def get_conversations( """ conversation_service = ConversationService(db) conversations = conversation_service.get_user_conversations( - group_id + end_user_id ) return success(data=[ { @@ -63,7 +63,7 @@ def get_conversations( ], msg="get conversations success") -@router.get("/{group_id}/messages", response_model=ApiResponse) +@router.get("/{end_user_id}/messages", response_model=ApiResponse) def get_messages( conversation_id: uuid.UUID, current_user: User = Depends(get_current_user), @@ -100,7 +100,7 @@ def get_messages( return success(data=messages, msg="get conversation history success") -@router.get("/{group_id}/detail", response_model=ApiResponse) +@router.get("/{end_user_id}/detail", response_model=ApiResponse) async def get_conversation_detail( conversation_id: uuid.UUID, current_user: User = Depends(get_current_user), diff --git a/api/app/controllers/service/memory_api_controller.py b/api/app/controllers/service/memory_api_controller.py index 30ca1306..accd749e 100644 --- a/api/app/controllers/service/memory_api_controller.py +++ b/api/app/controllers/service/memory_api_controller.py @@ -39,7 +39,7 @@ async def write_memory_api_service( Stores memory content for the specified end user using the Memory API Service. """ - logger.info(f"Memory write request - end_user_id: {payload.end_user_id}") + logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}") memory_api_service = MemoryAPIService(db) diff --git a/api/app/controllers/user_memory_controllers.py b/api/app/controllers/user_memory_controllers.py index 6f02f8f9..39cbe523 100644 --- a/api/app/controllers/user_memory_controllers.py +++ b/api/app/controllers/user_memory_controllers.py @@ -135,27 +135,27 @@ async def generate_cache_api( api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - group_id = request.end_user_id + end_user_id = request.end_user_id api_logger.info( f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, " - f"end_user_id={group_id if group_id else '全部用户'}" + f"end_user_id={end_user_id if end_user_id else '全部用户'}" ) try: - if group_id: + if end_user_id: # 为单个用户生成 - api_logger.info(f"开始为单个用户生成缓存: end_user_id={group_id}") + api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}") # 生成记忆洞察 - insight_result = await user_memory_service.generate_and_cache_insight(db, group_id, workspace_id) + insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id) # 生成用户摘要 - summary_result = await user_memory_service.generate_and_cache_summary(db, group_id, workspace_id) + summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id) # 构建响应 result = { - "end_user_id": group_id, + "end_user_id": end_user_id, "insight_success": insight_result["success"], "summary_success": summary_result["success"], "errors": [] @@ -175,9 +175,9 @@ async def generate_cache_api( # 记录结果 if result["insight_success"] and result["summary_success"]: - api_logger.info(f"成功为用户 {group_id} 生成缓存") + api_logger.info(f"成功为用户 {end_user_id} 生成缓存") else: - api_logger.warning(f"用户 {group_id} 的缓存生成部分失败: {result['errors']}") + api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {result['errors']}") return success(data=result, msg="生成完成") diff --git a/api/app/core/agent/langchain_agent.py b/api/app/core/agent/langchain_agent.py index 87b46e6f..ddacb094 100644 --- a/api/app/core/agent/langchain_agent.py +++ b/api/app/core/agent/langchain_agent.py @@ -155,13 +155,13 @@ class LangChainAgent: # userid=end_user_end, # messages=messages, # apply_id=end_user_end, - # group_id=end_user_end, + # end_user_id=end_user_end, # aimessages=aimessages # ) # store.delete_duplicate_sessions() # # logger.info(f'Redis_Agent:{end_user_end};{session_id}') # return session_id - + # TODO 乐力齐 - 累积多组对话批量写入功能已禁用 # async def term_memory_redis_read(self,end_user_end): # end_user_end = f"Term_{end_user_end}" @@ -179,7 +179,7 @@ class LangChainAgent: async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id): """ 写入记忆（支持结构化消息） - + Args: storage_type: 存储类型 (neo4j/rag) end_user_id: 终端用户ID @@ -188,7 +188,7 @@ class LangChainAgent: user_rag_memory_id: RAG 记忆ID actual_end_user_id: 实际用户ID actual_config_id: 配置ID - + 逻辑说明： - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变 - Neo4j 模式：使用结构化消息列表 @@ -204,20 +204,20 @@ class LangChainAgent: else: # Neo4j 模式：使用结构化消息列表 structured_messages = [] - + # 始终添加用户消息（如果不为空） if user_message: structured_messages.append({"role": "user", "content": user_message}) - + # 只有当 AI 回复不为空时才添加 assistant 消息 if ai_message: structured_messages.append({"role": "assistant", "content": ai_message}) - + # 如果没有消息，直接返回 if not structured_messages: logger.warning(f"No messages to write for user {actual_end_user_id}") return - + # 调用 Celery 任务，传递结构化消息列表 # 数据流： # 1. structured_messages 传递给 write_message_task @@ -228,7 +228,7 @@ class LangChainAgent: # 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段 logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}") write_id = write_message_task.delay( - actual_end_user_id, # group_id: 用户ID + actual_end_user_id, # end_user_id: 用户ID structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}] actual_config_id, # config_id: 配置ID storage_type, # storage_type: "neo4j" diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py index 2bad650a..ac1fb9a6 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py @@ -35,10 +35,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState: """问题分解节点""" # 从状态中获取数据 content = state.get('data', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) # 生成 JSON schema 以指导 LLM 输出正确格式 json_schema = ProblemExtensionResponse.model_json_schema() @@ -140,7 +140,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: start = time.time() content = state.get('data', '') data = state.get('spit_data', '')['context'] - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') memory_config = state.get('memory_config', None) @@ -156,7 +156,7 @@ async def Problem_Extension(state: ReadState) -> ReadState: databasets = {} data = [] - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) # 生成 JSON schema 以指导 LLM 输出正确格式 json_schema = ProblemExtensionResponse.model_json_schema() diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py index 14f8fa8b..1880357c 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py @@ -52,9 +52,9 @@ async def rag_config(state): return kb_config async def rag_knowledge(state,question): kb_config = await rag_config(state) - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') user_rag_memory_id=state.get("user_rag_memory_id",'') - retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(group_id)]) + retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(end_user_id)]) try: retrieval_knowledge = [i.page_content for i in retrieve_chunks_result] clean_content = '\n\n'.join(retrieval_knowledge) @@ -159,7 +159,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: problem_extension=state.get('problem_extension', '')['context'] storage_type=state.get('storage_type', '') user_rag_memory_id=state.get('user_rag_memory_id', '') - group_id=state.get('group_id', '') + end_user_id=state.get('end_user_id', '') memory_config = state.get('memory_config', None) original=state.get('data', '') problem_list=[] @@ -172,7 +172,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState: try: # Prepare search parameters based on storage type search_params = { - "group_id": group_id, + "end_user_id": end_user_id, "question": question, "return_raw_results": True } @@ -263,13 +263,13 @@ async def retrieve_nodes(state: ReadState) -> ReadState: async def retrieve(state: ReadState) -> ReadState: - # 从state中获取group_id + # 从state中获取end_user_id import time start=time.time() problem_extension = state.get('problem_extension', '')['context'] storage_type = state.get('storage_type', '') user_rag_memory_id = state.get('user_rag_memory_id', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) original = state.get('data', '') problem_list = [] @@ -295,13 +295,13 @@ async def retrieve(state: ReadState) -> ReadState: temperature=0.2, ) - time_retrieval_tool = create_time_retrieval_tool(group_id) - search_params = { "group_id": group_id, "return_raw_results": True } + time_retrieval_tool = create_time_retrieval_tool(end_user_id) + search_params = { "end_user_id": end_user_id, "return_raw_results": True } hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params) agent = create_agent( llm, tools=[time_retrieval_tool,hybrid_retrieval], - system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的group_id是: {group_id}" + system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}" ) # 创建异步任务处理单个问题 diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py index fb0484d2..0144c0e9 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py @@ -34,8 +34,8 @@ class SummaryNodeService(LLMServiceMixin): summary_service = SummaryNodeService() async def summary_history(state: ReadState) -> ReadState: - group_id = state.get("group_id", '') - history = await SessionService(store).get_history(group_id, group_id, group_id) + end_user_id = state.get("end_user_id", '') + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) return history async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str: @@ -122,12 +122,12 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o async def summary_redis_save(state: ReadState,aimessages) -> ReadState: data = state.get("data", '') - group_id = state.get("group_id", '') + end_user_id = state.get("end_user_id", '') await SessionService(store).save_session( - user_id=group_id, + user_id=end_user_id, query=data, - apply_id=group_id, - group_id=group_id, + apply_id=end_user_id, + end_user_id=end_user_id, ai_response=aimessages ) await SessionService(store).cleanup_duplicates() @@ -175,11 +175,11 @@ async def Input_Summary(state: ReadState) -> ReadState: memory_config = state.get('memory_config', None) user_rag_memory_id=state.get("user_rag_memory_id",'') data=state.get("data", '') - group_id=state.get("group_id", '') + end_user_id=state.get("end_user_id", '') logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}") history = await summary_history( state) search_params = { - "group_id": group_id, + "end_user_id": end_user_id, "question": data, "return_raw_results": True, "include": ["summaries"] # Only search summary nodes for faster performance diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py index 10ce8db4..b809faf2 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py @@ -62,12 +62,12 @@ async def Verify(state: ReadState): logger.info("=== Verify 节点开始执行 ===") try: content = state.get('data', '') - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', None) - logger.info(f"Verify: content={content[:50] if content else 'empty'}..., group_id={group_id}") + logger.info(f"Verify: content={content[:50] if content else 'empty'}..., end_user_id={end_user_id}") - history = await SessionService(store).get_history(group_id, group_id, group_id) + history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id) logger.info(f"Verify: 获取历史记录完成，history length={len(history)}") retrieve = state.get("retrieve", {}) diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py index 6af313c3..b85130ad 100644 --- a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py +++ b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py @@ -1,23 +1,24 @@ - -from app.core.memory.agent.utils.llm_tools import WriteState +from app.core.memory.agent.utils.llm_tools import WriteState from app.core.memory.agent.utils.write_tools import write from app.core.logging_config import get_agent_logger logger = get_agent_logger(__name__) + + async def write_node(state: WriteState) -> WriteState: """ Write data to the database/file system. Args: - state: WriteState containing messages, group_id, and memory_config + state: WriteState containing messages, end_user_id, and memory_config Returns: dict: Contains 'write_result' with status and data fields """ messages = state.get('messages', []) - group_id = state.get('group_id', '') + end_user_id = state.get('end_user_id', '') memory_config = state.get('memory_config', '') - + # Convert LangChain messages to structured format expected by write() structured_messages = [] for msg in messages: @@ -28,13 +29,11 @@ async def write_node(state: WriteState) -> WriteState: "role": role, "content": msg.content # content is now guaranteed to be a string }) - + try: result = await write( messages=structured_messages, - user_id=group_id, - apply_id=group_id, - group_id=group_id, + end_user_id=end_user_id, memory_config=memory_config, ) logger.info(f"Write completed successfully! Config: {memory_config.config_name}") diff --git a/api/app/core/memory/agent/langgraph_graph/read_graph.py b/api/app/core/memory/agent/langgraph_graph/read_graph.py index 19011a5f..3476d0ec 100644 --- a/api/app/core/memory/agent/langgraph_graph/read_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/read_graph.py @@ -79,7 +79,7 @@ async def make_read_graph(): async def main(): """主函数 - 运行工作流""" message = "昨天有什么好看的电影" - group_id = '88a459f5_text09' # 组ID + end_user_id = '88a459f5_text09' # 组ID storage_type = 'neo4j' # 存储类型 search_switch = '1' # 搜索开关 user_rag_memory_id = 'wwwwwwww' # 用户RAG记忆ID @@ -95,9 +95,9 @@ async def main(): start=time.time() try: async with make_read_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"group_id":group_id + initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"end_user_id":end_user_id ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config} # 获取节点更新信息 _intermediate_outputs = [] diff --git a/api/app/core/memory/agent/langgraph_graph/tools/tool.py b/api/app/core/memory/agent/langgraph_graph/tools/tool.py index ce6d5dd4..c4814de1 100644 --- a/api/app/core/memory/agent/langgraph_graph/tools/tool.py +++ b/api/app/core/memory/agent/langgraph_graph/tools/tool.py @@ -48,11 +48,11 @@ def extract_tool_message_content(response): class TimeRetrievalInput(BaseModel): """时间检索工具的输入模式""" context: str = Field(description="用户输入的查询内容") - group_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") + end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果") -def create_time_retrieval_tool(group_id: str): +def create_time_retrieval_tool(end_user_id: str): """ - 创建一个带有特定group_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) + 创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements) """ def clean_temporal_result_fields(data): @@ -93,26 +93,26 @@ def create_time_retrieval_tool(group_id: str): return data @tool - def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, group_id_param: str = None, clean_output: bool = True) -> str: + def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str: """ 优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段 显式接收参数： - context: 查询上下文内容 - start_date: 开始时间（可选，格式：YYYY-MM-DD） - end_date: 结束时间（可选，格式：YYYY-MM-DD） - - group_id_param: 组ID（可选，用于覆盖默认组ID） + - end_user_id_param: 组ID（可选，用于覆盖默认组ID） - clean_output: 是否清理输出中的元数据字段 -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d") """ async def _async_search(): # 使用传入的参数或默认值 - actual_group_id = group_id_param or group_id + actual_end_user_id = end_user_id_param or end_user_id actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d") actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d") # 基本时间搜索 results = await search_by_temporal( - group_id=actual_group_id, + end_user_id=actual_end_user_id, start_date=actual_start_date, end_date=actual_end_date, limit=10 @@ -147,7 +147,7 @@ def create_time_retrieval_tool(group_id: str): # 关键词时间搜索 results = await search_by_keyword_temporal( query_text=context, - group_id=group_id, + end_user_id=end_user_id, start_date=actual_start_date, end_date=actual_end_date, limit=15 @@ -172,7 +172,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): Args: memory_config: 内存配置对象 - **search_params: 搜索参数，包含group_id, limit, include等 + **search_params: 搜索参数，包含end_user_id, limit, include等 """ def clean_result_fields(data): @@ -211,7 +211,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): context: str, search_type: str = "hybrid", limit: int = 10, - group_id: str = None, + end_user_id: str = None, rerank_alpha: float = 0.6, use_forgetting_rerank: bool = False, use_llm_rerank: bool = False, @@ -224,7 +224,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): context: 查询内容 search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') limit: 结果数量限制 - group_id: 组ID，用于过滤搜索结果 + end_user_id: 组ID，用于过滤搜索结果 rerank_alpha: 重排序权重参数 use_forgetting_rerank: 是否使用遗忘重排序 use_llm_rerank: 是否使用LLM重排序 @@ -238,7 +238,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params): final_params = { "query_text": context, "search_type": search_type, - "group_id": group_id or search_params.get("group_id"), + "end_user_id": end_user_id or search_params.get("end_user_id"), "limit": limit or search_params.get("limit", 10), "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]), "output_path": None, # 不保存到文件 @@ -291,7 +291,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): context: str, search_type: str = "hybrid", limit: int = 10, - group_id: str = None, + end_user_id: str = None, clean_output: bool = True ) -> str: """ @@ -301,7 +301,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): context: 查询内容 search_type: 搜索类型 ('keyword', 'embedding', 'hybrid') limit: 结果数量限制 - group_id: 组ID，用于过滤搜索结果 + end_user_id: 组ID，用于过滤搜索结果 clean_output: 是否清理输出中的元数据字段 """ async def _async_search(): @@ -311,7 +311,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params): "context": context, "search_type": search_type, "limit": limit, - "group_id": group_id, + "end_user_id": end_user_id, "clean_output": clean_output }) diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index fe281a23..8b5de444 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -14,6 +14,7 @@ from app.db import get_db from app.core.logging_config import get_agent_logger from app.core.memory.agent.utils.llm_tools import WriteState from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node +from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_write from app.services.memory_config_service import MemoryConfigService warnings.filterwarnings("ignore", category=RuntimeWarning) @@ -26,9 +27,21 @@ async def make_write_graph(): """ Create a write graph workflow for memory operations. - The workflow directly processes messages from the initial state - and saves them to Neo4j storage. + Args: + user_id: User identifier + tools: MCP tools loaded from session + apply_id: Application identifier + end_user_id: Group identifier + memory_config: MemoryConfig object containing all configuration """ + # workflow = StateGraph(WriteState) + # workflow.add_node("content_input", content_input_write) + # workflow.add_node("save_neo4j", write_node) + # workflow.add_edge(START, "content_input") + # workflow.add_edge("content_input", "save_neo4j") + # workflow.add_edge("save_neo4j", END) + # + # graph = workflow.compile() workflow = StateGraph(WriteState) workflow.add_node("save_neo4j", write_node) workflow.add_edge(START, "save_neo4j") @@ -42,7 +55,7 @@ async def make_write_graph(): async def main(): """主函数 - 运行工作流""" message = "今天周一" - group_id = 'new_2025test1103' # 组ID + end_user_id = 'new_2025test1103' # 组ID # 获取数据库会话 @@ -54,9 +67,9 @@ async def main(): ) try: async with make_write_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 - initial_state = {"messages": [HumanMessage(content=message)], "group_id": group_id, "memory_config": memory_config} + initial_state = {"messages": [HumanMessage(content=message)], "end_user_id": end_user_id, "memory_config": memory_config} # 获取节点更新信息 async for update_event in graph.astream( diff --git a/api/app/core/memory/agent/services/parameter_builder.py b/api/app/core/memory/agent/services/parameter_builder.py index a58fcf1a..74382ade 100644 --- a/api/app/core/memory/agent/services/parameter_builder.py +++ b/api/app/core/memory/agent/services/parameter_builder.py @@ -24,7 +24,7 @@ class ParameterBuilder: tool_call_id: str, search_switch: str, apply_id: str, - group_id: str, + end_user_id: str, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None ) -> Dict[str, Any]: @@ -44,7 +44,7 @@ class ParameterBuilder: tool_call_id: Extracted tool call identifier search_switch: Search routing parameter apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier storage_type: Storage type for the workspace (optional) user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional) @@ -55,7 +55,7 @@ class ParameterBuilder: base_args = { "usermessages": tool_call_id, "apply_id": apply_id, - "group_id": group_id + "end_user_id": end_user_id } # Always add storage_type and user_rag_memory_id (with defaults if None) diff --git a/api/app/core/memory/agent/services/search_service.py b/api/app/core/memory/agent/services/search_service.py index 8a2e7cfe..4fc4256e 100644 --- a/api/app/core/memory/agent/services/search_service.py +++ b/api/app/core/memory/agent/services/search_service.py @@ -91,7 +91,7 @@ class SearchService: async def execute_hybrid_search( self, - group_id: str, + end_user_id: str, question: str, limit: int = 5, search_type: str = "hybrid", @@ -105,7 +105,7 @@ class SearchService: Execute hybrid search and return clean content. Args: - group_id: Group identifier for filtering results + end_user_id: Group identifier for filtering results question: Search query text limit: Maximum number of results to return (default: 5) search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid") @@ -130,7 +130,7 @@ class SearchService: answer = await run_hybrid_search( query_text=cleaned_query, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, output_path=output_path, @@ -186,7 +186,7 @@ class SearchService: except Exception as e: logger.error( - f"Search failed for query '{question}' in group '{group_id}': {e}", + f"Search failed for query '{question}' in group '{end_user_id}': {e}", exc_info=True ) # Return empty results on failure diff --git a/api/app/core/memory/agent/services/session_service.py b/api/app/core/memory/agent/services/session_service.py index b2d4f0ff..f7389984 100644 --- a/api/app/core/memory/agent/services/session_service.py +++ b/api/app/core/memory/agent/services/session_service.py @@ -59,7 +59,7 @@ class SessionService: self, user_id: str, apply_id: str, - group_id: str + end_user_id: str ) -> List[dict]: """ Retrieve conversation history from Redis. @@ -67,20 +67,20 @@ class SessionService: Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier Returns: List of conversation history items with Query and Answer keys Returns empty list if no history found or on error """ try: - history = self.store.find_user_apply_group(user_id, apply_id, group_id) + history = self.store.find_user_apply_group(user_id, apply_id, end_user_id) # Validate history structure if not isinstance(history, list): logger.warning( f"Invalid history format for user {user_id}, " - f"apply {apply_id}, group {group_id}: expected list, got {type(history)}" + f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}" ) return [] @@ -89,7 +89,7 @@ class SessionService: except Exception as e: logger.error( f"Failed to retrieve history for user {user_id}, " - f"apply {apply_id}, group {group_id}: {e}", + f"apply {apply_id}, group {end_user_id}: {e}", exc_info=True ) # Return empty list on error to allow execution to continue @@ -100,7 +100,7 @@ class SessionService: user_id: str, query: str, apply_id: str, - group_id: str, + end_user_id: str, ai_response: str ) -> Optional[str]: """ @@ -110,7 +110,7 @@ class SessionService: user_id: User identifier query: User query/message apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier ai_response: AI response/answer Returns: @@ -131,7 +131,7 @@ class SessionService: userid=user_id, messages=query, apply_id=apply_id, - group_id=group_id, + end_user_id=end_user_id, aimessages=ai_response ) @@ -152,7 +152,7 @@ class SessionService: Duplicates are identified by matching: - sessionid - user_id (id field) - - group_id + - end_user_id - messages - aimessages diff --git a/api/app/core/memory/agent/utils/get_dialogs.py b/api/app/core/memory/agent/utils/get_dialogs.py index 82a41773..bfb0f675 100644 --- a/api/app/core/memory/agent/utils/get_dialogs.py +++ b/api/app/core/memory/agent/utils/get_dialogs.py @@ -9,9 +9,7 @@ from app.core.memory.models.message_models import DialogData, ConversationContex async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", - group_id: str = "group_1", - user_id: str = "user1", - apply_id: str = "applyid", + end_user_id: str = "group_1", messages: list = None, ref_id: str = "wyl_20251027", config_id: str = None @@ -20,9 +18,7 @@ async def get_chunked_dialogs( Args: chunker_strategy: The chunking strategy to use (default: RecursiveChunker) - group_id: Group identifier - user_id: User identifier - apply_id: Application identifier + end_user_id: Group identifier messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference identifier config_id: Configuration ID for processing @@ -32,42 +28,40 @@ async def get_chunked_dialogs( """ from app.core.logging_config import get_agent_logger logger = get_agent_logger(__name__) - + if not messages or not isinstance(messages, list) or len(messages) == 0: raise ValueError("messages parameter must be a non-empty list") - + conversation_messages = [] - + for idx, msg in enumerate(messages): if not isinstance(msg, dict) or 'role' not in msg or 'content' not in msg: raise ValueError(f"Message {idx} format error: must contain 'role' and 'content' fields") - + role = msg['role'] content = msg['content'] - + if role not in ['user', 'assistant']: raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}") - + if content.strip(): conversation_messages.append(ConversationMessage(role=role, msg=content.strip())) - + if not conversation_messages: raise ValueError("Message list cannot be empty after filtering") - + conversation_context = ConversationContext(msgs=conversation_messages) dialog_data = DialogData( context=conversation_context, ref_id=ref_id, - group_id=group_id, - user_id=user_id, - apply_id=apply_id, + end_user_id=end_user_id, config_id=config_id ) - + chunker = DialogueChunker(chunker_strategy) extracted_chunks = await chunker.process_dialogue(dialog_data) dialog_data.chunks = extracted_chunks - + logger.info(f"DialogData created with {len(extracted_chunks)} chunks") return [dialog_data] diff --git a/api/app/core/memory/agent/utils/llm_tools.py b/api/app/core/memory/agent/utils/llm_tools.py index e73d5653..7f1041cb 100644 --- a/api/app/core/memory/agent/utils/llm_tools.py +++ b/api/app/core/memory/agent/utils/llm_tools.py @@ -13,13 +13,11 @@ class WriteState(TypedDict): Langgrapg Writing TypedDict ''' messages: Annotated[list[AnyMessage], add_messages] - user_id:str - apply_id:str - group_id:str + end_user_id: str errors: list[dict] # Track errors: [{"tool": "tool_name", "error": "message"}] memory_config: object write_result: dict - data:str + data: str class ReadState(TypedDict): """ @@ -29,7 +27,7 @@ class ReadState(TypedDict): messages: 消息列表，支持自动追加 loop_count: 遍历次数 search_switch: 搜索类型开关 - group_id: 组标识 + end_user_id: 组标识 config_id: 配置ID，用于过滤结果 data: 从content_input_node传递的内容数据 spit_data: 从Split_The_Problem传递的分解结果 @@ -40,7 +38,7 @@ class ReadState(TypedDict): messages: Annotated[list[AnyMessage], add_messages] # 消息追加模式 loop_count: int search_switch: str - group_id: str + end_user_id: str config_id: str data: str # 新增字段用于传递内容 spit_data: dict # 新增字段用于传递问题分解结果 diff --git a/api/app/core/memory/agent/utils/redis_tool.py b/api/app/core/memory/agent/utils/redis_tool.py index 31a76a11..505545b3 100644 --- a/api/app/core/memory/agent/utils/redis_tool.py +++ b/api/app/core/memory/agent/utils/redis_tool.py @@ -28,7 +28,7 @@ class RedisSessionStore: return text # 修改后的 save_session 方法 - def save_session(self, userid, messages, aimessages, apply_id, group_id): + def save_session(self, userid, messages, aimessages, apply_id, end_user_id): """ 写入一条会话数据，返回 session_id 优化版本：确保写入时间不超过1秒 @@ -46,7 +46,7 @@ class RedisSessionStore: "id": self.uudi, "sessionid": userid, "apply_id": apply_id, - "group_id": group_id, + "end_user_id": end_user_id, "messages": messages, "aimessages": aimessages, "starttime": starttime @@ -67,7 +67,7 @@ class RedisSessionStore: def save_sessions_batch(self, sessions_data): """ 批量写入多条会话数据，返回 session_id 列表 - sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, group_id + sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, end_user_id 优化版本：批量操作，大幅提升性能 """ try: @@ -83,7 +83,7 @@ class RedisSessionStore: "id": self.uudi, "sessionid": session.get('userid'), "apply_id": session.get('apply_id'), - "group_id": session.get('group_id'), + "end_user_id": session.get('end_user_id'), "messages": session.get('messages'), "aimessages": session.get('aimessages'), "starttime": starttime @@ -108,9 +108,9 @@ class RedisSessionStore: data = self.r.hgetall(key) return data if data else None - def get_session_apply_group(self, sessionid, apply_id, group_id): + def get_session_apply_group(self, sessionid, apply_id, end_user_id): """ - 根据 sessionid、apply_id 和 group_id 三个条件查询会话数据 + 根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据 """ result_items = [] @@ -124,7 +124,7 @@ class RedisSessionStore: # 检查三个条件是否都匹配 if (data.get('sessionid') == sessionid and data.get('apply_id') == apply_id and - data.get('group_id') == group_id): + data.get('end_user_id') == end_user_id): result_items.append(data) return result_items @@ -172,7 +172,7 @@ class RedisSessionStore: def delete_duplicate_sessions(self): """ 删除重复会话数据，条件： - "sessionid"、"user_id"、"group_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除 + "sessionid"、"user_id"、"end_user_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除 优化版本：使用 pipeline 批量操作，确保在1秒内完成 """ import time @@ -202,12 +202,12 @@ class RedisSessionStore: # 获取五个字段的值 sessionid = data.get('sessionid', '') user_id = data.get('id', '') - group_id = data.get('group_id', '') + end_user_id = data.get('end_user_id', '') messages = data.get('messages', '') aimessages = data.get('aimessages', '') # 用五元组作为唯一标识 - identifier = (sessionid, user_id, group_id, messages, aimessages) + identifier = (sessionid, user_id, end_user_id, messages, aimessages) if identifier in seen: # 重复，标记为待删除 @@ -248,9 +248,9 @@ class RedisSessionStore: result_items = [] return (result_items) - def find_user_apply_group(self, sessionid, apply_id, group_id): + def find_user_apply_group(self, sessionid, apply_id, end_user_id): """ - 根据 sessionid、apply_id 和 group_id 三个条件查询会话数据，返回最新的6条 + 根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据，返回最新的6条 """ import time start_time = time.time() @@ -276,7 +276,7 @@ class RedisSessionStore: # 检查是否符合三个条件 if (data.get('apply_id') == apply_id and - data.get('group_id') == group_id): + data.get('end_user_id') == end_user_id): # 支持模糊匹配 sessionid 或者完全匹配 if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid: matched_items.append({ diff --git a/api/app/core/memory/agent/utils/session_tools.py b/api/app/core/memory/agent/utils/session_tools.py index b2d4f0ff..f7389984 100644 --- a/api/app/core/memory/agent/utils/session_tools.py +++ b/api/app/core/memory/agent/utils/session_tools.py @@ -59,7 +59,7 @@ class SessionService: self, user_id: str, apply_id: str, - group_id: str + end_user_id: str ) -> List[dict]: """ Retrieve conversation history from Redis. @@ -67,20 +67,20 @@ class SessionService: Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier Returns: List of conversation history items with Query and Answer keys Returns empty list if no history found or on error """ try: - history = self.store.find_user_apply_group(user_id, apply_id, group_id) + history = self.store.find_user_apply_group(user_id, apply_id, end_user_id) # Validate history structure if not isinstance(history, list): logger.warning( f"Invalid history format for user {user_id}, " - f"apply {apply_id}, group {group_id}: expected list, got {type(history)}" + f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}" ) return [] @@ -89,7 +89,7 @@ class SessionService: except Exception as e: logger.error( f"Failed to retrieve history for user {user_id}, " - f"apply {apply_id}, group {group_id}: {e}", + f"apply {apply_id}, group {end_user_id}: {e}", exc_info=True ) # Return empty list on error to allow execution to continue @@ -100,7 +100,7 @@ class SessionService: user_id: str, query: str, apply_id: str, - group_id: str, + end_user_id: str, ai_response: str ) -> Optional[str]: """ @@ -110,7 +110,7 @@ class SessionService: user_id: User identifier query: User query/message apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier ai_response: AI response/answer Returns: @@ -131,7 +131,7 @@ class SessionService: userid=user_id, messages=query, apply_id=apply_id, - group_id=group_id, + end_user_id=end_user_id, aimessages=ai_response ) @@ -152,7 +152,7 @@ class SessionService: Duplicates are identified by matching: - sessionid - user_id (id field) - - group_id + - end_user_id - messages - aimessages diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py index 1df0b336..446ab86a 100644 --- a/api/app/core/memory/agent/utils/write_tools.py +++ b/api/app/core/memory/agent/utils/write_tools.py @@ -29,20 +29,18 @@ logger = get_agent_logger(__name__) async def write( - user_id: str, - apply_id: str, - group_id: str, + end_user_id: str, memory_config: MemoryConfig, messages: list, ref_id: str = "wyl20251027", ) -> None: """ Execute the complete knowledge extraction pipeline. - + Args: user_id: User identifier apply_id: Application identifier - group_id: Group identifier + end_user_id: Group identifier memory_config: MemoryConfig object containing all configuration messages: Structured message list [{"role": "user", "content": "..."}, ...] ref_id: Reference ID, defaults to "wyl20251027" @@ -51,14 +49,14 @@ async def write( embedding_model_id = str(memory_config.embedding_model_id) chunker_strategy = memory_config.chunker_strategy config_id = str(memory_config.config_id) - + logger.info("=== MemSci Knowledge Extraction Pipeline ===") logger.info(f"Config: {memory_config.config_name} (ID: {config_id})") logger.info(f"Workspace: {memory_config.workspace_name}") logger.info(f"LLM model: {memory_config.llm_model_name}") logger.info(f"Embedding model: {memory_config.embedding_model_name}") logger.info(f"Chunker strategy: {chunker_strategy}") - logger.info(f"Group ID: {group_id}") + logger.info(f"end_user_id ID: {end_user_id}") # Construct clients from memory_config using factory pattern with db session with get_db_context() as db: @@ -83,9 +81,7 @@ async def write( step_start = time.time() chunked_dialogs = await get_chunked_dialogs( chunker_strategy=chunker_strategy, - group_id=group_id, - user_id=user_id, - apply_id=apply_id, + end_user_id=end_user_id, messages=messages, ref_id=ref_id, config_id=config_id, diff --git a/api/app/core/memory/analytics/hot_memory_tags.py b/api/app/core/memory/analytics/hot_memory_tags.py index cab6cacd..95302726 100644 --- a/api/app/core/memory/analytics/hot_memory_tags.py +++ b/api/app/core/memory/analytics/hot_memory_tags.py @@ -16,13 +16,13 @@ class FilteredTags(BaseModel): """用于接收LLM筛选后的核心标签列表的模型。""" meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。") -async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: +async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]: """ 使用LLM筛选标签列表，仅保留具有代表性的核心名词。 Args: tags: 原始标签列表 - group_id: 用户组ID，用于获取配置 + end_user_id: 用户组ID，用于获取配置 Returns: 筛选后的标签列表 @@ -37,12 +37,12 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: get_end_user_connected_config, ) - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if not config_id: raise ValueError( - f"No memory_config_id found for group_id: {group_id}. " + f"No memory_config_id found for end_user_id: {end_user_id}. " "Please ensure the user has a valid memory configuration." ) @@ -87,7 +87,7 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]: async def get_raw_tags_from_db( connector: Neo4jConnector, - group_id: str, + end_user_id: str, limit: int, by_user: bool = False ) -> List[Tuple[str, int]]: @@ -99,9 +99,9 @@ async def get_raw_tags_from_db( Args: connector: Neo4j连接器实例 - group_id: 如果by_user=False，则为group_id；如果by_user=True，则为user_id + end_user_id: 如果by_user=False，则为end_user_id；如果by_user=True，则为user_id limit: 返回的标签数量限制 - by_user: 是否按user_id查询（默认False，按group_id查询） + by_user: 是否按user_id查询（默认False，按end_user_id查询） Returns: List[Tuple[str, int]]: 标签名称和频率的元组列表 @@ -119,7 +119,7 @@ async def get_raw_tags_from_db( else: query = ( "MATCH (e:ExtractedEntity) " - "WHERE e.group_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " + "WHERE e.end_user_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC " "LIMIT $limit" @@ -128,44 +128,44 @@ async def get_raw_tags_from_db( # 使用项目的Neo4jConnector执行查询 results = await connector.execute_query( query, - id=group_id, + id=end_user_id, limit=limit, names_to_exclude=names_to_exclude ) return [(record["name"], record["frequency"]) for record in results] -async def get_hot_memory_tags(group_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]: +async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]: """ 获取原始标签，然后使用LLM进行筛选，返回最终的热门标签列表。 查询更多的标签(limit=40)给LLM提供更丰富的上下文进行筛选。 Args: - group_id: 必需参数。如果by_user=False，则为group_id；如果by_user=True，则为user_id + end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id limit: 返回的标签数量限制 - by_user: 是否按user_id查询（默认False，按group_id查询） + by_user: 是否按user_id查询（默认False，按end_user_id查询） Raises: - ValueError: 如果group_id未提供或为空 + ValueError: 如果end_user_id未提供或为空 """ - # 验证group_id必须提供且不为空 - if not group_id or not group_id.strip(): + # 验证end_user_id必须提供且不为空 + if not end_user_id or not end_user_id.strip(): raise ValueError( - "group_id is required. Please provide a valid group_id or user_id." + "end_user_id is required. Please provide a valid end_user_id or user_id." ) # 使用项目的Neo4jConnector connector = Neo4jConnector() try: # 1. 从数据库获取原始排名靠前的标签 - raw_tags_with_freq = await get_raw_tags_from_db(connector, group_id, limit, by_user=by_user) + raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, limit, by_user=by_user) if not raw_tags_with_freq: return [] raw_tag_names = [tag for tag, freq in raw_tags_with_freq] # 2. 初始化LLM客户端并使用LLM筛选出有意义的标签 - meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, group_id) + meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, end_user_id) # 3. 根据LLM的筛选结果，构建最终的标签列表（保留原始频率和顺序） final_tags = [] diff --git a/api/app/core/memory/analytics/implicit_memory/data_source.py b/api/app/core/memory/analytics/implicit_memory/data_source.py index d277a05e..18678a55 100644 --- a/api/app/core/memory/analytics/implicit_memory/data_source.py +++ b/api/app/core/memory/analytics/implicit_memory/data_source.py @@ -75,8 +75,8 @@ class MemoryDataSource: start_date = time_range.start_date if time_range else None end_date = time_range.end_date if time_range else None - summary_dicts = await self.memory_summary_repo.find_by_group_id( - group_id=user_id, + summary_dicts = await self.memory_summary_repo.find_by_end_user_id( + end_user_id=user_id, limit=limit, start_date=start_date, end_date=end_date diff --git a/api/app/core/memory/evaluation/dialogue_queries.py b/api/app/core/memory/evaluation/dialogue_queries.py index fd7fa671..25abe64e 100644 --- a/api/app/core/memory/evaluation/dialogue_queries.py +++ b/api/app/core/memory/evaluation/dialogue_queries.py @@ -41,7 +41,7 @@ DIALOGUE_EMBEDDING_SEARCH = """ WITH $embedding AS q MATCH (d:Dialogue) WHERE d.dialog_embedding IS NOT NULL - AND ($group_id IS NULL OR d.group_id = $group_id) + AND ($end_user_id IS NULL OR d.end_user_id = $end_user_id) WITH d, q, d.dialog_embedding AS v WITH d, reduce(dot = 0.0, i IN range(0, size(q)-1) | dot + toFloat(q[i]) * toFloat(v[i])) AS dot, @@ -50,7 +50,7 @@ WITH d, WITH d, CASE WHEN qnorm = 0 OR vnorm = 0 THEN 0.0 ELSE dot / (qnorm * vnorm) END AS score WHERE score > $threshold RETURN d.id AS dialog_id, - d.group_id AS group_id, + d.end_user_id AS end_user_id, d.content AS content, d.created_at AS created_at, d.expired_at AS expired_at, diff --git a/api/app/core/memory/evaluation/extraction_utils.py b/api/app/core/memory/evaluation/extraction_utils.py index 9afa228c..9e70bc28 100644 --- a/api/app/core/memory/evaluation/extraction_utils.py +++ b/api/app/core/memory/evaluation/extraction_utils.py @@ -36,7 +36,7 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector async def ingest_contexts_via_full_pipeline( contexts: List[str], - group_id: str, + end_user_id: str, chunker_strategy: str | None = None, embedding_name: str | None = None, save_chunk_output: bool = False, @@ -48,7 +48,7 @@ async def ingest_contexts_via_full_pipeline( This function mirrors the steps in main(), but starts from raw text contexts. Args: contexts: List of dialogue texts, each containing lines like "role: message". - group_id: Group ID to assign to generated DialogData and graph nodes. + end_user_id: Group ID to assign to generated DialogData and graph nodes. chunker_strategy: Optional chunker strategy; defaults to SELECTED_CHUNKER_STRATEGY. embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID. save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging. @@ -109,7 +109,7 @@ async def ingest_contexts_via_full_pipeline( dialog = DialogData( context=context_model, ref_id=f"pipeline_item_{idx}", - group_id=group_id, + end_user_id=end_user_id, user_id="default_user", apply_id="default_application", ) @@ -318,16 +318,16 @@ async def handle_context_processing(args): print("No contexts provided for processing.") return False - return await main_from_contexts(contexts, args.context_group_id) + return await main_from_contexts(contexts, args.context_end_user_id) -async def main_from_contexts(contexts: List[str], group_id: str): +async def main_from_contexts(contexts: List[str], end_user_id: str): """Run the pipeline from provided dialogue contexts instead of test data.""" print("=== Running pipeline from provided contexts ===") success = await ingest_contexts_via_full_pipeline( contexts=contexts, - group_id=group_id, + end_user_id=end_user_id, chunker_strategy=SELECTED_CHUNKER_STRATEGY, embedding_name=SELECTED_EMBEDDING_ID, save_chunk_output=True diff --git a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py index b7d988c5..1c70c28e 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py +++ b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py @@ -47,7 +47,7 @@ from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient from app.core.memory.utils.definitions import ( PROJECT_ROOT, SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, + SELECTED_end_user_id, SELECTED_LLM_ID, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -59,7 +59,7 @@ from app.services.memory_config_service import MemoryConfigService async def run_locomo_benchmark( sample_size: int = 20, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, search_type: str = "hybrid", search_limit: int = 12, context_char_budget: int = 8000, @@ -85,7 +85,7 @@ async def run_locomo_benchmark( Args: sample_size: Number of QA pairs to evaluate (from first conversation) - group_id: Database group ID for retrieval (uses default if None) + end_user_id: Database group ID for retrieval (uses default if None) search_type: "keyword", "embedding", or "hybrid" search_limit: Max documents to retrieve per query context_char_budget: Max characters for context @@ -96,8 +96,8 @@ async def run_locomo_benchmark( Returns: Dictionary with evaluation results including metrics, timing, and samples """ - # Use default group_id if not provided - group_id = group_id or SELECTED_GROUP_ID + # Use default end_user_id if not provided + end_user_id = end_user_id or SELECTED_end_user_id # Determine data path data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") @@ -110,7 +110,7 @@ async def run_locomo_benchmark( print(f"{'='*60}") print("📊 Configuration:") print(f" Sample size: {sample_size}") - print(f" Group ID: {group_id}") + print(f" Group ID: {end_user_id}") print(f" Search type: {search_type}") print(f" Search limit: {search_limit}") print(f" Context budget: {context_char_budget} chars") @@ -134,7 +134,7 @@ async def run_locomo_benchmark( # Step 2: Extract conversations and ingest if needed if skip_ingest: print("⏭️ Skipping data ingestion (using existing data in Neo4j)") - print(f" Group ID: {group_id}\n") + print(f" Group ID: {end_user_id}\n") else: print("💾 Checking database ingestion...") try: @@ -142,10 +142,10 @@ async def run_locomo_benchmark( print(f"📝 Extracted {len(conversations)} conversations") # Always ingest for now (ingestion check not implemented) - print(f"🔄 Ingesting conversations into group '{group_id}'...") + print(f"🔄 Ingesting conversations into group '{end_user_id}'...") success = await ingest_conversations_if_needed( conversations=conversations, - group_id=group_id, + end_user_id=end_user_id, reset=reset_group ) @@ -224,7 +224,7 @@ async def run_locomo_benchmark( try: retrieved_info = await retrieve_relevant_information( question=question, - group_id=group_id, + end_user_id=end_user_id, search_type=search_type, search_limit=search_limit, connector=connector, @@ -409,7 +409,7 @@ async def run_locomo_benchmark( "sample_size": len(qa_items), "timestamp": datetime.now().isoformat(), "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_type": search_type, "search_limit": search_limit, "context_char_budget": context_char_budget, @@ -467,7 +467,7 @@ def main(): help="Number of QA pairs to evaluate" ) parser.add_argument( - "--group_id", + "--end_user_id", type=str, default=None, help="Database group ID for retrieval (uses default if not specified)" @@ -516,7 +516,7 @@ def main(): # Run benchmark result = asyncio.run(run_locomo_benchmark( sample_size=args.sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_type=args.search_type, search_limit=args.search_limit, context_char_budget=args.context_char_budget, diff --git a/api/app/core/memory/evaluation/locomo/locomo_test.py b/api/app/core/memory/evaluation/locomo/locomo_test.py index affedd0f..01c45123 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_test.py +++ b/api/app/core/memory/evaluation/locomo/locomo_test.py @@ -556,7 +556,7 @@ async def run_enhanced_evaluation(): search_results = await run_hybrid_search( query_text=q, search_type="hybrid", - group_id="locomo_sk", + end_user_id="locomo_sk", limit=20, include=["statements", "chunks", "entities", "summaries"], alpha=0.6, # BM25权重 diff --git a/api/app/core/memory/evaluation/locomo/locomo_utils.py b/api/app/core/memory/evaluation/locomo/locomo_utils.py index 69be5da9..d3b74947 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_utils.py +++ b/api/app/core/memory/evaluation/locomo/locomo_utils.py @@ -348,7 +348,7 @@ def select_and_format_information( async def retrieve_relevant_information( question: str, - group_id: str, + end_user_id: str, search_type: str, search_limit: int, connector: Any, @@ -368,7 +368,7 @@ async def retrieve_relevant_information( Args: question: Question to search for - group_id: Database group ID (identifies which conversation memory to search) + end_user_id: Database group ID (identifies which conversation memory to search) search_type: "keyword", "embedding", or "hybrid" search_limit: Max memory pieces to retrieve connector: Neo4j connector instance @@ -396,7 +396,7 @@ async def retrieve_relevant_information( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -455,7 +455,7 @@ async def retrieve_relevant_information( search_results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit ) @@ -491,7 +491,7 @@ async def retrieve_relevant_information( search_results = await run_hybrid_search( query_text=question, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], output_path=None, @@ -524,7 +524,7 @@ async def retrieve_relevant_information( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -584,7 +584,7 @@ async def retrieve_relevant_information( async def ingest_conversations_if_needed( conversations: List[str], - group_id: str, + end_user_id: str, reset: bool = False ) -> bool: """ @@ -603,7 +603,7 @@ async def ingest_conversations_if_needed( Args: conversations: List of raw conversation texts from LoCoMo dataset Example: ["User: I went to Paris. AI: When was that?", ...] - group_id: Target group ID for database storage + end_user_id: Target group ID for database storage reset: Whether to clear existing data first (not implemented in wrapper) Returns: @@ -617,7 +617,7 @@ async def ingest_conversations_if_needed( try: success = await ingest_contexts_via_full_pipeline( contexts=conversations, - group_id=group_id, + end_user_id=end_user_id, save_chunk_output=True ) return success diff --git a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py index 87a70a29..6a5caa0c 100644 --- a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py @@ -249,7 +249,7 @@ def get_search_params_by_category(category: str): async def run_locomo_eval( sample_size: int = 1, - group_id: str | None = None, + end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, # 保持默认值不变 llm_temperature: float = 0.0, @@ -262,7 +262,7 @@ async def run_locomo_eval( ) -> Dict[str, Any]: # 函数内部使用三路检索逻辑，但保持参数签名不变 - group_id = group_id or SELECTED_GROUP_ID + end_user_id = end_user_id or SELECTED_end_user_id data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") if not os.path.exists(data_path): data_path = os.path.join(os.getcwd(), "data", "locomo10.json") @@ -340,7 +340,7 @@ async def run_locomo_eval( # 关键修复：强制重新摄入纯净的对话数据 print("🔄 强制重新摄入纯净的对话数据...") - await ingest_contexts_via_full_pipeline(contents, group_id, save_chunk_output=True) + await ingest_contexts_via_full_pipeline(contents, end_user_id, save_chunk_output=True) # 使用异步LLM客户端 with get_db_context() as db: @@ -405,7 +405,7 @@ async def run_locomo_eval( connector=connector, embedder_client=embedder, query_text=q, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], # 修复：使用正确的类型 ) @@ -456,7 +456,7 @@ async def run_locomo_eval( search_results = await search_graph( connector=connector, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit ) dialogs = search_results.get("dialogues", []) @@ -486,7 +486,7 @@ async def run_locomo_eval( search_results = await run_hybrid_search( query_text=q, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], output_path=None, @@ -524,7 +524,7 @@ async def run_locomo_eval( connector=connector, embedder_client=embedder, query_text=q, - group_id=group_id, + end_user_id=end_user_id, limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -597,7 +597,7 @@ async def run_locomo_eval( "dialogues": [ { "uuid": d.get("uuid", ""), - "group_id": d.get("group_id", ""), + "end_user_id": d.get("end_user_id", ""), "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""), "score": d.get("score", 0.0) } @@ -795,7 +795,7 @@ async def run_locomo_eval( }, "samples": samples, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, @@ -825,7 +825,7 @@ async def run_locomo_eval( def main(): parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search") parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate") - parser.add_argument("--group_id", type=str, default=None, help="Group ID for retrieval") + parser.add_argument("--end_user_id", type=str, default=None, help="Group ID for retrieval") parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query") parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context") parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature") @@ -841,7 +841,7 @@ def main(): result = asyncio.run(run_locomo_eval( sample_size=args.sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py index 292e7288..8710a504 100644 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py @@ -524,11 +524,11 @@ def generate_query_keywords_cn(question: str) -> List[str]: # 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]: +async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]: results: List[Dict[str, Any]] = [] try: for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit) + rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit) if rows: results.extend(rows) except Exception: @@ -548,15 +548,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st # 通过对话/陈述中的entity_ids反查实体名称 _FETCH_ENTITIES_BY_IDS = """ MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type """ -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]: +async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]: if not ids: return [] try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id) + rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id) return rows or [] except Exception: return [] @@ -566,18 +566,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou _TIME_ENTITY_SEARCH = """ MATCH (e:ExtractedEntity) WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type LIMIT $limit """ -async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: +async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: """专门搜索时间相关的实体""" try: date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" rows = await connector.execute_query(_TIME_ENTITY_SEARCH, date_pattern=date_pattern, - group_id=group_id, + end_user_id=end_user_id, limit=limit) return rows or [] except Exception: @@ -624,7 +624,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: async def run_longmemeval_test( sample_size: int = 3, - group_id: str = "longmemeval_zh_bak_3", + end_user_id: str = "longmemeval_zh_bak_3", search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -678,13 +678,13 @@ async def run_longmemeval_test( contexts.extend(selected) print(f"📥 摄入 {len(contexts)} 个上下文到数据库") - if reset_group_before_ingest and group_id: + if reset_group_before_ingest and end_user_id: try: _tmp_conn = Neo4jConnector() - await _tmp_conn.delete_group(group_id) - print(f"🧹 已清空组 {group_id} 的历史图数据") + await _tmp_conn.delete_group(end_user_id) + print(f"🧹 已清空组 {end_user_id} 的历史图数据") except Exception as _e: - print(f"⚠️ 清空组数据失败（忽略继续）: {group_id} - {_e}") + print(f"⚠️ 清空组数据失败（忽略继续）: {end_user_id} - {_e}") finally: try: await _tmp_conn.close() @@ -696,7 +696,7 @@ async def run_longmemeval_test( else: await _ingest_fn( contexts, - group_id, + end_user_id, save_chunk_output=save_chunk_output, save_chunk_output_path=save_chunk_output_path, ) @@ -751,7 +751,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -796,7 +796,7 @@ async def run_longmemeval_test( search_results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) chunks = search_results.get("chunks", []) @@ -831,7 +831,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], ) @@ -849,7 +849,7 @@ async def run_longmemeval_test( kw_res = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) if isinstance(kw_res, dict): @@ -860,7 +860,7 @@ async def run_longmemeval_test( # 时间推理问题的特殊处理 if is_temporal: # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, group_id, search_limit//2) + time_entities = await _search_time_entities(connector, end_user_id, search_limit//2) if time_entities: kw_entities.extend(time_entities) # 添加时间相关关键词检索 @@ -870,7 +870,7 @@ async def run_longmemeval_test( time_res = await search_graph( connector=connector, q=tk, - group_id=group_id, + end_user_id=end_user_id, limit=2, ) if isinstance(time_res, dict): @@ -881,7 +881,7 @@ async def run_longmemeval_test( # 中文关键词拆分后做别名匹配 cn_tokens = _extract_cn_tokens(question) - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit) + alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit) if alias_entities: kw_entities.extend(alias_entities) @@ -895,7 +895,7 @@ async def run_longmemeval_test( except Exception: pass if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, group_id) + id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id) if id_entities: kw_entities.extend(id_entities) @@ -909,7 +909,7 @@ async def run_longmemeval_test( sub_res = await search_graph( connector=connector, q=str(kw), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(sub_res, dict): @@ -928,7 +928,7 @@ async def run_longmemeval_test( opt_res = await search_graph( connector=connector, q=str(opt), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(opt_res, dict): @@ -1010,7 +1010,7 @@ async def run_longmemeval_test( kw_fallback = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=max(search_limit, 5), ) fb_dialogs = kw_fallback.get("dialogues", []) or [] @@ -1224,7 +1224,7 @@ async def run_longmemeval_test( "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, }, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, @@ -1307,7 +1307,7 @@ def main(): result = asyncio.run( run_longmemeval_test( sample_size=sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/longmemeval/test_eval.py b/api/app/core/memory/evaluation/longmemeval/test_eval.py index 08a763e3..67bd6ec2 100644 --- a/api/app/core/memory/evaluation/longmemeval/test_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/test_eval.py @@ -498,11 +498,11 @@ def smart_context_selection(contexts: List[str], question: str, max_chars: int = # 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]: +async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]: results: List[Dict[str, Any]] = [] try: for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit) + rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit) if rows: results.extend(rows) except Exception: @@ -522,15 +522,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st # 通过对话/陈述中的entity_ids反查实体名称 _FETCH_ENTITIES_BY_IDS = """ MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type """ -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]: +async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]: if not ids: return [] try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id) + rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id) return rows or [] except Exception: return [] @@ -540,18 +540,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou _TIME_ENTITY_SEARCH = """ MATCH (e:ExtractedEntity) WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($group_id IS NULL OR e.group_id = $group_id) -RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type +AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) +RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type LIMIT $limit """ -async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: +async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: """专门搜索时间相关的实体""" try: date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" rows = await connector.execute_query(_TIME_ENTITY_SEARCH, date_pattern=date_pattern, - group_id=group_id, + end_user_id=end_user_id, limit=limit) return rows or [] except Exception: @@ -559,25 +559,25 @@ async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, # 技术术语专门检索 -async def _search_tech_terms(connector: Neo4jConnector, question: str, group_id: str | None, limit: int = 3) -> List[Dict[str, Any]]: +async def _search_tech_terms(connector: Neo4jConnector, question: str, end_user_id: str | None, limit: int = 3) -> List[Dict[str, Any]]: """专门搜索技术术语相关的实体""" tech_entities = [] try: # GPS相关 if any(term in question for term in ["GPS", "导航", "定位系统"]): - gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", group_id=group_id, limit=limit) + gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", end_user_id=end_user_id, limit=limit) if gps_rows: tech_entities.extend(gps_rows) # 活动相关 if any(term in question for term in ["工作坊", "研讨会", "网络研讨会"]): - workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", group_id=group_id, limit=limit) + workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", end_user_id=end_user_id, limit=limit) if workshop_rows: tech_entities.extend(workshop_rows) # 时间顺序相关 if any(term in question for term in ["先", "后", "第一个"]): - time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", group_id=group_id, limit=limit) + time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", end_user_id=end_user_id, limit=limit) if time_rows: tech_entities.extend(time_rows) @@ -627,7 +627,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: async def run_longmemeval_test( sample_size: int = 3, - group_id: str = "longmemeval_zh_bak_2", + end_user_id: str = "longmemeval_zh_bak_2", search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -707,7 +707,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["dialogues", "statements", "entities"], ) @@ -746,7 +746,7 @@ async def run_longmemeval_test( search_results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) dialogs = search_results.get("dialogues", []) @@ -776,7 +776,7 @@ async def run_longmemeval_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["dialogues", "statements", "entities"], ) @@ -792,7 +792,7 @@ async def run_longmemeval_test( kw_res = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, ) if isinstance(kw_res, dict): @@ -801,14 +801,14 @@ async def run_longmemeval_test( kw_entities = kw_res.get("entities", []) or [] # 技术术语专门检索 - tech_entities = await _search_tech_terms(connector, question, group_id, search_limit//2) + tech_entities = await _search_tech_terms(connector, question, end_user_id, search_limit//2) if tech_entities: kw_entities.extend(tech_entities) # 时间推理问题的特殊处理 if is_temporal: # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, group_id, search_limit//2) + time_entities = await _search_time_entities(connector, end_user_id, search_limit//2) if time_entities: kw_entities.extend(time_entities) # 添加时间相关关键词检索 @@ -818,7 +818,7 @@ async def run_longmemeval_test( time_res = await search_graph( connector=connector, q=tk, - group_id=group_id, + end_user_id=end_user_id, limit=2, ) if isinstance(time_res, dict): @@ -829,7 +829,7 @@ async def run_longmemeval_test( # 中文关键词拆分后做别名匹配 cn_tokens = generate_query_keywords_cn(question) # 使用增强版关键词提取 - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit) + alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit) if alias_entities: kw_entities.extend(alias_entities) @@ -843,7 +843,7 @@ async def run_longmemeval_test( except Exception: pass if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, group_id) + id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id) if id_entities: kw_entities.extend(id_entities) @@ -857,7 +857,7 @@ async def run_longmemeval_test( sub_res = await search_graph( connector=connector, q=str(kw), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(sub_res, dict): @@ -876,7 +876,7 @@ async def run_longmemeval_test( opt_res = await search_graph( connector=connector, q=str(opt), - group_id=group_id, + end_user_id=end_user_id, limit=max(3, search_limit // 2), ) if isinstance(opt_res, dict): @@ -971,7 +971,7 @@ async def run_longmemeval_test( kw_fallback = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=max(search_limit, 5), ) fb_dialogs = kw_fallback.get("dialogues", []) or [] @@ -1199,7 +1199,7 @@ async def run_longmemeval_test( "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, }, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, @@ -1278,7 +1278,7 @@ def main(): result = asyncio.run( run_longmemeval_test( sample_size=sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py index 6efb66ff..869fdb60 100644 --- a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py +++ b/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py @@ -135,8 +135,8 @@ def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any return merged -async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: - group_id = group_id or SELECTED_GROUP_ID +async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: + end_user_id = end_user_id or SELECTED_GROUP_ID # Load data data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") if not os.path.exists(data_path): @@ -147,7 +147,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入 # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略 contexts: List[str] = [build_context_from_dialog(item) for item in items] - await ingest_contexts_via_full_pipeline(contexts, group_id) + await ingest_contexts_via_full_pipeline(contexts, end_user_id) # LLM client (使用异步调用) with get_db_context() as db: @@ -173,7 +173,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s results = await run_hybrid_search( query_text=question, search_type=search_type, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["dialogues", "statements", "entities"], output_path=None, @@ -298,7 +298,7 @@ def main(): load_dotenv() parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen") parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量") - parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json") + parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json") parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数") parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") @@ -309,7 +309,7 @@ def main(): result = asyncio.run( run_memsciqa_eval( sample_size=args.sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py index 900cda9d..3023020a 100644 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py @@ -199,7 +199,7 @@ def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]: async def run_memsciqa_test( sample_size: int = 3, - group_id: str | None = None, + end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -217,7 +217,7 @@ async def run_memsciqa_test( """ # 默认使用指定的 memsci 组 ID - group_id = group_id or "group_memsci" + end_user_id = end_user_id or "group_memsci" # 数据路径解析（项目根与当前工作目录兜底） if not data_path: @@ -283,7 +283,7 @@ async def run_memsciqa_test( connector=connector, embedder_client=embedder, query_text=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues ) @@ -292,7 +292,7 @@ async def run_memsciqa_test( results = await search_graph( connector=connector, q=question, - group_id=group_id, + end_user_id=end_user_id, limit=search_limit, include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues ) @@ -500,7 +500,7 @@ async def run_memsciqa_test( }, "samples": samples, "params": { - "group_id": group_id, + "end_user_id": end_user_id, "search_limit": search_limit, "context_char_budget": context_char_budget, "llm_temperature": llm_temperature, @@ -543,7 +543,7 @@ def main(): result = asyncio.run( run_memsciqa_test( sample_size=sample_size, - group_id=args.group_id, + end_user_id=args.end_user_id, search_limit=args.search_limit, context_char_budget=args.context_char_budget, llm_temperature=args.llm_temperature, diff --git a/api/app/core/memory/evaluation/run_eval.py b/api/app/core/memory/evaluation/run_eval.py index 1de3de89..c5aacb2f 100644 --- a/api/app/core/memory/evaluation/run_eval.py +++ b/api/app/core/memory/evaluation/run_eval.py @@ -26,7 +26,7 @@ async def run( dataset: str, sample_size: int, reset_group: bool, - group_id: str | None, + end_user_id: str | None, judge_model: str | None = None, search_limit: int | None = None, context_char_budget: int | None = None, @@ -37,17 +37,17 @@ async def run( max_contexts_per_item: int | None = None, ) -> Dict[str, Any]: # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认 - group_id = group_id or SELECTED_GROUP_ID + end_user_id = end_user_id or SELECTED_GROUP_ID if reset_group: connector = Neo4jConnector() try: - await connector.delete_group(group_id) + await connector.delete_group(end_user_id) finally: await connector.close() if dataset == "locomo": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} + kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} if search_limit is not None: kwargs["search_limit"] = search_limit if context_char_budget is not None: @@ -61,7 +61,7 @@ async def run( return await run_locomo_eval(**kwargs) if dataset == "memsciqa": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} + kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} if search_limit is not None: kwargs["search_limit"] = search_limit if context_char_budget is not None: @@ -75,7 +75,7 @@ async def run( return await run_memsciqa_eval(**kwargs) if dataset == "longmemeval": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id} + kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} if search_limit is not None: kwargs["search_limit"] = search_limit if context_char_budget is not None: @@ -99,8 +99,8 @@ def main(): parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo") parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True) parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通") - parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 group_id 的图数据") - parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json") + parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 end_user_id 的图数据") + parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json") parser.add_argument("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名") parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）") parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）") @@ -117,7 +117,7 @@ def main(): args.dataset, args.sample_size, args.reset_group, - args.group_id, + args.end_user_id, args.judge_model, args.search_limit, args.context_char_budget, diff --git a/api/app/core/memory/llm_tools/chunker_client.py b/api/app/core/memory/llm_tools/chunker_client.py index 87cdb9f4..93a2df82 100644 --- a/api/app/core/memory/llm_tools/chunker_client.py +++ b/api/app/core/memory/llm_tools/chunker_client.py @@ -187,11 +187,11 @@ class ChunkerClient: async def generate_chunks(self, dialogue: DialogData): """ Generate chunks following 1 Message = 1 Chunk strategy. - + Each message creates one chunk, directly inheriting role information. If a message is too long, it will be split into multiple sub-chunks, each maintaining the same speaker. - + Raises: ValueError: If dialogue has no messages or chunking fails """ @@ -201,9 +201,9 @@ class ChunkerClient: f"Dialogue {dialogue.ref_id} has no messages. " f"Cannot generate chunks from empty dialogue." ) - + dialogue.chunks = [] - + # 按消息分块：每个消息创建一个或多个 chunk，直接继承角色 for msg_idx, msg in enumerate(dialogue.context.msgs): # Validate message has required attributes @@ -212,13 +212,13 @@ class ChunkerClient: f"Message {msg_idx} in dialogue {dialogue.ref_id} " f"missing 'role' or 'msg' attribute" ) - + msg_content = msg.msg.strip() - + # Skip empty messages if not msg_content: continue - + # 如果消息太长，可以进一步分块 if len(msg_content) > self.chunk_size: # 对单个消息的内容进行分块 @@ -228,14 +228,14 @@ class ChunkerClient: raise ValueError( f"Failed to chunk long message {msg_idx} in dialogue {dialogue.ref_id}: {e}" ) - + for idx, sub_chunk in enumerate(sub_chunks): sub_chunk_text = sub_chunk.text if hasattr(sub_chunk, 'text') else str(sub_chunk) sub_chunk_text = sub_chunk_text.strip() - + if len(sub_chunk_text) < (self.min_characters_per_chunk or 50): continue - + chunk = Chunk( content=f"{msg.role}: {sub_chunk_text}", speaker=msg.role, # 直接继承角色 @@ -260,7 +260,7 @@ class ChunkerClient: }, ) dialogue.chunks.append(chunk) - + # Validate we generated at least one chunk if not dialogue.chunks: raise ValueError( @@ -268,7 +268,7 @@ class ChunkerClient: f"All messages were either empty or too short. " f"Messages count: {len(dialogue.context.msgs)}" ) - + return dialogue def evaluate_chunking(self, dialogue: DialogData) -> dict: diff --git a/api/app/core/memory/models/config_models.py b/api/app/core/memory/models/config_models.py index f3341cc5..ca1780aa 100644 --- a/api/app/core/memory/models/config_models.py +++ b/api/app/core/memory/models/config_models.py @@ -72,7 +72,7 @@ class TemporalSearchParams(BaseModel): """Parameters for temporal search queries in the knowledge graph. Attributes: - group_id: Group ID to filter search results (default: 'test') + end_user_id: Group ID to filter search results (default: 'test') apply_id: Application ID to filter search results user_id: User ID to filter search results start_date: Start date for temporal filtering (format: 'YYYY-MM-DD') @@ -81,7 +81,7 @@ class TemporalSearchParams(BaseModel): invalid_date: Date when memory should be invalid (format: 'YYYY-MM-DD') limit: Maximum number of results to return (default: 3) """ - group_id: Optional[str] = Field("test", description="The group ID to filter the search.") + end_user_id: Optional[str] = Field("test", description="The group ID to filter the search.") apply_id: Optional[str] = Field(None, description="The apply ID to filter the search.") user_id: Optional[str] = Field(None, description="The user ID to filter the search.") start_date: Optional[str] = Field(None, description="The start date for the search.") diff --git a/api/app/core/memory/models/graph_models.py b/api/app/core/memory/models/graph_models.py index 7a48d6cb..79b88fdc 100644 --- a/api/app/core/memory/models/graph_models.py +++ b/api/app/core/memory/models/graph_models.py @@ -103,9 +103,7 @@ class Edge(BaseModel): id: Unique identifier for the edge source: ID of the source node target: ID of the target node - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy run_id: Unique identifier for the pipeline run that created this edge created_at: Timestamp when the edge was created (system perspective) expired_at: Optional timestamp when the edge expires (system perspective) @@ -113,9 +111,7 @@ class Edge(BaseModel): id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the edge.") source: str = Field(..., description="The ID of the source node.") target: str = Field(..., description="The ID of the target node.") - group_id: str = Field(..., description="The group ID of the edge.") - user_id: str = Field(..., description="The user ID of the edge.") - apply_id: str = Field(..., description="The apply ID of the edge.") + end_user_id: str = Field(..., description="The end user ID of the edge.") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(..., description="The valid time of the edge from system perspective.") expired_at: Optional[datetime] = Field(None, description="The expired time of the edge from system perspective.") @@ -185,18 +181,14 @@ class Node(BaseModel): Attributes: id: Unique identifier for the node name: Name of the node - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy run_id: Unique identifier for the pipeline run that created this node created_at: Timestamp when the node was created (system perspective) expired_at: Optional timestamp when the node expires (system perspective) """ id: str = Field(..., description="The unique identifier for the node.") name: str = Field(..., description="The name of the node.") - group_id: str = Field(..., description="The group ID of the node.") - user_id: str = Field(..., description="The user ID of the edge.") - apply_id: str = Field(..., description="The apply ID of the edge.") + end_user_id: str = Field(..., description="The end user ID of the node.") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(..., description="The valid time of the node from system perspective.") expired_at: Optional[datetime] = Field(None, description="The expired time of the node from system perspective.") diff --git a/api/app/core/memory/models/message_models.py b/api/app/core/memory/models/message_models.py index bcf08999..2f8660af 100644 --- a/api/app/core/memory/models/message_models.py +++ b/api/app/core/memory/models/message_models.py @@ -55,7 +55,7 @@ class Statement(BaseModel): Attributes: id: Unique identifier for the statement chunk_id: ID of the parent chunk this statement belongs to - group_id: Optional group ID for multi-tenancy + end_user_id: Optional group ID for multi-tenancy statement: The actual statement text content speaker: Optional speaker identifier ('用户' for user, 'AI' for AI responses) statement_embedding: Optional embedding vector for the statement @@ -73,7 +73,7 @@ class Statement(BaseModel): """ id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the statement.") chunk_id: str = Field(..., description="ID of the parent chunk this statement belongs to.") - group_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.") + end_user_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.") statement: str = Field(..., description="The text content of the statement.") speaker: Optional[str] = Field(None, description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses") statement_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the statement.") @@ -159,9 +159,7 @@ class DialogData(BaseModel): context: Full conversation context dialog_embedding: Optional embedding vector for the entire dialog ref_id: Reference ID linking to external dialog system - group_id: Group ID for multi-tenancy - user_id: User ID for user-specific data - apply_id: Application ID for application-specific data + end_user_id: End user ID for multi-tenancy created_at: Timestamp when the dialog was created expired_at: Timestamp when the dialog expires (default: far future) metadata: Additional metadata as key-value pairs @@ -175,9 +173,7 @@ class DialogData(BaseModel): context: ConversationContext = Field(..., description="The full conversation context as a single string.") dialog_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the dialog.") ref_id: str = Field(..., description="Refer to external dialog id. This is used to link to the original dialog.") - group_id: str = Field(default=..., description="Group ID of dialogue data") - user_id: str = Field(..., description="USER ID of dialogue data") - apply_id: str = Field(..., description="APPLY ID of dialogue data") + end_user_id: str = Field(default=..., description="End user ID of dialogue data") run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.") created_at: datetime = Field(default_factory=datetime.now, description="The timestamp when the dialog was created.") expired_at: datetime = Field(default_factory=lambda: datetime(9999, 12, 31), description="The timestamp when the dialog expires.") @@ -250,11 +246,11 @@ class DialogData(BaseModel): return [] def assign_group_id_to_statements(self) -> None: - """Assign this dialog's group_id to all statements in all chunks. + """Assign this dialog's end_user_id to all statements in all chunks. - This method updates statements that don't have a group_id set. + This method updates statements that don't have a end_user_id set. """ for chunk in self.chunks: for statement in chunk.statements: - if statement.group_id is None: - statement.group_id = self.group_id + if statement.end_user_id is None: + statement.end_user_id = self.end_user_id diff --git a/api/app/core/memory/src/search.py b/api/app/core/memory/src/search.py index 91e47eae..0e1d8424 100644 --- a/api/app/core/memory/src/search.py +++ b/api/app/core/memory/src/search.py @@ -6,6 +6,7 @@ import os import time from datetime import datetime from typing import TYPE_CHECKING, Any, Dict, List, Optional +from uuid import UUID if TYPE_CHECKING: from app.schemas.memory_config_schema import MemoryConfig @@ -396,13 +397,13 @@ def rerank_with_activation( return reranked -def log_search_query(query_text: str, search_type: str, group_id: str | None, limit: int, include: List[str], log_file: str = None): +def log_search_query(query_text: str, search_type: str, end_user_id: str | None, limit: int, include: List[str], log_file: str = None): """Log search query information using the logger. Args: query_text: The search query text search_type: Type of search (keyword, embedding, hybrid) - group_id: Group identifier for filtering + end_user_id: Group identifier for filtering limit: Maximum number of results include: List of result types to include log_file: Deprecated parameter, kept for backward compatibility @@ -413,7 +414,7 @@ def log_search_query(query_text: str, search_type: str, group_id: str | None, li # Log using the standard logger logger.info( f"Search query: query='{cleaned_query}', type={search_type}, " - f"group_id={group_id}, limit={limit}, include={include}" + f"end_user_id={end_user_id}, limit={limit}, include={include}" ) @@ -672,7 +673,7 @@ def apply_reranker_placeholder( async def run_hybrid_search( query_text: str, search_type: str, - group_id: str | None, + end_user_id: str | None, limit: int, include: List[str], output_path: str | None, @@ -715,7 +716,7 @@ async def run_hybrid_search( } # Log the search query - log_search_query(query_text, search_type, group_id, limit, include) + log_search_query(query_text, search_type, end_user_id, limit, include) connector = Neo4jConnector() results = {} @@ -732,7 +733,7 @@ async def run_hybrid_search( search_graph( connector=connector, q=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include ) @@ -769,7 +770,7 @@ async def run_hybrid_search( connector=connector, embedder_client=embedder, query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, ) @@ -916,9 +917,7 @@ async def run_hybrid_search( async def search_by_temporal( - group_id: Optional[str] = "test", - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = "test", start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -929,7 +928,7 @@ async def search_by_temporal( Temporal search across Statements. - Matches statements created between start_date and end_date - - Optionally filters by group_id + - Optionally filters by end_user_id - Returns up to 'limit' statements """ connector = Neo4jConnector() @@ -939,9 +938,7 @@ async def search_by_temporal( end_date = normalize_date_safe(end_date) params = TemporalSearchParams.model_validate({ - "group_id": group_id, - "apply_id": apply_id, - "user_id": user_id, + "end_user_id": end_user_id, "start_date": start_date, "end_date": end_date, "valid_date": valid_date, @@ -950,9 +947,7 @@ async def search_by_temporal( }) statements = await search_graph_by_temporal( connector=connector, - group_id=params.group_id, - apply_id=params.apply_id, - user_id=params.user_id, + end_user_id=params.end_user_id, start_date=params.start_date, end_date=params.end_date, valid_date=params.valid_date, @@ -964,9 +959,7 @@ async def search_by_temporal( async def search_by_keyword_temporal( query_text: str, - group_id: Optional[str] = "test", - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = "test", start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -987,9 +980,7 @@ async def search_by_keyword_temporal( invalid_date = normalize_date_safe(invalid_date) params = TemporalSearchParams.model_validate({ - "group_id": group_id, - "apply_id": apply_id, - "user_id": user_id, + "end_user_id": end_user_id, "start_date": start_date, "end_date": end_date, "valid_date": valid_date, @@ -999,9 +990,7 @@ async def search_by_keyword_temporal( statements = await search_graph_by_keyword_temporal( connector=connector, query_text=query_text, - group_id=params.group_id, - apply_id=params.apply_id, - user_id=params.user_id, + end_user_id=params.end_user_id, start_date=params.start_date, end_date=params.end_date, valid_date=params.valid_date, @@ -1013,7 +1002,7 @@ async def search_by_keyword_temporal( async def search_chunk_by_chunk_id( chunk_id: str, - group_id: Optional[str] = "test", + end_user_id: Optional[str] = "test", limit: int = 1, ): """ @@ -1023,7 +1012,7 @@ async def search_chunk_by_chunk_id( chunks = await search_graph_by_chunk_id( connector=connector, chunk_id=chunk_id, - group_id=group_id, + end_user_id=end_user_id, limit=limit ) return {"chunks": chunks} diff --git a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py index f5e72517..4dafd3ed 100644 --- a/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py +++ b/api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py @@ -555,8 +555,8 @@ class DataPreprocessor: dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}'))) - # 获取group_id，如果不存在则生成默认值 - group_id = item.get('group_id', f'group_default_{i}') + # 获取end_user_id，如果不存在则生成默认值 + end_user_id = item.get('end_user_id', f'group_default_{i}') user_id = item.get('user_id', f'user_default_{i}') apply_id = item.get('apply_id', f'apply_default_{i}') @@ -574,7 +574,7 @@ class DataPreprocessor: dialog_data = DialogData( context=context, ref_id=dialog_id, - group_id=group_id, + end_user_id=end_user_id, user_id=user_id, apply_id=apply_id, metadata=metadata @@ -644,7 +644,7 @@ class DataPreprocessor: context = ConversationContext(msgs=messages) dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}'))) - group_id = item.get('group_id', f'group_default_{i}') + end_user_id = item.get('end_user_id', f'group_default_{i}') user_id = item.get('user_id', f'user_default_{i}') apply_id = item.get('apply_id', f'apply_default_{i}') @@ -657,7 +657,7 @@ class DataPreprocessor: dialog_data = DialogData( context=context, ref_id=dialog_id, - group_id=group_id, + end_user_id=end_user_id, user_id=user_id, apply_id=apply_id, metadata=metadata diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py index 62b656b0..a425e0ed 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py @@ -199,7 +199,7 @@ def accurate_match( entity_nodes: List[ExtractedEntityNode] ) -> Tuple[List[ExtractedEntityNode], Dict[str, str], Dict[str, Dict]]: """ - 精确匹配：按 (group_id, name, entity_type) 合并实体并建立重定向与合并记录。 + 精确匹配：按 (end_user_id, name, entity_type) 合并实体并建立重定向与合并记录。 返回: (deduped_entities, id_redirect, exact_merge_map) """ exact_merge_map: Dict[str, Dict] = {} @@ -210,8 +210,8 @@ def accurate_match( for ent in entity_nodes: name_norm = (getattr(ent, "name", "") or "").strip() type_norm = (getattr(ent, "entity_type", "") or "").strip() - key = f"{getattr(ent, 'group_id', None)}|{name_norm}|{type_norm}" - # 为避免跨业务组误并，明确以 group_id 为范围边界 + key = f"{getattr(ent, 'end_user_id', None)}|{name_norm}|{type_norm}" + # 为避免跨业务组误并，明确以 end_user_id 为范围边界 if key not in canonical_map: canonical_map[key] = ent id_redirect[ent.id] = ent.id @@ -223,11 +223,11 @@ def accurate_match( id_redirect[ent.id] = canonical.id # 记录精确匹配的合并项（使用规范化键，避免外层变量误用） try: - k = f"{canonical.group_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}" + k = f"{canonical.end_user_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}" if k not in exact_merge_map: exact_merge_map[k] = { "canonical_id": canonical.id, - "group_id": canonical.group_id, + "end_user_id": canonical.end_user_id, "name": canonical.name, "entity_type": canonical.entity_type, "merged_ids": set(), @@ -596,7 +596,7 @@ def fuzzy_match( b = deduped_entities[j] # 跳过不同业务组的实体 - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): j += 1 continue @@ -671,7 +671,7 @@ def fuzzy_match( merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]" merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]" fuzzy_merge_records.append( - f"{merge_reason} 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type}) | " + f"{merge_reason} 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type}) | " f"s_name={s_name:.3f}, s_type={s_type:.3f}, overall={overall:.3f}, exact_alias={has_exact_match}" ) except Exception: @@ -779,7 +779,7 @@ async def LLM_decision( # 决策中包含去重和消歧的功能 # 记录 LLM 融合日志 try: llm_records.append( - f"[LLM融合] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})" + f"[LLM融合] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})" ) # 详细的“同类名称相似”记录改由 LLM 去重模块统一生成以携带 conf/reason except Exception: @@ -847,7 +847,7 @@ async def LLM_disamb_decision( id_redirect[k] = a.id try: disamb_records.append( - f"[DISAMB合并应用] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})" + f"[DISAMB合并应用] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py index 734f7b69..0249ac1f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py @@ -174,7 +174,7 @@ async def _judge_pair( pass # 3. 构建LLM判断的“上下文信息”（规则层计算的所有特征） 判断上下文特征有助于实体消歧首先判断的类型关系 ctx = { - "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None), + "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None), "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "type_similarity": _type_similarity(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "name_text_sim": name_text_sim, @@ -235,7 +235,7 @@ async def _judge_pair_disamb( except Exception: pass ctx = { - "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None), + "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None), "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)), "name_text_sim": name_text_sim, "name_embed_sim": name_embed_sim, @@ -317,8 +317,8 @@ async def llm_dedup_entities( # 保留对偶判断作为子流程，是为了 a = entity_nodes[i] for j in range(i + 1, len(entity_nodes)): b = entity_nodes[j] - # 规则1：必须属于同一组（group_id相同，不同组的实体不重复） - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + # 规则1：必须属于同一组（end_user_id相同，不同组的实体不重复） + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): continue # 规则2：类型必须兼容（调用_simple_type_ok判断） if not _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)): @@ -474,7 +474,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 - max_rounds: upper bound for iterative passes (default 3) - auto_merge_threshold: decision confidence for auto-merge when no co-occurrence (default 0.90) - co_ctx_threshold: lower threshold when co-occurrence is detected (default 0.83) - - shuffle_each_round: whether to shuffle entities within group_id each round to vary block composition + - shuffle_each_round: whether to shuffle entities within end_user_id each round to vary block composition Returns: - global_redirect: dict losing_id -> canonical_id accumulated across rounds @@ -509,7 +509,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 def _partition_blocks(nodes: List[ExtractedEntityNode]) -> List[List[ExtractedEntityNode]]: """ - 按 group_id 分块，避免跨组实体在同一块，减少无效候选对 + 按 end_user_id 分块，避免跨组实体在同一块，减少无效候选对 Args: nodes: 实体节点列表 @@ -519,7 +519,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 """ groups: Dict[str, List[ExtractedEntityNode]] = {} for e in nodes: - gid = getattr(e, "group_id", None) + gid = getattr(e, "end_user_id", None) groups.setdefault(str(gid), []).append(e) blocks: List[List[ExtractedEntityNode]] = [] for gid, arr in groups.items(): @@ -559,7 +559,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重 # Collapse nodes to canonical reps before each round to avoid redundant comparisons # 步骤1：折叠实体（合并已确定的重复实体，减少后续计算量） current_nodes = _collapse_nodes(current_nodes) - # 步骤2：分块（按group_id分块，避免跨组处理） + # 步骤2：分块（按end_user_id分块，避免跨组处理） blocks = _partition_blocks(current_nodes) if not blocks: # 无块可处理（实体已全部折叠），退出循环 break @@ -645,7 +645,7 @@ async def llm_disambiguate_pairs_iterative( a = entity_nodes[i] b = entity_nodes[j] # 必须同组 - if getattr(a, "group_id", None) != getattr(b, "group_id", None): + if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None): continue ta = getattr(a, "entity_type", None) tb = getattr(b, "entity_type", None) diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py index b41f35a4..dbc697d9 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py @@ -61,7 +61,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode: return ExtractedEntityNode( id=row.get("id"), name=row.get("name") or "", - group_id=row.get("group_id") or "", + end_user_id=row.get("end_user_id") or "", user_id=row.get("user_id") or "", apply_id=row.get("apply_id") or "", created_at=_parse_dt(row.get("created_at")), @@ -79,7 +79,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode: async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑，与 Neo4j 中同组实体联合去重 connector: Neo4jConnector, - group_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重 + end_user_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重 entity_nodes: List[ExtractedEntityNode], # 输入的实体节点列表，包含待去重的实体 statement_entity_edges: List[StatementEntityEdge], # 输入的语句实体边列表，用于处理实体之间的关系 entity_entity_edges: List[EntityEntityEdge], # 输入的实体实体边列表，用于处理实体之间的关系 @@ -88,7 +88,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑 ) -> Tuple[List[ExtractedEntityNode], List[StatementEntityEdge], List[EntityEntityEdge]]: """ 第二层去重消歧： - - 以第一层结果为索引，检索相同 group_id 下的 DB 候选实体 + - 以第一层结果为索引，检索相同 end_user_id 下的 DB 候选实体 - 将 DB 候选与当前实体集合联合，按既有精确/模糊/LLM 决策进行融合 - 返回融合后的实体与重定向后的边（边已指向规范 ID，优先 DB ID） """ @@ -102,7 +102,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑 ] candidates_map = await get_dedup_candidates_for_entities( # 从 Neo4j 中查询候选实体，并将结果赋值给candidates_map（等待异步操作完成）。 - connector=connector, group_id=group_id, + connector=connector, end_user_id=end_user_id, entities=incoming_rows, # 传入参数：第一层实体的核心信息（作为查询索引） use_contains_fallback=True # 传入参数：启用 “包含关系” 作为匹配失败的降级策略（若精确匹配无结果，用包含关系召回候选），与src\database\cypher_queries.py的307产生联动 ) diff --git a/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py b/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py index 11845d7d..f28b8a5f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py +++ b/api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py @@ -57,11 +57,11 @@ async def dedup_layers_and_merge_and_return( if pipeline_config is None: raise ValueError("pipeline_config is required for dedup_layers_and_merge_and_return") - # 先探测 group_id，决定报告写入策略 - group_id: Optional[str] = None + # 先探测 end_user_id，决定报告写入策略 + end_user_id: Optional[str] = None for dd in dialog_data_list: - group_id = getattr(dd, "group_id", None) - if group_id: + end_user_id = getattr(dd, "end_user_id", None) + if end_user_id: break # 第一层去重消歧 @@ -82,11 +82,11 @@ async def dedup_layers_and_merge_and_return( # 第二层去重消歧：与 Neo4j 中同组实体联合融合 try: - if group_id: + if end_user_id: if connector: fused_entity_nodes, fused_statement_entity_edges, fused_entity_entity_edges = await second_layer_dedup_and_merge_with_neo4j( connector=connector, - group_id=group_id, + end_user_id=end_user_id, entity_nodes=dedup_entity_nodes, statement_entity_edges=dedup_statement_entity_edges, entity_entity_edges=dedup_entity_entity_edges, @@ -96,7 +96,7 @@ async def dedup_layers_and_merge_and_return( else: print("Skip second-layer dedup: missing connector") else: - print("Skip second-layer dedup: missing group_id") + print("Skip second-layer dedup: missing end_user_id") except Exception as e: print(f"Second-layer dedup failed: {e}") diff --git a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py index 46ba1dde..8c69c7cf 100644 --- a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py +++ b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py @@ -287,7 +287,7 @@ class ExtractionOrchestrator: for d_idx, dialog in enumerate(dialog_data_list): dialogue_content = dialog.content if self.config.statement_extraction.include_dialogue_context else None for c_idx, chunk in enumerate(dialog.chunks): - all_chunks.append((chunk, dialog.group_id, dialogue_content)) + all_chunks.append((chunk, dialog.end_user_id, dialogue_content)) chunk_metadata.append((d_idx, c_idx)) logger.info(f"收集到 {len(all_chunks)} 个分块，开始全局并行提取") @@ -299,9 +299,9 @@ class ExtractionOrchestrator: # 全局并行处理所有分块 async def extract_for_chunk(chunk_data, chunk_index): nonlocal completed_chunks - chunk, group_id, dialogue_content = chunk_data + chunk, end_user_id, dialogue_content = chunk_data try: - statements = await self.statement_extractor._extract_statements(chunk, group_id, dialogue_content) + statements = await self.statement_extractor._extract_statements(chunk, end_user_id, dialogue_content) # 流式输出：每提取完一个分块的陈述句，立即发送进度 # 注意：只在试运行模式下发送陈述句详情，正式模式不发送 @@ -569,32 +569,32 @@ class ExtractionOrchestrator: if dialog_data_list and hasattr(dialog_data_list[0], 'config_id'): config_id = dialog_data_list[0].config_id - # 加载DataConfig - data_config = None + # 加载MemoryConfig + memory_config = None if config_id: try: from app.db import SessionLocal - from app.repositories.data_config_repository import DataConfigRepository + from app.repositories.memory_config_repository import MemoryConfigRepository db = SessionLocal() try: - data_config = DataConfigRepository.get_by_id(db, config_id) + memory_config = MemoryConfigRepository.get_by_id(db, config_id) finally: db.close() - if data_config and not data_config.emotion_enabled: + if memory_config and not memory_config.emotion_enabled: logger.info("情绪提取已在配置中禁用，跳过情绪提取") return [{} for _ in dialog_data_list] except Exception as e: - logger.warning(f"加载DataConfig失败: {e}，将跳过情绪提取") + logger.warning(f"加载MemoryConfig失败: {e}，将跳过情绪提取") return [{} for _ in dialog_data_list] else: logger.info("未找到config_id，跳过情绪提取") return [{} for _ in dialog_data_list] # 如果配置未启用情绪提取，直接返回空映射 - if not data_config or not data_config.emotion_enabled: + if not memory_config or not memory_config.emotion_enabled: logger.info("情绪提取未启用，跳过") return [{} for _ in dialog_data_list] @@ -608,7 +608,7 @@ class ExtractionOrchestrator: total_statements += 1 # 只处理用户的陈述句 (role 为 "user") if hasattr(statement, 'speaker') and statement.speaker == "user": - all_statements.append((statement, data_config)) + all_statements.append((statement, memory_config)) statement_metadata.append((d_idx, statement.id)) filtered_statements += 1 @@ -617,7 +617,7 @@ class ExtractionOrchestrator: # 初始化情绪提取服务 from app.services.emotion_extraction_service import EmotionExtractionService emotion_service = EmotionExtractionService( - llm_id=data_config.emotion_model_id if data_config.emotion_model_id else None + llm_id=memory_config.emotion_model_id if memory_config.emotion_model_id else None ) # 全局并行处理所有陈述句 @@ -992,9 +992,7 @@ class ExtractionOrchestrator: id=dialog_data.id, name=f"Dialog_{dialog_data.id}", # 添加必需的 name 字段 ref_id=dialog_data.ref_id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id content=dialog_data.context.content if dialog_data.context else "", dialog_embedding=dialog_data.dialog_embedding if hasattr(dialog_data, 'dialog_embedding') else None, @@ -1012,9 +1010,7 @@ class ExtractionOrchestrator: id=chunk.id, name=f"Chunk_{chunk.id}", # 添加必需的 name 字段 dialog_id=dialog_data.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id content=chunk.content, chunk_embedding=chunk.chunk_embedding, @@ -1035,9 +1031,7 @@ class ExtractionOrchestrator: stmt_type=getattr(statement, 'stmt_type', 'general'), # 添加必需的 stmt_type 字段 temporal_info=getattr(statement, 'temporal_info', TemporalInfo.ATEMPORAL), # 添加必需的 temporal_info 字段 connect_strength=statement.connect_strength if statement.connect_strength is not None else 'Strong', # 添加必需的 connect_strength 字段 - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id statement=statement.statement, speaker=getattr(statement, 'speaker', None), # 添加 speaker 字段 @@ -1060,9 +1054,7 @@ class ExtractionOrchestrator: statement_chunk_edge = StatementChunkEdge( source=statement.id, target=chunk.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, ) @@ -1095,9 +1087,7 @@ class ExtractionOrchestrator: aliases=getattr(entity, 'aliases', []) or [], # 传递从三元组提取阶段获取的aliases name_embedding=getattr(entity, 'name_embedding', None), is_explicit_memory=getattr(entity, 'is_explicit_memory', False), # 新增：传递语义记忆标记 - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, expired_at=dialog_data.expired_at, @@ -1112,9 +1102,7 @@ class ExtractionOrchestrator: source=statement.id, target=entity.id, connect_strength=entity_connect_strength if entity_connect_strength is not None else 'Strong', - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, ) @@ -1134,9 +1122,7 @@ class ExtractionOrchestrator: relation_type=triplet.predicate, statement=statement.statement, source_statement_id=statement.id, - group_id=dialog_data.group_id, - user_id=dialog_data.user_id, - apply_id=dialog_data.apply_id, + end_user_id=dialog_data.end_user_id, run_id=dialog_data.run_id, # 使用 dialog_data 的 run_id created_at=dialog_data.created_at, expired_at=dialog_data.expired_at, @@ -1763,14 +1749,14 @@ class ExtractionOrchestrator: async def get_chunked_dialogs( chunker_strategy: str = "RecursiveChunker", - group_id: str = "group_1", + end_user_id: str = "group_1", indices: Optional[List[int]] = None, ) -> List[DialogData]: """从测试数据生成分块对话 Args: chunker_strategy: 分块策略（默认: RecursiveChunker） - group_id: 组ID + end_user_id: 组ID indices: 要处理的数据索引列表（可选） Returns: @@ -1834,7 +1820,7 @@ async def get_chunked_dialogs( dialog_data = DialogData( context=conversation_context, ref_id=data['id'], - group_id=group_id, + end_user_id=end_user_id, metadata=dialog_metadata, ) @@ -1936,7 +1922,7 @@ async def get_chunked_dialogs_from_preprocessed( async def get_chunked_dialogs_with_preprocessing( chunker_strategy: str = "RecursiveChunker", - group_id: str = "default", + end_user_id: str = "default", user_id: str = "default", apply_id: str = "default", indices: Optional[List[int]] = None, @@ -1948,7 +1934,7 @@ async def get_chunked_dialogs_with_preprocessing( Args: chunker_strategy: 分块策略 - group_id: 组ID + end_user_id: 组ID user_id: 用户ID apply_id: 应用ID indices: 要处理的数据索引列表 @@ -1976,11 +1962,9 @@ async def get_chunked_dialogs_with_preprocessing( indices=indices, ) - # 设置 group_id, user_id, apply_id + # 设置 end_user_id for dd in preprocessed_data: - dd.group_id = group_id - dd.user_id = user_id - dd.apply_id = apply_id + dd.end_user_id = end_user_id # 步骤2: 语义剪枝 try: diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py index 7e75fd2d..f39313a8 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py @@ -193,9 +193,9 @@ async def _process_chunk_summary( node = MemorySummaryNode( id=uuid4().hex, name=title if title else f"MemorySummaryChunk_{chunk.id}", - group_id=dialog.group_id, - user_id=dialog.user_id, - apply_id=dialog.apply_id, + end_user_id=dialog.end_user_id, + user_id=dialog.end_user_id, + apply_id=dialog.end_user_id, run_id=dialog.run_id, # 使用 dialog 的 run_id created_at=datetime.now(), expired_at=datetime(9999, 12, 31), diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py index fb1b539a..b06bd70f 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py @@ -82,12 +82,12 @@ class StatementExtractor: logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty") return None - async def _extract_statements(self, chunk, group_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]: + async def _extract_statements(self, chunk, end_user_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]: """Process a single chunk and return extracted statements Args: chunk: Chunk object to process - group_id: Group ID to assign to all statements in this chunk + end_user_id: Group ID to assign to all statements in this chunk dialogue_content: Full dialogue content to provide as context Returns: @@ -158,7 +158,7 @@ class StatementExtractor: temporal_info=temporal_type, relevence_info=relevence_info, chunk_id=chunk.id, - group_id=group_id, + end_user_id=end_user_id, speaker=chunk_speaker, ) @@ -184,10 +184,10 @@ class StatementExtractor: logger.info(f"Processing {len(chunks_to_process)} chunks for statement extraction") - # Process all chunks concurrently, passing the group_id and dialogue content from dialog_data + # Process all chunks concurrently, passing the end_user_id and dialogue content from dialog_data dialogue_content = dialog_data.content if self.config.include_dialogue_context else None results = await asyncio.gather( - *[self._extract_statements(chunk, dialog_data.group_id, dialogue_content) for chunk in chunks_to_process], + *[self._extract_statements(chunk, dialog_data.end_user_id, dialogue_content) for chunk in chunks_to_process], return_exceptions=True ) @@ -225,7 +225,7 @@ class StatementExtractor: for i, statement in enumerate(statements, 1): f.write(f"Statement {i}:\n") f.write(f"Id: {statement.id}\n") - f.write(f"Group Id: {statement.group_id}\n") + f.write(f"Group Id: {statement.end_user_id}\n") f.write(f"Content: {statement.statement}\n") f.write(f"Type: {statement.stmt_type.value}\n") f.write(f"Temporal Info: {statement.temporal_info.value}\n") @@ -298,7 +298,7 @@ class StatementExtractor: dialog_sections.append({ "dialog_id": dialog.ref_id, - "group_id": dialog.group_id, + "end_user_id": dialog.end_user_id, "content": dialog.content if getattr(dialog, "content", None) else "", "strong": strong_relations, "weak": weak_relations, @@ -312,7 +312,7 @@ class StatementExtractor: for idx, section in enumerate(dialog_sections, 1): f.write(f"Dialog {idx}:\n") f.write(f"Dialog ID: {section.get('dialog_id', '')}\n") - f.write(f"Group ID: {section.get('group_id', '')}\n") + f.write(f"Group ID: {section.get('end_user_id', '')}\n") f.write("Content:\n") f.write(f"{section.get('content', '')}\n") f.write("-" * 40 + "\n\n") diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py index 9528e638..499027a4 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py @@ -132,7 +132,7 @@ class TemporalExtractor: prompt_logger.info("") prompt_logger.info("=== TEMPORAL EXTRACTION RESULTS ===") prompt_logger.info( - f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}" + f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py index d3d059b0..bfc0bc88 100644 --- a/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py +++ b/api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py @@ -116,7 +116,7 @@ class TripletExtractor: logger.info(f"Processing {len(all_statements)} statements for triplet extraction...") try: prompt_logger.info( - f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}, statements_to_process={len(all_statements)}" + f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}, statements_to_process={len(all_statements)}" ) except Exception: pass diff --git a/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py b/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py index 5722769a..a71c0957 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py +++ b/api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py @@ -75,7 +75,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_time: Optional[datetime] = None ) -> Dict[str, Any]: """ @@ -91,7 +91,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签（Statement, ExtractedEntity, MemorySummary） - group_id: 组ID（可选，用于过滤） + end_user_id: 组ID（可选，用于过滤） current_time: 当前时间（可选，默认使用系统时间） Returns: @@ -123,7 +123,7 @@ class AccessHistoryManager: for attempt in range(self.max_retries): try: # 步骤1：读取当前节点状态 - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: raise ValueError( @@ -142,7 +142,7 @@ class AccessHistoryManager: node_id=node_id, node_label=node_label, update_data=update_data, - group_id=group_id + end_user_id=end_user_id ) logger.info( @@ -172,7 +172,7 @@ class AccessHistoryManager: self, node_ids: List[str], node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, current_time: Optional[datetime] = None ) -> List[Dict[str, Any]]: """ @@ -184,7 +184,7 @@ class AccessHistoryManager: Args: node_ids: 节点ID列表 node_label: 节点标签（所有节点必须是同一类型） - group_id: 组ID（可选） + end_user_id: 组ID（可选） current_time: 当前时间（可选） Returns: @@ -202,7 +202,7 @@ class AccessHistoryManager: task = self.record_access( node_id=node_id, node_label=node_label, - group_id=group_id, + end_user_id=end_user_id, current_time=current_time ) tasks.append(task) @@ -235,7 +235,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Tuple[ConsistencyCheckResult, Optional[str]]: """ 检查节点数据的一致性 @@ -249,14 +249,14 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Tuple[ConsistencyCheckResult, Optional[str]]: - 一致性检查结果枚举 - 错误描述（如果不一致） """ - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: return ConsistencyCheckResult.CONSISTENT, None @@ -305,7 +305,7 @@ class AccessHistoryManager: async def check_batch_consistency( self, node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1000 ) -> Dict[str, Any]: """ @@ -313,7 +313,7 @@ class AccessHistoryManager: Args: node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） limit: 检查的最大节点数 Returns: @@ -329,16 +329,16 @@ class AccessHistoryManager: MATCH (n:{node_label}) WHERE n.access_history IS NOT NULL """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ RETURN n.id as id LIMIT $limit """ params = {"limit": limit} - if group_id: - params["group_id"] = group_id + if end_user_id: + params["end_user_id"] = end_user_id results = await self.connector.execute_query(query, **params) node_ids = [r['id'] for r in results] @@ -351,7 +351,7 @@ class AccessHistoryManager: result, message = await self.check_consistency( node_id=node_id, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) if result == ConsistencyCheckResult.CONSISTENT: @@ -387,7 +387,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> bool: """ 自动修复节点的数据不一致问题 @@ -401,7 +401,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: bool: 修复成功返回True，否则返回False @@ -411,7 +411,7 @@ class AccessHistoryManager: result, message = await self.check_consistency( node_id=node_id, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) if result == ConsistencyCheckResult.CONSISTENT: @@ -419,7 +419,7 @@ class AccessHistoryManager: return True # 获取节点数据 - node_data = await self._fetch_node(node_id, node_label, group_id) + node_data = await self._fetch_node(node_id, node_label, end_user_id) if not node_data: logger.error(f"节点不存在，无法修复: {node_label}[{node_id}]") return False @@ -457,8 +457,8 @@ class AccessHistoryManager: query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - query += " WHERE n.group_id = $group_id" + if end_user_id: + query += " WHERE n.end_user_id = $end_user_id" query += """ SET n += $repair_data RETURN n @@ -468,8 +468,8 @@ class AccessHistoryManager: 'node_id': node_id, 'repair_data': repair_data } - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id await self.connector.execute_query(query, **params) @@ -491,7 +491,7 @@ class AccessHistoryManager: self, node_id: str, node_label: str, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Optional[Dict[str, Any]]: """ 获取节点数据 @@ -499,7 +499,7 @@ class AccessHistoryManager: Args: node_id: 节点ID node_label: 节点标签 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Optional[Dict[str, Any]]: 节点数据，如果不存在返回None @@ -507,8 +507,8 @@ class AccessHistoryManager: query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - query += " WHERE n.group_id = $group_id" + if end_user_id: + query += " WHERE n.end_user_id = $end_user_id" query += """ RETURN n.id as id, n.importance_score as importance_score, @@ -519,8 +519,8 @@ class AccessHistoryManager: """ params = {'node_id': node_id} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) @@ -585,7 +585,7 @@ class AccessHistoryManager: node_id: str, node_label: str, update_data: Dict[str, Any], - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Dict[str, Any]: """ 原子性更新节点（使用乐观锁） @@ -597,7 +597,7 @@ class AccessHistoryManager: node_id: 节点ID node_label: 节点标签 update_data: 更新数据 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Dict[str, Any]: 更新后的节点数据 @@ -606,13 +606,13 @@ class AccessHistoryManager: RuntimeError: 如果更新失败或发生版本冲突 """ # 定义事务函数 - async def update_transaction(tx, node_id, node_label, update_data, group_id): + async def update_transaction(tx, node_id, node_label, update_data, end_user_id): # 步骤1：读取当前节点并获取版本号 read_query = f""" MATCH (n:{node_label} {{id: $node_id}}) """ - if group_id: - read_query += " WHERE n.group_id = $group_id" + if end_user_id: + read_query += " WHERE n.end_user_id = $end_user_id" read_query += """ RETURN n.id as id, n.version as version, @@ -624,8 +624,8 @@ class AccessHistoryManager: """ read_params = {'node_id': node_id} - if group_id: - read_params['group_id'] = group_id + if end_user_id: + read_params['end_user_id'] = end_user_id read_result = await tx.run(read_query, **read_params) current_node = await read_result.single() @@ -656,8 +656,8 @@ class AccessHistoryManager: # 构建 WHERE 子句 where_conditions = [] - if group_id: - where_conditions.append("n.group_id = $group_id") + if end_user_id: + where_conditions.append("n.end_user_id = $end_user_id") # 添加版本检查 if current_version > 0: @@ -695,8 +695,8 @@ class AccessHistoryManager: 'last_access_time': update_data['last_access_time'], 'access_count': update_data['access_count'] } - if group_id: - update_params['group_id'] = group_id + if end_user_id: + update_params['end_user_id'] = end_user_id update_result = await tx.run(update_query, **update_params) updated_node = await update_result.single() @@ -720,7 +720,7 @@ class AccessHistoryManager: node_id=node_id, node_label=node_label, update_data=update_data, - group_id=group_id + end_user_id=end_user_id ) return result except Exception as e: diff --git a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py index ea9a6358..25daa968 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/config_utils.py +++ b/api/app/core/memory/storage_services/forgetting_engine/config_utils.py @@ -11,9 +11,10 @@ Functions: import logging from typing import Optional, Dict, Any +from uuid import UUID from sqlalchemy.orm import Session -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator @@ -61,12 +62,12 @@ def calculate_forgetting_rate(lambda_time: float, lambda_mem: float) -> float: def load_actr_config_from_db( db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 从数据库加载 ACT-R 配置参数 - 从 PostgreSQL 的 data_config 表读取配置参数， + 从 PostgreSQL 的 memory_config 表读取配置参数， 并计算派生参数（如 forgetting_rate）。 Args: @@ -99,7 +100,7 @@ def load_actr_config_from_db( # 从数据库加载配置 try: - repository = DataConfigRepository() + repository = MemoryConfigRepository() db_config = repository.get_by_id(db, config_id) if db_config is None: @@ -150,7 +151,7 @@ def load_actr_config_from_db( def create_actr_calculator_from_config( db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> ACTRCalculator: """ 从数据库配置创建 ACTRCalculator 实例 @@ -168,11 +169,6 @@ def create_actr_calculator_from_config( ValueError: 如果指定的 config_id 不存在 Examples: - >>> from sqlalchemy.orm import Session - >>> db = Session() - >>> calculator = create_actr_calculator_from_config(db, config_id=1) - >>> # 使用计算器 - >>> activation = calculator.calculate_memory_activation(...) """ # 加载配置 config = load_actr_config_from_db(db, config_id) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py index 6d42af53..5a178fc2 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py @@ -16,6 +16,7 @@ Classes: import logging from typing import Dict, Any, Optional +from uuid import UUID from datetime import datetime from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy @@ -66,10 +67,10 @@ class ForgettingScheduler: async def run_forgetting_cycle( self, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, max_merge_batch_size: int = 100, min_days_since_access: int = 30, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> Dict[str, Any]: """ @@ -77,7 +78,7 @@ class ForgettingScheduler: Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） max_merge_batch_size: 单次最大融合节点对数（默认 100） min_days_since_access: 最小未访问天数（默认 30 天） config_id: 配置ID（可选，用于获取 llm_id） @@ -107,19 +108,19 @@ class ForgettingScheduler: start_time_iso = start_time.isoformat() logger.info( - f"开始遗忘周期: group_id={group_id}, " + f"开始遗忘周期: end_user_id={end_user_id}, " f"max_batch={max_merge_batch_size}, " f"min_days={min_days_since_access}" ) try: # 步骤1：统计遗忘前的节点数量 - nodes_before = await self._count_knowledge_nodes(group_id) + nodes_before = await self._count_knowledge_nodes(end_user_id) logger.info(f"遗忘前节点总数: {nodes_before}") # 步骤2：识别可遗忘的节点对 forgettable_pairs = await self.forgetting_strategy.find_forgettable_nodes( - group_id=group_id, + end_user_id=end_user_id, min_days_since_access=min_days_since_access ) @@ -213,7 +214,7 @@ class ForgettingScheduler: 'statement_text': pair['statement_text'], 'statement_activation': pair['statement_activation'], 'statement_importance': pair['statement_importance'], - 'group_id': group_id + 'end_user_id': end_user_id } entity_node = { @@ -222,7 +223,7 @@ class ForgettingScheduler: 'entity_type': pair['entity_type'], 'entity_activation': pair['entity_activation'], 'entity_importance': pair['entity_importance'], - 'group_id': group_id + 'end_user_id': end_user_id } # 融合节点 @@ -262,7 +263,7 @@ class ForgettingScheduler: continue # 步骤6：统计遗忘后的节点数量 - nodes_after = await self._count_knowledge_nodes(group_id) + nodes_after = await self._count_knowledge_nodes(end_user_id) logger.info(f"遗忘后节点总数: {nodes_after}") # 步骤7：生成遗忘报告 @@ -315,7 +316,7 @@ class ForgettingScheduler: async def _count_knowledge_nodes( self, - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> int: """ 统计知识层节点总数 @@ -323,7 +324,7 @@ class ForgettingScheduler: 统计 Statement、ExtractedEntity 和 MemorySummary 节点的总数。 Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） Returns: int: 知识层节点总数 @@ -333,16 +334,16 @@ class ForgettingScheduler: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ RETURN count(n) as total """ params = {} - if group_id: - params['group_id'] = group_id + if end_user_id: + end_user_id['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py index ccd8d2ca..a8c62dd4 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py @@ -13,6 +13,7 @@ Classes: import logging from typing import List, Dict, Any, Optional +from uuid import UUID from datetime import datetime, timedelta from app.repositories.neo4j.neo4j_connector import Neo4jConnector @@ -90,7 +91,7 @@ class ForgettingStrategy: async def find_forgettable_nodes( self, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, min_days_since_access: int = 30 ) -> List[Dict[str, Any]]: """ @@ -102,7 +103,7 @@ class ForgettingStrategy: 3. Statement 和 Entity 之间存在关系边 Args: - group_id: 组 ID（可选，用于过滤特定组的节点） + end_user_id: 组 ID（可选，用于过滤特定组的节点） min_days_since_access: 最小未访问天数（默认 30 天） Returns: @@ -136,8 +137,8 @@ class ForgettingStrategy: AND (e.entity_type IS NULL OR e.entity_type <> 'Person') """ - if group_id: - query += " AND s.group_id = $group_id AND e.group_id = $group_id" + if end_user_id: + query += " AND s.end_user_id = $end_user_id AND e.end_user_id = $end_user_id" query += """ RETURN s.id as statement_id, @@ -159,8 +160,8 @@ class ForgettingStrategy: 'threshold': self.forgetting_threshold, 'cutoff_time': cutoff_time_iso } - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) @@ -176,7 +177,7 @@ class ForgettingStrategy: self, statement_node: Dict[str, Any], entity_node: Dict[str, Any], - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> str: """ @@ -247,8 +248,8 @@ class ForgettingStrategy: entity_activation = entity_node['entity_activation'] entity_importance = entity_node['entity_importance'] - # 获取 group_id（从 statement 或 entity 节点） - group_id = statement_node.get('group_id') or entity_node.get('group_id') + # 获取 end_user_id（从 statement 或 entity 节点） + end_user_id = statement_node.get('end_user_id') or entity_node.get('end_user_id') # 生成摘要内容 summary_text = await self._generate_summary( @@ -325,7 +326,7 @@ class ForgettingStrategy: last_access_time: $current_time, access_count: 1, version: 1, - group_id: $group_id, + end_user_id: $end_user_id, created_at: datetime($current_time), merged_at: datetime($current_time) }) @@ -423,7 +424,7 @@ class ForgettingStrategy: 'inherited_activation': inherited_activation, 'inherited_importance': inherited_importance, 'current_time': current_time_iso, - 'group_id': group_id + 'end_user_id': end_user_id } try: @@ -462,7 +463,7 @@ class ForgettingStrategy: statement_text: str, entity_name: str, entity_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, db = None ) -> str: """ @@ -527,7 +528,7 @@ class ForgettingStrategy: statement_text, entity_name, entity_type ) - async def _get_llm_client(self, db, config_id: int): + async def _get_llm_client(self, db, config_id: UUID): """ 从数据库获取 LLM 客户端 @@ -539,11 +540,11 @@ class ForgettingStrategy: LLM 客户端实例，如果无法获取则返回 None """ try: - from app.repositories.data_config_repository import DataConfigRepository + from app.repositories.memory_config_repository import MemoryConfigRepository from app.core.memory.utils.llm.llm_utils import MemoryClientFactory # 从数据库读取配置 - repository = DataConfigRepository() + repository = MemoryConfigRepository() db_config = repository.get_by_id(db, config_id) if db_config is None or db_config.llm_id is None: diff --git a/api/app/core/memory/storage_services/search/__init__.py b/api/app/core/memory/storage_services/search/__init__.py index 2bec5bf1..c12c39b0 100644 --- a/api/app/core/memory/storage_services/search/__init__.py +++ b/api/app/core/memory/storage_services/search/__init__.py @@ -37,7 +37,7 @@ __all__ = [ async def run_hybrid_search( query_text: str, search_type: str = "hybrid", - group_id: str | None = None, + end_user_id: str | None = None, apply_id: str | None = None, user_id: str | None = None, limit: int = 50, @@ -54,7 +54,7 @@ async def run_hybrid_search( Args: query_text: 查询文本 search_type: 搜索类型（"hybrid", "keyword", "semantic"） - group_id: 组ID过滤 + end_user_id: 组ID过滤 apply_id: 应用ID过滤 user_id: 用户ID过滤 limit: 每个类别的最大结果数 @@ -104,7 +104,7 @@ async def run_hybrid_search( # 执行搜索 result = await strategy.search( query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include, alpha=alpha, diff --git a/api/app/core/memory/storage_services/search/hybrid_search.py b/api/app/core/memory/storage_services/search/hybrid_search.py index 43215df5..4111b09c 100644 --- a/api/app/core/memory/storage_services/search/hybrid_search.py +++ b/api/app/core/memory/storage_services/search/hybrid_search.py @@ -77,7 +77,7 @@ # async def search( # self, # query_text: str, -# group_id: Optional[str] = None, +# end_user_id: Optional[str] = None, # limit: int = 50, # include: Optional[List[str]] = None, # **kwargs @@ -86,7 +86,7 @@ # Args: # query_text: 查询文本 -# group_id: 可选的组ID过滤 +# end_user_id: 可选的组ID过滤 # limit: 每个类别的最大结果数 # include: 要包含的搜索类别列表 # **kwargs: 其他搜索参数（如alpha, use_forgetting_curve） @@ -94,7 +94,7 @@ # Returns: # SearchResult: 搜索结果对象 # """ -# logger.info(f"执行混合搜索: query='{query_text}', group_id={group_id}, limit={limit}") +# logger.info(f"执行混合搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # # 从kwargs中获取参数 # alpha = kwargs.get("alpha", self.alpha) @@ -107,14 +107,14 @@ # # 并行执行关键词搜索和语义搜索 # keyword_result = await self.keyword_strategy.search( # query_text=query_text, -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list # ) # semantic_result = await self.semantic_strategy.search( # query_text=query_text, -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list # ) @@ -139,7 +139,7 @@ # metadata = self._create_metadata( # query_text=query_text, # search_type="hybrid", -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # include=include_list, # alpha=alpha, @@ -165,7 +165,7 @@ # metadata=self._create_metadata( # query_text=query_text, # search_type="hybrid", -# group_id=group_id, +# end_user_id=end_user_id, # limit=limit, # error=str(e) # ) diff --git a/api/app/core/memory/storage_services/search/keyword_search.py b/api/app/core/memory/storage_services/search/keyword_search.py index 95dd0581..d2591945 100644 --- a/api/app/core/memory/storage_services/search/keyword_search.py +++ b/api/app/core/memory/storage_services/search/keyword_search.py @@ -44,7 +44,7 @@ class KeywordSearchStrategy(SearchStrategy): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -53,7 +53,7 @@ class KeywordSearchStrategy(SearchStrategy): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表 **kwargs: 其他搜索参数 @@ -61,7 +61,7 @@ class KeywordSearchStrategy(SearchStrategy): Returns: SearchResult: 搜索结果对象 """ - logger.info(f"执行关键词搜索: query='{query_text}', group_id={group_id}, limit={limit}") + logger.info(f"执行关键词搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # 获取有效的搜索类别 include_list = self._get_include_list(include) @@ -75,7 +75,7 @@ class KeywordSearchStrategy(SearchStrategy): results_dict = await search_graph( connector=self.connector, q=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -84,7 +84,7 @@ class KeywordSearchStrategy(SearchStrategy): metadata = self._create_metadata( query_text=query_text, search_type="keyword", - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -115,7 +115,7 @@ class KeywordSearchStrategy(SearchStrategy): metadata=self._create_metadata( query_text=query_text, search_type="keyword", - group_id=group_id, + end_user_id=end_user_id, limit=limit, error=str(e) ) diff --git a/api/app/core/memory/storage_services/search/search_strategy.py b/api/app/core/memory/storage_services/search/search_strategy.py index 27c02c89..3a670dd6 100644 --- a/api/app/core/memory/storage_services/search/search_strategy.py +++ b/api/app/core/memory/storage_services/search/search_strategy.py @@ -58,7 +58,7 @@ class SearchStrategy(ABC): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -67,7 +67,7 @@ class SearchStrategy(ABC): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表（statements, chunks, entities, summaries） **kwargs: 其他搜索参数 @@ -81,7 +81,7 @@ class SearchStrategy(ABC): self, query_text: str, search_type: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, **kwargs ) -> Dict[str, Any]: @@ -90,7 +90,7 @@ class SearchStrategy(ABC): Args: query_text: 查询文本 search_type: 搜索类型 - group_id: 组ID + end_user_id: 组ID limit: 结果限制 **kwargs: 其他元数据 @@ -100,7 +100,7 @@ class SearchStrategy(ABC): metadata = { "query": query_text, "search_type": search_type, - "group_id": group_id, + "end_user_id": end_user_id, "limit": limit, "timestamp": datetime.now().isoformat() } diff --git a/api/app/core/memory/storage_services/search/semantic_search.py b/api/app/core/memory/storage_services/search/semantic_search.py index b20f90a5..8d4eb05f 100644 --- a/api/app/core/memory/storage_services/search/semantic_search.py +++ b/api/app/core/memory/storage_services/search/semantic_search.py @@ -85,7 +85,7 @@ class SemanticSearchStrategy(SearchStrategy): async def search( self, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: Optional[List[str]] = None, **kwargs @@ -94,7 +94,7 @@ class SemanticSearchStrategy(SearchStrategy): Args: query_text: 查询文本 - group_id: 可选的组ID过滤 + end_user_id: 可选的组ID过滤 limit: 每个类别的最大结果数 include: 要包含的搜索类别列表 **kwargs: 其他搜索参数 @@ -102,7 +102,7 @@ class SemanticSearchStrategy(SearchStrategy): Returns: SearchResult: 搜索结果对象 """ - logger.info(f"执行语义搜索: query='{query_text}', group_id={group_id}, limit={limit}") + logger.info(f"执行语义搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}") # 获取有效的搜索类别 include_list = self._get_include_list(include) @@ -119,7 +119,7 @@ class SemanticSearchStrategy(SearchStrategy): connector=self.connector, embedder_client=self.embedder_client, query_text=query_text, - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -128,7 +128,7 @@ class SemanticSearchStrategy(SearchStrategy): metadata = self._create_metadata( query_text=query_text, search_type="semantic", - group_id=group_id, + end_user_id=end_user_id, limit=limit, include=include_list ) @@ -159,7 +159,7 @@ class SemanticSearchStrategy(SearchStrategy): metadata=self._create_metadata( query_text=query_text, search_type="semantic", - group_id=group_id, + end_user_id=end_user_id, limit=limit, error=str(e) ) diff --git a/api/app/core/memory/utils/config/get_data.py b/api/app/core/memory/utils/config/get_data.py index 1de6f6aa..e37ad723 100644 --- a/api/app/core/memory/utils/config/get_data.py +++ b/api/app/core/memory/utils/config/get_data.py @@ -23,7 +23,7 @@ async def _load_(data: List[Any]) -> List[Dict]: target_keys = [ "id", "statement", - "group_id", + "end_user_id", "chunk_id", "created_at", "expired_at", @@ -75,7 +75,7 @@ async def get_data(result): """ EXCLUDE_FIELDS = { "user_id", - "group_id", + "end_user_id", "entity_type", "connect_strength", "relationship_type", diff --git a/api/app/core/memory/utils/log/audit_logger.py b/api/app/core/memory/utils/log/audit_logger.py index 9010aad5..f80ad4d5 100644 --- a/api/app/core/memory/utils/log/audit_logger.py +++ b/api/app/core/memory/utils/log/audit_logger.py @@ -62,7 +62,7 @@ class ConfigAuditLogger: self, config_id: str, user_id: Optional[str] = None, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, success: bool = True, details: Optional[Dict[str, Any]] = None ): @@ -72,14 +72,14 @@ class ConfigAuditLogger: Args: config_id: 配置 ID user_id: 用户 ID（可选） - group_id: 组 ID（可选） + end_user_id: 组 ID（可选） success: 是否成功 details: 详细信息（可选） """ result = "SUCCESS" if success else "FAILED" msg = ( f"CONFIG_LOAD config_id={config_id} " - f"user={user_id or 'N/A'} group={group_id or 'N/A'} " + f"user={user_id or 'N/A'} group={end_user_id or 'N/A'} " f"result={result}" ) if details: @@ -121,7 +121,7 @@ class ConfigAuditLogger: self, operation: str, config_id: str, - group_id: str, + end_user_id: str, success: bool = True, duration: Optional[float] = None, error: Optional[str] = None, @@ -133,7 +133,7 @@ class ConfigAuditLogger: Args: operation: 操作类型（WRITE, READ 等） config_id: 配置 ID - group_id: 组 ID + end_user_id: 组 ID success: 是否成功 duration: 操作耗时（秒） error: 错误信息（可选） @@ -142,7 +142,7 @@ class ConfigAuditLogger: result = "SUCCESS" if success else "FAILED" msg = ( f"{operation.upper()} config_id={config_id} " - f"group={group_id} result={result}" + f"group={end_user_id} result={result}" ) if duration is not None: msg += f" duration={duration:.2f}s" diff --git a/api/app/core/rag/vdb/field.py b/api/app/core/rag/vdb/field.py index 86d39060..99d872c2 100644 --- a/api/app/core/rag/vdb/field.py +++ b/api/app/core/rag/vdb/field.py @@ -4,7 +4,7 @@ from enum import StrEnum, auto class Field(StrEnum): CONTENT_KEY = "page_content" METADATA_KEY = "metadata" - GROUP_KEY = "group_id" + GROUP_KEY = "end_user_id" VECTOR = auto() # Sparse Vector aims to support full text search SPARSE_VECTOR = auto() diff --git a/api/app/core/validators/memory_config_validators.py b/api/app/core/validators/memory_config_validators.py index 333572e6..ba26c5f2 100644 --- a/api/app/core/validators/memory_config_validators.py +++ b/api/app/core/validators/memory_config_validators.py @@ -26,7 +26,7 @@ logger = get_config_logger() def _parse_model_id(model_id: Union[str, UUID, None], model_type: str, - config_id: Optional[int] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]: + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]: """Parse model ID from string or UUID.""" if model_id is None: return None @@ -59,7 +59,7 @@ def validate_model_exists_and_active( model_type: str, db: Session, tenant_id: Optional[UUID] = None, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None ) -> tuple[str, bool]: """Validate that a model exists and is active. @@ -166,7 +166,7 @@ def validate_and_resolve_model_id( db: Session, tenant_id: Optional[UUID] = None, required: bool = False, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None ) -> tuple[Optional[UUID], Optional[str]]: """Validate and resolve a model ID, checking existence and active status. @@ -204,7 +204,7 @@ def validate_and_resolve_model_id( def validate_embedding_model( - config_id: int, + config_id: UUID, embedding_id: Union[str, UUID, None], db: Session, tenant_id: Optional[UUID] = None, @@ -256,7 +256,7 @@ def validate_embedding_model( def validate_llm_model( - config_id: int, + config_id: UUID, llm_id: Union[str, UUID, None], db: Session, tenant_id: Optional[UUID] = None, diff --git a/api/app/core/workflow/nodes/memory/config.py b/api/app/core/workflow/nodes/memory/config.py index 987230c1..4c8c43eb 100644 --- a/api/app/core/workflow/nodes/memory/config.py +++ b/api/app/core/workflow/nodes/memory/config.py @@ -1,4 +1,5 @@ import uuid +from uuid import UUID from pydantic import Field from typing import Literal @@ -11,7 +12,7 @@ class MemoryReadNodeConfig(BaseNodeConfig): ... ) - config_id: int = Field( + config_id: UUID = Field( ... ) @@ -26,6 +27,6 @@ class MemoryWriteNodeConfig(BaseNodeConfig): ... ) - config_id: int = Field( + config_id: UUID = Field( ... ) diff --git a/api/app/core/workflow/nodes/memory/node.py b/api/app/core/workflow/nodes/memory/node.py index 08a2b280..0589cc82 100644 --- a/api/app/core/workflow/nodes/memory/node.py +++ b/api/app/core/workflow/nodes/memory/node.py @@ -22,7 +22,7 @@ class MemoryReadNode(BaseNode): raise RuntimeError("End user id is required") return await MemoryAgentService().read_memory( - group_id=end_user_id, + end_user_id=end_user_id, message=self._render_template(self.typed_config.message, state), config_id=str(self.typed_config.config_id), search_switch=self.typed_config.search_switch, diff --git a/api/app/models/__init__.py b/api/app/models/__init__.py index bf3a1b3d..e069b40d 100644 --- a/api/app/models/__init__.py +++ b/api/app/models/__init__.py @@ -18,7 +18,7 @@ from .appshare_model import AppShare from .release_share_model import ReleaseShare from .conversation_model import Conversation, Message from .api_key_model import ApiKey, ApiKeyLog, ApiKeyType -from .data_config_model import DataConfig +from .memory_config_model import MemoryConfig from .multi_agent_model import MultiAgentConfig, AgentInvocation from .workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution from .retrieval_info import RetrievalInfo @@ -57,7 +57,7 @@ __all__ = [ "ApiKey", "ApiKeyLog", "ApiKeyType", - "DataConfig", + "MemoryConfig", "MultiAgentConfig", "AgentInvocation", "WorkflowConfig", diff --git a/api/app/models/data_config_model.py b/api/app/models/data_config_model.py deleted file mode 100644 index 06f87cb2..00000000 --- a/api/app/models/data_config_model.py +++ /dev/null @@ -1,88 +0,0 @@ -import datetime -from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float -from sqlalchemy.dialects.postgresql import UUID -from app.db import Base - - -class DataConfig(Base): - """数据配置表 - 用于存储记忆系统的配置参数""" - __tablename__ = "data_config" - - # 主键 - config_id = Column(Integer, primary_key=True, autoincrement=True, comment="配置ID") - - # 基本信息 - config_name = Column(String, nullable=False, comment="配置名称") - config_desc = Column(String, nullable=True, comment="配置描述") - - # 组织信息 - workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") - group_id = Column(String, nullable=True, comment="组ID") - user_id = Column(String, nullable=True, comment="用户ID") - apply_id = Column(String, nullable=True, comment="应用ID") - - # 模型选择（从workspace继承） - llm_id = Column(String, nullable=True, comment="LLM模型配置ID") - embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID") - rerank_id = Column(String, nullable=True, comment="重排序模型配置ID") - - # 记忆萃取引擎配置 - enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重") - enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧") - deep_retrieval = Column(Boolean, default=True, comment="深度检索开关") - - # 阈值配置 (0-1 之间的浮点数) - t_type_strict = Column(Float, default=0.8, comment="类型严格阈值") - t_name_strict = Column(Float, default=0.8, comment="名称严格阈值") - t_overall = Column(Float, default=0.8, comment="综合阈值") - - # 状态配置 - state = Column(Boolean, default=False, comment="配置使用状态") - - # 分块策略 - chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略") - - # 剪枝配置 - pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝") - pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound") - pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）") - - # 自我反思配置 - enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思") - iteration_period = Column(String, default="3", comment="反思迭代周期") - reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部") - baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实") - reflection_model_id = Column(String, nullable=True, comment="反思模型ID") - memory_verify = Column(Boolean, default=True, comment="记忆验证") - quality_assessment = Column(Boolean, default=True, comment="质量评估") - - # 遗忘引擎配置 - statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3") - include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文") - max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量") - lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数") - lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数") - offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数") - - # ACT-R 遗忘引擎配置 - decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5") - forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3") - forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24") - enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True") - max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100") - max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100") - min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30") - - # 情绪引擎配置 - emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取") - emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID") - emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词") - emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值") - emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类") - - # 时间戳 - created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") - updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") - - def __repr__(self): - return f"" diff --git a/api/app/models/memory_config_model.py b/api/app/models/memory_config_model.py index d47c3b52..b468e2a2 100644 --- a/api/app/models/memory_config_model.py +++ b/api/app/models/memory_config_model.py @@ -1,39 +1,88 @@ -# -*- coding: utf-8 -*- -"""Memory Configuration Model - Backward Compatibility +import datetime +from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float +from sqlalchemy.dialects.postgresql import UUID +from app.db import Base -This module provides backward compatibility for imports. -All classes have been moved to app.schemas.memory_config_schema. -DEPRECATED: Import from app.schemas.memory_config_schema instead. -""" +class MemoryConfig(Base): + """记忆配置表 - 用于存储记忆系统的配置参数""" + __tablename__ = "memory_config" -# Re-export for backward compatibility -from app.schemas.memory_config_schema import ( - ConfigurationError, - InvalidConfigError, - MemoryConfig, - MemoryConfigValidation, - ModelInactiveError, - ModelNotFoundError, - ModelValidation, - WorkspaceNotFoundError, - WorkspaceValidation, - validate_memory_config_data, - validate_model_data, - validate_workspace_data, -) + # 主键 + config_id = Column(UUID(as_uuid=True), primary_key=True, comment="配置ID") -__all__ = [ - "ConfigurationError", - "InvalidConfigError", - "MemoryConfig", - "MemoryConfigValidation", - "ModelInactiveError", - "ModelNotFoundError", - "ModelValidation", - "WorkspaceNotFoundError", - "WorkspaceValidation", - "validate_memory_config_data", - "validate_model_data", - "validate_workspace_data", -] + # 基本信息 + config_name = Column(String, nullable=False, comment="配置名称") + config_desc = Column(String, nullable=True, comment="配置描述") + + # 组织信息 + workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID") + end_user_id = Column(String, nullable=True, comment="组ID") + user_id = Column(String, nullable=True, comment="用户ID") + apply_id = Column(String, nullable=True, comment="应用ID") + + # 模型选择（从workspace继承） + llm_id = Column(String, nullable=True, comment="LLM模型配置ID") + embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID") + rerank_id = Column(String, nullable=True, comment="重排序模型配置ID") + + # 记忆萃取引擎配置 + enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重") + enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧") + deep_retrieval = Column(Boolean, default=True, comment="深度检索开关") + + # 阈值配置 (0-1 之间的浮点数) + t_type_strict = Column(Float, default=0.8, comment="类型严格阈值") + t_name_strict = Column(Float, default=0.8, comment="名称严格阈值") + t_overall = Column(Float, default=0.8, comment="综合阈值") + + # 状态配置 + state = Column(Boolean, default=False, comment="配置使用状态") + + # 分块策略 + chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略") + + # 剪枝配置 + pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝") + pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound") + pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）") + + # 自我反思配置 + enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思") + iteration_period = Column(String, default="3", comment="反思迭代周期") + reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部") + baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实") + reflection_model_id = Column(String, nullable=True, comment="反思模型ID") + memory_verify = Column(Boolean, default=True, comment="记忆验证") + quality_assessment = Column(Boolean, default=True, comment="质量评估") + + # 遗忘引擎配置 + statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3") + include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文") + max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量") + lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数") + lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数") + offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数") + + # ACT-R 遗忘引擎配置 + decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5") + forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3") + forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24") + enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True") + max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100") + max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100") + min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30") + + # 情绪引擎配置 + emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取") + emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID") + emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词") + emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值") + emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类") + + # 时间戳 + created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间") + updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间") + + def __repr__(self): + return f"" diff --git a/api/app/models/memory_perceptual_model.py b/api/app/models/memory_perceptual_model.py index 59eb0222..cafb18d4 100644 --- a/api/app/models/memory_perceptual_model.py +++ b/api/app/models/memory_perceptual_model.py @@ -16,7 +16,7 @@ class PerceptualType(IntEnum): CONVERSATION = 4 -class FileStorageType(IntEnum): +class FileStorageService(IntEnum): LOCAL = 1 REMOTE = 2 diff --git a/api/app/repositories/data_config_repository.py b/api/app/repositories/memory_config_repository.py similarity index 73% rename from api/app/repositories/data_config_repository.py rename to api/app/repositories/memory_config_repository.py index 3df7f800..12e564e2 100644 --- a/api/app/repositories/data_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -1,18 +1,19 @@ # -*- coding: utf-8 -*- -"""数据配置Repository模块 +"""记忆配置Repository模块 -本模块提供data_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。 +本模块提供memory_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。 包括CRUD操作和Neo4j Cypher查询常量。 Classes: - DataConfigRepository: 数据配置仓储类，提供CRUD操作 + MemoryConfigRepository: 记忆配置仓储类，提供CRUD操作 """ import uuid +from uuid import UUID from typing import Dict, List, Optional, Tuple from app.core.exceptions import BusinessException from app.core.logging_config import get_config_logger, get_db_logger -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig from app.schemas.memory_storage_schema import ( ConfigKey, ConfigParamsCreate, @@ -28,11 +29,11 @@ db_logger = get_db_logger() # 获取配置专用日志器 config_logger = get_config_logger() -TABLE_NAME = "data_config" -class DataConfigRepository: - """数据配置Repository +TABLE_NAME = "memory_config" +class MemoryConfigRepository: + """记忆配置Repository - 提供data_config表的数据访问方法，包括： + 提供memory_config表的数据访问方法，包括： - SQLAlchemy ORM 数据库操作 - Neo4j Cypher查询常量 """ @@ -41,48 +42,48 @@ class DataConfigRepository: # Dialogue count by group SEARCH_FOR_DIALOGUE = """ - MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # Chunk count by group SEARCH_FOR_CHUNK = """ - MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # Statement count by group SEARCH_FOR_STATEMENT = """ - MATCH (n:Statement) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # ExtractedEntity count by group SEARCH_FOR_ENTITY = """ - MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN COUNT(n) AS num + MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num """ # All counts by label and total SEARCH_FOR_ALL = """ - OPTIONAL MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN 'Chunk' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN 'Chunk' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:Statement) WHERE n.group_id = $group_id RETURN 'Statement' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN 'Statement' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count UNION ALL - OPTIONAL MATCH (n) WHERE n.group_id = $group_id RETURN 'ALL' AS Label, COUNT(n) AS Count + OPTIONAL MATCH (n) WHERE n.end_user_id = $end_user_id RETURN 'ALL' AS Label, COUNT(n) AS Count """ # Extracted entity details within group/app/user SEARCH_FOR_DETIALS = """ MATCH (n:ExtractedEntity) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN n.entity_idx AS entity_idx, n.connect_strength AS connect_strength, n.description AS description, n.entity_type AS entity_type, n.name AS name, COALESCE(n.fact_summary, '') AS fact_summary, - n.group_id AS group_id, + n.end_user_id AS end_user_id, n.apply_id AS apply_id, n.user_id AS user_id, n.id AS id @@ -91,9 +92,9 @@ class DataConfigRepository: # Edges between extracted entities within group/app/user SEARCH_FOR_EDGES = """ MATCH (n:ExtractedEntity)-[r]->(m:ExtractedEntity) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN - r.group_id AS group_id, + r.end_user_id AS end_user_id, r.apply_id AS apply_id, r.user_id AS user_id, elementId(r) AS rel_id, @@ -107,7 +108,7 @@ class DataConfigRepository: @staticmethod def update_reflection_config( db: Session, - config_id: int, + config_id: uuid.UUID, enable_self_reflexion: bool, iteration_period: str, reflexion_range: str, @@ -115,7 +116,7 @@ class DataConfigRepository: reflection_model_id: str, memory_verify: bool, quality_assessment: bool - ) -> DataConfig: + ) -> MemoryConfig: """构建反思配置更新语句（SQLAlchemy text() 命名参数） Args: @@ -130,28 +131,28 @@ class DataConfigRepository: config_id: 配置ID Returns: - Data + MemoryConfig Raises: ValueError: 没有字段需要更新时抛出 """ db_logger.debug(f"构建反思配置更新语句: config_id={config_id}") - stmt = select(DataConfig).where(DataConfig.config_id == config_id) - data_config_obj = db.scalars(stmt).first() - if not data_config_obj: + stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id) + memory_config_obj = db.scalars(stmt).first() + if not memory_config_obj: raise BusinessException - data_config_obj.enable_self_reflexion = enable_self_reflexion - data_config_obj.iteration_period = iteration_period - data_config_obj.reflexion_range = reflexion_range - data_config_obj.baseline = baseline - data_config_obj.reflection_model_id = reflection_model_id - data_config_obj.memory_verify = memory_verify - data_config_obj.quality_assessment = quality_assessment + memory_config_obj.enable_self_reflexion = enable_self_reflexion + memory_config_obj.iteration_period = iteration_period + memory_config_obj.reflexion_range = reflexion_range + memory_config_obj.baseline = baseline + memory_config_obj.reflection_model_id = reflection_model_id + memory_config_obj.memory_verify = memory_verify + memory_config_obj.quality_assessment = quality_assessment - return data_config_obj + return memory_config_obj @staticmethod - def query_reflection_config_by_id(db: Session, config_id: int) -> DataConfig: + def query_reflection_config_by_id(db: Session, config_id: uuid.UUID) -> MemoryConfig: """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数） Args: @@ -162,13 +163,13 @@ class DataConfigRepository: Tuple[str, Dict]: (SQL查询字符串, 参数字典) """ db_logger.debug(f"构建反思配置查询语句: config_id={config_id}") - stmt = select(DataConfig).where(DataConfig.config_id == config_id) - data_config = db.scalars(stmt).first() - if not data_config: + stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id) + memory_config = db.scalars(stmt).first() + if not memory_config: raise RuntimeError("reflection config not found") - return data_config + return memory_config @staticmethod - def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> DataConfig: + def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> MemoryConfig: """构建查询所有配置的语句（SQLAlchemy text() 命名参数） Args: @@ -180,11 +181,11 @@ class DataConfigRepository: """ db_logger.debug(f"构建查询所有配置语句: workspace_id={workspace_id}") - stmt = select(DataConfig).where(DataConfig.workspace_id == workspace_id) - data_config = db.scalars(stmt).first() - if not data_config: + stmt = select(MemoryConfig).where(MemoryConfig.workspace_id == workspace_id) + memory_config = db.scalars(stmt).first() + if not memory_config: raise RuntimeError("reflection config not found") - return data_config + return memory_config @staticmethod @@ -208,20 +209,21 @@ class DataConfigRepository: return query, params @staticmethod - def create(db: Session, params: ConfigParamsCreate) -> DataConfig: - """创建数据配置 + def create(db: Session, params: ConfigParamsCreate) -> MemoryConfig: + """创建记忆配置 Args: db: 数据库会话 params: 配置参数创建模型 Returns: - DataConfig: 创建的配置对象 + MemoryConfig: 创建的配置对象 """ - db_logger.debug(f"创建数据配置: config_name={params.config_name}, workspace_id={params.workspace_id}") + db_logger.debug(f"创建记忆配置: config_name={params.config_name}, workspace_id={params.workspace_id}") try: - db_config = DataConfig( + db_config = MemoryConfig( + config_id=uuid.uuid4(), config_name=params.config_name, config_desc=params.config_desc, workspace_id=params.workspace_id, @@ -232,16 +234,16 @@ class DataConfigRepository: db.add(db_config) db.flush() # 获取自增ID但不提交事务 - db_logger.info(f"数据配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})") + db_logger.info(f"记忆配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})") return db_config except Exception as e: db.rollback() - db_logger.error(f"创建数据配置失败: {params.config_name} - {str(e)}") + db_logger.error(f"创建记忆配置失败: {params.config_name} - {str(e)}") raise @staticmethod - def update(db: Session, update: ConfigUpdate) -> Optional[DataConfig]: + def update(db: Session, update: ConfigUpdate) -> Optional[MemoryConfig]: """更新基础配置 Args: @@ -249,17 +251,17 @@ class DataConfigRepository: update: 配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 """ - db_logger.debug(f"更新数据配置: config_id={update.config_id}") + db_logger.debug(f"更新记忆配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段 @@ -277,17 +279,17 @@ class DataConfigRepository: db.commit() db.refresh(db_config) - db_logger.info(f"数据配置更新成功: {db_config.config_name} (ID: {update.config_id})") + db_logger.info(f"记忆配置更新成功: {db_config.config_name} (ID: {update.config_id})") return db_config except Exception as e: db.rollback() - db_logger.error(f"更新数据配置失败: config_id={update.config_id} - {str(e)}") + db_logger.error(f"更新记忆配置失败: config_id={update.config_id} - {str(e)}") raise @staticmethod - def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[DataConfig]: + def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[MemoryConfig]: """更新记忆萃取引擎配置 Args: @@ -295,7 +297,7 @@ class DataConfigRepository: update: 萃取配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 @@ -303,9 +305,9 @@ class DataConfigRepository: db_logger.debug(f"更新萃取配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段映射 @@ -360,7 +362,7 @@ class DataConfigRepository: raise @staticmethod - def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[DataConfig]: + def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[MemoryConfig]: """更新遗忘引擎配置 Args: @@ -368,7 +370,7 @@ class DataConfigRepository: update: 遗忘配置更新模型 Returns: - Optional[DataConfig]: 更新后的配置对象，不存在则返回None + Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None Raises: ValueError: 没有字段需要更新时抛出 @@ -376,9 +378,9 @@ class DataConfigRepository: db_logger.debug(f"更新遗忘配置: config_id={update.config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={update.config_id}") + db_logger.warning(f"记忆配置不存在: config_id={update.config_id}") return None # 更新字段 @@ -408,7 +410,7 @@ class DataConfigRepository: raise @staticmethod - def get_extracted_config(db: Session, config_id: int) -> Optional[Dict]: + def get_extracted_config(db: Session, config_id: UUID) -> Optional[Dict]: """获取萃取配置，通过主键查询某条配置 Args: @@ -421,7 +423,7 @@ class DataConfigRepository: db_logger.debug(f"查询萃取配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: db_logger.debug(f"萃取配置不存在: config_id={config_id}") return None @@ -457,7 +459,7 @@ class DataConfigRepository: raise @staticmethod - def get_forget_config(db: Session, config_id: int) -> Optional[Dict]: + def get_forget_config(db: Session, config_id: UUID) -> Optional[Dict]: """获取遗忘配置，通过主键查询某条配置 Args: @@ -470,7 +472,7 @@ class DataConfigRepository: db_logger.debug(f"查询遗忘配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: db_logger.debug(f"遗忘配置不存在: config_id={config_id}") return None @@ -489,39 +491,39 @@ class DataConfigRepository: raise @staticmethod - def get_by_id(db: Session, config_id: int) -> Optional[DataConfig]: - """根据ID获取数据配置 + def get_by_id(db: Session, config_id: uuid.UUID) -> Optional[MemoryConfig]: + """根据ID获取记忆配置 Args: db: 数据库会话 config_id: 配置ID Returns: - Optional[DataConfig]: 配置对象，不存在则返回None + Optional[MemoryConfig]: 配置对象，不存在则返回None """ - db_logger.debug(f"根据ID查询数据配置: config_id={config_id}") + db_logger.debug(f"根据ID查询记忆配置: config_id={config_id}") try: - config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if config: - db_logger.debug(f"数据配置查询成功: {config.config_name} (ID: {config_id})") + db_logger.debug(f"记忆配置查询成功: {config.config_name} (ID: {config_id})") else: - db_logger.debug(f"数据配置不存在: config_id={config_id}") + db_logger.debug(f"记忆配置不存在: config_id={config_id}") return config except Exception as e: - db_logger.error(f"根据ID查询数据配置失败: config_id={config_id} - {str(e)}") + db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}") raise @staticmethod - def get_config_with_workspace(db: Session, config_id: int) -> Optional[tuple]: - """Get data config and its associated workspace information + def get_config_with_workspace(db: Session, config_id: uuid.UUID) -> Optional[tuple]: + """Get memory config and its associated workspace information Args: db: Database session config_id: Configuration ID Returns: - Optional[tuple]: (DataConfig, Workspace) tuple, None if not found + Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found Raises: ValueError: Raised when config exists but workspace doesn't @@ -541,19 +543,19 @@ class DataConfigRepository: } ) - db_logger.debug(f"Querying data config and workspace: config_id={config_id}") + db_logger.debug(f"Querying memory config and workspace: config_id={config_id}") try: # Use join query to get both config and workspace - result = db.query(DataConfig, Workspace).join( - Workspace, DataConfig.workspace_id == Workspace.id - ).filter(DataConfig.config_id == config_id).first() + result = db.query(MemoryConfig, Workspace).join( + Workspace, MemoryConfig.workspace_id == Workspace.id + ).filter(MemoryConfig.config_id == config_id).first() elapsed_ms = (time.time() - start_time) * 1000 if not result: # Check if config exists but workspace is missing - config_only = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + config_only = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if config_only: if config_only.workspace_id is None: config_logger.error( @@ -566,7 +568,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Data config {config_id} has no associated workspace ID") + db_logger.error(f"Memory config {config_id} has no associated workspace ID") raise ValueError(f"Configuration {config_id} has no associated workspace") else: config_logger.error( @@ -579,7 +581,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.error(f"Data config {config_id} references non-existent workspace {config_only.workspace_id}") + db_logger.error(f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}") raise ValueError(f"Workspace {config_only.workspace_id} not found for configuration {config_id}") config_logger.debug( @@ -591,7 +593,7 @@ class DataConfigRepository: "elapsed_ms": elapsed_ms } ) - db_logger.debug(f"Data config not found: config_id={config_id}") + db_logger.debug(f"Memory config not found: config_id={config_id}") return None config, workspace = result @@ -611,7 +613,7 @@ class DataConfigRepository: } ) - db_logger.debug(f"Data config and workspace query successful: config={config.config_name}, workspace={workspace.name}") + db_logger.debug(f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}") return (config, workspace) except ValueError: @@ -633,10 +635,10 @@ class DataConfigRepository: exc_info=True ) - db_logger.error(f"Failed to query data config and workspace: config_id={config_id} - {str(e)}") + db_logger.error(f"Failed to query memory config and workspace: config_id={config_id} - {str(e)}") raise @staticmethod - def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[DataConfig]: + def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[MemoryConfig]: """获取所有配置参数 Args: @@ -644,17 +646,17 @@ class DataConfigRepository: workspace_id: 工作空间ID，用于过滤查询结果 Returns: - List[DataConfig]: 配置列表 + List[MemoryConfig]: 配置列表 """ db_logger.debug(f"查询所有配置: workspace_id={workspace_id}") try: - query = db.query(DataConfig) + query = db.query(MemoryConfig) if workspace_id: - query = query.filter(DataConfig.workspace_id == workspace_id) + query = query.filter(MemoryConfig.workspace_id == workspace_id) - configs = query.order_by(desc(DataConfig.updated_at)).all() + configs = query.order_by(desc(MemoryConfig.updated_at)).all() db_logger.debug(f"配置列表查询成功: 数量={len(configs)}") return configs @@ -664,8 +666,8 @@ class DataConfigRepository: raise @staticmethod - def delete(db: Session, config_id: int) -> bool: - """删除数据配置 + def delete(db: Session, config_id: uuid.UUID) -> bool: + """删除记忆配置 Args: db: 数据库会话 @@ -674,22 +676,22 @@ class DataConfigRepository: Returns: bool: 删除成功返回True，配置不存在返回False """ - db_logger.debug(f"删除数据配置: config_id={config_id}") + db_logger.debug(f"删除记忆配置: config_id={config_id}") try: - db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first() + db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: - db_logger.warning(f"数据配置不存在: config_id={config_id}") + db_logger.warning(f"记忆配置不存在: config_id={config_id}") return False db.delete(db_config) db.commit() - db_logger.info(f"数据配置删除成功: config_id={config_id}") + db_logger.info(f"记忆配置删除成功: config_id={config_id}") return True except Exception as e: db.rollback() - db_logger.error(f"删除数据配置失败: config_id={config_id} - {str(e)}") + db_logger.error(f"删除记忆配置失败: config_id={config_id} - {str(e)}") raise diff --git a/api/app/repositories/memory_perceptual_repository.py b/api/app/repositories/memory_perceptual_repository.py index 8415c2d0..9fa9536e 100644 --- a/api/app/repositories/memory_perceptual_repository.py +++ b/api/app/repositories/memory_perceptual_repository.py @@ -6,7 +6,7 @@ from sqlalchemy import and_, desc from sqlalchemy.orm import Session from app.core.logging_config import get_db_logger -from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageType +from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageService from app.schemas.memory_perceptual_schema import PerceptualQuerySchema db_logger = get_db_logger() @@ -28,7 +28,7 @@ class MemoryPerceptualRepository: file_ext: str, summary: Optional[str] = None, meta_data: Optional[dict] = None, - storage_service: FileStorageType = FileStorageType.LOCAL + storage_service: FileStorageService = FileStorageService.LOCAL ) -> MemoryPerceptualModel: diff --git a/api/app/repositories/neo4j/add_edges.py b/api/app/repositories/neo4j/add_edges.py index 3b45867e..162bf411 100644 --- a/api/app/repositories/neo4j/add_edges.py +++ b/api/app/repositories/neo4j/add_edges.py @@ -32,7 +32,7 @@ async def add_chunk_statement_edges(chunks: List[Chunk], connector: Neo4jConnect "id": stable_edge_id, "source": chunk.id, "target": stmt.id, - "group_id": getattr(stmt, 'group_id', None), + "end_user_id": getattr(stmt, 'end_user_id', None), "user_id":getattr(stmt, 'user_id', None), "apply_id": getattr(stmt, 'apply_id', None), "run_id": getattr(stmt, 'run_id', None) or getattr(chunk, 'run_id', None), @@ -83,7 +83,7 @@ async def add_memory_summary_statement_edges(summaries: List[MemorySummaryNode], edges.append({ "summary_id": s.id, "chunk_id": chunk_id, - "group_id": s.group_id, + "end_user_id": s.end_user_id, "run_id": s.run_id, "created_at": s.created_at.isoformat() if s.created_at else None, "expired_at": s.expired_at.isoformat() if s.expired_at else None, diff --git a/api/app/repositories/neo4j/add_nodes.py b/api/app/repositories/neo4j/add_nodes.py index cf60a773..fcf700b5 100644 --- a/api/app/repositories/neo4j/add_nodes.py +++ b/api/app/repositories/neo4j/add_nodes.py @@ -6,10 +6,10 @@ from app.core.memory.models.graph_models import DialogueNode, StatementNode, Chu from app.repositories.neo4j.neo4j_connector import Neo4jConnector -async def delete_all_nodes(group_id: str, connector: Neo4jConnector): +async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector): """Delete all nodes in the database.""" - result = await connector.execute_query(f"MATCH (n {{group_id: '{group_id}'}}) DETACH DELETE n") - print(f"All group_id: {group_id} node and edge deleted successfully") + result = await connector.execute_query(f"MATCH (n {{end_user_id: '{end_user_id}'}}) DETACH DELETE n") + print(f"All end_user_id: {end_user_id} node and edge deleted successfully") return result async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConnector) -> Optional[List[str]]: @@ -32,9 +32,7 @@ async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConn for dialogue in dialogues: flattened_dialogues.append({ "id": dialogue.id, - "group_id": dialogue.group_id, - "user_id": dialogue.user_id, - "apply_id": dialogue.apply_id, + "end_user_id": dialogue.end_user_id, "run_id": dialogue.run_id, "ref_id": dialogue.ref_id, "name": dialogue.name, @@ -79,9 +77,7 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC flattened_statement = { "id": statement.id, "name": statement.name, - "group_id": statement.group_id, - "user_id": statement.user_id, - "apply_id": statement.apply_id, + "end_user_id": statement.end_user_id, "run_id": statement.run_id, "chunk_id": statement.chunk_id, # "created_at": statement.created_at.isoformat(), @@ -154,9 +150,7 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) -> flattened_chunk = { "id": chunk.id, "name": chunk.name, - "group_id": chunk.group_id, - "user_id": chunk.user_id, - "apply_id": chunk.apply_id, + "end_user_id": chunk.end_user_id, "run_id": chunk.run_id, "created_at": chunk.created_at.isoformat() if chunk.created_at else None, "expired_at": chunk.expired_at.isoformat() if chunk.expired_at else None, @@ -206,9 +200,7 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector flattened.append({ "id": s.id, "name": s.name, - "group_id": s.group_id, - "user_id": s.user_id, - "apply_id": s.apply_id, + "end_user_id": s.end_user_id, "run_id": s.run_id, "created_at": s.created_at.isoformat() if s.created_at else None, "expired_at": s.expired_at.isoformat() if s.expired_at else None, diff --git a/api/app/repositories/neo4j/base_neo4j_repository.py b/api/app/repositories/neo4j/base_neo4j_repository.py index 959a1e68..df953eb9 100644 --- a/api/app/repositories/neo4j/base_neo4j_repository.py +++ b/api/app/repositories/neo4j/base_neo4j_repository.py @@ -152,7 +152,7 @@ class BaseNeo4jRepository(BaseRepository[T]): Example: >>> results = await repository.find( - ... {"group_id": "group_123", "user_id": "user_456"}, + ... {"end_user_id": "group_123", "user_id": "user_456"}, ... limit=50 ... ) """ diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py index cd3cbed7..c93e75b3 100644 --- a/api/app/repositories/neo4j/cypher_queries.py +++ b/api/app/repositories/neo4j/cypher_queries.py @@ -3,9 +3,7 @@ DIALOGUE_NODE_SAVE = """ UNWIND $dialogues AS dialogue MERGE (n:Dialogue {id: dialogue.id}) SET n.uuid = coalesce(n.uuid, dialogue.id), - n.group_id = dialogue.group_id, - n.user_id = dialogue.user_id, - n.apply_id = dialogue.apply_id, + n.end_user_id = dialogue.end_user_id, n.run_id = dialogue.run_id, n.ref_id = dialogue.ref_id, n.created_at = dialogue.created_at, @@ -22,9 +20,7 @@ SET s += { id: statement.id, run_id: statement.run_id, chunk_id: statement.chunk_id, - group_id: statement.group_id, - user_id: statement.user_id, - apply_id: statement.apply_id, + end_user_id: statement.end_user_id, stmt_type: statement.stmt_type, statement: statement.statement, emotion_intensity: statement.emotion_intensity, @@ -54,9 +50,7 @@ MERGE (c:Chunk {id: chunk.id}) SET c += { id: chunk.id, name: chunk.name, - group_id: chunk.group_id, - user_id: chunk.user_id, - apply_id: chunk.apply_id, + end_user_id: chunk.end_user_id, run_id: chunk.run_id, created_at: chunk.created_at, expired_at: chunk.expired_at, @@ -76,9 +70,7 @@ EXTRACTED_ENTITY_NODE_SAVE = """ UNWIND $entities AS entity MERGE (e:ExtractedEntity {id: entity.id}) SET e.name = CASE WHEN entity.name IS NOT NULL AND entity.name <> '' THEN entity.name ELSE e.name END, - e.group_id = CASE WHEN entity.group_id IS NOT NULL AND entity.group_id <> '' THEN entity.group_id ELSE e.group_id END, - e.user_id = CASE WHEN entity.user_id IS NOT NULL AND entity.user_id <> '' THEN entity.user_id ELSE e.user_id END, - e.apply_id = CASE WHEN entity.apply_id IS NOT NULL AND entity.apply_id <> '' THEN entity.apply_id ELSE e.apply_id END, + e.end_user_id = CASE WHEN entity.end_user_id IS NOT NULL AND entity.end_user_id <> '' THEN entity.end_user_id ELSE e.end_user_id END, e.run_id = CASE WHEN entity.run_id IS NOT NULL AND entity.run_id <> '' THEN entity.run_id ELSE e.run_id END, e.created_at = CASE WHEN entity.created_at IS NOT NULL AND (e.created_at IS NULL OR entity.created_at < e.created_at) @@ -134,9 +126,9 @@ RETURN e.id AS uuid # Add back ENTITY_RELATIONSHIP_SAVE to be used by graph_saver.save_entities_and_relationships ENTITY_RELATIONSHIP_SAVE = """ UNWIND $relationships AS rel -// Match entities by stable id within group, do not constrain by run_id -MATCH (subject:ExtractedEntity {id: rel.source_id, group_id: rel.group_id}) -MATCH (object:ExtractedEntity {id: rel.target_id, group_id: rel.group_id}) +// Match entities by stable id within end_user_id, do not constrain by run_id +MATCH (subject:ExtractedEntity {id: rel.source_id, end_user_id: rel.end_user_id}) +MATCH (object:ExtractedEntity {id: rel.target_id, end_user_id: rel.end_user_id}) // Avoid duplicate edges across runs for the same endpoints MERGE (subject)-[r:EXTRACTED_RELATIONSHIP]->(object) SET r.predicate = rel.predicate, @@ -148,7 +140,7 @@ SET r.predicate = rel.predicate, r.created_at = rel.created_at, r.expired_at = rel.expired_at, r.run_id = rel.run_id, - r.group_id = rel.group_id + r.end_user_id = rel.end_user_id RETURN elementId(r) AS uuid """ @@ -160,7 +152,7 @@ UNWIND $weak_entities AS entity MERGE (e:ExtractedEntity {id: entity.id, run_id: entity.run_id}) SET e += { name: entity.name, - group_id: entity.group_id, + end_user_id: entity.end_user_id, run_id: entity.run_id, description: entity.description, chunk_id: entity.chunk_id, @@ -175,11 +167,11 @@ RETURN e.id AS id SAVE_STRONG_TRIPLE_ENTITIES = """ UNWIND $items AS item MERGE (s:ExtractedEntity {id: item.source_id, run_id: item.run_id}) -SET s += {name: item.subject, group_id: item.group_id, run_id: item.run_id} +SET s += {name: item.subject, end_user_id: item.end_user_id, run_id: item.run_id} // Independent strong flag SET s.is_strong = true MERGE (o:ExtractedEntity {id: item.target_id, run_id: item.run_id}) -SET o += {name: item.object, group_id: item.group_id, run_id: item.run_id} +SET o += {name: item.object, end_user_id: item.end_user_id, run_id: item.run_id} // Independent strong flag SET o.is_strong = true """ @@ -194,7 +186,7 @@ DIALOGUE_STATEMENT_EDGE_SAVE = """ // 仅按端点去重，关系属性可更新 MERGE (dialogue)-[e:MENTIONS]->(statement) SET e.uuid = edge.id, - e.group_id = edge.group_id, + e.end_user_id = edge.end_user_id, e.created_at = edge.created_at, e.expired_at = edge.expired_at RETURN e.uuid AS uuid @@ -208,7 +200,7 @@ CHUNK_STATEMENT_EDGE_SAVE = """ MATCH (statement:Statement {id: edge.source, run_id: edge.run_id}) MATCH (chunk:Chunk {id: edge.target, run_id: edge.run_id}) MERGE (chunk)-[e:CONTAINS {id: edge.id}]->(statement) - SET e.group_id = edge.group_id, + SET e.end_user_id = edge.end_user_id, e.run_id = edge.run_id, e.created_at = edge.created_at, e.expired_at = edge.expired_at @@ -218,13 +210,12 @@ CHUNK_STATEMENT_EDGE_SAVE = """ STATEMENT_ENTITY_EDGE_SAVE = """ UNWIND $relationships AS rel // Statement nodes are per-run; keep run_id constraint on statements -// Statement nodes are per-run; keep run_id constraint on statements MATCH (statement:Statement {id: rel.source, run_id: rel.run_id}) -// Entities are shared across runs within a group; do not constrain by run_id -MATCH (entity:ExtractedEntity {id: rel.target, group_id: rel.group_id}) +// Entities are shared across runs within end_user_id; do not constrain by run_id +MATCH (entity:ExtractedEntity {id: rel.target, end_user_id: rel.end_user_id}) // Avoid duplicate edges across runs for same endpoints MERGE (statement)-[r:REFERENCES_ENTITY]->(entity) -SET r.group_id = rel.group_id, +SET r.end_user_id = rel.end_user_id, r.run_id = rel.run_id, r.created_at = rel.created_at, r.expired_at = rel.expired_at, @@ -236,10 +227,10 @@ ENTITY_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('entity_embedding_index', $limit * 100, $embedding) YIELD node AS e, score WHERE e.name_embedding IS NOT NULL - AND ($group_id IS NULL OR e.group_id = $group_id) + AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) RETURN e.id AS id, e.name AS name, - e.group_id AS group_id, + e.end_user_id AS end_user_id, e.entity_type AS entity_type, COALESCE(e.activation_value, e.importance_score, 0.5) AS activation_value, COALESCE(e.importance_score, 0.5) AS importance_score, @@ -254,10 +245,10 @@ STATEMENT_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('statement_embedding_index', $limit * 100, $embedding) YIELD node AS s, score WHERE s.statement_embedding IS NOT NULL - AND ($group_id IS NULL OR s.group_id = $group_id) + AND ($end_user_id IS NULL OR s.end_user_id = $end_user_id) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.expired_at AS expired_at, @@ -277,9 +268,9 @@ CHUNK_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('chunk_embedding_index', $limit * 100, $embedding) YIELD node AS c, score WHERE c.chunk_embedding IS NOT NULL - AND ($group_id IS NULL OR c.group_id = $group_id) + AND ($end_user_id IS NULL OR c.end_user_id = $end_user_id) RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, COALESCE(c.activation_value, 0.5) AS activation_value, @@ -292,12 +283,12 @@ LIMIT $limit SEARCH_STATEMENTS_BY_KEYWORD = """ CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score -WHERE ($group_id IS NULL OR s.group_id = $group_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.expired_at AS expired_at, @@ -316,15 +307,13 @@ LIMIT $limit # 查询实体名称包含指定字符串的实体 SEARCH_ENTITIES_BY_NAME = """ CALL db.index.fulltext.queryNodes("entitiesFulltext", $q) YIELD node AS e, score -WHERE ($group_id IS NULL OR e.group_id = $group_id) +WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e) OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) RETURN e.id AS id, e.name AS name, - e.group_id AS group_id, + e.end_user_id AS end_user_id, e.entity_type AS entity_type, - e.apply_id AS apply_id, - e.user_id AS user_id, e.created_at AS created_at, e.expired_at AS expired_at, e.entity_idx AS entity_idx, @@ -347,11 +336,11 @@ LIMIT $limit SEARCH_CHUNKS_BY_CONTENT = """ CALL db.index.fulltext.queryNodes("chunksFulltext", $q) YIELD node AS c, score -WHERE ($group_id IS NULL OR c.group_id = $group_id) +WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id) OPTIONAL MATCH (c)-[:CONTAINS]->(s:Statement) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, c.sequence_number AS sequence_number, @@ -413,10 +402,10 @@ LIMIT $limit SEARCH_DIALOGUE_BY_DIALOG_ID = """ MATCH (d:Dialogue) -WHERE ($group_id IS NULL OR d.group_id = $group_id) +WHERE ($end_user_id IS NULL OR d.end_user_id = $end_user_id) AND d.id = $dialog_id RETURN d.id AS dialog_id, - d.group_id AS group_id, + d.end_user_id AS end_user_id, d.content AS content, d.created_at AS created_at, d.expired_at AS expired_at @@ -426,10 +415,10 @@ LIMIT $limit SEARCH_CHUNK_BY_CHUNK_ID = """ MATCH (c:Chunk) -WHERE ($group_id IS NULL OR c.group_id = $group_id) +WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id) AND c.id = $chunk_id RETURN c.id AS chunk_id, - c.group_id AS group_id, + c.end_user_id AS end_user_id, c.content AS content, c.dialog_id AS dialog_id, c.created_at AS created_at, @@ -441,18 +430,14 @@ LIMIT $limit SEARCH_STATEMENTS_BY_TEMPORAL = """ MATCH (s:Statement) -WHERE ($group_id IS NULL OR s.group_id = $group_id) - AND ($apply_id IS NULL OR s.apply_id = $apply_id) - AND ($user_id IS NULL OR s.user_id = $user_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND ((($start_date IS NULL OR datetime(s.created_at) >= datetime($start_date)) AND ($end_date IS NULL OR datetime(s.created_at) <= datetime($end_date))) OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date))) AND ($invalid_date IS NULL OR (s.invalid_at IS NOT NULL AND datetime(s.invalid_at) <= datetime($invalid_date))))) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, - s.apply_id AS apply_id, - s.user_id AS user_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.valid_at AS valid_at, @@ -468,9 +453,7 @@ LIMIT $limit SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL = """ CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score -WHERE ($group_id IS NULL OR s.group_id = $group_id) - AND ($apply_id IS NULL OR s.apply_id = $apply_id) - AND ($user_id IS NULL OR s.user_id = $user_id) +WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND ((($start_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) >= datetime($start_date))) AND ($end_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) <= datetime($end_date)))) OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date))) @@ -479,9 +462,7 @@ OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s) OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity) RETURN s.id AS id, s.statement AS statement, - s.group_id AS group_id, - s.apply_id AS apply_id, - s.user_id AS user_id, + s.end_user_id AS end_user_id, s.chunk_id AS chunk_id, s.created_at AS created_at, s.valid_at AS valid_at, @@ -499,15 +480,11 @@ LIMIT $limit SEARCH_STATEMENTS_BY_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 10)) = date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -519,15 +496,11 @@ LIMIT $limit SEARCH_STATEMENTS_BY_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) = date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -539,15 +512,11 @@ LIMIT $limit SEARCH_STATEMENTS_G_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) = date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -559,15 +528,11 @@ LIMIT $limit SEARCH_STATEMENTS_L_CREATED_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) < date($created_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -579,15 +544,11 @@ LIMIT $limit SEARCH_STATEMENTS_G_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) > date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -599,15 +560,11 @@ LIMIT $limit SEARCH_STATEMENTS_L_VALID_AT = """ MATCH (n:Statement) -WHERE ($group_id IS NULL OR n.group_id = $group_id) - AND ($apply_id IS NULL OR n.apply_id = $apply_id) - AND ($user_id IS NULL OR n.user_id = $user_id) +WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id) AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) < date($valid_at)) RETURN n.id AS id, n.statement AS statement, - n.group_id AS group_id, - n.apply_id AS apply_id, - n.user_id AS user_id, + n.end_user_id AS end_user_id, n.chunk_id AS chunk_id, n.created_at AS created_at, n.valid_at AS valid_at, @@ -665,18 +622,18 @@ LIMIT $limit # 根据id修改句子的invalid_at的值 UPDATE_STATEMENT_INVALID_AT = """ -MATCH (n:Statement {group_id: $group_id, id: $id}) +MATCH (n:Statement {end_user_id: $end_user_id, id: $id}) SET n.invalid_at = $new_invalid_at """ # MemorySummary keyword search using fulltext index SEARCH_MEMORY_SUMMARIES_BY_KEYWORD = """ CALL db.index.fulltext.queryNodes("summariesFulltext", $q) YIELD node AS m, score -WHERE ($group_id IS NULL OR m.group_id = $group_id) +WHERE ($end_user_id IS NULL OR m.end_user_id = $end_user_id) OPTIONAL MATCH (m)-[:DERIVED_FROM_STATEMENT]->(s:Statement) RETURN m.id AS id, m.name AS name, - m.group_id AS group_id, + m.end_user_id AS end_user_id, m.dialog_id AS dialog_id, m.chunk_ids AS chunk_ids, m.content AS content, @@ -695,10 +652,10 @@ MEMORY_SUMMARY_EMBEDDING_SEARCH = """ CALL db.index.vector.queryNodes('summary_embedding_index', $limit * 100, $embedding) YIELD node AS m, score WHERE m.summary_embedding IS NOT NULL - AND ($group_id IS NULL OR m.group_id = $group_id) + AND ($end_user_id IS NULL OR m.end_user_id = $end_user_id) RETURN m.id AS id, m.name AS name, - m.group_id AS group_id, + m.end_user_id AS end_user_id, m.dialog_id AS dialog_id, m.chunk_ids AS chunk_ids, m.content AS content, @@ -718,9 +675,7 @@ MERGE (m:MemorySummary {id: summary.id}) SET m += { id: summary.id, name: summary.name, - group_id: summary.group_id, - user_id: summary.user_id, - apply_id: summary.apply_id, + end_user_id: summary.end_user_id, run_id: summary.run_id, created_at: summary.created_at, expired_at: summary.expired_at, @@ -745,7 +700,7 @@ MATCH (ms:MemorySummary {id: e.summary_id, run_id: e.run_id}) MATCH (c:Chunk {id: e.chunk_id, run_id: e.run_id}) MATCH (c)-[:CONTAINS]->(s:Statement {run_id: e.run_id}) MERGE (ms)-[r:DERIVED_FROM_STATEMENT]->(s) -SET r.group_id = e.group_id, +SET r.end_user_id = e.end_user_id, r.run_id = e.run_id, r.created_at = e.created_at, r.expired_at = e.expired_at @@ -774,7 +729,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END | source_statement_id: rel.source_statement_id, valid_at: rel.valid_at, invalid_at: rel.invalid_at, - group_id: rel.group_id, + end_user_id: rel.end_user_id, user_id: rel.user_id, apply_id: rel.apply_id, run_id: rel.run_id, @@ -796,7 +751,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END | source_statement_id: rel.source_statement_id, valid_at: rel.valid_at, invalid_at: rel.invalid_at, - group_id: rel.group_id, + end_user_id: rel.end_user_id, user_id: rel.user_id, apply_id: rel.apply_id, run_id: rel.run_id, @@ -814,7 +769,7 @@ RETURN count(losing) as deleted neo4j_statement_part = ''' MATCH (n:Statement) -WHERE n.group_id = "{}" +WHERE n.end_user_id = "{}" AND datetime(n.created_at) >= datetime() - duration('P3D') RETURN n.statement as statement_name, @@ -824,7 +779,7 @@ RETURN ''' neo4j_statement_all = ''' MATCH (n:Statement) -WHERE n.group_id = "{}" +WHERE n.end_user_id = "{}" RETURN n.statement as statement_name, n.id as statement_id @@ -832,7 +787,7 @@ RETURN ''' neo4j_query_part = """ MATCH (n)-[r]-(m:ExtractedEntity) - WHERE n.group_id = "{}" + WHERE n.end_user_id = "{}" AND datetime(n.created_at) >= datetime() - duration('P3D') WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) @@ -853,7 +808,7 @@ neo4j_query_part = """ """ neo4j_query_all = """ MATCH (n)-[r]-(m:ExtractedEntity) - WHERE n.group_id = "{}" + WHERE n.end_user_id = "{}" WITH DISTINCT m OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity) RETURN @@ -1027,14 +982,14 @@ RETURN DISTINCT Memory_Space_User=""" MATCH (n)-[r]->(m) -WHERE n.group_id = $group_id AND m.name="用户" +WHERE n.end_user_id = $end_user_id AND m.name="用户" return DISTINCT elementId(m) as id """ Memory_Space_Entity=""" MATCH (n)-[]-(m) WHERE elementId(m) = $id AND m.entity_type = "Person" RETURN -DISTINCT m.name as name,m.group_id as group_id +DISTINCT m.name as name,m.end_user_id as end_user_id """ Memory_Space_Associative=""" MATCH (u)-[]-(x)-[]-(h) diff --git a/api/app/repositories/neo4j/dialog_repository.py b/api/app/repositories/neo4j/dialog_repository.py index ccb3d94c..020e7346 100644 --- a/api/app/repositories/neo4j/dialog_repository.py +++ b/api/app/repositories/neo4j/dialog_repository.py @@ -19,7 +19,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """对话仓储 管理对话节点的创建、查询、更新和删除操作。 - 提供按group_id、user_id、ref_id等条件查询对话的方法。 + 提供按end_user_id、user_id、ref_id等条件查询对话的方法。 Attributes: connector: Neo4j连接器实例 @@ -54,17 +54,17 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): return DialogueNode(**n) - async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[DialogueNode]: - """根据group_id查询对话 + async def find_by_end_user_id(self, end_user_id: str, limit: int = 100) -> List[DialogueNode]: + """根据end_user_id查询对话 Args: - group_id: 组ID + end_user_id: 组ID limit: 返回结果的最大数量 Returns: List[DialogueNode]: 对话列表 """ - return await self.find({"group_id": group_id}, limit=limit) + return await self.find({"end_user_id": end_user_id}, limit=limit) async def find_by_user_id(self, user_id: str, limit: int = 100) -> List[DialogueNode]: """根据user_id查询对话 @@ -94,14 +94,14 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): async def find_by_group_and_user( self, - group_id: str, + end_user_id: str, user_id: str, limit: int = 100 ) -> List[DialogueNode]: - """根据group_id和user_id查询对话 + """根据end_user_id和user_id查询对话 Args: - group_id: 组ID + end_user_id: 组ID user_id: 用户ID limit: 返回结果的最大数量 @@ -109,20 +109,20 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): List[DialogueNode]: 对话列表 """ return await self.find( - {"group_id": group_id, "user_id": user_id}, + {"end_user_id": end_user_id, "user_id": user_id}, limit=limit ) async def find_recent_dialogs( self, - group_id: str, + end_user_id: str, days: int = 7, limit: int = 100 ) -> List[DialogueNode]: """查询最近的对话 Args: - group_id: 组ID + end_user_id: 组ID days: 查询最近多少天的对话 limit: 返回结果的最大数量 @@ -131,7 +131,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND n.created_at >= datetime() - duration({{days: $days}}) RETURN n ORDER BY n.created_at DESC @@ -139,7 +139,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): """ results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, days=days, limit=limit ) @@ -164,22 +164,22 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]): async def find_by_config_and_group( self, config_id: str, - group_id: str, + end_user_id: str, limit: int = 100 ) -> List[DialogueNode]: - """根据config_id和group_id查询对话 + """根据config_id和end_user_id查询对话 支持按配置ID和组ID同时过滤,确保只返回使用特定配置处理的对话。 Args: config_id: 配置ID - group_id: 组ID + end_user_id: 组ID limit: 返回结果的最大数量 Returns: List[DialogueNode]: 对话列表 """ return await self.find( - {"config_id": config_id, "group_id": group_id}, + {"config_id": config_id, "end_user_id": end_user_id}, limit=limit ) diff --git a/api/app/repositories/neo4j/emotion_repository.py b/api/app/repositories/neo4j/emotion_repository.py index d445c8d4..e39968ac 100644 --- a/api/app/repositories/neo4j/emotion_repository.py +++ b/api/app/repositories/neo4j/emotion_repository.py @@ -40,7 +40,7 @@ class EmotionRepository: async def get_emotion_tags( self, - group_id: str, + end_user_id: str, emotion_type: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, @@ -51,7 +51,7 @@ class EmotionRepository: 查询指定用户的情绪类型分布，包括计数、百分比和平均强度。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） emotion_type: 可选的情绪类型过滤（joy/sadness/anger/fear/surprise/neutral） start_date: 可选的开始日期（ISO格式字符串） end_date: 可选的结束日期（ISO格式字符串） @@ -65,8 +65,8 @@ class EmotionRepository: - avg_intensity: 平均强度 """ # 构建查询条件 - where_clauses = ["s.group_id = $group_id", "s.emotion_type IS NOT NULL"] - params = {"group_id": group_id, "limit": limit} + where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_type IS NOT NULL"] + params = {"end_user_id": end_user_id, "limit": limit} if emotion_type: where_clauses.append("s.emotion_type = $emotion_type") @@ -119,7 +119,7 @@ class EmotionRepository: async def get_emotion_wordcloud( self, - group_id: str, + end_user_id: str, emotion_type: Optional[str] = None, limit: int = 50 ) -> List[Dict[str, Any]]: @@ -128,7 +128,7 @@ class EmotionRepository: 查询情绪关键词及其频率，用于生成词云可视化。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） emotion_type: 可选的情绪类型过滤 limit: 返回关键词的最大数量 @@ -140,8 +140,8 @@ class EmotionRepository: - avg_intensity: 平均强度 """ # 构建查询条件 - where_clauses = ["s.group_id = $group_id", "s.emotion_keywords IS NOT NULL"] - params = {"group_id": group_id, "limit": limit} + where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_keywords IS NOT NULL"] + params = {"end_user_id": end_user_id, "limit": limit} if emotion_type: where_clauses.append("s.emotion_type = $emotion_type") @@ -186,7 +186,7 @@ class EmotionRepository: async def get_emotions_in_range( self, - group_id: str, + end_user_id: str, time_range: str = "30d" ) -> List[Dict[str, Any]]: """获取时间范围内的情绪数据 @@ -194,7 +194,7 @@ class EmotionRepository: 查询指定时间范围内的所有情绪数据，用于健康指数计算。 Args: - group_id: 用户组ID（宿主ID） + end_user_id: 用户组ID（宿主ID） time_range: 时间范围（7d/30d/90d） Returns: @@ -214,7 +214,7 @@ class EmotionRepository: # 优化的 Cypher 查询：使用字符串比较避免时区问题 query = """ MATCH (s:Statement) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id AND s.emotion_type IS NOT NULL AND s.created_at >= $start_date RETURN s.id as statement_id, @@ -227,7 +227,7 @@ class EmotionRepository: try: results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, start_date=start_date ) formatted_results = [ diff --git a/api/app/repositories/neo4j/graph_saver.py b/api/app/repositories/neo4j/graph_saver.py index 13215e0f..1575315f 100644 --- a/api/app/repositories/neo4j/graph_saver.py +++ b/api/app/repositories/neo4j/graph_saver.py @@ -44,9 +44,7 @@ async def save_entities_and_relationships( 'created_at': edge.created_at.isoformat(), 'expired_at': edge.expired_at.isoformat(), 'run_id': edge.run_id, - 'group_id': edge.group_id, - 'user_id': edge.user_id, - 'apply_id': edge.apply_id, + 'end_user_id': edge.end_user_id, } all_relationships.append(relationship) @@ -101,9 +99,7 @@ async def save_statement_chunk_edges( "id": edge.id, "source": edge.source, "target": edge.target, - "group_id": edge.group_id, - "user_id": edge.user_id, - "apply_id": edge.apply_id, + "end_user_id": edge.end_user_id, "run_id": edge.run_id, "created_at": edge.created_at.isoformat() if edge.created_at else None, "expired_at": edge.expired_at.isoformat() if edge.expired_at else None, @@ -132,9 +128,7 @@ async def save_statement_entity_edges( edge_data = { "source": edge.source, "target": edge.target, - "group_id": edge.group_id, - "user_id": edge.user_id, - "apply_id": edge.apply_id, + "end_user_id": edge.end_user_id, "run_id": edge.run_id, "connect_strength": edge.connect_strength, "created_at": edge.created_at.isoformat() if edge.created_at else None, diff --git a/api/app/repositories/neo4j/graph_search.py b/api/app/repositories/neo4j/graph_search.py index 6f5764b4..e8f52535 100644 --- a/api/app/repositories/neo4j/graph_search.py +++ b/api/app/repositories/neo4j/graph_search.py @@ -33,7 +33,7 @@ async def _update_activation_values_batch( connector: Neo4jConnector, nodes: List[Dict[str, Any]], node_label: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, max_retries: int = 3 ) -> List[Dict[str, Any]]: """ @@ -46,7 +46,7 @@ async def _update_activation_values_batch( connector: Neo4j连接器 nodes: 节点列表，每个节点必须包含 'id' 字段 node_label: 节点标签（Statement, ExtractedEntity, MemorySummary） - group_id: 组ID（可选） + end_user_id: 组ID（可选） max_retries: 最大重试次数 Returns: @@ -97,7 +97,7 @@ async def _update_activation_values_batch( updated_nodes = await access_manager.record_batch_access( node_ids=unique_node_ids, node_label=node_label, - group_id=group_id + end_user_id=end_user_id ) logger.info( @@ -118,7 +118,7 @@ async def _update_activation_values_batch( async def _update_search_results_activation( connector: Neo4jConnector, results: Dict[str, List[Dict[str, Any]]], - group_id: Optional[str] = None + end_user_id: Optional[str] = None ) -> Dict[str, List[Dict[str, Any]]]: """ 更新搜索结果中所有知识节点的激活值 @@ -129,7 +129,7 @@ async def _update_search_results_activation( Args: connector: Neo4j连接器 results: 搜索结果字典，包含不同类型节点的列表 - group_id: 组ID（可选） + end_user_id: 组ID（可选） Returns: Dict[str, List[Dict[str, Any]]]: 更新后的搜索结果 @@ -152,7 +152,7 @@ async def _update_search_results_activation( connector=connector, nodes=results[key], node_label=label, - group_id=group_id + end_user_id=end_user_id ) ) update_keys.append(key) @@ -218,7 +218,7 @@ async def _update_search_results_activation( async def search_graph( connector: Neo4jConnector, q: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: List[str] = None, ) -> Dict[str, List[Dict[str, Any]]]: @@ -236,7 +236,7 @@ async def search_graph( Args: connector: Neo4j connector q: Query text - group_id: Optional group filter + end_user_id: Optional group filter limit: Max results per category include: List of categories to search (default: all) @@ -254,7 +254,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_STATEMENTS_BY_KEYWORD, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("statements") @@ -263,7 +263,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("entities") @@ -272,7 +272,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_CHUNKS_BY_CONTENT, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("chunks") @@ -281,7 +281,7 @@ async def search_graph( tasks.append(connector.execute_query( SEARCH_MEMORY_SUMMARIES_BY_KEYWORD, q=q, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("summaries") @@ -310,12 +310,12 @@ async def search_graph( key in include and key in results and results[key] for key in ['statements', 'entities', 'chunks'] ) - + if needs_activation_update: results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -325,7 +325,7 @@ async def search_graph_by_embedding( connector: Neo4jConnector, embedder_client, query_text: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 50, include: List[str] = ["statements", "chunks", "entities","summaries"], ) -> Dict[str, List[Dict[str, Any]]]: @@ -337,7 +337,7 @@ async def search_graph_by_embedding( - Computes query embedding with the provided embedder_client - Ranks by cosine similarity in Cypher - - Filters by group_id if provided + - Filters by end_user_id if provided - Returns up to 'limit' per included type """ import time @@ -346,7 +346,7 @@ async def search_graph_by_embedding( embed_start = time.time() embeddings = await embedder_client.response([query_text]) embed_time = time.time() - embed_start - logger.info(f"[PERF] Embedding generation took: {embed_time:.4f}s") + print(f"[PERF] Embedding generation took: {embed_time:.4f}s") if not embeddings or not embeddings[0]: return {"statements": [], "chunks": [], "entities": [], "summaries": []} @@ -361,7 +361,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( STATEMENT_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("statements") @@ -371,7 +371,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( CHUNK_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("chunks") @@ -381,7 +381,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( ENTITY_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("entities") @@ -391,7 +391,7 @@ async def search_graph_by_embedding( tasks.append(connector.execute_query( MEMORY_SUMMARY_EMBEDDING_SEARCH, embedding=embedding, - group_id=group_id, + end_user_id=end_user_id, limit=limit, )) task_keys.append("summaries") @@ -400,7 +400,7 @@ async def search_graph_by_embedding( query_start = time.time() task_results = await asyncio.gather(*tasks, return_exceptions=True) query_time = time.time() - query_start - logger.info(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s") + print(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s") # Build results dictionary results: Dict[str, List[Dict[str, Any]]] = { @@ -429,13 +429,13 @@ async def search_graph_by_embedding( key in include and key in results and results[key] for key in ['statements', 'entities', 'chunks'] ) - + if needs_activation_update: update_start = time.time() results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) update_time = time.time() - update_start logger.info(f"[PERF] Activation value updates took: {update_time:.4f}s") @@ -445,7 +445,7 @@ async def search_graph_by_embedding( return results async def get_dedup_candidates_for_entities( # 适配新版查询：使用全文索引按名称检索候选实体 connector: Neo4jConnector, - group_id: str, + end_user_id: str, entities: List[Dict[str, Any]], use_contains_fallback: bool = True, batch_size: int = 500, @@ -453,7 +453,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 ) -> Dict[str, List[Dict[str, Any]]]: """ 为第二层去重消歧批量检索候选实体（适配新版 cypher_queries）： - - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (group_id, name) 检索候选； + - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (end_user_id, name) 检索候选； - 保留并发控制与返回结构（incoming_id -> [db_entity_props...]）； - 若提供 `entity_type`，在本地对返回结果做类型过滤； - `use_contains_fallback` 保留形参以兼容，必要时可扩展二次查询策略。 @@ -477,7 +477,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 rows = await connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=name, - group_id=group_id, + end_user_id=end_user_id, limit=100, ) except Exception: @@ -501,7 +501,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 rows = await connector.execute_query( SEARCH_ENTITIES_BY_NAME, q=name.lower(), - group_id=group_id, + end_user_id=end_user_id, limit=100, ) for r in rows: @@ -532,9 +532,7 @@ async def get_dedup_candidates_for_entities( # 适配新版查询：使用全 async def search_graph_by_keyword_temporal( connector: Neo4jConnector, query_text: str, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -547,32 +545,30 @@ async def search_graph_by_keyword_temporal( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements containing query_text created between start_date and end_date - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ if not query_text: - logger.warning(f"query_text cannot be empty") + print(f"query_text不能为空") return {"statements": []} statements = await connector.execute_query( SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL, q=query_text, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, start_date=start_date, end_date=end_date, valid_date=valid_date, invalid_date=invalid_date, limit=limit, ) - logger.debug(f"Temporal keyword search results: {len(statements)} statements found") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -580,9 +576,7 @@ async def search_graph_by_keyword_temporal( async def search_graph_by_temporal( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None, valid_date: Optional[str] = None, @@ -595,14 +589,12 @@ async def search_graph_by_temporal( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created between start_date and end_date - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_TEMPORAL, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, start_date=start_date, end_date=end_date, valid_date=valid_date, @@ -610,16 +602,16 @@ async def search_graph_by_temporal( limit=limit, ) - logger.debug(f"Temporal search query: {SEARCH_STATEMENTS_BY_TEMPORAL}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, start_date={start_date}, end_date={end_date}, valid_date={valid_date}, invalid_date={invalid_date}, limit={limit}") - logger.debug(f"Temporal search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_TEMPORAL}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results @@ -628,23 +620,23 @@ async def search_graph_by_temporal( async def search_graph_by_dialog_id( connector: Neo4jConnector, dialog_id: str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: """ Temporal search across Dialogues. - Matches dialogues with dialog_id - - Optionally filters by group_id + - Optionally filters by end_user_id - Returns up to 'limit' dialogues """ if not dialog_id: - logger.warning(f"dialog_id cannot be empty") + print(f"dialog_id不能为空") return {"dialogues": []} dialogues = await connector.execute_query( SEARCH_DIALOGUE_BY_DIALOG_ID, - group_id=group_id, + end_user_id=end_user_id, dialog_id=dialog_id, limit=limit, ) @@ -654,15 +646,15 @@ async def search_graph_by_dialog_id( async def search_graph_by_chunk_id( connector: Neo4jConnector, chunk_id : str, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: if not chunk_id: - logger.warning(f"chunk_id cannot be empty") + print(f"chunk_id不能为空") return {"chunks": []} chunks = await connector.execute_query( SEARCH_CHUNK_BY_CHUNK_ID, - group_id=group_id, + end_user_id=end_user_id, chunk_id=chunk_id, limit=limit, ) @@ -671,9 +663,9 @@ async def search_graph_by_chunk_id( async def search_graph_by_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -683,37 +675,37 @@ async def search_graph_by_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search by created_at query: {SEARCH_STATEMENTS_BY_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id} created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_by_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -723,37 +715,37 @@ async def search_graph_by_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_BY_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search by valid_at query: {SEARCH_STATEMENTS_BY_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}， valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_g_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -763,37 +755,37 @@ async def search_graph_g_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_G_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search greater than created_at query: {SEARCH_STATEMENTS_G_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_G_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_g_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -803,37 +795,37 @@ async def search_graph_g_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_G_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search greater than valid_at query: {SEARCH_STATEMENTS_G_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_G_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_l_created_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + created_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -843,37 +835,37 @@ async def search_graph_l_created_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements created at created_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_L_CREATED_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + created_at=created_at, limit=limit, ) - logger.debug(f"Search less than created_at query: {SEARCH_STATEMENTS_L_CREATED_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_L_CREATED_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results async def search_graph_l_valid_at( connector: Neo4jConnector, - group_id: Optional[str] = None, - apply_id: Optional[str] = None, - user_id: Optional[str] = None, + end_user_id: Optional[str] = None, + + valid_at: Optional[str] = None, limit: int = 1, ) -> Dict[str, List[Dict[str, Any]]]: @@ -883,28 +875,28 @@ async def search_graph_l_valid_at( INTEGRATED: Updates activation values for Statement nodes before returning results - Matches statements valid at valid_at - - Optionally filters by group_id, apply_id, user_id + - Optionally filters by end_user_id, apply_id, user_id - Returns up to 'limit' statements """ statements = await connector.execute_query( SEARCH_STATEMENTS_L_VALID_AT, - group_id=group_id, - apply_id=apply_id, - user_id=user_id, + end_user_id=end_user_id, + + valid_at=valid_at, limit=limit, ) - logger.debug(f"Search less than valid_at query: {SEARCH_STATEMENTS_L_VALID_AT}") - logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}") - logger.debug(f"Search results: {len(statements)} statements found") + print(f"查询语句为：\n{SEARCH_STATEMENTS_L_VALID_AT}") + print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}") + print(f"查询结果为：\n{statements}") # 更新 Statement 节点的激活值 results = {"statements": statements} results = await _update_search_results_activation( connector=connector, results=results, - group_id=group_id + end_user_id=end_user_id ) return results diff --git a/api/app/repositories/neo4j/memory_summary_repository.py b/api/app/repositories/neo4j/memory_summary_repository.py index fc743f33..d7cd4fd4 100644 --- a/api/app/repositories/neo4j/memory_summary_repository.py +++ b/api/app/repositories/neo4j/memory_summary_repository.py @@ -18,7 +18,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """Memory Summary Repository Manages CRUD operations for MemorySummary nodes. - Provides methods to query summaries by group_id, user_id, and time ranges. + Provides methods to query summaries by end_user_id, user_id, and time ranges. Attributes: connector: Neo4j connector instance @@ -51,17 +51,17 @@ class MemorySummaryRepository(BaseNeo4jRepository): return dict(n) - async def find_by_group_id( + async def find_by_end_user_id( self, - group_id: str, + end_user_id: str, limit: int = 1000, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None ) -> List[Dict[str, Any]]: - """Query memory summaries by group_id + """Query memory summaries by end_user_id Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by limit: Maximum number of results to return start_date: Optional start date filter end_date: Optional end date filter @@ -71,10 +71,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id """ - params = {"group_id": group_id, "limit": limit} + params = {"end_user_id": end_user_id, "limit": limit} # Add date range filters if provided if start_date: @@ -139,16 +139,16 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_by_group_and_user( self, - group_id: str, + end_user_id: str, user_id: str, limit: int = 1000, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None ) -> List[Dict[str, Any]]: - """Query memory summaries by both group_id and user_id + """Query memory summaries by both end_user_id and user_id Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by user_id: User ID to filter by limit: Maximum number of results to return start_date: Optional start date filter @@ -159,10 +159,10 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id AND n.user_id = $user_id + WHERE n.end_user_id = $end_user_id AND n.user_id = $user_id """ - params = {"group_id": group_id, "user_id": user_id, "limit": limit} + params = {"end_user_id": end_user_id, "user_id": user_id, "limit": limit} # Add date range filters if provided if start_date: @@ -184,14 +184,14 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_recent_summaries( self, - group_id: str, + end_user_id: str, days: int = 7, limit: int = 1000 ) -> List[Dict[str, Any]]: """Query recent memory summaries Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by days: Number of recent days to query limit: Maximum number of results to return @@ -200,7 +200,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND n.created_at >= datetime() - duration({{days: $days}}) RETURN n ORDER BY n.created_at DESC @@ -209,7 +209,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): results = await self.connector.execute_query( query, - group_id=group_id, + end_user_id=end_user_id, days=days, limit=limit ) @@ -217,14 +217,14 @@ class MemorySummaryRepository(BaseNeo4jRepository): async def find_by_content_keywords( self, - group_id: str, + end_user_id: str, keywords: List[str], limit: int = 100 ) -> List[Dict[str, Any]]: """Query memory summaries by content keywords Args: - group_id: Group ID to filter by + end_user_id: Group ID to filter by keywords: List of keywords to search for in content limit: Maximum number of results to return @@ -233,7 +233,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): """ # Build keyword search conditions keyword_conditions = [] - params = {"group_id": group_id, "limit": limit} + params = {"end_user_id": end_user_id, "limit": limit} for i, keyword in enumerate(keywords): keyword_conditions.append(f"toLower(n.content) CONTAINS toLower($keyword_{i})") @@ -243,7 +243,7 @@ class MemorySummaryRepository(BaseNeo4jRepository): query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND ({keyword_filter}) RETURN n ORDER BY n.created_at DESC @@ -253,21 +253,21 @@ class MemorySummaryRepository(BaseNeo4jRepository): results = await self.connector.execute_query(query, **params) return [self._map_to_dict(r) for r in results] - async def get_summary_count_by_group(self, group_id: str) -> int: + async def get_summary_count_by_group(self, end_user_id: str) -> int: """Get count of memory summaries for a group Args: - group_id: Group ID to count summaries for + end_user_id: Group ID to count summaries for Returns: int: Number of memory summaries """ query = f""" MATCH (n:{self.node_label}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - results = await self.connector.execute_query(query, group_id=group_id) + results = await self.connector.execute_query(query, end_user_id=end_user_id) return results[0]['count'] if results else 0 \ No newline at end of file diff --git a/api/app/repositories/neo4j/neo4j_connector.py b/api/app/repositories/neo4j/neo4j_connector.py index 7c4b43b5..d96e4431 100644 --- a/api/app/repositories/neo4j/neo4j_connector.py +++ b/api/app/repositories/neo4j/neo4j_connector.py @@ -70,11 +70,7 @@ class Neo4jConnector: List[Dict[str, Any]]: 查询结果列表，每个元素是一个字典 Example: - >>> connector = Neo4jConnector() - >>> results = await connector.execute_query( - ... "MATCH (n:Person {name: $name}) RETURN n", - ... name="Alice" - ... ) + """ result = await self.driver.execute_query( query, @@ -98,17 +94,7 @@ class Neo4jConnector: Any: 事务函数的返回值 Example: - >>> async def create_node(tx, name): - ... result = await tx.run( - ... "CREATE (n:Person {name: $name}) RETURN n", - ... name=name - ... ) - ... return await result.single() - >>> - >>> connector = Neo4jConnector() - >>> result = await connector.execute_write_transaction( - ... create_node, name="Alice" - ... ) + """ async with self.driver.session(database="neo4j") as session: return await session.execute_write(transaction_func, **kwargs) @@ -126,45 +112,33 @@ class Neo4jConnector: Any: 事务函数的返回值 Example: - >>> async def get_node(tx, name): - ... result = await tx.run( - ... "MATCH (n:Person {name: $name}) RETURN n", - ... name=name - ... ) - ... return await result.single() - >>> - >>> connector = Neo4jConnector() - >>> result = await connector.execute_read_transaction( - ... get_node, name="Alice" - ... ) + """ async with self.driver.session(database="neo4j") as session: return await session.execute_read(transaction_func, **kwargs) - async def delete_group(self, group_id: str): + async def delete_group(self, end_user_id: str): """删除指定组的所有数据 - 删除所有属于指定group_id的节点和边。 + 删除所有属于指定end_user_id的节点和边。 这是一个危险操作，会永久删除数据。 Args: - group_id: 要删除的组ID + end_user_id: 要删除的组ID Example: - >>> connector = Neo4jConnector() - >>> await connector.delete_group("group_123") Group group_123 deleted. """ # 删除节点（DETACH DELETE会同时删除相关的边） await self.driver.execute_query( - "MATCH (n) WHERE n.group_id = $group_id DETACH DELETE n", + "MATCH (n) WHERE n.end_user_id = $end_user_id DETACH DELETE n", database="neo4j", - group_id=group_id + end_user_id=end_user_id ) # 删除独立的边（如果有的话） await self.driver.execute_query( - "MATCH ()-[r]->() WHERE r.group_id = $group_id DELETE r", + "MATCH ()-[r]->() WHERE r.end_user_id = $end_user_id DELETE r", database="neo4j", - group_id=group_id + end_user_id=end_user_id ) - print(f"Group {group_id} deleted.") + print(f"Group {end_user_id} deleted.") diff --git a/api/app/repositories/neo4j/statement_repository.py b/api/app/repositories/neo4j/statement_repository.py index cd9f2fac..4f12af83 100644 --- a/api/app/repositories/neo4j/statement_repository.py +++ b/api/app/repositories/neo4j/statement_repository.py @@ -20,7 +20,7 @@ class StatementRepository(BaseNeo4jRepository[StatementNode]): """陈述句仓储 管理陈述句节点的创建、查询、更新和删除操作。 - 提供按chunk_id、group_id、向量相似度等条件查询陈述句的方法。 + 提供按chunk_id、end_user_id、向量相似度等条件查询陈述句的方法。 Attributes: connector: Neo4j连接器实例 diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index 35d2e424..09410091 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -299,6 +299,18 @@ class AppRelease(BaseModel): created_at: datetime.datetime updated_at: datetime.datetime + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v if v is not None else {} + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None diff --git a/api/app/schemas/emotion_schema.py b/api/app/schemas/emotion_schema.py index c48fbd41..13c802b5 100644 --- a/api/app/schemas/emotion_schema.py +++ b/api/app/schemas/emotion_schema.py @@ -1,11 +1,12 @@ """情绪分析相关的请求和响应模型""" from typing import Optional +from uuid import UUID from pydantic import BaseModel, Field class EmotionTagsRequest(BaseModel): """获取情绪标签统计请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）") end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）") @@ -14,14 +15,14 @@ class EmotionTagsRequest(BaseModel): class EmotionWordcloudRequest(BaseModel): """获取情绪词云数据请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）") limit: int = Field(50, ge=1, le=200, description="返回词语数量") class EmotionHealthRequest(BaseModel): """获取情绪健康指数请求""" - group_id: str = Field(..., description="组ID") + end_user_id: str = Field(..., description="组ID") time_range: str = Field("30d", description="时间范围（7d/30d/90d）") @@ -29,8 +30,8 @@ class EmotionHealthRequest(BaseModel): class EmotionSuggestionsRequest(BaseModel): """获取个性化情绪建议请求""" - group_id: str = Field(..., description="组ID") - config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）") + end_user_id: str = Field(..., description="组ID") + config_id: Optional[UUID] = Field(None, description="配置ID（用于指定LLM模型）") class EmotionGenerateSuggestionsRequest(BaseModel): diff --git a/api/app/schemas/memory_agent_schema.py b/api/app/schemas/memory_agent_schema.py index d4354c40..b6f50dd7 100644 --- a/api/app/schemas/memory_agent_schema.py +++ b/api/app/schemas/memory_agent_schema.py @@ -7,11 +7,11 @@ class UserInput(BaseModel): message: str history: list[dict] search_switch: str - group_id: str + end_user_id: str config_id: Optional[str] = None class Write_UserInput(BaseModel): messages: list[dict] - group_id: str - config_id: Optional[str] = None + end_user_id: str + config_id: Optional[str] = None \ No newline at end of file diff --git a/api/app/schemas/memory_config_schema.py b/api/app/schemas/memory_config_schema.py index 0443dcc4..76acee5c 100644 --- a/api/app/schemas/memory_config_schema.py +++ b/api/app/schemas/memory_config_schema.py @@ -35,7 +35,7 @@ class ConfigurationError(Exception): def __init__( self, message: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, context: Optional[Dict[str, Any]] = None, ): @@ -72,7 +72,7 @@ class WorkspaceNotFoundError(ConfigurationError): def __init__( self, workspace_id: UUID, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, message: Optional[str] = None, ): if message is None: @@ -89,7 +89,7 @@ class ModelNotFoundError(ConfigurationError): self, model_id: Union[str, UUID], model_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, message: Optional[str] = None, ): @@ -112,7 +112,7 @@ class ModelInactiveError(ConfigurationError): model_id: Union[str, UUID], model_name: str, model_type: str, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, message: Optional[str] = None, ): @@ -136,7 +136,7 @@ class InvalidConfigError(ConfigurationError): message: str, field_name: Optional[str] = None, invalid_value: Optional[Any] = None, - config_id: Optional[int] = None, + config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None, ): context = {} @@ -155,7 +155,7 @@ class InvalidConfigError(ConfigurationError): class MemoryConfigValidation(BaseModel): """Pydantic model for validating memory configuration data from database.""" - config_id: int = Field(..., gt=0, description="Configuration ID must be positive") + config_id: UUID = Field(..., description="Configuration ID (UUID)") config_name: str = Field(..., min_length=1, max_length=255) workspace_id: UUID = Field(..., description="Workspace UUID") workspace_name: str = Field(..., min_length=1, max_length=255) @@ -275,7 +275,7 @@ class ModelValidation(BaseModel): def validate_memory_config_data( - config_data: Dict[str, Any], config_id: Optional[int] = None + config_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> MemoryConfigValidation: """Validate memory configuration data using Pydantic model.""" try: @@ -302,7 +302,7 @@ def validate_memory_config_data( def validate_workspace_data( - workspace_data: Dict[str, Any], config_id: Optional[int] = None + workspace_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> WorkspaceValidation: """Validate workspace data using Pydantic model.""" try: @@ -331,7 +331,7 @@ def validate_workspace_data( def validate_model_data( - model_data: Dict[str, Any], config_id: Optional[int] = None + model_data: Dict[str, Any], config_id: Optional[UUID] = None ) -> ModelValidation: """Validate model data using Pydantic model.""" try: @@ -364,7 +364,7 @@ def validate_model_data( class MemoryConfig: """Immutable memory configuration loaded from database.""" - config_id: int + config_id: UUID config_name: str workspace_id: UUID workspace_name: str diff --git a/api/app/schemas/memory_perceptual_schema.py b/api/app/schemas/memory_perceptual_schema.py index 05e01d2a..7dfefe01 100644 --- a/api/app/schemas/memory_perceptual_schema.py +++ b/api/app/schemas/memory_perceptual_schema.py @@ -4,7 +4,7 @@ from typing import Optional from pydantic import BaseModel, Field -from app.models.memory_perceptual_model import PerceptualType, FileStorageType +from app.models.memory_perceptual_model import PerceptualType, FileStorageService class PerceptualFilter(BaseModel): @@ -38,12 +38,14 @@ class PerceptualMemoryItem(BaseModel): """感知记忆项""" id: uuid.UUID = Field(..., description="Unique memory ID") perceptual_type: PerceptualType = Field(..., description="Type of perception, e.g., text, audio, or video") + storage_service: FileStorageService = Field(..., description="Storage service for file") file_path: str = Field(..., description="File path in the storage service") - file_ext: str = Field(..., description="File extension") file_name: str = Field(..., description="File name") + file_ext: str = Field(..., description="File extension") summary: Optional[str] = Field(None, description="summary") - storage_type: FileStorageType = Field(..., description="Storage type for file") + meta_data: Optional[dict] = Field(None, description="Metadata information") created_time: int = Field(..., description="create time") + topic: str = Field(..., description="topic") domain: str = Field(..., description="domain") keywords: list[str] = Field(..., description="keywords") diff --git a/api/app/schemas/memory_reflection_schemas.py b/api/app/schemas/memory_reflection_schemas.py index 860f1ef1..df841fb1 100644 --- a/api/app/schemas/memory_reflection_schemas.py +++ b/api/app/schemas/memory_reflection_schemas.py @@ -1,5 +1,6 @@ from pydantic import BaseModel, Field from typing import Optional +from uuid import UUID from enum import Enum @@ -9,7 +10,7 @@ class OptimizationStrategy(str, Enum): ACCURACY_FIRST = "accuracy_first" BALANCED = "balanced" class Memory_Reflection(BaseModel): - config_id: Optional[int] = None + config_id: Optional[UUID] = None reflection_enabled: bool reflection_period_in_hours: str reflexion_range: Optional[str] = "partial" diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index d17a9f2c..d9c04f8f 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -1,5 +1,5 @@ """ -所有的内容是放错误地方了，应该放在models + """ from typing import Any, Optional, List, Dict, Literal, Union @@ -8,20 +8,8 @@ import uuid from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator -# ============================================================================ -# 原 UserInput 相关 Schema (保留原有功能) -# ============================================================================ -class UserInput(BaseModel): - message: str - history: list[dict] - search_switch: str - group_id: str -class Write_UserInput(BaseModel): - message: str - group_id: str - # ============================================================================ # 从 json_schema.py 迁移的 Schema @@ -159,7 +147,7 @@ class ReflexionResultSchema(BaseModel): # Composite key identifying a config row class ConfigKey(BaseModel): # 配置参数键模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field("config_id", description="配置唯一标识（字符串）") + config_id: uuid.UUID = Field("config_id", description="配置唯一标识（UUID）") user_id: str = Field("user_id", description="用户标识（字符串）") apply_id: str = Field("apply_id", description="应用或场景标识（字符串）") @@ -250,17 +238,17 @@ class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body， class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体） model_config = ConfigDict(populate_by_name=True, extra="forbid") # config_name: str = Field("配置名称", description="配置名称（字符串）") - config_id: int = Field("配置ID", description="配置ID（字符串）") + config_id: uuid.UUID = Field("配置ID", description="配置ID（UUID）") class ConfigUpdate(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型 - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None config_name: str = Field("配置名称", description="配置名称（字符串）") config_desc: str = Field("配置描述", description="配置描述（字符串）") class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型 - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None llm_id: Optional[str] = Field(None, description="LLM模型配置ID") embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID") rerank_id: Optional[str] = Field(None, description="重排序模型配置ID") @@ -327,14 +315,14 @@ class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数 class ConfigUpdateForget(BaseModel): # 更新遗忘引擎配置参数时使用的模型 # 遗忘引擎配置参数更新模型 - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5") lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5") offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0") class ConfigPilotRun(BaseModel): # 试运行触发请求模型 - config_id: int = Field(..., description="配置ID（唯一）") + config_id: uuid.UUID = Field(..., description="配置ID（唯一）") dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填") model_config = ConfigDict(populate_by_name=True, extra="forbid") @@ -342,7 +330,7 @@ class ConfigPilotRun(BaseModel): # 试运行触发请求模型 class ConfigFilter(BaseModel): # 查询配置参数时使用的模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: Optional[int] = None + config_id: Optional[uuid.UUID] = None user_id: Optional[str] = None apply_id: Optional[str] = None @@ -418,7 +406,7 @@ class ForgettingConfigResponse(BaseModel): """遗忘引擎配置响应模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field(..., description="配置ID") + config_id: uuid.UUID = Field(..., description="配置ID") decay_constant: float = Field(..., description="衰减常数 d") lambda_time: float = Field(..., description="时间衰减参数") lambda_mem: float = Field(..., description="记忆衰减参数") @@ -436,7 +424,7 @@ class ForgettingConfigUpdateRequest(BaseModel): """遗忘引擎配置更新请求模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: int = Field(..., description="配置ID") + config_id: uuid.UUID = Field(..., description="配置ID") decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d") lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数") lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数") @@ -511,7 +499,7 @@ class ForgettingCurveRequest(BaseModel): importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）") days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）") - config_id: Optional[int] = Field(None, description="配置ID（可选，如果为None则使用默认配置）") + config_id: Optional[uuid.UUID] = Field(None, description="配置ID（可选，如果为None则使用默认配置）") class ForgettingCurveResponse(BaseModel): diff --git a/api/app/schemas/model_schema.py b/api/app/schemas/model_schema.py index 5b1fe6d9..68f15115 100644 --- a/api/app/schemas/model_schema.py +++ b/api/app/schemas/model_schema.py @@ -1,4 +1,4 @@ -from pydantic import BaseModel, Field, field_serializer, ConfigDict +from pydantic import BaseModel, Field, field_serializer, field_validator, ConfigDict from typing import Optional, List, Dict, Any import datetime import uuid @@ -91,6 +91,18 @@ class ModelApiKey(ModelApiKeyBase): created_at: datetime.datetime updated_at: datetime.datetime + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v + @field_serializer("created_at", when_used="json") def _serialize_created_at(self, dt: datetime.datetime): return int(dt.timestamp() * 1000) if dt else None diff --git a/api/app/schemas/release_share_schema.py b/api/app/schemas/release_share_schema.py index 069b78a9..47897847 100644 --- a/api/app/schemas/release_share_schema.py +++ b/api/app/schemas/release_share_schema.py @@ -1,7 +1,7 @@ import uuid import datetime from typing import Optional, List, Dict, Any -from pydantic import BaseModel, Field, ConfigDict, field_serializer +from pydantic import BaseModel, Field, ConfigDict, field_serializer, field_validator # ---------- Input Schemas ---------- @@ -88,6 +88,18 @@ class SharedReleaseInfo(BaseModel): # 嵌入配置 allow_embed: bool + @field_validator("config", mode="before") + @classmethod + def parse_config(cls, v): + """处理 config 字段，如果是字符串则解析为字典""" + if isinstance(v, str): + import json + try: + return json.loads(v) + except json.JSONDecodeError: + return {} + return v if v is not None else {} + class EmbedCode(BaseModel): """嵌入代码""" diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 4f20f6d9..9766eec0 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -92,7 +92,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str try: memory_content = asyncio.run( MemoryAgentService().read_memory( - group_id=end_user_id, + end_user_id=end_user_id, message=question, history=[], search_switch="2", diff --git a/api/app/services/emotion_analytics_service.py b/api/app/services/emotion_analytics_service.py index 601d2921..af98fb52 100644 --- a/api/app/services/emotion_analytics_service.py +++ b/api/app/services/emotion_analytics_service.py @@ -75,7 +75,7 @@ class EmotionAnalyticsService: # 调用仓储层查询 tags = await self.emotion_repo.get_emotion_tags( - group_id=end_user_id, + end_user_id=end_user_id, emotion_type=emotion_type, start_date=start_date, end_date=end_date, @@ -157,7 +157,7 @@ class EmotionAnalyticsService: # 调用仓储层查询 keywords = await self.emotion_repo.get_emotion_wordcloud( - group_id=end_user_id, + end_user_id=end_user_id, emotion_type=emotion_type, limit=limit ) @@ -339,7 +339,7 @@ class EmotionAnalyticsService: # 获取时间范围内的情绪数据 emotions = await self.emotion_repo.get_emotions_in_range( - group_id=end_user_id, + end_user_id=end_user_id, time_range=time_range ) @@ -505,7 +505,7 @@ class EmotionAnalyticsService: ) config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config( - config_id=int(config_id), + config_id=(config_id), service_name="EmotionAnalyticsService.generate_emotion_suggestions" ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory @@ -519,7 +519,7 @@ class EmotionAnalyticsService: # 3. 获取情绪数据用于模式分析 emotions = await self.emotion_repo.get_emotions_in_range( - group_id=end_user_id, + end_user_id=end_user_id, time_range="30d" ) @@ -598,13 +598,13 @@ class EmotionAnalyticsService: # 查询用户的实体和标签 query = """ MATCH (e:Entity) - WHERE e.group_id = $group_id + WHERE e.end_user_id = $end_user_id RETURN e.name as name, e.type as type ORDER BY e.created_at DESC LIMIT 20 """ - entities = await connector.execute_query(query, group_id=end_user_id) + entities = await connector.execute_query(query, end_user_id=end_user_id) # 提取兴趣标签 interests = [e["name"] for e in entities if e.get("type") in ["INTEREST", "HOBBY"]][:5] diff --git a/api/app/services/emotion_config_service.py b/api/app/services/emotion_config_service.py index 37171640..9880d4e1 100644 --- a/api/app/services/emotion_config_service.py +++ b/api/app/services/emotion_config_service.py @@ -8,9 +8,11 @@ Classes: """ from typing import Dict, Any +from uuid import UUID + from sqlalchemy.orm import Session -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig from app.core.logging_config import get_business_logger logger = get_business_logger() @@ -37,7 +39,7 @@ class EmotionConfigService: self.db = db logger.info("情绪配置服务初始化完成") - def get_emotion_config(self, config_id: int) -> Dict[str, Any]: + def get_emotion_config(self, config_id: UUID) -> Dict[str, Any]: """获取情绪引擎配置 查询指定配置ID的情绪相关配置字段。 @@ -61,8 +63,8 @@ class EmotionConfigService: logger.info(f"获取情绪配置: config_id={config_id}") # 查询配置 - config = self.db.query(DataConfig).filter( - DataConfig.config_id == config_id + config = self.db.query(MemoryConfig).filter( + MemoryConfig.config_id == config_id ).first() if not config: @@ -144,7 +146,7 @@ class EmotionConfigService: def update_emotion_config( self, - config_id: int, + config_id: UUID, config_data: Dict[str, Any] ) -> Dict[str, Any]: """更新情绪引擎配置 @@ -173,8 +175,8 @@ class EmotionConfigService: self.validate_emotion_config(config_data) # 查询配置 - config = self.db.query(DataConfig).filter( - DataConfig.config_id == config_id + config = self.db.query(MemoryConfig).filter( + MemoryConfig.config_id == config_id ).first() if not config: diff --git a/api/app/services/emotion_extraction_service.py b/api/app/services/emotion_extraction_service.py index d134251d..6b596a80 100644 --- a/api/app/services/emotion_extraction_service.py +++ b/api/app/services/emotion_extraction_service.py @@ -14,7 +14,7 @@ from app.core.memory.llm_tools.llm_client import LLMClientException from app.core.memory.models.emotion_models import EmotionExtraction from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context -from app.models.data_config_model import DataConfig +from app.models.memory_config_model import MemoryConfig logger = logging.getLogger(__name__) @@ -60,7 +60,7 @@ class EmotionExtractionService: async def extract_emotion( self, statement: str, - config: DataConfig + config: MemoryConfig ) -> Optional[EmotionExtraction]: """Extract emotion information from a statement. diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 1e1cde89..6e72a53f 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -9,6 +9,7 @@ import os import re import time import uuid +from uuid import UUID from typing import Any, AsyncGenerator, Dict, List, Optional import redis @@ -27,6 +28,7 @@ from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags from app.core.memory.utils.llm.llm_utils import MemoryClientFactory from app.db import get_db_context from app.models.knowledge_model import Knowledge, KnowledgeType +from app.repositories.memory_short_repository import ShortTermMemoryRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_agent_schema import Write_UserInput from app.schemas.memory_config_schema import ConfigurationError @@ -35,6 +37,7 @@ from app.services.memory_config_service import MemoryConfigService from app.services.memory_konwledges_server import ( write_rag, ) +from langchain_core.messages import AIMessage from langchain_core.messages import HumanMessage from pydantic import BaseModel, Field from sqlalchemy import func @@ -54,25 +57,24 @@ _neo4j_connector = Neo4jConnector() class MemoryAgentService: """Service for memory agent operations""" - def writer_messages_deal(self, messages, start_time, group_id, config_id, message, context): + def writer_messages_deal(self, messages, start_time, end_user_id, config_id, message, context): duration = time.time() - start_time - if str(messages) == 'success': - logger.info(f"Write operation successful for group {group_id} with config_id {config_id}") + logger.info(f"Write operation successful for group {end_user_id} with config_id {config_id}") # 记录成功的操作 if audit_logger: - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=True, + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=True, duration=duration, details={"message_length": len(message)}) return context else: - logger.warning(f"Write operation failed for group {group_id}") + logger.warning(f"Write operation failed for group {end_user_id}") # 记录失败的操作 if audit_logger: audit_logger.log_operation( operation="WRITE", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=f"写入失败: {messages[:100]}" @@ -263,13 +265,13 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, group_id: str, messages: list[dict], config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID], db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id Args: - group_id: Group identifier (also used as end_user_id) - messages: Structured message list [{"role": "user", "content": "..."}, ...] + end_user_id: Group identifier (also used as end_user_id) + message: Message to write config_id: Configuration ID from database db: SQLAlchemy database session storage_type: Storage type (neo4j or rag) @@ -284,15 +286,15 @@ class MemoryAgentService: # Resolve config_id if None using end_user's connected config if config_id is None: try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") if config_id is None: - raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): - raise - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + raise # Re-raise our specific error + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") import time start_time = time.time() @@ -312,7 +314,7 @@ class MemoryAgentService: # Log failed operation if audit_logger: duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg) + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) raise ValueError(error_msg) @@ -320,24 +322,23 @@ class MemoryAgentService: if storage_type == "rag": # For RAG storage, convert messages to single string message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - result = await write_rag(group_id, message_text, user_rag_memory_id) + result = await write_rag(end_user_id, message_text, user_rag_memory_id) return result else: async with make_write_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # Convert structured messages to LangChain messages langchain_messages = [] for msg in messages: if msg['role'] == 'user': langchain_messages.append(HumanMessage(content=msg['content'])) elif msg['role'] == 'assistant': - from langchain_core.messages import AIMessage langchain_messages.append(AIMessage(content=msg['content'])) - + # 初始状态 - 包含所有必要字段 initial_state = { "messages": langchain_messages, - "group_id": group_id, + "end_user_id": end_user_id, "memory_config": memory_config } @@ -354,14 +355,14 @@ class MemoryAgentService: contents = massages.get('write_result') # Convert messages back to string for logging message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) - return self.writer_messages_deal(massagesstatus, start_time, group_id, config_id, message_text, contents) + return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents) except Exception as e: # Ensure proper error handling and logging error_msg = f"Write operation failed: {str(e)}" logger.error(error_msg) if audit_logger: duration = time.time() - start_time - audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg) + audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg) raise ValueError(error_msg) @@ -369,15 +370,14 @@ class MemoryAgentService: async def read_memory( self, - group_id: str, + end_user_id: str, message: str, history: List[Dict], search_switch: str, - config_id: Optional[str], + config_id: Optional[UUID], db: Session, storage_type: str, - user_rag_memory_id: str - ) -> Dict: + user_rag_memory_id: str) -> Dict: """ Process read operation with config_id @@ -387,7 +387,7 @@ class MemoryAgentService: - "2": Direct answer based on context Args: - group_id: Group identifier (also used as end_user_id) + end_user_id: Group identifier (also used as end_user_id) message: User message history: Conversation history search_switch: Search mode switch @@ -405,22 +405,22 @@ class MemoryAgentService: import time start_time = time.time() - logger.info(f"[PERF] read_memory started for group_id={group_id}, search_switch={search_switch}") + ori_message= message # Resolve config_id if None using end_user's connected config if config_id is None: try: - config_id = get_end_user_connected_config(group_id, db) - config_id=config_id.get('memory_config_id') + connected_config = get_end_user_connected_config(end_user_id, db) + config_id = connected_config.get("memory_config_id") if config_id is None: - raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): raise # Re-raise our specific error - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") - logger.info(f"Read operation for group {group_id} with config_id {config_id}") + logger.info(f"Read operation for group {end_user_id} with config_id {config_id}") # 导入审计日志记录器 try: @@ -448,7 +448,7 @@ class MemoryAgentService: audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=error_msg @@ -458,16 +458,16 @@ class MemoryAgentService: # Step 2: Prepare history history.append({"role": "user", "content": message}) - logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}") + logger.debug(f"Group ID:{end_user_id}, Message:{message}, History:{history}, Config ID:{config_id}") # Step 3: Initialize MCP client and execute read workflow graph_exec_start = time.time() try: async with make_read_graph() as graph: - config = {"configurable": {"thread_id": group_id}} + config = {"configurable": {"thread_id": end_user_id}} # 初始状态 - 包含所有必要字段 initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch, - "group_id": group_id + "end_user_id": end_user_id , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id, "memory_config": memory_config} # 获取节点更新信息 @@ -562,13 +562,13 @@ class MemoryAgentService: if '信息不足，无法回答。' != str(summary) and str(search_switch).strip() != "2": # 使用 upsert 方法 repo.upsert( - end_user_id=group_id, - messages=message, + end_user_id=end_user_id, + messages=ori_message, aimessages=summary, retrieved_content=retrieved_content, search_switch=str(search_switch) ) - logger.info(f"成功保存短期记忆: group_id={group_id}, search_switch={search_switch}") + logger.info(f"成功保存短期记忆: end_user_id={end_user_id}, search_switch={search_switch}") else: logger.debug(f"跳过保存短期记忆: summary={summary[:50] if summary else 'None'}, search_switch={search_switch}") @@ -584,7 +584,7 @@ class MemoryAgentService: audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=True, duration=duration ) @@ -596,20 +596,20 @@ class MemoryAgentService: except Exception as e: # Ensure proper error handling and logging error_msg = f"Read operation failed: {str(e)}" - total_time = time.time() - start_time - logger.error(f"[PERF] read_memory failed after {total_time:.4f}s: {error_msg}") + logger.error(error_msg) if audit_logger: duration = time.time() - start_time audit_logger.log_operation( operation="READ", config_id=config_id, - group_id=group_id, + end_user_id=end_user_id, success=False, duration=duration, error=error_msg ) raise ValueError(error_msg) + def get_messages_list(self, user_input: Write_UserInput) -> list[dict]: """ Get standardized message list from user input. @@ -654,7 +654,7 @@ class MemoryAgentService: logger.info(f"Validation successful: Structured message list, count: {len(user_input.messages)}") return user_input.messages - async def classify_message_type(self, message: str, config_id: int, db: Session) -> Dict: + async def classify_message_type(self, message: str, config_id: UUID, db: Session) -> Dict: """ Determine the type of user message (read or write) Updated to eliminate global variables in favor of explicit parameters. @@ -681,10 +681,9 @@ class MemoryAgentService: status = await status_typle(message, memory_config.llm_model_id) logger.debug(f"Message type: {status}") return status - async def generate_summary_from_retrieve( self, - group_id: str, + end_user_id: str, retrieve_info: str, history: List[Dict], query: str, @@ -708,16 +707,16 @@ class MemoryAgentService: """ if config_id is None: try: - config_id = get_end_user_connected_config(group_id, db) + config_id = get_end_user_connected_config(end_user_id, db) config_id = config_id.get('memory_config_id') if config_id is None: raise ValueError( - f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.") + f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.") except Exception as e: if "No memory configuration found" in str(e): raise # Re-raise our specific error - logger.error(f"Failed to get connected config for end_user {group_id}: {e}") - raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}") + logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}") + raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}") logger.info(f"Generating summary from retrieve info for query: {query[:50]}...") try: @@ -727,6 +726,7 @@ class MemoryAgentService: config_id=config_id, service_name="MemoryAgentService" ) + # 导入必要的模块 from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import summary_llm from app.core.memory.agent.models.summary_models import RetrieveSummaryResponse @@ -766,7 +766,7 @@ class MemoryAgentService: """ 统计知识库类型分布，包含： 1. PostgreSQL 中的知识库类型：General, Web, Third-party, Folder（根据 workspace_id 过滤） - 2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/group_id 过滤） + 2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/end_user_id 过滤） 3. total: 所有类型的总和 参数： @@ -852,11 +852,11 @@ class MemoryAgentService: for end_user in end_users: end_user_id_str = str(end_user.id) memory_query = """ - MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN count(n) AS Count + MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN count(n) AS Count """ neo4j_result = await _neo4j_connector.execute_query( memory_query, - group_id=end_user_id_str, + end_user_id=end_user_id_str, ) chunk_count = neo4j_result[0]["Count"] if neo4j_result else 0 total_chunks += chunk_count @@ -896,7 +896,7 @@ class MemoryAgentService: 获取指定用户的热门记忆标签 参数： - - end_user_id: 用户ID（可选），对应Neo4j中的group_id字段 + - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段 - limit: 返回标签数量限制 返回格式： @@ -906,7 +906,7 @@ class MemoryAgentService: ] """ try: - # by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度） + # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度） tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False) payload=[] for tag, freq in tags: @@ -981,21 +981,21 @@ class MemoryAgentService: # 查询该用户的语句 query = ( "MATCH (s:Statement) " - "WHERE ($group_id IS NULL OR s.group_id = $group_id) AND s.statement IS NOT NULL " + "WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND s.statement IS NOT NULL " "RETURN s.statement AS statement " "ORDER BY s.created_at DESC LIMIT 100" ) - rows = await connector.execute_query(query, group_id=end_user_id) + rows = await connector.execute_query(query, end_user_id=end_user_id) statements = [r.get("statement", "") for r in rows if r.get("statement")] # 查询该用户的热门实体 entity_query = ( "MATCH (e:ExtractedEntity) " - "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL " + "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC LIMIT 20" ) - entity_rows = await connector.execute_query(entity_query, group_id=end_user_id) + entity_rows = await connector.execute_query(entity_query, end_user_id=end_user_id) entities = [f"{r['name']} ({r['frequency']})" for r in entity_rows] await connector.close() @@ -1048,14 +1048,14 @@ class MemoryAgentService: names_to_exclude = ['AI', 'Caroline', 'Melanie', 'Jon', 'Gina', '用户', 'AI助手', 'John', 'Maria'] hot_tag_query = ( "MATCH (e:ExtractedEntity) " - "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' " + "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' " "AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude " "RETURN e.name AS name, count(e) AS frequency " "ORDER BY frequency DESC LIMIT 4" ) hot_tag_rows = await connector.execute_query( hot_tag_query, - group_id=end_user_id, + end_user_id=end_user_id, names_to_exclude=names_to_exclude ) await connector.close() @@ -1189,6 +1189,16 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An # 3. 从 config 中提取 memory_config_id config = latest_release.config or {} + + # 如果 config 是字符串，解析为字典 + if isinstance(config, str): + import json + try: + config = json.loads(config) + except json.JSONDecodeError: + logger.warning(f"Failed to parse config JSON for release {latest_release.id}") + config = {} + memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None @@ -1227,7 +1237,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) """ from app.models.app_release_model import AppRelease from app.models.end_user_model import EndUser - from app.models.data_config_model import DataConfig + from app.models.memory_config_model import MemoryConfig from sqlalchemy import select logger.info(f"Batch getting connected configs for {len(end_user_ids)} end_users") @@ -1240,10 +1250,10 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 1. 批量查询所有 end_user 及其 app_id end_users = db.query(EndUser).filter(EndUser.id.in_(end_user_ids)).all() - + # 创建 end_user_id -> app_id 的映射 user_to_app = {str(eu.id): eu.app_id for eu in end_users} - + # 记录未找到的用户 found_user_ids = set(user_to_app.keys()) missing_user_ids = set(end_user_ids) - found_user_ids @@ -1285,13 +1295,13 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 批量查询 memory_config_name config_id_to_name = {} if memory_config_ids: - memory_configs = db.query(DataConfig).filter(DataConfig.config_id.in_(memory_config_ids)).all() - config_id_to_name = {str(mc.config_id): mc.config_name for mc in memory_configs} + memory_configs = db.query(MemoryConfig).filter(MemoryConfig.id.in_(memory_config_ids)).all() + config_id_to_name = {str(mc.id): mc.config_name for mc in memory_configs} # 4. 构建最终结果 for end_user_id, app_id in user_to_app.items(): release = app_to_release.get(app_id) - + if not release: logger.warning(f"No active release found for app: {app_id} (end_user: {end_user_id})") result[end_user_id] = {"memory_config_id": None, "memory_config_name": None} @@ -1303,7 +1313,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None # 获取配置名称 - memory_config_name = config_id_to_name.get(str(memory_config_id)) if memory_config_id else None + memory_config_name = config_id_to_name.get(memory_config_id) if memory_config_id else None result[end_user_id] = { "memory_config_id": memory_config_id, diff --git a/api/app/services/memory_api_service.py b/api/app/services/memory_api_service.py index 2d3d047e..a8c39a5a 100644 --- a/api/app/services/memory_api_service.py +++ b/api/app/services/memory_api_service.py @@ -25,7 +25,7 @@ class MemoryAPIService: This service provides a thin layer that: 1. Validates end_user exists and belongs to the authorized workspace - 2. Maps end_user_id to group_id for memory operations + 2. Maps end_user_id to end_user_id for memory operations 3. Delegates to MemoryAgentService for actual memory read/write operations """ @@ -68,7 +68,7 @@ class MemoryAPIService: ) end_user = self.db.query(EndUser).filter(EndUser.id == end_user_uuid).first() - + if not end_user: logger.warning(f"End user not found: {end_user_id}") raise ResourceNotFoundException( @@ -118,7 +118,7 @@ class MemoryAPIService: Args: workspace_id: Workspace ID for resource validation - end_user_id: End user identifier (used as group_id) + end_user_id: End user identifier (used as end_user_id) message: Message content to store config_id: Optional memory configuration ID storage_type: Storage backend (neo4j or rag) @@ -136,14 +136,13 @@ class MemoryAPIService: # Validate end_user exists and belongs to workspace self.validate_end_user(end_user_id, workspace_id) - # Use end_user_id as group_id for memory operations - group_id = end_user_id + # Use end_user_id as end_user_id for memory operations try: # Delegate to MemoryAgentService result = await MemoryAgentService().write_memory( - group_id=group_id, - message=message, + end_user_id=end_user_id, + messages=message, config_id=config_id, db=self.db, storage_type=storage_type, @@ -189,7 +188,7 @@ class MemoryAPIService: Args: workspace_id: Workspace ID for resource validation - end_user_id: End user identifier (used as group_id) + end_user_id: End user identifier (used as end_user_id) message: Query message search_switch: Search mode (0=deep search with verification, 1=deep search, 2=fast search) config_id: Optional memory configuration ID @@ -208,13 +207,13 @@ class MemoryAPIService: # Validate end_user exists and belongs to workspace self.validate_end_user(end_user_id, workspace_id) - # Use end_user_id as group_id for memory operations - group_id = end_user_id + # Use end_user_id as end_user_id for memory operations + try: # Delegate to MemoryAgentService result = await MemoryAgentService().read_memory( - group_id=group_id, + end_user_id=end_user_id, message=message, history=[], search_switch=search_switch, diff --git a/api/app/services/memory_base_service.py b/api/app/services/memory_base_service.py index 25a8281d..bc647752 100644 --- a/api/app/services/memory_base_service.py +++ b/api/app/services/memory_base_service.py @@ -326,7 +326,7 @@ class MemoryBaseService: Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 最大emotion_intensity对应的emotion_type，如果没有则返回None @@ -334,7 +334,7 @@ class MemoryBaseService: try: query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) WHERE stmt.emotion_type IS NOT NULL AND stmt.emotion_intensity IS NOT NULL @@ -347,7 +347,7 @@ class MemoryBaseService: result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) if result and len(result) > 0: @@ -381,10 +381,10 @@ class MemoryBaseService: if end_user_id: query = """ MATCH (n:MemorySummary) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await self.neo4j_connector.execute_query(query, group_id=end_user_id) + result = await self.neo4j_connector.execute_query(query, end_user_id=end_user_id) else: query = """ MATCH (n:MemorySummary) @@ -423,12 +423,12 @@ class MemoryBaseService: if end_user_id: semantic_query = """ MATCH (e:ExtractedEntity) - WHERE e.group_id = $group_id AND e.is_explicit_memory = true + WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN count(e) as count """ semantic_result = await self.neo4j_connector.execute_query( semantic_query, - group_id=end_user_id + end_user_id=end_user_id ) else: semantic_query = """ @@ -519,7 +519,7 @@ class MemoryBaseService: """ if end_user_id: - query += " AND n.group_id = $group_id" + query += " AND n.end_user_id = $end_user_id" query += """ RETURN sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes @@ -528,7 +528,7 @@ class MemoryBaseService: # 设置查询参数 params = {'threshold': forgetting_threshold} if end_user_id: - params['group_id'] = end_user_id + params['end_user_id'] = end_user_id # 执行查询 result = await self.neo4j_connector.execute_query(query, **params) diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index 0099eb18..e901d65d 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -14,7 +14,7 @@ from app.core.validators.memory_config_validators import ( validate_embedding_model, validate_model_exists_and_active, ) -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.schemas.memory_config_schema import ( ConfigurationError, InvalidConfigError, @@ -23,20 +23,24 @@ from app.schemas.memory_config_schema import ( ModelNotFoundError, ) from sqlalchemy.orm import Session +from uuid import UUID logger = get_logger(__name__) config_logger = get_config_logger() - +import uuid def _validate_config_id(config_id): - """Validate configuration ID format.""" + """Validate configuration ID format (supports both UUID and integer).""" + if isinstance(config_id, uuid.UUID): + return config_id + if config_id is None: raise InvalidConfigError( "Configuration ID cannot be None", field_name="config_id", invalid_value=config_id, ) - + if isinstance(config_id, int): if config_id <= 0: raise InvalidConfigError( @@ -45,10 +49,19 @@ def _validate_config_id(config_id): invalid_value=config_id, ) return config_id - + if isinstance(config_id, str): + config_id_stripped = config_id.strip() + + # Try parsing as UUID first try: - parsed_id = int(config_id.strip()) + return uuid.UUID(config_id_stripped) + except ValueError: + pass + + # Fall back to integer parsing + try: + parsed_id = config_id_stripped if parsed_id <= 0: raise InvalidConfigError( f"Configuration ID must be positive: {parsed_id}", @@ -58,13 +71,13 @@ def _validate_config_id(config_id): return parsed_id except ValueError: raise InvalidConfigError( - f"Invalid configuration ID format: '{config_id}'", + f"Invalid configuration ID format: '{config_id}' (must be UUID or positive integer)", field_name="config_id", invalid_value=config_id, ) - + raise InvalidConfigError( - f"Invalid type for configuration ID: expected int or str, got {type(config_id).__name__}", + f"Invalid type for configuration ID: expected UUID, int or str, got {type(config_id).__name__}", field_name="config_id", invalid_value=config_id, ) @@ -73,61 +86,61 @@ def _validate_config_id(config_id): class MemoryConfigService: """ Centralized service for memory configuration loading and validation. - + This class provides a single implementation of configuration loading logic that can be shared across multiple services, eliminating code duplication. - + Usage: config_service = MemoryConfigService(db) memory_config = config_service.load_memory_config(config_id) model_config = config_service.get_model_config(model_id) """ - + def __init__(self, db: Session): """Initialize the service with a database session. - + Args: db: SQLAlchemy database session """ self.db = db - + def load_memory_config( self, - config_id: int, + config_id: UUID, service_name: str = "MemoryConfigService", ) -> MemoryConfig: """ Load memory configuration from database by config_id. - + Args: - config_id: Configuration ID from database + config_id: Configuration ID (UUID) from database service_name: Name of the calling service (for logging purposes) - + Returns: MemoryConfig: Immutable configuration object - + Raises: ConfigurationError: If validation fails """ start_time = time.time() - + config_logger.info( "Starting memory configuration loading", extra={ "operation": "load_memory_config", "service": service_name, - "config_id": config_id, + "config_id": str(config_id), }, ) - + logger.info(f"Loading memory configuration from database: config_id={config_id}") - + try: validated_config_id = _validate_config_id(config_id) - + # Step 1: Get config and workspace db_query_start = time.time() - result = DataConfigRepository.get_config_with_workspace(self.db, validated_config_id) + result = MemoryConfigRepository.get_config_with_workspace(self.db, validated_config_id) db_query_time = time.time() - db_query_start logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s") if not result: @@ -136,18 +149,18 @@ class MemoryConfigService: "Configuration not found in database", extra={ "operation": "load_memory_config", - "config_id": validated_config_id, + "config_id": str(config_id), "load_result": "not_found", "elapsed_ms": elapsed_ms, "service": service_name, }, ) raise ConfigurationError( - f"Configuration {validated_config_id} not found in database" + f"Configuration {config_id} not found in database" ) - + memory_config, workspace = result - + # Step 2: Validate embedding model (returns both UUID and name) embed_start = time.time() embedding_uuid, embedding_name = validate_embedding_model( @@ -159,7 +172,7 @@ class MemoryConfigService: ) embed_time = time.time() - embed_start logger.info(f"[PERF] Embedding validation: {embed_time:.4f}s") - + # Step 3: Resolve LLM model llm_start = time.time() llm_uuid, llm_name = validate_and_resolve_model_id( @@ -173,7 +186,7 @@ class MemoryConfigService: ) llm_time = time.time() - llm_start logger.info(f"[PERF] LLM validation: {llm_time:.4f}s") - + # Step 4: Resolve optional rerank model rerank_start = time.time() rerank_uuid = None @@ -191,10 +204,10 @@ class MemoryConfigService: rerank_time = time.time() - rerank_start if memory_config.rerank_id: logger.info(f"[PERF] Rerank validation: {rerank_time:.4f}s") - + # Note: embedding_name is now returned from validate_embedding_model above # No need for redundant query! - + # Create immutable MemoryConfig object config = MemoryConfig( config_id=memory_config.config_id, @@ -235,9 +248,9 @@ class MemoryConfigService: pruning_scene=memory_config.pruning_scene or "education", pruning_threshold=float(memory_config.pruning_threshold) if memory_config.pruning_threshold is not None else 0.5, ) - + elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.info( "Memory configuration loaded successfully", extra={ @@ -250,13 +263,13 @@ class MemoryConfigService: "elapsed_ms": elapsed_ms, }, ) - + logger.info(f"Memory configuration loaded successfully: {config.config_name}") return config - + except Exception as e: elapsed_ms = (time.time() - start_time) * 1000 - + config_logger.error( "Failed to load memory configuration", extra={ @@ -270,7 +283,7 @@ class MemoryConfigService: }, exc_info=True, ) - + logger.error(f"Failed to load memory configuration {config_id}: {e}") if isinstance(e, (ConfigurationError, ValueError)): raise diff --git a/api/app/services/memory_entity_relationship_service.py b/api/app/services/memory_entity_relationship_service.py index 9b5f3c99..7081d28b 100644 --- a/api/app/services/memory_entity_relationship_service.py +++ b/api/app/services/memory_entity_relationship_service.py @@ -717,8 +717,8 @@ class MemoryInteraction: ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id) if ori_data!=[]: # name = ori_data[0]['name'] - group_id = [i['group_id'] for i in ori_data][0] - Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id) + end_user_id = [i['end_user_id'] for i in ori_data][0] + Space_User = await self.connector.execute_query(Memory_Space_User, end_user_id=end_user_id) if not Space_User: return [] user_id=Space_User[0]['id'] diff --git a/api/app/services/memory_episodic_service.py b/api/app/services/memory_episodic_service.py index 12eeff6e..08751fd1 100644 --- a/api/app/services/memory_episodic_service.py +++ b/api/app/services/memory_episodic_service.py @@ -34,7 +34,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: (标题, 类型)元组，如果不存在则返回默认值 @@ -43,14 +43,14 @@ class MemoryEpisodicService(MemoryBaseService): # 查询Summary节点的name(作为title)和memory_type(作为type) query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id RETURN s.name AS title, s.memory_type AS type """ result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) if not result or len(result) == 0: @@ -77,7 +77,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 前3个实体的name属性列表 @@ -87,7 +87,7 @@ class MemoryEpisodicService(MemoryBaseService): # 按activation_value降序排序,返回前3个 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity) WHERE entity.activation_value IS NOT NULL @@ -99,7 +99,7 @@ class MemoryEpisodicService(MemoryBaseService): result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 提取实体名称 @@ -123,7 +123,7 @@ class MemoryEpisodicService(MemoryBaseService): Args: summary_id: Summary节点的ID - end_user_id: 终端用户ID (group_id) + end_user_id: 终端用户ID (end_user_id) Returns: 所有Statement节点的statement属性内容列表 @@ -132,7 +132,7 @@ class MemoryEpisodicService(MemoryBaseService): # 查询Summary节点指向的所有Statement节点 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement) WHERE stmt.statement IS NOT NULL AND stmt.statement <> '' RETURN stmt.statement AS statement @@ -141,7 +141,7 @@ class MemoryEpisodicService(MemoryBaseService): result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 提取statement内容 @@ -214,12 +214,12 @@ class MemoryEpisodicService(MemoryBaseService): # 1. 先查询所有情景记忆的总数（不受筛选条件限制） total_all_query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id RETURN count(s) AS total_all """ total_all_result = await self.neo4j_connector.execute_query( total_all_query, - group_id=end_user_id + end_user_id=end_user_id ) total_all = total_all_result[0]["total_all"] if total_all_result else 0 @@ -229,7 +229,7 @@ class MemoryEpisodicService(MemoryBaseService): # 3. 构建Cypher查询 query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id """ # 添加时间范围过滤 @@ -248,7 +248,7 @@ class MemoryEpisodicService(MemoryBaseService): ORDER BY s.created_at DESC """ - params = {"group_id": end_user_id} + params = {"end_user_id": end_user_id} if time_filter: params["time_filter"] = time_filter if title_keyword: @@ -333,14 +333,14 @@ class MemoryEpisodicService(MemoryBaseService): # 1. 查询指定的MemorySummary节点 query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $summary_id AND s.group_id = $group_id + WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id RETURN elementId(s) AS id, s.created_at AS created_at """ result = await self.neo4j_connector.execute_query( query, summary_id=summary_id, - group_id=end_user_id + end_user_id=end_user_id ) # 2. 如果节点不存在，返回错误 diff --git a/api/app/services/memory_explicit_service.py b/api/app/services/memory_explicit_service.py index 713215c3..f8d39ae8 100644 --- a/api/app/services/memory_explicit_service.py +++ b/api/app/services/memory_explicit_service.py @@ -60,7 +60,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 1. 查询情景记忆（MemorySummary节点） ========== episodic_query = """ MATCH (s:MemorySummary) - WHERE s.group_id = $group_id + WHERE s.end_user_id = $end_user_id RETURN elementId(s) AS id, s.name AS title, s.content AS content, @@ -70,7 +70,7 @@ class MemoryExplicitService(MemoryBaseService): episodic_result = await self.neo4j_connector.execute_query( episodic_query, - group_id=end_user_id + end_user_id=end_user_id ) # 处理情景记忆数据 @@ -96,7 +96,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 2. 查询语义记忆（ExtractedEntity节点） ========== semantic_query = """ MATCH (e:ExtractedEntity) - WHERE e.group_id = $group_id + WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN elementId(e) AS id, e.name AS name, @@ -107,7 +107,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_result = await self.neo4j_connector.execute_query( semantic_query, - group_id=end_user_id + end_user_id=end_user_id ) # 处理语义记忆数据 @@ -189,7 +189,7 @@ class MemoryExplicitService(MemoryBaseService): # ========== 1. 先尝试查询情景记忆 ========== episodic_query = """ MATCH (s:MemorySummary) - WHERE elementId(s) = $memory_id AND s.group_id = $group_id + WHERE elementId(s) = $memory_id AND s.end_user_id = $end_user_id RETURN s.name AS title, s.content AS content, s.created_at AS created_at @@ -198,7 +198,7 @@ class MemoryExplicitService(MemoryBaseService): episodic_result = await self.neo4j_connector.execute_query( episodic_query, memory_id=memory_id, - group_id=end_user_id + end_user_id=end_user_id ) if episodic_result and len(episodic_result) > 0: @@ -229,7 +229,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_query = """ MATCH (e:ExtractedEntity) WHERE elementId(e) = $memory_id - AND e.group_id = $group_id + AND e.end_user_id = $end_user_id AND e.is_explicit_memory = true RETURN e.name AS name, e.description AS core_definition, @@ -240,7 +240,7 @@ class MemoryExplicitService(MemoryBaseService): semantic_result = await self.neo4j_connector.execute_query( semantic_query, memory_id=memory_id, - group_id=end_user_id + end_user_id=end_user_id ) if semantic_result and len(semantic_result) > 0: diff --git a/api/app/services/memory_forget_service.py b/api/app/services/memory_forget_service.py index 2db4cdc7..e1030b24 100644 --- a/api/app/services/memory_forget_service.py +++ b/api/app/services/memory_forget_service.py @@ -12,6 +12,7 @@ from typing import Optional, Dict, Any, Tuple from datetime import datetime, timezone +from uuid import UUID from sqlalchemy.orm import Session @@ -23,7 +24,7 @@ from app.core.memory.storage_services.forgetting_engine.config_utils import ( load_actr_config_from_db, ) from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.forgetting_cycle_history_repository import ForgettingCycleHistoryRepository @@ -70,7 +71,7 @@ class MemoryForgetService: def __init__(self): """初始化服务""" - self.config_repository = DataConfigRepository() + self.config_repository = MemoryConfigRepository() self.history_repository = ForgettingCycleHistoryRepository() def _get_neo4j_connector(self) -> Neo4jConnector: @@ -87,7 +88,7 @@ class MemoryForgetService: async def _get_forgetting_components( self, db: Session, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Tuple[ACTRCalculator, ForgettingStrategy, ForgettingScheduler, Dict[str, Any]]: """ 获取遗忘引擎组件（计算器、策略、调度器） @@ -132,7 +133,7 @@ class MemoryForgetService: async def _get_knowledge_stats( self, connector: Neo4jConnector, - group_id: Optional[str] = None, + end_user_id: Optional[str] = None, forgetting_threshold: float = 0.3 ) -> Dict[str, Any]: """ @@ -140,7 +141,7 @@ class MemoryForgetService: Args: connector: Neo4j 连接器 - group_id: 组ID（可选） + end_user_id: 组ID（可选） forgetting_threshold: 遗忘阈值 Returns: @@ -152,8 +153,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) """ - if group_id: - query += " AND n.group_id = $group_id" + if end_user_id: + query += " AND n.end_user_id = $end_user_id" query += """ WITH n, @@ -172,8 +173,8 @@ class MemoryForgetService: """ params = {'threshold': forgetting_threshold} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id results = await connector.execute_query(query, **params) @@ -200,7 +201,7 @@ class MemoryForgetService: async def _get_pending_forgetting_nodes( self, connector: Neo4jConnector, - group_id: str, + end_user_id: str, forgetting_threshold: float, min_days_since_access: int, limit: int = 20 @@ -212,7 +213,7 @@ class MemoryForgetService: Args: connector: Neo4j 连接器 - group_id: 组ID + end_user_id: 组ID forgetting_threshold: 遗忘阈值 min_days_since_access: 最小未访问天数 limit: 返回节点数量限制 @@ -229,7 +230,7 @@ class MemoryForgetService: query = """ MATCH (n) WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary) - AND n.group_id = $group_id + AND n.end_user_id = $end_user_id AND n.activation_value IS NOT NULL AND n.activation_value < $threshold AND n.last_access_time IS NOT NULL @@ -250,7 +251,7 @@ class MemoryForgetService: """ params = { - 'group_id': group_id, + 'end_user_id': end_user_id, 'threshold': forgetting_threshold, 'min_access_time_str': min_access_time_str, 'limit': limit @@ -291,10 +292,10 @@ class MemoryForgetService: async def trigger_forgetting_cycle( self, db: Session, - group_id: str, + end_user_id: str, max_merge_batch_size: Optional[int] = None, min_days_since_access: Optional[int] = None, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 手动触发遗忘周期 @@ -303,10 +304,10 @@ class MemoryForgetService: Args: db: 数据库会话 - group_id: 组ID（即终端用户ID，必填） + end_user_id: 组ID（即终端用户ID，必填） max_merge_batch_size: 最大融合批次大小（可选） min_days_since_access: 最小未访问天数（可选） - config_id: 配置ID（必填，由控制器层通过 group_id 获取） + config_id: 配置ID（必填，由控制器层通过 end_user_id 获取） Returns: dict: 遗忘报告 @@ -319,7 +320,7 @@ class MemoryForgetService: # 运行遗忘周期（LLM 客户端将在需要时由 forgetting_strategy 内部获取） report = await forgetting_scheduler.run_forgetting_cycle( - group_id=group_id, + end_user_id=end_user_id, max_merge_batch_size=max_merge_batch_size, min_days_since_access=min_days_since_access, config_id=config_id, @@ -338,7 +339,7 @@ class MemoryForgetService: stats_query = """ MATCH (n) WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) - AND n.group_id = $group_id + AND n.end_user_id = $end_user_id RETURN count(n) as total_nodes, avg(n.activation_value) as average_activation, @@ -347,7 +348,7 @@ class MemoryForgetService: stats_results = await connector.execute_query( stats_query, - group_id=group_id, + end_user_id=end_user_id, threshold=config['forgetting_threshold'] ) @@ -364,7 +365,7 @@ class MemoryForgetService: # 保存历史记录到数据库 self.history_repository.create( db=db, - end_user_id=group_id, + end_user_id=end_user_id, execution_time=execution_time, merged_count=report['merged_count'], failed_count=report['failed_count'], @@ -376,7 +377,7 @@ class MemoryForgetService: ) api_logger.info( - f"已保存遗忘周期历史记录: end_user_id={group_id}, " + f"已保存遗忘周期历史记录: end_user_id={end_user_id}, " f"merged_count={report['merged_count']}" ) @@ -389,7 +390,7 @@ class MemoryForgetService: def read_forgetting_config( self, db: Session, - config_id: int + config_id: UUID ) -> Dict[str, Any]: """ 获取遗忘引擎配置 @@ -416,7 +417,7 @@ class MemoryForgetService: def update_forgetting_config( self, db: Session, - config_id: int, + config_id: UUID, update_fields: Dict[str, Any] ) -> Dict[str, Any]: """ @@ -465,8 +466,8 @@ class MemoryForgetService: async def get_forgetting_stats( self, db: Session, - group_id: Optional[str] = None, - config_id: Optional[int] = None + end_user_id: Optional[str] = None, + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 获取遗忘引擎统计信息 @@ -475,7 +476,7 @@ class MemoryForgetService: Args: db: 数据库会话 - group_id: 组ID（可选） + end_user_id: 组ID（可选） config_id: 配置ID（可选，用于获取遗忘阈值） Returns: @@ -493,8 +494,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) """ - if group_id: - activation_query += " AND n.group_id = $group_id" + if end_user_id: + activation_query += " AND n.end_user_id = $end_user_id" activation_query += """ RETURN @@ -506,8 +507,8 @@ class MemoryForgetService: """ params = {'threshold': forgetting_threshold} - if group_id: - params['group_id'] = group_id + if end_user_id: + params['end_user_id'] = end_user_id activation_results = await connector.execute_query(activation_query, **params) @@ -539,8 +540,8 @@ class MemoryForgetService: WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk) """ - if group_id: - distribution_query += " AND n.group_id = $group_id" + if end_user_id: + distribution_query += " AND n.end_user_id = $end_user_id" distribution_query += """ WITH n, @@ -558,8 +559,8 @@ class MemoryForgetService: """ dist_params = {} - if group_id: - dist_params['group_id'] = group_id + if end_user_id: + dist_params['end_user_id'] = end_user_id distribution_results = await connector.execute_query(distribution_query, **dist_params) @@ -582,11 +583,11 @@ class MemoryForgetService: # 获取最近7个日期的历史趋势数据（每天取最后一次执行） recent_trends = [] try: - if group_id: + if end_user_id: # 查询所有历史记录 history_records = self.history_repository.get_recent_by_end_user( db=db, - end_user_id=group_id + end_user_id=end_user_id ) # 按日期分组（一天可能有多次执行，取最后一次） @@ -632,7 +633,7 @@ class MemoryForgetService: # 获取待遗忘节点列表（前20个满足遗忘条件的节点） pending_nodes = [] try: - if group_id: + if end_user_id: # 验证 min_days_since_access 配置值 min_days = config.get('min_days_since_access') if min_days is None or not isinstance(min_days, (int, float)) or min_days < 0: @@ -643,7 +644,7 @@ class MemoryForgetService: pending_nodes = await self._get_pending_forgetting_nodes( connector=connector, - group_id=group_id, + end_user_id=end_user_id, forgetting_threshold=forgetting_threshold, min_days_since_access=int(min_days), limit=20 @@ -677,7 +678,7 @@ class MemoryForgetService: db: Session, importance_score: float, days: int, - config_id: Optional[int] = None + config_id: Optional[UUID] = None ) -> Dict[str, Any]: """ 获取遗忘曲线数据 diff --git a/api/app/services/memory_konwledges_server.py b/api/app/services/memory_konwledges_server.py index c6297e12..420f7ca1 100644 --- a/api/app/services/memory_konwledges_server.py +++ b/api/app/services/memory_konwledges_server.py @@ -450,12 +450,12 @@ async def create_document_chunk( return success(data=chunk, msg="文档块创建成功") -async def write_rag(group_id, message, user_rag_memory_id): +async def write_rag(end_user_id, message, user_rag_memory_id): """ 将消息写入 RAG 知识库 Args: - group_id: 组ID，用作文件标题 + end_user_id: 组ID，用作文件标题 message: 消息内容 user_rag_memory_id: 知识库ID（必须是有效的UUID） @@ -487,10 +487,10 @@ async def write_rag(group_id, message, user_rag_memory_id): db = next(db_gen) try: - create_data = CustomTextFileCreate(title=group_id, content=message) + create_data = CustomTextFileCreate(title=end_user_id, content=message) current_user = SimpleUser(user_rag_memory_id) # 检查文档是否已存在 - document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{group_id}.txt") + document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{end_user_id}.txt") print('======',document) api_logger.info(f"查找文档结果: document_id={document}") if document is not None: @@ -508,7 +508,7 @@ async def write_rag(group_id, message, user_rag_memory_id): return result else: # 文档不存在，创建新文档 - api_logger.info(f"文档不存在，创建新文档: group_id={group_id}") + api_logger.info(f"文档不存在，创建新文档: end_user_id={end_user_id}") result = await memory_konwledges_up( kb_id=user_rag_memory_id, parent_id=user_rag_memory_id, @@ -520,13 +520,13 @@ async def write_rag(group_id, message, user_rag_memory_id): new_document_id = find_document_id_by_kb_and_filename( db=db, kb_id=user_rag_memory_id, - file_name=f"{group_id}.txt" + file_name=f"{end_user_id}.txt" ) if new_document_id: await parse_document_by_id(new_document_id, db=db, current_user=current_user) else: - api_logger.error(f"创建文档后无法找到文档ID: group_id={group_id}") + api_logger.error(f"创建文档后无法找到文档ID: end_user_id={end_user_id}") return result finally: # 确保数据库会话被关闭 diff --git a/api/app/services/memory_perceptual_service.py b/api/app/services/memory_perceptual_service.py index d257e80f..b9d96a0b 100644 --- a/api/app/services/memory_perceptual_service.py +++ b/api/app/services/memory_perceptual_service.py @@ -6,7 +6,7 @@ from sqlalchemy.orm import Session from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger -from app.models.memory_perceptual_model import PerceptualType, FileStorageType +from app.models.memory_perceptual_model import PerceptualType, FileStorageService from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository from app.schemas.memory_perceptual_schema import ( PerceptualQuerySchema, @@ -137,8 +137,19 @@ class MemoryPerceptualService: memory_items = [] for memory in memories: meta_data = memory.meta_data or {} - content = meta_data.get("content") - content = Content(**content) + content = meta_data.get("content", {}) + + # 安全地提取 content 字段，提供默认值 + if content: + content_obj = Content(**content) + topic = content_obj.topic + domain = content_obj.domain + keywords = content_obj.keywords + else: + topic = "Unknown" + domain = "Unknown" + keywords = [] + memory_item = PerceptualMemoryItem( id=memory.id, perceptual_type=PerceptualType(memory.perceptual_type), @@ -146,11 +157,12 @@ class MemoryPerceptualService: file_name=memory.file_name, file_ext=memory.file_ext, summary=memory.summary, - topic=content.topic, - domain=content.domain, - keywords=content.keywords, + meta_data=meta_data, + topic=topic, + domain=domain, + keywords=keywords, created_time=int(memory.created_time.timestamp()*1000), - storage_type=FileStorageType(memory.storage_service), + storage_service=FileStorageService(memory.storage_service), ) memory_items.append(memory_item) diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py index af72e3cc..402a40a1 100644 --- a/api/app/services/memory_reflection_service.py +++ b/api/app/services/memory_reflection_service.py @@ -13,7 +13,7 @@ from app.db import get_db from app.core.logging_config import get_api_logger from app.core.memory.storage_services.reflection_engine import ReflectionConfig, ReflectionEngine from app.core.memory.storage_services.reflection_engine.self_reflexion import ReflectionRange, ReflectionBaseline -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.models.app_model import App from app.models.app_release_model import AppRelease @@ -73,7 +73,7 @@ class WorkspaceAppService: "created_at": app.created_at.isoformat() if app.created_at else None, "updated_at": app.updated_at.isoformat() if app.updated_at else None, "releases": [], - "data_configs": [], + "memory_configs": [], "end_users": [] } @@ -101,11 +101,11 @@ class WorkspaceAppService: if memory_content: processed_configs.add(memory_content) - data_config_info = self._get_data_config(memory_content) + memory_config_info = self._get_memory_config(memory_content) - if data_config_info: - if not any(dc["config_id"] == data_config_info["config_id"] for dc in app_info["data_configs"]): - app_info["data_configs"].append(data_config_info) + if memory_config_info: + if not any(dc["config_id"] == memory_config_info["config_id"] for dc in app_info["memory_configs"]): + app_info["memory_configs"].append(memory_config_info) app_info["releases"].append(release_info) @@ -120,30 +120,30 @@ class WorkspaceAppService: return None - def _get_data_config(self, memory_content: str) -> Dict[str, Any]: - """Retrieve data_comfig information based on memory_comtent""" + def _get_memory_config(self, memory_content: str) -> Dict[str, Any]: + """Retrieve memory_config information based on memory_content""" try: - data_config_result = DataConfigRepository.query_reflection_config_by_id(self.db, int(memory_content)) + memory_config_result = MemoryConfigRepository.query_reflection_config_by_id(self.db, int(memory_content)) - # data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content) - # data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone() - # if data_config_result is None: + # memory_config_query, memory_config_params = MemoryConfigRepository.build_select_reflection(memory_content) + # memory_config_result = self.db.execute(text(memory_config_query), memory_config_params).fetchone() + # if memory_config_result is None: # return None - if data_config_result: + if memory_config_result: return { - "config_id": data_config_result.config_id, - "enable_self_reflexion": data_config_result.enable_self_reflexion, - "iteration_period": data_config_result.iteration_period, - "reflexion_range": data_config_result.reflexion_range, - "baseline": data_config_result.baseline, - "reflection_model_id": data_config_result.reflection_model_id, - "memory_verify": data_config_result.memory_verify, - "quality_assessment": data_config_result.quality_assessment, - "user_id": data_config_result.user_id + "config_id": memory_config_result.config_id, + "enable_self_reflexion": memory_config_result.enable_self_reflexion, + "iteration_period": memory_config_result.iteration_period, + "reflexion_range": memory_config_result.reflexion_range, + "baseline": memory_config_result.baseline, + "reflection_model_id": memory_config_result.reflection_model_id, + "memory_verify": memory_config_result.memory_verify, + "quality_assessment": memory_config_result.quality_assessment, + "user_id": memory_config_result.user_id } except Exception as e: - api_logger.warning(f"查询data_config失败，memory_content: {memory_content}, 错误: {str(e)}") + api_logger.warning(f"查询memory_config失败，memory_content: {memory_content}, 错误: {str(e)}") return None @@ -226,7 +226,7 @@ class MemoryReflectionService: } config_data_id = config_data['config_id'] - reflection_config = WorkspaceAppService(self.db)._get_data_config(config_data_id) + reflection_config = WorkspaceAppService(self.db)._get_memory_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config = self._create_reflection_config_from_data(reflection_config) # 3. 执行反思引擎 @@ -280,7 +280,7 @@ class MemoryReflectionService: config_data_id=config_data['config_id'] - reflection_config=WorkspaceAppService(self.db)._get_data_config(config_data_id) + reflection_config=WorkspaceAppService(self.db)._get_memory_config(config_data_id) if reflection_config is not None and reflection_config['enable_self_reflexion']: reflection_config= self._create_reflection_config_from_data(reflection_config) iteration_period = int(reflection_config.iteration_period) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index c276f337..80d8c717 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -19,7 +19,7 @@ from app.core.memory.analytics.hot_memory_tags import ( ) from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats from app.models.user_model import User -from app.repositories.data_config_repository import DataConfigRepository +from app.repositories.memory_config_repository import MemoryConfigRepository from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.schemas.memory_config_schema import ConfigurationError from app.schemas.memory_storage_schema import ( @@ -129,7 +129,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） if not params.rerank_id: params.rerank_id = configs.get('rerank') - config = DataConfigRepository.create(self.db, params) + config = MemoryConfigRepository.create(self.db, params) self.db.commit() return {"affected": 1, "config_id": config.config_id} @@ -146,20 +146,20 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Delete --- def delete(self, key: ConfigParamsDelete) -> Dict[str, Any]: # 删除配置参数（按配置ID） - success = DataConfigRepository.delete(self.db, key.config_id) + success = MemoryConfigRepository.delete(self.db, key.config_id) if not success: raise ValueError("未找到配置") return {"affected": 1} # --- Update --- def update(self, update: ConfigUpdate) -> Dict[str, Any]: # 部分更新配置参数 - config = DataConfigRepository.update(self.db, update) + config = MemoryConfigRepository.update(self.db, update) if not config: raise ValueError("未找到配置") return {"affected": 1} def update_extracted(self, update: ConfigUpdateExtracted) -> Dict[str, Any]: # 更新记忆萃取引擎配置参数 - config = DataConfigRepository.update_extracted(self.db, update) + config = MemoryConfigRepository.update_extracted(self.db, update) if not config: raise ValueError("未找到配置") return {"affected": 1} @@ -170,14 +170,14 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Read --- def get_extracted(self, key: ConfigKey) -> Dict[str, Any]: # 获取萃取配置参数 - result = DataConfigRepository.get_extracted_config(self.db, key.config_id) + result = MemoryConfigRepository.get_extracted_config(self.db, key.config_id) if not result: raise ValueError("未找到配置") return result # --- Read All --- def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数 - configs = DataConfigRepository.get_all(self.db, workspace_id) + configs = MemoryConfigRepository.get_all(self.db, workspace_id) # 将 ORM 对象转换为字典列表 data_list = [] @@ -187,7 +187,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, - "group_id": config.group_id, + "end_user_id": config.end_user_id, "user_id": config.user_id, "apply_id": config.apply_id, "llm_id": config.llm_id, @@ -395,8 +395,8 @@ _neo4j_connector = Neo4jConnector() async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_DIALOGUE, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_DIALOGUE, + end_user_id=end_user_id, ) data = {"search_for": "dialogue", "num": result[0]["num"]} return data @@ -404,8 +404,8 @@ async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_CHUNK, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_CHUNK, + end_user_id=end_user_id, ) data = {"search_for": "chunk", "num": result[0]["num"]} return data @@ -413,8 +413,8 @@ async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_STATEMENT, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_STATEMENT, + end_user_id=end_user_id, ) data = {"search_for": "statement", "num": result[0]["num"]} return data @@ -422,8 +422,8 @@ async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ENTITY, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_ENTITY, + end_user_id=end_user_id, ) data = {"search_for": "entity", "num": result[0]["num"]} return data @@ -431,8 +431,8 @@ async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]: async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ALL, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_ALL, + end_user_id=end_user_id, ) # 检查结果是否为空或长度不足 @@ -466,8 +466,8 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A 聚合 dialogue/chunk/statement/entity 四类计数，返回统一的分布结构，便于前端一次性消费。 """ result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_ALL, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_ALL, + end_user_id=end_user_id, ) # 检查结果是否为空或长度不足 @@ -497,21 +497,19 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_DETIALS, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_DETIALS, + end_user_id=end_user_id, ) return result async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]: result = await _neo4j_connector.execute_query( - DataConfigRepository.SEARCH_FOR_EDGES, - group_id=end_user_id, + MemoryConfigRepository.SEARCH_FOR_EDGES, + end_user_id=end_user_id, ) return result - - async def analytics_hot_memory_tags( db: Session, current_user: User, @@ -574,7 +572,7 @@ async def analytics_hot_memory_tags( # 步骤4: 只调用一次LLM进行筛选 tag_names = [tag for tag, _ in sorted_tags] - # 使用第一个用户的group_id来获取LLM配置 + # 使用第一个用户的end_user_id来获取LLM配置 # 因为同一工作空间下的用户应该使用相同的配置 first_end_user_id = str(end_users[0].id) filtered_tag_names = await filter_tags_with_llm(tag_names, first_end_user_id) diff --git a/api/app/services/pilot_run_service.py b/api/app/services/pilot_run_service.py index 17dfd7eb..755dda14 100644 --- a/api/app/services/pilot_run_service.py +++ b/api/app/services/pilot_run_service.py @@ -91,7 +91,7 @@ async def run_pilot_extraction( dialog = DialogData( context=context, ref_id="pilot_dialog_1", - group_id=str(memory_config.workspace_id), + end_user_id=str(memory_config.workspace_id), user_id=str(memory_config.tenant_id), apply_id=str(memory_config.config_id), metadata={"source": "pilot_run", "input_type": "frontend_text"}, diff --git a/api/app/services/user_memory_service.py b/api/app/services/user_memory_service.py index 863bccb0..3a90a821 100644 --- a/api/app/services/user_memory_service.py +++ b/api/app/services/user_memory_service.py @@ -155,10 +155,10 @@ class MemoryInsightHelper: """ query = """ MATCH (d:Dialogue) - WHERE d.group_id = $group_id AND d.created_at IS NOT NULL AND d.created_at <> '' + WHERE d.end_user_id = $end_user_id AND d.created_at IS NOT NULL AND d.created_at <> '' RETURN d.created_at AS creation_time """ - records = await self.neo4j_connector.execute_query(query, group_id=self.user_id) + records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id) if not records: return [] @@ -211,17 +211,17 @@ class MemoryInsightHelper: async def get_social_connections(self) -> dict | None: """Find the user with whom the most memories are shared.""" query = """ - MATCH (c1:Chunk {group_id: $group_id}) + MATCH (c1:Chunk {end_user_id: $end_user_id}) OPTIONAL MATCH (c1)-[:CONTAINS]->(s:Statement) OPTIONAL MATCH (s)<-[:CONTAINS]-(c2:Chunk) - WHERE c1.group_id <> c2.group_id AND s IS NOT NULL AND c2 IS NOT NULL - WITH c2.group_id AS other_user_id, COUNT(DISTINCT s) AS common_statements + WHERE c1.end_user_id <> c2.end_user_id AND s IS NOT NULL AND c2 IS NOT NULL + WITH c2.end_user_id AS other_user_id, COUNT(DISTINCT s) AS common_statements WHERE common_statements > 0 RETURN other_user_id, common_statements ORDER BY common_statements DESC LIMIT 1 """ - records = await self.neo4j_connector.execute_query(query, group_id=self.user_id) + records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id) if not records or not records[0].get("other_user_id"): return None @@ -230,7 +230,7 @@ class MemoryInsightHelper: time_range_query = """ MATCH (c:Chunk) - WHERE c.group_id IN [$user_id, $other_user_id] + WHERE c.end_user_id IN [$user_id, $other_user_id] RETURN min(c.created_at) AS start_time, max(c.created_at) AS end_time """ time_records = await self.neo4j_connector.execute_query( @@ -294,11 +294,11 @@ class UserSummaryHelper: """Fetch recent statements authored by the user/group for context.""" query = ( "MATCH (s:Statement) " - "WHERE s.group_id = $group_id AND s.statement IS NOT NULL " + "WHERE s.end_user_id = $end_user_id AND s.statement IS NOT NULL " "RETURN s.statement AS statement, s.created_at AS created_at " "ORDER BY created_at DESC LIMIT $limit" ) - rows = await self.connector.execute_query(query, group_id=self.user_id, limit=limit) + rows = await self.connector.execute_query(query, end_user_id=self.user_id, limit=limit) records = [] for r in rows: try: @@ -1152,7 +1152,7 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, import re # 创建 UserSummaryHelper 实例 - user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_GROUP_ID", "group_123")) + user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_end_user_id", "group_123")) try: # 1) 收集上下文数据 @@ -1273,10 +1273,10 @@ async def analytics_node_statistics( if end_user_id: query = f""" MATCH (n:{node_type}) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await _neo4j_connector.execute_query(query, group_id=end_user_id) + result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id) else: query = f""" MATCH (n:{node_type}) @@ -1387,10 +1387,10 @@ async def analytics_memory_types( # 查询 Statement 节点数量 query = """ MATCH (n:Statement) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN count(n) as count """ - result = await _neo4j_connector.execute_query(query, group_id=end_user_id) + result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id) statement_count = result[0]["count"] if result and len(result) > 0 else 0 # 取三分之一作为隐性记忆数量 implicit_count = round(statement_count / 3) @@ -1504,7 +1504,7 @@ async def analytics_graph_data( 包含节点、边和统计信息的字典 """ try: - # 1. 获取 group_id + # 1. 获取 end_user_id user_uuid = uuid.UUID(end_user_id) repo = EndUserRepository(db) end_user = repo.get_by_id(user_uuid) @@ -1528,7 +1528,7 @@ async def analytics_graph_data( # 基于中心节点的扩展查询 node_query = f""" MATCH path = (center)-[*1..{depth}]-(connected) - WHERE center.group_id = $group_id + WHERE center.end_user_id = $end_user_id AND elementId(center) = $center_node_id WITH collect(DISTINCT center) + collect(DISTINCT connected) as all_nodes UNWIND all_nodes as n @@ -1539,7 +1539,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "center_node_id": center_node_id, "limit": limit } @@ -1547,7 +1547,7 @@ async def analytics_graph_data( # 按节点类型过滤查询 node_query = """ MATCH (n) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id AND labels(n)[0] IN $node_types RETURN elementId(n) as id, @@ -1556,7 +1556,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "node_types": node_types, "limit": limit } @@ -1564,7 +1564,7 @@ async def analytics_graph_data( # 查询所有节点 node_query = """ MATCH (n) - WHERE n.group_id = $group_id + WHERE n.end_user_id = $end_user_id RETURN elementId(n) as id, labels(n)[0] as label, @@ -1572,7 +1572,7 @@ async def analytics_graph_data( LIMIT $limit """ node_params = { - "group_id": end_user_id, + "end_user_id": end_user_id, "limit": limit } diff --git a/api/app/tasks.py b/api/app/tasks.py index 5f2b1ef5..cdd7945e 100644 --- a/api/app/tasks.py +++ b/api/app/tasks.py @@ -4,6 +4,7 @@ import os import re import time import uuid +from uuid import UUID from datetime import datetime, timezone from math import ceil from typing import Any, Dict, List, Optional @@ -382,16 +383,16 @@ def build_graphrag_for_kb(kb_id: uuid.UUID): @celery_app.task(name="app.core.memory.agent.read_message", bind=True) -def read_message_task(self, group_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]: +def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a read message via MemoryAgentService. Args: - group_id: Group ID for the memory agent (also used as end_user_id) + end_user_id: Group ID for the memory agent (also used as end_user_id) message: User message to process history: Conversation history search_switch: Search switch parameter - config_id: Optional configuration ID + config_id: Configuration ID as string (will be converted to UUID) Returns: Dict containing the result and metadata @@ -401,14 +402,22 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, """ start_time = time.time() + # Convert config_id string to UUID + actual_config_id = None + if config_id: + try: + actual_config_id = uuid.UUID(config_id) if isinstance(config_id, str) else config_id + except (ValueError, AttributeError): + # If conversion fails, leave as None and try to resolve + pass + # Resolve config_id if None - actual_config_id = config_id if actual_config_id is None: try: from app.services.memory_agent_service import get_end_user_connected_config db = next(get_db()) try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) actual_config_id = connected_config.get("memory_config_id") finally: db.close() @@ -420,24 +429,42 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, db = next(get_db()) try: service = MemoryAgentService() - return await service.read_memory(group_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id) + return await service.read_memory(end_user_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id) finally: db.close() try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time return { "status": "SUCCESS", "result": result, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id } except BaseException as e: elapsed_time = time.time() - start_time + # Handle ExceptionGroup from TaskGroup if hasattr(e, 'exceptions'): error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions] detailed_error = "; ".join(error_messages) @@ -446,7 +473,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, return { "status": "FAILURE", "error": detailed_error, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id @@ -454,19 +481,13 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str, @celery_app.task(name="app.core.memory.agent.write_message", bind=True) -def write_message_task(self, group_id: str, message, config_id: str, storage_type: str, user_rag_memory_id: str) -> Dict[str, Any]: +def write_message_task(self, end_user_id: str, message: str, config_id: str, storage_type:str, user_rag_memory_id:str) -> Dict[str, Any]: """Celery task to process a write message via MemoryAgentService. - 支持两种消息格式： - 1. 字符串格式（向后兼容）：message="user: xxx\nassistant: yyy" - 2. 结构化消息列表（推荐）：message=[{"role": "user", "content": "xxx"}, {"role": "assistant", "content": "yyy"}] - Args: - group_id: Group ID for the memory agent (also used as end_user_id) - message: Message to write (str or list[dict]) - config_id: Optional configuration ID - storage_type: Storage type (neo4j/rag) - user_rag_memory_id: RAG memory ID + end_user_id: Group ID for the memory agent (also used as end_user_id) + message: Message to write + config_id: Configuration ID as string (will be converted to UUID) Returns: Dict containing the result and metadata @@ -477,30 +498,46 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ from app.core.logging_config import get_logger logger = get_logger(__name__) - logger.info(f"[CELERY WRITE] Starting write task - group_id={group_id}, config_id={config_id}, storage_type={storage_type}") + logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") start_time = time.time() + # Convert config_id string to UUID + actual_config_id = None + if config_id: + try: + actual_config_id = uuid.UUID(config_id) if isinstance(config_id, str) else config_id + logger.info(f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} (type: {type(actual_config_id).__name__})") + except (ValueError, AttributeError) as e: + logger.error(f"[CELERY WRITE] Invalid config_id format: {config_id}, error: {e}") + return { + "status": "FAILURE", + "error": f"Invalid config_id format: {config_id}", + "end_user_id": end_user_id, + "config_id": config_id, + "elapsed_time": 0.0, + "task_id": self.request.id + } + # Resolve config_id if None - actual_config_id = config_id if actual_config_id is None: try: from app.services.memory_agent_service import get_end_user_connected_config db = next(get_db()) try: - connected_config = get_end_user_connected_config(group_id, db) + connected_config = get_end_user_connected_config(end_user_id, db) actual_config_id = connected_config.get("memory_config_id") finally: db.close() except Exception: # Log but continue - will fail later with proper error pass - + async def _run() -> str: db = next(get_db()) try: - logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory") + logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory with config_id={actual_config_id} (type: {type(actual_config_id).__name__})") service = MemoryAgentService() - result = await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id) + result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type, user_rag_memory_id) logger.info(f"[CELERY WRITE] Write completed successfully: {result}") return result except Exception as e: @@ -510,7 +547,24 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ db.close() try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time logger.info(f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}") @@ -518,13 +572,14 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ return { "status": "SUCCESS", "result": result, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id } except BaseException as e: elapsed_time = time.time() - start_time + # Handle ExceptionGroup from TaskGroup if hasattr(e, 'exceptions'): error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions] detailed_error = "; ".join(error_messages) @@ -536,7 +591,7 @@ def write_message_task(self, group_id: str, message, config_id: str, storage_typ return { "status": "FAILURE", "error": detailed_error, - "group_id": group_id, + "end_user_id": end_user_id, "config_id": config_id, "elapsed_time": elapsed_time, "task_id": self.request.id @@ -878,7 +933,24 @@ def regenerate_memory_cache(self) -> Dict[str, Any]: } try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time result["elapsed_time"] = elapsed_time result["task_id"] = self.request.id @@ -951,7 +1023,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: end_users = data['end_users'] for base, config, user in zip(releases, data_configs, end_users): - if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']: + if str(base['config']) == str(config['config_id']) and str(base['app_id']) == str(user['app_id']): # 调用反思服务 api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}") @@ -1005,7 +1077,24 @@ def workspace_reflection_task(self) -> Dict[str, Any]: } try: - result = asyncio.run(_run()) + # 使用 nest_asyncio 来避免事件循环冲突 + try: + import nest_asyncio + nest_asyncio.apply() + except ImportError: + pass + + # 尝试获取现有事件循环，如果不存在则创建新的 + try: + loop = asyncio.get_event_loop() + if loop.is_closed(): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + result = loop.run_until_complete(_run()) elapsed_time = time.time() - start_time result["elapsed_time"] = elapsed_time result["task_id"] = self.request.id @@ -1023,7 +1112,7 @@ def workspace_reflection_task(self) -> Dict[str, Any]: @celery_app.task(name="app.tasks.run_forgetting_cycle_task", bind=True) -def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str, Any]: +def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Dict[str, Any]: """定时任务：运行遗忘周期 定期执行遗忘周期，识别并融合低激活值的知识节点。 @@ -1051,7 +1140,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str # 运行遗忘周期 report = await forget_service.trigger_forgetting( db=db, - group_id=None, # 处理所有组 + end_user_id=None, # 处理所有组 config_id=config_id ) @@ -1081,4 +1170,11 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str "duration_seconds": duration } - return asyncio.run(_run()) + # 运行异步函数 + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + result = loop.run_until_complete(_run()) + return result + finally: + loop.close() diff --git a/api/app/utils/app_config_utils.py b/api/app/utils/app_config_utils.py index 514e4565..ae41d8bf 100644 --- a/api/app/utils/app_config_utils.py +++ b/api/app/utils/app_config_utils.py @@ -83,6 +83,13 @@ class AgentConfigProxy: def agent_config_4_app_release(release: AppRelease) -> AgentConfig: config_dict = release.config + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} agent_config = AgentConfig( app_id=release.app_id, @@ -100,6 +107,14 @@ def agent_config_4_app_release(release: AppRelease) -> AgentConfig: def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig: config_dict = release.config + + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} agent_config = MultiAgentConfig( app_id=release.app_id, @@ -120,6 +135,14 @@ def multi_agent_config_4_app_release(release: AppRelease) -> MultiAgentConfig: def workflow_config_4_app_release(release: AppRelease) -> WorkflowConfig: config_dict = release.config + + # 如果 config 是字符串，解析为字典 + if isinstance(config_dict, str): + import json + try: + config_dict = json.loads(config_dict) + except json.JSONDecodeError: + config_dict = {} config = WorkflowConfig( id=config_dict.get("id"), diff --git a/api/uv.lock b/api/uv.lock index bccaef2c..f3b23325 100644 --- a/api/uv.lock +++ b/api/uv.lock @@ -4462,4 +4462,4 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/ff/8d/0309daffea4fcac7981021dbf21cdb2e3427a9e76bafbcdbdf5392ff99a4/zstandard-0.25.0-cp312-cp312-win32.whl", hash = "sha256:23ebc8f17a03133b4426bcc04aabd68f8236eb78c3760f12783385171b0fd8bd", size = 436922, upload-time = "2025-09-14T22:17:24.398Z" }, { url = "https://files.pythonhosted.org/packages/79/3b/fa54d9015f945330510cb5d0b0501e8253c127cca7ebe8ba46a965df18c5/zstandard-0.25.0-cp312-cp312-win_amd64.whl", hash = "sha256:ffef5a74088f1e09947aecf91011136665152e0b4b359c42be3373897fb39b01", size = 506276, upload-time = "2025-09-14T22:17:21.429Z" }, { url = "https://files.pythonhosted.org/packages/ea/6b/8b51697e5319b1f9ac71087b0af9a40d8a6288ff8025c36486e0c12abcc4/zstandard-0.25.0-cp312-cp312-win_arm64.whl", hash = "sha256:181eb40e0b6a29b3cd2849f825e0fa34397f649170673d385f3598ae17cca2e9", size = 462679, upload-time = "2025-09-14T22:17:23.147Z" }, -] +] \ No newline at end of file From e3b6ede99240faab54f41dc07aea72971791d385 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 11:54:38 +0800 Subject: [PATCH 062/175] feat(sandbox): add Python 3 code execution sandbox support --- .gitignore | 3 + sandbox/Dockerfile | 42 ++++ sandbox/app/config.py | 134 ++++++++++++ sandbox/app/controllers/__init__.py | 8 + sandbox/app/controllers/health_controller.py | 12 ++ sandbox/app/controllers/sandbox_controller.py | 59 ++++++ sandbox/app/core/__init__.py | 1 + sandbox/app/core/encryption.py | 32 +++ sandbox/app/core/executor.py | 48 +++++ sandbox/app/core/runners/__init__.py | 1 + sandbox/app/core/runners/python/__init__.py | 4 + sandbox/app/core/runners/python/env.py | 50 +++++ sandbox/app/core/runners/python/prescript.py | 56 +++++ .../app/core/runners/python/python_runner.py | 151 ++++++++++++++ sandbox/app/core/runners/python/settings.py | 62 ++++++ sandbox/app/dependencies.py | 161 +++++++++++++++ sandbox/app/logger.py | 42 ++++ sandbox/app/middleware/__init__.py | 1 + sandbox/app/middleware/auth.py | 15 ++ sandbox/app/middleware/concurrency.py | 48 +++++ sandbox/app/models.py | 80 +++++++ sandbox/app/services/__init__.py | 1 + sandbox/app/services/python_service.py | 80 +++++++ sandbox/config.yaml | 20 ++ sandbox/dependencies/python-requirements.txt | 4 + sandbox/lib/seccomp_nodejs/Cargo.lock | 7 + sandbox/lib/seccomp_nodejs/Cargo.toml | 6 + sandbox/lib/seccomp_nodejs/src/lib.rs | 0 sandbox/lib/seccomp_python/Cargo.lock | 23 +++ sandbox/lib/seccomp_python/Cargo.toml | 12 ++ sandbox/lib/seccomp_python/src/lib.rs | 195 ++++++++++++++++++ sandbox/lib/seccomp_python/src/syscalls.rs | 85 ++++++++ sandbox/main.py | 97 +++++++++ sandbox/requirements.txt | 20 ++ sandbox/script/env.sh | 53 +++++ 35 files changed, 1613 insertions(+) create mode 100644 sandbox/Dockerfile create mode 100644 sandbox/app/config.py create mode 100644 sandbox/app/controllers/__init__.py create mode 100644 sandbox/app/controllers/health_controller.py create mode 100644 sandbox/app/controllers/sandbox_controller.py create mode 100644 sandbox/app/core/__init__.py create mode 100644 sandbox/app/core/encryption.py create mode 100644 sandbox/app/core/executor.py create mode 100644 sandbox/app/core/runners/__init__.py create mode 100644 sandbox/app/core/runners/python/__init__.py create mode 100644 sandbox/app/core/runners/python/env.py create mode 100644 sandbox/app/core/runners/python/prescript.py create mode 100644 sandbox/app/core/runners/python/python_runner.py create mode 100644 sandbox/app/core/runners/python/settings.py create mode 100644 sandbox/app/dependencies.py create mode 100644 sandbox/app/logger.py create mode 100644 sandbox/app/middleware/__init__.py create mode 100644 sandbox/app/middleware/auth.py create mode 100644 sandbox/app/middleware/concurrency.py create mode 100644 sandbox/app/models.py create mode 100644 sandbox/app/services/__init__.py create mode 100644 sandbox/app/services/python_service.py create mode 100644 sandbox/config.yaml create mode 100644 sandbox/dependencies/python-requirements.txt create mode 100644 sandbox/lib/seccomp_nodejs/Cargo.lock create mode 100644 sandbox/lib/seccomp_nodejs/Cargo.toml create mode 100644 sandbox/lib/seccomp_nodejs/src/lib.rs create mode 100644 sandbox/lib/seccomp_python/Cargo.lock create mode 100644 sandbox/lib/seccomp_python/Cargo.toml create mode 100644 sandbox/lib/seccomp_python/src/lib.rs create mode 100644 sandbox/lib/seccomp_python/src/syscalls.rs create mode 100644 sandbox/main.py create mode 100644 sandbox/requirements.txt create mode 100644 sandbox/script/env.sh diff --git a/.gitignore b/.gitignore index c2648945..de160688 100644 --- a/.gitignore +++ b/.gitignore @@ -35,3 +35,6 @@ nltk_data/ tika-server*.jar* cl100k_base.tiktoken libssl*.deb + +sandbox/lib/seccomp_python/target +sandbox/lib/seccomp_nodejs/target diff --git a/sandbox/Dockerfile b/sandbox/Dockerfile new file mode 100644 index 00000000..677b991c --- /dev/null +++ b/sandbox/Dockerfile @@ -0,0 +1,42 @@ +FROM python:3.12-slim +USER root +WORKDIR /code +LABEL authors="Eterntiy" + +ARG NEED_MIRROR=0 + +RUN --mount=type=cache,id=mem_apt,target=/var/cache/apt,sharing=locked \ + if [ "$NEED_MIRROR" == "1" ]; then \ + sed -i 's|https://ports.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \ + sed -i 's|https://archive.ubuntu.com|https://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \ + fi; \ + rm -f /etc/apt/apt.conf.d/docker-clean && \ + echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \ + chmod 1777 /tmp && \ + apt update && \ + apt --no-install-recommends install -y ca-certificates && \ + apt update && \ + apt install -y python3-pip pipx nginx unzip curl wget git vim less && \ + apt-get install -y --no-install-recommends tzdata libseccomp2 libseccomp-dev && \ + ln -snf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && \ + echo "Asia/Shanghai" > /etc/timezone && \ + apt install -y cargo + +COPY ./app /code/app +COPY ./dependencies /code/dependencies +COPY ./lib /code/lib +COPY ./script /code/script +COPY ./config.yaml /code/config.yaml +COPY ./main.py /code/main.py +COPY ./requirements.txt /code/requirements.txt + +RUN python -m venv .venv +RUN .venv/bin/python3 -m pip install -r requirements.txt + +RUN cargo build --release --manifest-path lib/seccomp_python/Cargo.toml + +HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \ + CMD curl 127.0.0.1:8194/health + + +CMD [".venv/bin/python3", "main.py"] \ No newline at end of file diff --git a/sandbox/app/config.py b/sandbox/app/config.py new file mode 100644 index 00000000..3fa4cab5 --- /dev/null +++ b/sandbox/app/config.py @@ -0,0 +1,134 @@ +"""Configuration management""" +import os +from typing import List, Optional +from pydantic import BaseModel, Field +import yaml + +SANDBOX_USER_ID = 1000 +SANDBOX_GROUP_ID = 1000 + +DEFAULT_PYTHON_LIB_REQUIREMENTS_AMD = [ + "/usr/local/lib/python3.12", + "/usr/lib/python3", + "/usr/lib/x86_64-linux-gnu", + "/etc/ssl/certs/ca-certificates.crt", + "/etc/nsswitch.conf", + "/etc/hosts", + "/etc/resolv.conf", + "/run/systemd/resolve/stub-resolv.conf", + "/run/resolvconf/resolv.conf", + "/etc/localtime", + "/usr/share/zoneinfo", + "/etc/timezone", +] + + +class AppConfig(BaseModel): + """Application configuration""" + port: int = 8194 + debug: bool = True + key: str = "redbear-sandbox" + + +class ProxyConfig(BaseModel): + """Proxy configuration""" + socks5: str = "" + http: str = "" + https: str = "" + + +class Config(BaseModel): + """Global configuration""" + app: AppConfig = Field(default_factory=AppConfig) + max_workers: int = 4 + max_requests: int = 50 + worker_timeout: int = 30 + nodejs_path: str = "node" + enable_network: bool = True + enable_preload: bool = False + + python_path: str = "" + python_lib_paths: list = Field(default=DEFAULT_PYTHON_LIB_REQUIREMENTS_AMD) + python_deps_update_interval: str = "30m" + allowed_syscalls: List[int] = Field(default_factory=list) + proxy: ProxyConfig = Field(default_factory=ProxyConfig) + + +# Global configuration instance +_config: Optional[Config] = None + + +def load_config(config_path: str) -> Config: + """Load configuration from YAML file""" + global _config + + # Load from file + if os.path.exists(config_path): + with open(config_path, 'r') as f: + data = yaml.safe_load(f) + _config = Config(**data) + else: + _config = Config() + + # Override with environment variables + if os.getenv("DEBUG"): + _config.app.debug = os.getenv("DEBUG").lower() in ("true", "1", "yes") + + if os.getenv("MAX_WORKERS"): + _config.max_workers = int(os.getenv("MAX_WORKERS")) + + if os.getenv("MAX_REQUESTS"): + _config.max_requests = int(os.getenv("MAX_REQUESTS")) + + if os.getenv("SANDBOX_PORT"): + _config.app.port = int(os.getenv("SANDBOX_PORT")) + + if os.getenv("WORKER_TIMEOUT"): + _config.worker_timeout = int(os.getenv("WORKER_TIMEOUT")) + + if os.getenv("API_KEY"): + _config.app.key = os.getenv("API_KEY") + + if os.getenv("NODEJS_PATH"): + _config.nodejs_path = os.getenv("NODEJS_PATH") + + if os.getenv("ENABLE_NETWORK"): + _config.enable_network = os.getenv("ENABLE_NETWORK").lower() in ("true", "1", "yes") + + if os.getenv("ENABLE_PRELOAD"): + _config.enable_preload = os.getenv("ENABLE_PRELOAD").lower() in ("true", "1", "yes") + + if os.getenv("ALLOWED_SYSCALLS"): + _config.allowed_syscalls = [int(x) for x in os.getenv("ALLOWED_SYSCALLS").split(",")] + + if os.getenv("SOCKS5_PROXY"): + _config.proxy.socks5 = os.getenv("SOCKS5_PROXY") + + if os.getenv("HTTP_PROXY"): + _config.proxy.http = os.getenv("HTTP_PROXY") + + if os.getenv("HTTPS_PROXY"): + _config.proxy.https = os.getenv("HTTPS_PROXY") + + # python + if os.getenv("PYTHON_PATH"): + _config.python_path = os.getenv("PYTHON_PATH") + + if os.getenv("PYTHON_LIB_PATH"): + _config.python_lib_paths = os.getenv("PYTHON_LIB_PATH").split(',') + + if os.getenv("PYTHON_DEPS_UPDATE_INTERVAL"): + _config.python_deps_update_interval = os.getenv("PYTHON_DEPS_UPDATE_INTERVAL") + + return _config + + +config_path = os.getenv("CONFIG_PATH", "config.yaml") +load_config(config_path) + + +def get_config() -> Config: + """Get global configuration""" + if _config is None: + raise RuntimeError("Configuration not loaded. Call load_config() first.") + return _config diff --git a/sandbox/app/controllers/__init__.py b/sandbox/app/controllers/__init__.py new file mode 100644 index 00000000..b1d965ae --- /dev/null +++ b/sandbox/app/controllers/__init__.py @@ -0,0 +1,8 @@ +from fastapi import APIRouter + +from . import health_controller, sandbox_controller + +manager_router = APIRouter() + +manager_router.include_router(health_controller.router) +manager_router.include_router(sandbox_controller.router) diff --git a/sandbox/app/controllers/health_controller.py b/sandbox/app/controllers/health_controller.py new file mode 100644 index 00000000..4d872e58 --- /dev/null +++ b/sandbox/app/controllers/health_controller.py @@ -0,0 +1,12 @@ +"""Health check endpoint""" +from fastapi import APIRouter + +from app.models import HealthResponse + +router = APIRouter() + + +@router.get("/health", response_model=HealthResponse) +async def health_check(): + """Health check endpoint""" + return HealthResponse(status="healthy", version="2.0.0") diff --git a/sandbox/app/controllers/sandbox_controller.py b/sandbox/app/controllers/sandbox_controller.py new file mode 100644 index 00000000..1a713f52 --- /dev/null +++ b/sandbox/app/controllers/sandbox_controller.py @@ -0,0 +1,59 @@ +"""Sandbox API endpoints""" +from fastapi import APIRouter, Depends + +from app.middleware.auth import verify_api_key +from app.middleware.concurrency import check_max_requests, acquire_worker +from app.models import ( + RunCodeRequest, + ApiResponse, + UpdateDependencyRequest, + error_response +) +from app.services.python_service import ( + run_python_code, + list_python_dependencies, + update_python_dependencies +) + +router = APIRouter( + prefix="/v1/sandbox", + tags=["sandbox"], + dependencies=[Depends(verify_api_key)] +) + + +@router.post( + "/run", + response_model=ApiResponse, + dependencies=[Depends(check_max_requests), + Depends(acquire_worker)] +) +async def run_code(request: RunCodeRequest): + """Execute code in sandbox""" + if request.language == "python3": + return await run_python_code(request.code, request.preload, request.options) + elif request.language == "nodejs": + # TODO + return error_response(-400, "TODO") + else: + return error_response(-400, "unsupported language") + + +@router.get("/dependencies", response_model=ApiResponse) +async def get_dependencies(language: str): + """Get installed dependencies""" + if language == "python3": + return await list_python_dependencies() + else: + return error_response(-400, "unsupported language") + + +@router.post("/dependencies/update", response_model=ApiResponse) +async def update_dependencies(request: UpdateDependencyRequest): + """Update dependencies""" + if request.language == "python3": + return await update_python_dependencies() + else: + return error_response(-400, "unsupported language") + + diff --git a/sandbox/app/core/__init__.py b/sandbox/app/core/__init__.py new file mode 100644 index 00000000..e1abba12 --- /dev/null +++ b/sandbox/app/core/__init__.py @@ -0,0 +1 @@ +"""Core functionality package""" diff --git a/sandbox/app/core/encryption.py b/sandbox/app/core/encryption.py new file mode 100644 index 00000000..5e0855c9 --- /dev/null +++ b/sandbox/app/core/encryption.py @@ -0,0 +1,32 @@ +"""Code encryption utilities""" +import base64 + + +def encrypt_code(code: bytes, key: bytes) -> str: + """Encrypt code using XOR cipher with base64 encoding + + Args: + code: Plain code string + key: Encryption key bytes + + Returns: + Base64 encoded encrypted code + """ + encrypted_code = bytearray(len(code)) + for i in range(len(code)): + encrypted_code[i] = code[i] ^ key[i % 64] + encoded_code = base64.b64encode(encrypted_code).decode("utf-8") + return encoded_code + + +def generate_key(length: int = 64) -> bytes: + """Generate random encryption key + + Args: + length: Key length in bytes (default 64 for 512 bits) + + Returns: + Random key bytes + """ + import secrets + return secrets.token_bytes(length) diff --git a/sandbox/app/core/executor.py b/sandbox/app/core/executor.py new file mode 100644 index 00000000..6edc48c0 --- /dev/null +++ b/sandbox/app/core/executor.py @@ -0,0 +1,48 @@ +"""Code execution engine""" +import os +from typing import Optional +from abc import ABC, abstractmethod + +from app.config import get_config +from app.logger import get_logger +from app.models import RunnerOptions + + +class ExecutionResult: + """Result of code execution""" + + def __init__(self, stdout: str = "", stderr: str = "", exit_code: int = 0, error: Optional[str] = None): + self.stdout = stdout + self.stderr = stderr + self.exit_code = exit_code + self.error = error + + +class CodeExecutor(ABC): + """Base code executor""" + + def __init__(self): + self.logger = get_logger() + self.config = get_config() + + @abstractmethod + async def run( + self, + code: str, + options: RunnerOptions, + preload: str = "", + timeout: Optional[int] = None + ) -> ExecutionResult: + pass + + def cleanup_temp_file(self, file_path: str) -> None: + """Remove temporary file + + Args: + file_path: Path to file to remove + """ + try: + if os.path.exists(file_path): + os.remove(file_path) + except Exception as e: + self.logger.warning(f"Failed to cleanup temp file {file_path}: {e}") diff --git a/sandbox/app/core/runners/__init__.py b/sandbox/app/core/runners/__init__.py new file mode 100644 index 00000000..96c5e380 --- /dev/null +++ b/sandbox/app/core/runners/__init__.py @@ -0,0 +1 @@ +"""Code runners package""" diff --git a/sandbox/app/core/runners/python/__init__.py b/sandbox/app/core/runners/python/__init__.py new file mode 100644 index 00000000..99a56ef7 --- /dev/null +++ b/sandbox/app/core/runners/python/__init__.py @@ -0,0 +1,4 @@ +# -*- coding: UTF-8 -*- +# Author: Eternity +# @Email: 1533512157@qq.com +# @Time : 2026/1/23 11:27 diff --git a/sandbox/app/core/runners/python/env.py b/sandbox/app/core/runners/python/env.py new file mode 100644 index 00000000..d82b0522 --- /dev/null +++ b/sandbox/app/core/runners/python/env.py @@ -0,0 +1,50 @@ +import asyncio +import tempfile +import stat +from pathlib import Path + +from app.config import get_config +from app.core.runners.python.settings import LIB_PATH +from app.logger import get_logger + +logger = get_logger() + + +async def prepare_python_dependencies_env(): + config = get_config() + + with tempfile.TemporaryDirectory(dir="/") as root_path: + root = Path(root_path) + + env_sh = root / "env.sh" + with open("script/env.sh") as f: + env_sh.write_text(f.read()) + env_sh.chmod(env_sh.stat().st_mode | stat.S_IXUSR) + + for lib_path in config.python_lib_paths: + lib_path = Path(lib_path) + + if not lib_path.exists(): + logger.warning("python lib path %s is not available", lib_path) + continue + + cmd = [ + "bash", + str(env_sh), + str(lib_path), + str(LIB_PATH), + ] + + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + retcode = process.returncode + + if retcode != 0: + logger.error( + f"create env error for file {lib_path}: retcode={retcode}, stderr={stderr.decode()}" + ) diff --git a/sandbox/app/core/runners/python/prescript.py b/sandbox/app/core/runners/python/prescript.py new file mode 100644 index 00000000..4790be73 --- /dev/null +++ b/sandbox/app/core/runners/python/prescript.py @@ -0,0 +1,56 @@ +import ctypes +import os +import sys +import traceback +from base64 import b64decode + + +# Setup exception hook +def excepthook(etype, value, tb): + sys.stderr.write("".join(traceback.format_exception(etype, value, tb))) + sys.stderr.flush() + sys.exit(-1) + + +sys.excepthook = excepthook + +# Load security library if available +lib = ctypes.CDLL("./libpython.so") +lib.init_seccomp.argtypes = [ctypes.c_uint32, ctypes.c_uint32, ctypes.c_bool] +lib.init_seccomp.restype = None + +# Get running path +running_path = sys.argv[1] +if not running_path: + exit(-1) + +# Get decrypt key +key = sys.argv[2] +if not key: + exit(-1) + +key = b64decode(key) + +os.chdir(running_path) + +# Preload code +{{preload}} + +# Apply security if library is available +lib.init_seccomp({{uid}}, {{gid}}, {{enable_network}}) + +# Decrypt and execute code +code = b64decode("{{code}}") + + +def decrypt(code, key): + key_len = len(key) + code_len = len(code) + code = bytearray(code) + for i in range(code_len): + code[i] = code[i] ^ key[i % key_len] + return bytes(code) + + +code = decrypt(code, key) +exec(code) diff --git a/sandbox/app/core/runners/python/python_runner.py b/sandbox/app/core/runners/python/python_runner.py new file mode 100644 index 00000000..faac5f0c --- /dev/null +++ b/sandbox/app/core/runners/python/python_runner.py @@ -0,0 +1,151 @@ +"""Python code runner""" +import asyncio +import base64 +import os +import uuid +from typing import Optional + +from app.config import SANDBOX_USER_ID, SANDBOX_GROUP_ID, get_config +from app.core.encryption import generate_key, encrypt_code +from app.core.executor import CodeExecutor, ExecutionResult +from app.core.runners.python.settings import check_lib_avaiable, release_lib_binary, LIB_PATH +from app.models import RunnerOptions + +# Python sandbox prescript template +with open("app/core/runners/python/prescript.py") as f: + PYTHON_PRESCRIPT = f.read() + + +class PythonRunner(CodeExecutor): + """Python code runner with security isolation""" + + def __init__(self): + super().__init__() + + @staticmethod + def init_enviroment(code: bytes, preload, options: RunnerOptions) -> tuple[str, str]: + if not check_lib_avaiable(): + release_lib_binary(False) + config = get_config() + code_file_name = uuid.uuid4().hex.replace("-", "_") + + script = PYTHON_PRESCRIPT.replace("{{uid}}", str(SANDBOX_USER_ID), 1) + script = script.replace("{{gid}}", str(SANDBOX_GROUP_ID), 1) + script = script.replace( + "{{enable_network}}", + str(int(options.enable_network and config.enable_network) + ), + 1 + ) + script = script.replace("{{preload}}", f"{preload}\n", 1) + + key = generate_key(64) + + encoded_code = encrypt_code(code, key) + encoded_key = base64.b64encode(key).decode("utf-8") + + script = script.replace("{{code}}", encoded_code, 1) + + code_path = f"{LIB_PATH}/tmp/{code_file_name}.py" + try: + os.makedirs(os.path.dirname(code_path), mode=0o755, exist_ok=True) + with open(code_path, "w", encoding="utf-8") as f: + f.write(script) + os.chmod(code_path, 0o755) + + except OSError as e: + raise RuntimeError(f"Failed to write {code_path}") from e + + return code_path, encoded_key + + async def run( + self, + code: str, + options: RunnerOptions, + preload: str = "", + timeout: Optional[int] = None + ) -> ExecutionResult: + """Run Python code in sandbox + + Args: + options: + code: Base64 encoded encrypted code + preload: Preload code to execute before main code + timeout: Execution timeout in seconds + + Returns: + ExecutionResult with stdout, stderr, and exit code + """ + config = self.config + + if timeout is None: + timeout = config.worker_timeout + + # Check if preload is allowed + if not config.enable_preload: + preload = "" + code = base64.b64decode(code) + script_path, encoded_key = self.init_enviroment(code, preload, options=options) + + try: + # Setup environment + env = {} + + # Add proxy settings if configured + if config.proxy.socks5: + env["HTTPS_PROXY"] = config.proxy.socks5 + env["HTTP_PROXY"] = config.proxy.socks5 + elif config.proxy.https or config.proxy.http: + if config.proxy.https: + env["HTTPS_PROXY"] = config.proxy.https + if config.proxy.http: + env["HTTP_PROXY"] = config.proxy.http + + # Add allowed syscalls if configured + if config.allowed_syscalls: + env["ALLOWED_SYSCALLS"] = ",".join(map(str, config.allowed_syscalls)) + + # Execute with Python interpreter + + process = await asyncio.create_subprocess_exec( + config.python_path, + script_path, + LIB_PATH, + encoded_key, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + env=env, + cwd=LIB_PATH + ) + + # Wait for completion with timeout + try: + stdout, stderr = await asyncio.wait_for( + process.communicate(), + timeout=timeout + ) + + return ExecutionResult( + stdout=stdout.decode('utf-8', errors='replace'), + stderr=stderr.decode('utf-8', errors='replace'), + exit_code=process.returncode + ) + + except asyncio.TimeoutError: + # Kill process on timeout + try: + process.kill() + await process.wait() + except: + pass + + return ExecutionResult( + stdout="", + stderr="Execution timeout", + exit_code=-1, + error="Execution timeout" + ) + + finally: + # Cleanup temporary file + self.cleanup_temp_file(script_path) diff --git a/sandbox/app/core/runners/python/settings.py b/sandbox/app/core/runners/python/settings.py new file mode 100644 index 00000000..aee8827b --- /dev/null +++ b/sandbox/app/core/runners/python/settings.py @@ -0,0 +1,62 @@ +import os + +from app.logger import get_logger + +logger = get_logger() + +RELEASE_LIB_PATH = "./lib/seccomp_python/target/release/libpython.so" +LIB_PATH = "/var/sandbox/sandbox-python" +LIB_NAME = "libpython.so" + +try: + with open(RELEASE_LIB_PATH, "rb") as f: + _PYTHON_LIB = f.read() +except: + logger.critical("failed to load python lib") + raise + + +def check_lib_avaiable(): + return os.path.exists(os.path.join(LIB_PATH, LIB_NAME)) + + +def release_lib_binary(force_remove: bool): + logger.info("init runtime enviroment") + lib_file = os.path.join(LIB_PATH, LIB_NAME) + if os.path.exists(lib_file): + if force_remove: + try: + os.remove(lib_file) + except OSError: + logger.critical(f"failed to remove {os.path.join(LIB_PATH, LIB_NAME)}") + raise + + try: + os.makedirs(LIB_PATH, mode=0o755, exist_ok=True) + except OSError: + logger.critical(f"failed to create {LIB_PATH}") + raise + + try: + with open(lib_file, "wb") as f: + f.write(_PYTHON_LIB) + os.chmod(lib_file, 0o755) + except OSError: + logger.critical(f"failed to write {lib_file}") + raise + else: + try: + os.makedirs(LIB_PATH, mode=0o755, exist_ok=True) + except OSError: + logger.critical(f"failed to create {LIB_PATH}") + raise + + try: + with open(lib_file, "wb") as f: + f.write(_PYTHON_LIB) + os.chmod(lib_file, 0o755) + except OSError: + logger.critical(f"failed to write {lib_file}") + raise + + logger.info("python runner environment initialized") diff --git a/sandbox/app/dependencies.py b/sandbox/app/dependencies.py new file mode 100644 index 00000000..6e88aaf2 --- /dev/null +++ b/sandbox/app/dependencies.py @@ -0,0 +1,161 @@ +"""Dependency management""" +import asyncio +from pathlib import Path +from typing import List, Dict + +from app.config import get_config +from app.core.runners.python.env import prepare_python_dependencies_env +from app.logger import get_logger + + +async def setup_dependencies(): + """Setup initial dependencies""" + logger = get_logger() + + try: + logger.info("Installing Python dependencies...") + await install_python_dependencies() + logger.info("Python dependencies installed") + + logger.info("Preparing Python dependencies environment...") + await prepare_python_dependencies_env() + logger.info("Python dependencies environment ready") + + except Exception as e: + logger.error(f"Failed to setup dependencies: {e}") + + +async def update_dependencies(): + # TODO + return + + +async def install_python_dependencies(): + """Install Python dependencies from requirements file""" + logger = get_logger() + config = get_config() + + # Check if requirements file exists + req_file = Path("dependencies/python-requirements.txt") + if not req_file.exists(): + logger.warning("Python requirements file not found, skipping installation") + return + + # Read requirements + requirements = req_file.read_text().strip() + if not requirements: + logger.info("No Python requirements to install") + return + + # Install using pip + cmd = [ + config.python_path, + "-m", + "pip", + "install", + "--upgrade" + ] + + # Add packages from requirements + for line in requirements.split("\n"): + line = line.strip() + if line and not line.startswith("#"): + cmd.append(line) + + try: + process = await asyncio.create_subprocess_exec( + *cmd, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + if process.returncode != 0: + logger.error(f"Failed to install Python dependencies: {stderr.decode()}") + else: + logger.info("Python dependencies installed successfully") + + except Exception as e: + logger.error(f"Error installing Python dependencies: {e}") + + +async def list_dependencies(language: str) -> List[Dict[str, str]]: + """List installed dependencies + + Args: + language: Language (python or Node.js) + + Returns: + List of dependencies with name and version + """ + if language == "python": + return await list_python_packages() + else: + return [] + + +async def list_python_packages() -> List[Dict[str, str]]: + """List installed Python packages""" + config = get_config() + + try: + process = await asyncio.create_subprocess_exec( + config.python_path, + "-m", + "pip", + "list", + "--format=freeze", + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE + ) + + stdout, stderr = await process.communicate() + + if process.returncode != 0: + return [] + + # Parse output + packages = [] + for line in stdout.decode().split("\n"): + line = line.strip() + if line and "==" in line: + name, version = line.split("==", 1) + packages.append({"name": name, "version": version}) + + return packages + + except Exception as e: + get_logger().error(f"Failed to list Python packages: {e}") + return [] + + +async def update_dependencies_periodically(): + """Periodically update dependencies""" + logger = get_logger() + config = get_config() + + # Parse interval + interval_str = config.python_deps_update_interval + + # Convert to seconds + if interval_str.endswith("m"): + interval = int(interval_str[:-1]) * 60 + elif interval_str.endswith("h"): + interval = int(interval_str[:-1]) * 3600 + elif interval_str.endswith("s"): + interval = int(interval_str[:-1]) + else: + interval = 1800 # Default 30 minutes + + logger.info(f"Starting periodic dependency updates every {interval} seconds") + + while True: + await asyncio.sleep(interval) + + try: + logger.info("Updating Python dependencies...") + # TODO: await update_dependencies("python") + logger.info("Python dependencies updated successfully") + except Exception as e: + logger.error(f"Failed to update Python dependencies: {e}") diff --git a/sandbox/app/logger.py b/sandbox/app/logger.py new file mode 100644 index 00000000..de2ccc9e --- /dev/null +++ b/sandbox/app/logger.py @@ -0,0 +1,42 @@ +"""Logging configuration""" +import logging +import sys +from typing import Optional + +from app.config import get_config + +_logger: Optional[logging.Logger] = None + + +def setup_logger() -> logging.Logger: + """Setup application logger""" + global _logger + + config = get_config() + + # Create logger + _logger = logging.getLogger("sandbox") + _logger.setLevel(logging.DEBUG if config.app.debug else logging.INFO) + + # Create console handler + handler = logging.StreamHandler(sys.stdout) + handler.setLevel(logging.DEBUG if config.app.debug else logging.INFO) + + # Create formatter + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + handler.setFormatter(formatter) + + # Add handler to logger + _logger.addHandler(handler) + + return _logger + + +def get_logger() -> logging.Logger: + """Get application logger""" + if _logger is None: + return setup_logger() + return _logger diff --git a/sandbox/app/middleware/__init__.py b/sandbox/app/middleware/__init__.py new file mode 100644 index 00000000..77d6403c --- /dev/null +++ b/sandbox/app/middleware/__init__.py @@ -0,0 +1 @@ +"""Middleware package""" diff --git a/sandbox/app/middleware/auth.py b/sandbox/app/middleware/auth.py new file mode 100644 index 00000000..8a93a793 --- /dev/null +++ b/sandbox/app/middleware/auth.py @@ -0,0 +1,15 @@ +"""Authentication middleware""" +from fastapi import Header, HTTPException, status + +from app.config import get_config + + +async def verify_api_key(x_api_key: str = Header(..., alias="X-Api-Key")): + """Verify API key from request header""" + config = get_config() + if x_api_key != config.app.key: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid API key" + ) + return x_api_key diff --git a/sandbox/app/middleware/concurrency.py b/sandbox/app/middleware/concurrency.py new file mode 100644 index 00000000..8d8325a4 --- /dev/null +++ b/sandbox/app/middleware/concurrency.py @@ -0,0 +1,48 @@ +"""Concurrency control middleware""" +import asyncio +from fastapi import HTTPException, status + +from app.config import get_config +from app.models import error_response + + +# Global semaphores +_worker_semaphore: None | asyncio.Semaphore = None +_request_counter = 0 +_request_lock = asyncio.Lock() + + +def init_concurrency_control(): + """Initialize concurrency control""" + global _worker_semaphore + config = get_config() + _worker_semaphore = asyncio.Semaphore(config.max_workers) + + +async def check_max_requests(): + """Check if max requests limit is reached""" + global _request_counter + config = get_config() + + async with _request_lock: + if _request_counter >= config.max_requests: + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail=error_response(-503, "Too many requests") + ) + _request_counter += 1 + + try: + yield + finally: + async with _request_lock: + _request_counter -= 1 + + +async def acquire_worker(): + """Acquire a worker slot""" + if _worker_semaphore is None: + init_concurrency_control() + + async with _worker_semaphore: + yield diff --git a/sandbox/app/models.py b/sandbox/app/models.py new file mode 100644 index 00000000..e7492b4c --- /dev/null +++ b/sandbox/app/models.py @@ -0,0 +1,80 @@ +"""Data models""" +from typing import Optional, Any + +from pydantic import BaseModel, Field + + +class RunnerOptions(BaseModel): + enable_network: bool = Field(default=False, description="Sandbox network flag") + + +class RunCodeRequest(BaseModel): + """Request model for code execution""" + language: str = Field(..., description="Programming language (python3 or nodejs)") + code: str = Field(..., description="Base64 encoded encrypted code") + preload: Optional[str] = Field(default="", description="Preload code") + options: RunnerOptions = Field(default_factory=RunnerOptions, description="Enable network access") + + +class RunCodeResponse(BaseModel): + """Response model for code execution""" + stdout: str = Field(default="", description="Standard output") + stderr: str = Field(default="", description="Standard error") + + +class DependencyRequest(BaseModel): + """Request model for dependency operations""" + language: str = Field(..., description="Programming language") + + +class UpdateDependencyRequest(BaseModel): + """Request model for updating dependencies""" + language: str = Field(..., description="Programming language") + packages: list[str] = Field(default_factory=list, description="Packages to install") + + +class Dependency(BaseModel): + """Dependency information""" + name: str + version: str + + +class ListDependenciesResponse(BaseModel): + """Response model for listing dependencies""" + dependencies: list[Dependency] = Field(default_factory=list) + + +class RefreshDependenciesResponse(BaseModel): + """Response model for refreshing dependencies""" + dependencies: list[Dependency] = Field(default_factory=list) + + +class UpdateDependenciesResponse(BaseModel): + """Response model for updating dependencies""" + success: bool = True + installed: list[str] = Field(default_factory=list) + + +class HealthResponse(BaseModel): + """Health check response""" + status: str = "healthy" + version: str = "2.0.0" + + +class ApiResponse(BaseModel): + """Standard API response wrapper""" + code: int = Field(default=0, description="Response code (0 for success, negative for error)") + message: str = Field(default="success", description="Response message") + data: Optional[Any] = Field(default=None, description="Response data") + + +def success_response(data: Any) -> ApiResponse: + """Create success response""" + return ApiResponse(code=0, message="success", data=data) + + +def error_response(code: int, message: str) -> ApiResponse: + """Create error response""" + if code >= 0: + code = -1 + return ApiResponse(code=code, message=message, data=None) diff --git a/sandbox/app/services/__init__.py b/sandbox/app/services/__init__.py new file mode 100644 index 00000000..e3726046 --- /dev/null +++ b/sandbox/app/services/__init__.py @@ -0,0 +1 @@ +"""Services package""" diff --git a/sandbox/app/services/python_service.py b/sandbox/app/services/python_service.py new file mode 100644 index 00000000..71cfda0d --- /dev/null +++ b/sandbox/app/services/python_service.py @@ -0,0 +1,80 @@ +"""Python execution service""" +import signal + +from app.core.runners.python.python_runner import PythonRunner +from app.dependencies import ( + list_dependencies as list_deps, + update_dependencies as update_deps +) +from app.logger import get_logger +from app.models import ( + success_response, + error_response, + RunCodeResponse, + ListDependenciesResponse, + UpdateDependenciesResponse, + Dependency, + RunnerOptions +) + + +async def run_python_code(code: str, preload: str, options: RunnerOptions): + """Execute Python code in sandbox + + Args: + options: + code: Base64 encoded encrypted code + preload: Preload code + + Returns: + API response with execution result + """ + logger = get_logger() + + try: + runner = PythonRunner() + result = await runner.run(code, options, preload) + if result.exit_code == -signal.SIGSYS: + return error_response(31, "sandbox security policy violation") + + if result.error: + return error_response(-500, result.error) + + return success_response(RunCodeResponse( + stdout=result.stdout, + stderr=result.stderr + )) + + except Exception as e: + logger.error(f"Python execution failed: {e}", exc_info=True) + return error_response(-500, str(e)) + + +async def list_python_dependencies(): + """List installed Python dependencies + + Returns: + API response with dependency list + """ + try: + deps = await list_deps("python") + dependencies = [ + Dependency(name=dep["name"], version=dep["version"]) + for dep in deps + ] + return success_response(ListDependenciesResponse(dependencies=dependencies)) + except Exception as e: + return error_response(500, str(e)) + + +async def update_python_dependencies(): + """Update Python dependencies + + Returns: + API response with update result + """ + try: + await update_deps() + return success_response(UpdateDependenciesResponse(success=True)) + except Exception as e: + return error_response(500, str(e)) diff --git a/sandbox/config.yaml b/sandbox/config.yaml new file mode 100644 index 00000000..d9581b34 --- /dev/null +++ b/sandbox/config.yaml @@ -0,0 +1,20 @@ +app: + port: 8194 + debug: true + key: redbear-sandbox + +max_workers: 4 +max_requests: 50 +worker_timeout: 30 +python_path: /usr/local/bin/python +nodejs_path: /usr/local/bin/node +enable_network: true +enable_preload: false +python_deps_update_interval: 30m + +allowed_syscalls: [] + +proxy: + socks5: '' + http: '' + https: '' diff --git a/sandbox/dependencies/python-requirements.txt b/sandbox/dependencies/python-requirements.txt new file mode 100644 index 00000000..1c3c2901 --- /dev/null +++ b/sandbox/dependencies/python-requirements.txt @@ -0,0 +1,4 @@ +requests==2.31.0 +# numpy==1.26.0 +# pandas==2.0.0 +jinja2==3.1.2 \ No newline at end of file diff --git a/sandbox/lib/seccomp_nodejs/Cargo.lock b/sandbox/lib/seccomp_nodejs/Cargo.lock new file mode 100644 index 00000000..b37698ee --- /dev/null +++ b/sandbox/lib/seccomp_nodejs/Cargo.lock @@ -0,0 +1,7 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "seccomp_nodejs" +version = "0.1.0" diff --git a/sandbox/lib/seccomp_nodejs/Cargo.toml b/sandbox/lib/seccomp_nodejs/Cargo.toml new file mode 100644 index 00000000..a8bd8932 --- /dev/null +++ b/sandbox/lib/seccomp_nodejs/Cargo.toml @@ -0,0 +1,6 @@ +[package] +name = "seccomp_nodejs" +version = "0.1.0" +edition = "2024" + +[dependencies] \ No newline at end of file diff --git a/sandbox/lib/seccomp_nodejs/src/lib.rs b/sandbox/lib/seccomp_nodejs/src/lib.rs new file mode 100644 index 00000000..e69de29b diff --git a/sandbox/lib/seccomp_python/Cargo.lock b/sandbox/lib/seccomp_python/Cargo.lock new file mode 100644 index 00000000..881ad177 --- /dev/null +++ b/sandbox/lib/seccomp_python/Cargo.lock @@ -0,0 +1,23 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "libc" +version = "0.2.180" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bcc35a38544a891a5f7c865aca548a982ccb3b8650a5b06d0fd33a10283c56fc" + +[[package]] +name = "libseccomp-sys" +version = "0.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "60276e2d41bbb68b323e566047a1bfbf952050b157d8b5cdc74c07c1bf4ca3b6" + +[[package]] +name = "seccomp_python" +version = "0.1.0" +dependencies = [ + "libc", + "libseccomp-sys", +] diff --git a/sandbox/lib/seccomp_python/Cargo.toml b/sandbox/lib/seccomp_python/Cargo.toml new file mode 100644 index 00000000..07037172 --- /dev/null +++ b/sandbox/lib/seccomp_python/Cargo.toml @@ -0,0 +1,12 @@ +[package] +name = "seccomp_python" +version = "0.1.0" +edition = "2024" + +[lib] +name = "python" +crate-type = ["cdylib"] + +[dependencies] +libc = "0.2.180" +libseccomp-sys = "0.3.0" diff --git a/sandbox/lib/seccomp_python/src/lib.rs b/sandbox/lib/seccomp_python/src/lib.rs new file mode 100644 index 00000000..08b46c54 --- /dev/null +++ b/sandbox/lib/seccomp_python/src/lib.rs @@ -0,0 +1,195 @@ +mod syscalls; + +use crate::syscalls::*; +use libc::{chdir, chroot, gid_t, uid_t, c_int}; +use libseccomp_sys::*; +use std::env; +use std::ffi::CString; +use std::str::FromStr; + + +/* + * get_allowed_syscalls - retrieve allowed syscalls for the sandbox + * @enable_network: enable network-related syscalls if non-zero + * + * Syscall selection order: + * 1. ALLOWED_SYSCALLS environment variable + * 2. Built-in default allowlist + * 3. Optional network syscall extension + * + * Returns: + * (allowed_syscalls, allowed_not_kill_syscalls) + * allowed_syscalls: syscalls fully allowed + * allowed_not_kill_syscalls: syscalls returning EPERM + */ +pub fn get_allowed_syscalls(enable_network: bool) -> (Vec, Vec) { + let mut allowed_syscalls = Vec::new(); + let mut allowed_not_kill_syscalls = Vec::new(); + + /* Syscalls that return error instead of killing */ + allowed_not_kill_syscalls.extend(ALLOW_ERROR_SYSCALLS); + + /* Load from environment variable ALLOWED_SYSCALLS */ + if let Ok(env_val) = env::var("ALLOWED_SYSCALLS") { + if !env_val.is_empty() { + for s in env_val.split(',') { + if let Ok(sc) = i32::from_str(s) { + allowed_syscalls.push(sc); + } + } + } + } + + /* Fallback to default syscalls if env not set */ + if allowed_syscalls.is_empty() { + allowed_syscalls.extend(ALLOW_SYSCALLS); + if enable_network { + allowed_syscalls.extend(ALLOW_NETWORK_SYSCALLS); + } + } + + (allowed_syscalls, allowed_not_kill_syscalls) +} + +/* + * setup_root - setup restricted filesystem root + * + * Perform chroot(".") and change working directory to "/". + * + * Return: + * 0 on success + * negative error code on failure + */ +fn setup_root() -> Result<(), c_int> { + let root = CString::new(".").unwrap(); + if unsafe { chroot(root.as_ptr()) } != 0 { + return Err(-1); + } + + let root_dir = CString::new("/").unwrap(); + if unsafe { chdir(root_dir.as_ptr()) } != 0 { + return Err(-2); + } + + Ok(()) +} + +/* + * set_no_new_privs - enable PR_SET_NO_NEW_PRIVS + * + * Prevent privilege escalation via execve. + * + * Return: + * 0 on success + * negative error code on failure + */ +fn set_no_new_privs() -> Result<(), c_int> { + if unsafe { libc::prctl(libc::PR_SET_NO_NEW_PRIVS, 1, 0, 0, 0) } != 0 { + return Err(-3); + } + Ok(()) +} + +/* + * drop_privileges - drop process privileges + * @uid: target user ID + * @gid: target group ID + * + * Permanently reduce process privileges. + * + * Return: + * 0 on success + * negative error code on failure + */ +fn drop_privileges(uid: uid_t, gid: gid_t) -> Result<(), c_int> { + if unsafe { libc::setgid(gid) } != 0 { + return Err(-4); + } + if unsafe { libc::setuid(uid) } != 0 { + return Err(-5); + } + Ok(()) +} + +/* + * install_seccomp - install seccomp filter + * @enable_network: enable network-related syscalls if non-zero + * + * Default action is SCMP_ACT_KILL_PROCESS. + * Allowed syscalls are explicitly whitelisted. + * + * Return: + * 0 on success + * negative error code on failure + */ +fn install_seccomp(enable_network: bool) -> Result<(), c_int> { + unsafe { + let ctx = seccomp_init(SCMP_ACT_KILL_PROCESS); + if ctx.is_null() { + return Err(-6); /* failed to init seccomp context */ + } + + let (allowed_syscalls, allowed_not_kill_syscalls) = get_allowed_syscalls(enable_network); + + /* add fully allowed syscalls */ + for &sc in &allowed_syscalls { + if seccomp_rule_add(ctx, SCMP_ACT_ALLOW, sc, 0) != 0 { + seccomp_release(ctx); + return Err(-7); + } + } + + /* add syscalls returning EPERM */ + for &sc in &allowed_not_kill_syscalls { + if seccomp_rule_add(ctx, SCMP_ACT_ERRNO(libc::EPERM as u16), sc, 0) != 0 { + seccomp_release(ctx); + return Err(-8); + } + } + + if seccomp_load(ctx) != 0 { + seccomp_release(ctx); + return Err(-9); + } + + seccomp_release(ctx); + Ok(()) + } +} + +/* + * init_seccomp - initialize seccomp sandbox + * @uid: target user ID + * @gid: target group ID + * @enable_network: enable network syscalls if non-zero + * + * Initialize the sandbox and apply privilege restrictions + * in the following order: + * 1. setup_root() + * 2. set_no_new_privs() + * 3. drop_privileges() + * 4. install_seccomp() + * + * This function must be called before executing any untrusted code. + * It is not thread-safe and must be invoked once per process. + * + * Return: + * 0 on success + * negative error code on failure + */ +#[unsafe(no_mangle)] +pub unsafe extern "C" fn init_seccomp(uid: uid_t, gid: gid_t, enable_network: i32) -> c_int { + if let Err(code) = setup_root() { + return code; + } + if let Err(code) = set_no_new_privs() { + return code; + } + if let Err(code) = drop_privileges(uid, gid) { + return code; + } + match install_seccomp(enable_network != 0) { + Ok(_) => 0, + Err(code) => code, + } +} diff --git a/sandbox/lib/seccomp_python/src/syscalls.rs b/sandbox/lib/seccomp_python/src/syscalls.rs new file mode 100644 index 00000000..07070d22 --- /dev/null +++ b/sandbox/lib/seccomp_python/src/syscalls.rs @@ -0,0 +1,85 @@ +// src/syscalls.rs + +pub static ALLOW_SYSCALLS: &[i32] = &[ + // file io + libc::SYS_read as i32, + libc::SYS_write as i32, + libc::SYS_openat as i32, + libc::SYS_close as i32, + libc::SYS_newfstatat as i32, + libc::SYS_ioctl as i32, + libc::SYS_lseek as i32, + libc::SYS_getdents64 as i32, + + // thread + libc::SYS_futex as i32, + + // memory + libc::SYS_mmap as i32, + libc::SYS_brk as i32, + libc::SYS_mprotect as i32, + libc::SYS_munmap as i32, + libc::SYS_rt_sigreturn as i32, + libc::SYS_mremap as i32, + + // user / group + libc::SYS_setuid as i32, + libc::SYS_setgid as i32, + libc::SYS_getuid as i32, + + // process + libc::SYS_getpid as i32, + libc::SYS_getppid as i32, + libc::SYS_gettid as i32, + libc::SYS_exit as i32, + libc::SYS_exit_group as i32, + libc::SYS_tgkill as i32, + libc::SYS_rt_sigaction as i32, + libc::SYS_sched_yield as i32, + libc::SYS_set_robust_list as i32, + libc::SYS_get_robust_list as i32, + libc::SYS_rseq as i32, + + // time + libc::SYS_clock_gettime as i32, + libc::SYS_gettimeofday as i32, + libc::SYS_nanosleep as i32, + libc::SYS_epoll_create1 as i32, + libc::SYS_epoll_ctl as i32, + libc::SYS_clock_nanosleep as i32, + libc::SYS_pselect6 as i32, + libc::SYS_rt_sigprocmask as i32, + libc::SYS_sigaltstack as i32, + libc::SYS_getrandom as i32, + +]; + +pub static ALLOW_ERROR_SYSCALLS: &[i32] = &[ + libc::SYS_clone as i32, + libc::SYS_mkdirat as i32, + libc::SYS_mkdir as i32, +]; + +pub static ALLOW_NETWORK_SYSCALLS: &[i32] = &[ + libc::SYS_socket as i32, + libc::SYS_connect as i32, + libc::SYS_bind as i32, + libc::SYS_listen as i32, + libc::SYS_accept as i32, + libc::SYS_sendto as i32, + libc::SYS_recvfrom as i32, + libc::SYS_getsockname as i32, + libc::SYS_recvmsg as i32, + libc::SYS_getpeername as i32, + libc::SYS_setsockopt as i32, + libc::SYS_ppoll as i32, + libc::SYS_uname as i32, + libc::SYS_sendmsg as i32, + libc::SYS_sendmmsg as i32, + libc::SYS_getsockopt as i32, + libc::SYS_fstat as i32, + libc::SYS_fcntl as i32, + libc::SYS_fstatfs as i32, + libc::SYS_poll as i32, + libc::SYS_epoll_pwait as i32, +]; diff --git a/sandbox/main.py b/sandbox/main.py new file mode 100644 index 00000000..fc417563 --- /dev/null +++ b/sandbox/main.py @@ -0,0 +1,97 @@ +""" +Redbear Sandbox - Main Entry Point +""" +import asyncio +import os +import sys +from contextlib import asynccontextmanager + +import uvicorn +from fastapi import FastAPI + +from app.config import get_config +from app.controllers import manager_router +from app.dependencies import setup_dependencies, update_dependencies_periodically +from app.logger import setup_logger, get_logger + +logger = get_logger() + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Application lifespan manager""" + logger = get_logger() + + # Startup + logger.info("Starting RedBear Sandbox...") + + # Setup dependencies in background + asyncio.create_task(setup_dependencies()) + + # Start periodic dependency updates + config = get_config() + if config.python_deps_update_interval: + asyncio.create_task(update_dependencies_periodically()) + + yield + + # Shutdown + logger.info("Shutting down Redbear Sandbox...") + + +def create_app() -> FastAPI: + """Create FastAPI application""" + config = get_config() + + app = FastAPI( + title="Sandbox", + description="Secure code execution sandbox", + version="2.0.0", + lifespan=lifespan, + debug=config.app.debug + ) + + app.include_router(manager_router) + + return app + + +def check_root_privileges(): + """Check if running with root privileges""" + if os.geteuid() != 0: + logger.info("Error: Sandbox must be run as root for security features (chroot, setuid)") + sys.exit(1) + + +def main(): + """Main entry point""" + # Check root privileges + check_root_privileges() + + # Setup logging + setup_logger() + + config = get_config() + logger = get_logger() + + logger.info(f"Starting server on port {config.app.port}") + logger.info(f"Debug mode: {config.app.debug}") + logger.info(f"Max workers: {config.max_workers}") + logger.info(f"Max requests: {config.max_requests}") + logger.info(f"Network enabled: {config.enable_network}") + + # Create app + app = create_app() + + # Run server + uvicorn.run( + app, + host="0.0.0.0", + port=config.app.port, + log_level="debug" if config.app.debug else "info", + access_log=config.app.debug + ) + + +if __name__ == "__main__": + main() diff --git a/sandbox/requirements.txt b/sandbox/requirements.txt new file mode 100644 index 00000000..0c91018a --- /dev/null +++ b/sandbox/requirements.txt @@ -0,0 +1,20 @@ +# Web Framework +fastapi==0.115.0 +uvicorn[standard]==0.32.0 +pydantic==2.9.0 +pydantic-settings==2.5.0 + +# Configuration +PyYAML==6.0.2 + +# Security +pyseccomp==0.1.2 + + +# Async & Concurrency +aiofiles==24.1.0 + +# Testing +pytest==8.3.0 +pytest-asyncio==0.24.0 +httpx==0.27.0 diff --git a/sandbox/script/env.sh b/sandbox/script/env.sh new file mode 100644 index 00000000..f44f7208 --- /dev/null +++ b/sandbox/script/env.sh @@ -0,0 +1,53 @@ +#!/bin/bash + +# Check if the correct number of arguments are provided +if [ "$#" -ne 2 ]; then + echo "Usage: $0 " + exit 1 +fi + +src="$1" +dest="$2" + +# Function to copy and link files +copy_and_link() { + local src_file="$1" + local dest_file="$2" + + if [ -L "$src_file" ]; then + # If src_file is a symbolic link, copy it without changing permissions + cp -P "$src_file" "$dest_file" + elif [ -b "$src_file" ] || [ -c "$src_file" ]; then + # If src_file is a device file, copy it and change permissions + cp "$src_file" "$dest_file" + chmod 444 "$dest_file" + else + # Otherwise, create a hard link and change the permissions to read-only + ln -f "$src_file" "$dest_file" 2>/dev/null || { cp "$src_file" "$dest_file" && chmod 444 "$dest_file"; } + fi +} + +# Check if src is a file or directory +if [ -f "$src" ]; then + # src is a file, create hard link directly in dest + mkdir -p "$(dirname "$dest/$src")" + copy_and_link "$src" "$dest/$src" +elif [ -d "$src" ]; then + # src is a directory, process as before + mkdir -p "$dest/$src" + + # Find all files in the source directory + find "$src" -type f,l | while read -r file; do + # Get the relative path of the file + rel_path="${file#$src/}" + # Get the directory of the relative path + rel_dir=$(dirname "$rel_path") + # Create the same directory structure in the destination + mkdir -p "$dest/$src/$rel_dir" + # Copy and link the file + copy_and_link "$file" "$dest/$src/$rel_path" + done +else + echo "Error: $src is neither a file nor a directory" + exit 1 +fi From 0fd8a122fb01a71e497ba15692b63bd5657b8204 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 11:59:13 +0800 Subject: [PATCH 063/175] feat(workflow): emit SSE events for node exception output --- api/app/core/workflow/executor.py | 50 ++++++++++++++++-------- api/app/core/workflow/nodes/base_node.py | 5 +++ 2 files changed, 38 insertions(+), 17 deletions(-) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index 6721d7b0..f3feff60 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -261,7 +261,7 @@ class WorkflowExecutor: "data": { "execution_id": self.execution_id, "workspace_id": self.workspace_id, - "timestamp": start_time.isoformat() + "timestamp": int(start_time.timestamp() * 1000) } } @@ -293,20 +293,33 @@ class WorkflowExecutor: # Handle custom streaming events (chunks from nodes via stream writer) chunk_count += 1 event_type = data.get("type", "node_chunk") # "message" or "node_chunk" - logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}" - f"- execution_id: {self.execution_id}") - yield { - "event": event_type, # "message" or "node_chunk" - "data": { - "node_id": data.get("node_id"), - "chunk": data.get("chunk"), - "full_content": data.get("full_content"), - "chunk_index": data.get("chunk_index"), - "is_prefix": data.get("is_prefix"), - "is_suffix": data.get("is_suffix"), - "conversation_id": input_data.get("conversation_id"), + if event_type in ("message", "node_chunk"): + logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}" + f"- execution_id: {self.execution_id}") + yield { + "event": event_type, # "message" or "node_chunk" + "data": { + "node_id": data.get("node_id"), + "chunk": data.get("chunk"), + "full_content": data.get("full_content"), + "chunk_index": data.get("chunk_index"), + "is_prefix": data.get("is_prefix"), + "is_suffix": data.get("is_suffix"), + "conversation_id": input_data.get("conversation_id"), + } + } + elif event_type == "node_error": + yield { + "event": event_type, # "message" or "node_chunk" + "data": { + "node_id": data.get("node_id"), + "status": "failed", + "input": data.get("input_data"), + "elapsed_time": data.get("elapsed_time"), + "output": None, + "error": data.get("error") + } } - } elif mode == "debug": # Handle debug information (node execution status) @@ -325,14 +338,15 @@ class WorkflowExecutor: conversation_id = input_data.get("conversation_id") logger.info(f"[NODE-START] Node starts execution: {node_name} " f"- execution_id: {self.execution_id}") - yield { "event": "node_start", "data": { "node_id": node_name, "conversation_id": conversation_id, "execution_id": self.execution_id, - "timestamp": data.get("timestamp"), + "timestamp": int(datetime.datetime.fromisoformat( + data.get("timestamp") + ).timestamp() * 1000), } } elif event_type == "task_result": @@ -351,7 +365,9 @@ class WorkflowExecutor: "node_id": node_name, "conversation_id": conversation_id, "execution_id": self.execution_id, - "timestamp": data.get("timestamp"), + "timestamp": int(datetime.datetime.fromisoformat( + data.get("timestamp") + ).timestamp() * 1000), "state": result.get("node_outputs", {}).get(node_name), } } diff --git a/api/app/core/workflow/nodes/base_node.py b/api/app/core/workflow/nodes/base_node.py index 0c015c89..61d5ca1e 100644 --- a/api/app/core/workflow/nodes/base_node.py +++ b/api/app/core/workflow/nodes/base_node.py @@ -541,6 +541,11 @@ class BaseNode(ABC): "error_node": self.node_id } else: + writer = get_stream_writer() + writer({ + "type": "node_error", + **node_output + }) # 无错误边：抛出异常停止工作流 logger.error(f"节点 {self.node_id} 执行失败，停止工作流: {error_message}") raise Exception(f"节点 {self.node_id} 执行失败: {error_message}") From 1fc04c37d3456790f2bfac96e743eb92629981e9 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 12:22:54 +0800 Subject: [PATCH 064/175] perf(sandbox): optimize code encryption handling --- sandbox/app/core/encryption.py | 3 ++- sandbox/app/core/runners/python/prescript.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/sandbox/app/core/encryption.py b/sandbox/app/core/encryption.py index 5e0855c9..47a756c8 100644 --- a/sandbox/app/core/encryption.py +++ b/sandbox/app/core/encryption.py @@ -12,9 +12,10 @@ def encrypt_code(code: bytes, key: bytes) -> str: Returns: Base64 encoded encrypted code """ + key_length = len(key) encrypted_code = bytearray(len(code)) for i in range(len(code)): - encrypted_code[i] = code[i] ^ key[i % 64] + encrypted_code[i] = code[i] ^ key[i % key_length] encoded_code = base64.b64encode(encrypted_code).decode("utf-8") return encoded_code diff --git a/sandbox/app/core/runners/python/prescript.py b/sandbox/app/core/runners/python/prescript.py index 4790be73..950710ea 100644 --- a/sandbox/app/core/runners/python/prescript.py +++ b/sandbox/app/core/runners/python/prescript.py @@ -17,7 +17,7 @@ sys.excepthook = excepthook # Load security library if available lib = ctypes.CDLL("./libpython.so") lib.init_seccomp.argtypes = [ctypes.c_uint32, ctypes.c_uint32, ctypes.c_bool] -lib.init_seccomp.restype = None +lib.init_seccomp.restype = None # TODO: raise error info # Get running path running_path = sys.argv[1] From 85681db7b70017a4f1caf86f7ece84ab29741e13 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 12:28:40 +0800 Subject: [PATCH 065/175] perf(workflow): update standard node output structure --- api/app/core/workflow/executor.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index f3feff60..c4662113 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -368,7 +368,9 @@ class WorkflowExecutor: "timestamp": int(datetime.datetime.fromisoformat( data.get("timestamp") ).timestamp() * 1000), - "state": result.get("node_outputs", {}).get(node_name), + "input": result.get("node_outputs", {}).get(node_name, {}).get("input"), + "output": result.get("node_outputs", {}).get(node_name, {}).get("output"), + "elapsed_time": result.get("node_outputs", {}).get(node_name, {}).get("elapsed_time"), } } From 8228d38859e6ed1782543363d16b9ee093351f71 Mon Sep 17 00:00:00 2001 From: Mark Date: Mon, 26 Jan 2026 14:26:32 +0800 Subject: [PATCH 066/175] [add] migration script --- .../versions/325b759cd66b_2026011240.py | 50 +++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 api/migrations/versions/325b759cd66b_2026011240.py diff --git a/api/migrations/versions/325b759cd66b_2026011240.py b/api/migrations/versions/325b759cd66b_2026011240.py new file mode 100644 index 00000000..66c8681c --- /dev/null +++ b/api/migrations/versions/325b759cd66b_2026011240.py @@ -0,0 +1,50 @@ +"""2026011240 + +Revision ID: 325b759cd66b +Revises: 9a936a9ebb20 +Create Date: 2026-01-26 12:37:35.946749 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +revision: str = '325b759cd66b' +down_revision: Union[str, None] = '9a936a9ebb20' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # 1. 重命名表 data_config -> memory_config + op.rename_table('data_config', 'memory_config') + + # 2. 重命名列 group_id -> end_user_id + op.alter_column('memory_config', 'group_id', new_column_name='end_user_id') + + # 3. config_id: INTEGER -> UUID（保留旧值以便回滚） + op.alter_column('memory_config', 'config_id', new_column_name='config_id_old') + op.add_column('memory_config', sa.Column('config_id', sa.UUID(), nullable=True)) + op.execute("UPDATE memory_config SET config_id = apply_id::uuid") + op.drop_constraint('data_config_pkey', 'memory_config', type_='primary') + op.alter_column('memory_config', 'config_id', nullable=False) + op.create_primary_key('memory_config_pkey', 'memory_config', ['config_id']) + op.execute("DROP SEQUENCE IF EXISTS data_config_config_id_seq") + + +def downgrade() -> None: + # 1. config_id: UUID -> INTEGER（恢复旧值） + op.drop_constraint('memory_config_pkey', 'memory_config', type_='primary') + op.drop_column('memory_config', 'config_id') + op.alter_column('memory_config', 'config_id_old', new_column_name='config_id') + op.create_primary_key('data_config_pkey', 'memory_config', ['config_id']) + op.execute("CREATE SEQUENCE IF NOT EXISTS data_config_config_id_seq OWNED BY memory_config.config_id") + op.execute("SELECT setval('data_config_config_id_seq', COALESCE((SELECT MAX(config_id) FROM memory_config), 1))") + + # 2. 重命名列 end_user_id -> group_id + op.alter_column('memory_config', 'end_user_id', new_column_name='group_id') + + # 3. 重命名表 memory_config -> data_config + op.rename_table('memory_config', 'data_config') From b046411302553158e2af49eeb984ad3e564bf79b Mon Sep 17 00:00:00 2001 From: Mark Date: Mon, 26 Jan 2026 15:39:35 +0800 Subject: [PATCH 067/175] [modify] migration script --- api/migrations/versions/325b759cd66b_2026011240.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/api/migrations/versions/325b759cd66b_2026011240.py b/api/migrations/versions/325b759cd66b_2026011240.py index 66c8681c..763b0289 100644 --- a/api/migrations/versions/325b759cd66b_2026011240.py +++ b/api/migrations/versions/325b759cd66b_2026011240.py @@ -25,22 +25,24 @@ def upgrade() -> None: op.alter_column('memory_config', 'group_id', new_column_name='end_user_id') # 3. config_id: INTEGER -> UUID（保留旧值以便回滚） - op.alter_column('memory_config', 'config_id', new_column_name='config_id_old') + op.drop_constraint('data_config_pkey', 'memory_config', type_='primary') + op.alter_column('memory_config', 'config_id', new_column_name='config_id_old', nullable=True) op.add_column('memory_config', sa.Column('config_id', sa.UUID(), nullable=True)) op.execute("UPDATE memory_config SET config_id = apply_id::uuid") - op.drop_constraint('data_config_pkey', 'memory_config', type_='primary') op.alter_column('memory_config', 'config_id', nullable=False) op.create_primary_key('memory_config_pkey', 'memory_config', ['config_id']) op.execute("DROP SEQUENCE IF EXISTS data_config_config_id_seq") def downgrade() -> None: - # 1. config_id: UUID -> INTEGER（恢复旧值） + # 1. config_id: UUID -> INTEGER（恢复旧值，空值生成新ID） + op.execute("CREATE SEQUENCE IF NOT EXISTS data_config_config_id_seq") + op.execute("UPDATE memory_config SET config_id_old = nextval('data_config_config_id_seq') WHERE config_id_old IS NULL") op.drop_constraint('memory_config_pkey', 'memory_config', type_='primary') op.drop_column('memory_config', 'config_id') - op.alter_column('memory_config', 'config_id_old', new_column_name='config_id') + op.alter_column('memory_config', 'config_id_old', new_column_name='config_id', nullable=False) op.create_primary_key('data_config_pkey', 'memory_config', ['config_id']) - op.execute("CREATE SEQUENCE IF NOT EXISTS data_config_config_id_seq OWNED BY memory_config.config_id") + op.execute("ALTER SEQUENCE data_config_config_id_seq OWNED BY memory_config.config_id") op.execute("SELECT setval('data_config_config_id_seq', COALESCE((SELECT MAX(config_id) FROM memory_config), 1))") # 2. 重命名列 end_user_id -> group_id From 2eff6b2e9da716ba88664ea9d830f8e57705e8e4 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 26 Jan 2026 15:46:28 +0800 Subject: [PATCH 068/175] feat(web): add workflow runtime info --- web/src/components/Chat/ChatContent.tsx | 17 +- web/src/components/Chat/types.ts | 5 +- web/src/components/Markdown/CodeBlock.tsx | 16 +- web/src/i18n/en.ts | 4 + web/src/i18n/zh.ts | 4 + web/src/utils/stream.ts | 14 ++ .../views/Workflow/components/Chat/Chat.tsx | 206 ++++++++++++++++-- .../Workflow/components/Chat/chat.module.css | 45 ++++ 8 files changed, 286 insertions(+), 25 deletions(-) create mode 100644 web/src/views/Workflow/components/Chat/chat.module.css diff --git a/web/src/components/Chat/ChatContent.tsx b/web/src/components/Chat/ChatContent.tsx index c90f9208..a5d02b2b 100644 --- a/web/src/components/Chat/ChatContent.tsx +++ b/web/src/components/Chat/ChatContent.tsx @@ -8,6 +8,7 @@ import { type FC, useRef, useEffect } from 'react' import clsx from 'clsx' import Markdown from '@/components/Markdown' import type { ChatContentProps } from './types' +import { Spin } from 'antd' /** * 聊天内容显示组件 @@ -21,7 +22,8 @@ const ChatContent: FC = ({ empty, labelPosition = 'bottom', labelFormat, - errorDesc + errorDesc, + renderRuntime }) => { // 滚动容器引用，用于控制自动滚动到底部 const scrollContainerRef = useRef<(HTMLDivElement | null)>(null) @@ -45,8 +47,8 @@ const ChatContent: FC = ({ 'rb:left-0 rb:text-left': item.role === 'assistant', // 助手消息左对齐 })}> {/* 流式加载时且内容为空则不显示 */} - {streamLoading && item.content === '' - ? null + {streamLoading && item.content === '' && !renderRuntime + ? : <> {/* 顶部标签（如时间戳、用户名等） */} {labelPosition === 'top' && @@ -55,16 +57,17 @@ const ChatContent: FC = ({ } {/* 消息气泡框 */} -
+ {item.subContent && renderRuntime && renderRuntime(item, index)} {/* 使用Markdown组件渲染消息内容 */} - +
{/* 底部标签（如时间戳、用户名等） */} {labelPosition === 'bottom' && diff --git a/web/src/components/Chat/types.ts b/web/src/components/Chat/types.ts index 851a8ccc..264ce39c 100644 --- a/web/src/components/Chat/types.ts +++ b/web/src/components/Chat/types.ts @@ -19,7 +19,9 @@ export interface ChatItem { /** 消息内容 */ content?: string | null; /** 创建时间 */ - created_at?: number | string + created_at?: number | string; + status?: string; + subContent?: Record[] } /** @@ -81,4 +83,5 @@ export interface ChatContentProps { /** 标签格式化函数 */ labelFormat: (item: ChatItem) => any; errorDesc?: string; + renderRuntime?: (item: ChatItem, index: number) => ReactNode; } \ No newline at end of file diff --git a/web/src/components/Markdown/CodeBlock.tsx b/web/src/components/Markdown/CodeBlock.tsx index 23d54c34..a125a997 100644 --- a/web/src/components/Markdown/CodeBlock.tsx +++ b/web/src/components/Markdown/CodeBlock.tsx @@ -6,6 +6,9 @@ import CopyBtn from './CopyBtn'; type ICodeBlockProps = { value: string; + needCopy?: boolean; + size?: 'small' | 'default'; + showLineNumbers?: boolean; } // enum languageType { @@ -16,6 +19,9 @@ type ICodeBlockProps = { const CodeBlock: FC = ({ value, + needCopy = true, + size = 'default', + showLineNumbers = false }) => { return ( @@ -23,24 +29,26 @@ const CodeBlock: FC = ({ {value} - + />} ) } diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 1df2eb6d..87a95c40 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1982,6 +1982,10 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re arrange: 'Arrange', redo: 'Redo', undo: 'Undo', + + input: 'Input', + output: 'Output', + error: 'Error Message', }, emotionEngine: { emotionEngineConfig: 'Emotion Engine Configuration', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 39908757..fc683a66 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -2076,6 +2076,10 @@ export const zh = { arrange: '整理', redo: '重做', undo: '撤销', + + input: '输入', + output: '输出', + error: '错误信息', }, emotionEngine: { emotionEngineConfig: '情感引擎配置', diff --git a/web/src/utils/stream.ts b/web/src/utils/stream.ts index e4179e25..2501fde5 100644 --- a/web/src/utils/stream.ts +++ b/web/src/utils/stream.ts @@ -123,6 +123,20 @@ export const handleSSE = async (url: string, data: any, onMessage?: (data: SSEMe let response = await makeSSERequest(url, data, token || '', config); switch (response.status) { + case 500: + case 502: + const errorData = await response.json(); + errorData.error || i18n.t('common.serviceUpgrading'); + message.warning(errorData.error || i18n.t('common.serviceUpgrading')); + break + case 400: + const error = await response.json(); + message.warning(errorData.error); + throw error || 'Bad Request'; + case 504: + const errorJson = await response.json(); + message.warning(errorJson.error || i18n.t('common.serverError')); + break case 401: if (url?.includes('/public')) { return message.warning(i18n.t('common.publicApiCannotRefreshToken')); diff --git a/web/src/views/Workflow/components/Chat/Chat.tsx b/web/src/views/Workflow/components/Chat/Chat.tsx index 246c2e4c..4a1ac5a7 100644 --- a/web/src/views/Workflow/components/Chat/Chat.tsx +++ b/web/src/views/Workflow/components/Chat/Chat.tsx @@ -1,8 +1,9 @@ import { forwardRef, useImperativeHandle, useState, useRef } from 'react' import { useTranslation } from 'react-i18next' import clsx from 'clsx' -import { Input, Form, App } from 'antd' -import { Space, Button } from 'antd' +import { Input, Form, App, Space, Button, Collapse } from 'antd' +import { CheckCircleFilled, CloseCircleFilled, LoadingOutlined } from '@ant-design/icons' +import CodeBlock from '@/components/Markdown/CodeBlock' import ChatIcon from '@/assets/images/application/chat.png' import RbDrawer from '@/components/RbDrawer'; @@ -13,8 +14,11 @@ import ChatContent from '@/components/Chat/ChatContent' import type { ChatItem } from '@/components/Chat/types' import ChatSendIcon from '@/assets/images/application/chatSend.svg' import dayjs from 'dayjs' -import type { ChatRef, VariableConfigModalRef, StartVariableItem, GraphRef } from '../../types' +import type { ChatRef, VariableConfigModalRef, GraphRef } from '../../types' import { type SSEMessage } from '@/utils/stream' +import type { Variable } from '../Properties/VariableList/types' +import styles from './chat.module.css' +import Markdown from '@/components/Markdown' const Chat = forwardRef(({ appId, graphRef }, ref) => { const { t } = useTranslation() @@ -24,7 +28,7 @@ const Chat = forwardRef(({ appId const [open, setOpen] = useState(false) const [loading, setLoading] = useState(false) const [chatList, setChatList] = useState([]) - const [variables, setVariables] = useState([]) + const [variables, setVariables] = useState([]) const [streamLoading, setStreamLoading] = useState(false) const [conversationId, setConversationId] = useState(null) @@ -39,7 +43,7 @@ const Chat = forwardRef(({ appId if (startNodes.length) { const curVariables = startNodes[0].config.variables?.defaultValue - curVariables.forEach((vo: StartVariableItem) => { + curVariables.forEach((vo: Variable) => { if (typeof vo.default !== 'undefined') { vo.value = vo.default } @@ -60,7 +64,7 @@ const Chat = forwardRef(({ appId const handleEditVariables = () => { variableConfigModalRef.current?.handleOpen(variables) } - const handleSave = (values: StartVariableItem[]) => { + const handleSave = (values: Variable[]) => { setVariables([...values]) } const handleSend = () => { @@ -97,13 +101,28 @@ const Chat = forwardRef(({ appId role: 'assistant', content: '', created_at: Date.now(), + subContent: [], }]) const handleStreamMessage = (data: SSEMessage[]) => { - setStreamLoading(false) - data.forEach(item => { - const { chunk, conversation_id } = item.data as { chunk: string; conversation_id: string | null; }; + const { chunk, conversation_id, node_id, input, output, error, elapsed_time, status } = item.data as { + chunk: string; + conversation_id: string | null; + node_id: string; + node_name?: string; + input?: any; + output?: any; + elapsed_time?: string; + error?: any; + state: Record; + status?: 'completed' | 'failed' + }; + + const node = graphRef.current?.getNodes().find(n => n.id === node_id); + const { name, icon } = node?.getData() || {} + + console.log('node', node?.getData()) switch(item.event) { case 'message': @@ -119,6 +138,66 @@ const Chat = forwardRef(({ appId return newList }) break + case 'node_start': + setChatList(prev => { + const newList = [...prev] + const lastIndex = newList.length - 1 + if (lastIndex >= 0) { + const newSubContent = newList[lastIndex].subContent || [] + const filterIndex = newSubContent.findIndex(vo => vo.id === node_id) + if (filterIndex > -1) { + newSubContent[filterIndex] = { + ...newSubContent[filterIndex], + node_id: node_id, + node_name: name, + icon, + content: {}, + } + } else { + newSubContent.push({ + id: node_id, + node_id: node_id, + node_name: name, + icon, + content: {}, + }) + } + newList[lastIndex] = { + ...newList[lastIndex], + subContent: newSubContent + } + } + return newList + }) + break + case 'node_end': + case 'node_error': + setChatList(prev => { + const newList = [...prev] + const lastIndex = newList.length - 1 + if (lastIndex >= 0) { + const newSubContent = newList[lastIndex].subContent || [] + const filterIndex = newSubContent.findIndex(vo => vo.node_id === node_id) + if (filterIndex > -1 && newSubContent[filterIndex].content) { + newSubContent[filterIndex] = { + ...newSubContent[filterIndex], + content: { + input, + output, + error, + }, + status: status || 'completed', + elapsed_time + } + } + newList[lastIndex] = { + ...newList[lastIndex], + subContent: newSubContent + } + } + return newList + }) + break case 'workflow_end': setChatList(prev => { const newList = [...prev] @@ -126,6 +205,7 @@ const Chat = forwardRef(({ appId if (lastIndex >= 0) { newList[lastIndex] = { ...newList[lastIndex], + status, content: newList[lastIndex].content === '' ? null : newList[lastIndex].content } } @@ -142,14 +222,31 @@ const Chat = forwardRef(({ appId } form.setFieldValue('message', undefined) + setStreamLoading(true) draftRun(appId, { message: message, variables: params, stream: true, conversation_id: conversationId }, handleStreamMessage) + .catch((error) => { + setChatList(prev => { + const newList = [...prev] + const lastIndex = newList.length - 1 + if (lastIndex >= 0) { + newList[lastIndex] = { + ...newList[lastIndex], + status: 'failed', + content: null, + subContent: error.error + } + } + return newList + }) + }) .finally(() => { setLoading(false) + setStreamLoading(false) }) } // 暴露给父组件的方法 @@ -158,6 +255,11 @@ const Chat = forwardRef(({ appId handleClose })); + const getStatus = (status?: string) => { + return status === 'completed' ? 'rb:text-[#369F21]' : status === 'failed' ? 'rb:text-[#FF5D34]' : 'rb:text-[#5B6167]' + } + + console.log('chatList', chatList) return ( @@ -173,10 +275,7 @@ const Chat = forwardRef(({ appId onClose={handleClose} > } data={chatList} @@ -184,6 +283,87 @@ const Chat = forwardRef(({ appId labelPosition="bottom" labelFormat={(item) => dayjs(item.created_at).locale('en').format('MMMM D, YYYY [at] h:mm A')} errorDesc={t('application.ReplyException')} + renderRuntime={(item, index) => { + return ( +
+ + {item.status === 'completed' ? : item.status === 'failed' ? : } + {t('application.workflow')} +
, + className: styles.collapseItem, + children: ( + Array.isArray(item.subContent) + ? + {item.subContent?.map(vo => ( + +
+ {vo.icon && } +
{vo.node_name || vo.node_id}
+
+ + {typeof vo.elapsed_time == 'number' && <>{vo.elapsed_time?.toFixed(3)}ms} + {vo.status === 'completed' ? : vo.status === 'failed' ? : } + + , + className: styles.collapseItem, + children: ( + + {vo.status === 'failed' && +
+
+ {t(`workflow.error`)} + +
+
+ +
+
+ } + {['input', 'output'].map(key => ( +
+
+ {t(`workflow.${key}`)} + +
+
+ +
+
+ ))} + + ) + }]} + /> + ))} + + :
+ +
+ ) + }]} + /> + + ) + }} />
diff --git a/web/src/views/Workflow/components/Chat/chat.module.css b/web/src/views/Workflow/components/Chat/chat.module.css new file mode 100644 index 00000000..99fe11f7 --- /dev/null +++ b/web/src/views/Workflow/components/Chat/chat.module.css @@ -0,0 +1,45 @@ +.completed { + background-color: rgba(54, 159, 33, 0.06); + border-color: rgba(54, 159, 33, 0.25); + border-radius: 8px; +} +.failed { + background-color: rgba(255, 138, 76, 0.08); + border-color: rgba(255, 138, 76, 0.20); + border-radius: 8px; +} +.default { + background-color: rgba(91, 97, 103, 0.08); + border-color: rgba(91, 97, 103, 0.30); + border-radius: 8px; +} +.collapse-item { + font-size: 12px; + line-height: 16px; +} +.collapse-item:global(.ant-collapse-item>.ant-collapse-header) { + padding: 8px 12px; +} +.collapse-item:global(.ant-collapse-item>.ant-collapse-header .ant-collapse-expand-icon) { + height: 16px; +} +.completed:global(.ant-collapse .ant-collapse-content), +.failed:global(.ant-collapse .ant-collapse-content) { + background-color: transparent; + border-top: none; +} +:global(.ant-collapse .ant-collapse-content>.ant-collapse-content-box) { + padding-top: 0; +} +.collapse-item :global(.ant-collapse) { + /* background-color: #F0F3F8; */ + background-color: #FBFDFF; + border-radius: 6px; +} +.collapse-item :global(.ant-collapse>.ant-collapse-item:last-child), +.collapse-item :global(.ant-collapse>.ant-collapse-item:last-child>.ant-collapse-header) { + border-radius: 0 0 6px 6px; +} +.collapse-item :global(.ant-collapse .ant-collapse-content>.ant-collapse-content-box) { + padding: 0 4px 4px 4px; +} \ No newline at end of file From 7bfa7b3f029812a8966f639e0de3f6458c84adf1 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 26 Jan 2026 16:00:47 +0800 Subject: [PATCH 069/175] fix(web): handleSSE bugfix --- web/src/utils/stream.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/src/utils/stream.ts b/web/src/utils/stream.ts index 2501fde5..be2220da 100644 --- a/web/src/utils/stream.ts +++ b/web/src/utils/stream.ts @@ -131,7 +131,7 @@ export const handleSSE = async (url: string, data: any, onMessage?: (data: SSEMe break case 400: const error = await response.json(); - message.warning(errorData.error); + message.warning(error.error); throw error || 'Bad Request'; case 504: const errorJson = await response.json(); From 3b4b474ce869e4416db314df8669671a1c931a6f Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 16:32:58 +0800 Subject: [PATCH 070/175] fix(sandbox): prevent imports from being blocked when network is disabled --- sandbox/lib/seccomp_python/src/syscalls.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sandbox/lib/seccomp_python/src/syscalls.rs b/sandbox/lib/seccomp_python/src/syscalls.rs index 07070d22..961fffac 100644 --- a/sandbox/lib/seccomp_python/src/syscalls.rs +++ b/sandbox/lib/seccomp_python/src/syscalls.rs @@ -10,6 +10,7 @@ pub static ALLOW_SYSCALLS: &[i32] = &[ libc::SYS_ioctl as i32, libc::SYS_lseek as i32, libc::SYS_getdents64 as i32, + libc::SYS_fstat as i32, // thread libc::SYS_futex as i32, @@ -77,7 +78,6 @@ pub static ALLOW_NETWORK_SYSCALLS: &[i32] = &[ libc::SYS_sendmsg as i32, libc::SYS_sendmmsg as i32, libc::SYS_getsockopt as i32, - libc::SYS_fstat as i32, libc::SYS_fcntl as i32, libc::SYS_fstatfs as i32, libc::SYS_poll as i32, From 399357f75293e9bbd24aa7cf8591ddb1d629156e Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:06:55 +0800 Subject: [PATCH 071/175] =?UTF-8?q?user=5Fid->=E7=8E=B0=E5=AE=9E=E4=B8=BAc?= =?UTF-8?q?onfig=5Fid=5Fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 80d8c717..1a25c779 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -188,7 +188,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "user_id": config.user_id, + "config_id_old": config.user_id, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, From 7cd0d78424659f8cda7a9cd381798274f74131b5 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:21:10 +0800 Subject: [PATCH 072/175] =?UTF-8?q?user=5Fid->=E6=98=BE=E7=A4=BA=E4=B8=BAc?= =?UTF-8?q?onfig=5Fid=5Fold=E4=BC=A0=E8=BE=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 1a25c779..1707f8fa 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -188,7 +188,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "config_id_old": config.user_id, + "config_id_old": config.config_id_old, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, From ebc41b2eec3ed5f1dcb46fc5b66e6c87abe1f437 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Mon, 26 Jan 2026 17:22:48 +0800 Subject: [PATCH 073/175] Fix/memory bug fix (#199) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 --------- Co-authored-by: lanceyq <1982376970@qq.com> --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 80d8c717..1707f8fa 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -188,7 +188,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "user_id": config.user_id, + "config_id_old": config.config_id_old, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, From e1f56078366ea06fa330853ca2bb0ddfd6d2eef2 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:37:40 +0800 Subject: [PATCH 074/175] =?UTF-8?q?user=5Fid->=E6=98=BE=E7=A4=BA=E4=B8=BAc?= =?UTF-8?q?onfig=5Fid=5Fold=E4=BC=A0=E8=BE=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 1707f8fa..1a25c779 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -188,7 +188,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "config_id_old": config.config_id_old, + "config_id_old": config.user_id, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, From 46f0f3cee90f7cf852bf5bcf89866b57448f1ffa Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 26 Jan 2026 17:43:25 +0800 Subject: [PATCH 075/175] feat(web): update read_all_config select valueKey --- web/src/components/CustomSelect/index.tsx | 19 +++++++++++++------ web/src/views/ApplicationConfig/Agent.tsx | 6 ++++-- web/src/views/Workflow/constant.ts | 4 ++-- web/src/views/Workflow/types.ts | 2 +- 4 files changed, 20 insertions(+), 11 deletions(-) diff --git a/web/src/components/CustomSelect/index.tsx b/web/src/components/CustomSelect/index.tsx index 1887d635..6153a76d 100644 --- a/web/src/components/CustomSelect/index.tsx +++ b/web/src/components/CustomSelect/index.tsx @@ -15,7 +15,7 @@ interface ApiResponse { interface CustomSelectProps extends Omit { url: string; params?: Record; - valueKey?: string; + valueKey?: string | string[]; labelKey?: string; placeholder?: string; hasAll?: boolean; @@ -66,11 +66,18 @@ const CustomSelect: FC = ({ {...props} > {hasAll && {allTitle || t('common.all')}} - {displayOptions.map((option) => ( - - {String(option[labelKey])} - - ))} + {displayOptions.map((option) => { + const getValue = () => { + if (typeof valueKey === 'string') return option[valueKey]; + return valueKey.find(key => option[key] != null) ? option[valueKey.find(key => option[key] != null)!] : undefined; + }; + const value = getValue(); + return ( + + {String(option[labelKey])} + + ); + })} ); }; diff --git a/web/src/views/ApplicationConfig/Agent.tsx b/web/src/views/ApplicationConfig/Agent.tsx index 77e90440..97a622d1 100644 --- a/web/src/views/ApplicationConfig/Agent.tsx +++ b/web/src/views/ApplicationConfig/Agent.tsx @@ -79,7 +79,7 @@ const SelectWrapper: FC<{ title: string, desc: string, name: string | string[], placeholder={t('common.pleaseSelect')} url={url} hasAll={false} - valueKey='config_id' + valueKey={['config_id_old', 'config_id']} labelKey="config_name" /> @@ -126,12 +126,14 @@ const Agent = forwardRef((_props, ref) => { getApplicationConfig(id as string).then(res => { const response = res as Config let allTools = Array.isArray(response.tools) ? response.tools : [] + const memoryContent = response.memory?.memory_content + const convertedMemoryContent = memoryContent && !isNaN(Number(memoryContent)) ? Number(memoryContent) : memoryContent form.setFieldsValue({ ...response, tools: allTools, memory: { ...response.memory, - memory_content: response.memory?.memory_content ? Number(response.memory?.memory_content) : undefined + memory_content: convertedMemoryContent } }) setData({ diff --git a/web/src/views/Workflow/constant.ts b/web/src/views/Workflow/constant.ts index e250e184..aab8be7d 100644 --- a/web/src/views/Workflow/constant.ts +++ b/web/src/views/Workflow/constant.ts @@ -200,7 +200,7 @@ export const nodeLibrary: NodeLibrary[] = [ config_id: { type: 'customSelect', url: memoryConfigListUrl, - valueKey: 'config_id', + valueKey: ['config_id_old', 'config_id'], labelKey: 'config_name' }, search_switch: { @@ -223,7 +223,7 @@ export const nodeLibrary: NodeLibrary[] = [ config_id: { type: 'customSelect', url: memoryConfigListUrl, - valueKey: 'config_id', + valueKey: ['config_id_old', 'config_id'], labelKey: 'config_name' } } diff --git a/web/src/views/Workflow/types.ts b/web/src/views/Workflow/types.ts index 909c30e4..31d1f512 100644 --- a/web/src/views/Workflow/types.ts +++ b/web/src/views/Workflow/types.ts @@ -14,7 +14,7 @@ export interface NodeConfig { url?: string; params?: { [key: string]: unknown; } - valueKey?: string; + valueKey?: string | string[]; labelKey?: string; defaultValue?: any; From 722746c78b7c95a2d72e11ac637945fa65daac5a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:47:05 +0800 Subject: [PATCH 076/175] =?UTF-8?q?user=5Fid->=E6=98=BE=E7=A4=BA=E4=B8=BAc?= =?UTF-8?q?onfig=5Fid=5Fold=E4=BC=A0=E8=BE=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 1a25c779..0ede7bd3 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -188,7 +188,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "config_id_old": config.user_id, + "config_id_old": int(config.user_id), "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, From f1f887faaebc3caa74feb05730a76faae6bb30f3 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 17:29:44 +0800 Subject: [PATCH 077/175] feat(workflow): Add a new node for executing code --- api/app/core/workflow/nodes/code/__init__.py | 3 + api/app/core/workflow/nodes/code/config.py | 50 +++++++ api/app/core/workflow/nodes/code/node.py | 122 ++++++++++++++++++ api/app/core/workflow/nodes/configs.py | 14 +- api/app/core/workflow/nodes/node_factory.py | 5 +- sandbox/app/core/executor.py | 1 - .../app/core/runners/python/python_runner.py | 5 +- sandbox/app/services/python_service.py | 4 +- 8 files changed, 193 insertions(+), 11 deletions(-) create mode 100644 api/app/core/workflow/nodes/code/config.py create mode 100644 api/app/core/workflow/nodes/code/node.py diff --git a/api/app/core/workflow/nodes/code/__init__.py b/api/app/core/workflow/nodes/code/__init__.py index e69de29b..e42af93d 100644 --- a/api/app/core/workflow/nodes/code/__init__.py +++ b/api/app/core/workflow/nodes/code/__init__.py @@ -0,0 +1,3 @@ +from app.core.workflow.nodes.code.node import CodeNode + +__all__ = ["CodeNode"] \ No newline at end of file diff --git a/api/app/core/workflow/nodes/code/config.py b/api/app/core/workflow/nodes/code/config.py new file mode 100644 index 00000000..35b757e9 --- /dev/null +++ b/api/app/core/workflow/nodes/code/config.py @@ -0,0 +1,50 @@ +from typing import Literal +from pydantic import Field, BaseModel + +from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableType + + +class InputVariable(BaseModel): + name: str = Field( + ..., + description="variable name" + ) + + variable: str = Field( + ..., + description="variable selector" + ) + + +class OutputVariable(BaseModel): + name: str = Field( + ..., + description="variable name" + ) + + type: VariableType = Field( + ..., + description="variable selector" + ) + + +class CodeNodeConfig(BaseNodeConfig): + input_variables: list[InputVariable] = Field( + default_factory=list, + description="input variables" + ) + + output_variables: list[OutputVariable] = Field( + default_factory=list, + description="output variables" + ) + + code_content: str = Field( + default="", + description="code content" + ) + + language: Literal['python3', 'nodejs'] = Field( + ..., + description="language" + ) diff --git a/api/app/core/workflow/nodes/code/node.py b/api/app/core/workflow/nodes/code/node.py new file mode 100644 index 00000000..3e15089b --- /dev/null +++ b/api/app/core/workflow/nodes/code/node.py @@ -0,0 +1,122 @@ +import base64 +import json +import logging +import re +from string import Template +from textwrap import dedent +from typing import Any + +import httpx +from sympy.physics.vector import vlatex + +from app.core.workflow.nodes import BaseNode, WorkflowState +from app.core.workflow.nodes.base_config import VariableType +from app.core.workflow.nodes.code.config import CodeNodeConfig + +logger = logging.getLogger(__name__) + +SCRIPT_TEMPLATE = Template(dedent(""" +$code + +import json +from base64 import b64decode + +# decode and prepare input dict +inputs_obj = json.loads(b64decode('$inputs_variable').decode('utf-8')) + +# execute main function +output_obj = main(**inputs_obj) + +# convert output to json and print +output_json = json.dumps(output_obj, indent=4) +result = "<>" + output_json + "<>" +print(result) +""")) + + +class CodeNode(BaseNode): + def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): + super().__init__(node_config, workflow_config) + self.typed_config: CodeNodeConfig | None = None + + def extract_result(self, content: str): + match = re.search(r'<>(.*?)<>', content, re.DOTALL) + if match: + extracted = match.group(1) + exec_result = json.loads(extracted) + result = {} + for output in self.typed_config.output_variables: + value = exec_result.get(output.name) + if not value: + raise RuntimeError(f"Return value {output.name} does not exist") + match output.type: + case VariableType.STRING: + if not isinstance(value, str): + raise RuntimeError(f"Return value {output.name} should be a string") + case VariableType.BOOLEAN: + if not isinstance(value, bool): + raise RuntimeError(f"Return value {output.name} should be a boolean") + case VariableType.NUMBER: + if not isinstance(value, (int, float)): + raise RuntimeError(f"Return value {output.name} should be a number") + case VariableType.OBJECT: + if not isinstance(value, dict): + raise RuntimeError(f"Return value {output.name} should be a dictionary") + case VariableType.ARRAY_STRING: + if not isinstance(value, list) or not all(isinstance(v, str) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of strings") + case VariableType.ARRAY_NUMBER: + if not isinstance(value, list) or not all(isinstance(v, (int, float)) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of numbers") + case VariableType.ARRAY_OBJECT: + if not isinstance(value, list) or not all(isinstance(v, dict) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of dictionaries") + case VariableType.ARRAY_BOOLEAN: + if not isinstance(value, list) or not all(isinstance(v, bool) for v in value): + raise RuntimeError(f"Return value {output.name} should be a list of booleans") + result[output.name] = value + return result + else: + raise RuntimeError("The output of main must be a dictionary") + + async def execute(self, state: WorkflowState) -> Any: + self.typed_config = CodeNodeConfig(**self.config) + input_variable_dict = {} + for input_variable in self.typed_config.input_variables: + input_variable_dict[input_variable.name] = self.get_variable(input_variable.variable, state) + code = base64.b64decode( + self.typed_config.code + ).decode("utf-8") + + input_variable_dict = base64.b64encode( + json.dumps(input_variable_dict).encode("utf-8") + ).decode("utf-8") + + final_script = SCRIPT_TEMPLATE.substitute( + code=code, + inputs_variable=input_variable_dict, + ) + + async with httpx.AsyncClient() as client: + response = await client.post( + "http://sandbox:8194/v1/sandbox/run", + headers={ + "x-api-key": 'redbear-sandbox' + }, + json={ + "language": "python3", + "code": base64.b64encode(final_script.encode("utf-8")).decode("utf-8"), + "options": { + "enable_network": True + } + } + ) + resp = response.json() + + match resp['code']: + case 31: + raise RuntimeError("Operation not permitted") + case 0: + return self.extract_result(resp["data"]["stdout"]) + case _: + raise Exception(resp["message"]) diff --git a/api/app/core/workflow/nodes/configs.py b/api/app/core/workflow/nodes/configs.py index 4d31efaa..d73754f6 100644 --- a/api/app/core/workflow/nodes/configs.py +++ b/api/app/core/workflow/nodes/configs.py @@ -10,21 +10,22 @@ from app.core.workflow.nodes.base_config import ( VariableDefinition, VariableType, ) +from app.core.workflow.nodes.code.config import CodeNodeConfig +from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig from app.core.workflow.nodes.end.config import EndNodeConfig from app.core.workflow.nodes.http_request.config import HttpRequestNodeConfig from app.core.workflow.nodes.if_else.config import IfElseNodeConfig from app.core.workflow.nodes.jinja_render.config import JinjaRenderNodeConfig from app.core.workflow.nodes.knowledge.config import KnowledgeRetrievalNodeConfig from app.core.workflow.nodes.llm.config import LLMNodeConfig, MessageConfig -from app.core.workflow.nodes.start.config import StartNodeConfig -from app.core.workflow.nodes.transform.config import TransformNodeConfig -from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig +from app.core.workflow.nodes.memory.config import MemoryReadNodeConfig, MemoryWriteNodeConfig from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig from app.core.workflow.nodes.question_classifier.config import QuestionClassifierNodeConfig +from app.core.workflow.nodes.start.config import StartNodeConfig from app.core.workflow.nodes.tool.config import ToolNodeConfig -from app.core.workflow.nodes.memory.config import MemoryReadNodeConfig, MemoryWriteNodeConfig +from app.core.workflow.nodes.transform.config import TransformNodeConfig +from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig -from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig __all__ = [ # 基础类 "BaseNodeConfig", @@ -49,5 +50,6 @@ __all__ = [ "QuestionClassifierNodeConfig", "ToolNodeConfig", "MemoryReadNodeConfig", - "MemoryWriteNodeConfig" + "MemoryWriteNodeConfig", + "CodeNodeConfig" ] diff --git a/api/app/core/workflow/nodes/node_factory.py b/api/app/core/workflow/nodes/node_factory.py index 9fca8d7a..fb2fe00f 100644 --- a/api/app/core/workflow/nodes/node_factory.py +++ b/api/app/core/workflow/nodes/node_factory.py @@ -10,6 +10,7 @@ from typing import Any, Union from app.core.workflow.nodes.agent import AgentNode from app.core.workflow.nodes.assigner import AssignerNode from app.core.workflow.nodes.base_node import BaseNode +from app.core.workflow.nodes.code import CodeNode from app.core.workflow.nodes.cycle_graph.node import CycleGraphNode from app.core.workflow.nodes.end import EndNode from app.core.workflow.nodes.enums import NodeType @@ -49,7 +50,8 @@ WorkflowNode = Union[ QuestionClassifierNode, ToolNode, MemoryReadNode, - MemoryWriteNode + MemoryWriteNode, + CodeNode ] @@ -81,6 +83,7 @@ class NodeFactory: NodeType.TOOL: ToolNode, NodeType.MEMORY_READ: MemoryReadNode, NodeType.MEMORY_WRITE: MemoryWriteNode, + NodeType.CODE: CodeNode, } @classmethod diff --git a/sandbox/app/core/executor.py b/sandbox/app/core/executor.py index 6edc48c0..e87b510c 100644 --- a/sandbox/app/core/executor.py +++ b/sandbox/app/core/executor.py @@ -15,7 +15,6 @@ class ExecutionResult: self.stdout = stdout self.stderr = stderr self.exit_code = exit_code - self.error = error class CodeExecutor(ABC): diff --git a/sandbox/app/core/runners/python/python_runner.py b/sandbox/app/core/runners/python/python_runner.py index faac5f0c..30792b91 100644 --- a/sandbox/app/core/runners/python/python_runner.py +++ b/sandbox/app/core/runners/python/python_runner.py @@ -9,12 +9,15 @@ from app.config import SANDBOX_USER_ID, SANDBOX_GROUP_ID, get_config from app.core.encryption import generate_key, encrypt_code from app.core.executor import CodeExecutor, ExecutionResult from app.core.runners.python.settings import check_lib_avaiable, release_lib_binary, LIB_PATH +from app.logger import get_logger from app.models import RunnerOptions # Python sandbox prescript template with open("app/core/runners/python/prescript.py") as f: PYTHON_PRESCRIPT = f.read() +logger = get_logger() + class PythonRunner(CodeExecutor): """Python code runner with security isolation""" @@ -106,6 +109,7 @@ class PythonRunner(CodeExecutor): env["ALLOWED_SYSCALLS"] = ",".join(map(str, config.allowed_syscalls)) # Execute with Python interpreter + logger.info(encoded_key) process = await asyncio.create_subprocess_exec( config.python_path, @@ -143,7 +147,6 @@ class PythonRunner(CodeExecutor): stdout="", stderr="Execution timeout", exit_code=-1, - error="Execution timeout" ) finally: diff --git a/sandbox/app/services/python_service.py b/sandbox/app/services/python_service.py index 71cfda0d..5700841d 100644 --- a/sandbox/app/services/python_service.py +++ b/sandbox/app/services/python_service.py @@ -37,8 +37,8 @@ async def run_python_code(code: str, preload: str, options: RunnerOptions): if result.exit_code == -signal.SIGSYS: return error_response(31, "sandbox security policy violation") - if result.error: - return error_response(-500, result.error) + if result.stderr: + return error_response(500, result.stderr) return success_response(RunCodeResponse( stdout=result.stdout, From f76bffb4823252ed482867c3fe112a1cf09f5a16 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 26 Jan 2026 18:32:18 +0800 Subject: [PATCH 078/175] fix(web): KnowledgeConfigModal bugfix --- .../components/Knowledge/KnowledgeConfigModal.tsx | 2 +- .../Properties/Knowledge/KnowledgeConfigModal.tsx | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx index abf56b18..70b17a11 100644 --- a/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx +++ b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx @@ -66,7 +66,7 @@ const KnowledgeConfigModal = forwardRef { if (values?.retrieve_type) { const fieldsToReset = Object.keys(values).filter(key => - key !== 'kb_id' && key !== 'retrieve_type' + key !== 'kb_id' && key !== 'retrieve_type' && key !== 'top_k' ) as (keyof KnowledgeConfigForm)[]; form.resetFields(fieldsToReset); } diff --git a/web/src/views/Workflow/components/Properties/Knowledge/KnowledgeConfigModal.tsx b/web/src/views/Workflow/components/Properties/Knowledge/KnowledgeConfigModal.tsx index 77ca21a2..196ce8e3 100644 --- a/web/src/views/Workflow/components/Properties/Knowledge/KnowledgeConfigModal.tsx +++ b/web/src/views/Workflow/components/Properties/Knowledge/KnowledgeConfigModal.tsx @@ -66,7 +66,7 @@ const KnowledgeConfigModal = forwardRef { if (values?.retrieve_type) { const fieldsToReset = Object.keys(values).filter(key => - key !== 'kb_id' && key !== 'retrieve_type' + key !== 'kb_id' && key !== 'retrieve_type' && key !== 'top_k' ) as (keyof KnowledgeConfigForm)[]; form.resetFields(fieldsToReset); } @@ -108,6 +108,7 @@ const KnowledgeConfigModal = forwardRef {/* Top K */} @@ -116,13 +117,12 @@ const KnowledgeConfigModal = forwardRef form.setFieldValue('top_k', value)} + // onChange={(value) => form.setFieldValue('top_k', value)} /> {/* 语义相似度阈值 similarity_threshold */} From 5267bd60a566893d9269a20c4a073642b479fa33 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Mon, 26 Jan 2026 18:40:28 +0800 Subject: [PATCH 079/175] fix(web): iteration's variable add parameter-extractor node --- web/src/views/Workflow/constant.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/src/views/Workflow/constant.ts b/web/src/views/Workflow/constant.ts index e250e184..7b15c049 100644 --- a/web/src/views/Workflow/constant.ts +++ b/web/src/views/Workflow/constant.ts @@ -284,7 +284,7 @@ export const nodeLibrary: NodeLibrary[] = [ config: { input: { type: 'variableList', - filterNodeTypes: ['knowledge-retrieval', 'iteration', 'loop'], + filterNodeTypes: ['knowledge-retrieval', 'iteration', 'loop', 'parameter-extractor'], filterVariableNames: ['message'] }, parallel: { From 1f615a06add14d193f7d2840bddefb53712ced32 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Mon, 26 Jan 2026 18:50:22 +0800 Subject: [PATCH 080/175] fix(sandbox): treat non-zero exit codes as errors instead of relying only on stderr --- api/app/core/workflow/nodes/code/config.py | 2 +- api/app/core/workflow/nodes/code/node.py | 4 ++-- sandbox/app/services/python_service.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/api/app/core/workflow/nodes/code/config.py b/api/app/core/workflow/nodes/code/config.py index 35b757e9..8af13f12 100644 --- a/api/app/core/workflow/nodes/code/config.py +++ b/api/app/core/workflow/nodes/code/config.py @@ -39,7 +39,7 @@ class CodeNodeConfig(BaseNodeConfig): description="output variables" ) - code_content: str = Field( + code: str = Field( default="", description="code content" ) diff --git a/api/app/core/workflow/nodes/code/node.py b/api/app/core/workflow/nodes/code/node.py index 3e15089b..5262a7e2 100644 --- a/api/app/core/workflow/nodes/code/node.py +++ b/api/app/core/workflow/nodes/code/node.py @@ -47,7 +47,7 @@ class CodeNode(BaseNode): result = {} for output in self.typed_config.output_variables: value = exec_result.get(output.name) - if not value: + if value is None: raise RuntimeError(f"Return value {output.name} does not exist") match output.type: case VariableType.STRING: @@ -104,7 +104,7 @@ class CodeNode(BaseNode): "x-api-key": 'redbear-sandbox' }, json={ - "language": "python3", + "language": self.typed_config.language, "code": base64.b64encode(final_script.encode("utf-8")).decode("utf-8"), "options": { "enable_network": True diff --git a/sandbox/app/services/python_service.py b/sandbox/app/services/python_service.py index 5700841d..210b2086 100644 --- a/sandbox/app/services/python_service.py +++ b/sandbox/app/services/python_service.py @@ -37,7 +37,7 @@ async def run_python_code(code: str, preload: str, options: RunnerOptions): if result.exit_code == -signal.SIGSYS: return error_response(31, "sandbox security policy violation") - if result.stderr: + if result.stderr and result.exit_code != 0: return error_response(500, result.stderr) return success_response(RunCodeResponse( From a5b8d3afa5ef19723ae5cce57f3fbbee70ff51f8 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Mon, 26 Jan 2026 19:05:07 +0800 Subject: [PATCH 081/175] Fix/memory bug fix (#200) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 --------- Co-authored-by: lanceyq <1982376970@qq.com> --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 1707f8fa..0ede7bd3 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -188,7 +188,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "config_id_old": config.config_id_old, + "config_id_old": int(config.user_id), "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, From 80ca247435fe9d79a0c0c71fd4b0113284eaf359 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B9=90=E5=8A=9B=E9=BD=90?= <162269739+lanceyq@users.noreply.github.com> Date: Mon, 26 Jan 2026 19:05:20 +0800 Subject: [PATCH 082/175] Refactor/benchmark test (#196) * [changes]refactor locomo_test * [fix]Fix the circular import of ModelParameters * [changes]The benchmark test can run stably. * [fix]Complete end-to-end LoCoMo repair * [fix]Complete the end-to-end longmemeval and memsciqa fixes * [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect. * [changes]refactor locomo_test * [fix]Fix the circular import of ModelParameters * [changes]The benchmark test can run stably. * [fix]Complete end-to-end LoCoMo repair * [fix]Complete the end-to-end longmemeval and memsciqa fixes * [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect. * [changes]Benchmark test adaptation for end_user_id * [changes]refactor locomo_test * [fix]Fix the circular import of ModelParameters * [changes]The benchmark test can run stably. * [fix]Complete end-to-end LoCoMo repair * [fix]Complete the end-to-end longmemeval and memsciqa fixes * [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect. * [fix]Complete the end-to-end longmemeval and memsciqa fixes * [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect. * [changes]Benchmark test adaptation for end_user_id --- .../memory/evaluation/.env.evaluation.example | 224 +++++ api/app/core/memory/evaluation/.gitignore | 13 + api/app/core/memory/evaluation/benchmark.md | 772 +++++++++++++++- .../memory/evaluation/check_enduser_data.py | 371 ++++++++ .../core/memory/evaluation/common/metrics.py | 2 +- .../memory/evaluation/dialogue_queries.py | 6 +- .../memory/evaluation/extraction_utils.py | 369 +++++--- .../evaluation/locomo/locomo_benchmark.py | 863 +++++++++++------- .../memory/evaluation/locomo/locomo_test.py | 293 +++--- .../memory/evaluation/locomo/locomo_utils.py | 69 +- .../evaluation/locomo/qwen_search_eval.py | 96 +- ...earch_eval.py => longmemeval_benchmark.py} | 181 ++-- .../evaluation/longmemeval/test_eval.py | 132 ++- .../evaluation/memsciqa/memsciqa-test.py | 224 +++-- .../{evaluate_qa.py => memsciqa_benchmark.py} | 123 ++- api/app/core/memory/evaluation/run_eval.py | 21 +- .../extraction_orchestrator.py | 22 +- api/app/models/agent_app_config_model.py | 2 +- api/app/models/multi_agent_model.py | 2 +- api/app/schemas/multi_agent_schema.py | 2 +- api/app/services/master_agent_router.py | 2 +- api/app/utils/app_config_utils.py | 2 +- 22 files changed, 2760 insertions(+), 1031 deletions(-) create mode 100644 api/app/core/memory/evaluation/.env.evaluation.example create mode 100644 api/app/core/memory/evaluation/.gitignore create mode 100644 api/app/core/memory/evaluation/check_enduser_data.py rename api/app/core/memory/evaluation/longmemeval/{qwen_search_eval.py => longmemeval_benchmark.py} (93%) rename api/app/core/memory/evaluation/memsciqa/{evaluate_qa.py => memsciqa_benchmark.py} (76%) diff --git a/api/app/core/memory/evaluation/.env.evaluation.example b/api/app/core/memory/evaluation/.env.evaluation.example new file mode 100644 index 00000000..be089eb4 --- /dev/null +++ b/api/app/core/memory/evaluation/.env.evaluation.example @@ -0,0 +1,224 @@ +# ============================================================================ +# 基准测试统一配置文件示例 +# ============================================================================ +# 复制此文件为 .env.evaluation 并根据需要修改 +# 支持的基准测试：LoCoMo、LongMemEval、MemSciQA +# ============================================================================ + +# ============================================================================ +# 通用配置（所有基准测试共用） +# ============================================================================ + +# ---------------------------------------------------------------------------- +# Neo4j 配置 +# ---------------------------------------------------------------------------- +# 默认 Group ID（建议各基准测试使用独立的 group） +EVAL_GROUP_ID=benchmark_default + +# ---------------------------------------------------------------------------- +# 模型配置（必需） +# ---------------------------------------------------------------------------- +# ⚠️ 必填：从数据库 models 表中选择有效的模型 ID +# +# 如何获取模型 ID： +# 1. 查询数据库：SELECT id, model_name FROM models WHERE is_active = true; +# 2. 或通过系统管理界面查看 +# 3. 确保模型可用且配置正确 + +# LLM 模型 ID（必填） +EVAL_LLM_ID=your_llm_model_id_here + +# Embedding 模型 ID（必填） +EVAL_EMBEDDING_ID=your_embedding_model_id_here + +# ---------------------------------------------------------------------------- +# 检索参数 +# ---------------------------------------------------------------------------- +# 检索类型: "keyword", "embedding", "hybrid" +EVAL_SEARCH_TYPE=hybrid + +# 检索结果数量限制（默认值） +EVAL_SEARCH_LIMIT=12 + +# 上下文最大字符数（默认值） +EVAL_MAX_CONTEXT_CHARS=8000 + +# ---------------------------------------------------------------------------- +# LLM 参数 +# ---------------------------------------------------------------------------- +# LLM 温度参数（0.0 = 确定性输出） +EVAL_LLM_TEMPERATURE=0.0 + +# LLM 最大生成 token 数 +EVAL_LLM_MAX_TOKENS=32 + +# LLM 超时时间（秒） +EVAL_LLM_TIMEOUT=10.0 + +# LLM 最大重试次数 +EVAL_LLM_MAX_RETRIES=1 + +# ---------------------------------------------------------------------------- +# 数据处理参数 +# ---------------------------------------------------------------------------- +# Chunker 策略 +EVAL_CHUNKER_STRATEGY=RecursiveChunker + +# 是否在导入前清空现有数据 +EVAL_RESET_ON_INGEST=true + +# 是否保存详细日志 +EVAL_SAVE_DETAILED_LOGS=true + +# ============================================================================ +# LoCoMo 基准测试专用配置 +# ============================================================================ +# 数据集：locomo10.json +# 运行：python locomo_benchmark.py --sample_size 20 +# ---------------------------------------------------------------------------- + +# Group ID（LoCoMo 专用） +LOCOMO_GROUP_ID=locomo_benchmark + +# 测试样本数量 +# 建议值：20（快速测试）、100（中等测试）、1986（完整测试） +LOCOMO_SAMPLE_SIZE=20 + +# 检索结果数量限制 +LOCOMO_SEARCH_LIMIT=12 + +# 上下文最大字符数 +LOCOMO_CONTEXT_CHAR_BUDGET=8000 + +# 导入的对话数量 +LOCOMO_MAX_DIALOGUES=1 + +# 跳过数据摄入（true=跳过，false=摄入） +# 首次运行设置为 false，后续运行可设置为 true 以节省时间 +LOCOMO_SKIP_INGEST=false + +# 结果保存目录 +LOCOMO_OUTPUT_DIR=locomo/results + +# ============================================================================ +# LongMemEval 基准测试专用配置 +# ============================================================================ +# 数据集：longmemeval_oracle_zh.json +# 运行：python longmemeval_benchmark.py --sample_size 3 +# 特点：支持时间推理问题的增强检索 +# ---------------------------------------------------------------------------- + +# Group ID（LongMemEval 专用） +LONGMEMEVAL_GROUP_ID=longmemeval_zh_bak_3 + +# 测试样本数量（<=0 表示全部样本） +LONGMEMEVAL_SAMPLE_SIZE=3 + +# 起始样本索引 +LONGMEMEVAL_START_INDEX=0 + +# 检索结果数量限制 +LONGMEMEVAL_SEARCH_LIMIT=8 + +# 上下文最大字符数 +LONGMEMEVAL_CONTEXT_CHAR_BUDGET=4000 + +# LLM 最大生成 token 数 +LONGMEMEVAL_LLM_MAX_TOKENS=16 + +# 每条样本最多摄入的上下文段数 +LONGMEMEVAL_MAX_CONTEXTS_PER_ITEM=2 + +# 是否保存分块结果 +LONGMEMEVAL_SAVE_CHUNK_OUTPUT=true + +# 自定义分块输出路径（留空使用默认） +LONGMEMEVAL_SAVE_CHUNK_OUTPUT_PATH= + +# 摄入前是否清空组数据 +LONGMEMEVAL_RESET_GROUP_BEFORE_INGEST=false + +# 是否跳过摄入，仅检索评估 +LONGMEMEVAL_SKIP_INGEST=false + +# 结果保存目录 +LONGMEMEVAL_OUTPUT_DIR=longmemeval/results + +# ============================================================================ +# MemSciQA 基准测试专用配置 +# ============================================================================ +# 数据集：msc_self_instruct.jsonl +# 运行：python memsciqa_benchmark.py --sample_size 1 +# 特点：对话记忆检索评估 +# ---------------------------------------------------------------------------- + +# Group ID（MemSciQA 专用，独立数据集） +MEMSCIQA_GROUP_ID=memsciqa_benchmark + +# 测试样本数量 +MEMSCIQA_SAMPLE_SIZE=1 # 0或者-1标识测试数据集中的所有样本 + +# 检索结果数量限制 +MEMSCIQA_SEARCH_LIMIT=8 + +# 上下文最大字符数 +MEMSCIQA_CONTEXT_CHAR_BUDGET=4000 + +# LLM 最大生成 token 数 +MEMSCIQA_LLM_MAX_TOKENS=64 + +# 跳过数据摄入（true=跳过，false=摄入） +# 首次运行设置为 false，后续运行可设置为 true 以节省时间 +MEMSCIQA_SKIP_INGEST=false + +# 结果保存目录（相对于 memsciqa 脚本所在目录） +# 使用 "results" 会保存到 api/app/core/memory/evaluation/memsciqa/results/ +MEMSCIQA_OUTPUT_DIR=results + +# ============================================================================ +# 高级配置（可选） +# ============================================================================ + +# BM25 权重（用于混合检索，0.0-1.0） +EVAL_RERANK_ALPHA=0.6 + +# 是否使用遗忘重排序 +EVAL_USE_FORGETTING_RERANK=false + +# 是否使用 LLM 重排序 +EVAL_USE_LLM_RERANK=false + +# 连接重置间隔（每 N 个问题重置一次） +EVAL_RESET_INTERVAL=5 + +# 性能阈值（低于此值触发重置） +EVAL_PERFORMANCE_THRESHOLD=0.6 + +# ============================================================================ +# 快速配置指南 +# ============================================================================ +# 1. 复制此文件为 .env.evaluation +# 2. 修改 EVAL_LLM_ID 和 EVAL_EMBEDDING_ID 为你的模型 ID +# 3. 根据需要修改各基准测试的专用配置 +# 4. 运行测试： +# - LoCoMo: python locomo/locomo_benchmark.py --sample_size 20 +# - LongMemEval: python longmemeval/longmemeval_benchmark.py --sample_size 3 --all +# - MemSciQA: python memsciqa/memsciqa_benchmark.py --sample_size 10 +# 配置优先级： +# 命令行参数 > 特定配置（如 LOCOMO_*）> 通用配置（EVAL_*）> 代码默认值 +# ============================================================================ + + +# 执行LoCoMo测试 +# 只摄入前5条消息，评估3个问题（最小测试） +# python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 3 --max_ingest_messages 5 +# +# 如果数据已经摄入，跳过摄入阶段直接测试 +# python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 5 --skip_ingest + + +# 执行longmemeval测试 +# python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark --sample-size 10 --max-contexts-per-item 3 --reset-group-before-ingest + +# 执行memsciqa测试 +# python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark --sample-size 1 diff --git a/api/app/core/memory/evaluation/.gitignore b/api/app/core/memory/evaluation/.gitignore new file mode 100644 index 00000000..38b1055a --- /dev/null +++ b/api/app/core/memory/evaluation/.gitignore @@ -0,0 +1,13 @@ +# 忽略实际的评估配置文件（包含敏感信息） +.env.evaluation + +# 保留示例文件 +!.env.evaluation.example + +# 忽略测试结果文件 +*/results/*.json +*/results/*.log + +# 忽略数据集文件（文件过大，不应提交到 Git） +dataset/*.json +dataset/*.jsonl diff --git a/api/app/core/memory/evaluation/benchmark.md b/api/app/core/memory/evaluation/benchmark.md index 2853b22b..7c31cccd 100644 --- a/api/app/core/memory/evaluation/benchmark.md +++ b/api/app/core/memory/evaluation/benchmark.md @@ -1,30 +1,748 @@ -⏬数据集下载地址： - Locomo10.json：https://github.com/snap-research/locomo/tree/main/data - LongMemEval_oracle.json：https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned - msc_self_instruct.jsonl:https://huggingface.co/datasets/MemGPT/MSC-Self-Instruct - 上方数据集下载好后全部放入app/core/memory/data文件夹中 +# 1.数据集下载地址 +Locomo10.json : https://github.com/snap-research/locomo/tree/main/data +LongMemEval_oracle.json : https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned +msc_self_instruct.jsonl : https://huggingface.co/datasets/MemGPT/MSC-Self-Instruct -全流程基准测试运行： - locomo： - python -m app.core.memory.evaluation.run_eval --dataset locomo --sample-size 1 --reset-group --group-id yyw1 --search-type hybrid --search-limit 8 --context-char-budget 12000 --llm-max-tokens 32 - LongMemEval： - python -m app.core.memory.evaluation.run_eval --dataset longmemeval --sample-size 10 --start-index 0 --group-id longmemeval_zh_bak_2 --search-limit 8 --context-char-budget 4000 --search-type hybrid --max-contexts-per-item 2 --reset-group - memsciqa： - python -m app.core.memory.evaluation.run_eval --dataset memsciqa --sample-size 10 --reset-group --group-id group_memsci +数据集下载之后保存至api\app\core\memory\evaluation\dataset目录下 +# 2.配置说明 +文件api\app\core\memory\evaluation\.env.evaluation.example对三个基准测试所需配置有着详细的说明 +**实际配置文件**：api\app\core\memory\evaluation\.env.evaluation +```python +# 当使用不带配置参数的命令行执行基准测试，基准测试所需的配置参数根据.env.evaluation中的参数执行 +python -m app.core.memory.evaluation.locomo.locomo_benchmark +``` +**检查neo4j指定的grou_id是否摄入数据** +```python +# 1. 进入交互模式 +python -m app.core.memory.evaluation.check_enduser_data -单独检索评估运行命令： - python -m app.core.memory.evaluation.locomo.locomo_test - python -m app.core.memory.evaluation.longmemeval.test_eval - python -m app.core.memory.evaluation.memsciqa.memsciqa-test - 需要先在项目中修改需要检测评估的group_id。 +# 2. 选择 "1" 检查指定 group +# 3. 输入 group_id，例如: locomo_benchmark +# 4. 选择是否显示详细统计 (y/n) +``` +# 3.locomo -参数及解释： - ● --dataset longmemeval - 指定数据集 - ● --sample-size 10 - 评估10个样本 - ● --start-index 0 - 从第0个样本开始 - ● --group-id longmemeval_zh_bak_2 - 使用指定的组ID - ● --search-limit 8 - 检索限制8条 - ● --context-char-budget 4000 - 上下文字符预算4000 - ● --search-type hybrid - 使用混合检索 - ● --max-contexts-per-item 2 - 每个样本最多摄入2个上下文 - ● --reset-group - 运行前清空组数据 \ No newline at end of file +### (1)locomo执行命令 +```python +# 首先进入api目录 +cd api + +# 只摄入前5条消息，评估3个问题（最小测试） +python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 3 --max_ingest_messages 5 + +# 如果数据已经摄入，跳过摄入阶段直接测试(使用skip_ingest参数) +python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 5 --skip_ingest +``` +### (2)locomo结果说明 + +#### 结果示例 +```json +{ + "dataset": "locomo", + "sample_size": 0, + "timestamp": "2026-01-26T11:24:28.239156", + "params": { + "group_id": "locomo_benchmark", + "search_type": "hybrid", + "search_limit": 12, + "context_char_budget": 8000, + "llm_id": "2c9b0782-7a85-4740-ba84-4baf77f256c4", + "embedding_id": "e2a6392d-ca63-4d59-a523-647420b59cb2" + }, + "overall_metrics": { + "f1": 0.0, + "bleu1": 0.0, + "jaccard": 0.0, + "locomo_f1": 0.0 + }, + "by_category": {}, + "latency": { + "search": { + "mean": 0.0, + "p50": 0.0, + "p95": 0.0, + "iqr": 0.0 + }, + "llm": { + "mean": 0.0, + "p50": 0.0, + "p95": 0.0, + "iqr": 0.0 + } + }, + "context_stats": { + "avg_retrieved_docs": 0.0, + "avg_context_chars": 0.0, + "avg_context_tokens": 0.0 + }, + "samples": [] +} +``` + +#### 参数详解 + +##### 1. 核心评估指标 (overall_metrics) + +**🎯 关键进步指标：** + +- **`f1`** (F1 Score): 精确率和召回率的调和平均值 + - 范围：0.0 - 1.0 + - **越高越好**，衡量检索和生成答案的准确性 + - 这是最重要的综合性能指标 + - 优秀标准：> 0.85 + +- **`bleu1`** (BLEU-1): 单词级别的匹配度 + - 范围：0.0 - 1.0 + - **越高越好**，衡量生成答案与标准答案的词汇重叠度 + - 关注词汇层面的准确性 + +- **`jaccard`** (Jaccard 相似度): 集合相似度 + - 范围：0.0 - 1.0 + - **越高越好**，衡量答案集合的相似性 + - 计算公式：交集大小 / 并集大小 + +- **`locomo_f1`**: Locomo 特定的 F1 分数 + - 范围：0.0 - 1.0 + - **越高越好**，针对 Locomo 数据集优化的评估指标 + - 考虑了长对话记忆的特殊性 + +##### 2. 性能指标 (latency) + +**⚡ 关键效率指标：** + +- **`search`**: 检索延迟统计（单位：毫秒） + - `mean`: 平均延迟 + - `p50`: 中位数延迟（50%的请求在此时间内完成） + - `p95`: 95分位数延迟（95%的请求在此时间内完成） + - `iqr`: 四分位距（Q3-Q1，衡量稳定性） + - **越低越好**，衡量记忆检索速度 + - 优秀标准：p95 < 2000ms + +- **`llm`**: LLM 推理延迟统计（单位：毫秒） + - `mean`: 平均推理时间 + - `p50`: 中位数推理时间 + - `p95`: 95分位数推理时间 + - `iqr`: 四分位距（越小越稳定） + - **越低越好**，衡量答案生成速度 + - 优秀标准：p95 < 3000ms + +##### 3. 上下文统计 (context_stats) + +**📊 资源效率指标：** + +- **`avg_retrieved_docs`**: 平均检索文档数 + - 反映检索策略的广度 + - 需要平衡：太少可能信息不足，太多增加噪音和延迟 + - 建议范围：8-15 个文档 + +- **`avg_context_chars`**: 平均上下文字符数 + - 反映检索内容的总量 + - 应在满足准确性前提下尽量精简 + - 受 `context_char_budget` 参数限制 + +- **`avg_context_tokens`**: 平均上下文 token 数 + - **越低越好**（在保持准确性前提下） + - 直接影响 API 调用成本和推理速度 + - 成本效益比 = f1 / avg_context_tokens + +##### 4. 分类统计 (by_category) + +- 按问题类型分类的性能指标 +- 帮助识别系统在不同场景下的强弱项 +- 可针对性优化特定类型的问题 + +#### 系统进步衡量标准 + +**一级指标（最重要）：** +- `f1` 和 `locomo_f1` 提升 → 核心能力提升 +- 目标：f1 > 0.85 + +**二级指标（重要）：** +- `latency.p95` 降低 → 用户体验提升 +- 目标：search.p95 < 2000ms, llm.p95 < 3000ms + +**三级指标（辅助）：** +- `avg_context_tokens` 降低（在保持 f1 前提下）→ 成本优化 +- `iqr` 降低 → 性能稳定性提升 +# 4.longmemeval +支持时间推理问题的增强检索 +### (1)执行命令 +```python +# 首先进入api目录 +cd api + +# 不带参数运行 - 使用环境变量 +python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark + +# 命令行参数覆盖环境变量 +python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark --sample-size 2 + +# 如果数据已经摄入，跳过摄入阶段直接测试(使用skip_ingest参数) +python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark --skip_ingest +``` +### (2)结果说明 + +#### 结果示例 +```json +{ + "dataset": "longmemeval", + "items": 1, + "accuracy_by_type": { + "single-session-user": 1.0 + }, + "f1_by_type": { + "single-session-user": 1.0 + }, + "jaccard_by_type": { + "single-session-user": 1.0 + }, + "samples": [ + { + "question": "What degree did I graduate with?", + "prediction": "Business Administration", + "answer": "Business Administration", + "question_type": "single-session-user", + "is_temporal": false, + "question_id": "e47becba", + "options": [], + "context_count": 13, + "context_chars": 1268, + "retrieved_dialogue_count": 0, + "retrieved_statement_count": 12, + "metrics": { + "exact_match": true, + "f1": 1.0, + "jaccard": 1.0 + }, + "timing": { + "search_ms": 1483.100175857544, + "llm_ms": 995.8682060241699 + } + } + ], + "latency": { + "search": { + "mean": 1483.100175857544, + "p50": 1483.100175857544, + "p95": 1483.100175857544, + "iqr": 0.0 + }, + "llm": { + "mean": 995.8682060241699, + "p50": 995.8682060241699, + "p95": 995.8682060241699, + "iqr": 0.0 + } + }, + "context": { + "avg_tokens": 204.0, + "avg_chars": 1268, + "count_avg": 13 + }, + "params": { + "group_id": "longmemeval_zh_bak_3", + "search_limit": 8, + "context_char_budget": 4000, + "search_type": "hybrid", + "llm_id": "6dc52e1b-9cec-4194-af66-a74c6307fc3f", + "embedding_id": "e2a6392d-ca63-4d59-a523-647420b59cb2", + "sample_size": 1, + "start_index": 0 + }, + "timestamp": "2026-01-24T21:36:10.818308", + "metric_summary": { + "score_accuracy": 100.0, + "latency_median_s": 2.478968381881714, + "latency_iqr_s": 0.0, + "avg_context_tokens_k": 0.204 + }, + "diagnostics": { + "duplicate_previews_top": [], + "unique_preview_count": 1 + } +} +``` + +#### 参数详解 + +##### 1. 核心评估指标 + +**🎯 关键进步指标：** + +- **`accuracy_by_type`**: 按问题类型分类的准确率 + - 范围：0.0 - 1.0 + - **越高越好**，1.0 表示 100% 准确 + - 问题类型包括： + - `single-session-user`: 单会话用户信息 + - `single-session-event`: 单会话事件信息 + - `multi-session-user`: 多会话用户信息 + - `multi-session-event`: 多会话事件信息 + - 可以识别系统在不同场景下的强弱项 + +- **`f1_by_type`**: 按问题类型的 F1 分数 + - 范围：0.0 - 1.0 + - **越高越好**，综合评估精确率和召回率 + - 比单纯的准确率更全面 + +- **`jaccard_by_type`**: 按问题类型的 Jaccard 相似度 + - 范围：0.0 - 1.0 + - **越高越好**，衡量答案集合匹配度 + - 对于集合类答案特别有用 + +##### 2. 样本级指标 (samples) + +**详细诊断指标：** + +- **`metrics.exact_match`**: 精确匹配（布尔值） + - **true 越多越好**，最严格的评估标准 + - 要求预测答案与标准答案完全一致 + +- **`metrics.f1`**: 单个样本的 F1 分数 + - 范围：0.0 - 1.0 + - **越高越好**，衡量单个问题的回答质量 + +- **`is_temporal`**: 是否为时间推理问题 + - 布尔值，标识问题是否涉及时间推理 + - 时间推理问题通常更具挑战性 + +- **`context_count`**: 检索到的上下文数量 + - 反映检索策略的有效性 + - 建议范围：8-15 个上下文片段 + +- **`retrieved_dialogue_count`**: 检索到的对话数 +- **`retrieved_statement_count`**: 检索到的陈述数 + - 这两个指标帮助理解检索的内容类型分布 + - 可用于优化检索策略 + +- **`timing.search_ms`**: 单个问题的检索延迟（毫秒） +- **`timing.llm_ms`**: 单个问题的 LLM 推理延迟（毫秒） + - **越低越好**，反映单次查询的响应速度 + +##### 3. 汇总指标 (metric_summary) + +**📊 关键 KPI：** + +- **`score_accuracy`**: 总体准确率百分比 + - 范围：0.0 - 100.0 + - **越高越好**，最直观的性能指标 + - 优秀标准：> 90.0 + +- **`latency_median_s`**: 中位延迟（秒） + - **越低越好**，反映真实响应速度 + - 优秀标准：< 3.0 秒 + +- **`latency_iqr_s`**: 延迟四分位距（秒） + - **越低越好**，反映性能稳定性 + - 越小说明响应时间越稳定 + +- **`avg_context_tokens_k`**: 平均上下文 token 数（千） + - **越低越好**（在保持准确性前提下） + - 直接影响 API 调用成本 + - 成本效益比 = score_accuracy / (avg_context_tokens_k * 1000) + +##### 4. 上下文统计 (context) + +- **`avg_tokens`**: 平均 token 数 +- **`avg_chars`**: 平均字符数 +- **`count_avg`**: 平均上下文片段数 + - 这些指标反映检索内容的规模 + - 需要在准确性和效率之间平衡 + +##### 5. 性能指标 (latency) + +**⚡ 效率指标：** + +- **`search`**: 检索延迟统计（单位：毫秒） + - `mean`: 平均延迟 + - `p50`: 中位数延迟 + - `p95`: 95分位数延迟 + - `iqr`: 四分位距 + - **越低越好**，衡量记忆检索速度 + +- **`llm`**: LLM 推理延迟统计（单位：毫秒） + - `mean`: 平均推理时间 + - `p50`: 中位数推理时间 + - `p95`: 95分位数推理时间 + - `iqr`: 四分位距 + - **越低越好**，衡量答案生成速度 + +##### 6. 诊断信息 (diagnostics) + +- **`duplicate_previews_top`**: 重复预览统计 + - 列出出现频率最高的重复内容 + - 帮助发现检索冗余问题 + - 应该尽量减少重复 + +- **`unique_preview_count`**: 唯一预览数量 + - 反映检索多样性 + - **越高越好**，说明检索到的内容更丰富 + +#### 系统进步衡量标准 + +**一级指标（最重要）：** +- `score_accuracy` 提升 → 核心能力提升 +- 目标：> 90.0% +- 各类型的 `accuracy_by_type` 均衡提升 → 全面能力提升 + +**二级指标（重要）：** +- `latency_median_s` 降低 → 用户体验提升 +- 目标：< 3.0 秒 +- `exact_match` 比例提升 → 精确度提升 + +**三级指标（辅助）：** +- `avg_context_tokens_k` 降低（在保持准确性前提下）→ 成本优化 +- `unique_preview_count` 提升 → 检索多样性提升 +- `latency_iqr_s` 降低 → 性能稳定性提升 + +**特殊关注：** +- 时间推理问题（`is_temporal: true`）的准确率 +- 多会话问题的准确率（通常更具挑战性） +# 5.memsciqa +对话记忆检索评估 +### (1)执行命令 +```python +# 首先进入api目录 +cd api + +# 不带参数运行 - 使用环境变量 +python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark + +# 命令行参数覆盖环境变量 +python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark --sample-size 100 + +# 如果数据已经摄入，跳过摄入阶段直接测试(使用skip_ingest参数) +python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark --skip_ingest +``` +### (2)结果说明 + +#### 结果示例 +```json +{ + "dataset": "memsciqa", + "items": 1, + "metrics": { + "accuracy": 0.0, + "f1": 0.0, + "bleu1": 0.0, + "jaccard": 0.0 + }, + "latency": { + "search": { + "mean": 0.0, + "p50": 0.0, + "p95": 0.0, + "iqr": 0.0 + }, + "llm": { + "mean": 3067.7285194396973, + "p50": 3067.7285194396973, + "p95": 3067.7285194396973, + "iqr": 0.0 + } + }, + "avg_context_tokens": 4.0 +} +``` + +#### 参数详解 + +##### 1. 核心评估指标 (metrics) + +**🎯 关键进步指标：** + +- **`accuracy`**: 准确率 + - 范围：0.0 - 1.0 + - **越高越好**，最直接的性能指标 + - 衡量系统回答正确的问题比例 + - 优秀标准：> 0.85 + +- **`f1`**: F1 分数 + - 范围：0.0 - 1.0 + - **越高越好**，平衡精确率和召回率 + - 计算公式：2 * (precision * recall) / (precision + recall) + - 比单纯的准确率更全面，特别适合不平衡数据集 + +- **`bleu1`**: BLEU-1 分数 + - 范围：0.0 - 1.0 + - **越高越好**，衡量词汇级别的匹配度 + - 关注生成答案与标准答案的单词重叠 + - 源自机器翻译评估，适用于自然语言生成 + +- **`jaccard`**: Jaccard 相似度 + - 范围：0.0 - 1.0 + - **越高越好**，衡量集合相似性 + - 计算公式：|A ∩ B| / |A ∪ B| + - 对于多答案或集合类问题特别有用 + +##### 2. 性能指标 (latency) + +**⚡ 效率指标：** + +- **`search`**: 检索延迟统计（单位：毫秒） + - `mean`: 平均检索延迟 + - `p50`: 中位数延迟（50%的请求在此时间内完成） + - `p95`: 95分位数延迟（95%的请求在此时间内完成） + - `iqr`: 四分位距（Q3-Q1，衡量稳定性） + - **越低越好**，衡量记忆检索效率 + - 优秀标准：p95 < 2000ms + +- **`llm`**: LLM 推理延迟统计（单位：毫秒） + - `mean`: 平均推理时间 + - `p50`: 中位数推理时间 + - `p95`: 95分位数推理时间 + - `iqr`: 四分位距（越小越稳定） + - **越低越好**，衡量答案生成速度 + - 优秀标准：p95 < 3000ms + - 注意：LLM 延迟通常占总延迟的大部分 + +##### 3. 资源指标 + +- **`avg_context_tokens`**: 平均上下文 token 数 + - **越低越好**（在保持准确性前提下） + - 直接影响： + - API 调用成本（按 token 计费） + - 推理速度（token 越多越慢） + - 上下文窗口占用 + - 成本效益比 = accuracy / avg_context_tokens + - 建议范围：根据模型上下文窗口和成本预算调整 + +##### 4. 数据集特点 + +- **`items`**: 评估的问题数量 + - 样本量越大，评估结果越可靠 + - 建议至少 100 个样本以获得稳定的评估结果 + +- **对话记忆特性**： + - MemSciQA 专注于对话历史中的记忆检索 + - 评估系统从多轮对话中提取和回忆信息的能力 + - 模拟真实的对话场景 + +#### 系统进步衡量标准 + +**一级指标（最重要）：** +- `accuracy` 提升 → 核心能力提升 +- 目标：> 0.85 +- `f1` 提升 → 综合性能提升 +- 目标：> 0.80 + +**二级指标（重要）：** +- `latency.p95` 降低 → 用户体验提升 + - search.p95 目标：< 2000ms + - llm.p95 目标：< 3000ms +- `iqr` 降低 → 性能稳定性提升 + +**三级指标（辅助）：** +- `avg_context_tokens` 降低（在保持准确性前提下）→ 成本优化 +- `bleu1` 和 `jaccard` 提升 → 答案质量提升 + +**综合评估：** +- 成本效益比 = accuracy / avg_context_tokens + - 该比值越高，说明系统在相同成本下性能越好 +- 总延迟 = search.p95 + llm.p95 + - 应控制在 5 秒以内以保证良好的用户体验 + +#### 优化建议 + +**提升准确性：** +- 优化检索算法（调整 hybrid search 参数） +- 改进 embedding 模型质量 +- 增加检索上下文数量（`search_limit`） +- 优化 prompt 工程 + +**提升效率：** +- 减少不必要的检索文档 +- 使用更快的 LLM 模型或量化版本 +- 实施缓存策略（相似问题复用结果） +- 优化数据库索引 + +**平衡性能：** +- 监控 accuracy vs latency 的权衡 +- 监控 accuracy vs cost (tokens) 的权衡 +- 根据业务需求调整优先级 + + +--- + +# 6. 三个基准测试对比总结 + +## 6.1 测试特点对比 + +| 基准测试 | 主要评估目标 | 数据集特点 | 适用场景 | +|---------|------------|-----------|---------| +| **Locomo** | 长对话记忆检索 | 长对话历史，多轮交互 | 评估长期记忆保持和检索能力 | +| **LongMemEval** | 时间推理和多会话记忆 | 支持时间推理，多会话场景 | 评估时间感知和跨会话记忆能力 | +| **MemSciQA** | 对话记忆问答 | 对话历史问答 | 评估对话上下文理解和记忆提取 | + +## 6.2 核心指标对比 + +### 准确性指标 + +| 指标 | Locomo | LongMemEval | MemSciQA | 说明 | +|-----|--------|-------------|----------|------| +| **F1 Score** | ✅ | ✅ | ✅ | 所有测试都使用，最重要的综合指标 | +| **Accuracy** | ❌ | ✅ | ✅ | 直观的准确率指标 | +| **BLEU-1** | ✅ | ❌ | ✅ | 词汇级别匹配度 | +| **Jaccard** | ✅ | ✅ | ✅ | 集合相似度 | +| **Exact Match** | ❌ | ✅ | ❌ | 最严格的评估标准 | + +### 性能指标 + +所有三个测试都包含： +- **检索延迟** (search latency): mean, p50, p95, iqr +- **LLM 延迟** (llm latency): mean, p50, p95, iqr +- **上下文统计**: token 数、字符数、文档数 + +## 6.3 关键进步指标优先级 + +### 🥇 一级指标（必须关注） + +1. **准确性指标** + - Locomo: `f1`, `locomo_f1` + - LongMemEval: `score_accuracy`, `accuracy_by_type` + - MemSciQA: `accuracy`, `f1` + - **目标**: > 85% 或 > 0.85 + +2. **综合性能** + - 所有测试的 F1 分数应保持一致性 + - 不同类型问题的准确率应均衡 + +### 🥈 二级指标（重要） + +3. **响应延迟** + - `latency.p95` (95分位数延迟) + - **目标**: + - search.p95 < 2000ms + - llm.p95 < 3000ms + - 总延迟 < 5000ms + +4. **性能稳定性** + - `iqr` (四分位距) + - **目标**: 越小越好，说明性能稳定 + +### 🥉 三级指标（优化） + +5. **成本效率** + - `avg_context_tokens` + - **目标**: 在保持准确性前提下最小化 + - 成本效益比 = accuracy / avg_context_tokens + +6. **检索质量** + - `avg_retrieved_docs` 的合理性 + - `unique_preview_count` (LongMemEval) + - 检索内容的多样性和相关性 + +## 6.4 系统优化路径 + +### 阶段一：提升准确性（优先级最高） + +**目标**: 所有测试的准确率 > 85% + +**优化方向**: +1. 改进 embedding 模型质量 +2. 优化检索算法（hybrid search 参数） +3. 增加检索上下文数量（`search_limit`） +4. 优化 prompt 工程 +5. 改进记忆存储结构 + +**监控指标**: +- Locomo: `f1`, `locomo_f1` +- LongMemEval: `score_accuracy`, `exact_match` 比例 +- MemSciQA: `accuracy`, `f1` + +### 阶段二：优化性能（准确性达标后） + +**目标**: p95 延迟 < 5 秒，性能稳定 + +**优化方向**: +1. 优化数据库索引和查询 +2. 实施缓存策略 +3. 使用更快的 LLM 模型 +4. 并行化检索和推理 +5. 减少不必要的检索 + +**监控指标**: +- `latency.p50`, `latency.p95` +- `iqr` (稳定性) +- 各阶段耗时分布 + +### 阶段三：降低成本（性能达标后） + +**目标**: 在保持准确性和性能前提下，最小化成本 + +**优化方向**: +1. 精简检索上下文 +2. 优化 context 选择策略 +3. 使用更小的 LLM 模型 +4. 实施智能缓存 +5. 批处理优化 + +**监控指标**: +- `avg_context_tokens` +- 成本效益比 = accuracy / avg_context_tokens +- API 调用成本 + +## 6.5 评估最佳实践 + +### 测试执行建议 + +1. **初始测试**: 使用小样本快速验证 + ```bash + --sample_size 10 + ``` + +2. **完整评估**: 使用足够大的样本量 + ```bash + --sample_size 100 # 或更多 + ``` + +3. **增量测试**: 数据已摄入时跳过摄入阶段 + ```bash + --skip_ingest + ``` + +4. **参数调优**: 系统性地调整参数并记录结果 + - 调整 `search_limit`: 4, 8, 12, 16 + - 调整 `context_char_budget`: 2000, 4000, 8000 + - 尝试不同的 `search_type`: vector, keyword, hybrid + +### 结果分析建议 + +1. **横向对比**: 比较三个测试的结果，识别系统的强弱项 +2. **纵向对比**: 跟踪同一测试在不同版本的表现 +3. **分类分析**: 关注不同问题类型的性能差异 +4. **异常诊断**: 分析失败案例，找出根本原因 + +### 持续监控 + +建议建立监控仪表板，跟踪： +- 核心指标趋势（准确率、延迟） +- 成本效益比趋势 +- 不同问题类型的性能分布 +- 异常样本和失败模式 + +## 6.6 性能基准参考 + +### 优秀水平（Production Ready） + +- **准确性**: accuracy/f1 > 0.90 +- **延迟**: p95 < 3 秒 +- **稳定性**: iqr < 500ms +- **成本效益**: accuracy/tokens > 0.0001 + +### 良好水平（Acceptable） + +- **准确性**: accuracy/f1 > 0.85 +- **延迟**: p95 < 5 秒 +- **稳定性**: iqr < 1000ms +- **成本效益**: accuracy/tokens > 0.00005 + +### 需要改进（Below Target） + +- **准确性**: accuracy/f1 < 0.85 +- **延迟**: p95 > 5 秒 +- **稳定性**: iqr > 1000ms +- **成本效益**: accuracy/tokens < 0.00005 + +--- + +**注**: 以上标准仅供参考，实际目标应根据具体业务需求和资源约束调整。 diff --git a/api/app/core/memory/evaluation/check_enduser_data.py b/api/app/core/memory/evaluation/check_enduser_data.py new file mode 100644 index 00000000..18ecbb34 --- /dev/null +++ b/api/app/core/memory/evaluation/check_enduser_data.py @@ -0,0 +1,371 @@ +""" +交互式 Neo4j End User 数据检查工具 + +用于查询指定 end_user_id 在 Neo4j 中是否存在数据，以及数据的详细统计信息。 + +使用方法: + python check_group_data.py + python check_group_data.py --group-id locomo_benchmark + python check_group_data.py --group-id memsciqa_benchmark --detailed +""" + +import asyncio +import argparse +import os +from pathlib import Path +from typing import Dict, Any +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}\n") + +from app.repositories.neo4j.neo4j_connector import Neo4jConnector + + +async def check_group_exists(end_user_id: str) -> Dict[str, Any]: + """ + 检查指定 end_user_id 是否存在数据 + + Args: + end_user_id: 要检查的 end_user ID + + Returns: + 包含统计信息的字典 + """ + connector = Neo4jConnector() + + try: + # 查询该 end_user 的节点总数 + query_total = """ + MATCH (n {end_user_id: $end_user_id}) + RETURN count(n) as total_nodes + """ + result_total = await connector.execute_query(query_total, end_user_id=end_user_id) + total_nodes = result_total[0]["total_nodes"] if result_total else 0 + + # 查询各类型节点的数量 + query_by_type = """ + MATCH (n {end_user_id: $end_user_id}) + RETURN labels(n) as labels, count(n) as count + ORDER BY count DESC + """ + result_by_type = await connector.execute_query(query_by_type, end_user_id=end_user_id) + + # 查询关系数量 + query_relationships = """ + MATCH (n {end_user_id: $end_user_id})-[r]-() + RETURN count(DISTINCT r) as total_relationships + """ + result_rel = await connector.execute_query(query_relationships, end_user_id=end_user_id) + total_relationships = result_rel[0]["total_relationships"] if result_rel else 0 + + return { + "exists": total_nodes > 0, + "total_nodes": total_nodes, + "total_relationships": total_relationships, + "nodes_by_type": result_by_type + } + + finally: + await connector.close() + + +async def get_detailed_stats(end_user_id: str) -> Dict[str, Any]: + """ + 获取详细的统计信息 + + Args: + end_user_id: 要检查的 end_user ID + + Returns: + 详细统计信息字典 + """ + connector = Neo4jConnector() + + try: + stats = {} + + # Chunk 节点统计 + query_chunks = """ + MATCH (c:Chunk {end_user_id: $end_user_id}) + RETURN count(c) as count, + avg(size(c.content)) as avg_content_length + """ + result_chunks = await connector.execute_query(query_chunks, end_user_id=end_user_id) + if result_chunks and result_chunks[0]["count"] > 0: + stats["chunks"] = { + "count": result_chunks[0]["count"], + "avg_content_length": int(result_chunks[0]["avg_content_length"]) if result_chunks[0]["avg_content_length"] else 0 + } + + # Statement 节点统计 + query_statements = """ + MATCH (s:Statement {end_user_id: $end_user_id}) + RETURN count(s) as count + """ + result_statements = await connector.execute_query(query_statements, end_user_id=end_user_id) + if result_statements and result_statements[0]["count"] > 0: + stats["statements"] = { + "count": result_statements[0]["count"] + } + + # Entity 节点统计 + query_entities = """ + MATCH (e:Entity {end_user_id: $end_user_id}) + RETURN count(e) as count, + count(DISTINCT e.entity_type) as unique_types + """ + result_entities = await connector.execute_query(query_entities, end_user_id=end_user_id) + if result_entities and result_entities[0]["count"] > 0: + stats["entities"] = { + "count": result_entities[0]["count"], + "unique_types": result_entities[0]["unique_types"] + } + + # Dialogue 节点统计 + query_dialogues = """ + MATCH (d:Dialogue {end_user_id: $end_user_id}) + RETURN count(d) as count + """ + result_dialogues = await connector.execute_query(query_dialogues, end_user_id=end_user_id) + if result_dialogues and result_dialogues[0]["count"] > 0: + stats["dialogues"] = { + "count": result_dialogues[0]["count"] + } + + # Summary 节点统计 + query_summaries = """ + MATCH (s:Summary {end_user_id: $end_user_id}) + RETURN count(s) as count + """ + result_summaries = await connector.execute_query(query_summaries, end_user_id=end_user_id) + if result_summaries and result_summaries[0]["count"] > 0: + stats["summaries"] = { + "count": result_summaries[0]["count"] + } + + return stats + + finally: + await connector.close() + + +async def list_all_end_users() -> list: + """ + 列出数据库中所有的 end_user_id + + Returns: + end_user_id 列表及其节点数量 + """ + connector = Neo4jConnector() + + try: + query = """ + MATCH (n) + WHERE n.end_user_id IS NOT NULL + RETURN DISTINCT n.end_user_id as end_user_id, count(n) as node_count + ORDER BY node_count DESC + """ + results = await connector.execute_query(query) + return results + + finally: + await connector.close() + + +def print_results(end_user_id: str, stats: Dict[str, Any], detailed_stats: Dict[str, Any] = None): + """ + 打印查询结果 + + Args: + end_user_id: End User ID + stats: 基本统计信息 + detailed_stats: 详细统计信息（可选） + """ + print(f"\n{'='*60}") + print(f"📊 End User ID: {end_user_id}") + print(f"{'='*60}\n") + + if not stats["exists"]: + print("❌ 该 end_user_id 不存在数据") + print("\n💡 提示: 请先运行基准测试以摄入数据") + return + + print(f"✅ 该 end_user_id 存在数据\n") + print(f"📈 基本统计:") + print(f" 总节点数: {stats['total_nodes']}") + print(f" 总关系数: {stats['total_relationships']}") + + if stats["nodes_by_type"]: + print(f"\n📋 节点类型分布:") + for item in stats["nodes_by_type"]: + labels = ", ".join(item["labels"]) + count = item["count"] + print(f" {labels}: {count}") + + if detailed_stats: + print(f"\n🔍 详细统计:") + + if "chunks" in detailed_stats: + print(f" Chunks: {detailed_stats['chunks']['count']} 个") + print(f" 平均内容长度: {detailed_stats['chunks']['avg_content_length']} 字符") + + if "statements" in detailed_stats: + print(f" Statements: {detailed_stats['statements']['count']} 个") + + if "entities" in detailed_stats: + print(f" Entities: {detailed_stats['entities']['count']} 个") + print(f" 唯一类型数: {detailed_stats['entities']['unique_types']}") + + if "dialogues" in detailed_stats: + print(f" Dialogues: {detailed_stats['dialogues']['count']} 个") + + if "summaries" in detailed_stats: + print(f" Summaries: {detailed_stats['summaries']['count']} 个") + + print(f"\n{'='*60}\n") + + +async def interactive_mode(): + """ + 交互式模式 + """ + print("\n" + "="*60) + print("🔍 Neo4j End User 数据检查工具 - 交互模式") + print("="*60 + "\n") + + while True: + print("\n请选择操作:") + print(" 1. 检查指定 end_user_id") + print(" 2. 列出所有 end_user_id") + print(" 3. 退出") + + choice = input("\n请输入选项 (1-3): ").strip() + + if choice == "1": + end_user_id = input("\n请输入 end_user_id: ").strip() + if not end_user_id: + print("❌ end_user_id 不能为空") + continue + + detailed = input("是否显示详细统计? (y/n, 默认 n): ").strip().lower() == 'y' + + print("\n🔄 正在查询...") + stats = await check_group_exists(end_user_id) + + detailed_stats = None + if detailed and stats["exists"]: + detailed_stats = await get_detailed_stats(end_user_id) + + print_results(end_user_id, stats, detailed_stats) + + elif choice == "2": + print("\n🔄 正在查询所有 end_user_id...") + end_users = await list_all_end_users() + + if not end_users: + print("\n❌ 数据库中没有任何 end_user 数据") + else: + print(f"\n{'='*60}") + print(f"📋 数据库中的所有 End User ID") + print(f"{'='*60}\n") + + for idx, end_user in enumerate(end_users, 1): + print(f" {idx}. {end_user['end_user_id']}") + print(f" 节点数: {end_user['node_count']}") + + print(f"\n{'='*60}\n") + + elif choice == "3": + print("\n👋 再见！") + break + + else: + print("\n❌ 无效的选项，请重新选择") + + +async def main(): + """ + 主函数 + """ + parser = argparse.ArgumentParser( + description="检查 Neo4j 中指定 end_user_id 的数据情况", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +示例: + # 交互模式 + python check_group_data.py + + # 检查指定 end_user + python check_group_data.py --end-user-id locomo_benchmark + + # 检查并显示详细统计 + python check_group_data.py --end-user-id memsciqa_benchmark --detailed + + # 列出所有 end_user + python check_group_data.py --list-all + """ + ) + + parser.add_argument( + "--end-user-id", + type=str, + help="要检查的 end_user ID" + ) + + parser.add_argument( + "--detailed", + action="store_true", + help="显示详细统计信息" + ) + + parser.add_argument( + "--list-all", + action="store_true", + help="列出所有 end_user_id" + ) + + args = parser.parse_args() + + # 如果没有提供任何参数，进入交互模式 + if not args.end_user_id and not args.list_all: + await interactive_mode() + return + + # 列出所有 end_user + if args.list_all: + print("\n🔄 正在查询所有 end_user_id...") + end_users = await list_all_end_users() + + if not end_users: + print("\n❌ 数据库中没有任何 end_user 数据") + else: + print(f"\n{'='*60}") + print(f"📋 数据库中的所有 End User ID") + print(f"{'='*60}\n") + + for idx, end_user in enumerate(end_users, 1): + print(f" {idx}. {end_user['end_user_id']}") + print(f" 节点数: {end_user['node_count']}") + + print(f"\n{'='*60}\n") + return + + # 检查指定 end_user + if args.end_user_id: + print(f"\n🔄 正在查询 end_user_id: {args.end_user_id}...") + stats = await check_group_exists(args.end_user_id) + + detailed_stats = None + if args.detailed and stats["exists"]: + print("🔄 正在获取详细统计...") + detailed_stats = await get_detailed_stats(args.end_user_id) + + print_results(args.end_user_id, stats, detailed_stats) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/api/app/core/memory/evaluation/common/metrics.py b/api/app/core/memory/evaluation/common/metrics.py index acc27fb9..961ce7f0 100644 --- a/api/app/core/memory/evaluation/common/metrics.py +++ b/api/app/core/memory/evaluation/common/metrics.py @@ -2,7 +2,7 @@ import math import re from typing import List, Dict - +# 评估指标的实现 def _normalize(text: str) -> List[str]: """Lowercase, strip punctuation, and split into tokens.""" text = text.lower().strip() diff --git a/api/app/core/memory/evaluation/dialogue_queries.py b/api/app/core/memory/evaluation/dialogue_queries.py index 25abe64e..0aace0ec 100644 --- a/api/app/core/memory/evaluation/dialogue_queries.py +++ b/api/app/core/memory/evaluation/dialogue_queries.py @@ -4,15 +4,17 @@ This file contains Cypher queries for searching dialogues, entities, and chunks. Placed in evaluation directory to avoid circular imports with src modules. """ +# 应该是neo4j browser的cypher语句，需要修改文件名 + # Entity search queries SEARCH_ENTITIES_BY_NAME = """ -MATCH (e:Entity) +MATCH (e:ExtractedEntity) WHERE e.name = $name RETURN e """ SEARCH_ENTITIES_BY_NAME_FALLBACK = """ -MATCH (e:Entity) +MATCH (e:ExtractedEntity) WHERE e.name CONTAINS $name RETURN e """ diff --git a/api/app/core/memory/evaluation/extraction_utils.py b/api/app/core/memory/evaluation/extraction_utils.py index 9e70bc28..43ef6fe0 100644 --- a/api/app/core/memory/evaluation/extraction_utils.py +++ b/api/app/core/memory/evaluation/extraction_utils.py @@ -1,34 +1,33 @@ +import os import asyncio import json -import os -import re +from typing import List, Dict, Any, Optional from datetime import datetime -from typing import Any, Dict, List, Optional +from uuid import UUID +import re from app.core.memory.llm_tools.openai_client import LLMClient -from app.core.memory.models.message_models import ( - ConversationContext, - ConversationMessage, - DialogData, -) +from app.core.memory.storage_services.extraction_engine.knowledge_extraction.chunk_extraction import DialogueChunker +from app.core.memory.models.message_models import DialogData, ConversationContext, ConversationMessage +import os +import sys +from pathlib import Path +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent / "app" / "core" / "memory" / "evaluation" / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") + +from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.core.memory.utils.llm.llm_utils import get_llm_client # 使用新的模块化架构 -from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ( - ExtractionOrchestrator, -) -from app.core.memory.storage_services.extraction_engine.knowledge_extraction.chunk_extraction import ( - DialogueChunker, -) -from app.core.memory.utils.config.definitions import ( - SELECTED_CHUNKER_STRATEGY, - SELECTED_EMBEDDING_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.db import get_db_context +from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ExtractionOrchestrator # Import from database module from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j -from app.repositories.neo4j.neo4j_connector import Neo4jConnector # Cypher queries for evaluation # Note: Entity, chunk, and dialogue search queries have been moved to evaluation/dialogue_queries.py @@ -41,11 +40,14 @@ async def ingest_contexts_via_full_pipeline( embedding_name: str | None = None, save_chunk_output: bool = False, save_chunk_output_path: str | None = None, + reset_group: bool = False, ) -> bool: - """DEPRECATED: 此函数使用旧的流水线架构，建议使用新的 ExtractionOrchestrator + """ + 使用新的 ExtractionOrchestrator 运行完整的提取流水线 Run the full extraction pipeline on provided dialogue contexts and save to Neo4j. - This function mirrors the steps in main(), but starts from raw text contexts. + This function uses the new ExtractionOrchestrator architecture for better maintainability. + Args: contexts: List of dialogue texts, each containing lines like "role: message". end_user_id: Group ID to assign to generated DialogData and graph nodes. @@ -53,25 +55,59 @@ async def ingest_contexts_via_full_pipeline( embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID. save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging. save_chunk_output_path: Optional output path; defaults to src/chunker_test_output.txt. + reset_group: If True, clear existing data for this group before ingestion. Returns: True if data saved successfully, False otherwise. """ - chunker_strategy = chunker_strategy or SELECTED_CHUNKER_STRATEGY - embedding_name = embedding_name or SELECTED_EMBEDDING_ID + chunker_strategy = chunker_strategy or os.getenv("EVAL_CHUNKER_STRATEGY", "RecursiveChunker") + embedding_name = embedding_name or os.getenv("EVAL_EMBEDDING_ID") + + # Check if we should reset from environment variable if not explicitly set + if not reset_group: + reset_group = os.getenv("EVAL_RESET_ON_INGEST", "false").lower() in ("true", "1", "yes") + + # Step 0: Reset group if requested + if reset_group: + print(f"[Ingestion] 🗑️ 清空 end_user '{end_user_id}' 的现有数据...") + try: + from app.repositories.neo4j.neo4j_connector import Neo4jConnector + connector = Neo4jConnector() + try: + # 删除该 end_user 的所有节点和关系 + query = """ + MATCH (n {end_user_id: $end_user_id}) + DETACH DELETE n + """ + await connector.execute_query(query, end_user_id=end_user_id) + print(f"[Ingestion] ✅ End User '{end_user_id}' 已清空") + finally: + await connector.close() + except Exception as e: + print(f"[Ingestion] ⚠️ 清空 end_user 失败: {e}") + # 继续执行，不中断摄入流程 - # Initialize llm client with graceful fallback + # Step 1: Initialize LLM client llm_client = None - llm_available = True try: - from app.core.memory.utils.config import definitions as config_defs - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(config_defs.SELECTED_LLM_ID) + # 使用评估配置中的 LLM ID + llm_id = os.getenv("EVAL_LLM_ID") + if not llm_id: + print("[Ingestion] ❌ EVAL_LLM_ID not set in .env.evaluation") + return False + + from app.db import get_db + + db = next(get_db()) + try: + llm_client = get_llm_client(llm_id, db) + finally: + db.close() except Exception as e: - print(f"[Ingestion] LLM client unavailable, will skip LLM-dependent steps: {e}") - llm_available = False + print(f"[Ingestion] LLM client unavailable: {e}") + return False - # Step A: Build DialogData list from contexts with robust parsing + # Step 2: Parse contexts and create DialogData with chunks + print(f"[Ingestion] Parsing {len(contexts)} contexts...") chunker = DialogueChunker(chunker_strategy) dialog_data_list: List[DialogData] = [] @@ -94,7 +130,7 @@ async def ingest_contexts_via_full_pipeline( line = raw.strip() if not line: continue - m = re.match(r'^\s*([^:：]+)\s*[：:]\s*(.+)$', line) + m = re.match(r'^\s*([^:：]+)\s*[：:]\s*(.+)', line) if m: role = m.group(1).strip() msg = m.group(2).strip() @@ -118,10 +154,12 @@ async def ingest_contexts_via_full_pipeline( dialog_data_list.append(dialog) if not dialog_data_list: - print("No dialogs to process for ingestion.") + print("[Ingestion] No dialogs to process.") return False - # Optionally save chunking outputs for debugging + print(f"[Ingestion] Parsed {len(dialog_data_list)} dialogs with chunks") + + # Step 3: Optionally save chunking outputs for debugging if save_chunk_output: try: def _serialize_datetime(obj): @@ -137,124 +175,185 @@ async def ingest_contexts_via_full_pipeline( combined_output = [dd.model_dump() for dd in dialog_data_list] with open(out_path, "w", encoding="utf-8") as f: json.dump(combined_output, f, ensure_ascii=False, indent=4, default=_serialize_datetime) - print(f"Saved chunking results to: {out_path}") + print(f"[Ingestion] Saved chunking results to: {out_path}") except Exception as e: - print(f"Failed to save chunking results: {e}") + print(f"[Ingestion] Failed to save chunking results: {e}") - # Step B-G: 使用新的 ExtractionOrchestrator 执行完整的提取流水线 - if not llm_available: - print("[Ingestion] Skipping extraction pipeline (no LLM).") - return False - - # 初始化 embedder 客户端 - from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient + # Step 4: Initialize embedder client from app.core.models.base import RedBearModelConfig - from app.services.memory_config_service import MemoryConfigService + from app.core.memory.utils.config.config_utils import get_embedder_config + from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient + from app.db import get_db try: - with get_db_context() as db: - embedder_config_dict = MemoryConfigService(db).get_embedder_config(embedding_name or SELECTED_EMBEDDING_ID) - embedder_config = RedBearModelConfig(**embedder_config_dict) - embedder_client = OpenAIEmbedderClient(embedder_config) + db = next(get_db()) + try: + embedder_config_dict = get_embedder_config(embedding_name, db) + embedder_config = RedBearModelConfig(**embedder_config_dict) + embedder_client = OpenAIEmbedderClient(embedder_config) + finally: + db.close() except Exception as e: print(f"[Ingestion] Failed to initialize embedder client: {e}") - print("[Ingestion] Skipping extraction pipeline (embedder initialization failed).") return False + # Step 5: Initialize Neo4j connector connector = Neo4jConnector() - # 初始化并运行 ExtractionOrchestrator - from app.core.memory.utils.config.config_utils import get_pipeline_config - config = get_pipeline_config() + # Step 6: 构建 MemoryConfig（从环境变量直接构建，不依赖数据库） + print("[Ingestion] 构建 MemoryConfig from environment variables...") + from app.schemas.memory_config_schema import MemoryConfig + + try: + # 从环境变量获取配置参数 + llm_id = os.getenv("EVAL_LLM_ID") + embedding_id = os.getenv("EVAL_EMBEDDING_ID") + chunker_strategy_env = os.getenv("EVAL_CHUNKER_STRATEGY", "RecursiveChunker") + + if not llm_id or not embedding_id: + print("[Ingestion] ❌ EVAL_LLM_ID or EVAL_EMBEDDING_ID is not set in .env.evaluation") + print("[Ingestion] Please set both EVAL_LLM_ID and EVAL_EMBEDDING_ID") + await connector.close() + return False + + # 从数据库获取模型信息（仅用于显示名称） + from app.db import get_db + db = next(get_db()) + try: + from sqlalchemy import text + # 获取 LLM 模型信息（从 model_configs 表） + llm_result = db.execute( + text("SELECT name FROM model_configs WHERE id = :id"), + {"id": llm_id} + ).fetchone() + llm_model_name = llm_result[0] if llm_result else "Unknown LLM" + + # 获取 Embedding 模型信息（从 model_configs 表） + emb_result = db.execute( + text("SELECT name FROM model_configs WHERE id = :id"), + {"id": embedding_id} + ).fetchone() + embedding_model_name = emb_result[0] if emb_result else "Unknown Embedding" + except Exception as e: + # 如果查询失败，使用默认名称 + print(f"[Ingestion] Warning: Failed to query model names from database: {e}") + llm_model_name = f"LLM ({llm_id[:8]}...)" + embedding_model_name = f"Embedding ({embedding_id[:8]}...)" + finally: + db.close() + + # 构建 MemoryConfig 对象（使用最小必需配置） + from uuid import uuid4 + memory_config = MemoryConfig( + config_id=0, # 评估环境不需要真实的 config_id + config_name="evaluation_config", + workspace_id=uuid4(), # 临时 workspace_id + workspace_name="evaluation_workspace", + tenant_id=uuid4(), # 临时 tenant_id + llm_model_id=UUID(llm_id), + llm_model_name=llm_model_name, + embedding_model_id=UUID(embedding_id), + embedding_model_name=embedding_model_name, + storage_type="neo4j", + chunker_strategy=chunker_strategy_env, + reflexion_enabled=False, + reflexion_iteration_period=3, + reflexion_range="partial", + reflexion_baseline="TIME", + loaded_at=datetime.now(), + # 可选字段使用默认值 + rerank_model_id=None, + rerank_model_name=None, + llm_params={}, + embedding_params={}, + config_version="2.0", + ) + + print(f"[Ingestion] ✅ 构建 MemoryConfig 成功") + print(f"[Ingestion] LLM: {llm_model_name}") + print(f"[Ingestion] Embedding: {embedding_model_name}") + print(f"[Ingestion] Chunker: {chunker_strategy_env}") + + except Exception as e: + print(f"[Ingestion] ❌ Failed to build MemoryConfig: {e}") + print(f"[Ingestion] Please check:") + print(f"[Ingestion] 1. EVAL_LLM_ID and EVAL_EMBEDDING_ID are set in .env.evaluation") + print(f"[Ingestion] 2. Model IDs exist in the models table") + print(f"[Ingestion] 3. Database connection is working") + await connector.close() + return False + + # Step 7: Initialize and run ExtractionOrchestrator + print("[Ingestion] Running extraction pipeline with ExtractionOrchestrator...") + from app.services.memory_config_service import MemoryConfigService + config = MemoryConfigService.get_pipeline_config(memory_config) orchestrator = ExtractionOrchestrator( llm_client=llm_client, embedder_client=embedder_client, connector=connector, config=config, + embedding_id=str(memory_config.embedding_model_id), # 传递 embedding_id ) - # 创建一个包装的 orchestrator 来修复时间提取器的输出 - # 保存原始的 _assign_extracted_data 方法 - original_assign = orchestrator._assign_extracted_data - - def clean_temporal_value(value): - """清理 temporal_validity 字段的值，将无效值转换为 None""" - if value is None: - return None - if isinstance(value, str): - # 处理字符串形式的 'null', 'None', 空字符串等 - if value.lower() in ('null', 'none', '') or value.strip() == '': - return None - return value - - async def patched_assign_extracted_data(*args, **kwargs): - """包装方法：在赋值后清理 temporal_validity 中的无效字符串""" - result = await original_assign(*args, **kwargs) + try: + # Run the complete extraction pipeline + result = await orchestrator.run(dialog_data_list, is_pilot_run=False) - # 清理返回的 dialog_data_list 中的 temporal_validity - for dialog in result: - if hasattr(dialog, 'chunks') and dialog.chunks: - for chunk in dialog.chunks: - if hasattr(chunk, 'statements') and chunk.statements: - for statement in chunk.statements: - if hasattr(statement, 'temporal_validity') and statement.temporal_validity: - tv = statement.temporal_validity - # 清理 valid_at 和 invalid_at - if hasattr(tv, 'valid_at'): - tv.valid_at = clean_temporal_value(tv.valid_at) - if hasattr(tv, 'invalid_at'): - tv.invalid_at = clean_temporal_value(tv.invalid_at) - return result - - # 替换方法 - orchestrator._assign_extracted_data = patched_assign_extracted_data - - # 同时包装 _create_nodes_and_edges 方法，在创建节点前再次清理 - original_create = orchestrator._create_nodes_and_edges - - async def patched_create_nodes_and_edges(dialog_data_list_arg): - """包装方法：在创建节点前再次清理 temporal_validity""" - # 最后一次清理，确保万无一失 - for dialog in dialog_data_list_arg: - if hasattr(dialog, 'chunks') and dialog.chunks: - for chunk in dialog.chunks: - if hasattr(chunk, 'statements') and chunk.statements: - for statement in chunk.statements: - if hasattr(statement, 'temporal_validity') and statement.temporal_validity: - tv = statement.temporal_validity - if hasattr(tv, 'valid_at'): - tv.valid_at = clean_temporal_value(tv.valid_at) - if hasattr(tv, 'invalid_at'): - tv.invalid_at = clean_temporal_value(tv.invalid_at) + # Handle different return formats: + # - Pilot mode: 7 values (without dedup_details) + # - Normal mode: 8 values (with dedup_details at the end) + if len(result) == 8: + # Normal mode: includes dedup_details + ( + dialogue_nodes, + chunk_nodes, + statement_nodes, + entity_nodes, + statement_chunk_edges, + statement_entity_edges, + entity_entity_edges, + _, # dedup_details - not needed here + ) = result + elif len(result) == 7: + # Pilot mode or older version: no dedup_details + ( + dialogue_nodes, + chunk_nodes, + statement_nodes, + entity_nodes, + statement_chunk_edges, + statement_entity_edges, + entity_entity_edges, + ) = result + else: + raise ValueError(f"Unexpected number of return values: {len(result)}") - return await original_create(dialog_data_list_arg) - - orchestrator._create_nodes_and_edges = patched_create_nodes_and_edges - - # 运行完整的提取流水线 - # orchestrator.run 返回 7 个元素的元组 - result = await orchestrator.run(dialog_data_list, is_pilot_run=False) - ( - dialogue_nodes, - chunk_nodes, - statement_nodes, - entity_nodes, - statement_chunk_edges, - statement_entity_edges, - entity_entity_edges, - ) = result - - # statement_chunk_edges 已经由 orchestrator 创建，无需重复创建 + print(f"[Ingestion] Extraction completed: {len(statement_nodes)} statements, {len(entity_nodes)} entities") + + except ValueError as e: + # If unpacking fails, provide helpful error message + print(f"[Ingestion] Extraction pipeline result unpacking failed: {e}") + print(f"[Ingestion] Result type: {type(result)}, length: {len(result) if hasattr(result, '__len__') else 'N/A'}") + if hasattr(result, '__len__') and len(result) > 0: + print(f"[Ingestion] First element type: {type(result[0])}") + await connector.close() + return False + except Exception as e: + print(f"[Ingestion] Extraction pipeline failed: {e}") + import traceback + traceback.print_exc() + await connector.close() + return False - # Step G: 生成记忆摘要 + # Step 7: Generate memory summaries print("[Ingestion] Generating memory summaries...") try: from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import ( memory_summary_generation, ) - from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges from app.repositories.neo4j.add_nodes import add_memory_summary_nodes + from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges summaries = await memory_summary_generation( chunked_dialogs=dialog_data_list, @@ -266,7 +365,8 @@ async def ingest_contexts_via_full_pipeline( print(f"[Ingestion] Warning: Failed to generate memory summaries: {e}") summaries = [] - # Step H: Save to Neo4j + # Step 8: Save to Neo4j + print("[Ingestion] Saving to Neo4j...") try: success = await save_dialog_and_statements_to_neo4j( dialogue_nodes=dialogue_nodes, @@ -284,18 +384,21 @@ async def ingest_contexts_via_full_pipeline( try: await add_memory_summary_nodes(summaries, connector) await add_memory_summary_statement_edges(summaries, connector) - print(f"Successfully saved {len(summaries)} memory summary nodes to Neo4j") + print(f"[Ingestion] Saved {len(summaries)} memory summary nodes to Neo4j") except Exception as e: - print(f"Warning: Failed to save summary nodes: {e}") + print(f"[Ingestion] Warning: Failed to save summary nodes: {e}") await connector.close() + if success: - print("Successfully saved extracted data to Neo4j!") + print("[Ingestion] Successfully saved all data to Neo4j!") else: - print("Failed to save data to Neo4j") + print("[Ingestion] Failed to save data to Neo4j") return success + except Exception as e: - print(f"Failed to save data to Neo4j: {e}") + print(f"[Ingestion] Failed to save data to Neo4j: {e}") + await connector.close() return False diff --git a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py index 1c70c28e..eed75016 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py +++ b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py @@ -15,134 +15,145 @@ import json import os import time from datetime import datetime -from typing import Any, Dict, List, Optional +from typing import List, Dict, Any, Optional +from pathlib import Path +from dotenv import load_dotenv -try: - from dotenv import load_dotenv -except ImportError: - def load_dotenv(): - pass +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") +from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient +from app.core.models.base import RedBearModelConfig +from app.core.memory.utils.config.config_utils import get_embedder_config +from app.core.memory.utils.llm.llm_utils import get_llm_client from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - bleu1, f1_score, + bleu1, jaccard, latency_stats, + avg_context_tokens ) from app.core.memory.evaluation.locomo.locomo_metrics import ( - get_category_name, locomo_f1_score, locomo_multi_f1, + get_category_name ) from app.core.memory.evaluation.locomo.locomo_utils import ( - extract_conversations, - ingest_conversations_if_needed, load_locomo_data, + extract_conversations, resolve_temporal_references, - retrieve_relevant_information, select_and_format_information, -) -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_end_user_id, - SELECTED_LLM_ID, + retrieve_relevant_information, ) from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig from app.db import get_db_context -from app.repositories.neo4j.neo4j_connector import Neo4jConnector from app.services.memory_config_service import MemoryConfigService +# Get configuration from environment variables +PROJECT_ROOT = str(Path(__file__).resolve().parents[5]) # api directory +SELECTED_EMBEDDING_ID = os.getenv("EVAL_EMBEDDING_ID", "e2a6392d-ca63-4d59-a523-647420b59cb2") +SELECTED_end_user_id = os.getenv("LOCOMO_END_USER_ID") or os.getenv("EVAL_END_USER_ID", "locomo_benchmark") +SELECTED_LLM_ID = os.getenv("EVAL_LLM_ID", "2c9b0782-7a85-4740-ba84-4baf77f256c4") -async def run_locomo_benchmark( - sample_size: int = 20, - end_user_id: Optional[str] = None, - search_type: str = "hybrid", - search_limit: int = 12, - context_char_budget: int = 8000, - reset_group: bool = False, - skip_ingest: bool = False, - output_dir: Optional[str] = None -) -> Dict[str, Any]: + +# ============================================================================ +# Step 1: Data Loading +# ============================================================================ + +def step_load_data(data_path: str, sample_size: int) -> List[Dict[str, Any]]: """ - Run LoCoMo benchmark evaluation. - - This function orchestrates the complete evaluation pipeline: - 1. Load LoCoMo dataset (only QA pairs from first conversation) - 2. Check/ingest conversations into database (only first conversation, unless skip_ingest=True) - 3. For each question: - - Retrieve relevant information - - Generate answer using LLM - - Calculate metrics - 4. Aggregate results and save to file - - Note: By default, only the first conversation is ingested into the database, - and only QA pairs from that conversation are evaluated. This ensures that - all questions have corresponding memory in the database for retrieval. + Load QA pairs from LoCoMo dataset. Args: - sample_size: Number of QA pairs to evaluate (from first conversation) - end_user_id: Database group ID for retrieval (uses default if None) - search_type: "keyword", "embedding", or "hybrid" - search_limit: Max documents to retrieve per query - context_char_budget: Max characters for context - reset_group: Whether to clear and re-ingest data (not implemented) - skip_ingest: If True, skip data ingestion and use existing data in Neo4j - output_dir: Directory to save results (uses default if None) + data_path: Path to locomo10.json file + sample_size: Number of QA pairs to load (0 for all) Returns: - Dictionary with evaluation results including metrics, timing, and samples + List of QA items from the first conversation """ - # Use default end_user_id if not provided - end_user_id = end_user_id or SELECTED_end_user_id + print("📂 Loading LoCoMo data...") - # Determine data path - data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") - if not os.path.exists(data_path): - # Fallback to current directory - data_path = os.path.join(os.getcwd(), "data", "locomo10.json") + # Load the dataset + qa_items = load_locomo_data(data_path, sample_size) - print(f"\n{'='*60}") - print("🚀 Starting LoCoMo Benchmark Evaluation") - print(f"{'='*60}") - print("📊 Configuration:") - print(f" Sample size: {sample_size}") - print(f" Group ID: {end_user_id}") - print(f" Search type: {search_type}") - print(f" Search limit: {search_limit}") - print(f" Context budget: {context_char_budget} chars") - print(f" Data path: {data_path}") - print(f"{'='*60}\n") + print(f"✅ Loaded {len(qa_items)} QA pairs from first conversation\n") + return qa_items + + +# ============================================================================ +# Step 2: Data Ingestion +# ============================================================================ + +async def ingest_conversations_if_needed( + conversations: List[str], + end_user_id: str, + reset: bool = False +) -> bool: + """ + Ingest conversations into Neo4j database. - # Step 1: Load LoCoMo data - print("📂 Loading LoCoMo dataset...") + Args: + conversations: List of conversation strings (already formatted) + end_user_id: Database end_user ID + reset: Whether to reset the group before ingestion + + Returns: + True if successful, False otherwise + """ try: - # Only load QA pairs from the first conversation (index 0) - # since we only ingest the first conversation into the database - qa_items = load_locomo_data(data_path, sample_size, conversation_index=0) - print(f"✅ Loaded {len(qa_items)} QA pairs from conversation 0\n") + from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline + + # Conversations are already formatted as strings, use them directly + await ingest_contexts_via_full_pipeline(conversations, end_user_id) + return True + except Exception as e: - print(f"❌ Failed to load data: {e}") - return { - "error": f"Data loading failed: {e}", - "timestamp": datetime.now().isoformat() - } + print(f"⚠️ Ingestion error: {e}") + import traceback + traceback.print_exc() + return False + + +async def step_ingest_data( + data_path: str, + end_user_id: str, + skip_ingest: bool, + reset_group: bool, + max_messages: Optional[int] = None +) -> bool: + """ + Ingest conversations into Neo4j database if needed. - # Step 2: Extract conversations and ingest if needed + Args: + data_path: Path to locomo10.json file + end_user_id: Database end_user ID + skip_ingest: Whether to skip ingestion + reset_group: Whether to reset the group before ingestion + max_messages: Maximum messages per dialogue to ingest (for testing) + + Returns: + True if ingestion succeeded or was skipped, False otherwise + """ if skip_ingest: print("⏭️ Skipping data ingestion (using existing data in Neo4j)") - print(f" Group ID: {end_user_id}\n") + print(f" End User ID: {end_user_id}\n") else: print("💾 Checking database ingestion...") try: - conversations = extract_conversations(data_path, max_dialogues=1) + # Extract conversations with optional message limit + conversations = extract_conversations( + data_path, + max_dialogues=1, + max_messages_per_dialogue=max_messages + ) print(f"📝 Extracted {len(conversations)} conversations") # Always ingest for now (ingestion check not implemented) - print(f"🔄 Ingesting conversations into group '{end_user_id}'...") + print(f"🔄 Ingesting conversations into end_user '{end_user_id}'...") success = await ingest_conversations_if_needed( conversations=conversations, end_user_id=end_user_id, @@ -156,238 +167,249 @@ async def run_locomo_benchmark( except Exception as e: print(f"❌ Ingestion failed: {e}") + import traceback + traceback.print_exc() print("⚠️ Continuing with evaluation (database may be empty)\n") - # Step 3: Initialize clients + return True + + +# ============================================================================ +# Step 3: Initialize Clients +# ============================================================================ + +def step_initialize_clients(llm_id: str, embedding_id: str): + """ + Initialize Neo4j connector, LLM client, and embedder. + + Args: + llm_id: LLM model ID + embedding_id: Embedding model ID + + Returns: + Tuple of (connector, llm_client, embedder) + """ print("🔧 Initializing clients...") + connector = Neo4jConnector() - # Initialize LLM client with database context - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) + # Get database session + from app.db import get_db + db = next(get_db()) + try: + llm_client = get_llm_client(llm_id, db) + cfg_dict = get_embedder_config(embedding_id, db) + embedder = OpenAIEmbedderClient( + model_config=RedBearModelConfig.model_validate(cfg_dict) + ) + finally: + db.close() - # Initialize embedder - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) print("✅ Clients initialized\n") - - # Step 4: Process questions + return connector, llm_client, embedder + + +# ============================================================================ +# Step 4: Process Questions +# ============================================================================ + +async def step_process_all_questions( + qa_items: List[Dict[str, Any]], + end_user_id: str, + search_type: str, + search_limit: int, + context_char_budget: int, + connector: Neo4jConnector, + embedder: OpenAIEmbedderClient, + llm_client: Any +) -> List[Dict[str, Any]]: + """Process all QA items: retrieve, generate, and calculate metrics.""" print(f"🔍 Processing {len(qa_items)} questions...") print(f"{'='*60}\n") - # Tracking variables - latencies_search: List[float] = [] - latencies_llm: List[float] = [] - context_counts: List[int] = [] - context_chars: List[int] = [] - context_tokens: List[int] = [] - - # Metric lists - f1_scores: List[float] = [] - bleu1_scores: List[float] = [] - jaccard_scores: List[float] = [] - locomo_f1_scores: List[float] = [] - - # Per-category tracking - category_counts: Dict[str, int] = {} - category_f1: Dict[str, List[float]] = {} - category_bleu1: Dict[str, List[float]] = {} - category_jaccard: Dict[str, List[float]] = {} - category_locomo_f1: Dict[str, List[float]] = {} - - # Detailed samples samples: List[Dict[str, Any]] = [] - - # Fixed anchor date for temporal resolution anchor_date = datetime(2023, 5, 8) - try: - for idx, item in enumerate(qa_items, 1): - question = item.get("question", "") - ground_truth = item.get("answer", "") - category = get_category_name(item) - - # Ensure ground truth is a string - ground_truth_str = str(ground_truth) if ground_truth is not None else "" - - print(f"[{idx}/{len(qa_items)}] Category: {category}") - print(f"❓ Question: {question}") - print(f"✅ Ground Truth: {ground_truth_str}") - - # Step 4a: Retrieve relevant information - t_search_start = time.time() - try: - retrieved_info = await retrieve_relevant_information( - question=question, - end_user_id=end_user_id, - search_type=search_type, - search_limit=search_limit, - connector=connector, - embedder=embedder - ) - t_search_end = time.time() - search_latency = (t_search_end - t_search_start) * 1000 - latencies_search.append(search_latency) - - print(f"🔍 Retrieved {len(retrieved_info)} documents ({search_latency:.1f}ms)") - - except Exception as e: - print(f"❌ Retrieval failed: {e}") - retrieved_info = [] - search_latency = 0.0 - latencies_search.append(search_latency) - - # Step 4b: Select and format context - context_text = select_and_format_information( - retrieved_info=retrieved_info, + for idx, item in enumerate(qa_items, 1): + question = item.get("question", "") + ground_truth = item.get("answer", "") + category = get_category_name(item) + ground_truth_str = str(ground_truth) if ground_truth is not None else "" + + print(f"[{idx}/{len(qa_items)}] Category: {category}") + print(f"❓ Question: {question}") + print(f"✅ Ground Truth: {ground_truth_str}") + + # Retrieve + t_search_start = time.time() + try: + retrieved_info = await retrieve_relevant_information( question=question, - max_chars=context_char_budget + end_user_id=end_user_id, + search_type=search_type, + search_limit=search_limit, + connector=connector, + embedder=embedder ) - - # Resolve temporal references - context_text = resolve_temporal_references(context_text, anchor_date) - - # Add reference date to context - if context_text: - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n{context_text}" + search_latency = (time.time() - t_search_start) * 1000 + print(f"🔍 Retrieved {len(retrieved_info)} documents ({search_latency:.1f}ms)") + except Exception as e: + print(f"❌ Retrieval failed: {e}") + retrieved_info = [] + search_latency = 0.0 + + # Format context + context_text = select_and_format_information( + retrieved_info=retrieved_info, + question=question, + max_chars=context_char_budget + ) + context_text = resolve_temporal_references(context_text, anchor_date) + if context_text: + context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n{context_text}" + else: + context_text = "No relevant context found." + + print(f"📝 Context: {len(context_text)} chars, {len(retrieved_info)} docs") + + # Generate answer + messages = [ + { + "role": "system", + "content": ( + "You are a precise QA assistant. Answer following these rules:\n" + "1) Extract the EXACT information mentioned in the context\n" + "2) For time questions: calculate actual dates from relative times\n" + "3) Return ONLY the answer text in simplest form\n" + "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" + "5) If no clear answer found, respond with 'Unknown'" + ) + }, + { + "role": "user", + "content": f"Question: {question}\n\nContext:\n{context_text}" + } + ] + + t_llm_start = time.time() + try: + response = await llm_client.chat(messages=messages) + llm_latency = (time.time() - t_llm_start) * 1000 + if hasattr(response, 'content'): + prediction = response.content.strip() + elif isinstance(response, dict): + prediction = response["choices"][0]["message"]["content"].strip() else: - context_text = "No relevant context found." - - # Track context statistics - context_counts.append(len(retrieved_info)) - context_chars.append(len(context_text)) - context_tokens.append(len(context_text.split())) - - print(f"📝 Context: {len(context_text)} chars, {len(retrieved_info)} docs") - - # Step 4c: Generate answer with LLM - messages = [ - { - "role": "system", - "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - ) - }, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}" - } - ] - - t_llm_start = time.time() - try: - response = await llm_client.chat(messages=messages) - t_llm_end = time.time() - llm_latency = (t_llm_end - t_llm_start) * 1000 - latencies_llm.append(llm_latency) - - # Extract prediction from response - if hasattr(response, 'content'): - prediction = response.content.strip() - elif isinstance(response, dict): - prediction = response["choices"][0]["message"]["content"].strip() - else: - prediction = "Unknown" - - print(f"🤖 Prediction: {prediction} ({llm_latency:.1f}ms)") - - except Exception as e: - print(f"❌ LLM failed: {e}") prediction = "Unknown" - llm_latency = 0.0 - latencies_llm.append(llm_latency) - - # Step 4d: Calculate metrics - f1_val = f1_score(prediction, ground_truth_str) - bleu1_val = bleu1(prediction, ground_truth_str) - jaccard_val = jaccard(prediction, ground_truth_str) - - # LoCoMo-specific F1: use multi-answer for category 1 (Multi-Hop) - if item.get("category") == 1: - locomo_f1_val = locomo_multi_f1(prediction, ground_truth_str) - else: - locomo_f1_val = locomo_f1_score(prediction, ground_truth_str) - - # Accumulate metrics - f1_scores.append(f1_val) - bleu1_scores.append(bleu1_val) - jaccard_scores.append(jaccard_val) - locomo_f1_scores.append(locomo_f1_val) - - # Track by category - category_counts[category] = category_counts.get(category, 0) + 1 - category_f1.setdefault(category, []).append(f1_val) - category_bleu1.setdefault(category, []).append(bleu1_val) - category_jaccard.setdefault(category, []).append(jaccard_val) - category_locomo_f1.setdefault(category, []).append(locomo_f1_val) - - print(f"📊 Metrics - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, " - f"Jaccard: {jaccard_val:.3f}, LoCoMo F1: {locomo_f1_val:.3f}") - print() - - # Save sample details - samples.append({ - "question": question, - "ground_truth": ground_truth_str, - "prediction": prediction, - "category": category, - "metrics": { - "f1": f1_val, - "bleu1": bleu1_val, - "jaccard": jaccard_val, - "locomo_f1": locomo_f1_val - }, - "retrieval": { - "num_docs": len(retrieved_info), - "context_length": len(context_text) - }, - "timing": { - "search_ms": search_latency, - "llm_ms": llm_latency - } - }) + print(f"🤖 Prediction: {prediction} ({llm_latency:.1f}ms)") + except Exception as e: + print(f"❌ LLM failed: {e}") + prediction = "Unknown" + llm_latency = 0.0 + + # Calculate metrics + f1_val = f1_score(prediction, ground_truth_str) + bleu1_val = bleu1(prediction, ground_truth_str) + jaccard_val = jaccard(prediction, ground_truth_str) + if item.get("category") == 1: + locomo_f1_val = locomo_multi_f1(prediction, ground_truth_str) + else: + locomo_f1_val = locomo_f1_score(prediction, ground_truth_str) + + print(f"📊 Metrics - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, " + f"Jaccard: {jaccard_val:.3f}, LoCoMo F1: {locomo_f1_val:.3f}") + print() + + samples.append({ + "question": question, + "ground_truth": ground_truth_str, + "prediction": prediction, + "category": category, + "metrics": { + "f1": f1_val, + "bleu1": bleu1_val, + "jaccard": jaccard_val, + "locomo_f1": locomo_f1_val + }, + "retrieval": { + "num_docs": len(retrieved_info), + "context_length": len(context_text) + }, + "context_tokens": len(context_text.split()), + "timing": { + "search_ms": search_latency, + "llm_ms": llm_latency + } + }) - finally: - # Close connector - await connector.close() - - # Step 5: Aggregate results + return samples + + +# ============================================================================ +# Step 5: Aggregate Results +# ============================================================================ + +def step_aggregate_results(samples: List[Dict[str, Any]]) -> Dict[str, Any]: + """Aggregate metrics from all samples.""" print(f"\n{'='*60}") print("📊 Aggregating Results") print(f"{'='*60}\n") + if not samples: + return { + "overall_metrics": {}, + "by_category": {}, + "latency": {}, + "context_stats": {} + } + + # Extract metrics + f1_scores = [s["metrics"]["f1"] for s in samples] + bleu1_scores = [s["metrics"]["bleu1"] for s in samples] + jaccard_scores = [s["metrics"]["jaccard"] for s in samples] + locomo_f1_scores = [s["metrics"]["locomo_f1"] for s in samples] + + # Extract timing + latencies_search = [s["timing"]["search_ms"] for s in samples] + latencies_llm = [s["timing"]["llm_ms"] for s in samples] + + # Extract context stats + context_counts = [s["retrieval"]["num_docs"] for s in samples] + context_chars = [s["retrieval"]["context_length"] for s in samples] + context_tokens = [s["context_tokens"] for s in samples] + # Overall metrics overall_metrics = { - "f1": sum(f1_scores) / max(len(f1_scores), 1) if f1_scores else 0.0, - "bleu1": sum(bleu1_scores) / max(len(bleu1_scores), 1) if bleu1_scores else 0.0, - "jaccard": sum(jaccard_scores) / max(len(jaccard_scores), 1) if jaccard_scores else 0.0, - "locomo_f1": sum(locomo_f1_scores) / max(len(locomo_f1_scores), 1) if locomo_f1_scores else 0.0 + "f1": sum(f1_scores) / len(f1_scores) if f1_scores else 0.0, + "bleu1": sum(bleu1_scores) / len(bleu1_scores) if bleu1_scores else 0.0, + "jaccard": sum(jaccard_scores) / len(jaccard_scores) if jaccard_scores else 0.0, + "locomo_f1": sum(locomo_f1_scores) / len(locomo_f1_scores) if locomo_f1_scores else 0.0 } # Per-category metrics + category_data: Dict[str, Dict[str, List[float]]] = {} + for sample in samples: + cat = sample["category"] + if cat not in category_data: + category_data[cat] = { + "f1": [], + "bleu1": [], + "jaccard": [], + "locomo_f1": [] + } + category_data[cat]["f1"].append(sample["metrics"]["f1"]) + category_data[cat]["bleu1"].append(sample["metrics"]["bleu1"]) + category_data[cat]["jaccard"].append(sample["metrics"]["jaccard"]) + category_data[cat]["locomo_f1"].append(sample["metrics"]["locomo_f1"]) + by_category: Dict[str, Dict[str, Any]] = {} - for cat in category_counts: - f1_list = category_f1.get(cat, []) - b1_list = category_bleu1.get(cat, []) - j_list = category_jaccard.get(cat, []) - lf_list = category_locomo_f1.get(cat, []) - + for cat, metrics_lists in category_data.items(): by_category[cat] = { - "count": category_counts[cat], - "f1": sum(f1_list) / max(len(f1_list), 1) if f1_list else 0.0, - "bleu1": sum(b1_list) / max(len(b1_list), 1) if b1_list else 0.0, - "jaccard": sum(j_list) / max(len(j_list), 1) if j_list else 0.0, - "locomo_f1": sum(lf_list) / max(len(lf_list), 1) if lf_list else 0.0 + "count": len(metrics_lists["f1"]), + "f1": sum(metrics_lists["f1"]) / len(metrics_lists["f1"]), + "bleu1": sum(metrics_lists["bleu1"]) / len(metrics_lists["bleu1"]), + "jaccard": sum(metrics_lists["jaccard"]) / len(metrics_lists["jaccard"]), + "locomo_f1": sum(metrics_lists["locomo_f1"]) / len(metrics_lists["locomo_f1"]) } # Latency statistics @@ -398,12 +420,181 @@ async def run_locomo_benchmark( # Context statistics context_stats = { - "avg_retrieved_docs": sum(context_counts) / max(len(context_counts), 1) if context_counts else 0.0, - "avg_context_chars": sum(context_chars) / max(len(context_chars), 1) if context_chars else 0.0, - "avg_context_tokens": sum(context_tokens) / max(len(context_tokens), 1) if context_tokens else 0.0 + "avg_retrieved_docs": sum(context_counts) / len(context_counts) if context_counts else 0.0, + "avg_context_chars": sum(context_chars) / len(context_chars) if context_chars else 0.0, + "avg_context_tokens": sum(context_tokens) / len(context_tokens) if context_tokens else 0.0 } - # Build result dictionary + return { + "overall_metrics": overall_metrics, + "by_category": by_category, + "latency": latency, + "context_stats": context_stats + } + + +# ============================================================================ +# Step 6: Result Saving +# ============================================================================ + +def step_save_results( + result: Dict[str, Any], + output_dir: Optional[str] +) -> str: + """ + Save evaluation results to JSON file. + + Args: + result: Complete result dictionary + output_dir: Directory to save results (uses default if None) + + Returns: + Path to saved file + """ + if output_dir is None: + # Use absolute path to ensure results are saved in the correct location + script_dir = Path(__file__).resolve().parent + output_dir = script_dir / "results" + else: + # Convert to Path object + output_dir = Path(output_dir) + # If relative path, make it relative to script directory + if not output_dir.is_absolute(): + script_dir = Path(__file__).resolve().parent + output_dir = script_dir / output_dir + + # Create directory if it doesn't exist + output_dir.mkdir(parents=True, exist_ok=True) + + timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = output_dir / f"locomo_{timestamp_str}.json" + + try: + with open(output_path, "w", encoding="utf-8") as f: + json.dump(result, f, ensure_ascii=False, indent=2) + print(f"✅ Results saved to: {output_path}\n") + return str(output_path) + except Exception as e: + print(f"❌ Failed to save results: {e}") + print("📊 Printing results to console instead:\n") + print(json.dumps(result, ensure_ascii=False, indent=2)) + return "" + + +# ============================================================================ +# Main Orchestration Function +# ============================================================================ + + +async def run_locomo_benchmark( + sample_size: int = 20, + end_user_id: Optional[str] = None, + search_type: str = "hybrid", + search_limit: int = 12, + context_char_budget: int = 8000, + reset_group: bool = False, + skip_ingest: bool = False, + output_dir: Optional[str] = None, + max_ingest_messages: Optional[int] = None +) -> Dict[str, Any]: + """ + Run LoCoMo benchmark evaluation. + + This function orchestrates the complete evaluation pipeline by calling + well-defined step functions: + 1. Load LoCoMo dataset (only QA pairs from first conversation) + 2. Ingest conversations into database (unless skip_ingest=True) + 3. Initialize clients (Neo4j, LLM, Embedder) + 4. Process all questions (retrieve, generate, calculate metrics) + 5. Aggregate results + 6. Save results to file + + Note: By default, only the first conversation is ingested into the database, + and only QA pairs from that conversation are evaluated. This ensures that + all questions have corresponding memory in the database for retrieval. + + Args: + sample_size: Number of QA pairs to evaluate (from first conversation) + end_user_id: Database end_user ID for retrieval (uses default if None) + search_type: "keyword", "embedding", or "hybrid" + search_limit: Max documents to retrieve per query + context_char_budget: Max characters for context + reset_group: Whether to clear and re-ingest data + skip_ingest: If True, skip data ingestion and use existing data in Neo4j + output_dir: Directory to save results (uses default if None) + max_ingest_messages: Max messages per dialogue to ingest (for testing, None = all) + + Returns: + Dictionary with evaluation results including metrics, timing, and samples + """ + # Use default end_user_id if not provided + # 优先级：命令行参数 > LOCOMO_END_USER_ID > EVAL_END_USER_ID > 默认值 + if end_user_id is None: + end_user_id = os.getenv("LOCOMO_END_USER_ID") or os.getenv("EVAL_END_USER_ID", "locomo_benchmark") + + # Get model IDs from config + llm_id = os.getenv("EVAL_LLM_ID", "6dc52e1b-9cec-4194-af66-a74c6307fc3f") + embedding_id = os.getenv("EVAL_EMBEDDING_ID", "e2a6392d-ca63-4d59-a523-647420b59cb2") + + # Determine data path + dataset_dir = Path(__file__).resolve().parent.parent / "dataset" + data_path = dataset_dir / "locomo10.json" + if not os.path.exists(data_path): + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 locomo10.json 放置在: {dataset_dir}" + ) + + # Print configuration + print(f"\n{'='*60}") + print("🚀 Starting LoCoMo Benchmark Evaluation") + print(f"{'='*60}") + print("📊 Configuration:") + print(f" Sample size: {sample_size}") + print(f" End User ID: {end_user_id}") + print(f" Search type: {search_type}") + print(f" Search limit: {search_limit}") + print(f" Context budget: {context_char_budget} chars") + print(f" Data path: {data_path}") + if max_ingest_messages: + print(f" Max ingest messages: {max_ingest_messages} (testing mode)") + print(f"{'='*60}\n") + + # Step 1: Load LoCoMo data （加载数据） + try: + qa_items = step_load_data(data_path, sample_size) + except Exception as e: + print(f"❌ Failed to load data: {e}") + return { + "error": f"Data loading failed: {e}", + "timestamp": datetime.now().isoformat() + } + + # Step 2: Ingest data if needed（数据摄入） + await step_ingest_data(data_path, end_user_id, skip_ingest, reset_group, max_ingest_messages) + + # Step 3: Initialize clients （初始化客户端） + connector, llm_client, embedder = step_initialize_clients(llm_id, embedding_id) + + # Step 4: Process all questions （处理所有问题） + try: + samples = await step_process_all_questions( + qa_items=qa_items, + end_user_id=end_user_id, + search_type=search_type, + search_limit=search_limit, + context_char_budget=context_char_budget, + connector=connector, + embedder=embedder, + llm_client=llm_client + ) + finally: + await connector.close() + + # Step 5: Aggregate results （聚合答案） + aggregated = step_aggregate_results(samples) + + # Build final result dictionary result = { "dataset": "locomo", "sample_size": len(qa_items), @@ -413,37 +604,18 @@ async def run_locomo_benchmark( "search_type": search_type, "search_limit": search_limit, "context_char_budget": context_char_budget, - "llm_id": SELECTED_LLM_ID, - "embedding_id": SELECTED_EMBEDDING_ID + "llm_id": llm_id, + "embedding_id": embedding_id }, - "overall_metrics": overall_metrics, - "by_category": by_category, - "latency": latency, - "context_stats": context_stats, + "overall_metrics": aggregated["overall_metrics"], + "by_category": aggregated["by_category"], + "latency": aggregated["latency"], + "context_stats": aggregated["context_stats"], "samples": samples } - # Step 6: Save results - if output_dir is None: - output_dir = os.path.join( - os.path.dirname(__file__), - "results" - ) - - os.makedirs(output_dir, exist_ok=True) - - # Generate timestamped filename - timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S") - output_path = os.path.join(output_dir, f"locomo_{timestamp_str}.json") - - try: - with open(output_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"✅ Results saved to: {output_path}\n") - except Exception as e: - print(f"❌ Failed to save results: {e}") - print("📊 Printing results to console instead:\n") - print(json.dumps(result, ensure_ascii=False, indent=2)) + # Step 6: Save results （保存结果） + step_save_results(result, output_dir) return result @@ -454,7 +626,25 @@ def main(): This function provides a CLI interface for running LoCoMo benchmarks with configurable parameters. + + Configuration priority: Command-line args > Environment variables > Code defaults """ + # Load environment variables first + load_dotenv() + + # Get defaults from environment variables + env_sample_size = os.getenv("LOCOMO_SAMPLE_SIZE") + env_search_limit = os.getenv("LOCOMO_SEARCH_LIMIT") + env_context_budget = os.getenv("LOCOMO_CONTEXT_CHAR_BUDGET") + env_output_dir = os.getenv("LOCOMO_OUTPUT_DIR") + env_skip_ingest = os.getenv("LOCOMO_SKIP_INGEST", "false").lower() in ("true", "1", "yes") + + # Convert to appropriate types with fallback to code defaults + default_sample_size = int(env_sample_size) if env_sample_size else 20 + default_search_limit = int(env_search_limit) if env_search_limit else 12 + default_context_budget = int(env_context_budget) if env_context_budget else 8000 + default_output_dir = env_output_dir if env_output_dir else None + parser = argparse.ArgumentParser( description="Run LoCoMo benchmark evaluation", formatter_class=argparse.ArgumentDefaultsHelpFormatter @@ -463,14 +653,14 @@ def main(): parser.add_argument( "--sample_size", type=int, - default=20, - help="Number of QA pairs to evaluate" + default=default_sample_size, + help=f"Number of QA pairs to evaluate (env: LOCOMO_SAMPLE_SIZE={env_sample_size or 'not set'}, 0 for all)" ) parser.add_argument( "--end_user_id", type=str, default=None, - help="Database group ID for retrieval (uses default if not specified)" + help="Database end user ID for retrieval (uses LOCOMO_END_USER_ID or EVAL_END_USER_ID if not specified)" ) parser.add_argument( "--search_type", @@ -482,14 +672,14 @@ def main(): parser.add_argument( "--search_limit", type=int, - default=12, - help="Maximum number of documents to retrieve per query" + default=default_search_limit, + help=f"Maximum number of documents to retrieve per query (env: LOCOMO_SEARCH_LIMIT={env_search_limit or 'not set'})" ) parser.add_argument( "--context_char_budget", type=int, - default=8000, - help="Maximum characters for context" + default=default_context_budget, + help=f"Maximum characters for context (env: LOCOMO_CONTEXT_CHAR_BUDGET={env_context_budget or 'not set'})" ) parser.add_argument( "--reset_group", @@ -499,20 +689,24 @@ def main(): parser.add_argument( "--skip_ingest", action="store_true", - help="Skip data ingestion and use existing data in Neo4j" + default=env_skip_ingest, + help=f"Skip data ingestion and use existing data in Neo4j (env: LOCOMO_SKIP_INGEST={os.getenv('LOCOMO_SKIP_INGEST', 'false')})" ) parser.add_argument( "--output_dir", type=str, + default=default_output_dir, + help=f"Directory to save results (env: LOCOMO_OUTPUT_DIR={env_output_dir or 'not set'})" + ) + parser.add_argument( + "--max_ingest_messages", + type=int, default=None, - help="Directory to save results (uses default if not specified)" + help="Maximum messages per dialogue to ingest (for testing, default: all messages)" ) args = parser.parse_args() - # Load environment variables - load_dotenv() - # Run benchmark result = asyncio.run(run_locomo_benchmark( sample_size=args.sample_size, @@ -522,7 +716,8 @@ def main(): context_char_budget=args.context_char_budget, reset_group=args.reset_group, skip_ingest=args.skip_ingest, - output_dir=args.output_dir + output_dir=args.output_dir, + max_ingest_messages=args.max_ingest_messages )) # Print summary diff --git a/api/app/core/memory/evaluation/locomo/locomo_test.py b/api/app/core/memory/evaluation/locomo/locomo_test.py index 01c45123..2cb0664c 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_test.py +++ b/api/app/core/memory/evaluation/locomo/locomo_test.py @@ -1,30 +1,29 @@ # file name: check_neo4j_connection_fixed.py import asyncio -import json -import math import os -import re import sys +import json import time +import math +import re from datetime import datetime, timedelta -from typing import Any, Dict, List +from typing import List, Dict, Any from pathlib import Path - from dotenv import load_dotenv -# 1 -# 添加项目根目录到路径 -current_dir = Path(__file__).resolve().parent -project_root = str(current_dir.parent) -if project_root not in sys.path: - sys.path.insert(0, project_root) -# 关键：将 src 目录置于最前，确保从当前仓库加载模块 -src_dir = os.path.join(project_root, "src") -if src_dir not in sys.path: - sys.path.insert(0, src_dir) - +# Load main .env load_dotenv() +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") + +# Get group_id from config +group_id = os.getenv("EVAL_GROUP_ID", "locomo_test") +print(f"✅ 使用配置的 group_id: {group_id}") + # 首先定义 _loc_normalize 函数，因为其他函数依赖它 def _loc_normalize(text: str) -> str: text = str(text) if text is not None else "" @@ -37,7 +36,7 @@ def _loc_normalize(text: str) -> str: # 尝试从 metrics.py 导入基础指标 try: - from common.metrics import bleu1, f1_score, jaccard + from app.core.memory.evaluation.common.metrics import f1_score, bleu1, jaccard print("✅ 从 metrics.py 导入基础指标成功") except ImportError as e: print(f"❌ 从 metrics.py 导入失败: {e}") @@ -107,23 +106,8 @@ except ImportError as e: # 尝试从 qwen_search_eval.py 导入 LoCoMo 特定指标 try: - # 添加 evaluation 目录路径 - evaluation_dir = os.path.join(project_root, "evaluation") - if evaluation_dir not in sys.path: - sys.path.insert(0, evaluation_dir) - - # 尝试从不同位置导入 - try: - from locomo.qwen_search_eval import ( - _resolve_relative_times, - loc_f1_score, - loc_multi_f1, - ) - print("✅ 从 locomo.qwen_search_eval 导入 LoCoMo 特定指标成功") - except ImportError: - from qwen_search_eval import _resolve_relative_times, loc_f1_score, loc_multi_f1 - print("✅ 从 qwen_search_eval 导入 LoCoMo 特定指标成功") - + from app.core.memory.evaluation.locomo.qwen_search_eval import loc_f1_score, loc_multi_f1, _resolve_relative_times + print("✅ 从 qwen_search_eval 导入 LoCoMo 特定指标成功") except ImportError as e: print(f"❌ 从 qwen_search_eval.py 导入失败: {e}") # 回退到本地实现 LoCoMo 特定函数 @@ -429,31 +413,36 @@ def enhanced_context_selection(contexts: List[str], question: str, question_inde async def run_enhanced_evaluation(): """使用增强方法进行完整评估 - 解决中间性能衰减问题""" - try: - from dotenv import load_dotenv - except Exception: - def load_dotenv(): - return None - + from dotenv import load_dotenv + from uuid import UUID + from datetime import datetime + from dataclasses import dataclass + # 修正导入路径：使用 app.core.memory.src 前缀 - from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient - from app.core.memory.utils.config.definitions import ( - SELECTED_EMBEDDING_ID, - SELECTED_LLM_ID, - ) - from app.core.memory.utils.llm.llm_utils import MemoryClientFactory - from app.core.models.base import RedBearModelConfig - from app.db import get_db_context - from app.repositories.neo4j.graph_search import search_graph_by_embedding from app.repositories.neo4j.neo4j_connector import Neo4jConnector + from app.repositories.neo4j.graph_search import search_graph_by_embedding + from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient + from app.core.models.base import RedBearModelConfig + from app.core.memory.utils.llm.llm_utils import get_llm_client + from app.core.memory.utils.config.config_utils import get_embedder_config + from app.schemas.memory_config_schema import MemoryConfig from app.services.memory_config_service import MemoryConfigService + + # Get model IDs from config + llm_id = os.getenv("EVAL_LLM_ID", "6dc52e1b-9cec-4194-af66-a74c6307fc3f") + embedding_id = os.getenv("EVAL_EMBEDDING_ID", "e2a6392d-ca63-4d59-a523-647420b59cb2") - # 加载数据 - # 获取项目根目录 - current_file = os.path.abspath(__file__) - evaluation_dir = os.path.dirname(os.path.dirname(current_file)) # evaluation目录 - memory_dir = os.path.dirname(evaluation_dir) # memory目录 - data_path = os.path.join(memory_dir, "data", "locomo10.json") + # 加载数据 - 使用统一的 dataset 目录 + data_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "dataset", "locomo10.json") + + if not os.path.exists(data_path): + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 locomo10.json 放置在: api/app/core/memory/evaluation/dataset/" + ) + + print(f"✅ 找到数据文件: {data_path}") + with open(data_path, "r", encoding="utf-8") as f: raw = json.load(f) @@ -463,64 +452,109 @@ async def run_enhanced_evaluation(): qa_items.extend(entry.get("qa", [])) else: qa_items.extend(raw.get("qa", [])) - - items = qa_items[:20] # 测试多少个问题 + + # 测试多少个问题 - 可通过环境变量设置 + sample_size = int(os.getenv("LOCOMO_SAMPLE_SIZE", "20")) + items = qa_items[:sample_size] + print(f"📊 将测试 {len(items)} 个问题（总共 {len(qa_items)} 个可用）") # 初始化增强监控器 monitor = EnhancedEvaluationMonitor(reset_interval=5, performance_threshold=0.6) - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm = factory.get_llm_client(SELECTED_LLM_ID) + # 获取数据库会话并初始化 LLM 客户端 + from app.db import get_db + db = next(get_db()) - # 初始化embedder - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 初始化连接器 - connector = Neo4jConnector() - - # 初始化结果字典 - results = { - "questions": [], - "overall_metrics": {"f1": 0.0, "b1": 0.0, "j": 0.0, "loc_f1": 0.0}, - "category_metrics": {}, - "retrieval_stats": {"total_questions": len(items), "avg_context_length": 0, "avg_retrieved_docs": 0}, - "performance_trend": "stable", - "timestamp": datetime.now().isoformat(), - "enhanced_strategy": True - } - - total_f1 = 0.0 - total_bleu1 = 0.0 - total_jaccard = 0.0 - total_loc_f1 = 0.0 - total_context_length = 0 - total_retrieved_docs = 0 - category_stats = {} - try: - for i, item in enumerate(items): - monitor.question_count += 1 + llm = get_llm_client(llm_id, db) + + # 初始化embedder + cfg_dict = get_embedder_config(embedding_id, db) + embedder = OpenAIEmbedderClient( + model_config=RedBearModelConfig.model_validate(cfg_dict) + ) + + # 🔧 创建 MemoryConfig 对象用于搜索 + # 方案1：如果有配置ID，从数据库加载 + config_id = os.getenv("EVAL_CONFIG_ID") + if config_id: + print(f"📋 从数据库加载配置 ID: {config_id}") + memory_config_service = MemoryConfigService(db) + memory_config = memory_config_service.load_memory_config(config_id, service_name="locomo_test") + else: + # 方案2：创建临时配置对象用于测试 + print(f"📋 创建临时测试配置") + from uuid import UUID + from datetime import datetime + + # 将字符串 ID 转换为 UUID + try: + embedding_uuid = UUID(embedding_id) + llm_uuid = UUID(llm_id) + except ValueError as e: + raise ValueError(f"无效的 UUID 格式: {e}") + + memory_config = MemoryConfig( + config_id=1, # 临时 ID + config_name="locomo_test_config", + workspace_id=UUID("00000000-0000-0000-0000-000000000000"), # 临时 workspace + workspace_name="test_workspace", + tenant_id=UUID("00000000-0000-0000-0000-000000000000"), # 临时 tenant + embedding_model_id=embedding_uuid, + embedding_model_name="test_embedding", + llm_model_id=llm_uuid, + llm_model_name="test_llm", + storage_type="neo4j", + chunker_strategy="RecursiveChunker", + reflexion_enabled=False, + reflexion_iteration_period=3, + reflexion_range="partial", + reflexion_baseline="Time", + loaded_at=datetime.now() + ) + + print(f"✅ MemoryConfig 已准备: embedding_id={memory_config.embedding_model_id}, llm_id={memory_config.llm_model_id}") + + # 初始化连接器 + connector = Neo4jConnector() - # 获取近期性能用于重置判断 - recent_performance = monitor.get_recent_performance() + # 初始化结果字典 + results = { + "questions": [], + "overall_metrics": {"f1": 0.0, "b1": 0.0, "j": 0.0, "loc_f1": 0.0}, + "category_metrics": {}, + "retrieval_stats": {"total_questions": len(items), "avg_context_length": 0, "avg_retrieved_docs": 0}, + "performance_trend": "stable", + "timestamp": datetime.now().isoformat(), + "enhanced_strategy": True + } - # 增强的重置判断 - should_reset = monitor.should_reset_connections(current_f1=recent_performance) - if should_reset and i > 0: - print(f"🔄 重置Neo4j连接 (问题 {i+1}/{len(items)}, 近期性能: {recent_performance:.3f})...") - await connector.close() - connector = Neo4jConnector() # 创建新连接 - print("✅ 连接重置完成") + total_f1 = 0.0 + total_bleu1 = 0.0 + total_jaccard = 0.0 + total_loc_f1 = 0.0 + total_context_length = 0 + total_retrieved_docs = 0 + category_stats = {} - q = item.get("question", "") - ref = item.get("answer", "") - ref_str = str(ref) if ref is not None else "" + try: + for i, item in enumerate(items): + monitor.question_count += 1 + + # 获取近期性能用于重置判断 + recent_performance = monitor.get_recent_performance() + + # 增强的重置判断 + should_reset = monitor.should_reset_connections(current_f1=recent_performance) + if should_reset and i > 0: + print(f"🔄 重置Neo4j连接 (问题 {i+1}/{len(items)}, 近期性能: {recent_performance:.3f})...") + await connector.close() + connector = Neo4jConnector() # 创建新连接 + print("✅ 连接重置完成") + + q = item.get("question", "") + ref = item.get("answer", "") + ref_str = str(ref) if ref is not None else "" print(f"\n🔍 [{i+1}/{len(items)}] 问题: {q}") print(f"✅ 真实答案: {ref_str}") @@ -548,10 +582,12 @@ async def run_enhanced_evaluation(): contexts_all = [] try: - # 使用统一的搜索服务 - from app.core.memory.storage_services.search import run_hybrid_search + # 使用旧版本的搜索服务（重构前的版本） + from app.core.memory.src.search import run_hybrid_search - print("🔀 使用混合搜索服务...") + print(f"🔀 使用混合搜索服务（旧版本）...") + print(f"📍 检索参数: group_id={group_id}, limit=20, search_type=hybrid") + print(f"📍 查询文本: {q}") search_results = await run_hybrid_search( query_text=q, @@ -559,15 +595,27 @@ async def run_enhanced_evaluation(): end_user_id="locomo_sk", limit=20, include=["statements", "chunks", "entities", "summaries"], - alpha=0.6, # BM25权重 - embedding_id=SELECTED_EMBEDDING_ID + output_path=None, + memory_config=memory_config, # 🔧 添加必需的 memory_config 参数 + rerank_alpha=0.6, # BM25权重 + use_forgetting_rerank=False, + use_llm_rerank=False ) - # 处理搜索结果 - 新的搜索服务返回统一的结构 - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) + # 处理搜索结果 - 旧版本返回包含 reranked_results 的结构 + # 对于 hybrid 搜索，使用 reranked_results + if "reranked_results" in search_results: + reranked = search_results["reranked_results"] + chunks = reranked.get("chunks", []) + statements = reranked.get("statements", []) + entities = reranked.get("entities", []) + summaries = reranked.get("summaries", []) + else: + # 单一搜索类型的结果 + chunks = search_results.get("chunks", []) + statements = search_results.get("statements", []) + entities = search_results.get("entities", []) + summaries = search_results.get("summaries", []) print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要") @@ -609,6 +657,8 @@ async def run_enhanced_evaluation(): print(f"📊 有效上下文数量: {len(contexts_all)}") except Exception as e: print(f"❌ 检索失败: {e}") + import traceback + print(f"详细错误信息:\n{traceback.format_exc()}") contexts_all = [] t1 = time.time() @@ -728,14 +778,17 @@ async def run_enhanced_evaluation(): print("="*60) - except Exception as e: - print(f"❌ 评估过程中发生错误: {e}") - # 即使出错，也返回已有的结果 - import traceback - traceback.print_exc() + except Exception as e: + print(f"❌ 评估过程中发生错误: {e}") + # 即使出错，也返回已有的结果 + import traceback + traceback.print_exc() + finally: + await connector.close() + finally: - await connector.close() + db.close() # 关闭数据库会话 # 计算总体指标 n = len(items) diff --git a/api/app/core/memory/evaluation/locomo/locomo_utils.py b/api/app/core/memory/evaluation/locomo/locomo_utils.py index d3b74947..6ad68470 100644 --- a/api/app/core/memory/evaluation/locomo/locomo_utils.py +++ b/api/app/core/memory/evaluation/locomo/locomo_utils.py @@ -15,8 +15,14 @@ import json import re from datetime import datetime, timedelta from typing import List, Dict, Any, Optional +from pathlib import Path +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) -from app.core.memory.utils.definitions import PROJECT_ROOT from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline @@ -82,7 +88,7 @@ def load_locomo_data( return qa_items[:sample_size] -def extract_conversations(data_path: str, max_dialogues: int = 1) -> List[str]: +def extract_conversations(data_path: str, max_dialogues: int = 1, max_messages_per_dialogue: Optional[int] = None) -> List[str]: """ Extract conversation texts from LoCoMo data for ingestion. @@ -93,6 +99,7 @@ def extract_conversations(data_path: str, max_dialogues: int = 1) -> List[str]: Args: data_path: Path to locomo10.json file max_dialogues: Maximum number of dialogues to extract (default: 1) + max_messages_per_dialogue: Maximum messages per dialogue (default: None = all messages) Returns: List of conversation strings formatted for ingestion. @@ -141,13 +148,21 @@ def extract_conversations(data_path: str, max_dialogues: int = 1) -> List[str]: continue lines.append(f"{role}: {text}") + + # Limit messages if specified + if max_messages_per_dialogue and len(lines) >= max_messages_per_dialogue: + break + + # Break outer loop if we've reached the message limit + if max_messages_per_dialogue and len(lines) >= max_messages_per_dialogue: + break if lines: contents.append("\n".join(lines)) return contents - +# 时间解析：将相对时间表达转换为绝对日期 def resolve_temporal_references(text: str, anchor_date: datetime) -> str: """ Resolve relative temporal references to absolute dates. @@ -225,6 +240,8 @@ def resolve_temporal_references(text: str, anchor_date: datetime) -> str: t, flags=re.IGNORECASE ) + + # 中文支持 t = re.sub( r"\bnext\s+week\b", (anchor_date + timedelta(days=7)).date().isoformat(), @@ -345,6 +362,50 @@ def select_and_format_information( return "\n\n".join(selected) +# 记忆系统核心能力：写入与读取 +async def ingest_conversations_if_needed( + conversations: List[str], + end_user_id: str, + reset: bool = False +) -> bool: + """ + Wrapper for conversation ingestion using external extraction pipeline. + + This function populates the Neo4j database with processed conversation data + (chunks, statements, entities) so that the retrieval system has memory to search. + + The ingestion process: + 1. Parses conversation text into dialogue messages + 2. Chunks the dialogues into semantic units + 3. Extracts statements and entities using LLM + 4. Generates embeddings for all content + 5. Stores everything in Neo4j graph database + + Args: + conversations: List of raw conversation texts from LoCoMo dataset + Example: ["User: I went to Paris. AI: When was that?", ...] + end_user_id: Target end_user ID for database storage + reset: Whether to clear existing data first (not implemented in wrapper) + + Returns: + True if successful, False otherwise + + Note: + The external function uses "contexts" to mean "conversation texts". + This runs the full extraction pipeline: chunking → entity extraction → + statement extraction → embedding → Neo4j storage. + """ + try: + success = await ingest_contexts_via_full_pipeline( + contexts=conversations, + end_user_id=end_user_id, + save_chunk_output=True, + reset_group=reset + ) + return success + except Exception as e: + print(f"[Ingestion] Failed to ingest conversations: {e}") + return False async def retrieve_relevant_information( question: str, @@ -385,7 +446,7 @@ async def retrieve_relevant_information( search_graph, search_graph_by_embedding ) - from app.core.memory.storage_services.search import run_hybrid_search + from app.core.memory.src.search import run_hybrid_search contexts_all: List[str] = [] diff --git a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py index 6a5caa0c..889c5065 100644 --- a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py @@ -2,43 +2,29 @@ import argparse import asyncio import json import os -import statistics import time from datetime import datetime, timedelta -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - +from typing import List, Dict, Any +import statistics import re +from pathlib import Path +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - bleu1, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.common.metrics import f1_score as common_f1 -from app.core.memory.evaluation.extraction_utils import ( - ingest_contexts_via_full_pipeline, -) -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.storage_services.search import run_hybrid_search -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService +from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding +from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient +from app.core.models.base import RedBearModelConfig +from app.core.memory.utils.config.config_utils import get_embedder_config +from app.core.memory.src.search import run_hybrid_search # 使用旧版本（重构前） +from app.core.memory.utils.llm.llm_utils import get_llm_client +from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline +from app.core.memory.evaluation.common.metrics import f1_score as common_f1, bleu1, jaccard, latency_stats, avg_context_tokens # 参考 evaluation/locomo/evaluation.py 的 F1 计算逻辑（移除外部依赖，内联实现） @@ -265,7 +251,10 @@ async def run_locomo_eval( end_user_id = end_user_id or SELECTED_end_user_id data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") if not os.path.exists(data_path): - data_path = os.path.join(os.getcwd(), "data", "locomo10.json") + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 locomo10.json 放置在: {dataset_dir}" + ) with open(data_path, "r", encoding="utf-8") as f: raw = json.load(f) # LoCoMo 数据结构：顶层为若干对象，每个对象下有 qa 列表 @@ -343,13 +332,9 @@ async def run_locomo_eval( await ingest_contexts_via_full_pipeline(contents, end_user_id, save_chunk_output=True) # 使用异步LLM客户端 - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) + llm_client = get_llm_client(llm_id) # 初始化embedder用于直接调用 - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) + cfg_dict = get_embedder_config(embedding_id) embedder = OpenAIEmbedderClient( model_config=RedBearModelConfig.model_validate(cfg_dict) ) @@ -480,8 +465,8 @@ async def run_locomo_eval( contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") else: # hybrid - # 🎯 关键修复：混合检索使用更严格的回退机制 - print("🔀 使用混合检索（带回退机制）...") + # 使用旧版本的混合检索（重构前） + print("🔀 使用混合检索（旧版本）...") try: search_results = await run_hybrid_search( query_text=q, @@ -490,16 +475,26 @@ async def run_locomo_eval( limit=adjusted_limit, include=["chunks", "statements", "entities", "summaries"], output_path=None, + rerank_alpha=0.6, + use_forgetting_rerank=False, + use_llm_rerank=False ) - # 🎯 关键修复：正确处理混合检索的扁平结构 - # 新的API返回扁平结构，直接从顶层获取结果 + # 处理旧版本的返回结构（包含 reranked_results） if search_results and isinstance(search_results, dict): - # 新API返回扁平结构：直接从顶层获取 - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) + # 对于 hybrid 搜索，使用 reranked_results + if "reranked_results" in search_results: + reranked = search_results["reranked_results"] + chunks = reranked.get("chunks", []) + statements = reranked.get("statements", []) + entities = reranked.get("entities", []) + summaries = reranked.get("summaries", []) + else: + # 单一搜索类型的结果 + chunks = search_results.get("chunks", []) + statements = search_results.get("statements", []) + entities = search_results.get("entities", []) + summaries = search_results.get("summaries", []) # 检查是否有有效结果 if chunks or statements or entities or summaries: @@ -799,8 +794,9 @@ async def run_locomo_eval( "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, - "llm_id": SELECTED_LLM_ID, - "retrieval_embedding_id": SELECTED_EMBEDDING_ID, + "llm_id": llm_id, + "retrieval_embedding_id": embedding_id, + "chunker_strategy": os.getenv("EVAL_CHUNKER_STRATEGY", "RecursiveChunker"), "skip_ingest_if_exists": skip_ingest_if_exists, "llm_timeout": llm_timeout, "llm_max_retries": llm_max_retries, diff --git a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py b/api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py similarity index 93% rename from api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py rename to api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py index 8710a504..aaf46e35 100644 --- a/api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py @@ -2,100 +2,67 @@ import argparse import asyncio import json import os +import time import re import statistics -import time from datetime import datetime, timedelta -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -# 确保可以找到 src 及项目根路径 -import sys +from typing import List, Dict, Any from pathlib import Path -_THIS_DIR = Path(__file__).resolve().parent -_PROJECT_ROOT = str(_THIS_DIR.parents[2]) -_SRC_DIR = os.path.join(_PROJECT_ROOT, "src") -for _p in (_SRC_DIR, _PROJECT_ROOT): - if _p not in sys.path: - sys.path.insert(0, _p) +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") -# 与现有评估脚本保持一致的导入方式 from app.repositories.neo4j.neo4j_connector import Neo4jConnector - -try: - # 优先从 extraction_utils1 导入 - from app.core.memory.evaluation.extraction_utils import ( - ingest_contexts_via_full_pipeline, # type: ignore - ) -except Exception: - ingest_contexts_via_full_pipeline = None # 在运行时做兜底检查 -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.common.metrics import f1_score as common_f1 -from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context +from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.services.memory_config_service import MemoryConfigService - -try: - from app.core.memory.evaluation.common.metrics import exact_match -except Exception: - # 兜底：简单的大小写不敏感比较 - def exact_match(pred: str, ref: str) -> bool: - return str(pred).strip().lower() == str(ref).strip().lower() +from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient +from app.core.models.base import RedBearModelConfig +from app.core.memory.utils.config.config_utils import get_embedder_config +from app.core.memory.utils.llm.llm_utils import get_llm_client +from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME +from app.core.memory.evaluation.common.metrics import f1_score as common_f1, jaccard, latency_stats, avg_context_tokens +from app.core.memory.evaluation.common.metrics import exact_match def load_dataset_any(path: str) -> List[Dict[str, Any]]: - """健壮地加载数据集（兼容 list 或多段 JSON）。""" + """健壮地加载数据集，支持三种格式： + 1. 标准 JSON 数组: [{...}, {...}] + 2. 单个 JSON 对象: {...} + 3. JSONL 格式（每行一个 JSON）: {...}\n{...}\n{...} + """ with open(path, "r", encoding="utf-8") as f: - s = f.read().strip() + content = f.read().strip() + + # 尝试标准 JSON 解析 try: - obj = json.loads(s) - if isinstance(obj, list): - return obj - elif isinstance(obj, dict): - return [obj] + data = json.loads(content) + if isinstance(data, list): + return [item for item in data if isinstance(item, dict)] + elif isinstance(data, dict): + return [data] except json.JSONDecodeError: pass - dec = json.JSONDecoder() - idx = 0 - items: List[Dict[str, Any]] = [] - while idx < len(s): - while idx < len(s) and s[idx].isspace(): - idx += 1 - if idx >= len(s): - break + + # 尝试 JSONL 格式（每行一个 JSON 对象） + items = [] + for line in content.splitlines(): + line = line.strip() + if not line: + continue try: - obj, end = dec.raw_decode(s, idx) - if isinstance(obj, list): - for it in obj: - if isinstance(it, dict): - items.append(it) - elif isinstance(obj, dict): + obj = json.loads(line) + if isinstance(obj, dict): items.append(obj) - idx = end + elif isinstance(obj, list): + items.extend(item for item in obj if isinstance(item, dict)) except json.JSONDecodeError: - nl = s.find("\n", idx) - if nl == -1: - break - idx = nl + 1 + continue + return items @@ -624,7 +591,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: async def run_longmemeval_test( sample_size: int = 3, - end_user_id: str = "longmemeval_zh_bak_3", + end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, @@ -639,18 +606,22 @@ async def run_longmemeval_test( skip_ingest: bool = False, ) -> Dict[str, Any]: """LongMemEval 评估测试：增强时间推理能力""" + + # Use environment variable with fallback chain + if end_user_id is None: + end_user_id = os.getenv("LONGMEMEVAL_END_USER_ID") or os.getenv("EVAL_END_USER_ID", "longmemeval_zh_bak_3") # 数据路径 if not data_path: - # 固定使用中文数据集：data/longmemeval_oracle_zh.json - zh_proj = os.path.join(PROJECT_ROOT, "data", "longmemeval_oracle_zh.json") - zh_cwd = os.path.join(os.getcwd(), "data", "longmemeval_oracle_zh.json") - if os.path.exists(zh_proj): - data_path = zh_proj - elif os.path.exists(zh_cwd): - data_path = zh_cwd - else: - raise FileNotFoundError("未找到数据集: data/longmemeval_oracle_zh.json，请确保其存在于项目根目录或当前工作目录的 data 目录下。") + # 固定使用中文数据集：dataset/longmemeval_oracle_zh.json + dataset_dir = Path(__file__).resolve().parent.parent / "dataset" + data_path = str(dataset_dir / "longmemeval_oracle_zh.json") + + if not os.path.exists(data_path): + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 longmemeval_oracle_zh.json 放置在: {dataset_dir}" + ) qa_list: List[Dict[str, Any]] = load_dataset_any(data_path) # 支持评估全部样本：当 sample_size <= 0 时，取从 start_index 到末尾 @@ -702,16 +673,19 @@ async def run_longmemeval_test( ) # 初始化组件（摄入后再初始化连接器）- 使用异步LLM客户端 - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) + from app.db import get_db + + db = next(get_db()) + try: + llm_client = get_llm_client(os.getenv("EVAL_LLM_ID"), db) + cfg_dict = get_embedder_config(os.getenv("EVAL_EMBEDDING_ID"), db) + embedder = OpenAIEmbedderClient( + model_config=RedBearModelConfig.model_validate(cfg_dict) + ) + finally: + db.close() + connector = Neo4jConnector() - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) # 指标收集 latencies_llm: List[float] = [] @@ -768,10 +742,10 @@ async def run_longmemeval_test( if stmt_text: contexts_all.append(stmt_text) - # for sm in summaries: - # summary_text = str(sm.get("summary", "")).strip() - # if summary_text: - # contexts_all.append(summary_text) + for sm in summaries: + summary_text = str(sm.get("summary", "")).strip() + if summary_text: + contexts_all.append(summary_text) # 实体摘要（最多3个） scored = [e for e in entities if e.get("score") is not None] @@ -1228,8 +1202,8 @@ async def run_longmemeval_test( "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, - "llm_id": SELECTED_LLM_ID, - "embedding_id": SELECTED_EMBEDDING_ID, + "llm_id": os.getenv("EVAL_LLM_ID"), + "embedding_id": os.getenv("EVAL_EMBEDDING_ID"), "sample_size": sample_size, "start_index": start_index, }, @@ -1288,7 +1262,7 @@ def main(): parser.add_argument("--sample-size", type=int, default=3, help="样本数量（<=0 表示全部）") parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--group-id", type=str, default="longmemeval_zh_bak_3", help="图数据库 Group ID") + parser.add_argument("--end-user-id", type=str, default=None, help="图数据库 End User ID，默认使用环境变量") parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") @@ -1349,7 +1323,8 @@ def main(): # 保存结果到文件 try: - out_dir = os.path.join(PROJECT_ROOT, "evaluation", "longmemeval", "results") + # 使用相对路径而不是 PROJECT_ROOT + out_dir = Path(__file__).resolve().parent / "results" os.makedirs(out_dir, exist_ok=True) ts = datetime.now().strftime("%Y%m%d_%H%M%S") out_path = os.path.join(out_dir, f"longmemeval_{result['params']['search_type']}_{ts}.json") diff --git a/api/app/core/memory/evaluation/longmemeval/test_eval.py b/api/app/core/memory/evaluation/longmemeval/test_eval.py index 67bd6ec2..08daa890 100644 --- a/api/app/core/memory/evaluation/longmemeval/test_eval.py +++ b/api/app/core/memory/evaluation/longmemeval/test_eval.py @@ -2,81 +2,67 @@ import argparse import asyncio import json import os +import time import re import statistics -import time from datetime import datetime, timedelta -from typing import Any, Dict, List +from typing import List, Dict, Any +from pathlib import Path -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") # 与现有评估脚本保持一致的导入方式 -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - jaccard, - latency_stats, -) -from app.core.memory.evaluation.common.metrics import f1_score as common_f1 -from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService - -try: - from app.core.memory.evaluation.common.metrics import exact_match -except Exception: - # 兜底：简单的大小写不敏感比较 - def exact_match(pred: str, ref: str) -> bool: - return str(pred).strip().lower() == str(ref).strip().lower() +from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding +from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient +from app.core.models.base import RedBearModelConfig +from app.core.memory.utils.config.config_utils import get_embedder_config +from app.core.memory.utils.llm.llm_utils import get_llm_client +from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME +from app.core.memory.evaluation.common.metrics import f1_score as common_f1, jaccard, latency_stats, avg_context_tokens +from app.core.memory.evaluation.common.metrics import exact_match def load_dataset_any(path: str) -> List[Dict[str, Any]]: - """健壮地加载数据集（兼容 list 或多段 JSON）。""" + """健壮地加载数据集，支持三种格式： + 1. 标准 JSON 数组: [{...}, {...}] + 2. 单个 JSON 对象: {...} + 3. JSONL 格式（每行一个 JSON）: {...}\n{...}\n{...} + """ with open(path, "r", encoding="utf-8") as f: - s = f.read().strip() + content = f.read().strip() + + # 尝试标准 JSON 解析 try: - obj = json.loads(s) - if isinstance(obj, list): - return obj - elif isinstance(obj, dict): - return [obj] + data = json.loads(content) + if isinstance(data, list): + return [item for item in data if isinstance(item, dict)] + elif isinstance(data, dict): + return [data] except json.JSONDecodeError: pass - dec = json.JSONDecoder() - idx = 0 - items: List[Dict[str, Any]] = [] - while idx < len(s): - while idx < len(s) and s[idx].isspace(): - idx += 1 - if idx >= len(s): - break + + # 尝试 JSONL 格式（每行一个 JSON 对象） + items = [] + for line in content.splitlines(): + line = line.strip() + if not line: + continue try: - obj, end = dec.raw_decode(s, idx) - if isinstance(obj, list): - for it in obj: - if isinstance(it, dict): - items.append(it) - elif isinstance(obj, dict): + obj = json.loads(line) + if isinstance(obj, dict): items.append(obj) - idx = end + elif isinstance(obj, list): + items.extend(item for item in obj if isinstance(item, dict)) except json.JSONDecodeError: - nl = s.find("\n", idx) - if nl == -1: - break - idx = nl + 1 + continue + return items @@ -640,15 +626,15 @@ async def run_longmemeval_test( # 数据路径 if not data_path: - # 固定使用中文数据集：data/longmemeval_oracle_zh.json - zh_proj = os.path.join(PROJECT_ROOT, "data", "longmemeval_oracle_zh.json") - zh_cwd = os.path.join(os.getcwd(), "data", "longmemeval_oracle_zh.json") - if os.path.exists(zh_proj): - data_path = zh_proj - elif os.path.exists(zh_cwd): - data_path = zh_cwd - else: - raise FileNotFoundError("未找到数据集: data/longmemeval_oracle_zh.json，请确保其存在于项目根目录或当前工作目录的 data 目录下。") + # 固定使用中文数据集：dataset/longmemeval_oracle_zh.json + dataset_dir = Path(__file__).resolve().parent.parent / "dataset" + data_path = str(dataset_dir / "longmemeval_oracle_zh.json") + + if not os.path.exists(data_path): + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 longmemeval_oracle_zh.json 放置在: {dataset_dir}" + ) qa_list: List[Dict[str, Any]] = load_dataset_any(data_path) # 支持评估全部样本：当 sample_size <= 0 时，取从 start_index 到末尾 @@ -658,13 +644,9 @@ async def run_longmemeval_test( items = qa_list[start_index:start_index + sample_size] # 初始化组件 - 使用异步LLM客户端 - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) + llm_client = get_llm_client(os.getenv("EVAL_LLM_ID")) connector = Neo4jConnector() - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) + cfg_dict = get_embedder_config(os.getenv("EVAL_EMBEDDING_ID")) embedder = OpenAIEmbedderClient( model_config=RedBearModelConfig.model_validate(cfg_dict) ) @@ -1203,8 +1185,8 @@ async def run_longmemeval_test( "search_limit": search_limit, "context_char_budget": context_char_budget, "search_type": search_type, - "llm_id": SELECTED_LLM_ID, - "embedding_id": SELECTED_EMBEDDING_ID, + "llm_id": os.getenv("EVAL_LLM_ID"), + "embedding_id": os.getenv("EVAL_EMBEDDING_ID"), "sample_size": sample_size, "start_index": start_index, }, diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py index 3023020a..e07b0cab 100644 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py @@ -2,81 +2,30 @@ import argparse import asyncio import json import os -import re import time from datetime import datetime -from typing import Any, Dict, List - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -# 路径与模块导入保持与现有评估脚本一致 -import sys +from typing import List, Dict, Any +import re from pathlib import Path -_THIS_DIR = Path(__file__).resolve().parent -_PROJECT_ROOT = str(_THIS_DIR.parents[1]) -_SRC_DIR = os.path.join(_PROJECT_ROOT, "src") -for _p in (_SRC_DIR, _PROJECT_ROOT): - if _p not in sys.path: - sys.path.insert(0, _p) +from dotenv import load_dotenv + +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) + print(f"✅ 加载评估配置: {eval_config_path}") -# 对齐 locomo_test 的检索逻辑：直接使用 graph_search 与 Neo4jConnector/Embedder1 -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - exact_match, - latency_stats, -) -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.core.models.base import RedBearModelConfig -from app.db import get_db_context -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.services.memory_config_service import MemoryConfigService +from app.core.memory.src.search import run_hybrid_search # 使用与 evaluate_qa.py 相同的检索函数 +from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient +from app.core.models.base import RedBearModelConfig +from app.core.memory.utils.config.config_utils import get_embedder_config -try: - from app.core.memory.evaluation.common.metrics import bleu1, f1_score, jaccard -except Exception: - # 兜底：简单实现（必要时） - def f1_score(pred: str, ref: str) -> float: - ps = pred.lower().split() - rs = ref.lower().split() - if not ps or not rs: - return 0.0 - tp = len(set(ps) & set(rs)) - if tp == 0: - return 0.0 - precision = tp / len(ps) - recall = tp / len(rs) - if precision + recall == 0: - return 0.0 - return 2 * precision * recall / (precision + recall) +from app.core.memory.utils.llm.llm_utils import get_llm_client +from app.core.memory.evaluation.common.metrics import exact_match, latency_stats, avg_context_tokens - def bleu1(pred: str, ref: str) -> float: - ps = pred.lower().split() - rs = ref.lower().split() - if not ps or not rs: - return 0.0 - overlap = len([w for w in ps if w in rs]) - return overlap / max(len(ps), 1) - - def jaccard(pred: str, ref: str) -> float: - ps = set(pred.lower().split()) - rs = set(ref.lower().split()) - union = len(ps | rs) - if union == 0: - return 0.0 - return len(ps & rs) / union +from app.core.memory.evaluation.common.metrics import f1_score, bleu1, jaccard def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: @@ -219,16 +168,16 @@ async def run_memsciqa_test( # 默认使用指定的 memsci 组 ID end_user_id = end_user_id or "group_memsci" - # 数据路径解析（项目根与当前工作目录兜底） + # 数据路径解析 if not data_path: - proj_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") - cwd_path = os.path.join(os.getcwd(), "data", "msc_self_instruct.jsonl") - if os.path.exists(proj_path): - data_path = proj_path - elif os.path.exists(cwd_path): - data_path = cwd_path - else: - raise FileNotFoundError("未找到数据集: data/msc_self_instruct.jsonl，请确保其存在于项目根目录或当前工作目录的 data 目录下。") + dataset_dir = Path(__file__).resolve().parent.parent / "dataset" + data_path = str(dataset_dir / "msc_self_instruct.jsonl") + + if not os.path.exists(data_path): + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 msc_self_instruct.jsonl 放置在: {dataset_dir}" + ) # 加载数据 all_items = load_dataset_memsciqa(data_path) @@ -238,17 +187,13 @@ async def run_memsciqa_test( items = all_items[start_index:start_index + sample_size] # 初始化 LLM（纯测试：不进行摄入） - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm = factory.get_llm_client(SELECTED_LLM_ID) + llm = get_llm_client(os.getenv("EVAL_LLM_ID")) # 初始化 Neo4j 连接与向量检索 Embedder（对齐 locomo_test） connector = Neo4jConnector() embedder = None if search_type in ("embedding", "hybrid"): - with get_db_context() as db: - config_service = MemoryConfigService(db) - cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID) + cfg_dict = get_embedder_config(os.getenv("EVAL_EMBEDDING_ID")) embedder = OpenAIEmbedderClient( model_config=RedBearModelConfig.model_validate(cfg_dict) ) @@ -273,7 +218,7 @@ async def run_memsciqa_test( question = item.get("self_instruct", {}).get("B", "") or item.get("question", "") reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "") - # 三路检索：chunks/statements/entities/summaries（对齐 qwen_search_eval.py） + # 检索：使用与 evaluate_qa.py 相同的 run_hybrid_search t0 = time.time() results = None try: @@ -302,57 +247,94 @@ async def run_memsciqa_test( search_ms = (t1 - t0) * 1000 latencies_search.append(search_ms) - # 构建上下文：包含 chunks、陈述、摘要和实体（对齐 qwen_search_eval.py） + # 构建上下文：与 evaluate_qa.py 完全一致的逻辑 contexts_all: List[str] = [] retrieved_counts: Dict[str, int] = {} if results: - chunks = results.get("chunks", []) - statements = results.get("statements", []) - entities = results.get("entities", []) - summaries = results.get("summaries", []) + # 处理 hybrid 搜索结果 + if search_type == "hybrid": + emb = results.get("embedding_search", {}) if isinstance(results.get("embedding_search"), dict) else {} + kw = results.get("keyword_search", {}) if isinstance(results.get("keyword_search"), dict) else {} + emb_dialogs = emb.get("dialogues", []) + emb_statements = emb.get("statements", []) + emb_entities = emb.get("entities", []) + kw_dialogs = kw.get("dialogues", []) + kw_statements = kw.get("statements", []) + kw_entities = kw.get("entities", []) + all_dialogs = emb_dialogs + kw_dialogs + all_statements = emb_statements + kw_statements + all_entities = emb_entities + kw_entities + + # 简单去重 + seen_dialog = set() + dialogues = [] + for d in all_dialogs: + key = (str(d.get("uuid", "")), str(d.get("content", ""))) + if key not in seen_dialog: + dialogues.append(d) + seen_dialog.add(key) + + seen_stmt = set() + statements = [] + for s in all_statements: + key = str(s.get("statement", "")) + if key not in seen_stmt: + statements.append(s) + seen_stmt.add(key) + + seen_ent = set() + entities = [] + for e in all_entities: + key = str(e.get("name", "")) + if key not in seen_ent: + entities.append(e) + seen_ent.add(key) + else: + # embedding 或 keyword 单独搜索 + dialogues = results.get("dialogues", []) + statements = results.get("statements", []) + entities = results.get("entities", []) + retrieved_counts = { - "chunks": len(chunks), + "dialogues": len(dialogues), "statements": len(statements), "entities": len(entities), - "summaries": len(summaries), } - # 优先使用 chunks - for c in chunks: - text = str(c.get("content", "")).strip() + + # 构建上下文文本 + for d in dialogues: + text = str(d.get("content", "")).strip() if text: contexts_all.append(text) - # 然后是 statements + for s in statements: text = str(s.get("statement", "")).strip() if text: contexts_all.append(text) - # 然后是 summaries - for sm in summaries: - text = str(sm.get("summary", "")).strip() - if text: - contexts_all.append(text) - # 实体摘要：最多加入前3个高分实体（对齐 qwen_search_eval.py） - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) + + # 实体摘要 + if entities: + scored = [e for e in entities if e.get("score") is not None] + top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] + if top_entities: + summary_lines = [] + for e in top_entities: + name = str(e.get("name", "")).strip() + etype = str(e.get("entity_type", "")).strip() + score = e.get("score") + if name: + meta = [] + if etype: + meta.append(f"type={etype}") + if isinstance(score, (int, float)): + meta.append(f"score={score:.3f}") + summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") + if summary_lines: + contexts_all.append("\n".join(summary_lines)) if verbose: if retrieved_counts: - print(f"✅ 检索成功: {retrieved_counts.get('chunks',0)} chunks, {retrieved_counts.get('statements',0)} 条陈述, {retrieved_counts.get('entities',0)} 个实体, {retrieved_counts.get('summaries',0)} 个摘要") + print(f"✅ 检索成功: {retrieved_counts.get('dialogues',0)} dialogues, {retrieved_counts.get('statements',0)} 条陈述, {retrieved_counts.get('entities',0)} 个实体, {retrieved_counts.get('summaries',0)} 个摘要") print(f"📊 有效上下文数量: {len(contexts_all)}") q_keywords = extract_question_keywords(question, max_keywords=8) if q_keywords: @@ -507,8 +489,8 @@ async def run_memsciqa_test( "llm_max_tokens": llm_max_tokens, "search_type": search_type, "start_index": start_index, - "llm_id": SELECTED_LLM_ID, - "retrieval_embedding_id": SELECTED_EMBEDDING_ID + "llm_id": os.getenv("EVAL_LLM_ID"), + "retrieval_embedding_id": os.getenv("EVAL_EMBEDDING_ID") }, "timestamp": datetime.now().isoformat(), } @@ -522,7 +504,7 @@ async def run_memsciqa_test( def main(): load_dotenv() parser = argparse.ArgumentParser(description="memsciqa 测试脚本（三路检索 + 智能上下文选择）") - parser.add_argument("--sample-size", type=int, default=30, help="样本数量（<=0 表示全部）") + parser.add_argument("--sample-size", type=int, default=10, help="样本数量（<=0 表示全部）") parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") parser.add_argument("--group-id", type=str, default="group_memsci", help="图数据库 Group ID（默认 group_memsci）") diff --git a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py b/api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py similarity index 76% rename from api/app/core/memory/evaluation/memsciqa/evaluate_qa.py rename to api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py index 869fdb60..40684f4c 100644 --- a/api/app/core/memory/evaluation/memsciqa/evaluate_qa.py +++ b/api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py @@ -4,35 +4,20 @@ import json import os import time from datetime import datetime -from typing import TYPE_CHECKING, Any, Dict, List +from typing import List, Dict, Any +from pathlib import Path +from dotenv import load_dotenv -if TYPE_CHECKING: - from app.schemas.memory_config_schema import MemoryConfig +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None - -from app.core.memory.evaluation.common.metrics import ( - avg_context_tokens, - exact_match, - latency_stats, -) -from app.core.memory.evaluation.extraction_utils import ( - ingest_contexts_via_full_pipeline, -) -from app.core.memory.storage_services.search import run_hybrid_search -from app.core.memory.utils.config.definitions import ( - PROJECT_ROOT, - SELECTED_EMBEDDING_ID, - SELECTED_GROUP_ID, - SELECTED_LLM_ID, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.db import get_db_context from app.repositories.neo4j.neo4j_connector import Neo4jConnector +from app.core.memory.src.search import run_hybrid_search # 使用旧版本（重构前） +from app.core.memory.utils.llm.llm_utils import get_llm_client +from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline +from app.core.memory.evaluation.common.metrics import exact_match, latency_stats, avg_context_tokens def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: @@ -135,24 +120,37 @@ def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any return merged + async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: end_user_id = end_user_id or SELECTED_GROUP_ID + # Load data - data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl") + dataset_dir = Path(__file__).resolve().parent.parent / "dataset" + data_path = dataset_dir / "msc_self_instruct.jsonl" + if not os.path.exists(data_path): - data_path = os.path.join(os.getcwd(), "data", "msc_self_instruct.jsonl") + raise FileNotFoundError( + f"数据集文件不存在: {data_path}\n" + f"请将 msc_self_instruct.jsonl 放置在: {dataset_dir}" + ) with open(data_path, "r", encoding="utf-8") as f: lines = f.readlines() items: List[Dict[str, Any]] = [json.loads(l) for l in lines[:sample_size]] + + # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入 # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略 contexts: List[str] = [build_context_from_dialog(item) for item in items] await ingest_contexts_via_full_pipeline(contexts, end_user_id) # LLM client (使用异步调用) - with get_db_context() as db: - factory = MemoryClientFactory(db) - llm_client = factory.get_llm_client(SELECTED_LLM_ID) + from app.db import get_db + + db = next(get_db()) + try: + llm_client = get_llm_client(os.getenv("EVAL_LLM_ID"), db) + finally: + db.close() # Evaluate each item connector = Neo4jConnector() @@ -177,7 +175,6 @@ async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None limit=search_limit, include=["dialogues", "statements", "entities"], output_path=None, - memory_config=memory_config, ) except Exception: results = None @@ -261,11 +258,7 @@ async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else str(resp).strip()) # Metrics: F1, BLEU-1, Jaccard; keep exact match for reference correct_flags.append(exact_match(pred, reference)) - from app.core.memory.evaluation.common.metrics import ( - bleu1, - f1_score, - jaccard, - ) + from app.core.memory.evaluation.common.metrics import f1_score, bleu1, jaccard f1s.append(f1_score(str(pred), str(reference))) b1s.append(bleu1(str(pred), str(reference))) jss.append(jaccard(str(pred), str(reference))) @@ -295,15 +288,39 @@ async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None def main(): + # Load environment variables first load_dotenv() + + # Get defaults from environment variables + env_sample_size = os.getenv("MEMSCIQA_SAMPLE_SIZE") + env_search_limit = os.getenv("MEMSCIQA_SEARCH_LIMIT") + env_context_budget = os.getenv("MEMSCIQA_CONTEXT_CHAR_BUDGET") + env_llm_max_tokens = os.getenv("MEMSCIQA_LLM_MAX_TOKENS") + env_skip_ingest = os.getenv("MEMSCIQA_SKIP_INGEST", "false").lower() in ("true", "1", "yes") + env_output_dir = os.getenv("MEMSCIQA_OUTPUT_DIR") + + # Convert to appropriate types with fallback to code defaults + default_sample_size = int(env_sample_size) if env_sample_size else 1 + default_search_limit = int(env_search_limit) if env_search_limit else 8 + default_context_budget = int(env_context_budget) if env_context_budget else 4000 + default_llm_max_tokens = int(env_llm_max_tokens) if env_llm_max_tokens else 64 + default_output_dir = env_output_dir if env_output_dir else None + parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen") + parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量") - parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json") + parser.add_argument("--end-user-id", type=str, default=None, help="可选 end_user_id，默认使用环境变量") parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数") parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") + parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=64, help="LLM 最大生成长度") + parser.add_argument("--llm-max-tokens", type=int, default=default_llm_max_tokens, + help=f"LLM 最大生成长度 (env: MEMSCIQA_LLM_MAX_TOKENS={env_llm_max_tokens or 'not set'})") parser.add_argument("--search-type", type=str, choices=["keyword","embedding","hybrid"], default="hybrid", help="检索类型") + parser.add_argument("--skip-ingest", action="store_true", default=env_skip_ingest, + help=f"跳过数据摄入，使用 Neo4j 中的现有数据 (env: MEMSCIQA_SKIP_INGEST={os.getenv('MEMSCIQA_SKIP_INGEST', 'false')})") + parser.add_argument("--output-dir", type=str, default=default_output_dir, + help=f"结果保存目录 (env: MEMSCIQA_OUTPUT_DIR={env_output_dir or 'not set'})") args = parser.parse_args() result = asyncio.run( @@ -315,9 +332,37 @@ def main(): llm_temperature=args.llm_temperature, llm_max_tokens=args.llm_max_tokens, search_type=args.search_type, + skip_ingest=args.skip_ingest, ) ) + + # Print results to console print(json.dumps(result, ensure_ascii=False, indent=2)) + + # Save results to file + output_dir = args.output_dir + if output_dir is None: + # Use absolute path to ensure results are saved in the correct location + script_dir = Path(__file__).resolve().parent + output_dir = script_dir / "results" + elif not Path(output_dir).is_absolute(): + # If relative path, make it relative to this script's directory + script_dir = Path(__file__).resolve().parent + output_dir = script_dir / output_dir + else: + output_dir = Path(output_dir) + + output_dir.mkdir(parents=True, exist_ok=True) + + timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = output_dir / f"memsciqa_{timestamp_str}.json" + + try: + with open(output_path, "w", encoding="utf-8") as f: + json.dump(result, f, ensure_ascii=False, indent=2) + print(f"\n✅ 结果已保存到: {output_path}") + except Exception as e: + print(f"\n❌ 保存结果失败: {e}") if __name__ == "__main__": diff --git a/api/app/core/memory/evaluation/run_eval.py b/api/app/core/memory/evaluation/run_eval.py index c5aacb2f..56b2e790 100644 --- a/api/app/core/memory/evaluation/run_eval.py +++ b/api/app/core/memory/evaluation/run_eval.py @@ -2,20 +2,16 @@ import argparse import asyncio import json import os -import sys from typing import Any, Dict +from pathlib import Path +from dotenv import load_dotenv -# Add src directory to Python path for proper imports when running from evaluation directory -sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'src')) - -try: - from dotenv import load_dotenv -except Exception: - def load_dotenv(): - return None +# Load evaluation config +eval_config_path = Path(__file__).resolve().parent / ".env.evaluation" +if eval_config_path.exists(): + load_dotenv(eval_config_path, override=True) from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.utils.config.definitions import SELECTED_GROUP_ID, PROJECT_ROOT from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test @@ -36,8 +32,9 @@ async def run( start_index: int | None = None, max_contexts_per_item: int | None = None, ) -> Dict[str, Any]: - # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认 - end_user_id = end_user_id or SELECTED_GROUP_ID + # Use environment variable with fallback chain if not provided + if end_user_id is None: + end_user_id = os.getenv("EVAL_END_USER_ID", "benchmark_default") if reset_group: connector = Neo4jConnector() diff --git a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py index 8c69c7cf..7b7e854b 100644 --- a/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py +++ b/api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py @@ -1064,13 +1064,16 @@ class ExtractionOrchestrator: if statement.triplet_extraction_info: triplet_info = statement.triplet_extraction_info - # 创建实体索引到ID的映射 + # 创建实体索引到ID的映射（支持多种索引方式） entity_idx_to_id = {} # 创建实体节点 for entity_idx, entity in enumerate(triplet_info.entities): - # 映射实体索引到实体ID + # 映射实体索引到实体ID（使用多个键以提高容错性） + # 1. 使用实体自己的 entity_idx entity_idx_to_id[entity.entity_idx] = entity.id + # 2. 使用枚举索引（从0开始） + entity_idx_to_id[entity_idx] = entity.id if entity.id not in entity_id_set: entity_connect_strength = getattr(entity, 'connect_strength', 'Strong') @@ -1149,9 +1152,18 @@ class ExtractionOrchestrator: relationship_result ) else: - logger.warning( - f"跳过三元组 - 无法找到实体ID: subject_id={triplet.subject_id}, " - f"object_id={triplet.object_id}, statement_id={statement.id}" + # 改进的警告信息，包含更多调试信息 + missing_subject = "subject" if not subject_entity_id else "" + missing_object = "object" if not object_entity_id else "" + missing_both = " and " if (not subject_entity_id and not object_entity_id) else "" + + logger.debug( + f"跳过三元组 - 无法找到{missing_subject}{missing_both}{missing_object}实体ID: " + f"subject_id={triplet.subject_id} ({triplet.subject_name}), " + f"object_id={triplet.object_id} ({triplet.object_name}), " + f"predicate={triplet.predicate}, " + f"statement_id={statement.id}, " + f"available_indices={sorted(entity_idx_to_id.keys())}" ) logger.info( diff --git a/api/app/models/agent_app_config_model.py b/api/app/models/agent_app_config_model.py index 0a7a5935..96752c8e 100644 --- a/api/app/models/agent_app_config_model.py +++ b/api/app/models/agent_app_config_model.py @@ -6,7 +6,7 @@ from sqlalchemy.orm import relationship from app.base.type import PydanticType from app.db import Base -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters class AgentConfig(Base): diff --git a/api/app/models/multi_agent_model.py b/api/app/models/multi_agent_model.py index 544ddb27..400c05ad 100644 --- a/api/app/models/multi_agent_model.py +++ b/api/app/models/multi_agent_model.py @@ -10,7 +10,7 @@ from sqlalchemy.orm import relationship from app.base.type import PydanticType from app.db import Base -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters class OrchestrationMode(StrEnum): diff --git a/api/app/schemas/multi_agent_schema.py b/api/app/schemas/multi_agent_schema.py index c0d72cdd..8fba2929 100644 --- a/api/app/schemas/multi_agent_schema.py +++ b/api/app/schemas/multi_agent_schema.py @@ -4,7 +4,7 @@ import datetime from typing import Optional, List, Dict, Any, Union from pydantic import BaseModel, Field, ConfigDict, field_serializer -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters # ==================== 子 Agent 配置 ==================== diff --git a/api/app/services/master_agent_router.py b/api/app/services/master_agent_router.py index 3971aab7..87fdb22c 100644 --- a/api/app/services/master_agent_router.py +++ b/api/app/services/master_agent_router.py @@ -5,7 +5,7 @@ import uuid from typing import Dict, Any, List, Optional, Tuple from sqlalchemy.orm import Session -from app.schemas import ModelParameters +from app.schemas.app_schema import ModelParameters from app.services.conversation_state_manager import ConversationStateManager from app.models import ModelConfig, AgentConfig from app.core.logging_config import get_business_logger diff --git a/api/app/utils/app_config_utils.py b/api/app/utils/app_config_utils.py index ae41d8bf..06549989 100644 --- a/api/app/utils/app_config_utils.py +++ b/api/app/utils/app_config_utils.py @@ -57,7 +57,7 @@ def dict_to_model_parameters(data: Optional[Dict[str, Any]]) -> Optional[Any]: if data is None: return None - from app.schemas import ModelParameters + from app.schemas.app_schema import ModelParameters if isinstance(data, ModelParameters): return data From 87731090cab1097dbb30909e388b62774bb10283 Mon Sep 17 00:00:00 2001 From: Mark Date: Mon, 26 Jan 2026 19:19:41 +0800 Subject: [PATCH 083/175] [modify] migration script --- api/migrations/versions/325b759cd66b_2026011240.py | 1 + 1 file changed, 1 insertion(+) diff --git a/api/migrations/versions/325b759cd66b_2026011240.py b/api/migrations/versions/325b759cd66b_2026011240.py index 763b0289..3d7443a8 100644 --- a/api/migrations/versions/325b759cd66b_2026011240.py +++ b/api/migrations/versions/325b759cd66b_2026011240.py @@ -31,6 +31,7 @@ def upgrade() -> None: op.execute("UPDATE memory_config SET config_id = apply_id::uuid") op.alter_column('memory_config', 'config_id', nullable=False) op.create_primary_key('memory_config_pkey', 'memory_config', ['config_id']) + op.execute("ALTER TABLE memory_config ALTER COLUMN config_id_old DROP DEFAULT") op.execute("DROP SEQUENCE IF EXISTS data_config_config_id_seq") From c3ea3b751b07c9325f80000e9a4d93a0a4790fe4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B9=90=E5=8A=9B=E9=BD=90?= <162269739+lanceyq@users.noreply.github.com> Date: Mon, 26 Jan 2026 20:30:07 +0800 Subject: [PATCH 084/175] delete benchmark-test (#204) * Refactor: Move evaluation folder to redbear-mem-benchmark submodule * [changes]Restore .gitmodules --- .../memory/evaluation/.env.evaluation.example | 224 --- api/app/core/memory/evaluation/.gitignore | 13 - api/app/core/memory/evaluation/__init__.py | 1 - api/app/core/memory/evaluation/benchmark.md | 748 --------- .../memory/evaluation/check_enduser_data.py | 371 ----- .../core/memory/evaluation/common/metrics.py | 100 -- .../memory/evaluation/dialogue_queries.py | 62 - .../memory/evaluation/extraction_utils.py | 444 ------ .../evaluation/locomo/locomo_benchmark.py | 770 ---------- .../evaluation/locomo/locomo_metrics.py | 225 --- .../memory/evaluation/locomo/locomo_test.py | 864 ----------- .../memory/evaluation/locomo/locomo_utils.py | 687 --------- .../evaluation/locomo/qwen_search_eval.py | 874 ----------- .../longmemeval/longmemeval_benchmark.py | 1339 ----------------- .../evaluation/longmemeval/test_eval.py | 1312 ---------------- .../evaluation/memsciqa/memsciqa-test.py | 559 ------- .../evaluation/memsciqa/memsciqa_benchmark.py | 369 ----- api/app/core/memory/evaluation/run_eval.py | 147 -- redbear-mem-benchmark | 2 +- 19 files changed, 1 insertion(+), 9110 deletions(-) delete mode 100644 api/app/core/memory/evaluation/.env.evaluation.example delete mode 100644 api/app/core/memory/evaluation/.gitignore delete mode 100644 api/app/core/memory/evaluation/__init__.py delete mode 100644 api/app/core/memory/evaluation/benchmark.md delete mode 100644 api/app/core/memory/evaluation/check_enduser_data.py delete mode 100644 api/app/core/memory/evaluation/common/metrics.py delete mode 100644 api/app/core/memory/evaluation/dialogue_queries.py delete mode 100644 api/app/core/memory/evaluation/extraction_utils.py delete mode 100644 api/app/core/memory/evaluation/locomo/locomo_benchmark.py delete mode 100644 api/app/core/memory/evaluation/locomo/locomo_metrics.py delete mode 100644 api/app/core/memory/evaluation/locomo/locomo_test.py delete mode 100644 api/app/core/memory/evaluation/locomo/locomo_utils.py delete mode 100644 api/app/core/memory/evaluation/locomo/qwen_search_eval.py delete mode 100644 api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py delete mode 100644 api/app/core/memory/evaluation/longmemeval/test_eval.py delete mode 100644 api/app/core/memory/evaluation/memsciqa/memsciqa-test.py delete mode 100644 api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py delete mode 100644 api/app/core/memory/evaluation/run_eval.py diff --git a/api/app/core/memory/evaluation/.env.evaluation.example b/api/app/core/memory/evaluation/.env.evaluation.example deleted file mode 100644 index be089eb4..00000000 --- a/api/app/core/memory/evaluation/.env.evaluation.example +++ /dev/null @@ -1,224 +0,0 @@ -# ============================================================================ -# 基准测试统一配置文件示例 -# ============================================================================ -# 复制此文件为 .env.evaluation 并根据需要修改 -# 支持的基准测试：LoCoMo、LongMemEval、MemSciQA -# ============================================================================ - -# ============================================================================ -# 通用配置（所有基准测试共用） -# ============================================================================ - -# ---------------------------------------------------------------------------- -# Neo4j 配置 -# ---------------------------------------------------------------------------- -# 默认 Group ID（建议各基准测试使用独立的 group） -EVAL_GROUP_ID=benchmark_default - -# ---------------------------------------------------------------------------- -# 模型配置（必需） -# ---------------------------------------------------------------------------- -# ⚠️ 必填：从数据库 models 表中选择有效的模型 ID -# -# 如何获取模型 ID： -# 1. 查询数据库：SELECT id, model_name FROM models WHERE is_active = true; -# 2. 或通过系统管理界面查看 -# 3. 确保模型可用且配置正确 - -# LLM 模型 ID（必填） -EVAL_LLM_ID=your_llm_model_id_here - -# Embedding 模型 ID（必填） -EVAL_EMBEDDING_ID=your_embedding_model_id_here - -# ---------------------------------------------------------------------------- -# 检索参数 -# ---------------------------------------------------------------------------- -# 检索类型: "keyword", "embedding", "hybrid" -EVAL_SEARCH_TYPE=hybrid - -# 检索结果数量限制（默认值） -EVAL_SEARCH_LIMIT=12 - -# 上下文最大字符数（默认值） -EVAL_MAX_CONTEXT_CHARS=8000 - -# ---------------------------------------------------------------------------- -# LLM 参数 -# ---------------------------------------------------------------------------- -# LLM 温度参数（0.0 = 确定性输出） -EVAL_LLM_TEMPERATURE=0.0 - -# LLM 最大生成 token 数 -EVAL_LLM_MAX_TOKENS=32 - -# LLM 超时时间（秒） -EVAL_LLM_TIMEOUT=10.0 - -# LLM 最大重试次数 -EVAL_LLM_MAX_RETRIES=1 - -# ---------------------------------------------------------------------------- -# 数据处理参数 -# ---------------------------------------------------------------------------- -# Chunker 策略 -EVAL_CHUNKER_STRATEGY=RecursiveChunker - -# 是否在导入前清空现有数据 -EVAL_RESET_ON_INGEST=true - -# 是否保存详细日志 -EVAL_SAVE_DETAILED_LOGS=true - -# ============================================================================ -# LoCoMo 基准测试专用配置 -# ============================================================================ -# 数据集：locomo10.json -# 运行：python locomo_benchmark.py --sample_size 20 -# ---------------------------------------------------------------------------- - -# Group ID（LoCoMo 专用） -LOCOMO_GROUP_ID=locomo_benchmark - -# 测试样本数量 -# 建议值：20（快速测试）、100（中等测试）、1986（完整测试） -LOCOMO_SAMPLE_SIZE=20 - -# 检索结果数量限制 -LOCOMO_SEARCH_LIMIT=12 - -# 上下文最大字符数 -LOCOMO_CONTEXT_CHAR_BUDGET=8000 - -# 导入的对话数量 -LOCOMO_MAX_DIALOGUES=1 - -# 跳过数据摄入（true=跳过，false=摄入） -# 首次运行设置为 false，后续运行可设置为 true 以节省时间 -LOCOMO_SKIP_INGEST=false - -# 结果保存目录 -LOCOMO_OUTPUT_DIR=locomo/results - -# ============================================================================ -# LongMemEval 基准测试专用配置 -# ============================================================================ -# 数据集：longmemeval_oracle_zh.json -# 运行：python longmemeval_benchmark.py --sample_size 3 -# 特点：支持时间推理问题的增强检索 -# ---------------------------------------------------------------------------- - -# Group ID（LongMemEval 专用） -LONGMEMEVAL_GROUP_ID=longmemeval_zh_bak_3 - -# 测试样本数量（<=0 表示全部样本） -LONGMEMEVAL_SAMPLE_SIZE=3 - -# 起始样本索引 -LONGMEMEVAL_START_INDEX=0 - -# 检索结果数量限制 -LONGMEMEVAL_SEARCH_LIMIT=8 - -# 上下文最大字符数 -LONGMEMEVAL_CONTEXT_CHAR_BUDGET=4000 - -# LLM 最大生成 token 数 -LONGMEMEVAL_LLM_MAX_TOKENS=16 - -# 每条样本最多摄入的上下文段数 -LONGMEMEVAL_MAX_CONTEXTS_PER_ITEM=2 - -# 是否保存分块结果 -LONGMEMEVAL_SAVE_CHUNK_OUTPUT=true - -# 自定义分块输出路径（留空使用默认） -LONGMEMEVAL_SAVE_CHUNK_OUTPUT_PATH= - -# 摄入前是否清空组数据 -LONGMEMEVAL_RESET_GROUP_BEFORE_INGEST=false - -# 是否跳过摄入，仅检索评估 -LONGMEMEVAL_SKIP_INGEST=false - -# 结果保存目录 -LONGMEMEVAL_OUTPUT_DIR=longmemeval/results - -# ============================================================================ -# MemSciQA 基准测试专用配置 -# ============================================================================ -# 数据集：msc_self_instruct.jsonl -# 运行：python memsciqa_benchmark.py --sample_size 1 -# 特点：对话记忆检索评估 -# ---------------------------------------------------------------------------- - -# Group ID（MemSciQA 专用，独立数据集） -MEMSCIQA_GROUP_ID=memsciqa_benchmark - -# 测试样本数量 -MEMSCIQA_SAMPLE_SIZE=1 # 0或者-1标识测试数据集中的所有样本 - -# 检索结果数量限制 -MEMSCIQA_SEARCH_LIMIT=8 - -# 上下文最大字符数 -MEMSCIQA_CONTEXT_CHAR_BUDGET=4000 - -# LLM 最大生成 token 数 -MEMSCIQA_LLM_MAX_TOKENS=64 - -# 跳过数据摄入（true=跳过，false=摄入） -# 首次运行设置为 false，后续运行可设置为 true 以节省时间 -MEMSCIQA_SKIP_INGEST=false - -# 结果保存目录（相对于 memsciqa 脚本所在目录） -# 使用 "results" 会保存到 api/app/core/memory/evaluation/memsciqa/results/ -MEMSCIQA_OUTPUT_DIR=results - -# ============================================================================ -# 高级配置（可选） -# ============================================================================ - -# BM25 权重（用于混合检索，0.0-1.0） -EVAL_RERANK_ALPHA=0.6 - -# 是否使用遗忘重排序 -EVAL_USE_FORGETTING_RERANK=false - -# 是否使用 LLM 重排序 -EVAL_USE_LLM_RERANK=false - -# 连接重置间隔（每 N 个问题重置一次） -EVAL_RESET_INTERVAL=5 - -# 性能阈值（低于此值触发重置） -EVAL_PERFORMANCE_THRESHOLD=0.6 - -# ============================================================================ -# 快速配置指南 -# ============================================================================ -# 1. 复制此文件为 .env.evaluation -# 2. 修改 EVAL_LLM_ID 和 EVAL_EMBEDDING_ID 为你的模型 ID -# 3. 根据需要修改各基准测试的专用配置 -# 4. 运行测试： -# - LoCoMo: python locomo/locomo_benchmark.py --sample_size 20 -# - LongMemEval: python longmemeval/longmemeval_benchmark.py --sample_size 3 --all -# - MemSciQA: python memsciqa/memsciqa_benchmark.py --sample_size 10 -# 配置优先级： -# 命令行参数 > 特定配置（如 LOCOMO_*）> 通用配置（EVAL_*）> 代码默认值 -# ============================================================================ - - -# 执行LoCoMo测试 -# 只摄入前5条消息，评估3个问题（最小测试） -# python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 3 --max_ingest_messages 5 -# -# 如果数据已经摄入，跳过摄入阶段直接测试 -# python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 5 --skip_ingest - - -# 执行longmemeval测试 -# python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark --sample-size 10 --max-contexts-per-item 3 --reset-group-before-ingest - -# 执行memsciqa测试 -# python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark --sample-size 1 diff --git a/api/app/core/memory/evaluation/.gitignore b/api/app/core/memory/evaluation/.gitignore deleted file mode 100644 index 38b1055a..00000000 --- a/api/app/core/memory/evaluation/.gitignore +++ /dev/null @@ -1,13 +0,0 @@ -# 忽略实际的评估配置文件（包含敏感信息） -.env.evaluation - -# 保留示例文件 -!.env.evaluation.example - -# 忽略测试结果文件 -*/results/*.json -*/results/*.log - -# 忽略数据集文件（文件过大，不应提交到 Git） -dataset/*.json -dataset/*.jsonl diff --git a/api/app/core/memory/evaluation/__init__.py b/api/app/core/memory/evaluation/__init__.py deleted file mode 100644 index e9d6aa6c..00000000 --- a/api/app/core/memory/evaluation/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Evaluation package with dataset-specific pipelines and a unified runner.""" diff --git a/api/app/core/memory/evaluation/benchmark.md b/api/app/core/memory/evaluation/benchmark.md deleted file mode 100644 index 7c31cccd..00000000 --- a/api/app/core/memory/evaluation/benchmark.md +++ /dev/null @@ -1,748 +0,0 @@ -# 1.数据集下载地址 -Locomo10.json : https://github.com/snap-research/locomo/tree/main/data -LongMemEval_oracle.json : https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned -msc_self_instruct.jsonl : https://huggingface.co/datasets/MemGPT/MSC-Self-Instruct - -数据集下载之后保存至api\app\core\memory\evaluation\dataset目录下 -# 2.配置说明 -文件api\app\core\memory\evaluation\.env.evaluation.example对三个基准测试所需配置有着详细的说明 -**实际配置文件**：api\app\core\memory\evaluation\.env.evaluation -```python -# 当使用不带配置参数的命令行执行基准测试，基准测试所需的配置参数根据.env.evaluation中的参数执行 -python -m app.core.memory.evaluation.locomo.locomo_benchmark -``` -**检查neo4j指定的grou_id是否摄入数据** -```python -# 1. 进入交互模式 -python -m app.core.memory.evaluation.check_enduser_data - -# 2. 选择 "1" 检查指定 group -# 3. 输入 group_id，例如: locomo_benchmark -# 4. 选择是否显示详细统计 (y/n) -``` -# 3.locomo - -### (1)locomo执行命令 -```python -# 首先进入api目录 -cd api - -# 只摄入前5条消息，评估3个问题（最小测试） -python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 3 --max_ingest_messages 5 - -# 如果数据已经摄入，跳过摄入阶段直接测试(使用skip_ingest参数) -python -m app.core.memory.evaluation.locomo.locomo_benchmark --sample_size 5 --skip_ingest -``` -### (2)locomo结果说明 - -#### 结果示例 -```json -{ - "dataset": "locomo", - "sample_size": 0, - "timestamp": "2026-01-26T11:24:28.239156", - "params": { - "group_id": "locomo_benchmark", - "search_type": "hybrid", - "search_limit": 12, - "context_char_budget": 8000, - "llm_id": "2c9b0782-7a85-4740-ba84-4baf77f256c4", - "embedding_id": "e2a6392d-ca63-4d59-a523-647420b59cb2" - }, - "overall_metrics": { - "f1": 0.0, - "bleu1": 0.0, - "jaccard": 0.0, - "locomo_f1": 0.0 - }, - "by_category": {}, - "latency": { - "search": { - "mean": 0.0, - "p50": 0.0, - "p95": 0.0, - "iqr": 0.0 - }, - "llm": { - "mean": 0.0, - "p50": 0.0, - "p95": 0.0, - "iqr": 0.0 - } - }, - "context_stats": { - "avg_retrieved_docs": 0.0, - "avg_context_chars": 0.0, - "avg_context_tokens": 0.0 - }, - "samples": [] -} -``` - -#### 参数详解 - -##### 1. 核心评估指标 (overall_metrics) - -**🎯 关键进步指标：** - -- **`f1`** (F1 Score): 精确率和召回率的调和平均值 - - 范围：0.0 - 1.0 - - **越高越好**，衡量检索和生成答案的准确性 - - 这是最重要的综合性能指标 - - 优秀标准：> 0.85 - -- **`bleu1`** (BLEU-1): 单词级别的匹配度 - - 范围：0.0 - 1.0 - - **越高越好**，衡量生成答案与标准答案的词汇重叠度 - - 关注词汇层面的准确性 - -- **`jaccard`** (Jaccard 相似度): 集合相似度 - - 范围：0.0 - 1.0 - - **越高越好**，衡量答案集合的相似性 - - 计算公式：交集大小 / 并集大小 - -- **`locomo_f1`**: Locomo 特定的 F1 分数 - - 范围：0.0 - 1.0 - - **越高越好**，针对 Locomo 数据集优化的评估指标 - - 考虑了长对话记忆的特殊性 - -##### 2. 性能指标 (latency) - -**⚡ 关键效率指标：** - -- **`search`**: 检索延迟统计（单位：毫秒） - - `mean`: 平均延迟 - - `p50`: 中位数延迟（50%的请求在此时间内完成） - - `p95`: 95分位数延迟（95%的请求在此时间内完成） - - `iqr`: 四分位距（Q3-Q1，衡量稳定性） - - **越低越好**，衡量记忆检索速度 - - 优秀标准：p95 < 2000ms - -- **`llm`**: LLM 推理延迟统计（单位：毫秒） - - `mean`: 平均推理时间 - - `p50`: 中位数推理时间 - - `p95`: 95分位数推理时间 - - `iqr`: 四分位距（越小越稳定） - - **越低越好**，衡量答案生成速度 - - 优秀标准：p95 < 3000ms - -##### 3. 上下文统计 (context_stats) - -**📊 资源效率指标：** - -- **`avg_retrieved_docs`**: 平均检索文档数 - - 反映检索策略的广度 - - 需要平衡：太少可能信息不足，太多增加噪音和延迟 - - 建议范围：8-15 个文档 - -- **`avg_context_chars`**: 平均上下文字符数 - - 反映检索内容的总量 - - 应在满足准确性前提下尽量精简 - - 受 `context_char_budget` 参数限制 - -- **`avg_context_tokens`**: 平均上下文 token 数 - - **越低越好**（在保持准确性前提下） - - 直接影响 API 调用成本和推理速度 - - 成本效益比 = f1 / avg_context_tokens - -##### 4. 分类统计 (by_category) - -- 按问题类型分类的性能指标 -- 帮助识别系统在不同场景下的强弱项 -- 可针对性优化特定类型的问题 - -#### 系统进步衡量标准 - -**一级指标（最重要）：** -- `f1` 和 `locomo_f1` 提升 → 核心能力提升 -- 目标：f1 > 0.85 - -**二级指标（重要）：** -- `latency.p95` 降低 → 用户体验提升 -- 目标：search.p95 < 2000ms, llm.p95 < 3000ms - -**三级指标（辅助）：** -- `avg_context_tokens` 降低（在保持 f1 前提下）→ 成本优化 -- `iqr` 降低 → 性能稳定性提升 -# 4.longmemeval -支持时间推理问题的增强检索 -### (1)执行命令 -```python -# 首先进入api目录 -cd api - -# 不带参数运行 - 使用环境变量 -python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark - -# 命令行参数覆盖环境变量 -python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark --sample-size 2 - -# 如果数据已经摄入，跳过摄入阶段直接测试(使用skip_ingest参数) -python -m app.core.memory.evaluation.longmemeval.longmemeval_benchmark --skip_ingest -``` -### (2)结果说明 - -#### 结果示例 -```json -{ - "dataset": "longmemeval", - "items": 1, - "accuracy_by_type": { - "single-session-user": 1.0 - }, - "f1_by_type": { - "single-session-user": 1.0 - }, - "jaccard_by_type": { - "single-session-user": 1.0 - }, - "samples": [ - { - "question": "What degree did I graduate with?", - "prediction": "Business Administration", - "answer": "Business Administration", - "question_type": "single-session-user", - "is_temporal": false, - "question_id": "e47becba", - "options": [], - "context_count": 13, - "context_chars": 1268, - "retrieved_dialogue_count": 0, - "retrieved_statement_count": 12, - "metrics": { - "exact_match": true, - "f1": 1.0, - "jaccard": 1.0 - }, - "timing": { - "search_ms": 1483.100175857544, - "llm_ms": 995.8682060241699 - } - } - ], - "latency": { - "search": { - "mean": 1483.100175857544, - "p50": 1483.100175857544, - "p95": 1483.100175857544, - "iqr": 0.0 - }, - "llm": { - "mean": 995.8682060241699, - "p50": 995.8682060241699, - "p95": 995.8682060241699, - "iqr": 0.0 - } - }, - "context": { - "avg_tokens": 204.0, - "avg_chars": 1268, - "count_avg": 13 - }, - "params": { - "group_id": "longmemeval_zh_bak_3", - "search_limit": 8, - "context_char_budget": 4000, - "search_type": "hybrid", - "llm_id": "6dc52e1b-9cec-4194-af66-a74c6307fc3f", - "embedding_id": "e2a6392d-ca63-4d59-a523-647420b59cb2", - "sample_size": 1, - "start_index": 0 - }, - "timestamp": "2026-01-24T21:36:10.818308", - "metric_summary": { - "score_accuracy": 100.0, - "latency_median_s": 2.478968381881714, - "latency_iqr_s": 0.0, - "avg_context_tokens_k": 0.204 - }, - "diagnostics": { - "duplicate_previews_top": [], - "unique_preview_count": 1 - } -} -``` - -#### 参数详解 - -##### 1. 核心评估指标 - -**🎯 关键进步指标：** - -- **`accuracy_by_type`**: 按问题类型分类的准确率 - - 范围：0.0 - 1.0 - - **越高越好**，1.0 表示 100% 准确 - - 问题类型包括： - - `single-session-user`: 单会话用户信息 - - `single-session-event`: 单会话事件信息 - - `multi-session-user`: 多会话用户信息 - - `multi-session-event`: 多会话事件信息 - - 可以识别系统在不同场景下的强弱项 - -- **`f1_by_type`**: 按问题类型的 F1 分数 - - 范围：0.0 - 1.0 - - **越高越好**，综合评估精确率和召回率 - - 比单纯的准确率更全面 - -- **`jaccard_by_type`**: 按问题类型的 Jaccard 相似度 - - 范围：0.0 - 1.0 - - **越高越好**，衡量答案集合匹配度 - - 对于集合类答案特别有用 - -##### 2. 样本级指标 (samples) - -**详细诊断指标：** - -- **`metrics.exact_match`**: 精确匹配（布尔值） - - **true 越多越好**，最严格的评估标准 - - 要求预测答案与标准答案完全一致 - -- **`metrics.f1`**: 单个样本的 F1 分数 - - 范围：0.0 - 1.0 - - **越高越好**，衡量单个问题的回答质量 - -- **`is_temporal`**: 是否为时间推理问题 - - 布尔值，标识问题是否涉及时间推理 - - 时间推理问题通常更具挑战性 - -- **`context_count`**: 检索到的上下文数量 - - 反映检索策略的有效性 - - 建议范围：8-15 个上下文片段 - -- **`retrieved_dialogue_count`**: 检索到的对话数 -- **`retrieved_statement_count`**: 检索到的陈述数 - - 这两个指标帮助理解检索的内容类型分布 - - 可用于优化检索策略 - -- **`timing.search_ms`**: 单个问题的检索延迟（毫秒） -- **`timing.llm_ms`**: 单个问题的 LLM 推理延迟（毫秒） - - **越低越好**，反映单次查询的响应速度 - -##### 3. 汇总指标 (metric_summary) - -**📊 关键 KPI：** - -- **`score_accuracy`**: 总体准确率百分比 - - 范围：0.0 - 100.0 - - **越高越好**，最直观的性能指标 - - 优秀标准：> 90.0 - -- **`latency_median_s`**: 中位延迟（秒） - - **越低越好**，反映真实响应速度 - - 优秀标准：< 3.0 秒 - -- **`latency_iqr_s`**: 延迟四分位距（秒） - - **越低越好**，反映性能稳定性 - - 越小说明响应时间越稳定 - -- **`avg_context_tokens_k`**: 平均上下文 token 数（千） - - **越低越好**（在保持准确性前提下） - - 直接影响 API 调用成本 - - 成本效益比 = score_accuracy / (avg_context_tokens_k * 1000) - -##### 4. 上下文统计 (context) - -- **`avg_tokens`**: 平均 token 数 -- **`avg_chars`**: 平均字符数 -- **`count_avg`**: 平均上下文片段数 - - 这些指标反映检索内容的规模 - - 需要在准确性和效率之间平衡 - -##### 5. 性能指标 (latency) - -**⚡ 效率指标：** - -- **`search`**: 检索延迟统计（单位：毫秒） - - `mean`: 平均延迟 - - `p50`: 中位数延迟 - - `p95`: 95分位数延迟 - - `iqr`: 四分位距 - - **越低越好**，衡量记忆检索速度 - -- **`llm`**: LLM 推理延迟统计（单位：毫秒） - - `mean`: 平均推理时间 - - `p50`: 中位数推理时间 - - `p95`: 95分位数推理时间 - - `iqr`: 四分位距 - - **越低越好**，衡量答案生成速度 - -##### 6. 诊断信息 (diagnostics) - -- **`duplicate_previews_top`**: 重复预览统计 - - 列出出现频率最高的重复内容 - - 帮助发现检索冗余问题 - - 应该尽量减少重复 - -- **`unique_preview_count`**: 唯一预览数量 - - 反映检索多样性 - - **越高越好**，说明检索到的内容更丰富 - -#### 系统进步衡量标准 - -**一级指标（最重要）：** -- `score_accuracy` 提升 → 核心能力提升 -- 目标：> 90.0% -- 各类型的 `accuracy_by_type` 均衡提升 → 全面能力提升 - -**二级指标（重要）：** -- `latency_median_s` 降低 → 用户体验提升 -- 目标：< 3.0 秒 -- `exact_match` 比例提升 → 精确度提升 - -**三级指标（辅助）：** -- `avg_context_tokens_k` 降低（在保持准确性前提下）→ 成本优化 -- `unique_preview_count` 提升 → 检索多样性提升 -- `latency_iqr_s` 降低 → 性能稳定性提升 - -**特殊关注：** -- 时间推理问题（`is_temporal: true`）的准确率 -- 多会话问题的准确率（通常更具挑战性） -# 5.memsciqa -对话记忆检索评估 -### (1)执行命令 -```python -# 首先进入api目录 -cd api - -# 不带参数运行 - 使用环境变量 -python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark - -# 命令行参数覆盖环境变量 -python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark --sample-size 100 - -# 如果数据已经摄入，跳过摄入阶段直接测试(使用skip_ingest参数) -python -m app.core.memory.evaluation.memsciqa.memsciqa_benchmark --skip_ingest -``` -### (2)结果说明 - -#### 结果示例 -```json -{ - "dataset": "memsciqa", - "items": 1, - "metrics": { - "accuracy": 0.0, - "f1": 0.0, - "bleu1": 0.0, - "jaccard": 0.0 - }, - "latency": { - "search": { - "mean": 0.0, - "p50": 0.0, - "p95": 0.0, - "iqr": 0.0 - }, - "llm": { - "mean": 3067.7285194396973, - "p50": 3067.7285194396973, - "p95": 3067.7285194396973, - "iqr": 0.0 - } - }, - "avg_context_tokens": 4.0 -} -``` - -#### 参数详解 - -##### 1. 核心评估指标 (metrics) - -**🎯 关键进步指标：** - -- **`accuracy`**: 准确率 - - 范围：0.0 - 1.0 - - **越高越好**，最直接的性能指标 - - 衡量系统回答正确的问题比例 - - 优秀标准：> 0.85 - -- **`f1`**: F1 分数 - - 范围：0.0 - 1.0 - - **越高越好**，平衡精确率和召回率 - - 计算公式：2 * (precision * recall) / (precision + recall) - - 比单纯的准确率更全面，特别适合不平衡数据集 - -- **`bleu1`**: BLEU-1 分数 - - 范围：0.0 - 1.0 - - **越高越好**，衡量词汇级别的匹配度 - - 关注生成答案与标准答案的单词重叠 - - 源自机器翻译评估，适用于自然语言生成 - -- **`jaccard`**: Jaccard 相似度 - - 范围：0.0 - 1.0 - - **越高越好**，衡量集合相似性 - - 计算公式：|A ∩ B| / |A ∪ B| - - 对于多答案或集合类问题特别有用 - -##### 2. 性能指标 (latency) - -**⚡ 效率指标：** - -- **`search`**: 检索延迟统计（单位：毫秒） - - `mean`: 平均检索延迟 - - `p50`: 中位数延迟（50%的请求在此时间内完成） - - `p95`: 95分位数延迟（95%的请求在此时间内完成） - - `iqr`: 四分位距（Q3-Q1，衡量稳定性） - - **越低越好**，衡量记忆检索效率 - - 优秀标准：p95 < 2000ms - -- **`llm`**: LLM 推理延迟统计（单位：毫秒） - - `mean`: 平均推理时间 - - `p50`: 中位数推理时间 - - `p95`: 95分位数推理时间 - - `iqr`: 四分位距（越小越稳定） - - **越低越好**，衡量答案生成速度 - - 优秀标准：p95 < 3000ms - - 注意：LLM 延迟通常占总延迟的大部分 - -##### 3. 资源指标 - -- **`avg_context_tokens`**: 平均上下文 token 数 - - **越低越好**（在保持准确性前提下） - - 直接影响： - - API 调用成本（按 token 计费） - - 推理速度（token 越多越慢） - - 上下文窗口占用 - - 成本效益比 = accuracy / avg_context_tokens - - 建议范围：根据模型上下文窗口和成本预算调整 - -##### 4. 数据集特点 - -- **`items`**: 评估的问题数量 - - 样本量越大，评估结果越可靠 - - 建议至少 100 个样本以获得稳定的评估结果 - -- **对话记忆特性**： - - MemSciQA 专注于对话历史中的记忆检索 - - 评估系统从多轮对话中提取和回忆信息的能力 - - 模拟真实的对话场景 - -#### 系统进步衡量标准 - -**一级指标（最重要）：** -- `accuracy` 提升 → 核心能力提升 -- 目标：> 0.85 -- `f1` 提升 → 综合性能提升 -- 目标：> 0.80 - -**二级指标（重要）：** -- `latency.p95` 降低 → 用户体验提升 - - search.p95 目标：< 2000ms - - llm.p95 目标：< 3000ms -- `iqr` 降低 → 性能稳定性提升 - -**三级指标（辅助）：** -- `avg_context_tokens` 降低（在保持准确性前提下）→ 成本优化 -- `bleu1` 和 `jaccard` 提升 → 答案质量提升 - -**综合评估：** -- 成本效益比 = accuracy / avg_context_tokens - - 该比值越高，说明系统在相同成本下性能越好 -- 总延迟 = search.p95 + llm.p95 - - 应控制在 5 秒以内以保证良好的用户体验 - -#### 优化建议 - -**提升准确性：** -- 优化检索算法（调整 hybrid search 参数） -- 改进 embedding 模型质量 -- 增加检索上下文数量（`search_limit`） -- 优化 prompt 工程 - -**提升效率：** -- 减少不必要的检索文档 -- 使用更快的 LLM 模型或量化版本 -- 实施缓存策略（相似问题复用结果） -- 优化数据库索引 - -**平衡性能：** -- 监控 accuracy vs latency 的权衡 -- 监控 accuracy vs cost (tokens) 的权衡 -- 根据业务需求调整优先级 - - ---- - -# 6. 三个基准测试对比总结 - -## 6.1 测试特点对比 - -| 基准测试 | 主要评估目标 | 数据集特点 | 适用场景 | -|---------|------------|-----------|---------| -| **Locomo** | 长对话记忆检索 | 长对话历史，多轮交互 | 评估长期记忆保持和检索能力 | -| **LongMemEval** | 时间推理和多会话记忆 | 支持时间推理，多会话场景 | 评估时间感知和跨会话记忆能力 | -| **MemSciQA** | 对话记忆问答 | 对话历史问答 | 评估对话上下文理解和记忆提取 | - -## 6.2 核心指标对比 - -### 准确性指标 - -| 指标 | Locomo | LongMemEval | MemSciQA | 说明 | -|-----|--------|-------------|----------|------| -| **F1 Score** | ✅ | ✅ | ✅ | 所有测试都使用，最重要的综合指标 | -| **Accuracy** | ❌ | ✅ | ✅ | 直观的准确率指标 | -| **BLEU-1** | ✅ | ❌ | ✅ | 词汇级别匹配度 | -| **Jaccard** | ✅ | ✅ | ✅ | 集合相似度 | -| **Exact Match** | ❌ | ✅ | ❌ | 最严格的评估标准 | - -### 性能指标 - -所有三个测试都包含： -- **检索延迟** (search latency): mean, p50, p95, iqr -- **LLM 延迟** (llm latency): mean, p50, p95, iqr -- **上下文统计**: token 数、字符数、文档数 - -## 6.3 关键进步指标优先级 - -### 🥇 一级指标（必须关注） - -1. **准确性指标** - - Locomo: `f1`, `locomo_f1` - - LongMemEval: `score_accuracy`, `accuracy_by_type` - - MemSciQA: `accuracy`, `f1` - - **目标**: > 85% 或 > 0.85 - -2. **综合性能** - - 所有测试的 F1 分数应保持一致性 - - 不同类型问题的准确率应均衡 - -### 🥈 二级指标（重要） - -3. **响应延迟** - - `latency.p95` (95分位数延迟) - - **目标**: - - search.p95 < 2000ms - - llm.p95 < 3000ms - - 总延迟 < 5000ms - -4. **性能稳定性** - - `iqr` (四分位距) - - **目标**: 越小越好，说明性能稳定 - -### 🥉 三级指标（优化） - -5. **成本效率** - - `avg_context_tokens` - - **目标**: 在保持准确性前提下最小化 - - 成本效益比 = accuracy / avg_context_tokens - -6. **检索质量** - - `avg_retrieved_docs` 的合理性 - - `unique_preview_count` (LongMemEval) - - 检索内容的多样性和相关性 - -## 6.4 系统优化路径 - -### 阶段一：提升准确性（优先级最高） - -**目标**: 所有测试的准确率 > 85% - -**优化方向**: -1. 改进 embedding 模型质量 -2. 优化检索算法（hybrid search 参数） -3. 增加检索上下文数量（`search_limit`） -4. 优化 prompt 工程 -5. 改进记忆存储结构 - -**监控指标**: -- Locomo: `f1`, `locomo_f1` -- LongMemEval: `score_accuracy`, `exact_match` 比例 -- MemSciQA: `accuracy`, `f1` - -### 阶段二：优化性能（准确性达标后） - -**目标**: p95 延迟 < 5 秒，性能稳定 - -**优化方向**: -1. 优化数据库索引和查询 -2. 实施缓存策略 -3. 使用更快的 LLM 模型 -4. 并行化检索和推理 -5. 减少不必要的检索 - -**监控指标**: -- `latency.p50`, `latency.p95` -- `iqr` (稳定性) -- 各阶段耗时分布 - -### 阶段三：降低成本（性能达标后） - -**目标**: 在保持准确性和性能前提下，最小化成本 - -**优化方向**: -1. 精简检索上下文 -2. 优化 context 选择策略 -3. 使用更小的 LLM 模型 -4. 实施智能缓存 -5. 批处理优化 - -**监控指标**: -- `avg_context_tokens` -- 成本效益比 = accuracy / avg_context_tokens -- API 调用成本 - -## 6.5 评估最佳实践 - -### 测试执行建议 - -1. **初始测试**: 使用小样本快速验证 - ```bash - --sample_size 10 - ``` - -2. **完整评估**: 使用足够大的样本量 - ```bash - --sample_size 100 # 或更多 - ``` - -3. **增量测试**: 数据已摄入时跳过摄入阶段 - ```bash - --skip_ingest - ``` - -4. **参数调优**: 系统性地调整参数并记录结果 - - 调整 `search_limit`: 4, 8, 12, 16 - - 调整 `context_char_budget`: 2000, 4000, 8000 - - 尝试不同的 `search_type`: vector, keyword, hybrid - -### 结果分析建议 - -1. **横向对比**: 比较三个测试的结果，识别系统的强弱项 -2. **纵向对比**: 跟踪同一测试在不同版本的表现 -3. **分类分析**: 关注不同问题类型的性能差异 -4. **异常诊断**: 分析失败案例，找出根本原因 - -### 持续监控 - -建议建立监控仪表板，跟踪： -- 核心指标趋势（准确率、延迟） -- 成本效益比趋势 -- 不同问题类型的性能分布 -- 异常样本和失败模式 - -## 6.6 性能基准参考 - -### 优秀水平（Production Ready） - -- **准确性**: accuracy/f1 > 0.90 -- **延迟**: p95 < 3 秒 -- **稳定性**: iqr < 500ms -- **成本效益**: accuracy/tokens > 0.0001 - -### 良好水平（Acceptable） - -- **准确性**: accuracy/f1 > 0.85 -- **延迟**: p95 < 5 秒 -- **稳定性**: iqr < 1000ms -- **成本效益**: accuracy/tokens > 0.00005 - -### 需要改进（Below Target） - -- **准确性**: accuracy/f1 < 0.85 -- **延迟**: p95 > 5 秒 -- **稳定性**: iqr > 1000ms -- **成本效益**: accuracy/tokens < 0.00005 - ---- - -**注**: 以上标准仅供参考，实际目标应根据具体业务需求和资源约束调整。 diff --git a/api/app/core/memory/evaluation/check_enduser_data.py b/api/app/core/memory/evaluation/check_enduser_data.py deleted file mode 100644 index 18ecbb34..00000000 --- a/api/app/core/memory/evaluation/check_enduser_data.py +++ /dev/null @@ -1,371 +0,0 @@ -""" -交互式 Neo4j End User 数据检查工具 - -用于查询指定 end_user_id 在 Neo4j 中是否存在数据，以及数据的详细统计信息。 - -使用方法: - python check_group_data.py - python check_group_data.py --group-id locomo_benchmark - python check_group_data.py --group-id memsciqa_benchmark --detailed -""" - -import asyncio -import argparse -import os -from pathlib import Path -from typing import Dict, Any -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}\n") - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector - - -async def check_group_exists(end_user_id: str) -> Dict[str, Any]: - """ - 检查指定 end_user_id 是否存在数据 - - Args: - end_user_id: 要检查的 end_user ID - - Returns: - 包含统计信息的字典 - """ - connector = Neo4jConnector() - - try: - # 查询该 end_user 的节点总数 - query_total = """ - MATCH (n {end_user_id: $end_user_id}) - RETURN count(n) as total_nodes - """ - result_total = await connector.execute_query(query_total, end_user_id=end_user_id) - total_nodes = result_total[0]["total_nodes"] if result_total else 0 - - # 查询各类型节点的数量 - query_by_type = """ - MATCH (n {end_user_id: $end_user_id}) - RETURN labels(n) as labels, count(n) as count - ORDER BY count DESC - """ - result_by_type = await connector.execute_query(query_by_type, end_user_id=end_user_id) - - # 查询关系数量 - query_relationships = """ - MATCH (n {end_user_id: $end_user_id})-[r]-() - RETURN count(DISTINCT r) as total_relationships - """ - result_rel = await connector.execute_query(query_relationships, end_user_id=end_user_id) - total_relationships = result_rel[0]["total_relationships"] if result_rel else 0 - - return { - "exists": total_nodes > 0, - "total_nodes": total_nodes, - "total_relationships": total_relationships, - "nodes_by_type": result_by_type - } - - finally: - await connector.close() - - -async def get_detailed_stats(end_user_id: str) -> Dict[str, Any]: - """ - 获取详细的统计信息 - - Args: - end_user_id: 要检查的 end_user ID - - Returns: - 详细统计信息字典 - """ - connector = Neo4jConnector() - - try: - stats = {} - - # Chunk 节点统计 - query_chunks = """ - MATCH (c:Chunk {end_user_id: $end_user_id}) - RETURN count(c) as count, - avg(size(c.content)) as avg_content_length - """ - result_chunks = await connector.execute_query(query_chunks, end_user_id=end_user_id) - if result_chunks and result_chunks[0]["count"] > 0: - stats["chunks"] = { - "count": result_chunks[0]["count"], - "avg_content_length": int(result_chunks[0]["avg_content_length"]) if result_chunks[0]["avg_content_length"] else 0 - } - - # Statement 节点统计 - query_statements = """ - MATCH (s:Statement {end_user_id: $end_user_id}) - RETURN count(s) as count - """ - result_statements = await connector.execute_query(query_statements, end_user_id=end_user_id) - if result_statements and result_statements[0]["count"] > 0: - stats["statements"] = { - "count": result_statements[0]["count"] - } - - # Entity 节点统计 - query_entities = """ - MATCH (e:Entity {end_user_id: $end_user_id}) - RETURN count(e) as count, - count(DISTINCT e.entity_type) as unique_types - """ - result_entities = await connector.execute_query(query_entities, end_user_id=end_user_id) - if result_entities and result_entities[0]["count"] > 0: - stats["entities"] = { - "count": result_entities[0]["count"], - "unique_types": result_entities[0]["unique_types"] - } - - # Dialogue 节点统计 - query_dialogues = """ - MATCH (d:Dialogue {end_user_id: $end_user_id}) - RETURN count(d) as count - """ - result_dialogues = await connector.execute_query(query_dialogues, end_user_id=end_user_id) - if result_dialogues and result_dialogues[0]["count"] > 0: - stats["dialogues"] = { - "count": result_dialogues[0]["count"] - } - - # Summary 节点统计 - query_summaries = """ - MATCH (s:Summary {end_user_id: $end_user_id}) - RETURN count(s) as count - """ - result_summaries = await connector.execute_query(query_summaries, end_user_id=end_user_id) - if result_summaries and result_summaries[0]["count"] > 0: - stats["summaries"] = { - "count": result_summaries[0]["count"] - } - - return stats - - finally: - await connector.close() - - -async def list_all_end_users() -> list: - """ - 列出数据库中所有的 end_user_id - - Returns: - end_user_id 列表及其节点数量 - """ - connector = Neo4jConnector() - - try: - query = """ - MATCH (n) - WHERE n.end_user_id IS NOT NULL - RETURN DISTINCT n.end_user_id as end_user_id, count(n) as node_count - ORDER BY node_count DESC - """ - results = await connector.execute_query(query) - return results - - finally: - await connector.close() - - -def print_results(end_user_id: str, stats: Dict[str, Any], detailed_stats: Dict[str, Any] = None): - """ - 打印查询结果 - - Args: - end_user_id: End User ID - stats: 基本统计信息 - detailed_stats: 详细统计信息（可选） - """ - print(f"\n{'='*60}") - print(f"📊 End User ID: {end_user_id}") - print(f"{'='*60}\n") - - if not stats["exists"]: - print("❌ 该 end_user_id 不存在数据") - print("\n💡 提示: 请先运行基准测试以摄入数据") - return - - print(f"✅ 该 end_user_id 存在数据\n") - print(f"📈 基本统计:") - print(f" 总节点数: {stats['total_nodes']}") - print(f" 总关系数: {stats['total_relationships']}") - - if stats["nodes_by_type"]: - print(f"\n📋 节点类型分布:") - for item in stats["nodes_by_type"]: - labels = ", ".join(item["labels"]) - count = item["count"] - print(f" {labels}: {count}") - - if detailed_stats: - print(f"\n🔍 详细统计:") - - if "chunks" in detailed_stats: - print(f" Chunks: {detailed_stats['chunks']['count']} 个") - print(f" 平均内容长度: {detailed_stats['chunks']['avg_content_length']} 字符") - - if "statements" in detailed_stats: - print(f" Statements: {detailed_stats['statements']['count']} 个") - - if "entities" in detailed_stats: - print(f" Entities: {detailed_stats['entities']['count']} 个") - print(f" 唯一类型数: {detailed_stats['entities']['unique_types']}") - - if "dialogues" in detailed_stats: - print(f" Dialogues: {detailed_stats['dialogues']['count']} 个") - - if "summaries" in detailed_stats: - print(f" Summaries: {detailed_stats['summaries']['count']} 个") - - print(f"\n{'='*60}\n") - - -async def interactive_mode(): - """ - 交互式模式 - """ - print("\n" + "="*60) - print("🔍 Neo4j End User 数据检查工具 - 交互模式") - print("="*60 + "\n") - - while True: - print("\n请选择操作:") - print(" 1. 检查指定 end_user_id") - print(" 2. 列出所有 end_user_id") - print(" 3. 退出") - - choice = input("\n请输入选项 (1-3): ").strip() - - if choice == "1": - end_user_id = input("\n请输入 end_user_id: ").strip() - if not end_user_id: - print("❌ end_user_id 不能为空") - continue - - detailed = input("是否显示详细统计? (y/n, 默认 n): ").strip().lower() == 'y' - - print("\n🔄 正在查询...") - stats = await check_group_exists(end_user_id) - - detailed_stats = None - if detailed and stats["exists"]: - detailed_stats = await get_detailed_stats(end_user_id) - - print_results(end_user_id, stats, detailed_stats) - - elif choice == "2": - print("\n🔄 正在查询所有 end_user_id...") - end_users = await list_all_end_users() - - if not end_users: - print("\n❌ 数据库中没有任何 end_user 数据") - else: - print(f"\n{'='*60}") - print(f"📋 数据库中的所有 End User ID") - print(f"{'='*60}\n") - - for idx, end_user in enumerate(end_users, 1): - print(f" {idx}. {end_user['end_user_id']}") - print(f" 节点数: {end_user['node_count']}") - - print(f"\n{'='*60}\n") - - elif choice == "3": - print("\n👋 再见！") - break - - else: - print("\n❌ 无效的选项，请重新选择") - - -async def main(): - """ - 主函数 - """ - parser = argparse.ArgumentParser( - description="检查 Neo4j 中指定 end_user_id 的数据情况", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -示例: - # 交互模式 - python check_group_data.py - - # 检查指定 end_user - python check_group_data.py --end-user-id locomo_benchmark - - # 检查并显示详细统计 - python check_group_data.py --end-user-id memsciqa_benchmark --detailed - - # 列出所有 end_user - python check_group_data.py --list-all - """ - ) - - parser.add_argument( - "--end-user-id", - type=str, - help="要检查的 end_user ID" - ) - - parser.add_argument( - "--detailed", - action="store_true", - help="显示详细统计信息" - ) - - parser.add_argument( - "--list-all", - action="store_true", - help="列出所有 end_user_id" - ) - - args = parser.parse_args() - - # 如果没有提供任何参数，进入交互模式 - if not args.end_user_id and not args.list_all: - await interactive_mode() - return - - # 列出所有 end_user - if args.list_all: - print("\n🔄 正在查询所有 end_user_id...") - end_users = await list_all_end_users() - - if not end_users: - print("\n❌ 数据库中没有任何 end_user 数据") - else: - print(f"\n{'='*60}") - print(f"📋 数据库中的所有 End User ID") - print(f"{'='*60}\n") - - for idx, end_user in enumerate(end_users, 1): - print(f" {idx}. {end_user['end_user_id']}") - print(f" 节点数: {end_user['node_count']}") - - print(f"\n{'='*60}\n") - return - - # 检查指定 end_user - if args.end_user_id: - print(f"\n🔄 正在查询 end_user_id: {args.end_user_id}...") - stats = await check_group_exists(args.end_user_id) - - detailed_stats = None - if args.detailed and stats["exists"]: - print("🔄 正在获取详细统计...") - detailed_stats = await get_detailed_stats(args.end_user_id) - - print_results(args.end_user_id, stats, detailed_stats) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/api/app/core/memory/evaluation/common/metrics.py b/api/app/core/memory/evaluation/common/metrics.py deleted file mode 100644 index 961ce7f0..00000000 --- a/api/app/core/memory/evaluation/common/metrics.py +++ /dev/null @@ -1,100 +0,0 @@ -import math -import re -from typing import List, Dict - -# 评估指标的实现 -def _normalize(text: str) -> List[str]: - """Lowercase, strip punctuation, and split into tokens.""" - text = text.lower().strip() - # Python's re doesn't support \p classes; use a simple non-word filter - text = re.sub(r"[^\w\s]", " ", text) - tokens = [t for t in text.split() if t] - return tokens - - -def exact_match(pred: str, ref: str) -> float: - return float(_normalize(pred) == _normalize(ref)) - - -def jaccard(pred: str, ref: str) -> float: - p = set(_normalize(pred)) - r = set(_normalize(ref)) - if not p and not r: - return 1.0 - if not p or not r: - return 0.0 - return len(p & r) / len(p | r) - - -def f1_score(pred: str, ref: str) -> float: - p_tokens = _normalize(pred) - r_tokens = _normalize(ref) - if not p_tokens and not r_tokens: - return 1.0 - if not p_tokens or not r_tokens: - return 0.0 - p_set = set(p_tokens) - r_set = set(r_tokens) - tp = len(p_set & r_set) - precision = tp / len(p_set) if p_set else 0.0 - recall = tp / len(r_set) if r_set else 0.0 - if precision + recall == 0: - return 0.0 - return 2 * precision * recall / (precision + recall) - - -def bleu1(pred: str, ref: str) -> float: - """Unigram BLEU (BLEU-1) with clipping and brevity penalty.""" - p_tokens = _normalize(pred) - r_tokens = _normalize(ref) - if not p_tokens: - return 0.0 - # Clipped count - r_counts: Dict[str, int] = {} - for t in r_tokens: - r_counts[t] = r_counts.get(t, 0) + 1 - clipped = 0 - p_counts: Dict[str, int] = {} - for t in p_tokens: - p_counts[t] = p_counts.get(t, 0) + 1 - for t, c in p_counts.items(): - clipped += min(c, r_counts.get(t, 0)) - precision = clipped / max(len(p_tokens), 1) - # Brevity penalty - ref_len = len(r_tokens) - pred_len = len(p_tokens) - if pred_len > ref_len or pred_len == 0: - bp = 1.0 - else: - bp = math.exp(1 - ref_len / max(pred_len, 1)) - return bp * precision - - -def percentile(values: List[float], p: float) -> float: - if not values: - return 0.0 - vals = sorted(values) - k = (len(vals) - 1) * p - f = math.floor(k) - c = math.ceil(k) - if f == c: - return vals[int(k)] - return vals[f] + (k - f) * (vals[c] - vals[f]) - - -def latency_stats(latencies_ms: List[float]) -> Dict[str, float]: - """Return basic latency stats: mean, p50, p95, iqr (p75-p25).""" - if not latencies_ms: - return {"mean": 0.0, "p50": 0.0, "p95": 0.0, "iqr": 0.0} - p25 = percentile(latencies_ms, 0.25) - p50 = percentile(latencies_ms, 0.50) - p75 = percentile(latencies_ms, 0.75) - p95 = percentile(latencies_ms, 0.95) - mean = sum(latencies_ms) / max(len(latencies_ms), 1) - return {"mean": mean, "p50": p50, "p95": p95, "iqr": p75 - p25} - - -def avg_context_tokens(contexts: List[str]) -> float: - if not contexts: - return 0.0 - return sum(len(_normalize(c)) for c in contexts) / len(contexts) diff --git a/api/app/core/memory/evaluation/dialogue_queries.py b/api/app/core/memory/evaluation/dialogue_queries.py deleted file mode 100644 index 0aace0ec..00000000 --- a/api/app/core/memory/evaluation/dialogue_queries.py +++ /dev/null @@ -1,62 +0,0 @@ -""" -Dialogue search queries for evaluation purposes. -This file contains Cypher queries for searching dialogues, entities, and chunks. -Placed in evaluation directory to avoid circular imports with src modules. -""" - -# 应该是neo4j browser的cypher语句，需要修改文件名 - -# Entity search queries -SEARCH_ENTITIES_BY_NAME = """ -MATCH (e:ExtractedEntity) -WHERE e.name = $name -RETURN e -""" - -SEARCH_ENTITIES_BY_NAME_FALLBACK = """ -MATCH (e:ExtractedEntity) -WHERE e.name CONTAINS $name -RETURN e -""" - -# Chunk search queries -SEARCH_CHUNKS_BY_CONTENT = """ -MATCH (c:Chunk) -WHERE c.content CONTAINS $content -RETURN c -""" - -# Dialogue search queries -SEARCH_DIALOGUE_BY_DIALOG_ID = """ -MATCH (d:Dialogue) -WHERE d.dialog_id = $dialog_id -RETURN d -""" - -SEARCH_DIALOGUES_BY_CONTENT = """ -MATCH (d:Dialogue) -WHERE d.content CONTAINS $q -RETURN d -""" - -DIALOGUE_EMBEDDING_SEARCH = """ -WITH $embedding AS q -MATCH (d:Dialogue) -WHERE d.dialog_embedding IS NOT NULL - AND ($end_user_id IS NULL OR d.end_user_id = $end_user_id) -WITH d, q, d.dialog_embedding AS v -WITH d, - reduce(dot = 0.0, i IN range(0, size(q)-1) | dot + toFloat(q[i]) * toFloat(v[i])) AS dot, - sqrt(reduce(qs = 0.0, i IN range(0, size(q)-1) | qs + toFloat(q[i]) * toFloat(q[i]))) AS qnorm, - sqrt(reduce(vs = 0.0, i IN range(0, size(v)-1) | vs + toFloat(v[i]) * toFloat(v[i]))) AS vnorm -WITH d, CASE WHEN qnorm = 0 OR vnorm = 0 THEN 0.0 ELSE dot / (qnorm * vnorm) END AS score -WHERE score > $threshold -RETURN d.id AS dialog_id, - d.end_user_id AS end_user_id, - d.content AS content, - d.created_at AS created_at, - d.expired_at AS expired_at, - score -ORDER BY score DESC -LIMIT $limit -""" diff --git a/api/app/core/memory/evaluation/extraction_utils.py b/api/app/core/memory/evaluation/extraction_utils.py deleted file mode 100644 index 43ef6fe0..00000000 --- a/api/app/core/memory/evaluation/extraction_utils.py +++ /dev/null @@ -1,444 +0,0 @@ -import os -import asyncio -import json -from typing import List, Dict, Any, Optional -from datetime import datetime -from uuid import UUID -import re - -from app.core.memory.llm_tools.openai_client import LLMClient -from app.core.memory.storage_services.extraction_engine.knowledge_extraction.chunk_extraction import DialogueChunker -from app.core.memory.models.message_models import DialogData, ConversationContext, ConversationMessage -import os -import sys -from pathlib import Path -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent / "app" / "core" / "memory" / "evaluation" / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.utils.llm.llm_utils import get_llm_client - -# 使用新的模块化架构 -from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ExtractionOrchestrator - -# Import from database module -from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j - -# Cypher queries for evaluation -# Note: Entity, chunk, and dialogue search queries have been moved to evaluation/dialogue_queries.py - - -async def ingest_contexts_via_full_pipeline( - contexts: List[str], - end_user_id: str, - chunker_strategy: str | None = None, - embedding_name: str | None = None, - save_chunk_output: bool = False, - save_chunk_output_path: str | None = None, - reset_group: bool = False, -) -> bool: - """ - 使用新的 ExtractionOrchestrator 运行完整的提取流水线 - - Run the full extraction pipeline on provided dialogue contexts and save to Neo4j. - This function uses the new ExtractionOrchestrator architecture for better maintainability. - - Args: - contexts: List of dialogue texts, each containing lines like "role: message". - end_user_id: Group ID to assign to generated DialogData and graph nodes. - chunker_strategy: Optional chunker strategy; defaults to SELECTED_CHUNKER_STRATEGY. - embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID. - save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging. - save_chunk_output_path: Optional output path; defaults to src/chunker_test_output.txt. - reset_group: If True, clear existing data for this group before ingestion. - Returns: - True if data saved successfully, False otherwise. - """ - chunker_strategy = chunker_strategy or os.getenv("EVAL_CHUNKER_STRATEGY", "RecursiveChunker") - embedding_name = embedding_name or os.getenv("EVAL_EMBEDDING_ID") - - # Check if we should reset from environment variable if not explicitly set - if not reset_group: - reset_group = os.getenv("EVAL_RESET_ON_INGEST", "false").lower() in ("true", "1", "yes") - - # Step 0: Reset group if requested - if reset_group: - print(f"[Ingestion] 🗑️ 清空 end_user '{end_user_id}' 的现有数据...") - try: - from app.repositories.neo4j.neo4j_connector import Neo4jConnector - connector = Neo4jConnector() - try: - # 删除该 end_user 的所有节点和关系 - query = """ - MATCH (n {end_user_id: $end_user_id}) - DETACH DELETE n - """ - await connector.execute_query(query, end_user_id=end_user_id) - print(f"[Ingestion] ✅ End User '{end_user_id}' 已清空") - finally: - await connector.close() - except Exception as e: - print(f"[Ingestion] ⚠️ 清空 end_user 失败: {e}") - # 继续执行，不中断摄入流程 - - # Step 1: Initialize LLM client - llm_client = None - try: - # 使用评估配置中的 LLM ID - llm_id = os.getenv("EVAL_LLM_ID") - if not llm_id: - print("[Ingestion] ❌ EVAL_LLM_ID not set in .env.evaluation") - return False - - from app.db import get_db - - db = next(get_db()) - try: - llm_client = get_llm_client(llm_id, db) - finally: - db.close() - except Exception as e: - print(f"[Ingestion] LLM client unavailable: {e}") - return False - - # Step 2: Parse contexts and create DialogData with chunks - print(f"[Ingestion] Parsing {len(contexts)} contexts...") - chunker = DialogueChunker(chunker_strategy) - dialog_data_list: List[DialogData] = [] - - for idx, ctx in enumerate(contexts): - messages: List[ConversationMessage] = [] - - # Improved parsing: capture multi-line message blocks, normalize roles - pattern = r"^\s*(用户|AI|assistant|user)\s*[：:]\s*(.+?)(?=\n\s*(?:用户|AI|assistant|user)\s*[：:]|\Z)" - matches = list(re.finditer(pattern, ctx, flags=re.MULTILINE | re.DOTALL)) - - if matches: - for m in matches: - raw_role = m.group(1).strip() - content = m.group(2).strip() - norm_role = "AI" if raw_role.lower() in ("ai", "assistant") else "用户" - messages.append(ConversationMessage(role=norm_role, msg=content)) - else: - # Fallback: line-by-line parsing - for raw in ctx.split("\n"): - line = raw.strip() - if not line: - continue - m = re.match(r'^\s*([^:：]+)\s*[：:]\s*(.+)', line) - if m: - role = m.group(1).strip() - msg = m.group(2).strip() - norm_role = "AI" if role.lower() in ("ai", "assistant") else "用户" - messages.append(ConversationMessage(role=norm_role, msg=msg)) - else: - # Final fallback: treat as user message - default_role = "AI" if re.match(r'^\s*(assistant|AI)\b', line, flags=re.IGNORECASE) else "用户" - messages.append(ConversationMessage(role=default_role, msg=line)) - - context_model = ConversationContext(msgs=messages) - dialog = DialogData( - context=context_model, - ref_id=f"pipeline_item_{idx}", - end_user_id=end_user_id, - user_id="default_user", - apply_id="default_application", - ) - # Generate chunks - dialog.chunks = await chunker.process_dialogue(dialog) - dialog_data_list.append(dialog) - - if not dialog_data_list: - print("[Ingestion] No dialogs to process.") - return False - - print(f"[Ingestion] Parsed {len(dialog_data_list)} dialogs with chunks") - - # Step 3: Optionally save chunking outputs for debugging - if save_chunk_output: - try: - def _serialize_datetime(obj): - if isinstance(obj, datetime): - return obj.isoformat() - raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable") - - from app.core.config import settings - settings.ensure_memory_output_dir() - default_path = settings.get_memory_output_path("chunker_test_output.txt") - out_path = save_chunk_output_path or default_path - - combined_output = [dd.model_dump() for dd in dialog_data_list] - with open(out_path, "w", encoding="utf-8") as f: - json.dump(combined_output, f, ensure_ascii=False, indent=4, default=_serialize_datetime) - print(f"[Ingestion] Saved chunking results to: {out_path}") - except Exception as e: - print(f"[Ingestion] Failed to save chunking results: {e}") - - # Step 4: Initialize embedder client - from app.core.models.base import RedBearModelConfig - from app.core.memory.utils.config.config_utils import get_embedder_config - from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient - from app.db import get_db - - try: - db = next(get_db()) - try: - embedder_config_dict = get_embedder_config(embedding_name, db) - embedder_config = RedBearModelConfig(**embedder_config_dict) - embedder_client = OpenAIEmbedderClient(embedder_config) - finally: - db.close() - except Exception as e: - print(f"[Ingestion] Failed to initialize embedder client: {e}") - return False - - # Step 5: Initialize Neo4j connector - connector = Neo4jConnector() - - # Step 6: 构建 MemoryConfig（从环境变量直接构建，不依赖数据库） - print("[Ingestion] 构建 MemoryConfig from environment variables...") - from app.schemas.memory_config_schema import MemoryConfig - - try: - # 从环境变量获取配置参数 - llm_id = os.getenv("EVAL_LLM_ID") - embedding_id = os.getenv("EVAL_EMBEDDING_ID") - chunker_strategy_env = os.getenv("EVAL_CHUNKER_STRATEGY", "RecursiveChunker") - - if not llm_id or not embedding_id: - print("[Ingestion] ❌ EVAL_LLM_ID or EVAL_EMBEDDING_ID is not set in .env.evaluation") - print("[Ingestion] Please set both EVAL_LLM_ID and EVAL_EMBEDDING_ID") - await connector.close() - return False - - # 从数据库获取模型信息（仅用于显示名称） - from app.db import get_db - db = next(get_db()) - try: - from sqlalchemy import text - # 获取 LLM 模型信息（从 model_configs 表） - llm_result = db.execute( - text("SELECT name FROM model_configs WHERE id = :id"), - {"id": llm_id} - ).fetchone() - llm_model_name = llm_result[0] if llm_result else "Unknown LLM" - - # 获取 Embedding 模型信息（从 model_configs 表） - emb_result = db.execute( - text("SELECT name FROM model_configs WHERE id = :id"), - {"id": embedding_id} - ).fetchone() - embedding_model_name = emb_result[0] if emb_result else "Unknown Embedding" - except Exception as e: - # 如果查询失败，使用默认名称 - print(f"[Ingestion] Warning: Failed to query model names from database: {e}") - llm_model_name = f"LLM ({llm_id[:8]}...)" - embedding_model_name = f"Embedding ({embedding_id[:8]}...)" - finally: - db.close() - - # 构建 MemoryConfig 对象（使用最小必需配置） - from uuid import uuid4 - memory_config = MemoryConfig( - config_id=0, # 评估环境不需要真实的 config_id - config_name="evaluation_config", - workspace_id=uuid4(), # 临时 workspace_id - workspace_name="evaluation_workspace", - tenant_id=uuid4(), # 临时 tenant_id - llm_model_id=UUID(llm_id), - llm_model_name=llm_model_name, - embedding_model_id=UUID(embedding_id), - embedding_model_name=embedding_model_name, - storage_type="neo4j", - chunker_strategy=chunker_strategy_env, - reflexion_enabled=False, - reflexion_iteration_period=3, - reflexion_range="partial", - reflexion_baseline="TIME", - loaded_at=datetime.now(), - # 可选字段使用默认值 - rerank_model_id=None, - rerank_model_name=None, - llm_params={}, - embedding_params={}, - config_version="2.0", - ) - - print(f"[Ingestion] ✅ 构建 MemoryConfig 成功") - print(f"[Ingestion] LLM: {llm_model_name}") - print(f"[Ingestion] Embedding: {embedding_model_name}") - print(f"[Ingestion] Chunker: {chunker_strategy_env}") - - except Exception as e: - print(f"[Ingestion] ❌ Failed to build MemoryConfig: {e}") - print(f"[Ingestion] Please check:") - print(f"[Ingestion] 1. EVAL_LLM_ID and EVAL_EMBEDDING_ID are set in .env.evaluation") - print(f"[Ingestion] 2. Model IDs exist in the models table") - print(f"[Ingestion] 3. Database connection is working") - await connector.close() - return False - - # Step 7: Initialize and run ExtractionOrchestrator - print("[Ingestion] Running extraction pipeline with ExtractionOrchestrator...") - from app.services.memory_config_service import MemoryConfigService - config = MemoryConfigService.get_pipeline_config(memory_config) - - orchestrator = ExtractionOrchestrator( - llm_client=llm_client, - embedder_client=embedder_client, - connector=connector, - config=config, - embedding_id=str(memory_config.embedding_model_id), # 传递 embedding_id - ) - - try: - # Run the complete extraction pipeline - result = await orchestrator.run(dialog_data_list, is_pilot_run=False) - - # Handle different return formats: - # - Pilot mode: 7 values (without dedup_details) - # - Normal mode: 8 values (with dedup_details at the end) - if len(result) == 8: - # Normal mode: includes dedup_details - ( - dialogue_nodes, - chunk_nodes, - statement_nodes, - entity_nodes, - statement_chunk_edges, - statement_entity_edges, - entity_entity_edges, - _, # dedup_details - not needed here - ) = result - elif len(result) == 7: - # Pilot mode or older version: no dedup_details - ( - dialogue_nodes, - chunk_nodes, - statement_nodes, - entity_nodes, - statement_chunk_edges, - statement_entity_edges, - entity_entity_edges, - ) = result - else: - raise ValueError(f"Unexpected number of return values: {len(result)}") - - print(f"[Ingestion] Extraction completed: {len(statement_nodes)} statements, {len(entity_nodes)} entities") - - except ValueError as e: - # If unpacking fails, provide helpful error message - print(f"[Ingestion] Extraction pipeline result unpacking failed: {e}") - print(f"[Ingestion] Result type: {type(result)}, length: {len(result) if hasattr(result, '__len__') else 'N/A'}") - if hasattr(result, '__len__') and len(result) > 0: - print(f"[Ingestion] First element type: {type(result[0])}") - await connector.close() - return False - except Exception as e: - print(f"[Ingestion] Extraction pipeline failed: {e}") - import traceback - traceback.print_exc() - await connector.close() - return False - - # Step 7: Generate memory summaries - print("[Ingestion] Generating memory summaries...") - try: - from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import ( - memory_summary_generation, - ) - from app.repositories.neo4j.add_nodes import add_memory_summary_nodes - from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges - - summaries = await memory_summary_generation( - chunked_dialogs=dialog_data_list, - llm_client=llm_client, - embedder_client=embedder_client - ) - print(f"[Ingestion] Generated {len(summaries)} memory summaries") - except Exception as e: - print(f"[Ingestion] Warning: Failed to generate memory summaries: {e}") - summaries = [] - - # Step 8: Save to Neo4j - print("[Ingestion] Saving to Neo4j...") - try: - success = await save_dialog_and_statements_to_neo4j( - dialogue_nodes=dialogue_nodes, - chunk_nodes=chunk_nodes, - statement_nodes=statement_nodes, - entity_nodes=entity_nodes, - entity_edges=entity_entity_edges, - statement_chunk_edges=statement_chunk_edges, - statement_entity_edges=statement_entity_edges, - connector=connector - ) - - # Save memory summaries separately - if summaries: - try: - await add_memory_summary_nodes(summaries, connector) - await add_memory_summary_statement_edges(summaries, connector) - print(f"[Ingestion] Saved {len(summaries)} memory summary nodes to Neo4j") - except Exception as e: - print(f"[Ingestion] Warning: Failed to save summary nodes: {e}") - - await connector.close() - - if success: - print("[Ingestion] Successfully saved all data to Neo4j!") - else: - print("[Ingestion] Failed to save data to Neo4j") - return success - - except Exception as e: - print(f"[Ingestion] Failed to save data to Neo4j: {e}") - await connector.close() - return False - - -async def handle_context_processing(args): - """Handle context-based processing from command line arguments.""" - contexts = [] - - if args.contexts: - contexts.extend(args.contexts) - - if args.context_file: - try: - with open(args.context_file, 'r', encoding='utf-8') as f: - contexts.extend(line.strip() for line in f if line.strip()) - except Exception as e: - print(f"Error reading context file: {e}") - return False - - if not contexts: - print("No contexts provided for processing.") - return False - - return await main_from_contexts(contexts, args.context_end_user_id) - - -async def main_from_contexts(contexts: List[str], end_user_id: str): - """Run the pipeline from provided dialogue contexts instead of test data.""" - print("=== Running pipeline from provided contexts ===") - - success = await ingest_contexts_via_full_pipeline( - contexts=contexts, - end_user_id=end_user_id, - chunker_strategy=SELECTED_CHUNKER_STRATEGY, - embedding_name=SELECTED_EMBEDDING_ID, - save_chunk_output=True - ) - - if success: - print("Successfully processed and saved contexts to Neo4j!") - else: - print("Failed to process contexts.") - - return success diff --git a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py b/api/app/core/memory/evaluation/locomo/locomo_benchmark.py deleted file mode 100644 index eed75016..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_benchmark.py +++ /dev/null @@ -1,770 +0,0 @@ -""" -LoCoMo Benchmark Script - -This module provides the main entry point for running LoCoMo benchmark evaluations. -It orchestrates data loading, ingestion, retrieval, LLM inference, and metric calculation -in a clean, maintainable way. - -Usage: - python locomo_benchmark.py --sample_size 20 --search_type hybrid -""" - -import argparse -import asyncio -import json -import os -import time -from datetime import datetime -from typing import List, Dict, Any, Optional -from pathlib import Path -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.models.base import RedBearModelConfig -from app.core.memory.utils.config.config_utils import get_embedder_config -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.evaluation.common.metrics import ( - f1_score, - bleu1, - jaccard, - latency_stats, - avg_context_tokens -) -from app.core.memory.evaluation.locomo.locomo_metrics import ( - locomo_f1_score, - locomo_multi_f1, - get_category_name -) -from app.core.memory.evaluation.locomo.locomo_utils import ( - load_locomo_data, - extract_conversations, - resolve_temporal_references, - select_and_format_information, - retrieve_relevant_information, -) -from app.core.memory.utils.llm.llm_utils import MemoryClientFactory -from app.db import get_db_context -from app.services.memory_config_service import MemoryConfigService - -# Get configuration from environment variables -PROJECT_ROOT = str(Path(__file__).resolve().parents[5]) # api directory -SELECTED_EMBEDDING_ID = os.getenv("EVAL_EMBEDDING_ID", "e2a6392d-ca63-4d59-a523-647420b59cb2") -SELECTED_end_user_id = os.getenv("LOCOMO_END_USER_ID") or os.getenv("EVAL_END_USER_ID", "locomo_benchmark") -SELECTED_LLM_ID = os.getenv("EVAL_LLM_ID", "2c9b0782-7a85-4740-ba84-4baf77f256c4") - - -# ============================================================================ -# Step 1: Data Loading -# ============================================================================ - -def step_load_data(data_path: str, sample_size: int) -> List[Dict[str, Any]]: - """ - Load QA pairs from LoCoMo dataset. - - Args: - data_path: Path to locomo10.json file - sample_size: Number of QA pairs to load (0 for all) - - Returns: - List of QA items from the first conversation - """ - print("📂 Loading LoCoMo data...") - - # Load the dataset - qa_items = load_locomo_data(data_path, sample_size) - - print(f"✅ Loaded {len(qa_items)} QA pairs from first conversation\n") - return qa_items - - -# ============================================================================ -# Step 2: Data Ingestion -# ============================================================================ - -async def ingest_conversations_if_needed( - conversations: List[str], - end_user_id: str, - reset: bool = False -) -> bool: - """ - Ingest conversations into Neo4j database. - - Args: - conversations: List of conversation strings (already formatted) - end_user_id: Database end_user ID - reset: Whether to reset the group before ingestion - - Returns: - True if successful, False otherwise - """ - try: - from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline - - # Conversations are already formatted as strings, use them directly - await ingest_contexts_via_full_pipeline(conversations, end_user_id) - return True - - except Exception as e: - print(f"⚠️ Ingestion error: {e}") - import traceback - traceback.print_exc() - return False - - -async def step_ingest_data( - data_path: str, - end_user_id: str, - skip_ingest: bool, - reset_group: bool, - max_messages: Optional[int] = None -) -> bool: - """ - Ingest conversations into Neo4j database if needed. - - Args: - data_path: Path to locomo10.json file - end_user_id: Database end_user ID - skip_ingest: Whether to skip ingestion - reset_group: Whether to reset the group before ingestion - max_messages: Maximum messages per dialogue to ingest (for testing) - - Returns: - True if ingestion succeeded or was skipped, False otherwise - """ - if skip_ingest: - print("⏭️ Skipping data ingestion (using existing data in Neo4j)") - print(f" End User ID: {end_user_id}\n") - else: - print("💾 Checking database ingestion...") - try: - # Extract conversations with optional message limit - conversations = extract_conversations( - data_path, - max_dialogues=1, - max_messages_per_dialogue=max_messages - ) - print(f"📝 Extracted {len(conversations)} conversations") - - # Always ingest for now (ingestion check not implemented) - print(f"🔄 Ingesting conversations into end_user '{end_user_id}'...") - success = await ingest_conversations_if_needed( - conversations=conversations, - end_user_id=end_user_id, - reset=reset_group - ) - - if success: - print("✅ Ingestion completed successfully\n") - else: - print("⚠️ Ingestion may have failed, continuing anyway\n") - - except Exception as e: - print(f"❌ Ingestion failed: {e}") - import traceback - traceback.print_exc() - print("⚠️ Continuing with evaluation (database may be empty)\n") - - return True - - -# ============================================================================ -# Step 3: Initialize Clients -# ============================================================================ - -def step_initialize_clients(llm_id: str, embedding_id: str): - """ - Initialize Neo4j connector, LLM client, and embedder. - - Args: - llm_id: LLM model ID - embedding_id: Embedding model ID - - Returns: - Tuple of (connector, llm_client, embedder) - """ - print("🔧 Initializing clients...") - - connector = Neo4jConnector() - - # Get database session - from app.db import get_db - db = next(get_db()) - try: - llm_client = get_llm_client(llm_id, db) - cfg_dict = get_embedder_config(embedding_id, db) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - finally: - db.close() - - print("✅ Clients initialized\n") - return connector, llm_client, embedder - - -# ============================================================================ -# Step 4: Process Questions -# ============================================================================ - -async def step_process_all_questions( - qa_items: List[Dict[str, Any]], - end_user_id: str, - search_type: str, - search_limit: int, - context_char_budget: int, - connector: Neo4jConnector, - embedder: OpenAIEmbedderClient, - llm_client: Any -) -> List[Dict[str, Any]]: - """Process all QA items: retrieve, generate, and calculate metrics.""" - print(f"🔍 Processing {len(qa_items)} questions...") - print(f"{'='*60}\n") - - samples: List[Dict[str, Any]] = [] - anchor_date = datetime(2023, 5, 8) - - for idx, item in enumerate(qa_items, 1): - question = item.get("question", "") - ground_truth = item.get("answer", "") - category = get_category_name(item) - ground_truth_str = str(ground_truth) if ground_truth is not None else "" - - print(f"[{idx}/{len(qa_items)}] Category: {category}") - print(f"❓ Question: {question}") - print(f"✅ Ground Truth: {ground_truth_str}") - - # Retrieve - t_search_start = time.time() - try: - retrieved_info = await retrieve_relevant_information( - question=question, - end_user_id=end_user_id, - search_type=search_type, - search_limit=search_limit, - connector=connector, - embedder=embedder - ) - search_latency = (time.time() - t_search_start) * 1000 - print(f"🔍 Retrieved {len(retrieved_info)} documents ({search_latency:.1f}ms)") - except Exception as e: - print(f"❌ Retrieval failed: {e}") - retrieved_info = [] - search_latency = 0.0 - - # Format context - context_text = select_and_format_information( - retrieved_info=retrieved_info, - question=question, - max_chars=context_char_budget - ) - context_text = resolve_temporal_references(context_text, anchor_date) - if context_text: - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n{context_text}" - else: - context_text = "No relevant context found." - - print(f"📝 Context: {len(context_text)} chars, {len(retrieved_info)} docs") - - # Generate answer - messages = [ - { - "role": "system", - "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - ) - }, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}" - } - ] - - t_llm_start = time.time() - try: - response = await llm_client.chat(messages=messages) - llm_latency = (time.time() - t_llm_start) * 1000 - if hasattr(response, 'content'): - prediction = response.content.strip() - elif isinstance(response, dict): - prediction = response["choices"][0]["message"]["content"].strip() - else: - prediction = "Unknown" - print(f"🤖 Prediction: {prediction} ({llm_latency:.1f}ms)") - except Exception as e: - print(f"❌ LLM failed: {e}") - prediction = "Unknown" - llm_latency = 0.0 - - # Calculate metrics - f1_val = f1_score(prediction, ground_truth_str) - bleu1_val = bleu1(prediction, ground_truth_str) - jaccard_val = jaccard(prediction, ground_truth_str) - if item.get("category") == 1: - locomo_f1_val = locomo_multi_f1(prediction, ground_truth_str) - else: - locomo_f1_val = locomo_f1_score(prediction, ground_truth_str) - - print(f"📊 Metrics - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, " - f"Jaccard: {jaccard_val:.3f}, LoCoMo F1: {locomo_f1_val:.3f}") - print() - - samples.append({ - "question": question, - "ground_truth": ground_truth_str, - "prediction": prediction, - "category": category, - "metrics": { - "f1": f1_val, - "bleu1": bleu1_val, - "jaccard": jaccard_val, - "locomo_f1": locomo_f1_val - }, - "retrieval": { - "num_docs": len(retrieved_info), - "context_length": len(context_text) - }, - "context_tokens": len(context_text.split()), - "timing": { - "search_ms": search_latency, - "llm_ms": llm_latency - } - }) - - return samples - - -# ============================================================================ -# Step 5: Aggregate Results -# ============================================================================ - -def step_aggregate_results(samples: List[Dict[str, Any]]) -> Dict[str, Any]: - """Aggregate metrics from all samples.""" - print(f"\n{'='*60}") - print("📊 Aggregating Results") - print(f"{'='*60}\n") - - if not samples: - return { - "overall_metrics": {}, - "by_category": {}, - "latency": {}, - "context_stats": {} - } - - # Extract metrics - f1_scores = [s["metrics"]["f1"] for s in samples] - bleu1_scores = [s["metrics"]["bleu1"] for s in samples] - jaccard_scores = [s["metrics"]["jaccard"] for s in samples] - locomo_f1_scores = [s["metrics"]["locomo_f1"] for s in samples] - - # Extract timing - latencies_search = [s["timing"]["search_ms"] for s in samples] - latencies_llm = [s["timing"]["llm_ms"] for s in samples] - - # Extract context stats - context_counts = [s["retrieval"]["num_docs"] for s in samples] - context_chars = [s["retrieval"]["context_length"] for s in samples] - context_tokens = [s["context_tokens"] for s in samples] - - # Overall metrics - overall_metrics = { - "f1": sum(f1_scores) / len(f1_scores) if f1_scores else 0.0, - "bleu1": sum(bleu1_scores) / len(bleu1_scores) if bleu1_scores else 0.0, - "jaccard": sum(jaccard_scores) / len(jaccard_scores) if jaccard_scores else 0.0, - "locomo_f1": sum(locomo_f1_scores) / len(locomo_f1_scores) if locomo_f1_scores else 0.0 - } - - # Per-category metrics - category_data: Dict[str, Dict[str, List[float]]] = {} - for sample in samples: - cat = sample["category"] - if cat not in category_data: - category_data[cat] = { - "f1": [], - "bleu1": [], - "jaccard": [], - "locomo_f1": [] - } - category_data[cat]["f1"].append(sample["metrics"]["f1"]) - category_data[cat]["bleu1"].append(sample["metrics"]["bleu1"]) - category_data[cat]["jaccard"].append(sample["metrics"]["jaccard"]) - category_data[cat]["locomo_f1"].append(sample["metrics"]["locomo_f1"]) - - by_category: Dict[str, Dict[str, Any]] = {} - for cat, metrics_lists in category_data.items(): - by_category[cat] = { - "count": len(metrics_lists["f1"]), - "f1": sum(metrics_lists["f1"]) / len(metrics_lists["f1"]), - "bleu1": sum(metrics_lists["bleu1"]) / len(metrics_lists["bleu1"]), - "jaccard": sum(metrics_lists["jaccard"]) / len(metrics_lists["jaccard"]), - "locomo_f1": sum(metrics_lists["locomo_f1"]) / len(metrics_lists["locomo_f1"]) - } - - # Latency statistics - latency = { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm) - } - - # Context statistics - context_stats = { - "avg_retrieved_docs": sum(context_counts) / len(context_counts) if context_counts else 0.0, - "avg_context_chars": sum(context_chars) / len(context_chars) if context_chars else 0.0, - "avg_context_tokens": sum(context_tokens) / len(context_tokens) if context_tokens else 0.0 - } - - return { - "overall_metrics": overall_metrics, - "by_category": by_category, - "latency": latency, - "context_stats": context_stats - } - - -# ============================================================================ -# Step 6: Result Saving -# ============================================================================ - -def step_save_results( - result: Dict[str, Any], - output_dir: Optional[str] -) -> str: - """ - Save evaluation results to JSON file. - - Args: - result: Complete result dictionary - output_dir: Directory to save results (uses default if None) - - Returns: - Path to saved file - """ - if output_dir is None: - # Use absolute path to ensure results are saved in the correct location - script_dir = Path(__file__).resolve().parent - output_dir = script_dir / "results" - else: - # Convert to Path object - output_dir = Path(output_dir) - # If relative path, make it relative to script directory - if not output_dir.is_absolute(): - script_dir = Path(__file__).resolve().parent - output_dir = script_dir / output_dir - - # Create directory if it doesn't exist - output_dir.mkdir(parents=True, exist_ok=True) - - timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S") - output_path = output_dir / f"locomo_{timestamp_str}.json" - - try: - with open(output_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"✅ Results saved to: {output_path}\n") - return str(output_path) - except Exception as e: - print(f"❌ Failed to save results: {e}") - print("📊 Printing results to console instead:\n") - print(json.dumps(result, ensure_ascii=False, indent=2)) - return "" - - -# ============================================================================ -# Main Orchestration Function -# ============================================================================ - - -async def run_locomo_benchmark( - sample_size: int = 20, - end_user_id: Optional[str] = None, - search_type: str = "hybrid", - search_limit: int = 12, - context_char_budget: int = 8000, - reset_group: bool = False, - skip_ingest: bool = False, - output_dir: Optional[str] = None, - max_ingest_messages: Optional[int] = None -) -> Dict[str, Any]: - """ - Run LoCoMo benchmark evaluation. - - This function orchestrates the complete evaluation pipeline by calling - well-defined step functions: - 1. Load LoCoMo dataset (only QA pairs from first conversation) - 2. Ingest conversations into database (unless skip_ingest=True) - 3. Initialize clients (Neo4j, LLM, Embedder) - 4. Process all questions (retrieve, generate, calculate metrics) - 5. Aggregate results - 6. Save results to file - - Note: By default, only the first conversation is ingested into the database, - and only QA pairs from that conversation are evaluated. This ensures that - all questions have corresponding memory in the database for retrieval. - - Args: - sample_size: Number of QA pairs to evaluate (from first conversation) - end_user_id: Database end_user ID for retrieval (uses default if None) - search_type: "keyword", "embedding", or "hybrid" - search_limit: Max documents to retrieve per query - context_char_budget: Max characters for context - reset_group: Whether to clear and re-ingest data - skip_ingest: If True, skip data ingestion and use existing data in Neo4j - output_dir: Directory to save results (uses default if None) - max_ingest_messages: Max messages per dialogue to ingest (for testing, None = all) - - Returns: - Dictionary with evaluation results including metrics, timing, and samples - """ - # Use default end_user_id if not provided - # 优先级：命令行参数 > LOCOMO_END_USER_ID > EVAL_END_USER_ID > 默认值 - if end_user_id is None: - end_user_id = os.getenv("LOCOMO_END_USER_ID") or os.getenv("EVAL_END_USER_ID", "locomo_benchmark") - - # Get model IDs from config - llm_id = os.getenv("EVAL_LLM_ID", "6dc52e1b-9cec-4194-af66-a74c6307fc3f") - embedding_id = os.getenv("EVAL_EMBEDDING_ID", "e2a6392d-ca63-4d59-a523-647420b59cb2") - - # Determine data path - dataset_dir = Path(__file__).resolve().parent.parent / "dataset" - data_path = dataset_dir / "locomo10.json" - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 locomo10.json 放置在: {dataset_dir}" - ) - - # Print configuration - print(f"\n{'='*60}") - print("🚀 Starting LoCoMo Benchmark Evaluation") - print(f"{'='*60}") - print("📊 Configuration:") - print(f" Sample size: {sample_size}") - print(f" End User ID: {end_user_id}") - print(f" Search type: {search_type}") - print(f" Search limit: {search_limit}") - print(f" Context budget: {context_char_budget} chars") - print(f" Data path: {data_path}") - if max_ingest_messages: - print(f" Max ingest messages: {max_ingest_messages} (testing mode)") - print(f"{'='*60}\n") - - # Step 1: Load LoCoMo data （加载数据） - try: - qa_items = step_load_data(data_path, sample_size) - except Exception as e: - print(f"❌ Failed to load data: {e}") - return { - "error": f"Data loading failed: {e}", - "timestamp": datetime.now().isoformat() - } - - # Step 2: Ingest data if needed（数据摄入） - await step_ingest_data(data_path, end_user_id, skip_ingest, reset_group, max_ingest_messages) - - # Step 3: Initialize clients （初始化客户端） - connector, llm_client, embedder = step_initialize_clients(llm_id, embedding_id) - - # Step 4: Process all questions （处理所有问题） - try: - samples = await step_process_all_questions( - qa_items=qa_items, - end_user_id=end_user_id, - search_type=search_type, - search_limit=search_limit, - context_char_budget=context_char_budget, - connector=connector, - embedder=embedder, - llm_client=llm_client - ) - finally: - await connector.close() - - # Step 5: Aggregate results （聚合答案） - aggregated = step_aggregate_results(samples) - - # Build final result dictionary - result = { - "dataset": "locomo", - "sample_size": len(qa_items), - "timestamp": datetime.now().isoformat(), - "params": { - "end_user_id": end_user_id, - "search_type": search_type, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "llm_id": llm_id, - "embedding_id": embedding_id - }, - "overall_metrics": aggregated["overall_metrics"], - "by_category": aggregated["by_category"], - "latency": aggregated["latency"], - "context_stats": aggregated["context_stats"], - "samples": samples - } - - # Step 6: Save results （保存结果） - step_save_results(result, output_dir) - - return result - - -def main(): - """ - Parse command-line arguments and run benchmark. - - This function provides a CLI interface for running LoCoMo benchmarks - with configurable parameters. - - Configuration priority: Command-line args > Environment variables > Code defaults - """ - # Load environment variables first - load_dotenv() - - # Get defaults from environment variables - env_sample_size = os.getenv("LOCOMO_SAMPLE_SIZE") - env_search_limit = os.getenv("LOCOMO_SEARCH_LIMIT") - env_context_budget = os.getenv("LOCOMO_CONTEXT_CHAR_BUDGET") - env_output_dir = os.getenv("LOCOMO_OUTPUT_DIR") - env_skip_ingest = os.getenv("LOCOMO_SKIP_INGEST", "false").lower() in ("true", "1", "yes") - - # Convert to appropriate types with fallback to code defaults - default_sample_size = int(env_sample_size) if env_sample_size else 20 - default_search_limit = int(env_search_limit) if env_search_limit else 12 - default_context_budget = int(env_context_budget) if env_context_budget else 8000 - default_output_dir = env_output_dir if env_output_dir else None - - parser = argparse.ArgumentParser( - description="Run LoCoMo benchmark evaluation", - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - - parser.add_argument( - "--sample_size", - type=int, - default=default_sample_size, - help=f"Number of QA pairs to evaluate (env: LOCOMO_SAMPLE_SIZE={env_sample_size or 'not set'}, 0 for all)" - ) - parser.add_argument( - "--end_user_id", - type=str, - default=None, - help="Database end user ID for retrieval (uses LOCOMO_END_USER_ID or EVAL_END_USER_ID if not specified)" - ) - parser.add_argument( - "--search_type", - type=str, - default="hybrid", - choices=["keyword", "embedding", "hybrid"], - help="Search strategy to use" - ) - parser.add_argument( - "--search_limit", - type=int, - default=default_search_limit, - help=f"Maximum number of documents to retrieve per query (env: LOCOMO_SEARCH_LIMIT={env_search_limit or 'not set'})" - ) - parser.add_argument( - "--context_char_budget", - type=int, - default=default_context_budget, - help=f"Maximum characters for context (env: LOCOMO_CONTEXT_CHAR_BUDGET={env_context_budget or 'not set'})" - ) - parser.add_argument( - "--reset_group", - action="store_true", - help="Clear and re-ingest data (not implemented)" - ) - parser.add_argument( - "--skip_ingest", - action="store_true", - default=env_skip_ingest, - help=f"Skip data ingestion and use existing data in Neo4j (env: LOCOMO_SKIP_INGEST={os.getenv('LOCOMO_SKIP_INGEST', 'false')})" - ) - parser.add_argument( - "--output_dir", - type=str, - default=default_output_dir, - help=f"Directory to save results (env: LOCOMO_OUTPUT_DIR={env_output_dir or 'not set'})" - ) - parser.add_argument( - "--max_ingest_messages", - type=int, - default=None, - help="Maximum messages per dialogue to ingest (for testing, default: all messages)" - ) - - args = parser.parse_args() - - # Run benchmark - result = asyncio.run(run_locomo_benchmark( - sample_size=args.sample_size, - end_user_id=args.end_user_id, - search_type=args.search_type, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - reset_group=args.reset_group, - skip_ingest=args.skip_ingest, - output_dir=args.output_dir, - max_ingest_messages=args.max_ingest_messages - )) - - # Print summary - print(f"\n{'='*60}") - - # Check if there was an error - if 'error' in result: - print("❌ Benchmark Failed!") - print(f"{'='*60}") - print(f"Error: {result['error']}") - return - - print("🎉 Benchmark Complete!") - print(f"{'='*60}") - print("📊 Final Results:") - print(f" Sample size: {result.get('sample_size', 0)}") - print(f" F1: {result['overall_metrics']['f1']:.3f}") - print(f" BLEU-1: {result['overall_metrics']['bleu1']:.3f}") - print(f" Jaccard: {result['overall_metrics']['jaccard']:.3f}") - print(f" LoCoMo F1: {result['overall_metrics']['locomo_f1']:.3f}") - - if result.get('context_stats'): - print("\n📈 Context Statistics:") - print(f" Avg retrieved docs: {result['context_stats']['avg_retrieved_docs']:.1f}") - print(f" Avg context chars: {result['context_stats']['avg_context_chars']:.0f}") - print(f" Avg context tokens: {result['context_stats']['avg_context_tokens']:.0f}") - - if result.get('latency'): - print("\n⏱️ Latency Statistics:") - print(f" Search - Mean: {result['latency']['search']['mean']:.1f}ms, " - f"P50: {result['latency']['search']['p50']:.1f}ms, " - f"P95: {result['latency']['search']['p95']:.1f}ms") - print(f" LLM - Mean: {result['latency']['llm']['mean']:.1f}ms, " - f"P50: {result['latency']['llm']['p50']:.1f}ms, " - f"P95: {result['latency']['llm']['p95']:.1f}ms") - - if result.get('by_category'): - print("\n📂 Results by Category:") - for cat, metrics in result['by_category'].items(): - print(f" {cat}:") - print(f" Count: {metrics['count']}") - print(f" F1: {metrics['f1']:.3f}") - print(f" LoCoMo F1: {metrics['locomo_f1']:.3f}") - print(f" Jaccard: {metrics['jaccard']:.3f}") - - print(f"\n{'='*60}\n") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/locomo/locomo_metrics.py b/api/app/core/memory/evaluation/locomo/locomo_metrics.py deleted file mode 100644 index 20d5f2b5..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_metrics.py +++ /dev/null @@ -1,225 +0,0 @@ -""" -LoCoMo-specific metric calculations. - -This module provides clean, simplified implementations of metrics used for -LoCoMo benchmark evaluation, including text normalization and F1 score variants. -""" - -import re -from typing import Dict, Any - - -def normalize_text(text: str) -> str: - """ - Normalize text for LoCoMo evaluation. - - Normalization steps: - - Convert to lowercase - - Remove commas - - Remove stop words (a, an, the, and) - - Remove punctuation - - Normalize whitespace - - Args: - text: Input text to normalize - - Returns: - Normalized text string with consistent formatting - - Examples: - >>> normalize_text("The cat, and the dog") - 'cat dog' - >>> normalize_text("Hello, World!") - 'hello world' - """ - # Ensure input is a string - text = str(text) if text is not None else "" - - # Convert to lowercase - text = text.lower() - - # Remove commas - text = re.sub(r"[\,]", " ", text) - - # Remove stop words - text = re.sub(r"\b(a|an|the|and)\b", " ", text) - - # Remove punctuation (keep only word characters and whitespace) - text = re.sub(r"[^\w\s]", " ", text) - - # Normalize whitespace (collapse multiple spaces to single space) - text = " ".join(text.split()) - - return text - - -def locomo_f1_score(prediction: str, ground_truth: str) -> float: - """ - Calculate LoCoMo F1 score for single-answer questions. - - Uses token-level precision and recall based on normalized text. - Treats tokens as sets (no duplicate counting). - - Args: - prediction: Model's predicted answer - ground_truth: Correct answer - - Returns: - F1 score between 0.0 and 1.0 - - Examples: - >>> locomo_f1_score("Paris", "Paris") - 1.0 - >>> locomo_f1_score("The cat", "cat") - 1.0 - >>> locomo_f1_score("dog", "cat") - 0.0 - """ - # Ensure inputs are strings - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - # Normalize and tokenize - pred_tokens = normalize_text(pred_str).split() - truth_tokens = normalize_text(truth_str).split() - - # Handle empty cases - if not pred_tokens or not truth_tokens: - return 0.0 - - # Convert to sets for comparison - pred_set = set(pred_tokens) - truth_set = set(truth_tokens) - - # Calculate true positives (intersection) - true_positives = len(pred_set & truth_set) - - # Calculate precision and recall - precision = true_positives / len(pred_set) if pred_set else 0.0 - recall = true_positives / len(truth_set) if truth_set else 0.0 - - # Calculate F1 score - if precision + recall == 0: - return 0.0 - - f1 = 2 * precision * recall / (precision + recall) - return f1 - - -def locomo_multi_f1(prediction: str, ground_truth: str) -> float: - """ - Calculate LoCoMo F1 score for multi-answer questions. - - Handles comma-separated answers by: - 1. Splitting both prediction and ground truth by commas - 2. For each ground truth answer, finding the best matching prediction - 3. Averaging the F1 scores across all ground truth answers - - Args: - prediction: Model's predicted answer (may contain multiple comma-separated answers) - ground_truth: Correct answer (may contain multiple comma-separated answers) - - Returns: - Average F1 score across all ground truth answers (0.0 to 1.0) - - Examples: - >>> locomo_multi_f1("Paris, London", "Paris, London") - 1.0 - >>> locomo_multi_f1("Paris", "Paris, London") - 0.5 - >>> locomo_multi_f1("Paris, Berlin", "Paris, London") - 0.5 - """ - # Ensure inputs are strings - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - # Split by commas and strip whitespace - predictions = [p.strip() for p in pred_str.split(',') if p.strip()] - ground_truths = [g.strip() for g in truth_str.split(',') if g.strip()] - - # Handle empty cases - if not predictions or not ground_truths: - return 0.0 - - # For each ground truth, find the best matching prediction - f1_scores = [] - for gt in ground_truths: - # Calculate F1 with each prediction and take the maximum - best_f1 = max(locomo_f1_score(pred, gt) for pred in predictions) - f1_scores.append(best_f1) - - # Return average F1 across all ground truths - return sum(f1_scores) / len(f1_scores) - - -def get_category_name(item: Dict[str, Any]) -> str: - """ - Extract and normalize category name from QA item. - - Handles both numeric categories (1-4) and string categories with various formats. - Supports multiple field names: "cat", "category", "type". - - Category mapping: - - 1 or "multi-hop" -> "Multi-Hop" - - 2 or "temporal" -> "Temporal" - - 3 or "open domain" -> "Open Domain" - - 4 or "single-hop" -> "Single-Hop" - - Args: - item: QA item dictionary containing category information - - Returns: - Standardized category name or "unknown" if not found - - Examples: - >>> get_category_name({"category": 1}) - 'Multi-Hop' - >>> get_category_name({"cat": "temporal"}) - 'Temporal' - >>> get_category_name({"type": "Single-Hop"}) - 'Single-Hop' - """ - # Numeric category mapping - CATEGORY_MAP = { - 1: "Multi-Hop", - 2: "Temporal", - 3: "Open Domain", - 4: "Single-Hop", - } - - # String category aliases (case-insensitive) - TYPE_ALIASES = { - "single-hop": "Single-Hop", - "singlehop": "Single-Hop", - "single hop": "Single-Hop", - "multi-hop": "Multi-Hop", - "multihop": "Multi-Hop", - "multi hop": "Multi-Hop", - "open domain": "Open Domain", - "opendomain": "Open Domain", - "temporal": "Temporal", - } - - # Try "cat" field first (string category) - cat = item.get("cat") - if isinstance(cat, str) and cat.strip(): - name = cat.strip() - lower = name.lower() - return TYPE_ALIASES.get(lower, name) - - # Try "category" field (can be int or string) - cat_num = item.get("category") - if isinstance(cat_num, int): - return CATEGORY_MAP.get(cat_num, "unknown") - elif isinstance(cat_num, str) and cat_num.strip(): - lower = cat_num.strip().lower() - return TYPE_ALIASES.get(lower, cat_num.strip()) - - # Try "type" field as fallback - cat_type = item.get("type") - if isinstance(cat_type, str) and cat_type.strip(): - lower = cat_type.strip().lower() - return TYPE_ALIASES.get(lower, cat_type.strip()) - - return "unknown" diff --git a/api/app/core/memory/evaluation/locomo/locomo_test.py b/api/app/core/memory/evaluation/locomo/locomo_test.py deleted file mode 100644 index 2cb0664c..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_test.py +++ /dev/null @@ -1,864 +0,0 @@ -# file name: check_neo4j_connection_fixed.py -import asyncio -import os -import sys -import json -import time -import math -import re -from datetime import datetime, timedelta -from typing import List, Dict, Any -from pathlib import Path -from dotenv import load_dotenv - -# Load main .env -load_dotenv() - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -# Get group_id from config -group_id = os.getenv("EVAL_GROUP_ID", "locomo_test") -print(f"✅ 使用配置的 group_id: {group_id}") - -# 首先定义 _loc_normalize 函数，因为其他函数依赖它 -def _loc_normalize(text: str) -> str: - text = str(text) if text is not None else "" - text = text.lower() - text = re.sub(r"[\,]", " ", text) - text = re.sub(r"\b(a|an|the|and)\b", " ", text) - text = re.sub(r"[^\w\s]", " ", text) - text = " ".join(text.split()) - return text - -# 尝试从 metrics.py 导入基础指标 -try: - from app.core.memory.evaluation.common.metrics import f1_score, bleu1, jaccard - print("✅ 从 metrics.py 导入基础指标成功") -except ImportError as e: - print(f"❌ 从 metrics.py 导入失败: {e}") - # 回退到本地实现 - def f1_score(pred: str, ref: str) -> float: - pred_str = str(pred) if pred is not None else "" - ref_str = str(ref) if ref is not None else "" - - p_tokens = _loc_normalize(pred_str).split() - r_tokens = _loc_normalize(ref_str).split() - if not p_tokens and not r_tokens: - return 1.0 - if not p_tokens or not r_tokens: - return 0.0 - p_set = set(p_tokens) - r_set = set(r_tokens) - tp = len(p_set & r_set) - precision = tp / len(p_set) if p_set else 0.0 - recall = tp / len(r_set) if r_set else 0.0 - if precision + recall == 0: - return 0.0 - return 2 * precision * recall / (precision + recall) - - def bleu1(pred: str, ref: str) -> float: - pred_str = str(pred) if pred is not None else "" - ref_str = str(ref) if ref is not None else "" - - p_tokens = _loc_normalize(pred_str).split() - r_tokens = _loc_normalize(ref_str).split() - if not p_tokens: - return 0.0 - - r_counts = {} - for t in r_tokens: - r_counts[t] = r_counts.get(t, 0) + 1 - - clipped = 0 - p_counts = {} - for t in p_tokens: - p_counts[t] = p_counts.get(t, 0) + 1 - - for t, c in p_counts.items(): - clipped += min(c, r_counts.get(t, 0)) - - precision = clipped / max(len(p_tokens), 1) - ref_len = len(r_tokens) - pred_len = len(p_tokens) - - if pred_len > ref_len or pred_len == 0: - bp = 1.0 - else: - bp = math.exp(1 - ref_len / max(pred_len, 1)) - - return bp * precision - - def jaccard(pred: str, ref: str) -> float: - pred_str = str(pred) if pred is not None else "" - ref_str = str(ref) if ref is not None else "" - - p = set(_loc_normalize(pred_str).split()) - r = set(_loc_normalize(ref_str).split()) - if not p and not r: - return 1.0 - if not p or not r: - return 0.0 - return len(p & r) / len(p | r) - -# 尝试从 qwen_search_eval.py 导入 LoCoMo 特定指标 -try: - from app.core.memory.evaluation.locomo.qwen_search_eval import loc_f1_score, loc_multi_f1, _resolve_relative_times - print("✅ 从 qwen_search_eval 导入 LoCoMo 特定指标成功") -except ImportError as e: - print(f"❌ 从 qwen_search_eval.py 导入失败: {e}") - # 回退到本地实现 LoCoMo 特定函数 - def _resolve_relative_times(text: str, anchor: datetime) -> str: - t = str(text) if text is not None else "" - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - return t - - def loc_f1_score(prediction: str, ground_truth: str) -> float: - p_tokens = _loc_normalize(prediction).split() - g_tokens = _loc_normalize(ground_truth).split() - if not p_tokens or not g_tokens: - return 0.0 - p = set(p_tokens) - g = set(g_tokens) - tp = len(p & g) - precision = tp / len(p) if p else 0.0 - recall = tp / len(g) if g else 0.0 - return (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0 - - def loc_multi_f1(prediction: str, ground_truth: str) -> float: - predictions = [p.strip() for p in str(prediction).split(',') if p.strip()] - ground_truths = [g.strip() for g in str(ground_truth).split(',') if g.strip()] - if not predictions or not ground_truths: - return 0.0 - def _f1(a: str, b: str) -> float: - return loc_f1_score(a, b) - vals = [] - for gt in ground_truths: - vals.append(max(_f1(pred, gt) for pred in predictions)) - return sum(vals) / len(vals) - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 8000) -> str: - """基于问题关键词智能选择上下文""" - if not contexts: - return "" - - # 提取问题关键词（只保留有意义的词） - question_lower = question.lower() - stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'} - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = {word for word in question_words if word not in stop_words and len(word) > 2} - - print(f"🔍 问题关键词: {question_words}") - - # 给每个上下文打分 - scored_contexts = [] - for i, context in enumerate(contexts): - context_lower = context.lower() - score = 0 - - # 关键词匹配得分 - keyword_matches = 0 - for word in question_words: - if word in context_lower: - keyword_matches += 1 - # 关键词出现次数越多，得分越高 - score += context_lower.count(word) * 2 - - # 上下文长度得分（适中的长度更好） - context_len = len(context) - if 100 < context_len < 2000: # 理想长度范围 - score += 5 - elif context_len >= 2000: # 太长可能包含无关信息 - score += 2 - - # 如果是前几个上下文，给予额外分数（通常相关性更高） - if i < 3: - score += 3 - - scored_contexts.append((score, context, keyword_matches)) - - # 按得分排序 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - - # 选择高得分的上下文，直到达到字符限制 - selected = [] - total_chars = 0 - selected_count = 0 - - print("📊 上下文相关性分析:") - for score, context, matches in scored_contexts[:5]: # 只显示前5个 - print(f" - 得分: {score}, 关键词匹配: {matches}, 长度: {len(context)}") - - for score, context, matches in scored_contexts: - if total_chars + len(context) <= max_chars: - selected.append(context) - total_chars += len(context) - selected_count += 1 - else: - # 如果这个上下文得分很高但放不下，尝试截取 - if score > 10 and total_chars < max_chars - 500: - remaining = max_chars - total_chars - # 找到包含关键词的部分 - lines = context.split('\n') - relevant_lines = [] - current_chars = 0 - - for line in lines: - line_lower = line.lower() - line_relevance = any(word in line_lower for word in question_words) - - if line_relevance and current_chars < remaining - 100: - relevant_lines.append(line) - current_chars += len(line) - - if relevant_lines: - truncated = '\n'.join(relevant_lines) - if len(truncated) > 100: # 确保有足够内容 - selected.append(truncated + "\n[相关内容截断...]") - total_chars += len(truncated) - selected_count += 1 - break # 不再尝试添加更多上下文 - - result = "\n\n".join(selected) - print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {total_chars}字符") - return result - - -def get_dynamic_search_params(question: str, question_index: int, total_questions: int): - """根据问题复杂度和进度动态调整检索参数""" - - # 分析问题复杂度 - word_count = len(question.split()) - has_temporal = any(word in question.lower() for word in ['when', 'date', 'time', 'ago']) - has_multi_hop = any(word in question.lower() for word in ['and', 'both', 'also', 'while']) - - # 根据进度调整 - 后期问题可能需要更精确的检索 - progress_factor = question_index / total_questions - - base_limit = 12 - if has_temporal and has_multi_hop: - base_limit = 20 - elif word_count > 8: - base_limit = 16 - - # 随着测试进行，逐渐收紧检索范围 - adjusted_limit = max(8, int(base_limit * (1 - progress_factor * 0.3))) - - # 动态调整最大字符数 - max_chars = 8000 + 4000 * (1 - progress_factor) - - return { - "limit": adjusted_limit, - "max_chars": int(max_chars) - } - - -class EnhancedEvaluationMonitor: - def __init__(self, reset_interval=5, performance_threshold=0.6): - self.question_count = 0 - self.reset_interval = reset_interval - self.performance_threshold = performance_threshold - self.consecutive_low_scores = 0 - self.performance_history = [] - self.recent_f1_scores = [] - - def should_reset_connections(self, current_f1=None): - """基于计数和性能双重判断""" - # 定期重置 - if self.question_count % self.reset_interval == 0: - return True - - # 性能驱动的重置 - if current_f1 is not None and current_f1 < self.performance_threshold: - self.consecutive_low_scores += 1 - if self.consecutive_low_scores >= 2: # 连续2个低分就重置 - print("🚨 连续低分，触发紧急重置") - self.consecutive_low_scores = 0 - return True - else: - self.consecutive_low_scores = 0 - - return False - - def record_performance(self, question_index, metrics, context_length, retrieved_docs): - """记录性能指标，检测衰减""" - self.performance_history.append({ - 'index': question_index, - 'metrics': metrics, - 'context_length': context_length, - 'retrieved_docs': retrieved_docs, - 'timestamp': time.time() - }) - - # 记录最近的F1分数 - self.recent_f1_scores.append(metrics['f1']) - if len(self.recent_f1_scores) > 5: - self.recent_f1_scores.pop(0) - - def get_recent_performance(self): - """获取近期平均性能""" - if not self.recent_f1_scores: - return 0.5 - return sum(self.recent_f1_scores) / len(self.recent_f1_scores) - - def get_performance_trend(self): - """分析性能趋势""" - if len(self.performance_history) < 2: - return "stable" - - recent_metrics = [item['metrics']['f1'] for item in self.performance_history[-5:]] - earlier_metrics = [item['metrics']['f1'] for item in self.performance_history[-10:-5]] - - if len(recent_metrics) < 2 or len(earlier_metrics) < 2: - return "stable" - - recent_avg = sum(recent_metrics) / len(recent_metrics) - earlier_avg = sum(earlier_metrics) / len(earlier_metrics) - - if recent_avg < earlier_avg * 0.8: - return "degrading" - elif recent_avg > earlier_avg * 1.1: - return "improving" - else: - return "stable" - - -def get_enhanced_search_params(question: str, question_index: int, total_questions: int, recent_performance: float): - """基于问题复杂度和近期性能动态调整检索参数""" - - # 基础参数 - base_params = get_dynamic_search_params(question, question_index, total_questions) - - # 性能自适应调整 - if recent_performance < 0.5: # 近期表现差 - # 增加检索范围，尝试获取更多上下文 - base_params["limit"] = min(base_params["limit"] + 5, 25) - base_params["max_chars"] = min(base_params["max_chars"] + 2000, 12000) - print(f"📈 性能自适应：增加检索范围 (limit={base_params['limit']}, max_chars={base_params['max_chars']})") - - elif recent_performance > 0.8: # 近期表现好 - # 收紧检索，提高精度 - base_params["limit"] = max(base_params["limit"] - 2, 8) - base_params["max_chars"] = max(base_params["max_chars"] - 1000, 6000) - print(f"🎯 性能自适应：提高检索精度 (limit={base_params['limit']}, max_chars={base_params['max_chars']})") - - # 中间阶段特殊处理 - mid_sequence_factor = abs(question_index / total_questions - 0.5) - if mid_sequence_factor < 0.2: # 在中间30%的问题 - print("🎯 中间阶段：使用更精确的检索策略") - base_params["limit"] = max(base_params["limit"] - 2, 10) # 减少数量，提高质量 - base_params["max_chars"] = max(base_params["max_chars"] - 1000, 7000) - - return base_params - - -def enhanced_context_selection(contexts: List[str], question: str, question_index: int, total_questions: int, max_chars: int = 8000) -> str: - """考虑问题序列位置的智能选择""" - - if not contexts: - return "" - - # 在序列中间阶段使用更严格的筛选 - mid_sequence_factor = abs(question_index / total_questions - 0.5) # 距离中心的距离 - - if mid_sequence_factor < 0.2: # 在中间30%的问题 - print("🎯 中间阶段：使用严格上下文筛选") - - # 提取问题关键词 - question_lower = question.lower() - stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'} - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = {word for word in question_words if word not in stop_words and len(word) > 2} - - # 只保留高度相关的上下文 - filtered_contexts = [] - for context in contexts: - context_lower = context.lower() - relevance_score = sum(3 if word in context_lower else 0 for word in question_words) - - # 额外加分给包含数字、日期的上下文（对事实性问题更重要） - if any(char.isdigit() for char in context): - relevance_score += 2 - - # 提高阈值：只有得分>=3的上下文才保留 - if relevance_score >= 3: - filtered_contexts.append(context) - else: - print(f" - 过滤低分上下文: 得分={relevance_score}") - - contexts = filtered_contexts - print(f"🔍 严格筛选后保留 {len(contexts)} 个上下文") - - # 使用原有的智能选择逻辑 - return smart_context_selection(contexts, question, max_chars) - - -async def run_enhanced_evaluation(): - """使用增强方法进行完整评估 - 解决中间性能衰减问题""" - from dotenv import load_dotenv - from uuid import UUID - from datetime import datetime - from dataclasses import dataclass - - # 修正导入路径：使用 app.core.memory.src 前缀 - from app.repositories.neo4j.neo4j_connector import Neo4jConnector - from app.repositories.neo4j.graph_search import search_graph_by_embedding - from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient - from app.core.models.base import RedBearModelConfig - from app.core.memory.utils.llm.llm_utils import get_llm_client - from app.core.memory.utils.config.config_utils import get_embedder_config - from app.schemas.memory_config_schema import MemoryConfig - from app.services.memory_config_service import MemoryConfigService - - # Get model IDs from config - llm_id = os.getenv("EVAL_LLM_ID", "6dc52e1b-9cec-4194-af66-a74c6307fc3f") - embedding_id = os.getenv("EVAL_EMBEDDING_ID", "e2a6392d-ca63-4d59-a523-647420b59cb2") - - # 加载数据 - 使用统一的 dataset 目录 - data_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "dataset", "locomo10.json") - - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 locomo10.json 放置在: api/app/core/memory/evaluation/dataset/" - ) - - print(f"✅ 找到数据文件: {data_path}") - - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - - qa_items = [] - if isinstance(raw, list): - for entry in raw: - qa_items.extend(entry.get("qa", [])) - else: - qa_items.extend(raw.get("qa", [])) - - # 测试多少个问题 - 可通过环境变量设置 - sample_size = int(os.getenv("LOCOMO_SAMPLE_SIZE", "20")) - items = qa_items[:sample_size] - print(f"📊 将测试 {len(items)} 个问题（总共 {len(qa_items)} 个可用）") - - # 初始化增强监控器 - monitor = EnhancedEvaluationMonitor(reset_interval=5, performance_threshold=0.6) - - # 获取数据库会话并初始化 LLM 客户端 - from app.db import get_db - db = next(get_db()) - - try: - llm = get_llm_client(llm_id, db) - - # 初始化embedder - cfg_dict = get_embedder_config(embedding_id, db) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 🔧 创建 MemoryConfig 对象用于搜索 - # 方案1：如果有配置ID，从数据库加载 - config_id = os.getenv("EVAL_CONFIG_ID") - if config_id: - print(f"📋 从数据库加载配置 ID: {config_id}") - memory_config_service = MemoryConfigService(db) - memory_config = memory_config_service.load_memory_config(config_id, service_name="locomo_test") - else: - # 方案2：创建临时配置对象用于测试 - print(f"📋 创建临时测试配置") - from uuid import UUID - from datetime import datetime - - # 将字符串 ID 转换为 UUID - try: - embedding_uuid = UUID(embedding_id) - llm_uuid = UUID(llm_id) - except ValueError as e: - raise ValueError(f"无效的 UUID 格式: {e}") - - memory_config = MemoryConfig( - config_id=1, # 临时 ID - config_name="locomo_test_config", - workspace_id=UUID("00000000-0000-0000-0000-000000000000"), # 临时 workspace - workspace_name="test_workspace", - tenant_id=UUID("00000000-0000-0000-0000-000000000000"), # 临时 tenant - embedding_model_id=embedding_uuid, - embedding_model_name="test_embedding", - llm_model_id=llm_uuid, - llm_model_name="test_llm", - storage_type="neo4j", - chunker_strategy="RecursiveChunker", - reflexion_enabled=False, - reflexion_iteration_period=3, - reflexion_range="partial", - reflexion_baseline="Time", - loaded_at=datetime.now() - ) - - print(f"✅ MemoryConfig 已准备: embedding_id={memory_config.embedding_model_id}, llm_id={memory_config.llm_model_id}") - - # 初始化连接器 - connector = Neo4jConnector() - - # 初始化结果字典 - results = { - "questions": [], - "overall_metrics": {"f1": 0.0, "b1": 0.0, "j": 0.0, "loc_f1": 0.0}, - "category_metrics": {}, - "retrieval_stats": {"total_questions": len(items), "avg_context_length": 0, "avg_retrieved_docs": 0}, - "performance_trend": "stable", - "timestamp": datetime.now().isoformat(), - "enhanced_strategy": True - } - - total_f1 = 0.0 - total_bleu1 = 0.0 - total_jaccard = 0.0 - total_loc_f1 = 0.0 - total_context_length = 0 - total_retrieved_docs = 0 - category_stats = {} - - try: - for i, item in enumerate(items): - monitor.question_count += 1 - - # 获取近期性能用于重置判断 - recent_performance = monitor.get_recent_performance() - - # 增强的重置判断 - should_reset = monitor.should_reset_connections(current_f1=recent_performance) - if should_reset and i > 0: - print(f"🔄 重置Neo4j连接 (问题 {i+1}/{len(items)}, 近期性能: {recent_performance:.3f})...") - await connector.close() - connector = Neo4jConnector() # 创建新连接 - print("✅ 连接重置完成") - - q = item.get("question", "") - ref = item.get("answer", "") - ref_str = str(ref) if ref is not None else "" - - print(f"\n🔍 [{i+1}/{len(items)}] 问题: {q}") - print(f"✅ 真实答案: {ref_str}") - - # 分类别统计 - category = "Unknown" - if item.get("category") == 1: - category = "Multi-Hop" - elif item.get("category") == 2: - category = "Temporal" - elif item.get("category") == 3: - category = "Open Domain" - elif item.get("category") == 4: - category = "Single-Hop" - - # 增强的检索参数 - search_params = get_enhanced_search_params(q, i, len(items), recent_performance) - search_limit = search_params["limit"] - max_chars = search_params["max_chars"] - - print(f"🏷️ 类别: {category}, 检索参数: limit={search_limit}, max_chars={max_chars}") - - # 使用项目标准的混合检索方法 - t0 = time.time() - contexts_all = [] - - try: - # 使用旧版本的搜索服务（重构前的版本） - from app.core.memory.src.search import run_hybrid_search - - print(f"🔀 使用混合搜索服务（旧版本）...") - print(f"📍 检索参数: group_id={group_id}, limit=20, search_type=hybrid") - print(f"📍 查询文本: {q}") - - search_results = await run_hybrid_search( - query_text=q, - search_type="hybrid", - end_user_id="locomo_sk", - limit=20, - include=["statements", "chunks", "entities", "summaries"], - output_path=None, - memory_config=memory_config, # 🔧 添加必需的 memory_config 参数 - rerank_alpha=0.6, # BM25权重 - use_forgetting_rerank=False, - use_llm_rerank=False - ) - - # 处理搜索结果 - 旧版本返回包含 reranked_results 的结构 - # 对于 hybrid 搜索，使用 reranked_results - if "reranked_results" in search_results: - reranked = search_results["reranked_results"] - chunks = reranked.get("chunks", []) - statements = reranked.get("statements", []) - entities = reranked.get("entities", []) - summaries = reranked.get("summaries", []) - else: - # 单一搜索类型的结果 - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要") - - # 构建上下文：优先使用 chunks、statements 和 summaries - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要：最多加入前3个高分实体，避免噪声 - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + ' '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - print(f"📊 有效上下文数量: {len(contexts_all)}") - except Exception as e: - print(f"❌ 检索失败: {e}") - import traceback - print(f"详细错误信息:\n{traceback.format_exc()}") - contexts_all = [] - - t1 = time.time() - search_time = (t1 - t0) * 1000 - - # 增强的上下文选择 - context_text = "" - if contexts_all: - # 使用增强的上下文选择 - context_text = enhanced_context_selection(contexts_all, q, i, len(items), max_chars=max_chars) - - # 如果智能选择后仍然过长，进行最终保护性截断 - if len(context_text) > max_chars: - print(f"⚠️ 智能选择后仍然过长 ({len(context_text)}字符)，进行最终截断") - context_text = context_text[:max_chars] + "\n\n[最终截断...]" - - # 时间解析 - anchor_date = datetime(2023, 5, 8) # 使用固定日期确保一致性 - context_text = _resolve_relative_times(context_text, anchor_date) - - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n" + context_text - - print(f"📝 最终上下文长度: {len(context_text)} 字符") - - # 显示不同上下文的预览（不只是第一条） - print("🔍 上下文预览:") - for j, context in enumerate(contexts_all[:3]): # 显示前3个上下文 - preview = context[:150].replace('\n', ' ') - print(f" 上下文{j+1}: {preview}...") - - # 🔍 调试：检查答案是否在上下文中 - if ref_str and ref_str.strip(): - answer_found = any(ref_str.lower() in ctx.lower() for ctx in contexts_all) - print(f"🔍 调试：答案 '{ref_str}' 是否在检索到的上下文中？ {'✅ 是' if answer_found else '❌ 否'}") - - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # LLM 回答 - messages = [ - {"role": "system", "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - )}, - {"role": "user", "content": f"Question: {q}\n\nContext:\n{context_text}"}, - ] - - t2 = time.time() - try: - # 使用异步调用 - resp = await llm.chat(messages=messages) - # 兼容不同的响应格式 - pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - except Exception as e: - print(f"❌ LLM 生成失败: {e}") - pred = "Unknown" - t3 = time.time() - llm_time = (t3 - t2) * 1000 - - # 计算指标 - 使用导入的指标函数 - f1_val = f1_score(pred, ref_str) - bleu1_val = bleu1(pred, ref_str) - jaccard_val = jaccard(pred, ref_str) - loc_f1_val = loc_f1_score(pred, ref_str) - - print(f"🤖 LLM 回答: {pred}") - print(f"📈 指标 - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, Jaccard: {jaccard_val:.3f}, LoCoMo F1: {loc_f1_val:.3f}") - print(f"⏱️ 时间 - 检索: {search_time:.1f}ms, LLM: {llm_time:.1f}ms") - - # 更新统计 - total_f1 += f1_val - total_bleu1 += bleu1_val - total_jaccard += jaccard_val - total_loc_f1 += loc_f1_val - total_context_length += len(context_text) - total_retrieved_docs += len(contexts_all) - - if category not in category_stats: - category_stats[category] = {"count": 0, "f1_sum": 0.0, "b1_sum": 0.0, "j_sum": 0.0, "loc_f1_sum": 0.0} - - category_stats[category]["count"] += 1 - category_stats[category]["f1_sum"] += f1_val - category_stats[category]["b1_sum"] += bleu1_val - category_stats[category]["j_sum"] += jaccard_val - category_stats[category]["loc_f1_sum"] += loc_f1_val - - # 记录性能指标 - metrics = {"f1": f1_val, "bleu1": bleu1_val, "jaccard": jaccard_val, "loc_f1": loc_f1_val} - monitor.record_performance(i, metrics, len(context_text), len(contexts_all)) - - # 保存结果 - question_result = { - "question": q, - "ground_truth": ref_str, - "prediction": pred, - "category": category, - "metrics": metrics, - "retrieval": { - "retrieved_documents": len(contexts_all), - "context_length": len(context_text), - "search_limit": search_limit, - "max_chars": max_chars, - "recent_performance": recent_performance - }, - "timing": { - "search_ms": search_time, - "llm_ms": llm_time - } - } - - results["questions"].append(question_result) - - print("="*60) - - except Exception as e: - print(f"❌ 评估过程中发生错误: {e}") - # 即使出错，也返回已有的结果 - import traceback - traceback.print_exc() - - finally: - await connector.close() - - finally: - db.close() # 关闭数据库会话 - - # 计算总体指标 - n = len(items) - if n > 0: - results["overall_metrics"] = { - "f1": total_f1 / n, - "b1": total_bleu1 / n, - "j": total_jaccard / n, - "loc_f1": total_loc_f1 / n - } - - for category, stats in category_stats.items(): - count = stats["count"] - results["category_metrics"][category] = { - "count": count, - "f1": stats["f1_sum"] / count, - "bleu1": stats["b1_sum"] / count, - "jaccard": stats["j_sum"] / count, - "loc_f1": stats["loc_f1_sum"] / count - } - - results["retrieval_stats"]["avg_context_length"] = total_context_length / n - results["retrieval_stats"]["avg_retrieved_docs"] = total_retrieved_docs / n - - # 分析性能趋势 - results["performance_trend"] = monitor.get_performance_trend() - results["reset_interval"] = monitor.reset_interval - results["total_questions_processed"] = monitor.question_count - - return results - - -if __name__ == "__main__": - print("🚀 运行增强版完整评估（解决中间性能衰减问题）...") - print("📋 增强特性:") - print(" - 双重重置策略：定期重置 + 性能驱动重置") - print(" - 动态检索参数：基于近期性能自适应调整") - print(" - 中间阶段严格筛选：提高上下文质量要求") - print(" - 连续性能监控：实时检测性能衰减") - - result = asyncio.run(run_enhanced_evaluation()) - - print("\n📊 最终评估结果:") - print("总体指标:") - print(f" F1: {result['overall_metrics']['f1']:.4f}") - print(f" BLEU-1: {result['overall_metrics']['b1']:.4f}") - print(f" Jaccard: {result['overall_metrics']['j']:.4f}") - print(f" LoCoMo F1: {result['overall_metrics']['loc_f1']:.4f}") - - print("\n分类别指标:") - for category, metrics in result['category_metrics'].items(): - print(f" {category}: F1={metrics['f1']:.4f}, BLEU-1={metrics['bleu1']:.4f}, Jaccard={metrics['jaccard']:.4f}, LoCoMo F1={metrics['loc_f1']:.4f} (样本数: {metrics['count']})") - - print("\n检索统计:") - stats = result['retrieval_stats'] - print(f" 平均上下文长度: {stats['avg_context_length']:.0f} 字符") - print(f" 平均检索文档数: {stats['avg_retrieved_docs']:.1f}") - - print(f"\n性能趋势: {result['performance_trend']}") - print(f"重置间隔: 每{result['reset_interval']}个问题") - print(f"处理问题总数: {result['total_questions_processed']}") - print(f"增强策略: {'启用' if result.get('enhanced_strategy', False) else '未启用'}") - - - # 保存结果到指定目录 - # 使用代码文件所在目录的绝对路径 - current_file_dir = os.path.dirname(os.path.abspath(__file__)) - output_dir = os.path.join(current_file_dir, "results") - os.makedirs(output_dir, exist_ok=True) - output_file = os.path.join(output_dir, "enhanced_evaluation_results.json") - with open(output_file, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n详细结果已保存到: {output_file}") diff --git a/api/app/core/memory/evaluation/locomo/locomo_utils.py b/api/app/core/memory/evaluation/locomo/locomo_utils.py deleted file mode 100644 index 6ad68470..00000000 --- a/api/app/core/memory/evaluation/locomo/locomo_utils.py +++ /dev/null @@ -1,687 +0,0 @@ -""" -LoCoMo Utilities Module - -This module provides helper functions for the LoCoMo benchmark evaluation: -- Data loading from JSON files -- Conversation extraction for ingestion -- Temporal reference resolution -- Context selection and formatting -- Retrieval wrapper functions -- Ingestion wrapper functions -""" - -import os -import json -import re -from datetime import datetime, timedelta -from typing import List, Dict, Any, Optional -from pathlib import Path -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - -from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline - - -def load_locomo_data( - data_path: str, - sample_size: int, - conversation_index: int = 0 -) -> List[Dict[str, Any]]: - """ - Load LoCoMo dataset from JSON file. - - The LoCoMo dataset structure is a list of conversation objects, where each - object contains a "qa" list of question-answer pairs. - - Args: - data_path: Path to locomo10.json file - sample_size: Number of QA pairs to load (limits total QA items returned) - conversation_index: Which conversation to load QA pairs from (default: 0 for first) - - Returns: - List of QA item dictionaries, each containing: - - question: str - - answer: str - - category: int (1-4) - - evidence: List[str] - - Raises: - FileNotFoundError: If data_path does not exist - json.JSONDecodeError: If file is not valid JSON - IndexError: If conversation_index is out of range - """ - if not os.path.exists(data_path): - raise FileNotFoundError(f"LoCoMo data file not found: {data_path}") - - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - - # LoCoMo data structure: list of objects, each with a "qa" list - qa_items: List[Dict[str, Any]] = [] - - if isinstance(raw, list): - # Only load QA pairs from the specified conversation - if conversation_index < len(raw): - entry = raw[conversation_index] - if isinstance(entry, dict) and "qa" in entry: - qa_items.extend(entry.get("qa", [])) - else: - raise IndexError( - f"Conversation index {conversation_index} out of range. " - f"Dataset has {len(raw)} conversations." - ) - else: - # Fallback: single object with qa list - if conversation_index == 0: - qa_items.extend(raw.get("qa", [])) - else: - raise IndexError( - f"Conversation index {conversation_index} out of range. " - f"Dataset has only 1 conversation." - ) - - # Return only the requested sample size - return qa_items[:sample_size] - - -def extract_conversations(data_path: str, max_dialogues: int = 1, max_messages_per_dialogue: Optional[int] = None) -> List[str]: - """ - Extract conversation texts from LoCoMo data for ingestion. - - This function extracts the raw conversation dialogues from the LoCoMo dataset - so they can be ingested into the memory system. Each conversation is formatted - as a multi-line string with "role: message" format. - - Args: - data_path: Path to locomo10.json file - max_dialogues: Maximum number of dialogues to extract (default: 1) - max_messages_per_dialogue: Maximum messages per dialogue (default: None = all messages) - - Returns: - List of conversation strings formatted for ingestion. - Each string contains multiple lines in format "role: message" - - Example output: - [ - "User: I went to the store yesterday.\\nAI: What did you buy?\\n...", - "User: I love hiking.\\nAI: Where do you like to hike?\\n..." - ] - """ - if not os.path.exists(data_path): - raise FileNotFoundError(f"LoCoMo data file not found: {data_path}") - - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - - # Ensure we have a list of entries - entries = raw if isinstance(raw, list) else [raw] - - contents: List[str] = [] - - for i, entry in enumerate(entries[:max_dialogues]): - if not isinstance(entry, dict): - continue - - conv = entry.get("conversation", {}) - - if not isinstance(conv, dict): - continue - - lines: List[str] = [] - - # Collect all session_* messages - for key, val in sorted(conv.items()): - if isinstance(val, list) and key.startswith("session_"): - for msg in val: - if not isinstance(msg, dict): - continue - - role = msg.get("speaker") or "User" - text = msg.get("text") or "" - text = str(text).strip() - - if not text: - continue - - lines.append(f"{role}: {text}") - - # Limit messages if specified - if max_messages_per_dialogue and len(lines) >= max_messages_per_dialogue: - break - - # Break outer loop if we've reached the message limit - if max_messages_per_dialogue and len(lines) >= max_messages_per_dialogue: - break - - if lines: - contents.append("\n".join(lines)) - - return contents - -# 时间解析：将相对时间表达转换为绝对日期 -def resolve_temporal_references(text: str, anchor_date: datetime) -> str: - """ - Resolve relative temporal references to absolute dates. - - This function converts relative time expressions (like "today", "yesterday", - "3 days ago") into absolute ISO date strings based on an anchor date. - - Supported patterns: - - today, yesterday, tomorrow - - X days ago, in X days - - last week, next week - - Args: - text: Text containing temporal references - anchor_date: Reference date for resolution (datetime object) - - Returns: - Text with temporal references replaced by ISO dates (YYYY-MM-DD format) - - Example: - >>> anchor = datetime(2023, 5, 8) - >>> resolve_temporal_references("I saw him yesterday", anchor) - "I saw him 2023-05-07" - """ - # Ensure input is a string - t = str(text) if text is not None else "" - - # today / yesterday / tomorrow - t = re.sub( - r"\btoday\b", - anchor_date.date().isoformat(), - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\byesterday\b", - (anchor_date - timedelta(days=1)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\btomorrow\b", - (anchor_date + timedelta(days=1)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - - # X days ago - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor_date - timedelta(days=n)).date().isoformat() - - # in X days - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor_date + timedelta(days=n)).date().isoformat() - - t = re.sub( - r"\b(\d+)\s+days?\s+ago\b", - _ago_repl, - t, - flags=re.IGNORECASE - ) - t = re.sub( - r"\bin\s+(\d+)\s+days?\b", - _in_repl, - t, - flags=re.IGNORECASE - ) - - # last week / next week (approximate as 7 days) - t = re.sub( - r"\blast\s+week\b", - (anchor_date - timedelta(days=7)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - - # 中文支持 - t = re.sub( - r"\bnext\s+week\b", - (anchor_date + timedelta(days=7)).date().isoformat(), - t, - flags=re.IGNORECASE - ) - - return t - - -def select_and_format_information( - retrieved_info: List[str], - question: str, - max_chars: int = 8000 -) -> str: - """ - Intelligently select and format most relevant retrieved information for LLM prompt. - - This function scores each piece of retrieved information based on keyword matching - with the question, then selects the highest-scoring pieces up to the character limit. - - Scoring criteria: - - Keyword matches (higher weight for multiple occurrences) - - Context length (moderate length preferred) - - Position (earlier contexts get bonus points) - - Args: - retrieved_info: List of retrieved information strings (chunks, statements, entities) - question: Question being answered - max_chars: Maximum total characters to include in final prompt - - Returns: - Formatted string combining the most relevant information for LLM prompt. - Contexts are separated by double newlines. - - Example: - >>> contexts = ["Alice went to Paris", "Bob likes pizza", "Alice visited the Eiffel Tower"] - >>> question = "Where did Alice go?" - >>> select_and_format_information(contexts, question, max_chars=100) - "Alice went to Paris\\n\\nAlice visited the Eiffel Tower" - """ - if not retrieved_info: - return "" - - # Extract question keywords (filter out stop words and short words) - question_lower = question.lower() - stop_words = { - 'what', 'when', 'where', 'who', 'why', 'how', - 'did', 'do', 'does', 'is', 'are', 'was', 'were', - 'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at' - } - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = { - word for word in question_words - if word not in stop_words and len(word) > 2 - } - - # Score each context - scored_contexts = [] - for i, context in enumerate(retrieved_info): - context_lower = context.lower() - score = 0 - - # Keyword matching score - keyword_matches = 0 - for word in question_words: - if word in context_lower: - keyword_matches += 1 - # Multiple occurrences increase score - score += context_lower.count(word) * 2 - - # Length score (prefer moderate length) - context_len = len(context) - if 100 < context_len < 2000: - score += 5 - elif context_len >= 2000: - score += 2 - - # Position bonus (earlier contexts often more relevant) - if i < 3: - score += 3 - - scored_contexts.append((score, context, keyword_matches)) - - # Sort by score (descending) - scored_contexts.sort(key=lambda x: x[0], reverse=True) - - # Select contexts up to character limit - selected = [] - total_chars = 0 - - for score, context, matches in scored_contexts: - if total_chars + len(context) <= max_chars: - selected.append(context) - total_chars += len(context) - else: - # Try to include high-scoring context by truncating - if score > 10 and total_chars < max_chars - 500: - remaining = max_chars - total_chars - # Find lines with keywords - lines = context.split('\n') - relevant_lines = [] - current_chars = 0 - - for line in lines: - line_lower = line.lower() - line_relevance = any(word in line_lower for word in question_words) - - if line_relevance and current_chars < remaining - 100: - relevant_lines.append(line) - current_chars += len(line) - - if relevant_lines and len('\n'.join(relevant_lines)) > 100: - truncated = '\n'.join(relevant_lines) - selected.append(truncated + "\n[Content truncated...]") - total_chars += len(truncated) - break - - return "\n\n".join(selected) - -# 记忆系统核心能力：写入与读取 -async def ingest_conversations_if_needed( - conversations: List[str], - end_user_id: str, - reset: bool = False -) -> bool: - """ - Wrapper for conversation ingestion using external extraction pipeline. - - This function populates the Neo4j database with processed conversation data - (chunks, statements, entities) so that the retrieval system has memory to search. - - The ingestion process: - 1. Parses conversation text into dialogue messages - 2. Chunks the dialogues into semantic units - 3. Extracts statements and entities using LLM - 4. Generates embeddings for all content - 5. Stores everything in Neo4j graph database - - Args: - conversations: List of raw conversation texts from LoCoMo dataset - Example: ["User: I went to Paris. AI: When was that?", ...] - end_user_id: Target end_user ID for database storage - reset: Whether to clear existing data first (not implemented in wrapper) - - Returns: - True if successful, False otherwise - - Note: - The external function uses "contexts" to mean "conversation texts". - This runs the full extraction pipeline: chunking → entity extraction → - statement extraction → embedding → Neo4j storage. - """ - try: - success = await ingest_contexts_via_full_pipeline( - contexts=conversations, - end_user_id=end_user_id, - save_chunk_output=True, - reset_group=reset - ) - return success - except Exception as e: - print(f"[Ingestion] Failed to ingest conversations: {e}") - return False - -async def retrieve_relevant_information( - question: str, - end_user_id: str, - search_type: str, - search_limit: int, - connector: Any, - embedder: Any -) -> List[str]: - """ - Retrieve relevant information from memory graph for a question. - - This function searches the Neo4j memory graph (populated during ingestion) and - returns relevant chunks, statements, and entity information that might help - answer the question. - - The function supports three search types: - - "keyword": Full-text search using Cypher queries - - "embedding": Vector similarity search using embeddings - - "hybrid": Combination of keyword and embedding search with reranking - - Args: - question: Question to search for - end_user_id: Database group ID (identifies which conversation memory to search) - search_type: "keyword", "embedding", or "hybrid" - search_limit: Max memory pieces to retrieve - connector: Neo4j connector instance - embedder: Embedder client instance - - Returns: - List of text strings (chunks, statements, entity summaries) from memory graph. - Each string represents a piece of retrieved information. - - Raises: - Exception: If search fails (caught and returns empty list) - """ - from app.repositories.neo4j.graph_search import ( - search_graph, - search_graph_by_embedding - ) - from app.core.memory.src.search import run_hybrid_search - - contexts_all: List[str] = [] - - try: - if search_type == "embedding": - # Embedding-based search - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # Build context from chunks - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - # Add statements - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - # Add summaries - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # Add top entities (limit to 3 to avoid noise) - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = ( - sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] - if scored else entities[:3] - ) - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append( - f"EntitySummary: {name}" - f"{(' [' + '; '.join(meta) + ']') if meta else ''}" - ) - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - # Keyword-based search - search_results = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=search_limit - ) - - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - # Build context from dialogues - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - - # Add statements - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - # Add entity names - if entities: - entity_names = [ - str(e.get("name", "")).strip() - for e in entities[:5] - if e.get("name") - ] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid - # Hybrid search with fallback to embedding - try: - search_results = await run_hybrid_search( - query_text=question, - search_type=search_type, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - output_path=None, - ) - - # Handle flat structure (new API format) - if search_results and isinstance(search_results, dict): - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # Check if we got results - if not (chunks or statements or entities or summaries): - # Try nested structure (backward compatibility) - reranked = search_results.get("reranked_results", {}) - if reranked and isinstance(reranked, dict): - chunks = reranked.get("chunks", []) - statements = reranked.get("statements", []) - entities = reranked.get("entities", []) - summaries = reranked.get("summaries", []) - else: - raise ValueError("Hybrid search returned empty results") - else: - raise ValueError("Hybrid search returned empty results") - - except Exception as e: - # Fallback to embedding search - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # Build context (same for both hybrid and fallback) - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # Add top entities - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = ( - sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] - if scored else entities[:3] - ) - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append( - f"EntitySummary: {name}" - f"{(' [' + '; '.join(meta) + ']') if meta else ''}" - ) - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - except Exception as e: - # Return empty list on error - contexts_all = [] - - return contexts_all - - -async def ingest_conversations_if_needed( - conversations: List[str], - end_user_id: str, - reset: bool = False -) -> bool: - """ - Wrapper for conversation ingestion using external extraction pipeline. - - This function populates the Neo4j database with processed conversation data - (chunks, statements, entities) so that the retrieval system has memory to search. - - The ingestion process: - 1. Parses conversation text into dialogue messages - 2. Chunks the dialogues into semantic units - 3. Extracts statements and entities using LLM - 4. Generates embeddings for all content - 5. Stores everything in Neo4j graph database - - Args: - conversations: List of raw conversation texts from LoCoMo dataset - Example: ["User: I went to Paris. AI: When was that?", ...] - end_user_id: Target group ID for database storage - reset: Whether to clear existing data first (not implemented in wrapper) - - Returns: - True if successful, False otherwise - - Note: - The external function uses "contexts" to mean "conversation texts". - This runs the full extraction pipeline: chunking → entity extraction → - statement extraction → embedding → Neo4j storage. - """ - try: - success = await ingest_contexts_via_full_pipeline( - contexts=conversations, - end_user_id=end_user_id, - save_chunk_output=True - ) - return success - except Exception as e: - print(f"[Ingestion] Failed to ingest conversations: {e}") - return False diff --git a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py b/api/app/core/memory/evaluation/locomo/qwen_search_eval.py deleted file mode 100644 index 889c5065..00000000 --- a/api/app/core/memory/evaluation/locomo/qwen_search_eval.py +++ /dev/null @@ -1,874 +0,0 @@ -import argparse -import asyncio -import json -import os -import time -from datetime import datetime, timedelta -from typing import List, Dict, Any -import statistics -import re -from pathlib import Path -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.models.base import RedBearModelConfig -from app.core.memory.utils.config.config_utils import get_embedder_config -from app.core.memory.src.search import run_hybrid_search # 使用旧版本（重构前） -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline -from app.core.memory.evaluation.common.metrics import f1_score as common_f1, bleu1, jaccard, latency_stats, avg_context_tokens - - -# 参考 evaluation/locomo/evaluation.py 的 F1 计算逻辑（移除外部依赖，内联实现） -def _loc_normalize(text: str) -> str: - import re - # 确保输入是字符串 - text = str(text) if text is not None else "" - text = text.lower() - text = re.sub(r"[\,]", " ", text) # 去掉逗号 - text = re.sub(r"\b(a|an|the|and)\b", " ", text) - text = re.sub(r"[^\w\s]", " ", text) - text = " ".join(text.split()) - return text - -# 追加：相对时间归一化为绝对日期（有限支持：today/yesterday/tomorrow/X days ago/in X days/last week/next week） -def _resolve_relative_times(text: str, anchor: datetime) -> str: - import re - # 确保输入是字符串 - t = str(text) if text is not None else "" - # today / yesterday / tomorrow - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - # X days ago / in X days - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - # last week / next week（以7天近似） - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - return t - -def loc_f1_score(prediction: str, ground_truth: str) -> float: - # 单答案 F1：按词集合计算（近似原始实现，去除词干依赖） - # 确保输入是字符串 - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - p_tokens = _loc_normalize(pred_str).split() - g_tokens = _loc_normalize(truth_str).split() - if not p_tokens or not g_tokens: - return 0.0 - p = set(p_tokens) - g = set(g_tokens) - tp = len(p & g) - precision = tp / len(p) if p else 0.0 - recall = tp / len(g) if g else 0.0 - return (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0 - -def loc_multi_f1(prediction: str, ground_truth: str) -> float: - # 多答案 F1：prediction 与 ground_truth 以逗号分隔，逐一匹配取最大，再对多个 GT 取平均 - # 确保输入是字符串 - pred_str = str(prediction) if prediction is not None else "" - truth_str = str(ground_truth) if ground_truth is not None else "" - - predictions = [p.strip() for p in str(pred_str).split(',') if p.strip()] - ground_truths = [g.strip() for g in str(truth_str).split(',') if g.strip()] - if not predictions or not ground_truths: - return 0.0 - def _f1(a: str, b: str) -> float: - return loc_f1_score(a, b) - vals = [] - for gt in ground_truths: - vals.append(max(_f1(pred, gt) for pred in predictions)) - return sum(vals) / len(vals) - -# 标准化 LoCoMo 类别名：支持数字 category 与字符串 cat/type -CATEGORY_MAP_NUM_TO_NAME = { - 4: "Single-Hop", - 1: "Multi-Hop", - 3: "Open Domain", - 2: "Temporal", -} - -_TYPE_ALIASES = { - "single-hop": "Single-Hop", - "singlehop": "Single-Hop", - "single hop": "Single-Hop", - "multi-hop": "Multi-Hop", - "multihop": "Multi-Hop", - "multi hop": "Multi-Hop", - "open domain": "Open Domain", - "opendomain": "Open Domain", - "temporal": "Temporal", -} - -def get_category_label(item: Dict[str, Any]) -> str: - # 1) 直接用字符串 cat - cat = item.get("cat") - if isinstance(cat, str) and cat.strip(): - name = cat.strip() - lower = name.lower() - return _TYPE_ALIASES.get(lower, name) - # 2) 数字 category 转名称 - cat_num = item.get("category") - if isinstance(cat_num, int): - return CATEGORY_MAP_NUM_TO_NAME.get(cat_num, "unknown") - # 3) 备用 type 字段 - t = item.get("type") - if isinstance(t, str) and t.strip(): - lower = t.strip().lower() - return _TYPE_ALIASES.get(lower, t.strip()) - return "unknown" - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 12000) -> str: - """基于问题关键词智能选择上下文""" - if not contexts: - return "" - - # 提取问题关键词（只保留有意义的词） - question_lower = question.lower() - stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'} - question_words = set(re.findall(r'\b\w+\b', question_lower)) - question_words = {word for word in question_words if word not in stop_words and len(word) > 2} - - print(f"🔍 问题关键词: {question_words}") - - # 给每个上下文打分 - scored_contexts = [] - for i, context in enumerate(contexts): - context_lower = context.lower() - score = 0 - - # 关键词匹配得分 - keyword_matches = 0 - for word in question_words: - if word in context_lower: - keyword_matches += 1 - # 关键词出现次数越多，得分越高 - score += context_lower.count(word) * 2 - - # 上下文长度得分（适中的长度更好） - context_len = len(context) - if 100 < context_len < 2000: # 理想长度范围 - score += 5 - elif context_len >= 2000: # 太长可能包含无关信息 - score += 2 - - # 如果是前几个上下文，给予额外分数（通常相关性更高） - if i < 3: - score += 3 - - scored_contexts.append((score, context, keyword_matches)) - - # 按得分排序 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - - # 选择高得分的上下文，直到达到字符限制 - selected = [] - total_chars = 0 - selected_count = 0 - - print("📊 上下文相关性分析:") - for score, context, matches in scored_contexts[:5]: # 只显示前5个 - print(f" - 得分: {score}, 关键词匹配: {matches}, 长度: {len(context)}") - - for score, context, matches in scored_contexts: - if total_chars + len(context) <= max_chars: - selected.append(context) - total_chars += len(context) - selected_count += 1 - else: - # 如果这个上下文得分很高但放不下，尝试截取 - if score > 10 and total_chars < max_chars - 500: - remaining = max_chars - total_chars - # 找到包含关键词的部分 - lines = context.split('\n') - relevant_lines = [] - current_chars = 0 - - for line in lines: - line_lower = line.lower() - line_relevance = any(word in line_lower for word in question_words) - - if line_relevance and current_chars < remaining - 100: - relevant_lines.append(line) - current_chars += len(line) - - if relevant_lines: - truncated = '\n'.join(relevant_lines) - if len(truncated) > 100: # 确保有足够内容 - selected.append(truncated + "\n[相关内容截断...]") - total_chars += len(truncated) - selected_count += 1 - break # 不再尝试添加更多上下文 - - result = "\n\n".join(selected) - print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {total_chars}字符") - return result - - -def get_search_params_by_category(category: str): - """根据问题类别调整检索参数""" - params_map = { - "Multi-Hop": {"limit": 20, "max_chars": 15000}, - "Temporal": {"limit": 16, "max_chars": 10000}, - "Open Domain": {"limit": 24, "max_chars": 18000}, - "Single-Hop": {"limit": 12, "max_chars": 8000}, - } - return params_map.get(category, {"limit": 16, "max_chars": 12000}) - - -async def run_locomo_eval( - sample_size: int = 1, - end_user_id: str | None = None, - search_limit: int = 8, - context_char_budget: int = 4000, # 保持默认值不变 - llm_temperature: float = 0.0, - llm_max_tokens: int = 32, - search_type: str = "hybrid", # 保持默认值不变 - output_path: str | None = None, - skip_ingest_if_exists: bool = True, - llm_timeout: float = 10.0, - llm_max_retries: int = 1 -) -> Dict[str, Any]: - - # 函数内部使用三路检索逻辑，但保持参数签名不变 - end_user_id = end_user_id or SELECTED_end_user_id - data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json") - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 locomo10.json 放置在: {dataset_dir}" - ) - with open(data_path, "r", encoding="utf-8") as f: - raw = json.load(f) - # LoCoMo 数据结构：顶层为若干对象，每个对象下有 qa 列表 - qa_items: List[Dict[str, Any]] = [] - if isinstance(raw, list): - for entry in raw: - qa_items.extend(entry.get("qa", [])) - else: - qa_items.extend(raw.get("qa", [])) - items: List[Dict[str, Any]] = qa_items[:sample_size] - - # === 保持原来的数据摄入逻辑 === - entries = raw if isinstance(raw, list) else [raw] - - # 只摄入前1条对话（保持原样） - max_dialogues_to_ingest = 1 - contents: List[str] = [] - print(f"📊 找到 {len(entries)} 个对话对象，只摄入前 {max_dialogues_to_ingest} 条") - - for i, entry in enumerate(entries[:max_dialogues_to_ingest]): - if not isinstance(entry, dict): - continue - - conv = entry.get("conversation", {}) - sample_id = entry.get("sample_id", f"unknown_{i}") - - print(f"🔍 处理对话 {i+1}: {sample_id}") - - lines: List[str] = [] - if isinstance(conv, dict): - # 收集所有 session_* 的消息 - session_count = 0 - for key, val in conv.items(): - if isinstance(val, list) and key.startswith("session_"): - session_count += 1 - for msg in val: - role = msg.get("speaker") or "用户" - text = msg.get("text") or "" - text = str(text).strip() - if not text: - continue - lines.append(f"{role}: {text}") - - print(f" - 包含 {session_count} 个session, {len(lines)} 条消息") - - if not lines: - print(f"⚠️ 警告: 对话 {sample_id} 没有对话内容，跳过摄入") - continue - - contents.append("\n".join(lines)) - - print(f"📥 总共摄入 {len(contents)} 个对话的conversation内容") - - # 选择要评测的QA对（从所有对话中选取） - indexed_items: List[tuple[int, Dict[str, Any]]] = [] - if isinstance(raw, list): - for e_idx, entry in enumerate(raw): - for qa in entry.get("qa", []): - indexed_items.append((e_idx, qa)) - else: - for qa in raw.get("qa", []): - indexed_items.append((0, qa)) - - # 这里使用sample_size来限制评测的QA数量 - selected = indexed_items[:sample_size] - items: List[Dict[str, Any]] = [qa for _, qa in selected] - - print(f"🎯 将评测 {len(items)} 个QA对，数据库中只包含 {len(contents)} 个对话") - # === 修改结束 === - - connector = Neo4jConnector() - - # 关键修复：强制重新摄入纯净的对话数据 - print("🔄 强制重新摄入纯净的对话数据...") - await ingest_contexts_via_full_pipeline(contents, end_user_id, save_chunk_output=True) - - # 使用异步LLM客户端 - llm_client = get_llm_client(llm_id) - # 初始化embedder用于直接调用 - cfg_dict = get_embedder_config(embedding_id) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # connector initialized above - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - # 上下文诊断收集 - per_query_context_counts: List[int] = [] - per_query_context_avg_tokens: List[float] = [] - per_query_context_chars: List[int] = [] - per_query_context_tokens_total: List[int] = [] - # 详细样本调试信息 - samples: List[Dict[str, Any]] = [] - # 通用指标 - f1s: List[float] = [] - b1s: List[float] = [] - jss: List[float] = [] - # 参考 LoCoMo 评测的类别专用 F1（multi-hop 使用多答案 F1） - loc_f1s: List[float] = [] - # Per-category aggregation - cat_counts: Dict[str, int] = {} - cat_f1s: Dict[str, List[float]] = {} - cat_b1s: Dict[str, List[float]] = {} - cat_jss: Dict[str, List[float]] = {} - cat_loc_f1s: Dict[str, List[float]] = {} - try: - for item in items: - q = item.get("question", "") - ref = item.get("answer", "") - # 确保答案是字符串 - ref_str = str(ref) if ref is not None else "" - cat = get_category_label(item) - - print(f"\n=== 处理问题: {q} ===") - - # 根据类别调整检索参数 - search_params = get_search_params_by_category(cat) - adjusted_limit = search_params["limit"] - max_chars = search_params["max_chars"] - - print(f"🏷️ 类别: {cat}, 检索参数: limit={adjusted_limit}, max_chars={max_chars}") - - # 改进的检索逻辑：使用三路检索（statements, dialogues, entities） - t0 = time.time() - contexts_all: List[str] = [] - search_results = None # 保存完整的检索结果 - - try: - if search_type == "embedding": - # 直接调用嵌入检索，包含三路数据 - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=q, - end_user_id=end_user_id, - limit=adjusted_limit, - include=["chunks", "statements", "entities", "summaries"], # 修复：使用正确的类型 - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - print(f"✅ 嵌入检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要") - - # 构建上下文：优先使用 chunks、statements 和 summaries - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要：最多加入前3个高分实体，避免噪声 - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - # 直接调用关键词检索 - search_results = await search_graph( - connector=connector, - q=q, - end_user_id=end_user_id, - limit=adjusted_limit - ) - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - print(f"🔤 关键词检索找到 {len(dialogs)} 条对话, {len(statements)} 条陈述, {len(entities)} 个实体") - - # 构建上下文 - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体处理（关键词检索的实体可能没有分数） - if entities: - entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid - # 使用旧版本的混合检索（重构前） - print("🔀 使用混合检索（旧版本）...") - try: - search_results = await run_hybrid_search( - query_text=q, - search_type=search_type, - end_user_id=end_user_id, - limit=adjusted_limit, - include=["chunks", "statements", "entities", "summaries"], - output_path=None, - rerank_alpha=0.6, - use_forgetting_rerank=False, - use_llm_rerank=False - ) - - # 处理旧版本的返回结构（包含 reranked_results） - if search_results and isinstance(search_results, dict): - # 对于 hybrid 搜索，使用 reranked_results - if "reranked_results" in search_results: - reranked = search_results["reranked_results"] - chunks = reranked.get("chunks", []) - statements = reranked.get("statements", []) - entities = reranked.get("entities", []) - summaries = reranked.get("summaries", []) - else: - # 单一搜索类型的结果 - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - # 检查是否有有效结果 - if chunks or statements or entities or summaries: - print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 陈述, {len(entities)} 实体, {len(summaries)} 摘要") - else: - # 如果顶层没有结果，尝试旧的嵌套结构（向后兼容） - reranked = search_results.get("reranked_results", {}) - if reranked and isinstance(reranked, dict): - chunks = reranked.get("chunks", []) - statements = reranked.get("statements", []) - entities = reranked.get("entities", []) - summaries = reranked.get("summaries", []) - print(f"✅ 混合检索成功（使用旧格式reranked结果）: {len(chunks)} chunks, {len(statements)} 陈述") - else: - raise ValueError("混合检索返回空结果") - else: - raise ValueError("混合检索返回空结果") - - except Exception as e: - print(f"❌ 混合检索失败: {e}，回退到嵌入检索") - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=q, - end_user_id=end_user_id, - limit=adjusted_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - print(f"✅ 回退嵌入检索成功: {len(chunks)} chunks, {len(statements)} 陈述") - - # 🎯 统一处理：构建上下文（所有检索类型共用） - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要：最多加入前3个高分实体 - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - # 关键修复：过滤掉包含当前问题答案的上下文 - filtered_contexts = [] - for context in contexts_all: - content = str(context) - # 排除包含当前问题标准答案的上下文 - if ref_str and ref_str.strip() and ref_str.strip() in content: - print("🚫 过滤掉包含标准答案的上下文") - continue - filtered_contexts.append(context) - - print(f"📊 过滤后保留 {len(filtered_contexts)} 个上下文 (原 {len(contexts_all)} 个)") - contexts_all = filtered_contexts - - # 输出完整的检索结果信息 - print("🔍 检索结果详情:") - if search_results: - output_data = { - "statements": [ - { - "statement": s.get("statement", "")[:200] + "..." if len(s.get("statement", "")) > 200 else s.get("statement", ""), - "score": s.get("score", 0.0) - } - for s in (statements[:2] if 'statements' in locals() else []) - ], - "dialogues": [ - { - "uuid": d.get("uuid", ""), - "end_user_id": d.get("end_user_id", ""), - "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""), - "score": d.get("score", 0.0) - } - for d in (dialogs[:2] if 'dialogs' in locals() else []) - ], - "entities": [ - { - "name": e.get("name", ""), - "entity_type": e.get("entity_type", ""), - "score": e.get("score", 0.0) - } - for e in (entities[:2] if 'entities' in locals() else []) - ] - } - print(json.dumps(output_data, ensure_ascii=False, indent=2)) - else: - print(" 无检索结果") - - except Exception as e: - print(f"❌ {search_type}检索失败: {e}") - contexts_all = [] - search_results = None - - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 使用智能上下文选择 - context_text = "" - if contexts_all: - context_text = smart_context_selection(contexts_all, q, max_chars=max_chars) - - # 如果智能选择后仍然过长，进行最终保护性截断 - if len(context_text) > max_chars: - print(f"⚠️ 智能选择后仍然过长 ({len(context_text)}字符)，进行最终截断") - context_text = context_text[:max_chars] + "\n\n[最终截断...]" - - # 时间解析 - anchor_date = datetime(2023, 5, 8) # 使用固定日期确保一致性 - context_text = _resolve_relative_times(context_text, anchor_date) - - context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n" + context_text - - print(f"📝 最终上下文长度: {len(context_text)} 字符") - - # 显示不同上下文的预览 - print("🔍 上下文预览:") - for j, context in enumerate(contexts_all[:3]): # 显示前3个上下文 - preview = context[:150].replace('\n', ' ') - print(f" 上下文{j+1}: {preview}...") - - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # 记录上下文诊断信息 - per_query_context_counts.append(len(contexts_all)) - per_query_context_avg_tokens.append(avg_context_tokens([context_text])) - per_query_context_chars.append(len(context_text)) - per_query_context_tokens_total.append(len(_loc_normalize(context_text).split())) - - # LLM 提示词 - messages = [ - {"role": "system", "content": ( - "You are a precise QA assistant. Answer following these rules:\n" - "1) Extract the EXACT information mentioned in the context\n" - "2) For time questions: calculate actual dates from relative times\n" - "3) Return ONLY the answer text in simplest form\n" - "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n" - "5) If no clear answer found, respond with 'Unknown'" - )}, - {"role": "user", "content": f"Question: {q}\n\nContext:\n{context_text}"}, - ] - - t2 = time.time() - # 使用异步调用 - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - - # 兼容不同的响应格式 - pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - - # 计算指标（确保使用字符串） - f1_val = common_f1(str(pred), ref_str) - b1_val = bleu1(str(pred), ref_str) - j_val = jaccard(str(pred), ref_str) - - f1s.append(f1_val) - b1s.append(b1_val) - jss.append(j_val) - - # Accumulate by category - cat_counts[cat] = cat_counts.get(cat, 0) + 1 - cat_f1s.setdefault(cat, []).append(f1_val) - cat_b1s.setdefault(cat, []).append(b1_val) - cat_jss.setdefault(cat, []).append(j_val) - - # LoCoMo 专用 F1：multi-hop(1) 使用多答案 F1，其它(2/3/4)使用单答案 F1 - if item.get("category") in [2, 3, 4]: - loc_val = loc_f1_score(str(pred), ref_str) - elif item.get("category") in [1]: - loc_val = loc_multi_f1(str(pred), ref_str) - else: - loc_val = loc_f1_score(str(pred), ref_str) - loc_f1s.append(loc_val) - cat_loc_f1s.setdefault(cat, []).append(loc_val) - - # 保存完整的检索结果信息 - samples.append({ - "question": q, - "answer": ref_str, - "category": cat, - "prediction": pred, - "metrics": { - "f1": f1_val, - "b1": b1_val, - "j": j_val, - "loc_f1": loc_val - }, - "retrieval": { - "retrieved_documents": len(contexts_all), - "context_length": len(context_text), - "search_limit": adjusted_limit, - "max_chars": max_chars - }, - "timing": { - "search_ms": (t1 - t0) * 1000, - "llm_ms": (t3 - t2) * 1000 - } - }) - - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {ref_str}") - print(f"📈 当前指标 - F1: {f1_val:.3f}, BLEU-1: {b1_val:.3f}, Jaccard: {j_val:.3f}, LoCoMo F1: {loc_val:.3f}") - - # Compute per-category averages and dispersion (std, iqr) - def _percentile(sorted_vals: List[float], p: float) -> float: - if not sorted_vals: - return 0.0 - if len(sorted_vals) == 1: - return sorted_vals[0] - k = (len(sorted_vals) - 1) * p - f = int(k) - c = f + 1 if f + 1 < len(sorted_vals) else f - if f == c: - return sorted_vals[f] - return sorted_vals[f] + (sorted_vals[c] - sorted_vals[f]) * (k - f) - - by_category: Dict[str, Dict[str, float | int]] = {} - for c in cat_counts: - f_list = cat_f1s.get(c, []) - b_list = cat_b1s.get(c, []) - j_list = cat_jss.get(c, []) - lf_list = cat_loc_f1s.get(c, []) - j_sorted = sorted(j_list) - j_std = statistics.stdev(j_list) if len(j_list) > 1 else 0.0 - j_q75 = _percentile(j_sorted, 0.75) - j_q25 = _percentile(j_sorted, 0.25) - by_category[c] = { - "count": cat_counts[c], - "f1": (sum(f_list) / max(len(f_list), 1)) if f_list else 0.0, - "b1": (sum(b_list) / max(len(b_list), 1)) if b_list else 0.0, - "j": (sum(j_list) / max(len(j_list), 1)) if j_list else 0.0, - "j_std": j_std, - "j_iqr": (j_q75 - j_q25) if j_list else 0.0, - # 参考 LoCoMo 评测的类别专用 F1 - "loc_f1": (sum(lf_list) / max(len(lf_list), 1)) if lf_list else 0.0, - } - - # 累加命中（cum accuracy by category）：与 evaluation_stats.py 输出形式相仿 - cum_accuracy_by_category = {c: sum(cat_loc_f1s.get(c, [])) for c in cat_counts} - - result = { - "dataset": "locomo", - "items": len(items), - "metrics": { - "f1": sum(f1s) / max(len(f1s), 1), - "b1": sum(b1s) / max(len(b1s), 1), - "j": sum(jss) / max(len(jss), 1), - # LoCoMo 类别专用 F1 的总体 - "loc_f1": sum(loc_f1s) / max(len(loc_f1s), 1), - }, - "by_category": by_category, - "category_counts": cat_counts, - "cum_accuracy_by_category": cum_accuracy_by_category, - "context": { - "avg_tokens": (sum(per_query_context_avg_tokens) / max(len(per_query_context_avg_tokens), 1)) if per_query_context_avg_tokens else 0.0, - "avg_chars": (sum(per_query_context_chars) / max(len(per_query_context_chars), 1)) if per_query_context_chars else 0.0, - "count_avg": (sum(per_query_context_counts) / max(len(per_query_context_counts), 1)) if per_query_context_counts else 0.0, - "avg_memory_tokens": (sum(per_query_context_tokens_total) / max(len(per_query_context_tokens_total), 1)) if per_query_context_tokens_total else 0.0, - }, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "samples": samples, - "params": { - "end_user_id": end_user_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "search_type": search_type, - "llm_id": llm_id, - "retrieval_embedding_id": embedding_id, - "chunker_strategy": os.getenv("EVAL_CHUNKER_STRATEGY", "RecursiveChunker"), - "skip_ingest_if_exists": skip_ingest_if_exists, - "llm_timeout": llm_timeout, - "llm_max_retries": llm_max_retries, - "llm_temperature": llm_temperature, - "llm_max_tokens": llm_max_tokens - }, - "timestamp": datetime.now().isoformat() - } - if output_path: - try: - os.makedirs(os.path.dirname(output_path), exist_ok=True) - with open(output_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"✅ 结果已保存到: {output_path}") - except Exception as e: - print(f"❌ 保存结果失败: {e}") - return result - finally: - await connector.close() - - -def main(): - parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search") - parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate") - parser.add_argument("--end_user_id", type=str, default=None, help="Group ID for retrieval") - parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query") - parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context") - parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature") - parser.add_argument("--llm_max_tokens", type=int, default=32, help="LLM max tokens") - parser.add_argument("--search_type", type=str, default="embedding", choices=["keyword", "embedding", "hybrid"], help="Search type") - parser.add_argument("--output_path", type=str, default=None, help="Output path for results") - parser.add_argument("--skip_ingest_if_exists", action="store_true", help="Skip ingest if group exists") - parser.add_argument("--llm_timeout", type=float, default=10.0, help="LLM timeout in seconds") - parser.add_argument("--llm_max_retries", type=int, default=1, help="LLM max retries") - args = parser.parse_args() - - load_dotenv() - - result = asyncio.run(run_locomo_eval( - sample_size=args.sample_size, - end_user_id=args.end_user_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - output_path=args.output_path, - skip_ingest_if_exists=args.skip_ingest_if_exists, - llm_timeout=args.llm_timeout, - llm_max_retries=args.llm_max_retries - )) - - print("\n" + "="*50) - print("📊 最终评测结果:") - print(f" 样本数量: {result['items']}") - print(f" F1: {result['metrics']['f1']:.3f}") - print(f" BLEU-1: {result['metrics']['b1']:.3f}") - print(f" Jaccard: {result['metrics']['j']:.3f}") - print(f" LoCoMo F1: {result['metrics']['loc_f1']:.3f}") - print(f" 平均上下文长度: {result['context']['avg_chars']:.0f} 字符") - print(f" 平均检索延迟: {result['latency']['search']['mean']:.1f}ms") - print(f" 平均LLM延迟: {result['latency']['llm']['mean']:.1f}ms") - - if result['by_category']: - print("\n📈 按类别细分:") - for cat, metrics in result['by_category'].items(): - print(f" {cat}:") - print(f" 样本数: {metrics['count']}") - print(f" F1: {metrics['f1']:.3f}") - print(f" LoCoMo F1: {metrics['loc_f1']:.3f}") - print(f" Jaccard: {metrics['j']:.3f} (±{metrics['j_std']:.3f}, IQR={metrics['j_iqr']:.3f})") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py b/api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py deleted file mode 100644 index aaf46e35..00000000 --- a/api/app/core/memory/evaluation/longmemeval/longmemeval_benchmark.py +++ /dev/null @@ -1,1339 +0,0 @@ -import argparse -import asyncio -import json -import os -import time -import re -import statistics -from datetime import datetime, timedelta -from typing import List, Dict, Any -from pathlib import Path - -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.models.base import RedBearModelConfig -from app.core.memory.utils.config.config_utils import get_embedder_config -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME -from app.core.memory.evaluation.common.metrics import f1_score as common_f1, jaccard, latency_stats, avg_context_tokens -from app.core.memory.evaluation.common.metrics import exact_match - - -def load_dataset_any(path: str) -> List[Dict[str, Any]]: - """健壮地加载数据集，支持三种格式： - 1. 标准 JSON 数组: [{...}, {...}] - 2. 单个 JSON 对象: {...} - 3. JSONL 格式（每行一个 JSON）: {...}\n{...}\n{...} - """ - with open(path, "r", encoding="utf-8") as f: - content = f.read().strip() - - # 尝试标准 JSON 解析 - try: - data = json.loads(content) - if isinstance(data, list): - return [item for item in data if isinstance(item, dict)] - elif isinstance(data, dict): - return [data] - except json.JSONDecodeError: - pass - - # 尝试 JSONL 格式（每行一个 JSON 对象） - items = [] - for line in content.splitlines(): - line = line.strip() - if not line: - continue - try: - obj = json.loads(line) - if isinstance(obj, dict): - items.append(obj) - elif isinstance(obj, list): - items.extend(item for item in obj if isinstance(item, dict)) - except json.JSONDecodeError: - continue - - return items - - -def is_chinese_text(s: str) -> bool: - return bool(re.search(r"[\u4e00-\u9fff]", s or "")) - - -def build_context_from_sessions(item: Dict[str, Any]) -> List[str]: - """从数据项的 haystack_sessions 构建上下文片段。 - - 优先返回包含 has_answer 的消息 - - 其次返回拼接后的整段会话 - """ - contexts: List[str] = [] - sessions = item.get("haystack_sessions", []) or item.get("sessions", []) - for session in sessions: - parts: List[str] = [] - if isinstance(session, list): - for msg in session: - role = msg.get("role", "") - content = msg.get("content", "") or msg.get("text", "") - if content: - parts.append(f"{role}: {content}" if role else str(content)) - if msg.get("has_answer", False): - contexts.append(f"{role}: {content}" if role else str(content)) - elif isinstance(session, dict): - role = session.get("role", "") - content = session.get("content", "") or session.get("text", "") - if content: - parts.append(f"{role}: {content}" if role else str(content)) - if session.get("has_answer", False): - contexts.append(f"{role}: {content}" if role else str(content)) - if parts: - contexts.append("\n".join(parts)) - # 兜底：存在单字段上下文 - if not contexts: - single_ctx = item.get("context") or item.get("dialogue") or item.get("conversation") - if isinstance(single_ctx, str) and single_ctx.strip(): - contexts.append(single_ctx.strip()) - return contexts - - -def extract_candidate_options(question: str) -> List[str]: - """从问题中提取候选选项（A-or-B 类问题）。""" - q = (question or "").strip() - options: List[str] = [] - - # 1) 引号包裹的片段 - for pat in [r"'([^']+)'", r'\"([^\"]+)\"', r'“([^”]+)”', r'‘([^’]+)’']: - for m in re.findall(pat, q): - val = (m or "").strip() - if val: - options.append(val) - - # 2) or/还是/或者 连接词 - if len(options) < 2: - pats = [ - r"([^,;，；]+?)\s+or\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+还是\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+或者\s+([^,;，；\?\.!.。！]+)", - ] - for pat in pats: - matches = list(re.finditer(pat, q, flags=re.IGNORECASE)) - if matches: - m = matches[-1] - cand1 = m.group(1).strip().strip("?？.,，;； ") - cand2 = m.group(2).strip().strip("?？.,，;； ") - options.extend([cand1, cand2]) - break - - # 去重 - seen = set() - uniq: List[str] = [] - for o in options: - o2 = o.strip() - key = o2.lower() if not is_chinese_text(o2) else o2 - if o2 and key not in seen: - uniq.append(o2) - seen.add(key) - return uniq - - -def extract_time_entities(text: str) -> List[Dict[str, Any]]: - """增强时间实体提取，专门用于时间推理问题""" - time_entities = [] - - # 日期模式 - date_patterns = [ - (r'\b(\d{4})-(\d{1,2})-(\d{1,2})\b', 'date'), # YYYY-MM-DD - (r'\b(\d{1,2})月(\d{1,2})日\b', 'date'), # 中文日期 - (r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份 - (r'\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份缩写 - ] - - # 时间间隔模式 - duration_patterns = [ - (r'(\d+)\s*天', 'days'), - (r'(\d+)\s*周', 'weeks'), - (r'(\d+)\s*个月', 'months'), - (r'(\d+)\s*年', 'years'), - (r'(\d+)\s*days?', 'days'), - (r'(\d+)\s*weeks?', 'weeks'), - (r'(\d+)\s*months?', 'months'), - (r'(\d+)\s*years?', 'years'), - ] - - # 事件时间关系模式 - temporal_relation_patterns = [ - (r'(之前|以前|前)\s*(\d+)\s*天', 'days_before'), - (r'(之后|以后|后)\s*(\d+)\s*天', 'days_after'), - (r'(\d+)\s*天\s*(之前|以前|前)', 'days_before'), - (r'(\d+)\s*天\s*(之后|以后|后)', 'days_after'), - (r'(\d+)\s*days?\s*(before|ago)', 'days_before'), - (r'(\d+)\s*days?\s*(after|later)', 'days_after'), - ] - - # 提取日期 - for pattern, entity_type in date_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间间隔 - for pattern, entity_type in duration_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间关系 - for pattern, entity_type in temporal_relation_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(2)) if match.groups() >= 2 else int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - return time_entities - - -def calculate_time_difference(date1: str, date2: str) -> int: - """计算两个日期之间的天数差""" - try: - # 解析日期格式 - def parse_date(date_str: str) -> datetime: - # 尝试多种日期格式 - formats = [ - '%Y-%m-%d', - '%m月%d日', - '%B %d, %Y', - '%b %d, %Y', - '%Y年%m月%d日' - ] - - for fmt in formats: - try: - return datetime.strptime(date_str, fmt) - except ValueError: - continue - - # 如果都无法解析，返回当前日期 - return datetime.now() - - d1 = parse_date(date1) - d2 = parse_date(date2) - - # 计算天数差（绝对值） - return abs((d2 - d1).days) - except Exception: - return -1 # 表示计算失败 - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """增强版上下文选择：特别优化时间推理问题的处理""" - if not contexts: - return "" - - # 检测是否为时间推理问题 - is_temporal_question = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 提取时间实体从问题中 - question_time_entities = extract_time_entities(question) - - # 英文关键词（去停用词） - question_lower = question.lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but','many','which','first' - } - eng_words = [w for w in set(re.findall(r'\b\w+\b', question_lower)) - if w not in stop_words and len(w) > 2] - - # 中文片段与候选选项 - cn_tokens = generate_query_keywords_cn(question) - options = extract_candidate_options(question) - - # 时间推理问题的特殊处理 - if is_temporal_question: - # 为时间问题添加时间相关关键词 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'days', 'first', '先后'] - eng_words = [w for w in eng_words if w not in ['days', 'first']] # 避免重复 - cn_tokens.extend([kw for kw in time_keywords if kw not in cn_tokens]) - - # 限制关键词数量，优先时间相关 - tokens = time_keywords[:2] + cn_tokens[:2] + eng_words[:1] + options[:1] - else: - # 常规问题处理 - tokens = cn_tokens[:3] + options[:2] + eng_words[:1] - - # 去重 - seen = set() - final_tokens: List[str] = [] - for t in tokens: - t2 = t.strip() - if t2 and t2 not in seen: - final_tokens.append(t2) - seen.add(t2) - - scored_contexts: List[tuple[float, str]] = [] - - # 时间推理问题的权重映射 - temporal_weight_map = { - "天": 2.0, "日": 2.0, "月": 1.8, "年": 1.8, "days": 2.0, - "before": 1.5, "after": 1.5, "first": 1.5, "先后": 1.5 - } - - # 常规问题的权重映射 - normal_weight_map = { - "问题": 2.0, "故障": 2.0, "异常": 1.8, "不正常": 1.8, "坏了": 1.8, - "系统": 1.3, "GPS": 1.5, "保养": 1.4, "设备": 1.2, "模块": 1.2, "功能": 1.1 - } - - weight_map = temporal_weight_map if is_temporal_question else normal_weight_map - - for i, context in enumerate(contexts): - context_str = str(context) - lines = re.split(r'[\r\n]+', context_str) - hit_lines: List[str] = [] - kw_hits: float = 0.0 - time_entity_count = 0 - - for line in lines: - ln = line.strip() - if not ln: - continue - - has_keyword = False - # 关键词匹配 - for tok in final_tokens: - if tok and tok in ln: - w = weight_map.get(tok, 1.0) - kw_hits += ln.count(tok) * w - has_keyword = True - - # 时间实体检测（特别针对时间推理问题） - if is_temporal_question: - time_entities = extract_time_entities(ln) - time_entity_count += len(time_entities) - if time_entities: - has_keyword = True - - if has_keyword: - # 对于时间推理问题，保留包含时间信息的完整行 - hit_lines.append(ln) - - snippet = "\n".join(hit_lines) if hit_lines else context_str.strip() - - # 限制单段长度，但对时间推理问题稍微放宽限制 - max_snippet_len = 600 if is_temporal_question else 500 - if len(snippet) > max_snippet_len: - snippet = snippet[:max_snippet_len] - - # 评分逻辑 - has_number = 1 if re.search(r'\d', snippet) else 0 - has_date = 1 if (re.search(r'\b\d{4}-\d{1,2}-\d{1,2}\b', snippet) or - re.search(r'\d{1,2}月\d{1,2}日', snippet)) else 0 - - # 时间推理问题的特殊评分 - if is_temporal_question: - time_bonus = time_entity_count * 2.0 # 时间实体奖励 - temporal_coherence = 3 if (has_date and time_entity_count >= 2) else 0 - else: - time_bonus = 0 - temporal_coherence = 0 - - length_bonus = 5 if 50 < len(snippet) < 1000 else (2 if len(snippet) >= 1000 else 0) - pos_bonus = 3 if i < 3 else 0 - - score = (kw_hits * 0.8 + (has_number + has_date) * 1.5 + - length_bonus + pos_bonus + time_bonus + temporal_coherence) - - scored_contexts.append((score, snippet)) - - # 选择累计至总字符预算 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - selected: List[str] = [] - total_chars = 0 - - for score, snippet in scored_contexts: - if total_chars + len(snippet) <= max_chars: - selected.append(snippet) - total_chars += len(snippet) - else: - if not selected and len(snippet) > max_chars: - selected.append(snippet[:max_chars]) - break - - final_context = "\n\n".join(selected) - - # 对于时间推理问题，添加时间计算提示 - if is_temporal_question and question_time_entities: - time_prompt = "\n\n[时间推理提示：请仔细分析上述上下文中的日期和时间关系，计算时间间隔或确定事件顺序]" - if total_chars + len(time_prompt) <= max_chars: - final_context += time_prompt - - return final_context - - -# 中文关键词提取（短语级，含数词/日期/常见领域词） -def _extract_cn_tokens(text: str) -> List[str]: - if not text: - return [] - t = str(text) - # 去掉常见功能词（粗略，不依赖分词库） - stop_words = [ - "我","我们","你","他","她","它","这","那","哪","一个","一次","一些","什么","怎么","是否","吗","呢", - "很","更","最","已经","正在","将要","马上","尽快","最近","关于","有关","以及","并且","或者","还是", - "因为","所以","如果","但是","而且","然后","之后","之前","同时","另外","并","但","却","被","把","让","给", - "和","与","跟","及","还有","就","都","在","对","对于","的","了","着","过","到","于","从","以","为","向","至","是" - ] - for sw in stop_words: - t = t.replace(sw, " ") - # 去标点 - t = re.sub(r"[，。！？、；：,.!?;:\"'（）()［］\[\]\-—…·]", " ", t) - # 基础中文片段（>=2） - base = re.findall(r"[\u4e00-\u9fff]{2,}", t) - # 特殊组合：第X次XXXX - specials = re.findall(r"第[一二三四五六七八九十]+次[\u4e00-\u9fff]{2,6}", text) - # 领域词（简单词典） - # 日期与数字 - dates = re.findall(r"\d{4}年\d{1,2}月\d{1,2}日|\d{1,2}月\d{1,2}日|\d{4}-\d{1,2}-\d{1,2}", text) - numbers = re.findall(r"\b\d+\b", text) - - tokens: List[str] = specials + base + dates + numbers - - generic = {"建议","推荐","帮助","提升","技能","有效","团队","参与度","喜欢","开始"} - tokens: List[str] = specials + base + dates + numbers - uniq: List[str] = [] - seen = set() - for tok in tokens: - tok2 = tok.strip() - if len(tok2) < 2 or len(tok2) > 6: - continue - if tok2 in generic: - continue - if tok2 not in seen: - uniq.append(tok2) - seen.add(tok2) - # 排除常见疑问型短语 - blacklist_exact = {"是什么","多少","多少天","哪个","哪些","之间","先","后","之前","之后"} - uniq2: List[str] = [u for u in uniq if u not in blacklist_exact] - return uniq2[:12] - - -# 面向检索的中文关键词生成：强调"短语、核心名词、问题/故障" -def generate_query_keywords_cn(question: str) -> List[str]: - if not question: - return [] - raw = _extract_cn_tokens(question) - core: List[str] = [] - seen = set() - - def push(x: str): - x2 = x.strip() - if not x2: - return - if 2 <= len(x2) <= 6 and x2 not in seen: - core.append(x2) - seen.add(x2) - - # 检测时间推理问题 - is_temporal = any(keyword in question for keyword in ['天', '日', 'before', 'after', 'first', '先后', '间隔']) - if is_temporal: - push("天") - push("日") - push("先后") - - # 明确优先的核心词 - if "新车" in question: - push("新车") - # 第X次保养/维修 - specials = re.findall(r"第[一二三四五六七八九十]+次[\u4e00-\u9fff]{2,6}", question) - for s in specials: - if "保养" in s or "维修" in s: - push(s) - if "保养" in question: - push("保养") - # 问题/故障类词，如题含"问题"则扩展同义词 - if "问题" in question: - for w in ["问题","故障","异常","不正常"]: - push(w) - - # 补充：从原始片段筛更短的名词短语（过滤疑问型词） - blacklist = {"是什么","多少","哪个","还是","或者","之间","先","后","之前","之后"} - for tok in raw: - if tok in blacklist: - continue - push(tok) - - # 限制数量，避免过长列表影响检索稳定性 - return core[:4] # 稍微增加限制 - - -# 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]: - results: List[Dict[str, Any]] = [] - try: - for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit) - if rows: - results.extend(rows) - except Exception: - pass - - # 按 name 去重 - deduped: List[Dict[str, Any]] = [] - seen = set() - for r in results: - k = str(r.get("name", "")) - if k and k not in seen: - deduped.append(r) - seen.add(k) - return deduped - - -# 通过对话/陈述中的entity_ids反查实体名称 -_FETCH_ENTITIES_BY_IDS = """ -MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) -RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type -""" - -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]: - if not ids: - return [] - try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id) - return rows or [] - except Exception: - return [] - - -# 增强的时间实体检索 -_TIME_ENTITY_SEARCH = """ -MATCH (e:ExtractedEntity) -WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) -RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type -LIMIT $limit -""" - -async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: - """专门搜索时间相关的实体""" - try: - date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" - rows = await connector.execute_query(_TIME_ENTITY_SEARCH, - date_pattern=date_pattern, - end_user_id=end_user_id, - limit=limit) - return rows or [] - except Exception: - return [] - - -# 中英相对时间解析：today/昨天/上周/3天后 等简单归一化为日期 -def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: - t = str(text) if text is not None else "" - # 英文 today/yesterday/tomorrow - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - - # 英文 X days ago / in X days - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - - # 中文 今天/昨天/明天 - t = re.sub(r"今天", anchor.date().isoformat(), t) - t = re.sub(r"昨日|昨天", (anchor - timedelta(days=1)).date().isoformat(), t) - t = re.sub(r"明天", (anchor + timedelta(days=1)).date().isoformat(), t) - # 中文 X天前 / X天后 - t = re.sub(r"(\d+)天前", lambda m: (anchor - timedelta(days=int(m.group(1)))).date().isoformat(), t) - t = re.sub(r"(\d+)天后", lambda m: (anchor + timedelta(days=int(m.group(1)))).date().isoformat(), t) - # 中文 上周 / 下周（近似7天） - t = re.sub(r"上周", (anchor - timedelta(days=7)).date().isoformat(), t) - t = re.sub(r"下周", (anchor + timedelta(days=7)).date().isoformat(), t) - # 中文 月日（无年份）补全年份 - def _md_repl(m: re.Match[str]) -> str: - mon = int(m.group(1)); day = int(m.group(2)) - return f"{anchor.year}-{mon:02d}-{day:02d}" - t = re.sub(r"(\d{1,2})月(\d{1,2})日", _md_repl, t) - return t - - -async def run_longmemeval_test( - sample_size: int = 3, - end_user_id: str | None = None, - search_limit: int = 8, - context_char_budget: int = 4000, - llm_temperature: float = 0.0, - llm_max_tokens: int = 16, - search_type: str = "hybrid", - data_path: str | None = None, - start_index: int = 0, - max_contexts_per_item: int = 2, - save_chunk_output: bool = True, - save_chunk_output_path: str | None = None, - reset_group_before_ingest: bool = False, - skip_ingest: bool = False, -) -> Dict[str, Any]: - """LongMemEval 评估测试：增强时间推理能力""" - - # Use environment variable with fallback chain - if end_user_id is None: - end_user_id = os.getenv("LONGMEMEVAL_END_USER_ID") or os.getenv("EVAL_END_USER_ID", "longmemeval_zh_bak_3") - - # 数据路径 - if not data_path: - # 固定使用中文数据集：dataset/longmemeval_oracle_zh.json - dataset_dir = Path(__file__).resolve().parent.parent / "dataset" - data_path = str(dataset_dir / "longmemeval_oracle_zh.json") - - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 longmemeval_oracle_zh.json 放置在: {dataset_dir}" - ) - - qa_list: List[Dict[str, Any]] = load_dataset_any(data_path) - # 支持评估全部样本：当 sample_size <= 0 时，取从 start_index 到末尾 - if sample_size is None or sample_size <= 0: - items = qa_list[start_index:] - else: - items = qa_list[start_index:start_index + sample_size] - - # 可选：摄入上下文（默认启用） - if not skip_ingest: - # 选择上下文并限量 - contexts: List[str] = [] - for it in items: - built = build_context_from_sessions(it) - full_transcripts = [c for c in built if "\n" in c] - evidence_msgs = [c for c in built if "\n" not in c] - selected: List[str] = [] - take_e = min(len(evidence_msgs), max_contexts_per_item) - selected.extend(evidence_msgs[:take_e]) - remain = max_contexts_per_item - len(selected) - if remain > 0 and full_transcripts: - selected.extend(full_transcripts[:remain]) - if not selected and built: - selected.append(built[0]) - contexts.extend(selected) - - print(f"📥 摄入 {len(contexts)} 个上下文到数据库") - if reset_group_before_ingest and end_user_id: - try: - _tmp_conn = Neo4jConnector() - await _tmp_conn.delete_group(end_user_id) - print(f"🧹 已清空组 {end_user_id} 的历史图数据") - except Exception as _e: - print(f"⚠️ 清空组数据失败（忽略继续）: {end_user_id} - {_e}") - finally: - try: - await _tmp_conn.close() - except Exception: - pass - _ingest_fn = ingest_contexts_via_full_pipeline - if _ingest_fn is None: - print("⚠️ 摄入函数不可用，已跳过摄入。请确认 PYTHONPATH 包含 'src' 或从项目根运行。") - else: - await _ingest_fn( - contexts, - end_user_id, - save_chunk_output=save_chunk_output, - save_chunk_output_path=save_chunk_output_path, - ) - - # 初始化组件（摄入后再初始化连接器）- 使用异步LLM客户端 - from app.db import get_db - - db = next(get_db()) - try: - llm_client = get_llm_client(os.getenv("EVAL_LLM_ID"), db) - cfg_dict = get_embedder_config(os.getenv("EVAL_EMBEDDING_ID"), db) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - finally: - db.close() - - connector = Neo4jConnector() - - # 指标收集 - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - per_query_context_counts: List[int] = [] - per_query_context_avg_tokens: List[float] = [] - per_query_context_chars: List[int] = [] - - type_correct: Dict[str, List[float]] = {} - type_f1: Dict[str, List[float]] = {} - type_jacc: Dict[str, List[float]] = {} - - samples: List[Dict[str, Any]] = [] - # 统计重复的上下文预览（跨样本），便于诊断"相同上下文"问题 - preview_counter: Dict[str, int] = {} - - try: - for item in items: - question = item.get("question", "") - reference = item.get("answer", "") - qtype = item.get("question_type") or item.get("type", "unknown") - - print(f"\n=== 处理问题: {question} ===") - - # 检测问题类型 - is_temporal = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 检索 - t0 = time.time() - contexts_all: List[str] = [] - dialogs, statements, entities = [], [], [] - - try: - if search_type == "embedding": - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - - # 实体摘要（最多3个） - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - search_results = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=search_limit, - ) - chunks = search_results.get("chunks", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - summaries = search_results.get("summaries", []) - - for c in chunks: - content = str(c.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - for sm in summaries: - summary_text = str(sm.get("summary", "")).strip() - if summary_text: - contexts_all.append(summary_text) - if entities: - entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid（增强版：特别优化时间推理问题） - emb_chunks, emb_statements, emb_entities, emb_summaries, emb_dialogs = [], [], [], [], [] - kw_dialogs, kw_statements, kw_entities = [], [], [] - - # 1) 嵌入检索 - try: - emb_res = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], - ) - if isinstance(emb_res, dict): - emb_chunks = emb_res.get("chunks", []) or [] - emb_statements = emb_res.get("statements", []) or [] - emb_entities = emb_res.get("entities", []) or [] - emb_summaries = emb_res.get("summaries", []) or [] - emb_dialogs = emb_res.get("dialogues", []) or [] - except Exception as e: - print(f"⚠️ 嵌入检索失败，将继续进行关键词检索: {e}") - - # 2) 关键词检索（增强版） - try: - kw_res = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=search_limit, - ) - if isinstance(kw_res, dict): - kw_dialogs = kw_res.get("dialogues", []) or [] - kw_statements = kw_res.get("statements", []) or [] - kw_entities = kw_res.get("entities", []) or [] - - # 时间推理问题的特殊处理 - if is_temporal: - # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, end_user_id, search_limit//2) - if time_entities: - kw_entities.extend(time_entities) - # 添加时间相关关键词检索 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'first'] - for tk in time_keywords: - try: - time_res = await search_graph( - connector=connector, - q=tk, - end_user_id=end_user_id, - limit=2, - ) - if isinstance(time_res, dict): - kw_dialogs.extend(time_res.get("dialogues", []) or []) - kw_statements.extend(time_res.get("statements", []) or []) - except Exception: - pass - - # 中文关键词拆分后做别名匹配 - cn_tokens = _extract_cn_tokens(question) - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit) - if alias_entities: - kw_entities.extend(alias_entities) - - # 从对话/陈述中的 entity_ids 反查实体 - ids = [] - try: - for d in kw_dialogs: - ids.extend(d.get("entity_ids", []) or []) - for s in kw_statements: - ids.extend(s.get("entity_ids", []) or []) - except Exception: - pass - if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id) - if id_entities: - kw_entities.extend(id_entities) - - # 多关键词检索 - try: - eng_words = [w for w in set(re.findall(r"\b\w+\b", question.lower())) if len(w) > 2] - kw_list = generate_query_keywords_cn(question)[:3] + eng_words[:1] - for kw in kw_list: - if not kw: - continue - sub_res = await search_graph( - connector=connector, - q=str(kw), - end_user_id=end_user_id, - limit=max(3, search_limit // 2), - ) - if isinstance(sub_res, dict): - kw_dialogs.extend(sub_res.get("dialogues", []) or []) - kw_statements.extend(sub_res.get("statements", []) or []) - kw_entities.extend(sub_res.get("entities", []) or []) - except Exception: - pass - - # 选项参与关键词检索 - try: - opt_list = extract_candidate_options(question)[:2] - for opt in opt_list: - if not opt: - continue - opt_res = await search_graph( - connector=connector, - q=str(opt), - end_user_id=end_user_id, - limit=max(3, search_limit // 2), - ) - if isinstance(opt_res, dict): - kw_dialogs.extend(opt_res.get("dialogues", []) or []) - kw_statements.extend(opt_res.get("statements", []) or []) - kw_entities.extend(opt_res.get("entities", []) or []) - except Exception: - pass - except Exception as e: - print(f"❌ 关键词检索失败: {e}") - - # 3) 合并、排序并去重 - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - def dedup(items: List[Dict[str, Any]], key_field: str = "uuid") -> List[Dict[str, Any]]: - seen = set() - out = [] - for it in items: - key = str(it.get(key_field, "")) + str(it.get("content", "") + str(it.get("statement", ""))) - if key not in seen: - out.append(it) - seen.add(key) - return out - - # 时间推理问题优先排序包含时间信息的文档 - if is_temporal: - def temporal_score(item: Dict[str, Any]) -> float: - base_score = float(item.get("score", 0.0)) - content = str(item.get("content", "") + str(item.get("statement", ""))) - time_entities = extract_time_entities(content) - time_bonus = len(time_entities) * 0.5 - return base_score + time_bonus - - dialogs = dedup(sorted(all_dialogs, key=temporal_score, reverse=True)) - statements = dedup(sorted(all_statements, key=temporal_score, reverse=True)) - else: - dialogs = dedup(sorted(all_dialogs, key=lambda d: float(d.get("score", 0.0)), reverse=True)) - statements = dedup(sorted(all_statements, key=lambda s: float(s.get("score", 0.0)), reverse=True)) - - entities = dedup(all_entities, key_field="name") - - # 4) 构建上下文 - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体摘要 - try: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - except Exception: - pass - - # 全局回退 - if not contexts_all and search_type in ("embedding", "hybrid"): - try: - print("🔁 检索为空，回退到关键词检索...") - kw_fallback = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=max(search_limit, 5), - ) - fb_dialogs = kw_fallback.get("dialogues", []) or [] - fb_statements = kw_fallback.get("statements", []) or [] - fb_entities = kw_fallback.get("entities", []) or [] - - for d in fb_dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in fb_statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - if fb_entities: - entity_names = [str(e.get("name", "")).strip() for e in fb_entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - dialogs = fb_dialogs if fb_dialogs else dialogs - statements = fb_statements if fb_statements else statements - entities = fb_entities if fb_entities else entities - print(f"↩️ 回退到关键词检索: {len(fb_dialogs)} 对话, {len(fb_statements)} 条陈述, {len(fb_entities)} 个实体") - except Exception as fe: - print(f"❌ 关键词回退失败: {fe}") - - ent_count = len(entities) if isinstance(entities, list) else 0 - print(f"✅ {search_type}检索成功: {len(dialogs)} 对话, {len(statements)} 条陈述, {ent_count} 个实体") - if is_temporal: - print("⏰ 检测为时间推理问题，已启用时间优化检索") - - except Exception as e: - print(f"❌ {search_type}检索失败: {e}") - contexts_all = [] - - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 智能上下文选择 - context_text = "" - if contexts_all: - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) - # 相对时间解析 - try: - context_text = _resolve_relative_times_cn_en(context_text, anchor=datetime.now()) - except Exception: - pass - # 诊断信息 - try: - cn_diag = generate_query_keywords_cn(question)[:3] - opts = extract_candidate_options(question)[:2] - qlw = [w for w in set(re.findall(r'\b\w+\b', question.lower())) if len(w) > 2][:1] - diag_tokens: List[str] = [] - for t in cn_diag + opts + qlw: - if t and t not in diag_tokens: - diag_tokens.append(t) - print(f"🔍 关键词/选项: {', '.join(diag_tokens)}") - preview = context_text[:200].replace('\n', ' ') - print(f"🔎 上下文预览: {preview}...") - key_preview = preview.strip() - if key_preview: - preview_counter[key_preview] = preview_counter.get(key_preview, 0) + 1 - except Exception: - pass - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # 记录上下文诊断信息 - per_query_context_counts.append(len(contexts_all)) - per_query_context_avg_tokens.append(avg_context_tokens([context_text])) - per_query_context_chars.append(len(context_text)) - - # LLM 推理（增强时间推理提示） - options = extract_candidate_options(question) - if len(options) >= 2: - opt_lines = "\n".join(f"- {o}" for o in options) - # 时间推理问题的特殊提示 - if is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "Return ONLY one string: exactly one option from the provided candidates. If the context is insufficient, respond with 'Unknown'. " - "Pay special attention to date sequences and time intervals." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. Return ONLY one string: exactly one option from the provided candidates. " - "If the context is insufficient, respond with 'Unknown'. If the context expresses a synonym or paraphrase of a candidate, return the closest candidate. " - "Do not include explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": ( - f"Question: {question}\n\nCandidates:\n{opt_lines}\n\nContext:\n{context_text}\n\nReturn EXACTLY one candidate string (or 'Unknown')." - ), - }, - ] - else: - # 时间推理问题的特殊提示 - if is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "If the context contains the answer, return a concise answer phrase focusing on temporal information. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. If the context contains the answer, return a concise answer phrase. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}\n\nReturn ONLY the answer (or 'Unknown').", - }, - ] - - t2 = time.time() - # 使用异步调用 - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - - # 兼容不同的响应格式 - pred_raw = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - - # 选项题输出规范化 - pred = pred_raw - if len(options) >= 2 and not pred_raw.lower().startswith("unknown"): - def _basic_norm(s: str) -> str: - s = s.lower().strip() - return re.sub(r"[^\w\s]", " ", s) - def _jaccard(a: str, b: str) -> float: - ta = set(t for t in _basic_norm(a).split() if t) - tb = set(t for t in _basic_norm(b).split() if t) - if not ta and not tb: - return 1.0 - if not ta or not tb: - return 0.0 - return len(ta & tb) / len(ta | tb) - best = None - best_score = -1.0 - for o in options: - score = _jaccard(pred_raw, o) - if score > best_score: - best = o - best_score = score - if best is not None and best_score > 0.0: - pred = best - - # 指标 - flag = exact_match(pred, reference) - f1_val = common_f1(str(pred), str(reference)) - j_val = jaccard(str(pred), str(reference)) - - type_correct.setdefault(qtype, []).append(flag) - type_f1.setdefault(qtype, []).append(f1_val) - type_jacc.setdefault(qtype, []).append(j_val) - - samples.append({ - "question": question, - "prediction": pred, - "answer": reference, - "question_type": qtype, - "is_temporal": is_temporal, - "question_id": item.get("question_id"), - "options": options, - "context_count": len(contexts_all), - "context_chars": len(context_text), - "retrieved_dialogue_count": len(dialogs), - "retrieved_statement_count": len(statements), - "metrics": { - "exact_match": bool(flag), - "f1": f1_val, - "jaccard": j_val - }, - "timing": { - "search_ms": (t1 - t0) * 1000, - "llm_ms": (t3 - t2) * 1000 - } - }) - - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {reference}") - print(f"📈 当前指标 - Exact Match: {flag}, F1: {f1_val:.3f}, Jaccard: {j_val:.3f}") - - # 聚合结果 - type_acc = {t: (sum(v) / max(len(v), 1)) for t, v in type_correct.items()} - f1_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_f1.items()} - jacc_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_jacc.items()} - - result = { - "dataset": "longmemeval", - "items": len(items), - "accuracy_by_type": type_acc, - "f1_by_type": f1_by_type, - "jaccard_by_type": jacc_by_type, - "samples": samples, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "context": { - "avg_tokens": statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0, - "avg_chars": statistics.mean(per_query_context_chars) if per_query_context_chars else 0.0, - "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, - }, - "params": { - "end_user_id": end_user_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "search_type": search_type, - "llm_id": os.getenv("EVAL_LLM_ID"), - "embedding_id": os.getenv("EVAL_EMBEDDING_ID"), - "sample_size": sample_size, - "start_index": start_index, - }, - "timestamp": datetime.now().isoformat() - } - - # 计算汇总指标 - try: - total_items = max(len(samples), 1) - correct_count = sum(1 for s in samples if s.get("metrics", {}).get("exact_match")) - score_accuracy = (correct_count / total_items) * 100.0 - - total_latencies_ms = [] - for s in samples: - t = s.get("timing", {}) - total_latencies_ms.append(float(t.get("search_ms", 0.0)) + float(t.get("llm_ms", 0.0))) - total_lat_stats = latency_stats(total_latencies_ms) if total_latencies_ms else {"p50": 0.0, "iqr": 0.0} - latency_median_s = total_lat_stats.get("p50", 0.0) / 1000.0 - latency_iqr_s = total_lat_stats.get("iqr", 0.0) / 1000.0 - - avg_ctx_tokens = statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0 - avg_ctx_tokens_k = avg_ctx_tokens / 1000.0 - - result["metric_summary"] = { - "score_accuracy": score_accuracy, - "latency_median_s": latency_median_s, - "latency_iqr_s": latency_iqr_s, - "avg_context_tokens_k": avg_ctx_tokens_k, - } - except Exception: - result["metric_summary"] = { - "score_accuracy": 0.0, - "latency_median_s": 0.0, - "latency_iqr_s": 0.0, - "avg_context_tokens_k": 0.0, - } - - # 诊断信息 - try: - dups = sorted([(k, c) for k, c in preview_counter.items() if c > 1], key=lambda x: -x[1])[:5] - result["diagnostics"] = { - "duplicate_previews_top": [{"count": c, "preview": k[:120]} for k, c in dups], - "unique_preview_count": len(preview_counter), - } - except Exception: - pass - - return result - - finally: - await connector.close() - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="LongMemEval 评估测试脚本（增强时间推理版）") - parser.add_argument("--sample-size", type=int, default=3, help="样本数量（<=0 表示全部）") - parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") - parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--end-user-id", type=str, default=None, help="图数据库 End User ID，默认使用环境变量") - parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=16, help="LLM 最大输出 token") - parser.add_argument("--search-type", type=str, default="hybrid", choices=["embedding","keyword","hybrid"], help="检索类型") - parser.add_argument("--data-path", type=str, default=None, help="数据集路径") - parser.add_argument("--max-contexts-per-item", type=int, default=2, help="每条样本最多摄入的上下文段数") - parser.add_argument("--no-save-chunk-output", action="store_true", help="不保存分块结果（默认保存）") - parser.add_argument("--save-chunk-output-path", type=str, default=None, help="自定义分块输出路径") - parser.add_argument("--reset-group-before-ingest", action="store_true", help="摄入前清空该 Group 在图数据库中的历史数据") - parser.add_argument("--skip-ingest", action="store_true", help="跳过摄入，仅检索评估") - args = parser.parse_args() - - sample_size = 0 if args.all else args.sample_size - - result = asyncio.run( - run_longmemeval_test( - sample_size=sample_size, - end_user_id=args.end_user_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - data_path=args.data_path, - start_index=args.start_index, - max_contexts_per_item=args.max_contexts_per_item, - save_chunk_output=(not args.no_save_chunk_output), - save_chunk_output_path=args.save_chunk_output_path, - reset_group_before_ingest=args.reset_group_before_ingest, - skip_ingest=args.skip_ingest, - ) - ) - - # 打印结果 - print("\n" + "="*50) - print("📊 LongMemEval 测试结果:") - print(f" 样本数量: {result['items']}") - - if result['accuracy_by_type']: - print("\n📈 按问题类型细分:") - for qtype, acc in result['accuracy_by_type'].items(): - print(f" {qtype}:") - print(f" Score (Accuracy): {acc:.3f}") - - print(f"\n📊 指标总览:") - ms = result.get('metric_summary', {}) - print(f" Score (Accuracy): {ms.get('score_accuracy', 0.0):.1f}%") - print(f" Latency (s): median {ms.get('latency_median_s', 0.0):.3f}s") - print(f" Latency IQR (s): {ms.get('latency_iqr_s', 0.0):.3f}s") - print(f" Avg Context Tokens (k): {ms.get('avg_context_tokens_k', 0.0):.3f}k") - - print(f"\n⏱️ 细分性能指标:") - print(f" 检索延迟(均值): {result['latency']['search']['mean']:.1f}ms") - print(f" LLM延迟(均值): {result['latency']['llm']['mean']:.1f}ms") - print(f" 上下文长度(均值): {result['context']['avg_chars']:.0f} 字符") - - - # 保存结果到文件 - try: - # 使用相对路径而不是 PROJECT_ROOT - out_dir = Path(__file__).resolve().parent / "results" - os.makedirs(out_dir, exist_ok=True) - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = os.path.join(out_dir, f"longmemeval_{result['params']['search_type']}_{ts}.json") - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n💾 结果已保存: {out_path}") - except Exception as e: - print(f"⚠️ 结果保存失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/longmemeval/test_eval.py b/api/app/core/memory/evaluation/longmemeval/test_eval.py deleted file mode 100644 index 08daa890..00000000 --- a/api/app/core/memory/evaluation/longmemeval/test_eval.py +++ /dev/null @@ -1,1312 +0,0 @@ -import argparse -import asyncio -import json -import os -import time -import re -import statistics -from datetime import datetime, timedelta -from typing import List, Dict, Any -from pathlib import Path - -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -# 与现有评估脚本保持一致的导入方式 -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.models.base import RedBearModelConfig -from app.core.memory.utils.config.config_utils import get_embedder_config -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.evaluation.dialogue_queries import SEARCH_ENTITIES_BY_NAME -from app.core.memory.evaluation.common.metrics import f1_score as common_f1, jaccard, latency_stats, avg_context_tokens -from app.core.memory.evaluation.common.metrics import exact_match - - -def load_dataset_any(path: str) -> List[Dict[str, Any]]: - """健壮地加载数据集，支持三种格式： - 1. 标准 JSON 数组: [{...}, {...}] - 2. 单个 JSON 对象: {...} - 3. JSONL 格式（每行一个 JSON）: {...}\n{...}\n{...} - """ - with open(path, "r", encoding="utf-8") as f: - content = f.read().strip() - - # 尝试标准 JSON 解析 - try: - data = json.loads(content) - if isinstance(data, list): - return [item for item in data if isinstance(item, dict)] - elif isinstance(data, dict): - return [data] - except json.JSONDecodeError: - pass - - # 尝试 JSONL 格式（每行一个 JSON 对象） - items = [] - for line in content.splitlines(): - line = line.strip() - if not line: - continue - try: - obj = json.loads(line) - if isinstance(obj, dict): - items.append(obj) - elif isinstance(obj, list): - items.extend(item for item in obj if isinstance(item, dict)) - except json.JSONDecodeError: - continue - - return items - - -def is_chinese_text(s: str) -> bool: - return bool(re.search(r"[\u4e00-\u9fff]", s or "")) - - -def extract_candidate_options(question: str) -> List[str]: - """从问题中提取候选选项（A-or-B 类问题）。""" - q = (question or "").strip() - options: List[str] = [] - - # 1) 引号包裹的片段 - for pat in [r"'([^']+)'", r'\"([^\"]+)\"', r'“([^”]+)”', r'‘([^’]+)’']: - for m in re.findall(pat, q): - val = (m or "").strip() - if val: - options.append(val) - - # 2) or/还是/或者 连接词 - if len(options) < 2: - pats = [ - r"([^,;，；]+?)\s+or\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+还是\s+([^,;，；\?\.!.。！]+)", - r"([^,;，；]+?)\s+或者\s+([^,;，；\?\.!.。！]+)", - ] - for pat in pats: - matches = list(re.finditer(pat, q, flags=re.IGNORECASE)) - if matches: - m = matches[-1] - cand1 = m.group(1).strip().strip("?？.,，;； ") - cand2 = m.group(2).strip().strip("?？.,，;； ") - options.extend([cand1, cand2]) - break - - # 去重 - seen = set() - uniq: List[str] = [] - for o in options: - o2 = o.strip() - key = o2.lower() if not is_chinese_text(o2) else o2 - if o2 and key not in seen: - uniq.append(o2) - seen.add(key) - return uniq - - -def extract_time_entities(text: str) -> List[Dict[str, Any]]: - """增强时间实体提取，专门用于时间推理问题""" - time_entities = [] - - # 日期模式 - date_patterns = [ - (r'\b(\d{4})-(\d{1,2})-(\d{1,2})\b', 'date'), # YYYY-MM-DD - (r'\b(\d{1,2})月(\d{1,2})日\b', 'date'), # 中文日期 - (r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份 - (r'\b(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s+(\d{1,2}),?\s+(\d{4})?', 'date'), # 英文月份缩写 - ] - - # 时间间隔模式 - duration_patterns = [ - (r'(\d+)\s*天', 'days'), - (r'(\d+)\s*周', 'weeks'), - (r'(\d+)\s*个月', 'months'), - (r'(\d+)\s*年', 'years'), - (r'(\d+)\s*days?', 'days'), - (r'(\d+)\s*weeks?', 'weeks'), - (r'(\d+)\s*months?', 'months'), - (r'(\d+)\s*years?', 'years'), - ] - - # 事件时间关系模式 - temporal_relation_patterns = [ - (r'(之前|以前|前)\s*(\d+)\s*天', 'days_before'), - (r'(之后|以后|后)\s*(\d+)\s*天', 'days_after'), - (r'(\d+)\s*天\s*(之前|以前|前)', 'days_before'), - (r'(\d+)\s*天\s*(之后|以后|后)', 'days_after'), - (r'(\d+)\s*days?\s*(before|ago)', 'days_before'), - (r'(\d+)\s*days?\s*(after|later)', 'days_after'), - ] - - # 提取日期 - for pattern, entity_type in date_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间间隔 - for pattern, entity_type in duration_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - # 提取时间关系 - for pattern, entity_type in temporal_relation_patterns: - matches = re.finditer(pattern, text, re.IGNORECASE) - for match in matches: - time_entities.append({ - 'text': match.group(), - 'type': entity_type, - 'value': int(match.group(2)) if match.groups() >= 2 else int(match.group(1)), - 'start': match.start(), - 'end': match.end() - }) - - return time_entities - - -def calculate_time_difference(date1: str, date2: str) -> int: - """计算两个日期之间的天数差""" - try: - # 解析日期格式 - def parse_date(date_str: str) -> datetime: - # 尝试多种日期格式 - formats = [ - '%Y-%m-%d', - '%m月%d日', - '%B %d, %Y', - '%b %d, %Y', - '%Y年%m月%d日' - ] - - for fmt in formats: - try: - return datetime.strptime(date_str, fmt) - except ValueError: - continue - - # 如果都无法解析，返回当前日期 - return datetime.now() - - d1 = parse_date(date1) - d2 = parse_date(date2) - - # 计算天数差（绝对值） - return abs((d2 - d1).days) - except Exception: - return -1 # 表示计算失败 - - -def _extract_cn_tokens(text: str) -> List[str]: - """中文关键词提取（短语级，含数词/日期/常见领域词）""" - if not text: - return [] - t = str(text) - # 去掉常见功能词（粗略，不依赖分词库） - stop_words = [ - "我","我们","你","他","她","它","这","那","哪","一个","一次","一些","什么","怎么","是否","吗","呢", - "很","更","最","已经","正在","将要","马上","尽快","最近","关于","有关","以及","并且","或者","还是", - "因为","所以","如果","但是","而且","然后","之后","之前","同时","另外","并","但","却","被","把","让","给", - "和","与","跟","及","还有","就","都","在","对","对于","的","了","着","过","到","于","从","以","为","向","至","是" - ] - for sw in stop_words: - t = t.replace(sw, " ") - # 去标点 - t = re.sub(r"[，。！？、；：,.!?;:\"'（）()［］\[\]\-—…·]", " ", t) - # 基础中文片段（>=2） - base = re.findall(r"[\u4e00-\u9fff]{2,}", t) - # 特殊组合：第X次XXXX - specials = re.findall(r"第[一二三四五六七八九十]+次[\u4e00-\u9fff]{2,6}", text) - # 日期与数字 - dates = re.findall(r"\d{4}年\d{1,2}月\d{1,2}日|\d{1,2}月\d{1,2}日|\d{4}-\d{1,2}-\d{1,2}", text) - numbers = re.findall(r"\b\d+\b", text) - - generic = {"建议","推荐","帮助","提升","技能","有效","团队","参与度","喜欢","开始"} - tokens: List[str] = specials + base + dates + numbers - uniq: List[str] = [] - seen = set() - for tok in tokens: - tok2 = tok.strip() - if len(tok2) < 2 or len(tok2) > 6: - continue - if tok2 in generic: - continue - if tok2 not in seen: - uniq.append(tok2) - seen.add(tok2) - # 排除常见疑问型短语 - blacklist_exact = {"是什么","多少","多少天","哪个","哪些","之间","先","后","之前","之后"} - uniq2: List[str] = [u for u in uniq if u not in blacklist_exact] - return uniq2[:12] - - -def generate_query_keywords_cn(question: str) -> List[str]: - """增强版关键词提取，特别关注技术术语和专有名词""" - if not question: - return [] - - # 提取专有名词（带引号的内容） - quoted_terms = re.findall(r'["""]([^"""]+)["""]', question) - - # 提取技术术语（中英文混合） - tech_terms = re.findall(r'[A-Z][a-zA-Z]+\s+[A-Z][a-zA-Z]+|[A-Za-z]+[\u4e00-\u9fff]+|[\u4e00-\u9fff]+[A-Za-z]+', question) - - # 提取核心名词短语 - core_nouns = re.findall(r'[\u4e00-\u9fff]{2,5}系统|[\u4e00-\u9fff]{2,5}管理|[\u4e00-\u9fff]{2,5}分析|[\u4e00-\u9fff]{2,5}工作坊|[\u4e00-\u9fff]{2,5}研讨会', question) - - # 基础中文片段 - base_tokens = _extract_cn_tokens(question) - - # 特定领域关键词增强 - domain_keywords = [] - # GPS相关 - if any(term in question for term in ["GPS", "导航", "定位系统", "系统运行"]): - domain_keywords.extend(["GPS", "导航系统", "定位", "系统故障", "功能异常"]) - # 活动相关 - if any(term in question for term in ["工作坊", "研讨会", "网络研讨会", "活动"]): - domain_keywords.extend(["工作坊", "研讨会", "参加", "参与", "活动"]) - # 时间顺序相关 - if any(term in question for term in ["先", "后", "第一个", "之前", "首先"]): - domain_keywords.extend(["先", "后", "之前", "之后", "第一次", "首先"]) - # 设备相关 - if any(term in question for term in ["设备", "手机", "电脑", "笔记本电脑"]): - domain_keywords.extend(["设备", "手机", "电脑", "笔记本电脑", "购买"]) - - # 合并并去重 - all_tokens = quoted_terms + tech_terms + core_nouns + base_tokens + domain_keywords - seen = set() - final_tokens = [] - - for token in all_tokens: - token = token.strip() - if len(token) >= 2 and token not in seen: - final_tokens.append(token) - seen.add(token) - - return final_tokens[:8] - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """增强版上下文选择：特别优化技术术语和精确匹配""" - if not contexts: - return "" - - # 检测是否为时间推理问题 - is_temporal_question = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 提取时间实体从问题中 - question_time_entities = extract_time_entities(question) - - # 提取关键技术实体 - key_entities = [] - # GPS相关 - if any(term in question for term in ["GPS", "导航", "定位系统", "系统运行"]): - key_entities.extend(["GPS", "导航", "定位", "系统", "功能", "问题", "故障"]) - # 活动相关 - if any(term in question for term in ["工作坊", "研讨会", "网络研讨会", "活动"]): - key_entities.extend(["工作坊", "研讨会", "参加", "参与", "活动", "时间"]) - # 时间顺序相关 - if any(term in question for term in ["先", "后", "第一个", "之前", "首先"]): - key_entities.extend(["先", "后", "之前", "之后", "第一次", "首先"]) - - # 英文关键词（去停用词） - question_lower = question.lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but','many','which','first' - } - eng_words = [w for w in set(re.findall(r'\b\w+\b', question_lower)) - if w not in stop_words and len(w) > 2] - - # 中文片段与候选选项 - cn_tokens = generate_query_keywords_cn(question) - options = extract_candidate_options(question) - - # 时间推理问题的特殊处理 - if is_temporal_question: - # 为时间问题添加时间相关关键词 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'days', 'first', '先后'] - eng_words = [w for w in eng_words if w not in ['days', 'first']] # 避免重复 - cn_tokens.extend([kw for kw in time_keywords if kw not in cn_tokens]) - - # 限制关键词数量，优先时间相关 - tokens = time_keywords[:2] + key_entities[:3] + cn_tokens[:2] + eng_words[:1] + options[:1] - else: - # 常规问题处理，优先关键技术实体 - tokens = key_entities[:4] + cn_tokens[:3] + options[:2] + eng_words[:1] - - # 去重 - seen = set() - final_tokens: List[str] = [] - for t in tokens: - t2 = t.strip() - if t2 and t2 not in seen: - final_tokens.append(t2) - seen.add(t2) - - scored_contexts: List[tuple[float, str]] = [] - - # 关键技术实体权重映射 - key_entity_weights = { - "GPS": 3.0, "导航": 2.5, "系统": 2.0, "功能": 2.0, "问题": 2.0, "故障": 2.5, - "工作坊": 2.5, "研讨会": 2.5, "参加": 2.0, "参与": 2.0, - "先": 2.0, "后": 2.0, "之前": 2.0, "之后": 2.0, "第一次": 2.5 - } - - # 时间推理问题的权重映射 - temporal_weight_map = { - "天": 2.0, "日": 2.0, "月": 1.8, "年": 1.8, "days": 2.0, - "before": 1.5, "after": 1.5, "first": 1.5, "先后": 1.5 - } - - # 常规问题的权重映射 - normal_weight_map = { - "问题": 2.0, "故障": 2.0, "异常": 1.8, "不正常": 1.8, "坏了": 1.8, - "系统": 1.3, "GPS": 1.5, "保养": 1.4, "设备": 1.2, "模块": 1.2, "功能": 1.1 - } - - # 合并权重映射 - weight_map = {**normal_weight_map, **temporal_weight_map, **key_entity_weights} - - for i, context in enumerate(contexts): - context_str = str(context) - lines = re.split(r'[\r\n]+', context_str) - hit_lines: List[str] = [] - kw_hits: float = 0.0 - time_entity_count = 0 - key_entity_hits = 0 - - for line in lines: - ln = line.strip() - if not ln: - continue - - has_keyword = False - # 关键词匹配 - for tok in final_tokens: - if tok and tok in ln: - w = weight_map.get(tok, 1.0) - hit_count = ln.count(tok) - kw_hits += hit_count * w - # 关键技术实体额外奖励 - if tok in key_entity_weights: - key_entity_hits += hit_count - has_keyword = True - - # 时间实体检测（特别针对时间推理问题） - if is_temporal_question: - time_entities = extract_time_entities(ln) - time_entity_count += len(time_entities) - if time_entities: - has_keyword = True - - # 精确匹配奖励（完整问题关键词出现在上下文中） - for q_word in question.split(): - if len(q_word) > 3 and q_word in ln: - kw_hits += 0.5 # 精确匹配奖励 - - if has_keyword: - # 对于包含关键信息的行，保留完整行 - hit_lines.append(ln) - - snippet = "\n".join(hit_lines) if hit_lines else context_str.strip() - - # 限制单段长度，但对包含关键信息的上下文稍微放宽限制 - max_snippet_len = 600 if (key_entity_hits > 0 or time_entity_count > 0) else 500 - if len(snippet) > max_snippet_len: - snippet = snippet[:max_snippet_len] - - # 评分逻辑 - has_number = 1 if re.search(r'\d', snippet) else 0 - has_date = 1 if (re.search(r'\b\d{4}-\d{1,2}-\d{1,2}\b', snippet) or - re.search(r'\d{1,2}月\d{1,2}日', snippet)) else 0 - - # 关键技术实体奖励 - key_entity_bonus = key_entity_hits * 1.0 - - # 时间推理问题的特殊评分 - if is_temporal_question: - time_bonus = time_entity_count * 2.0 # 时间实体奖励 - temporal_coherence = 3 if (has_date and time_entity_count >= 2) else 0 - else: - time_bonus = 0 - temporal_coherence = 0 - - length_bonus = 5 if 50 < len(snippet) < 1000 else (2 if len(snippet) >= 1000 else 0) - pos_bonus = 3 if i < 3 else 0 - - score = (kw_hits * 0.8 + (has_number + has_date) * 1.5 + - length_bonus + pos_bonus + time_bonus + temporal_coherence + key_entity_bonus) - - scored_contexts.append((score, snippet)) - - # 选择累计至总字符预算 - scored_contexts.sort(key=lambda x: x[0], reverse=True) - selected: List[str] = [] - total_chars = 0 - - for score, snippet in scored_contexts: - if total_chars + len(snippet) <= max_chars: - selected.append(snippet) - total_chars += len(snippet) - else: - if not selected and len(snippet) > max_chars: - selected.append(snippet[:max_chars]) - break - - final_context = "\n\n".join(selected) - - # 对于时间推理问题，添加时间计算提示 - if is_temporal_question and question_time_entities: - time_prompt = "\n\n[时间推理提示：请仔细分析上述上下文中的日期和时间关系，计算时间间隔或确定事件顺序]" - if total_chars + len(time_prompt) <= max_chars: - final_context += time_prompt - - return final_context - - -# 通过别名匹配进行实体关键词检索（多token合并） -async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]: - results: List[Dict[str, Any]] = [] - try: - for tok in tokens: - rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit) - if rows: - results.extend(rows) - except Exception: - pass - - # 按 name 去重 - deduped: List[Dict[str, Any]] = [] - seen = set() - for r in results: - k = str(r.get("name", "")) - if k and k not in seen: - deduped.append(r) - seen.add(k) - return deduped - - -# 通过对话/陈述中的entity_ids反查实体名称 -_FETCH_ENTITIES_BY_IDS = """ -MATCH (e:ExtractedEntity) -WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) -RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type -""" - -async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]: - if not ids: - return [] - try: - rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id) - return rows or [] - except Exception: - return [] - - -# 增强的时间实体检索 -_TIME_ENTITY_SEARCH = """ -MATCH (e:ExtractedEntity) -WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern -AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id) -RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type -LIMIT $limit -""" - -async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]: - """专门搜索时间相关的实体""" - try: - date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*" - rows = await connector.execute_query(_TIME_ENTITY_SEARCH, - date_pattern=date_pattern, - end_user_id=end_user_id, - limit=limit) - return rows or [] - except Exception: - return [] - - -# 技术术语专门检索 -async def _search_tech_terms(connector: Neo4jConnector, question: str, end_user_id: str | None, limit: int = 3) -> List[Dict[str, Any]]: - """专门搜索技术术语相关的实体""" - tech_entities = [] - try: - # GPS相关 - if any(term in question for term in ["GPS", "导航", "定位系统"]): - gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", end_user_id=end_user_id, limit=limit) - if gps_rows: - tech_entities.extend(gps_rows) - - # 活动相关 - if any(term in question for term in ["工作坊", "研讨会", "网络研讨会"]): - workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", end_user_id=end_user_id, limit=limit) - if workshop_rows: - tech_entities.extend(workshop_rows) - - # 时间顺序相关 - if any(term in question for term in ["先", "后", "第一个"]): - time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", end_user_id=end_user_id, limit=limit) - if time_rows: - tech_entities.extend(time_rows) - - except Exception: - pass - - return tech_entities - - -# 中英相对时间解析：today/昨天/上周/3天后 等简单归一化为日期 -def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str: - t = str(text) if text is not None else "" - # 英文 today/yesterday/tomorrow - t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE) - - # 英文 X days ago / in X days - def _ago_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor - timedelta(days=n)).date().isoformat() - def _in_repl(m: re.Match[str]) -> str: - n = int(m.group(1)) - return (anchor + timedelta(days=n)).date().isoformat() - t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE) - t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE) - - # 中文 今天/昨天/明天 - t = re.sub(r"今天", anchor.date().isoformat(), t) - t = re.sub(r"昨日|昨天", (anchor - timedelta(days=1)).date().isoformat(), t) - t = re.sub(r"明天", (anchor + timedelta(days=1)).date().isoformat(), t) - # 中文 X天前 / X天后 - t = re.sub(r"(\d+)天前", lambda m: (anchor - timedelta(days=int(m.group(1)))).date().isoformat(), t) - t = re.sub(r"(\d+)天后", lambda m: (anchor + timedelta(days=int(m.group(1)))).date().isoformat(), t) - # 中文 上周 / 下周（近似7天） - t = re.sub(r"上周", (anchor - timedelta(days=7)).date().isoformat(), t) - t = re.sub(r"下周", (anchor + timedelta(days=7)).date().isoformat(), t) - # 中文 月日（无年份）补全年份 - def _md_repl(m: re.Match[str]) -> str: - mon = int(m.group(1)); day = int(m.group(2)) - return f"{anchor.year}-{mon:02d}-{day:02d}" - t = re.sub(r"(\d{1,2})月(\d{1,2})日", _md_repl, t) - return t - - -async def run_longmemeval_test( - sample_size: int = 3, - end_user_id: str = "longmemeval_zh_bak_2", - search_limit: int = 8, - context_char_budget: int = 4000, - llm_temperature: float = 0.0, - llm_max_tokens: int = 16, - search_type: str = "hybrid", - data_path: str | None = None, - start_index: int = 0, -) -> Dict[str, Any]: - """LongMemEval 评估测试：增强技术术语检索能力""" - - # 数据路径 - if not data_path: - # 固定使用中文数据集：dataset/longmemeval_oracle_zh.json - dataset_dir = Path(__file__).resolve().parent.parent / "dataset" - data_path = str(dataset_dir / "longmemeval_oracle_zh.json") - - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 longmemeval_oracle_zh.json 放置在: {dataset_dir}" - ) - - qa_list: List[Dict[str, Any]] = load_dataset_any(data_path) - # 支持评估全部样本：当 sample_size <= 0 时，取从 start_index 到末尾 - if sample_size is None or sample_size <= 0: - items = qa_list[start_index:] - else: - items = qa_list[start_index:start_index + sample_size] - - # 初始化组件 - 使用异步LLM客户端 - llm_client = get_llm_client(os.getenv("EVAL_LLM_ID")) - connector = Neo4jConnector() - cfg_dict = get_embedder_config(os.getenv("EVAL_EMBEDDING_ID")) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 指标收集 - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - per_query_context_counts: List[int] = [] - per_query_context_avg_tokens: List[float] = [] - per_query_context_chars: List[int] = [] - - type_correct: Dict[str, List[float]] = {} - type_f1: Dict[str, List[float]] = {} - type_jacc: Dict[str, List[float]] = {} - - samples: List[Dict[str, Any]] = [] - # 统计重复的上下文预览（跨样本），便于诊断"相同上下文"问题 - preview_counter: Dict[str, int] = {} - - try: - for item in items: - question = item.get("question", "") - reference = item.get("answer", "") - qtype = item.get("question_type") or item.get("type", "unknown") - - print(f"\n=== 处理问题: {question} ===") - - # 检测问题类型 - is_temporal = any(keyword in question.lower() for keyword in - ['days', 'day', 'before', 'after', 'first', '先后', '顺序', '间隔', '多久', '多少天']) - - # 检索 - t0 = time.time() - contexts_all: List[str] = [] - dialogs, statements, entities = [], [], [] - - try: - if search_type == "embedding": - search_results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["dialogues", "statements", "entities"], - ) - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体摘要（最多3个） - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - elif search_type == "keyword": - search_results = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=search_limit, - ) - dialogs = search_results.get("dialogues", []) - statements = search_results.get("statements", []) - entities = search_results.get("entities", []) - - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - if entities: - entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - else: # hybrid（增强版：特别优化技术术语检索） - emb_dialogs, emb_statements, emb_entities = [], [], [] - kw_dialogs, kw_statements, kw_entities = [], [], [] - - # 1) 嵌入检索 - try: - emb_res = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["dialogues", "statements", "entities"], - ) - if isinstance(emb_res, dict): - emb_dialogs = emb_res.get("dialogues", []) or [] - emb_statements = emb_res.get("statements", []) or [] - emb_entities = emb_res.get("entities", []) or [] - except Exception as e: - print(f"⚠️ 嵌入检索失败，将继续进行关键词检索: {e}") - - # 2) 关键词检索（增强版） - try: - kw_res = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=search_limit, - ) - if isinstance(kw_res, dict): - kw_dialogs = kw_res.get("dialogues", []) or [] - kw_statements = kw_res.get("statements", []) or [] - kw_entities = kw_res.get("entities", []) or [] - - # 技术术语专门检索 - tech_entities = await _search_tech_terms(connector, question, end_user_id, search_limit//2) - if tech_entities: - kw_entities.extend(tech_entities) - - # 时间推理问题的特殊处理 - if is_temporal: - # 专门搜索时间实体 - time_entities = await _search_time_entities(connector, end_user_id, search_limit//2) - if time_entities: - kw_entities.extend(time_entities) - # 添加时间相关关键词检索 - time_keywords = ['天', '日', '月', '年', 'before', 'after', 'first'] - for tk in time_keywords: - try: - time_res = await search_graph( - connector=connector, - q=tk, - end_user_id=end_user_id, - limit=2, - ) - if isinstance(time_res, dict): - kw_dialogs.extend(time_res.get("dialogues", []) or []) - kw_statements.extend(time_res.get("statements", []) or []) - except Exception: - pass - - # 中文关键词拆分后做别名匹配 - cn_tokens = generate_query_keywords_cn(question) # 使用增强版关键词提取 - alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit) - if alias_entities: - kw_entities.extend(alias_entities) - - # 从对话/陈述中的 entity_ids 反查实体 - ids = [] - try: - for d in kw_dialogs: - ids.extend(d.get("entity_ids", []) or []) - for s in kw_statements: - ids.extend(s.get("entity_ids", []) or []) - except Exception: - pass - if ids: - id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id) - if id_entities: - kw_entities.extend(id_entities) - - # 多关键词检索（使用增强版关键词） - try: - eng_words = [w for w in set(re.findall(r"\b\w+\b", question.lower())) if len(w) > 2] - kw_list = generate_query_keywords_cn(question)[:4] # 使用更多关键词 - for kw in kw_list: - if not kw: - continue - sub_res = await search_graph( - connector=connector, - q=str(kw), - end_user_id=end_user_id, - limit=max(3, search_limit // 2), - ) - if isinstance(sub_res, dict): - kw_dialogs.extend(sub_res.get("dialogues", []) or []) - kw_statements.extend(sub_res.get("statements", []) or []) - kw_entities.extend(sub_res.get("entities", []) or []) - except Exception: - pass - - # 选项参与关键词检索 - try: - opt_list = extract_candidate_options(question)[:2] - for opt in opt_list: - if not opt: - continue - opt_res = await search_graph( - connector=connector, - q=str(opt), - end_user_id=end_user_id, - limit=max(3, search_limit // 2), - ) - if isinstance(opt_res, dict): - kw_dialogs.extend(opt_res.get("dialogues", []) or []) - kw_statements.extend(opt_res.get("statements", []) or []) - kw_entities.extend(opt_res.get("entities", []) or []) - except Exception: - pass - except Exception as e: - print(f"❌ 关键词检索失败: {e}") - - # 3) 合并、排序并去重 - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - def dedup(items: List[Dict[str, Any]], key_field: str = "uuid") -> List[Dict[str, Any]]: - seen = set() - out = [] - for it in items: - key = str(it.get(key_field, "")) + str(it.get("content", "") + str(it.get("statement", ""))) - if key not in seen: - out.append(it) - seen.add(key) - return out - - # 关键技术实体优先排序 - def enhanced_score(item: Dict[str, Any]) -> float: - score_val = item.get("score", 0.0) - base_score = float(score_val) if score_val is not None else 0.0 - content = str(item.get("content", "") + str(item.get("statement", ""))) - - # 关键技术实体奖励 - key_entities = [] - if any(term in question for term in ["GPS", "导航", "系统"]): - key_entities.extend(["GPS", "导航", "系统", "功能"]) - if any(term in question for term in ["工作坊", "研讨会", "活动"]): - key_entities.extend(["工作坊", "研讨会", "参加"]) - - key_bonus = 0 - for key_ent in key_entities: - if key_ent in content: - key_bonus += 1.0 - - # 时间实体奖励 - time_bonus = 0 - if is_temporal: - time_entities = extract_time_entities(content) - time_bonus = len(time_entities) * 0.5 - - return base_score + key_bonus + time_bonus - - dialogs = dedup(sorted(all_dialogs, key=enhanced_score, reverse=True)) - statements = dedup(sorted(all_statements, key=enhanced_score, reverse=True)) - entities = dedup(all_entities, key_field="name") - - # 4) 构建上下文 - for d in dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - # 实体摘要 - try: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - except Exception: - pass - - # 全局回退 - if not contexts_all and search_type in ("embedding", "hybrid"): - try: - print("🔁 检索为空，回退到关键词检索...") - kw_fallback = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=max(search_limit, 5), - ) - fb_dialogs = kw_fallback.get("dialogues", []) or [] - fb_statements = kw_fallback.get("statements", []) or [] - fb_entities = kw_fallback.get("entities", []) or [] - - for d in fb_dialogs: - content = str(d.get("content", "")).strip() - if content: - contexts_all.append(content) - for s in fb_statements: - stmt_text = str(s.get("statement", "")).strip() - if stmt_text: - contexts_all.append(stmt_text) - if fb_entities: - entity_names = [str(e.get("name", "")).strip() for e in fb_entities[:5] if e.get("name")] - if entity_names: - contexts_all.append(f"EntitySummary: {', '.join(entity_names)}") - - dialogs = fb_dialogs if fb_dialogs else dialogs - statements = fb_statements if fb_statements else statements - entities = fb_entities if fb_entities else entities - print(f"↩️ 回退到关键词检索: {len(fb_dialogs)} 对话, {len(fb_statements)} 条陈述, {len(fb_entities)} 个实体") - except Exception as fe: - print(f"❌ 关键词回退失败: {fe}") - - ent_count = len(entities) if isinstance(entities, list) else 0 - print(f"✅ {search_type}检索成功: {len(dialogs)} 对话, {len(statements)} 条陈述, {ent_count} 个实体") - if is_temporal: - print("⏰ 检测为时间推理问题，已启用时间优化检索") - - except Exception as e: - print(f"❌ {search_type}检索失败: {e}") - contexts_all = [] - - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 智能上下文选择 - context_text = "" - if contexts_all: - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) - # 相对时间解析 - try: - context_text = _resolve_relative_times_cn_en(context_text, anchor=datetime.now()) - except Exception: - pass - # 诊断信息 - try: - cn_diag = generate_query_keywords_cn(question)[:4] # 显示更多关键词 - opts = extract_candidate_options(question)[:2] - qlw = [w for w in set(re.findall(r'\b\w+\b', question.lower())) if len(w) > 2][:1] - diag_tokens: List[str] = [] - for t in cn_diag + opts + qlw: - if t and t not in diag_tokens: - diag_tokens.append(t) - print(f"🔍 关键词/选项: {', '.join(diag_tokens)}") - preview = context_text[:200].replace('\n', ' ') - print(f"🔎 上下文预览: {preview}...") - key_preview = preview.strip() - if key_preview: - preview_counter[key_preview] = preview_counter.get(key_preview, 0) + 1 - except Exception: - pass - else: - print("❌ 没有检索到有效上下文") - context_text = "No relevant context found." - - # 记录上下文诊断信息 - per_query_context_counts.append(len(contexts_all)) - per_query_context_avg_tokens.append(avg_context_tokens([context_text])) - per_query_context_chars.append(len(context_text)) - - # LLM 推理（增强技术术语提示） - options = extract_candidate_options(question) - if len(options) >= 2: - opt_lines = "\n".join(f"- {o}" for o in options) - # 技术术语问题的特殊提示 - if any(term in question for term in ["GPS", "系统", "功能", "工作坊", "研讨会"]): - system_prompt = ( - "You are a QA assistant specializing in technical and activity-related questions. " - "Pay special attention to technical terms like GPS, systems, functions, workshops, and seminars. " - "Return ONLY one string: exactly one option from the provided candidates. If the context is insufficient, respond with 'Unknown'. " - "Focus on matching technical details and activity sequences accurately." - ) - elif is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "Return ONLY one string: exactly one option from the provided candidates. If the context is insufficient, respond with 'Unknown'. " - "Pay special attention to date sequences and time intervals." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. Return ONLY one string: exactly one option from the provided candidates. " - "If the context is insufficient, respond with 'Unknown'. If the context expresses a synonym or paraphrase of a candidate, return the closest candidate. " - "Do not include explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": ( - f"Question: {question}\n\nCandidates:\n{opt_lines}\n\nContext:\n{context_text}\n\nReturn EXACTLY one candidate string (or 'Unknown')." - ), - }, - ] - else: - # 技术术语问题的特殊提示 - if any(term in question for term in ["GPS", "系统", "功能", "工作坊", "研讨会"]): - system_prompt = ( - "You are a QA assistant specializing in technical and activity-related questions. " - "Pay special attention to technical terms like GPS, systems, functions, workshops, and seminars. " - "If the context contains the answer, return a concise answer phrase focusing on technical details. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - elif is_temporal: - system_prompt = ( - "You are a QA assistant specializing in temporal reasoning. Analyze the dates and time relationships in the context carefully. " - "If the context contains the answer, return a concise answer phrase focusing on temporal information. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - else: - system_prompt = ( - "You are a QA assistant. Respond in the same language as the question. If the context contains the answer, return a concise answer phrase. " - "If the answer cannot be determined from the context, respond with 'Unknown'. Return ONLY the final answer string, no explanations." - ) - - messages = [ - {"role": "system", "content": system_prompt}, - { - "role": "user", - "content": f"Question: {question}\n\nContext:\n{context_text}\n\nReturn ONLY the answer (or 'Unknown').", - }, - ] - - t2 = time.time() - # 使用异步调用 - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - - # 兼容不同的响应格式 - pred_raw = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown") - - # 选项题输出规范化 - pred = pred_raw - if len(options) >= 2 and not pred_raw.lower().startswith("unknown"): - def _basic_norm(s: str) -> str: - s = s.lower().strip() - return re.sub(r"[^\w\s]", " ", s) - def _jaccard(a: str, b: str) -> float: - ta = set(t for t in _basic_norm(a).split() if t) - tb = set(t for t in _basic_norm(b).split() if t) - if not ta and not tb: - return 1.0 - if not ta or not tb: - return 0.0 - return len(ta & tb) / len(ta | tb) - best = None - best_score = -1.0 - for o in options: - score = _jaccard(pred_raw, o) - if score > best_score: - best = o - best_score = score - if best is not None and best_score > 0.0: - pred = best - - # 指标 - flag = exact_match(pred, reference) - f1_val = common_f1(str(pred), str(reference)) - j_val = jaccard(str(pred), str(reference)) - - type_correct.setdefault(qtype, []).append(flag) - type_f1.setdefault(qtype, []).append(f1_val) - type_jacc.setdefault(qtype, []).append(j_val) - - samples.append({ - "question": question, - "prediction": pred, - "answer": reference, - "question_type": qtype, - "is_temporal": is_temporal, - "question_id": item.get("question_id"), - "options": options, - "context_count": len(contexts_all), - "context_chars": len(context_text), - "retrieved_dialogue_count": len(dialogs), - "retrieved_statement_count": len(statements), - "metrics": { - "exact_match": bool(flag), - "f1": f1_val, - "jaccard": j_val - }, - "timing": { - "search_ms": (t1 - t0) * 1000, - "llm_ms": (t3 - t2) * 1000 - } - }) - - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {reference}") - print(f"📈 当前指标 - Exact Match: {flag}, F1: {f1_val:.3f}, Jaccard: {j_val:.3f}") - - # 聚合结果 - type_acc = {t: (sum(v) / max(len(v), 1)) for t, v in type_correct.items()} - f1_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_f1.items()} - jacc_by_type = {t: (sum(v) / max(len(v), 1)) for t, v in type_jacc.items()} - - result = { - "dataset": "longmemeval", - "items": len(items), - "accuracy_by_type": type_acc, - "f1_by_type": f1_by_type, - "jaccard_by_type": jacc_by_type, - "samples": samples, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "context": { - "avg_tokens": statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0, - "avg_chars": statistics.mean(per_query_context_chars) if per_query_context_chars else 0.0, - "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0, - }, - "params": { - "end_user_id": end_user_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "search_type": search_type, - "llm_id": os.getenv("EVAL_LLM_ID"), - "embedding_id": os.getenv("EVAL_EMBEDDING_ID"), - "sample_size": sample_size, - "start_index": start_index, - }, - "timestamp": datetime.now().isoformat() - } - - # 计算汇总指标 - try: - total_items = max(len(samples), 1) - correct_count = sum(1 for s in samples if s.get("metrics", {}).get("exact_match")) - score_accuracy = (correct_count / total_items) * 100.0 - - total_latencies_ms = [] - for s in samples: - t = s.get("timing", {}) - total_latencies_ms.append(float(t.get("search_ms", 0.0)) + float(t.get("llm_ms", 0.0))) - total_lat_stats = latency_stats(total_latencies_ms) if total_latencies_ms else {"p50": 0.0, "iqr": 0.0} - latency_median_s = total_lat_stats.get("p50", 0.0) / 1000.0 - latency_iqr_s = total_lat_stats.get("iqr", 0.0) / 1000.0 - - avg_ctx_tokens = statistics.mean(per_query_context_avg_tokens) if per_query_context_avg_tokens else 0.0 - avg_ctx_tokens_k = avg_ctx_tokens / 1000.0 - - result["metric_summary"] = { - "score_accuracy": score_accuracy, - "latency_median_s": latency_median_s, - "latency_iqr_s": latency_iqr_s, - "avg_context_tokens_k": avg_ctx_tokens_k, - } - except Exception: - result["metric_summary"] = { - "score_accuracy": 0.0, - "latency_median_s": 0.0, - "latency_iqr_s": 0.0, - "avg_context_tokens_k": 0.0, - } - - # 诊断信息 - try: - dups = sorted([(k, c) for k, c in preview_counter.items() if c > 1], key=lambda x: -x[1])[:5] - result["diagnostics"] = { - "duplicate_previews_top": [{"count": c, "preview": k[:120]} for k, c in dups], - "unique_preview_count": len(preview_counter), - } - except Exception: - pass - - return result - - finally: - await connector.close() - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="LongMemEval 评估测试脚本（增强技术术语检索版）") - parser.add_argument("--sample-size", type=int, default=3, help="样本数量（<=0 表示全部）") - parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") - parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--group-id", type=str, default="longmemeval_zh_bak_3", help="图数据库 Group ID") - parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=16, help="LLM 最大输出 token") - parser.add_argument("--search-type", type=str, default="hybrid", choices=["embedding","keyword","hybrid"], help="检索类型") - parser.add_argument("--data-path", type=str, default=None, help="数据集路径") - args = parser.parse_args() - - sample_size = 0 if args.all else args.sample_size - - result = asyncio.run( - run_longmemeval_test( - sample_size=sample_size, - end_user_id=args.end_user_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - data_path=args.data_path, - start_index=args.start_index, - ) - ) - - # 打印结果 - print("\n" + "="*50) - print("📊 LongMemEval 测试结果:") - print(f" 样本数量: {result['items']}") - - if result['accuracy_by_type']: - print("\n📈 按问题类型细分:") - for qtype, acc in result['accuracy_by_type'].items(): - print(f" {qtype}:") - print(f" Score (Accuracy): {acc:.3f}") - - print(f"\n📊 指标总览:") - ms = result.get('metric_summary', {}) - print(f" Score (Accuracy): {ms.get('score_accuracy', 0.0):.1f}%") - print(f" Latency (s): median {ms.get('latency_median_s', 0.0):.3f}s") - print(f" Latency IQR (s): {ms.get('latency_iqr_s', 0.0):.3f}s") - print(f" Avg Context Tokens (k): {ms.get('avg_context_tokens_k', 0.0):.3f}k") - - print(f"\n⏱️ 细分性能指标:") - print(f" 检索延迟(均值): {result['latency']['search']['mean']:.1f}ms") - print(f" LLM延迟(均值): {result['latency']['llm']['mean']:.1f}ms") - print(f" 上下文长度(均值): {result['context']['avg_chars']:.0f} 字符") - - - # 保存结果到文件 - try: - out_dir = os.path.join(PROJECT_ROOT, "evaluation", "longmemeval", "results") - os.makedirs(out_dir, exist_ok=True) - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = os.path.join(out_dir, f"longmemeval_{result['params']['search_type']}_{ts}.json") - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n💾 结果已保存: {out_path}") - except Exception as e: - print(f"⚠️ 结果保存失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py b/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py deleted file mode 100644 index e07b0cab..00000000 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa-test.py +++ /dev/null @@ -1,559 +0,0 @@ -import argparse -import asyncio -import json -import os -import time -from datetime import datetime -from typing import List, Dict, Any -import re -from pathlib import Path - -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - print(f"✅ 加载评估配置: {eval_config_path}") - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.src.search import run_hybrid_search # 使用与 evaluate_qa.py 相同的检索函数 -from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient -from app.core.models.base import RedBearModelConfig -from app.core.memory.utils.config.config_utils import get_embedder_config - -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.evaluation.common.metrics import exact_match, latency_stats, avg_context_tokens - -from app.core.memory.evaluation.common.metrics import f1_score, bleu1, jaccard - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """基于问题关键词对上下文进行评分选择，并在预算内拼接文本。 - - 参考 evaluation/memsciqa/evaluate_qa.py 的实现，避免路径导入带来的不稳定。 - """ - if not contexts: - return "" - question_lower = (question or "").lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but' - } - question_words = set(re.findall(r"\b\w+\b", question_lower)) - question_words = {w for w in question_words if w not in stop_words and len(w) > 2} - - scored = [] - for i, ctx in enumerate(contexts): - ctx_lower = (ctx or "").lower() - score = 0 - matches = 0 - for w in question_words: - if w in ctx_lower: - matches += 1 - score += ctx_lower.count(w) * 2 - length = len(ctx) - if 100 < length < 2000: - score += 5 - elif length >= 2000: - score += 2 - if i < 3: - score += 3 - scored.append((score, ctx, matches)) - - scored.sort(key=lambda x: x[0], reverse=True) - - selected: List[str] = [] - total = 0 - for score, ctx, _ in scored: - if total + len(ctx) <= max_chars: - selected.append(ctx) - total += len(ctx) - else: - if score > 10 and total < max_chars - 200: - remaining = max_chars - total - lines = ctx.split('\n') - rel_lines: List[str] = [] - cur = 0 - for line in lines: - l = line.lower() - if any(w in l for w in question_words) and cur < remaining - 50: - rel_lines.append(line) - cur += len(line) - if rel_lines: - truncated = '\n'.join(rel_lines) - if len(truncated) > 50: - selected.append(truncated + "\n[相关内容截断...]") - total += len(truncated) - break - return "\n\n".join(selected) - - -def extract_question_keywords(question: str, max_keywords: int = 8) -> List[str]: - """提取问题中的关键词（简单英文分词，去停用词，长度>=3）。""" - ql = (question or "").lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but','of','to','in','on','for','with','from','that','this' - } - words = re.findall(r"\b[\w-]+\b", ql) - kws = [w for w in words if w not in stop_words and len(w) >= 3] - # 去重保序 - seen = set() - uniq = [] - for w in kws: - if w not in seen: - uniq.append(w) - seen.add(w) - if len(uniq) >= max_keywords: - break - return uniq - - -def analyze_contexts_simple(contexts: List[str], keywords: List[str], top_n: int = 5) -> List[Dict[str, int | float]]: - """对上下文进行简单相关性打分，仅用于控制台可视化。 - - 评分: score = match_count*200 + min(len(text), 100000)/100 - """ - results = [] - for ctx in contexts: - tl = (ctx or "").lower() - match_count = sum(1 for k in keywords if k in tl) - length = len(ctx) - score = match_count * 200 + min(length, 100000) / 100.0 - results.append({"score": float(f"{score:.0f}"), "match": match_count, "length": length}) - results.sort(key=lambda x: (x["score"], x["match"], x["length"]), reverse=True) - return results[:max(top_n, 0)] - - -# 纯测试脚本不进行摄入；若需摄入请使用 evaluate_qa.py - - -def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]: - if not os.path.exists(data_path): - raise FileNotFoundError(f"未找到数据集: {data_path}") - items: List[Dict[str, Any]] = [] - with open(data_path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - try: - items.append(json.loads(line)) - except Exception: - # 跳过坏行但不中断 - continue - return items - - -async def run_memsciqa_test( - sample_size: int = 3, - end_user_id: str | None = None, - search_limit: int = 8, - context_char_budget: int = 4000, - llm_temperature: float = 0.0, - llm_max_tokens: int = 64, - search_type: str = "embedding", - data_path: str | None = None, - start_index: int = 0, - verbose: bool = True, -) -> Dict[str, Any]: - """memsciqa 增强测试脚本：结合 evaluate_qa 的三路检索与智能上下文选择。 - - - 支持从指定索引开始与评估全部样本（sample_size<=0） - - 支持在摄入前重置组（清空图）与跳过摄入 - - 支持 keyword / embedding / hybrid 三种检索 - """ - - # 默认使用指定的 memsci 组 ID - end_user_id = end_user_id or "group_memsci" - - # 数据路径解析 - if not data_path: - dataset_dir = Path(__file__).resolve().parent.parent / "dataset" - data_path = str(dataset_dir / "msc_self_instruct.jsonl") - - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 msc_self_instruct.jsonl 放置在: {dataset_dir}" - ) - - # 加载数据 - all_items = load_dataset_memsciqa(data_path) - if sample_size is None or sample_size <= 0: - items = all_items[start_index:] - else: - items = all_items[start_index:start_index + sample_size] - - # 初始化 LLM（纯测试：不进行摄入） - llm = get_llm_client(os.getenv("EVAL_LLM_ID")) - - # 初始化 Neo4j 连接与向量检索 Embedder（对齐 locomo_test） - connector = Neo4jConnector() - embedder = None - if search_type in ("embedding", "hybrid"): - cfg_dict = get_embedder_config(os.getenv("EVAL_EMBEDDING_ID")) - embedder = OpenAIEmbedderClient( - model_config=RedBearModelConfig.model_validate(cfg_dict) - ) - - # 评估循环 - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - # 存储完整上下文文本用于统计 - contexts_used: List[str] = [] - per_query_context_chars: List[int] = [] - per_query_context_counts: List[int] = [] - correct_flags: List[float] = [] - f1s: List[float] = [] - b1s: List[float] = [] - jss: List[float] = [] - samples: List[Dict[str, Any]] = [] - - total_items = len(items) - for idx, item in enumerate(items): - if verbose: - print(f"\n🧪 评估样本: {idx+1}/{total_items}") - question = item.get("self_instruct", {}).get("B", "") or item.get("question", "") - reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "") - - # 检索：使用与 evaluate_qa.py 相同的 run_hybrid_search - t0 = time.time() - results = None - try: - if search_type in ("embedding", "hybrid"): - # 使用嵌入检索（与 qwen_search_eval 对齐） - results = await search_graph_by_embedding( - connector=connector, - embedder_client=embedder, - query_text=question, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues - ) - elif search_type == "keyword": - # 关键词检索（直接调用 graph_search） - results = await search_graph( - connector=connector, - q=question, - end_user_id=end_user_id, - limit=search_limit, - include=["chunks", "statements", "entities", "summaries"], # 使用 chunks 而不是 dialogues - ) - except Exception: - results = None - t1 = time.time() - search_ms = (t1 - t0) * 1000 - latencies_search.append(search_ms) - - # 构建上下文：与 evaluate_qa.py 完全一致的逻辑 - contexts_all: List[str] = [] - retrieved_counts: Dict[str, int] = {} - if results: - # 处理 hybrid 搜索结果 - if search_type == "hybrid": - emb = results.get("embedding_search", {}) if isinstance(results.get("embedding_search"), dict) else {} - kw = results.get("keyword_search", {}) if isinstance(results.get("keyword_search"), dict) else {} - emb_dialogs = emb.get("dialogues", []) - emb_statements = emb.get("statements", []) - emb_entities = emb.get("entities", []) - kw_dialogs = kw.get("dialogues", []) - kw_statements = kw.get("statements", []) - kw_entities = kw.get("entities", []) - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - # 简单去重 - seen_dialog = set() - dialogues = [] - for d in all_dialogs: - key = (str(d.get("uuid", "")), str(d.get("content", ""))) - if key not in seen_dialog: - dialogues.append(d) - seen_dialog.add(key) - - seen_stmt = set() - statements = [] - for s in all_statements: - key = str(s.get("statement", "")) - if key not in seen_stmt: - statements.append(s) - seen_stmt.add(key) - - seen_ent = set() - entities = [] - for e in all_entities: - key = str(e.get("name", "")) - if key not in seen_ent: - entities.append(e) - seen_ent.add(key) - else: - # embedding 或 keyword 单独搜索 - dialogues = results.get("dialogues", []) - statements = results.get("statements", []) - entities = results.get("entities", []) - - retrieved_counts = { - "dialogues": len(dialogues), - "statements": len(statements), - "entities": len(entities), - } - - # 构建上下文文本 - for d in dialogues: - text = str(d.get("content", "")).strip() - if text: - contexts_all.append(text) - - for s in statements: - text = str(s.get("statement", "")).strip() - if text: - contexts_all.append(text) - - # 实体摘要 - if entities: - scored = [e for e in entities if e.get("score") is not None] - top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3] - if top_entities: - summary_lines = [] - for e in top_entities: - name = str(e.get("name", "")).strip() - etype = str(e.get("entity_type", "")).strip() - score = e.get("score") - if name: - meta = [] - if etype: - meta.append(f"type={etype}") - if isinstance(score, (int, float)): - meta.append(f"score={score:.3f}") - summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}") - if summary_lines: - contexts_all.append("\n".join(summary_lines)) - - if verbose: - if retrieved_counts: - print(f"✅ 检索成功: {retrieved_counts.get('dialogues',0)} dialogues, {retrieved_counts.get('statements',0)} 条陈述, {retrieved_counts.get('entities',0)} 个实体, {retrieved_counts.get('summaries',0)} 个摘要") - print(f"📊 有效上下文数量: {len(contexts_all)}") - q_keywords = extract_question_keywords(question, max_keywords=8) - if q_keywords: - print(f"🔍 问题关键词: {set(q_keywords)}") - if contexts_all: - analysis = analyze_contexts_simple(contexts_all, q_keywords, top_n=5) - if analysis: - print("📊 上下文相关性分析:") - for a in analysis: - print(f" - 得分: {int(a['score'])}, 关键词匹配: {a['match']}, 长度: {a['length']}") - # 打印检索到的上下文预览，便于定位为何为 Unknown - print("🔎 上下文预览（最多前10条，每条截断展示）:") - for i, ctx in enumerate(contexts_all[:10]): - preview = str(ctx).replace("\n", " ") - if len(preview) > 300: - preview = preview[:300] + "..." - print(f" [{i+1}] 长度: {len(ctx)} | 片段: {preview}") - # 标注参考答案是否出现在任一上下文中 - ref_lower = (str(reference) or "").lower() - if ref_lower: - hits = [] - for i, ctx in enumerate(contexts_all): - if ref_lower in str(ctx).lower(): - hits.append(i+1) - print(f"🔗 参考答案命中上下文条数: {len(hits)}" + (f" | 命中索引: {hits}" if hits else "")) - - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) if contexts_all else "" - if not context_text: - context_text = "No relevant context found." - contexts_used.append(context_text) - per_query_context_chars.append(len(context_text)) - per_query_context_counts.append(len(contexts_all)) - - if verbose: - selected_count = (context_text.count("\n\n") + 1) if context_text else 0 - print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {len(context_text)}字符") - # 展示拼接后的上下文片段，便于核查是否包含答案 - concat_preview = context_text.replace("\n", " ") - if len(concat_preview) > 600: - concat_preview = concat_preview[:600] + "..." - print(f"🧵 拼接上下文预览: {concat_preview}") - - messages = [ - { - "role": "system", - "content": ( - "You are a QA assistant. Answer in English. Follow these guidelines:\n" - "1) If the context contains information to answer the question, provide a concise answer based on the context;\n" - "2) If the context does not contain enough information to answer the question, respond with 'Unknown';\n" - "3) Keep your answer brief and to the point;\n" - "4) Do not add explanations or additional text beyond the answer." - ), - }, - {"role": "user", "content": f"Question: {question}\n\nContext:\n{context_text}"}, - ] - - t2 = time.time() - try: - # 使用异步调用 - resp = await llm.chat(messages=messages) - # 更健壮的响应解析，处理不同的LLM响应格式 - if hasattr(resp, 'content'): - pred = resp.content.strip() - elif isinstance(resp, dict) and "choices" in resp and len(resp["choices"]) > 0: - pred = resp["choices"][0]["message"]["content"].strip() - elif isinstance(resp, dict) and "content" in resp: - pred = resp["content"].strip() - elif isinstance(resp, str): - pred = resp.strip() - else: - pred = "Unknown" - print(f"⚠️ LLM响应格式异常: {type(resp)} - {resp}") - - # 检查预测是否为"Unknown"或空，如果是则检查上下文是否真的没有答案 - if pred.lower() in ["unknown", ""]: - # 如果参考答案在上下文中存在，但LLM返回Unknown，可能是提示词问题 - ref_lower = (str(reference) or "").lower() - if ref_lower and any(ref_lower in ctx.lower() for ctx in contexts_all): - print("⚠️ 参考答案在上下文中存在但LLM返回Unknown，检查提示词") - except Exception as e: - # 更详细的错误处理 - pred = "Unknown" - print(f"⚠️ LLM调用异常: {e}") - t3 = time.time() - llm_ms = (t3 - t2) * 1000 - latencies_llm.append(llm_ms) - - exact = exact_match(pred, reference) - correct_flags.append(exact) - f1_val = f1_score(str(pred), str(reference)) - b1_val = bleu1(str(pred), str(reference)) - j_val = jaccard(str(pred), str(reference)) - f1s.append(f1_val) - b1s.append(b1_val) - jss.append(j_val) - - if verbose: - print(f"🤖 LLM 回答: {pred}") - print(f"✅ 正确答案: {reference}") - print(f"📈 当前指标 - F1: {f1_val:.3f}, BLEU-1: {b1_val:.3f}, Jaccard: {j_val:.3f}") - print(f"⏱️ 延迟 - 检索: {search_ms:.0f}ms, LLM: {llm_ms:.0f}ms") - - # 对齐 locomo/qwen_search_eval.py 的样本输出结构 - samples.append({ - "question": str(question), - "answer": str(reference), - "prediction": str(pred), - "metrics": { - "f1": f1_val, - "b1": b1_val, - "j": j_val - }, - "retrieval": { - "retrieved_documents": len(contexts_all), - "context_length": len(context_text), - "search_limit": search_limit, - "max_chars": context_char_budget - }, - "timing": { - "search_ms": search_ms, - "llm_ms": llm_ms - } - }) - - # 计算总体指标与聚合 - acc = sum(correct_flags) / max(len(correct_flags), 1) - ctx_avg_tokens = avg_context_tokens(contexts_used) - result = { - "dataset": "memsciqa", - "items": len(items), - "metrics": { - "f1": (sum(f1s) / max(len(f1s), 1)) if f1s else 0.0, - "b1": (sum(b1s) / max(len(b1s), 1)) if b1s else 0.0, - "j": (sum(jss) / max(len(jss), 1)) if jss else 0.0, - }, - "context": { - "avg_tokens": ctx_avg_tokens, - "avg_chars": (sum(per_query_context_chars) / max(len(per_query_context_chars), 1)) if per_query_context_chars else 0.0, - "count_avg": (sum(per_query_context_counts) / max(len(per_query_context_counts), 1)) if per_query_context_counts else 0.0, - "avg_memory_tokens": 0.0 - }, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "samples": samples, - "params": { - "end_user_id": end_user_id, - "search_limit": search_limit, - "context_char_budget": context_char_budget, - "llm_temperature": llm_temperature, - "llm_max_tokens": llm_max_tokens, - "search_type": search_type, - "start_index": start_index, - "llm_id": os.getenv("EVAL_LLM_ID"), - "retrieval_embedding_id": os.getenv("EVAL_EMBEDDING_ID") - }, - "timestamp": datetime.now().isoformat(), - } - try: - await connector.close() - except Exception: - pass - return result - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="memsciqa 测试脚本（三路检索 + 智能上下文选择）") - parser.add_argument("--sample-size", type=int, default=10, help="样本数量（<=0 表示全部）") - parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）") - parser.add_argument("--start-index", type=int, default=0, help="起始样本索引") - parser.add_argument("--group-id", type=str, default="group_memsci", help="图数据库 Group ID（默认 group_memsci）") - parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=64, help="LLM 最大输出 token") - parser.add_argument("--search-type", type=str, default="embedding", choices=["embedding","keyword","hybrid"], help="检索类型（hybrid 等同于 embedding）") - parser.add_argument("--data-path", type=str, default=None, help="数据集路径（默认 data/msc_self_instruct.jsonl）") - parser.add_argument("--output", type=str, default=None, help="将评估结果保存到指定文件路径（JSON）") - parser.add_argument("--verbose", action="store_true", default=True, help="打印过程日志（默认开启）") - parser.add_argument("--quiet", action="store_true", help="关闭过程日志") - args = parser.parse_args() - - sample_size = 0 if args.all else args.sample_size - - verbose_flag = False if args.quiet else args.verbose - result = asyncio.run( - run_memsciqa_test( - sample_size=sample_size, - end_user_id=args.end_user_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - data_path=args.data_path, - start_index=args.start_index, - verbose=verbose_flag, - ) - ) - - print(json.dumps(result, ensure_ascii=False, indent=2)) - - # 结果保存 - out_path = args.output - if not out_path: - eval_dir = os.path.dirname(os.path.abspath(__file__)) - dataset_results_dir = os.path.join(eval_dir, "results") - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = os.path.join(dataset_results_dir, f"memsciqa_{result['params']['search_type']}_{ts}.json") - try: - os.makedirs(os.path.dirname(out_path), exist_ok=True) - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n💾 结果已保存: {out_path}") - except Exception as e: - print(f"⚠️ 结果保存失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py b/api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py deleted file mode 100644 index 40684f4c..00000000 --- a/api/app/core/memory/evaluation/memsciqa/memsciqa_benchmark.py +++ /dev/null @@ -1,369 +0,0 @@ -import argparse -import asyncio -import json -import os -import time -from datetime import datetime -from typing import List, Dict, Any -from pathlib import Path -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent.parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector -from app.core.memory.src.search import run_hybrid_search # 使用旧版本（重构前） -from app.core.memory.utils.llm.llm_utils import get_llm_client -from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline -from app.core.memory.evaluation.common.metrics import exact_match, latency_stats, avg_context_tokens - - -def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str: - """基于问题关键词对上下文进行评分选择，并在预算内拼接文本。""" - if not contexts: - return "" - import re - # 提取问题关键词（移除停用词） - question_lower = (question or "").lower() - stop_words = { - 'what','when','where','who','why','how','did','do','does','is','are','was','were', - 'the','a','an','and','or','but' - } - question_words = set(re.findall(r"\b\w+\b", question_lower)) - question_words = {w for w in question_words if w not in stop_words and len(w) > 2} - - # 评分 - scored = [] - for i, ctx in enumerate(contexts): - ctx_lower = (ctx or "").lower() - score = 0 - matches = 0 - for w in question_words: - if w in ctx_lower: - matches += 1 - score += ctx_lower.count(w) * 2 - length = len(ctx) - if 100 < length < 2000: - score += 5 - elif length >= 2000: - score += 2 - if i < 3: - score += 3 - scored.append((score, ctx, matches)) - - scored.sort(key=lambda x: x[0], reverse=True) - - # 选择直到达到字符限制，必要时截断包含关键词的段落 - selected: List[str] = [] - total = 0 - for score, ctx, _ in scored: - if total + len(ctx) <= max_chars: - selected.append(ctx) - total += len(ctx) - else: - if score > 10 and total < max_chars - 200: - remaining = max_chars - total - lines = ctx.split('\n') - rel_lines: List[str] = [] - cur = 0 - for line in lines: - l = line.lower() - if any(w in l for w in question_words) and cur < remaining - 50: - rel_lines.append(line) - cur += len(line) - if rel_lines: - truncated = '\n'.join(rel_lines) - if len(truncated) > 50: - selected.append(truncated + "\n[相关内容截断...]") - total += len(truncated) - break - return "\n\n".join(selected) - - -def build_context_from_dialog(dialog_obj: Dict[str, Any]) -> str: - """Compose a text context from `dialog` list in msc_self_instruct item.""" - parts: List[str] = [] - for turn in dialog_obj.get("dialog", []): - speaker = turn.get("speaker", "") - text = turn.get("text", "") - if text: - parts.append(f"{speaker}: {text}") - return "\n".join(parts) - - -def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any]]: - """Combine dialogues from embedding and keyword searches (embedding first).""" - if results is None: - return [] - emb = [] - kw = [] - if isinstance(results.get("embedding_search"), dict): - emb = results.get("embedding_search", {}).get("dialogues", []) or [] - elif isinstance(results.get("dialogues"), list): - emb = results.get("dialogues", []) or [] - if isinstance(results.get("keyword_search"), dict): - kw = results.get("keyword_search", {}).get("dialogues", []) or [] - seen = set() - merged: List[Dict[str, Any]] = [] - for d in emb: - k = (str(d.get("uuid", "")), str(d.get("content", ""))) - if k not in seen: - merged.append(d) - seen.add(k) - for d in kw: - k = (str(d.get("uuid", "")), str(d.get("content", ""))) - if k not in seen: - merged.append(d) - seen.add(k) - return merged - - - -async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]: - end_user_id = end_user_id or SELECTED_GROUP_ID - - # Load data - dataset_dir = Path(__file__).resolve().parent.parent / "dataset" - data_path = dataset_dir / "msc_self_instruct.jsonl" - - if not os.path.exists(data_path): - raise FileNotFoundError( - f"数据集文件不存在: {data_path}\n" - f"请将 msc_self_instruct.jsonl 放置在: {dataset_dir}" - ) - with open(data_path, "r", encoding="utf-8") as f: - lines = f.readlines() - items: List[Dict[str, Any]] = [json.loads(l) for l in lines[:sample_size]] - - - # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入 - # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略 - contexts: List[str] = [build_context_from_dialog(item) for item in items] - await ingest_contexts_via_full_pipeline(contexts, end_user_id) - - # LLM client (使用异步调用) - from app.db import get_db - - db = next(get_db()) - try: - llm_client = get_llm_client(os.getenv("EVAL_LLM_ID"), db) - finally: - db.close() - - # Evaluate each item - connector = Neo4jConnector() - latencies_llm: List[float] = [] - latencies_search: List[float] = [] - contexts_used: List[str] = [] - correct_flags: List[float] = [] - f1s: List[float] = [] - b1s: List[float] = [] - jss: List[float] = [] - try: - for item in items: - question = item.get("self_instruct", {}).get("B", "") or item.get("question", "") - reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "") - # 检索：对齐 locomo 的三路检索（dialogues/statements/entities） - t0 = time.time() - try: - results = await run_hybrid_search( - query_text=question, - search_type=search_type, - end_user_id=end_user_id, - limit=search_limit, - include=["dialogues", "statements", "entities"], - output_path=None, - ) - except Exception: - results = None - t1 = time.time() - latencies_search.append((t1 - t0) * 1000) - - # 构建上下文：包含对话、陈述和实体摘要，并智能选择 - contexts_all: List[str] = [] - if results: - if search_type == "hybrid": - emb = results.get("embedding_search", {}) if isinstance(results.get("embedding_search"), dict) else {} - kw = results.get("keyword_search", {}) if isinstance(results.get("keyword_search"), dict) else {} - emb_dialogs = emb.get("dialogues", []) - emb_statements = emb.get("statements", []) - emb_entities = emb.get("entities", []) - kw_dialogs = kw.get("dialogues", []) - kw_statements = kw.get("statements", []) - kw_entities = kw.get("entities", []) - all_dialogs = emb_dialogs + kw_dialogs - all_statements = emb_statements + kw_statements - all_entities = emb_entities + kw_entities - - # 简单去重与限制 - seen_texts = set() - for d in all_dialogs: - text = str(d.get("content", "")).strip() - if text and text not in seen_texts: - contexts_all.append(text) - seen_texts.add(text) - if len(contexts_all) >= search_limit: - break - for s in all_statements: - text = str(s.get("statement", "")).strip() - if text and text not in seen_texts: - contexts_all.append(text) - seen_texts.add(text) - if len(contexts_all) >= search_limit: - break - # 实体摘要（最多3个） - names = [] - merged_entities = all_entities[:] - for e in merged_entities: - name = str(e.get("name", "")).strip() - if name and name not in names: - names.append(name) - if len(names) >= 3: - break - if names: - contexts_all.append("EntitySummary: " + ", ".join(names)) - else: - dialogs = results.get("dialogues", []) - statements = results.get("statements", []) - entities = results.get("entities", []) - for d in dialogs: - text = str(d.get("content", "")).strip() - if text: - contexts_all.append(text) - for s in statements: - text = str(s.get("statement", "")).strip() - if text: - contexts_all.append(text) - names = [str(e.get("name", "")).strip() for e in entities[:3] if e.get("name")] - if names: - contexts_all.append("EntitySummary: " + ", ".join(names)) - - # 智能选择并截断到预算 - context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) if contexts_all else "" - if not context_text: - context_text = "No relevant context found." - contexts_used.append(context_text[:200]) - - # Call LLM (使用异步调用) - messages = [ - {"role": "system", "content": "You are a QA assistant. Answer in English. Strictly follow: 1) If the context contains the answer, copy the shortest exact span from the context as the answer; 2) If the answer cannot be determined from the context, respond with 'Unknown'; 3) Return ONLY the answer text, no explanations."}, - {"role": "user", "content": f"Question: {question}\n\nContext:\n{context_text}"}, - ] - t2 = time.time() - resp = await llm_client.chat(messages=messages) - t3 = time.time() - latencies_llm.append((t3 - t2) * 1000) - pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else str(resp).strip()) - # Metrics: F1, BLEU-1, Jaccard; keep exact match for reference - correct_flags.append(exact_match(pred, reference)) - from app.core.memory.evaluation.common.metrics import f1_score, bleu1, jaccard - f1s.append(f1_score(str(pred), str(reference))) - b1s.append(bleu1(str(pred), str(reference))) - jss.append(jaccard(str(pred), str(reference))) - - # Aggregate metrics - acc = sum(correct_flags) / max(len(correct_flags), 1) - ctx_avg_tokens = avg_context_tokens(contexts_used) - result = { - "dataset": "memsciqa", - "items": len(items), - "metrics": { - "accuracy": acc, - # Placeholders for extensibility - "f1": (sum(f1s) / max(len(f1s), 1)) if f1s else 0.0, - "bleu1": (sum(b1s) / max(len(b1s), 1)) if b1s else 0.0, - "jaccard": (sum(jss) / max(len(jss), 1)) if jss else 0.0, - }, - "latency": { - "search": latency_stats(latencies_search), - "llm": latency_stats(latencies_llm), - }, - "avg_context_tokens": ctx_avg_tokens, - } - return result - finally: - await connector.close() - - -def main(): - # Load environment variables first - load_dotenv() - - # Get defaults from environment variables - env_sample_size = os.getenv("MEMSCIQA_SAMPLE_SIZE") - env_search_limit = os.getenv("MEMSCIQA_SEARCH_LIMIT") - env_context_budget = os.getenv("MEMSCIQA_CONTEXT_CHAR_BUDGET") - env_llm_max_tokens = os.getenv("MEMSCIQA_LLM_MAX_TOKENS") - env_skip_ingest = os.getenv("MEMSCIQA_SKIP_INGEST", "false").lower() in ("true", "1", "yes") - env_output_dir = os.getenv("MEMSCIQA_OUTPUT_DIR") - - # Convert to appropriate types with fallback to code defaults - default_sample_size = int(env_sample_size) if env_sample_size else 1 - default_search_limit = int(env_search_limit) if env_search_limit else 8 - default_context_budget = int(env_context_budget) if env_context_budget else 4000 - default_llm_max_tokens = int(env_llm_max_tokens) if env_llm_max_tokens else 64 - default_output_dir = env_output_dir if env_output_dir else None - - parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen") - - parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量") - parser.add_argument("--end-user-id", type=str, default=None, help="可选 end_user_id，默认使用环境变量") - parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数") - parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算") - - parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度") - parser.add_argument("--llm-max-tokens", type=int, default=default_llm_max_tokens, - help=f"LLM 最大生成长度 (env: MEMSCIQA_LLM_MAX_TOKENS={env_llm_max_tokens or 'not set'})") - parser.add_argument("--search-type", type=str, choices=["keyword","embedding","hybrid"], default="hybrid", help="检索类型") - parser.add_argument("--skip-ingest", action="store_true", default=env_skip_ingest, - help=f"跳过数据摄入，使用 Neo4j 中的现有数据 (env: MEMSCIQA_SKIP_INGEST={os.getenv('MEMSCIQA_SKIP_INGEST', 'false')})") - parser.add_argument("--output-dir", type=str, default=default_output_dir, - help=f"结果保存目录 (env: MEMSCIQA_OUTPUT_DIR={env_output_dir or 'not set'})") - args = parser.parse_args() - - result = asyncio.run( - run_memsciqa_eval( - sample_size=args.sample_size, - end_user_id=args.end_user_id, - search_limit=args.search_limit, - context_char_budget=args.context_char_budget, - llm_temperature=args.llm_temperature, - llm_max_tokens=args.llm_max_tokens, - search_type=args.search_type, - skip_ingest=args.skip_ingest, - ) - ) - - # Print results to console - print(json.dumps(result, ensure_ascii=False, indent=2)) - - # Save results to file - output_dir = args.output_dir - if output_dir is None: - # Use absolute path to ensure results are saved in the correct location - script_dir = Path(__file__).resolve().parent - output_dir = script_dir / "results" - elif not Path(output_dir).is_absolute(): - # If relative path, make it relative to this script's directory - script_dir = Path(__file__).resolve().parent - output_dir = script_dir / output_dir - else: - output_dir = Path(output_dir) - - output_dir.mkdir(parents=True, exist_ok=True) - - timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S") - output_path = output_dir / f"memsciqa_{timestamp_str}.json" - - try: - with open(output_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n✅ 结果已保存到: {output_path}") - except Exception as e: - print(f"\n❌ 保存结果失败: {e}") - - -if __name__ == "__main__": - main() diff --git a/api/app/core/memory/evaluation/run_eval.py b/api/app/core/memory/evaluation/run_eval.py deleted file mode 100644 index 56b2e790..00000000 --- a/api/app/core/memory/evaluation/run_eval.py +++ /dev/null @@ -1,147 +0,0 @@ -import argparse -import asyncio -import json -import os -from typing import Any, Dict -from pathlib import Path -from dotenv import load_dotenv - -# Load evaluation config -eval_config_path = Path(__file__).resolve().parent / ".env.evaluation" -if eval_config_path.exists(): - load_dotenv(eval_config_path, override=True) - -from app.repositories.neo4j.neo4j_connector import Neo4jConnector - -from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval -from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test -from app.core.memory.evaluation.locomo.qwen_search_eval import run_locomo_eval - - -async def run( - dataset: str, - sample_size: int, - reset_group: bool, - end_user_id: str | None, - judge_model: str | None = None, - search_limit: int | None = None, - context_char_budget: int | None = None, - llm_temperature: float | None = None, - llm_max_tokens: int | None = None, - search_type: str | None = None, - start_index: int | None = None, - max_contexts_per_item: int | None = None, -) -> Dict[str, Any]: - # Use environment variable with fallback chain if not provided - if end_user_id is None: - end_user_id = os.getenv("EVAL_END_USER_ID", "benchmark_default") - - if reset_group: - connector = Neo4jConnector() - try: - await connector.delete_group(end_user_id) - finally: - await connector.close() - - if dataset == "locomo": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} - if search_limit is not None: - kwargs["search_limit"] = search_limit - if context_char_budget is not None: - kwargs["context_char_budget"] = context_char_budget - if llm_temperature is not None: - kwargs["llm_temperature"] = llm_temperature - if llm_max_tokens is not None: - kwargs["llm_max_tokens"] = llm_max_tokens - if search_type is not None: - kwargs["search_type"] = search_type - return await run_locomo_eval(**kwargs) - - if dataset == "memsciqa": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} - if search_limit is not None: - kwargs["search_limit"] = search_limit - if context_char_budget is not None: - kwargs["context_char_budget"] = context_char_budget - if llm_temperature is not None: - kwargs["llm_temperature"] = llm_temperature - if llm_max_tokens is not None: - kwargs["llm_max_tokens"] = llm_max_tokens - if search_type is not None: - kwargs["search_type"] = search_type - return await run_memsciqa_eval(**kwargs) - - if dataset == "longmemeval": - kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id} - if search_limit is not None: - kwargs["search_limit"] = search_limit - if context_char_budget is not None: - kwargs["context_char_budget"] = context_char_budget - if llm_temperature is not None: - kwargs["llm_temperature"] = llm_temperature - if llm_max_tokens is not None: - kwargs["llm_max_tokens"] = llm_max_tokens - if search_type is not None: - kwargs["search_type"] = search_type - if start_index is not None: - kwargs["start_index"] = start_index - if max_contexts_per_item is not None: - kwargs["max_contexts_per_item"] = max_contexts_per_item - return await run_longmemeval_test(**kwargs) - raise ValueError(f"未知数据集: {dataset}") - - -def main(): - load_dotenv() - parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo") - parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True) - parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通") - parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 end_user_id 的图数据") - parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json") - parser.add_argument("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名") - parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）") - parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）") - parser.add_argument("--llm-temperature", type=float, default=None, help="生成温度（不提供则使用各脚本默认）") - parser.add_argument("--llm-max-tokens", type=int, default=None, help="最大生成 tokens（不提供则使用各脚本默认）") - parser.add_argument("--search-type", type=str, default=None, choices=["keyword", "embedding", "hybrid"], help="检索类型（不提供则使用各脚本默认）") - # 仅透传到 longmemeval；其他数据集忽略 - parser.add_argument("--start-index", type=int, default=None, help="仅 longmemeval：起始样本索引（不提供则用脚本默认）") - parser.add_argument("--max-contexts-per-item", type=int, default=None, help="仅 longmemeval：每条样本摄入的上下文数量上限（不提供则用脚本默认）") - parser.add_argument("--output", type=str, default=None, help="可选：将评估结果保存到指定文件路径（JSON）；不提供时默认保存到 evaluation//results 目录") - args = parser.parse_args() - - result = asyncio.run(run( - args.dataset, - args.sample_size, - args.reset_group, - args.end_user_id, - args.judge_model, - args.search_limit, - args.context_char_budget, - args.llm_temperature, - args.llm_max_tokens, - args.search_type, - args.start_index, - args.max_contexts_per_item, - )) - print(json.dumps(result, ensure_ascii=False, indent=2)) - - # 结果输出逻辑保持不变 - if args.output: - out_path = args.output - else: - eval_dir = os.path.dirname(os.path.abspath(__file__)) - dataset_results_dir = os.path.join(eval_dir, args.dataset, "results") - out_filename = f"{args.dataset}_{args.sample_size}.json" - out_path = os.path.join(dataset_results_dir, out_filename) - - out_dir = os.path.dirname(out_path) - if out_dir and not os.path.exists(out_dir): - os.makedirs(out_dir, exist_ok=True) - with open(out_path, "w", encoding="utf-8") as f: - json.dump(result, f, ensure_ascii=False, indent=2) - print(f"\n结果已保存到: {out_path}") - - -if __name__ == "__main__": - main() diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index d9a00be6..558c023d 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit d9a00be62d974c0ad071c27e86f878b921c675b6 +Subproject commit 558c023dadb5327a05561b22d8fb363c6ee2be29 From 3af183f6c347d9ca0b279952ecbf812de0db56c1 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 27 Jan 2026 11:24:04 +0800 Subject: [PATCH 085/175] feat(web): workflow add code node --- web/src/i18n/en.ts | 13 +- web/src/i18n/zh.ts | 14 +- .../Workflow/components/Editor/index.tsx | 47 +++-- .../plugin/JavaScriptHighlightPlugin.tsx | 164 ++++++++++++++++++ .../Editor/plugin/Python3HighlightPlugin.tsx | 159 +++++++++++++++++ .../Properties/CodeExecution/OutputList.tsx | 86 +++++++++ .../Properties/CodeExecution/index.tsx | 128 ++++++++++++++ .../Properties/HttpRequest/EditableTable.tsx | 3 +- .../Properties/HttpRequest/index.tsx | 1 + .../Properties/JinjaRender/index.tsx | 4 +- .../Properties/MappingList/index.tsx | 30 ++-- .../components/Properties/MessageEditor.tsx | 22 +-- .../Properties/hooks/useVariableList.ts | 7 +- .../Workflow/components/Properties/index.tsx | 10 +- .../Properties/properties.module.css | 3 + web/src/views/Workflow/constant.ts | 33 +++- .../views/Workflow/hooks/useWorkflowGraph.ts | 85 ++------- 17 files changed, 678 insertions(+), 131 deletions(-) create mode 100644 web/src/views/Workflow/components/Editor/plugin/JavaScriptHighlightPlugin.tsx create mode 100644 web/src/views/Workflow/components/Editor/plugin/Python3HighlightPlugin.tsx create mode 100644 web/src/views/Workflow/components/Properties/CodeExecution/OutputList.tsx create mode 100644 web/src/views/Workflow/components/Properties/CodeExecution/index.tsx diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 1df2eb6d..3c4aa092 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -866,7 +866,7 @@ export const en = { minimumRetention: 'Minimum retention (λ_time)', minimumRetentionDesc: 'Controls the minimum retention threshold of memory retention', - forgettingRate: 'Forgetting rate (λ_mem)', + forgettingRate: 'Forgetting rate (λ_mem)', forgettingRateDesc: 'Control the speed of memory forgetting, the higher the value, the faster the forgetting', offset: 'Offset (offset)', offsetDesc: 'The offset of the minimum preservation degree', @@ -934,7 +934,7 @@ export const en = { number: 'Number', checkbox: 'Checkbox', apiVariable: 'API Variable', - + displayName: 'Display Name', maxLength: 'Max Length', required: 'Required', @@ -1765,7 +1765,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re externalInteraction: 'External Interaction', "http-request": 'HTTP Request', tool: 'Tools', - code_execution: 'Code Execution', + code: 'Code Execution', "jinja-render": 'Template Rendering', cognitiveUpgrading: 'Cognitive Upgrading (Innovation)', 'memory-read': 'Memory Retrieval', @@ -1858,6 +1858,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re 'array[number]': 'Array[Number]', 'array[boolean]': 'Array[Boolean]', 'array[object]': 'Array[Object]', + 'object': 'Object', addParams: 'Add Extract Variable', promptPlaceholder: 'Write prompts here, type "{" to insert variables, type "insert" to insert', }, @@ -1962,6 +1963,12 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re config_id: 'Memory Configuration', search_switch: 'Search Mode', }, + + 'code': { + input_variables: 'Input Variables', + output_variables: 'Output Variables', + refreshTip: '同步函数签名至代码', + }, name: 'Key', type: 'Type', value: 'Value', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 39908757..a79b9ae9 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1609,11 +1609,6 @@ export const zh = { loadingEmpty: '内容正在加载中…', loadingEmptyDesc: '您的内容正在火箭运输中！很快就会降落在您的屏幕上' }, - count: '计数: {{count}}', - increment: '增加', - decrement: '减少', - reset: '重置', - switchLanguage: '切换语言', home: { title: '首页', @@ -1858,7 +1853,7 @@ export const zh = { externalInteraction: '外部交互', "http-request": 'HTTP请求', tool: '工具 (Tool)', - code_execution: '代码执行', + code: '代码执行', "jinja-render": '模板渲染', cognitiveUpgrading: '认知升级（创新）', 'memory-read': '记忆提取', @@ -1952,6 +1947,7 @@ export const zh = { 'array[number]': 'Array[Number]', 'array[boolean]': 'Array[Boolean]', 'array[object]': 'Array[Object]', + 'object': 'Object', addParams: '添加提取变量', promptPlaceholder: '在此处编写提示，输入“{”插入变量，输入“insert”插入', }, @@ -2056,6 +2052,12 @@ export const zh = { config_id: '记忆配置', search_switch: '检索模式', }, + + 'code': { + input_variables: '输入变量', + output_variables: '输出变量', + refreshTip: '同步函数签名至代码', + }, name: '键', type: '类型', value: '值', diff --git a/web/src/views/Workflow/components/Editor/index.tsx b/web/src/views/Workflow/components/Editor/index.tsx index fd3e937b..e37c71de 100644 --- a/web/src/views/Workflow/components/Editor/index.tsx +++ b/web/src/views/Workflow/components/Editor/index.tsx @@ -15,22 +15,24 @@ import CharacterCountPlugin from './plugin/CharacterCountPlugin' import InitialValuePlugin from './plugin/InitialValuePlugin'; import CommandPlugin from './plugin/CommandPlugin'; import Jinja2HighlightPlugin from './plugin/Jinja2HighlightPlugin'; +import Python3HighlightPlugin from './plugin/Python3HighlightPlugin'; +import JavaScriptHighlightPlugin from './plugin/JavaScriptHighlightPlugin'; import LineNumberPlugin from './plugin/LineNumberPlugin'; import BlurPlugin from './plugin/BlurPlugin'; import { VariableNode } from './nodes/VariableNode' -interface LexicalEditorProps { +export interface LexicalEditorProps { placeholder?: string; value?: string; onChange?: (value: string) => void; - options: Suggestion[]; + options?: Suggestion[]; variant?: 'outlined' | 'borderless'; height?: number; fontSize?: number; lineHeight?: number; - enableJinja2?: boolean; size?: 'default' | 'small'; - type?: 'input' | 'textarea' + type?: 'input' | 'textarea', + language?: 'string' | 'jinja2' | 'python3' | 'javascript' } const theme = { @@ -54,20 +56,25 @@ const Editor: FC =({ placeholder = "请输入内容...", value = "", onChange, - options, + options = [], variant = 'borderless', - enableJinja2 = false, size = 'default', - type = 'textarea' + type = 'textarea', + language = 'string' }) => { - const [_count, setCount] = useState(0); + const [enableJinja2, setEnableJinja2] = useState(false) + const [enableLineNumbers, setEnableLineNumbers] = useState(false) useEffect(() => { - if (enableJinja2) { - const styleId = 'jinja2-styles'; + const needsLineNumbers = language === 'jinja2' || language === 'python3' || language === 'javascript'; + setEnableJinja2(language === 'jinja2'); + setEnableLineNumbers(needsLineNumbers); + + if (needsLineNumbers) { + const styleId = 'code-editor-styles'; let existingStyle = document.getElementById(styleId); - + if (!existingStyle) { const style = document.createElement('style'); style.id = styleId; @@ -119,6 +126,7 @@ const Editor: FC =({ } .editor-content-with-numbers { white-space: pre-wrap; + font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace; } .editor-content-with-numbers p { margin: 0; @@ -128,7 +136,8 @@ const Editor: FC =({ document.head.appendChild(style); } } - }, [enableJinja2]); + }, [language]) + const initialConfig = { namespace: 'AutocompleteEditor', theme: enableJinja2 ? jinja2Theme : theme, @@ -168,7 +177,7 @@ const Editor: FC =({
=({ style={{ minHeight: placeHolderMinheight, position: 'absolute', - top: enableJinja2 ? '4px' : variant === 'borderless' ? '0' : '6px', - left: enableJinja2 ? '16px' : (variant === 'borderless' ? '0' : '11px'), + top: enableLineNumbers ? '4px' : variant === 'borderless' ? '0' : '6px', + left: enableLineNumbers ? '16px' : (variant === 'borderless' ? '0' : '11px'), color: '#A8A9AA', fontSize: fontSize, lineHeight: placeHolderMinheight, @@ -227,12 +236,14 @@ const Editor: FC =({ /> - {enableJinja2 && } - {enableJinja2 && } + {language === 'jinja2' && } + {language === 'python3' && } + {language === 'javascript' && } + {enableLineNumbers && } { setCount(count) }} onChange={onChange} /> - {enableJinja2 && } + {enableLineNumbers && }
); diff --git a/web/src/views/Workflow/components/Editor/plugin/JavaScriptHighlightPlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/JavaScriptHighlightPlugin.tsx new file mode 100644 index 00000000..90053646 --- /dev/null +++ b/web/src/views/Workflow/components/Editor/plugin/JavaScriptHighlightPlugin.tsx @@ -0,0 +1,164 @@ +import { useEffect } from 'react'; +import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext'; +import { TextNode, $createTextNode, $getSelection, $isRangeSelection } from 'lexical'; + +const JS_KEYWORDS = new Set([ + 'async', 'await', 'break', 'case', 'catch', 'class', 'const', 'continue', 'debugger', 'default', + 'delete', 'do', 'else', 'export', 'extends', 'finally', 'for', 'function', 'if', 'import', + 'in', 'instanceof', 'let', 'new', 'return', 'super', 'switch', 'this', 'throw', 'try', + 'typeof', 'var', 'void', 'while', 'with', 'yield', 'true', 'false', 'null', 'undefined' +]); + +const JavaScriptHighlightPlugin = () => { + const [editor] = useLexicalComposerContext(); + + useEffect(() => { + return editor.registerNodeTransform(TextNode, (textNode: TextNode) => { + const text = textNode.getTextContent(); + + if (textNode.hasFormat('code')) return; + if (!needsHighlight(text)) return; + + const parent = textNode.getParent(); + if (!parent) return; + + const selection = $getSelection(); + let selectionOffset = null; + if ($isRangeSelection(selection)) { + const anchor = selection.anchor; + if (anchor.getNode() === textNode) { + selectionOffset = anchor.offset; + } + } + + const tokens = tokenizeJavaScript(text); + if (tokens.length <= 1) return; + + const newNodes = tokens.map(token => { + const newNode = $createTextNode(token.text); + newNode.toggleFormat('code'); + + switch (token.type) { + case 'keyword': + newNode.setStyle('color: #d73a49; font-weight: 600;'); + break; + case 'string': + newNode.setStyle('color: #032f62;'); + break; + case 'comment': + newNode.setStyle('color: #6a737d; font-style: italic;'); + break; + case 'number': + newNode.setStyle('color: #005cc5; font-weight: 500;'); + break; + case 'function': + newNode.setStyle('color: #6f42c1; font-weight: 500;'); + break; + } + + return newNode; + }); + + if (newNodes.length > 1) { + textNode.replace(newNodes[0]); + for (let i = 1; i < newNodes.length; i++) { + newNodes[i - 1].insertAfter(newNodes[i]); + } + + if (selectionOffset !== null && $isRangeSelection(selection)) { + let currentOffset = 0; + for (const node of newNodes) { + const nodeLength = node.getTextContent().length; + if (currentOffset + nodeLength >= selectionOffset) { + node.select(selectionOffset - currentOffset, selectionOffset - currentOffset); + break; + } + currentOffset += nodeLength; + } + } + } + }); + }, [editor]); + + return null; +}; + +function needsHighlight(text: string): boolean { + return /[a-zA-Z0-9_/"'`]/.test(text); +} + +function tokenizeJavaScript(text: string): Array<{text: string, type: string}> { + const tokens: Array<{text: string, type: string}> = []; + let i = 0; + + while (i < text.length) { + // Single-line comments + if (text.slice(i, i + 2) === '//') { + let start = i; + while (i < text.length && text[i] !== '\n') i++; + tokens.push({ text: text.slice(start, i), type: 'comment' }); + continue; + } + + // Multi-line comments + if (text.slice(i, i + 2) === '/*') { + let start = i; + i += 2; + while (i < text.length && text.slice(i, i + 2) !== '*/') i++; + if (i < text.length) i += 2; + tokens.push({ text: text.slice(start, i), type: 'comment' }); + continue; + } + + // Strings + if (text[i] === '"' || text[i] === "'" || text[i] === '`') { + const quote = text[i]; + let start = i++; + + while (i < text.length) { + if (text[i] === quote && text[i - 1] !== '\\') { + i++; + break; + } + i++; + } + tokens.push({ text: text.slice(start, i), type: 'string' }); + continue; + } + + // Numbers + if (/\d/.test(text[i])) { + let start = i; + while (i < text.length && /[\d.]/.test(text[i])) i++; + tokens.push({ text: text.slice(start, i), type: 'number' }); + continue; + } + + // Keywords and identifiers + if (/[a-zA-Z_$]/.test(text[i])) { + let start = i; + while (i < text.length && /[a-zA-Z0-9_$]/.test(text[i])) i++; + const word = text.slice(start, i); + + if (JS_KEYWORDS.has(word)) { + tokens.push({ text: word, type: 'keyword' }); + } else if (i < text.length && text[i] === '(') { + tokens.push({ text: word, type: 'function' }); + } else { + tokens.push({ text: word, type: 'text' }); + } + continue; + } + + // Other characters + let start = i; + while (i < text.length && !/[a-zA-Z0-9_$/"'`]/.test(text[i])) i++; + if (start < i) { + tokens.push({ text: text.slice(start, i), type: 'text' }); + } + } + + return tokens; +} + +export default JavaScriptHighlightPlugin; diff --git a/web/src/views/Workflow/components/Editor/plugin/Python3HighlightPlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/Python3HighlightPlugin.tsx new file mode 100644 index 00000000..387160ed --- /dev/null +++ b/web/src/views/Workflow/components/Editor/plugin/Python3HighlightPlugin.tsx @@ -0,0 +1,159 @@ +import { useEffect } from 'react'; +import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext'; +import { TextNode, $createTextNode, $getSelection, $isRangeSelection } from 'lexical'; + +const PYTHON_KEYWORDS = new Set([ + 'False', 'None', 'True', 'and', 'as', 'assert', 'async', 'await', 'break', 'class', 'continue', + 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', + 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', + 'with', 'yield' +]); + +const Python3HighlightPlugin = () => { + const [editor] = useLexicalComposerContext(); + + useEffect(() => { + return editor.registerNodeTransform(TextNode, (textNode: TextNode) => { + const text = textNode.getTextContent(); + + if (textNode.hasFormat('code')) return; + if (!needsHighlight(text)) return; + + const parent = textNode.getParent(); + if (!parent) return; + + const selection = $getSelection(); + let selectionOffset = null; + if ($isRangeSelection(selection)) { + const anchor = selection.anchor; + if (anchor.getNode() === textNode) { + selectionOffset = anchor.offset; + } + } + + const tokens = tokenizePython(text); + if (tokens.length <= 1) return; + + const newNodes = tokens.map(token => { + const newNode = $createTextNode(token.text); + newNode.toggleFormat('code'); + + switch (token.type) { + case 'keyword': + newNode.setStyle('color: #d73a49; font-weight: 600;'); + break; + case 'string': + newNode.setStyle('color: #032f62;'); + break; + case 'comment': + newNode.setStyle('color: #6a737d; font-style: italic;'); + break; + case 'number': + newNode.setStyle('color: #005cc5; font-weight: 500;'); + break; + case 'function': + newNode.setStyle('color: #6f42c1; font-weight: 500;'); + break; + } + + return newNode; + }); + + if (newNodes.length > 1) { + textNode.replace(newNodes[0]); + for (let i = 1; i < newNodes.length; i++) { + newNodes[i - 1].insertAfter(newNodes[i]); + } + + if (selectionOffset !== null && $isRangeSelection(selection)) { + let currentOffset = 0; + for (const node of newNodes) { + const nodeLength = node.getTextContent().length; + if (currentOffset + nodeLength >= selectionOffset) { + node.select(selectionOffset - currentOffset, selectionOffset - currentOffset); + break; + } + currentOffset += nodeLength; + } + } + } + }); + }, [editor]); + + return null; +}; + +function needsHighlight(text: string): boolean { + return /[a-zA-Z0-9_#"']/.test(text); +} + +function tokenizePython(text: string): Array<{text: string, type: string}> { + const tokens: Array<{text: string, type: string}> = []; + let i = 0; + + while (i < text.length) { + // Comments + if (text[i] === '#') { + let start = i; + while (i < text.length && text[i] !== '\n') i++; + tokens.push({ text: text.slice(start, i), type: 'comment' }); + continue; + } + + // Strings + if (text[i] === '"' || text[i] === "'") { + const quote = text[i]; + let start = i++; + const isTriple = text.slice(start, start + 3) === quote.repeat(3); + if (isTriple) i += 2; + + while (i < text.length) { + if (isTriple && text.slice(i, i + 3) === quote.repeat(3)) { + i += 3; + break; + } else if (!isTriple && text[i] === quote && text[i - 1] !== '\\') { + i++; + break; + } + i++; + } + tokens.push({ text: text.slice(start, i), type: 'string' }); + continue; + } + + // Numbers + if (/\d/.test(text[i])) { + let start = i; + while (i < text.length && /[\d.]/.test(text[i])) i++; + tokens.push({ text: text.slice(start, i), type: 'number' }); + continue; + } + + // Keywords and identifiers + if (/[a-zA-Z_]/.test(text[i])) { + let start = i; + while (i < text.length && /[a-zA-Z0-9_]/.test(text[i])) i++; + const word = text.slice(start, i); + + if (PYTHON_KEYWORDS.has(word)) { + tokens.push({ text: word, type: 'keyword' }); + } else if (i < text.length && text[i] === '(') { + tokens.push({ text: word, type: 'function' }); + } else { + tokens.push({ text: word, type: 'text' }); + } + continue; + } + + // Other characters + let start = i; + while (i < text.length && !/[a-zA-Z0-9_#"']/.test(text[i])) i++; + if (start < i) { + tokens.push({ text: text.slice(start, i), type: 'text' }); + } + } + + return tokens; +} + +export default Python3HighlightPlugin; diff --git a/web/src/views/Workflow/components/Properties/CodeExecution/OutputList.tsx b/web/src/views/Workflow/components/Properties/CodeExecution/OutputList.tsx new file mode 100644 index 00000000..8be8d97e --- /dev/null +++ b/web/src/views/Workflow/components/Properties/CodeExecution/OutputList.tsx @@ -0,0 +1,86 @@ +import { type FC, type ReactNode } from 'react'; +import { useTranslation } from 'react-i18next' +import { Button, Form, Input, Divider, Space, Select } from 'antd'; + +interface OutputListProps { + label: string; + name: string; + extra?: ReactNode; +} + +const types = [ + 'string', + 'number', + 'boolean', + 'array[string]', + 'array[number]', + 'array[boolean]', + 'array[object]', + 'object' +] +const OutputList: FC = ({ label, name, extra }) => { + const { t } = useTranslation() + return ( + <> + + {(fields, { add, remove }) => ( + <> +
+
+ {label} +
+ + + {extra} + + +
+ {fields.map(({ key, name, ...restField }) => ( +
+ + + + + + + + + + + + + + + + + + + ) +} + +export default CodeExecution diff --git a/web/src/views/Workflow/components/Properties/HttpRequest/EditableTable.tsx b/web/src/views/Workflow/components/Properties/HttpRequest/EditableTable.tsx index 671ae074..d1383f45 100644 --- a/web/src/views/Workflow/components/Properties/HttpRequest/EditableTable.tsx +++ b/web/src/views/Workflow/components/Properties/HttpRequest/EditableTable.tsx @@ -144,6 +144,7 @@ const EditableTable: React.FC = ({ icon={block ? undefined : } onClick={() => add(createNewRow())} size="small" + block={block} className={block ? "rb:mt-1 rb:text-[12px]! rb:bg-transparent!" : "rb:text-[12px]!"} > {block && `+${t('common.add')}`} @@ -155,7 +156,7 @@ const EditableTable: React.FC = ({ {title && (
{title}
- +
)} diff --git a/web/src/views/Workflow/components/Properties/HttpRequest/index.tsx b/web/src/views/Workflow/components/Properties/HttpRequest/index.tsx index 7fcd333e..a6b50e33 100644 --- a/web/src/views/Workflow/components/Properties/HttpRequest/index.tsx +++ b/web/src/views/Workflow/components/Properties/HttpRequest/index.tsx @@ -196,6 +196,7 @@ const HttpRequest: FC<{ options: Suggestion[]; selectedNode?: any; graphRef?: an placeholder={t('common.pleaseSelect')} options={options.filter(vo => vo.dataType.includes('file'))} filterBooleanType={true} + size="small" /> } diff --git a/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx b/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx index d1a392ae..7b466310 100644 --- a/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx +++ b/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx @@ -175,7 +175,7 @@ const JinjaRender: FC = ({ selectedNode, options, templateOpti return ( <> - + @@ -184,7 +184,7 @@ const JinjaRender: FC = ({ selectedNode, options, templateOpti title={t('workflow.config.jinja-render.template')} isArray={false} parentName="template" - enableJinja2={true} + language="jinja2" options={templateOptions} titleVariant="borderless" size="small" diff --git a/web/src/views/Workflow/components/Properties/MappingList/index.tsx b/web/src/views/Workflow/components/Properties/MappingList/index.tsx index 4da1f3c3..d0f56e1c 100644 --- a/web/src/views/Workflow/components/Properties/MappingList/index.tsx +++ b/web/src/views/Workflow/components/Properties/MappingList/index.tsx @@ -1,14 +1,17 @@ -import React from 'react'; +import { type FC, type ReactNode } from 'react'; import { useTranslation } from 'react-i18next' -import { Button, Form, Input, Divider } from 'antd'; +import { Button, Form, Input, Divider, Space } from 'antd'; import type { Suggestion } from '../../Editor/plugin/AutocompletePlugin' import VariableSelect from '../VariableSelect' interface MappingListProps { + label: string; name: string; options: Suggestion[]; + extra?: ReactNode; + valueKey?: string; } -const MappingList: React.FC = ({ name, options }) => { +const MappingList: FC = ({ label, name, options, extra, valueKey = 'value' }) => { const { t } = useTranslation() return ( <> @@ -17,16 +20,19 @@ const MappingList: React.FC = ({ name, options }) => { <>
- {t('workflow.config.jinja-render.mapping')} + {label}
- + + {extra} + +
{fields.map(({ key, name, ...restField }) => (
@@ -43,7 +49,7 @@ const MappingList: React.FC = ({ name, options }) => { void; size?: 'small' | 'default' } @@ -29,8 +29,8 @@ const MessageEditor: FC = ({ isArray = true, parentName = 'messages', placeholder, - options, - enableJinja2 = false, + options = [], + language, size = 'default' }) => { const { t } = useTranslation() @@ -81,13 +81,15 @@ const MessageEditor: FC = ({ -
{title ?? t('workflow.answerDesc')}
+ : title} - + ); @@ -132,7 +134,7 @@ const MessageEditor: FC = ({ )} - + ); diff --git a/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts b/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts index 37574f75..11d91d98 100644 --- a/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts +++ b/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts @@ -68,7 +68,7 @@ const processNodeVariables = ( if (p?.name) addVariable(variableList, addedKeys, `${dataNodeId}_${p.name}`, p.name, p.type || 'string', `${dataNodeId}.${p.name}`, nodeData); }); break; - + case 'var-aggregator': if (config.group.defaultValue) { (config.group_variables.defaultValue || []).forEach((gv: any) => { @@ -106,6 +106,11 @@ const processNodeVariables = ( if (cv.name?.trim()) addVariable(variableList, addedKeys, `${dataNodeId}_cycle_${cv.name}`, cv.name, cv.type || 'string', `${dataNodeId}.${cv.name}`, nodeData); }); break; + case 'code': + (config.output_variables.defaultValue || []).forEach((cv: any) => { + if (cv.name?.trim()) addVariable(variableList, addedKeys, `${dataNodeId}_cycle_${cv.name}`, cv.name, cv.type || 'string', `${dataNodeId}.${cv.name}`, nodeData); + }); + break; } }; diff --git a/web/src/views/Workflow/components/Properties/index.tsx b/web/src/views/Workflow/components/Properties/index.tsx index 38fd3005..aa757275 100644 --- a/web/src/views/Workflow/components/Properties/index.tsx +++ b/web/src/views/Workflow/components/Properties/index.tsx @@ -26,9 +26,10 @@ import MemoryConfig from './MemoryConfig' import VariableList from './VariableList' import { useVariableList, getCurrentNodeVariables, getChildNodeVariables } from './hooks/useVariableList' import styles from './properties.module.css' -import Editor from "../Editor"; +import Editor, { type LexicalEditorProps } from "../Editor"; import RbSlider from './RbSlider' import JinjaRender from './JinjaRender' +import CodeExecution from './CodeExecution' interface PropertiesProps { selectedNode?: Node | null; @@ -364,6 +365,11 @@ const Properties: FC = ({ options={getFilteredVariableList(selectedNode?.data?.type, 'mapping')} templateOptions={getFilteredVariableList(selectedNode?.data?.type, 'template')} /> + : selectedNode?.data?.type === 'code' + ? : configs && Object.keys(configs).length > 0 && Object.keys(configs).map((key) => { const config = configs[key] || {} @@ -438,7 +444,7 @@ const Properties: FC = ({ title={t(`workflow.config.${selectedNode?.data?.type}.${key}`)} isArray={!!config.isArray} parentName={key} - enableJinja2={config.enableJinja2 as boolean} + language={config.language as LexicalEditorProps['language']} options={getFilteredVariableList(selectedNode?.data?.type, key)} titleVariant={config.titleVariant} size="small" diff --git a/web/src/views/Workflow/components/Properties/properties.module.css b/web/src/views/Workflow/components/Properties/properties.module.css index 292a13e4..4820788f 100644 --- a/web/src/views/Workflow/components/Properties/properties.module.css +++ b/web/src/views/Workflow/components/Properties/properties.module.css @@ -87,4 +87,7 @@ .properties :global(.ant-select .ant-select-arrow) { font-size: 10px; inset-inline-end: 6px; +} +.properties :global(.ant-input-sm) { + padding: 3.6px 7px; } \ No newline at end of file diff --git a/web/src/views/Workflow/constant.ts b/web/src/views/Workflow/constant.ts index e250e184..2cdd60d9 100644 --- a/web/src/views/Workflow/constant.ts +++ b/web/src/views/Workflow/constant.ts @@ -284,7 +284,7 @@ export const nodeLibrary: NodeLibrary[] = [ config: { input: { type: 'variableList', - filterNodeTypes: ['knowledge-retrieval', 'iteration', 'loop'], + filterNodeTypes: ['knowledge-retrieval', 'iteration', 'loop', 'parameter-extractor', 'code'], filterVariableNames: ['message'] }, parallel: { @@ -431,7 +431,32 @@ export const nodeLibrary: NodeLibrary[] = [ } } }, - // { type: "code_execution", icon: codeExecutionIcon }, + { type: "code", icon: codeExecutionIcon, + config: { + input_variables: { + type: 'inputList', + defaultValue: [{ name: 'arg1' }, { name: 'arg2' }] + }, + language: { + type: 'select', + defaultValue: 'python3' + }, + code: { + type: 'messageEditor', + isArray: false, + language: ['python3', 'javascript'], + titleVariant: 'borderless', + defaultValue: `def main(arg1: str, arg2: str): + return { + "result": arg1 + arg2, + }` + }, + output_variables: { + type: 'outputList', + defaultValue: [{name: 'result', type: 'string'}] + }, + } + }, { type: "jinja-render", icon: templateRenderingIcon, config: { mapping: { @@ -441,12 +466,12 @@ export const nodeLibrary: NodeLibrary[] = [ template: { type: 'messageEditor', isArray: false, - enableJinja2: true, + language: 'jinja2', titleVariant: 'borderless', defaultValue: "{{arg1}}" }, } - } + }, ] }, // { diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts index 0cc69fea..48cd6652 100644 --- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts +++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts @@ -109,6 +109,12 @@ export const useWorkflowGraph = ({ : group_variables } else if (type === 'http-request' && (key === 'headers' || key === 'params') && config[key] && typeof config[key] === 'object' && !Array.isArray(config[key]) && nodeLibraryConfig.config && nodeLibraryConfig.config[key]) { nodeLibraryConfig.config[key].defaultValue = Object.entries(config[key]).map(([name, value]) => ({ name, value })) + } else if (type === 'code' && key === 'code' && config[key] && nodeLibraryConfig.config && nodeLibraryConfig.config[key]) { + try { + nodeLibraryConfig.config[key].defaultValue = decodeURIComponent(atob(config[key] as string)) + } catch { + nodeLibraryConfig.config[key].defaultValue = config[key] + } } else if (nodeLibraryConfig.config && nodeLibraryConfig.config[key] && config[key]) { nodeLibraryConfig.config[key].defaultValue = config[key] } @@ -588,77 +594,6 @@ export const useWorkflowGraph = ({ graphRef.current.resize(containerRef.current.offsetWidth, containerRef.current.offsetHeight); } }; - - const nodeChangePosition = ({ node, options }: { node: Node; options: { skipParentHandler?: boolean } }) => { - const embedPadding = 50; // Define the embed padding constant - if (options.skipParentHandler) { - return - } - - const children = node.getChildren() - if (children && children.length) { - node.prop('originPosition', node.getPosition()) - } - - const parent = node.getParent() - if (parent && parent.isNode()) { - let originSize = parent.prop('originSize') - if (originSize == null) { - originSize = parent.getSize() - parent.prop('originSize', originSize) - } - - let originPosition = parent.prop('originPosition') - if (originPosition == null) { - originPosition = parent.getPosition() - parent.prop('originPosition', originPosition) - } - - let x = originPosition.x - let y = originPosition.y - let cornerX = originPosition.x + originSize.width - let cornerY = originPosition.y + originSize.height - let hasChange = false - - const children = parent.getChildren() - if (children) { - children.forEach((child) => { - const bbox = child.getBBox().inflate(embedPadding) - const corner = bbox.getCorner() - - if (bbox.x < x) { - x = bbox.x - hasChange = true - } - - if (bbox.y < y) { - y = bbox.y - hasChange = true - } - - if (corner.x > cornerX) { - cornerX = corner.x - hasChange = true - } - - if (corner.y > cornerY) { - cornerY = corner.y - hasChange = true - } - }) - } - - if (hasChange) { - parent.prop( - { - position: { x, y }, - size: { width: cornerX - x, height: cornerY - y }, - }, - { skipParentHandler: true }, - ) - } - } - } // 初始化 const init = () => { @@ -912,7 +847,13 @@ export const useWorkflowGraph = ({ if (data.config) { Object.keys(data.config).forEach(key => { - if (key === 'memory' && data.config[key] && 'defaultValue' in data.config[key]) { + if (data.type === 'code' && key === 'code' && data.config[key] && 'defaultValue' in data.config[key]) { + const code = data.config[key].defaultValue || '' + itemConfig = { + ...itemConfig, + code: btoa(encodeURIComponent(code || '')) + } + } else if (key === 'memory' && data.config[key] && 'defaultValue' in data.config[key]) { const { messages, ...rest } = data.config[key].defaultValue let memoryMessage = { role: 'USER', content: data.config[key].defaultValue.messages } itemConfig = { From a53be317656c1c2f17db96e143be4ea29bba9a31 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 11:41:16 +0800 Subject: [PATCH 086/175] =?UTF-8?q?=E6=A3=80=E6=9F=A5=E9=9C=80=E8=A6=81?= =?UTF-8?q?=E6=9B=B4=E6=94=B9=E7=9A=84=E6=A0=BC=E5=BC=8F=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../forgetting_engine/forgetting_scheduler.py | 2 +- api/app/services/memory_storage_service.py | 12 ++++++++++-- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py index 5a178fc2..072d587c 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py @@ -343,7 +343,7 @@ class ForgettingScheduler: params = {} if end_user_id: - end_user_id['end_user_id'] = end_user_id + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 0ede7bd3..784288de 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -182,13 +182,21 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # 将 ORM 对象转换为字典列表 data_list = [] for config in configs: + # 安全地转换 user_id 为 int + config_id_old = None + if config.user_id: + try: + config_id_old = int(config.user_id) + except (ValueError, TypeError): + config_id_old = None + config_dict = { "config_id": config.config_id, "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "config_id_old": int(config.user_id), + "config_id_old": config_id_old, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, @@ -268,7 +276,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） try: config_service = MemoryConfigService(self.db) memory_config = config_service.load_memory_config( - config_id=int(cid), + config_id=str(cid), service_name="MemoryStorageService.pilot_run_stream" ) logger.info(f"Configuration loaded successfully: {memory_config.config_name}") From d160076267ff28653c39f94aafdbaee5082f69b0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B9=90=E5=8A=9B=E9=BD=90?= <162269739+lanceyq@users.noreply.github.com> Date: Tue, 27 Jan 2026 11:44:50 +0800 Subject: [PATCH 087/175] Fix/redbear benchmark (#205) * Refactor: Move evaluation folder to redbear-mem-benchmark submodule * [changes]Update submodule reference * Refactor: Move evaluation folder to redbear-mem-benchmark submodule * [changes]Update submodule reference * Remove duplicate evaluation submodule, use redbear-mem-benchmark instead --- redbear-mem-benchmark | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/redbear-mem-benchmark b/redbear-mem-benchmark index 558c023d..4b0257bb 160000 --- a/redbear-mem-benchmark +++ b/redbear-mem-benchmark @@ -1 +1 @@ -Subproject commit 558c023dadb5327a05561b22d8fb363c6ee2be29 +Subproject commit 4b0257bb4e7dc384b2aaf849b0bd6eae4b39835d From 73c78103108ff3b3a7cd4c053b48bf58b0f80150 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Tue, 27 Jan 2026 11:45:14 +0800 Subject: [PATCH 088/175] Fix/memory bug fix (#207) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * 检查需要更改的格式问题 --------- Co-authored-by: lanceyq <1982376970@qq.com> --- .../forgetting_engine/forgetting_scheduler.py | 2 +- api/app/services/memory_storage_service.py | 12 ++++++++++-- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py index 5a178fc2..072d587c 100644 --- a/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py +++ b/api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py @@ -343,7 +343,7 @@ class ForgettingScheduler: params = {} if end_user_id: - end_user_id['end_user_id'] = end_user_id + params['end_user_id'] = end_user_id results = await self.connector.execute_query(query, **params) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 0ede7bd3..784288de 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -182,13 +182,21 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # 将 ORM 对象转换为字典列表 data_list = [] for config in configs: + # 安全地转换 user_id 为 int + config_id_old = None + if config.user_id: + try: + config_id_old = int(config.user_id) + except (ValueError, TypeError): + config_id_old = None + config_dict = { "config_id": config.config_id, "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, "end_user_id": config.end_user_id, - "config_id_old": int(config.user_id), + "config_id_old": config_id_old, "apply_id": config.apply_id, "llm_id": config.llm_id, "embedding_id": config.embedding_id, @@ -268,7 +276,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） try: config_service = MemoryConfigService(self.db) memory_config = config_service.load_memory_config( - config_id=int(cid), + config_id=str(cid), service_name="MemoryStorageService.pilot_run_stream" ) logger.info(f"Configuration loaded successfully: {memory_config.config_name}") From 1324ba3a494e9f2f7e311f095ed17f9f82dcd1c0 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 27 Jan 2026 13:47:55 +0800 Subject: [PATCH 089/175] fix(web): remove URI decode and encode --- web/src/views/Workflow/hooks/useWorkflowGraph.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts index 48cd6652..4c010de0 100644 --- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts +++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts @@ -111,7 +111,7 @@ export const useWorkflowGraph = ({ nodeLibraryConfig.config[key].defaultValue = Object.entries(config[key]).map(([name, value]) => ({ name, value })) } else if (type === 'code' && key === 'code' && config[key] && nodeLibraryConfig.config && nodeLibraryConfig.config[key]) { try { - nodeLibraryConfig.config[key].defaultValue = decodeURIComponent(atob(config[key] as string)) + nodeLibraryConfig.config[key].defaultValue = atob(config[key] as string) } catch { nodeLibraryConfig.config[key].defaultValue = config[key] } @@ -851,7 +851,7 @@ export const useWorkflowGraph = ({ const code = data.config[key].defaultValue || '' itemConfig = { ...itemConfig, - code: btoa(encodeURIComponent(code || '')) + code: btoa(code || '') } } else if (key === 'memory' && data.config[key] && 'defaultValue' in data.config[key]) { const { messages, ...rest } = data.config[key].defaultValue From 2694576a32c80b5746f0bf8b1dc428469dced72a Mon Sep 17 00:00:00 2001 From: Mark Date: Tue, 27 Jan 2026 14:04:44 +0800 Subject: [PATCH 090/175] [add] plugin system and base sso module --- api/app/core/config.py | 23 +++++++++++ api/app/models/tenant_model.py | 4 ++ api/app/models/user_model.py | 4 ++ api/app/plugins/__init__.py | 74 ++++++++++++++++++++++++++++++++++ 4 files changed, 105 insertions(+) create mode 100644 api/app/plugins/__init__.py diff --git a/api/app/core/config.py b/api/app/core/config.py index 59c6ff5f..85add288 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -9,6 +9,25 @@ load_dotenv() class Settings: + # ======================================================================== + # Deployment Mode Configuration + # ======================================================================== + # community: 社区版（开源，功能受限） + # cloud: SaaS 云服务版（全功能，按量计费） + # enterprise: 企业私有化版（License 控制） + DEPLOYMENT_MODE: str = os.getenv("DEPLOYMENT_MODE", "community") + + # License 配置（企业版） + LICENSE_FILE: str = os.getenv("LICENSE_FILE", "/etc/app/license.json") + LICENSE_SERVER_URL: str = os.getenv("LICENSE_SERVER_URL", "https://license.yourcompany.com") + + # 计费服务配置（SaaS 版） + BILLING_SERVICE_URL: str = os.getenv("BILLING_SERVICE_URL", "") + + # 基础 URL（用于 SSO 回调等） + BASE_URL: str = os.getenv("BASE_URL", "http://localhost:8000") + FRONTEND_URL: str = os.getenv("FRONTEND_URL", "http://localhost:3000") + ENABLE_SINGLE_WORKSPACE: bool = os.getenv("ENABLE_SINGLE_WORKSPACE", "true").lower() == "true" # API Keys Configuration OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "") @@ -72,6 +91,10 @@ class Settings: # Single Sign-On configuration ENABLE_SINGLE_SESSION: bool = os.getenv("ENABLE_SINGLE_SESSION", "false").lower() == "true" + + # SSO 免登配置 + SSO_TOKEN_EXPIRE_SECONDS: int = int(os.getenv("SSO_TOKEN_EXPIRE_SECONDS", "300")) + SSO_TRUSTED_SOURCES_CONFIG: str = os.getenv("SSO_TRUSTED_SOURCES_CONFIG", "{}") # File Upload MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", "52428800")) diff --git a/api/app/models/tenant_model.py b/api/app/models/tenant_model.py index 552e87b5..54a3e347 100644 --- a/api/app/models/tenant_model.py +++ b/api/app/models/tenant_model.py @@ -16,6 +16,10 @@ class Tenants(Base): updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now) is_active = Column(Boolean, default=True) + # SSO 外部关联字段 + external_id = Column(String(100), nullable=True, index=True) # 外部企业ID + external_source = Column(String(50), nullable=True) # 来源系统 + # Relationship to users - one tenant has many users users = relationship("User", back_populates="tenant") diff --git a/api/app/models/user_model.py b/api/app/models/user_model.py index 89971a3a..663bfc71 100644 --- a/api/app/models/user_model.py +++ b/api/app/models/user_model.py @@ -18,6 +18,10 @@ class User(Base): updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now) last_login_at = Column(DateTime, nullable=True) # 最后登录时间，可为空 + # SSO 外部关联字段 + external_id = Column(String(100), nullable=True) # 外部用户ID + external_source = Column(String(50), nullable=True) # 来源系统 + current_workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id"), nullable=True) # 当前工作空间ID，可为空 # Foreign key to tenant - each user belongs to exactly one tenant diff --git a/api/app/plugins/__init__.py b/api/app/plugins/__init__.py new file mode 100644 index 00000000..e9ef92fd --- /dev/null +++ b/api/app/plugins/__init__.py @@ -0,0 +1,74 @@ +# app/plugins/__init__.py +""" +插件系统 - 支持开源核心 + 闭源增值模块 + +使用方式： +1. 开源版（community）：基础功能 +2. 商业版（enterprise）：加载 premium 包中的高级实现 +""" +import os +from typing import Dict, Any, Optional +from app.core.logging_config import get_logger + +logger = get_logger(__name__) + +# 版本标识 +EDITION = os.environ.get("EDITION", "community") +IS_ENTERPRISE = EDITION == "enterprise" + +# 插件注册表 +_plugins: Dict[str, Any] = {} + +# 路由注册表（用于动态注册闭源模块的路由） +_routers: list = [] + + +def is_enterprise() -> bool: + """是否为商业版""" + return IS_ENTERPRISE + + +def list_plugins() -> list: + """列出所有已注册插件""" + return list(_plugins.keys()) + + +def register_plugin(name: str, instance: Any): + """注册插件""" + _plugins[name] = instance + logger.info(f"插件已注册: {name}") + + +def get_plugin(name: str) -> Optional[Any]: + """获取插件实例""" + return _plugins.get(name) + + +def register_router(router, prefix: str = "", tags: list = None): + """注册路由（供闭源模块使用）""" + _routers.append({ + "router": router, + "prefix": prefix, + "tags": tags or [] + }) + logger.info(f"路由已注册: {prefix}") + + +def get_registered_routers() -> list: + """获取所有注册的路由""" + return _routers + + +def register_premium_routers(app): + """ + 注册 premium 模块的路由到 FastAPI app + + 在商业版 main.py 中调用 + """ + for router_info in _routers: + app.include_router( + router_info["router"], + prefix=f"/api{router_info['prefix']}", + tags=router_info["tags"] + ) + logger.info(f"Premium 路由已挂载: /api{router_info['prefix']}") From a047cf2e91d3973442b67ae285730e1040d2a146 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 14:32:48 +0800 Subject: [PATCH 091/175] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E5=AE=BF=E4=B8=BB?= =?UTF-8?q?=E5=88=97=E8=A1=A8=E8=8E=B7=E5=8F=96memory=5Fconfig=5FidBUG?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 6e72a53f..09a14c32 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1263,7 +1263,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) result[user_id] = {"memory_config_id": None, "memory_config_name": None} # 2. 批量获取所有相关应用的最新发布版本 - app_ids = list(user_to_app.values()) + app_ids = list(set(user_to_app.values())) if not app_ids: return result @@ -1295,8 +1295,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 批量查询 memory_config_name config_id_to_name = {} if memory_config_ids: - memory_configs = db.query(MemoryConfig).filter(MemoryConfig.id.in_(memory_config_ids)).all() - config_id_to_name = {str(mc.id): mc.config_name for mc in memory_configs} + memory_configs = db.query(MemoryConfig).filter(MemoryConfig.config_id.in_(memory_config_ids)).all() + config_id_to_name = {str(mc.config_id): mc.config_name for mc in memory_configs} # 4. 构建最终结果 for end_user_id, app_id in user_to_app.items(): From 8847039d76b96c286c0c81e00f4ff466c3561c2a Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Tue, 27 Jan 2026 14:36:37 +0800 Subject: [PATCH 092/175] Fix/memory bug fix (#209) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * 检查需要更改的格式问题 * 修复宿主列表获取memory_config_idBUG --------- Co-authored-by: lanceyq <1982376970@qq.com> --- api/app/services/memory_agent_service.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 6e72a53f..09a14c32 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1263,7 +1263,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) result[user_id] = {"memory_config_id": None, "memory_config_name": None} # 2. 批量获取所有相关应用的最新发布版本 - app_ids = list(user_to_app.values()) + app_ids = list(set(user_to_app.values())) if not app_ids: return result @@ -1295,8 +1295,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 批量查询 memory_config_name config_id_to_name = {} if memory_config_ids: - memory_configs = db.query(MemoryConfig).filter(MemoryConfig.id.in_(memory_config_ids)).all() - config_id_to_name = {str(mc.id): mc.config_name for mc in memory_configs} + memory_configs = db.query(MemoryConfig).filter(MemoryConfig.config_id.in_(memory_config_ids)).all() + config_id_to_name = {str(mc.config_id): mc.config_name for mc in memory_configs} # 4. 构建最终结果 for end_user_id, app_id in user_to_app.items(): From 28e6939884435b9da0d5d73e53fff55b14d79aef Mon Sep 17 00:00:00 2001 From: Mark Date: Tue, 27 Jan 2026 15:06:50 +0800 Subject: [PATCH 093/175] [modify] file local server url --- api/app/controllers/file_storage_controller.py | 2 +- api/app/core/config.py | 1 + api/app/core/storage/url_signer.py | 2 +- api/env.example | 1 + 4 files changed, 4 insertions(+), 2 deletions(-) diff --git a/api/app/controllers/file_storage_controller.py b/api/app/controllers/file_storage_controller.py index c28ffe6c..1a7e8ad2 100644 --- a/api/app/controllers/file_storage_controller.py +++ b/api/app/controllers/file_storage_controller.py @@ -310,7 +310,7 @@ async def get_file_url( try: if permanent: # Generate permanent URL (no expiration check) - server_url = f"http://{settings.SERVER_IP}:8000/api" + server_url = settings.FILE_LOCAL_SERVER_URL url = f"{server_url}/storage/permanent/{file_id}" return success( data={ diff --git a/api/app/core/config.py b/api/app/core/config.py index 85add288..44d29c8f 100644 --- a/api/app/core/config.py +++ b/api/app/core/config.py @@ -130,6 +130,7 @@ class Settings: # Server Configuration SERVER_IP: str = os.getenv("SERVER_IP", "127.0.0.1") + FILE_LOCAL_SERVER_URL : str = os.getenv("FILE_LOCAL_SERVER_URL", "http://localhost:8000/api") # ======================================================================== # Internal Configuration (not in .env, used by application code) diff --git a/api/app/core/storage/url_signer.py b/api/app/core/storage/url_signer.py index 480c8ef4..712b298e 100644 --- a/api/app/core/storage/url_signer.py +++ b/api/app/core/storage/url_signer.py @@ -36,7 +36,7 @@ def generate_signed_url( """ if base_url is None: # Use SERVER_IP or default to localhost - server_url = f"http://{settings.SERVER_IP}:8000/api" + server_url = settings.FILE_LOCAL_SERVER_URL base_url = server_url # Calculate expiration timestamp diff --git a/api/env.example b/api/env.example index 45ab6c70..274049b9 100644 --- a/api/env.example +++ b/api/env.example @@ -75,6 +75,7 @@ ENABLE_SINGLE_SESSION= MAX_FILE_SIZE=52428800 # 50MB:10 * 1024 * 1024 FILE_PATH=/files +FILE_LOCAL_SERVER_URL="http://localhost:8000/api" # Storage Backend Configuration # Supported values: local, oss, s3 # Default: local From 93c9e76c4b63182b1897afe042b606e77d599e70 Mon Sep 17 00:00:00 2001 From: Mark Date: Tue, 27 Jan 2026 15:31:29 +0800 Subject: [PATCH 094/175] [add] migration script --- api/app/models/memory_config_model.py | 2 +- .../versions/75f0ec80e50b_202601271517.py | 57 +++++++++++++++++++ 2 files changed, 58 insertions(+), 1 deletion(-) create mode 100644 api/migrations/versions/75f0ec80e50b_202601271517.py diff --git a/api/app/models/memory_config_model.py b/api/app/models/memory_config_model.py index b468e2a2..454b1b48 100644 --- a/api/app/models/memory_config_model.py +++ b/api/app/models/memory_config_model.py @@ -10,7 +10,7 @@ class MemoryConfig(Base): # 主键 config_id = Column(UUID(as_uuid=True), primary_key=True, comment="配置ID") - + config_id_old = Column(Integer, nullable=True, comment="备份的配置ID") # 基本信息 config_name = Column(String, nullable=False, comment="配置名称") config_desc = Column(String, nullable=True, comment="配置描述") diff --git a/api/migrations/versions/75f0ec80e50b_202601271517.py b/api/migrations/versions/75f0ec80e50b_202601271517.py new file mode 100644 index 00000000..a70d7315 --- /dev/null +++ b/api/migrations/versions/75f0ec80e50b_202601271517.py @@ -0,0 +1,57 @@ +"""202601271517 + +Revision ID: 75f0ec80e50b +Revises: 325b759cd66b +Create Date: 2026-01-27 15:26:48.696600 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '75f0ec80e50b' +down_revision: Union[str, None] = '325b759cd66b' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.alter_column('memory_config', 'config_id', + existing_type=sa.UUID(), + comment='配置ID', + existing_nullable=False) + op.alter_column('memory_config', 'config_id_old', + existing_type=sa.INTEGER(), + comment='备份的配置ID', + existing_comment='配置ID', + existing_nullable=True) + op.add_column('tenants', sa.Column('external_id', sa.String(length=100), nullable=True)) + op.add_column('tenants', sa.Column('external_source', sa.String(length=50), nullable=True)) + op.create_index(op.f('ix_tenants_external_id'), 'tenants', ['external_id'], unique=False) + op.add_column('users', sa.Column('external_id', sa.String(length=100), nullable=True)) + op.add_column('users', sa.Column('external_source', sa.String(length=50), nullable=True)) + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('users', 'external_source') + op.drop_column('users', 'external_id') + op.drop_index(op.f('ix_tenants_external_id'), table_name='tenants') + op.drop_column('tenants', 'external_source') + op.drop_column('tenants', 'external_id') + op.alter_column('memory_config', 'config_id_old', + existing_type=sa.INTEGER(), + comment='配置ID', + existing_comment='备份的配置ID', + existing_nullable=True) + op.alter_column('memory_config', 'config_id', + existing_type=sa.UUID(), + comment=None, + existing_comment='配置ID', + existing_nullable=False) + # ### end Alembic commands ### From 2eff8d1962b603be3df7336958994f90366e6f1e Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 17:23:53 +0800 Subject: [PATCH 095/175] fix(workflow): fix activation and branch control issues in streaming output --- api/app/core/workflow/executor.py | 375 +++++++++-------------- api/app/core/workflow/graph_builder.py | 338 ++++++++++++++++---- api/app/core/workflow/nodes/base_node.py | 88 +----- api/app/core/workflow/nodes/end/node.py | 291 +----------------- api/app/core/workflow/nodes/llm/node.py | 49 +-- api/docker-compose.yml | 12 + 6 files changed, 458 insertions(+), 695 deletions(-) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index a465846b..a98a9c9a 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -11,16 +11,12 @@ from typing import Any from langchain_core.runnables import RunnableConfig from langgraph.graph.state import CompiledStateGraph -from app.core.workflow.graph_builder import GraphBuilder +from app.core.workflow.expression_evaluator import evaluate_expression +from app.core.workflow.graph_builder import GraphBuilder, StreamOutputConfig from app.core.workflow.nodes import WorkflowState from app.core.workflow.nodes.base_config import VariableType from app.core.workflow.nodes.enums import NodeType - -# from app.core.tools.registry import ToolRegistry -# from app.core.tools.executor import ToolExecutor -# from app.core.tools.langchain_adapter import LangchainAdapter -# TOOL_MANAGEMENT_AVAILABLE = True -# from app.db import get_db +from app.core.workflow.template_renderer import render_template logger = logging.getLogger(__name__) @@ -55,6 +51,8 @@ class WorkflowExecutor: self.execution_config = workflow_config.get("execution_config", {}) self.start_node_id = None + self.end_outputs: dict[str, StreamOutputConfig] = {} + self.activate_end: str | None = None self.checkpoint_config = RunnableConfig( configurable={ @@ -127,7 +125,6 @@ class WorkflowExecutor: "user_id": self.user_id, "error": None, "error_node": None, - "streaming_buffer": {}, # 流式缓冲区 "cycle_nodes": [ node.get("id") for node in self.workflow_config.get("nodes") @@ -139,9 +136,8 @@ class WorkflowExecutor: } } - def _build_final_output(self, result, elapsed_time): + def _build_final_output(self, result, elapsed_time, final_output): node_outputs = result.get("node_outputs", {}) - final_output = self._extract_final_output(node_outputs) token_usage = self._aggregate_token_usage(node_outputs) conversation_id = None for node_id, node_output in node_outputs.items(): @@ -161,6 +157,12 @@ class WorkflowExecutor: "error": result.get("error"), } + def _update_end_activate(self, node_id): + for node in self.end_outputs.keys(): + self.end_outputs[node].update_activate(node_id) + if self.end_outputs[node].activate and self.activate_end is None: + self.activate_end = node + def build_graph(self, stream=False) -> CompiledStateGraph: """构建 LangGraph @@ -173,6 +175,7 @@ class WorkflowExecutor: stream=stream, ) self.start_node_id = builder.start_node_id + self.end_outputs = builder.end_node_map graph = builder.build() logger.info(f"工作流图构建完成: execution_id={self.execution_id}") @@ -205,14 +208,34 @@ class WorkflowExecutor: try: result = await graph.ainvoke(initial_state, config=self.checkpoint_config) - + full_content = '' + for end_info in self.end_outputs.values(): + output_template = "".join([output.literal for output in end_info.outputs]) + full_content += render_template( + output_template, + result.get("variables", {}), + result.get("runtime_vars", {}), + strict=False + ) + result["messages"].extend( + [ + { + "role": "user", + "content": input_data.get("message", '') + }, + { + "role": "assistant", + "content": full_content + } + ] + ) # 计算耗时 end_time = datetime.datetime.now() elapsed_time = (end_time - start_time).total_seconds() logger.info(f"工作流执行完成: execution_id={self.execution_id}, elapsed_time={elapsed_time:.2f}s") - return self._build_final_output(result, elapsed_time) + return self._build_final_output(result, elapsed_time, full_content) except Exception as e: # 计算耗时（即使失败也记录） @@ -273,6 +296,7 @@ class WorkflowExecutor: # 3. Execute workflow try: chunk_count = 0 + full_content = '' async for event in graph.astream( initial_state, @@ -293,21 +317,25 @@ class WorkflowExecutor: # Handle custom streaming events (chunks from nodes via stream writer) chunk_count += 1 event_type = data.get("type", "node_chunk") # "message" or "node_chunk" - if event_type in ("message", "node_chunk"): + if event_type == "node_chunk": + node_id = data.get("node_id") + if self.activate_end: + end_info = self.end_outputs[self.activate_end] + current_output = end_info.outputs[end_info.cursor] + if current_output.is_variable and node_id in current_output.literal: + if data.get("done"): + end_info.cursor += 1 + else: + full_content += data.get("chunk") + yield { + "event": "message", + "data": { + "chunk": data.get("chunk") + } + } logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}" f"- execution_id: {self.execution_id}") - yield { - "event": event_type, # "message" or "node_chunk" - "data": { - "node_id": data.get("node_id"), - "chunk": data.get("chunk"), - "full_content": data.get("full_content"), - "chunk_index": data.get("chunk_index"), - "is_prefix": data.get("is_prefix"), - "is_suffix": data.get("is_suffix"), - "conversation_id": input_data.get("conversation_id"), - } - } + elif event_type == "node_error": yield { "event": event_type, # "message" or "node_chunk" @@ -376,14 +404,107 @@ class WorkflowExecutor: elif mode == "updates": # Handle state updates - store final state - # TODO:流式输出点 + for node_id in data.keys(): + self._update_end_activate(node_id) + wait = False + state = graph.get_state(config=self.checkpoint_config) + node_outputs = state.values.get("runtime_vars", {}) + for _ in data.keys(): + node_outputs = node_outputs | data.get(_).get("runtime_vars", {}) + + while self.activate_end and not wait: + message = '' + logger.info(self.activate_end) + end_info = self.end_outputs[self.activate_end] + content = end_info.outputs[end_info.cursor] + while content.activate: + if not content.is_variable: + full_content += content.literal + message += content.literal + else: + try: + chunk = evaluate_expression( + content.literal, + variables={}, + node_outputs=node_outputs + ) + message += chunk + full_content += chunk + except ValueError: + pass + end_info.cursor += 1 + if end_info.cursor == len(end_info.outputs): + break + content = end_info.outputs[end_info.cursor] + if end_info.cursor != len(end_info.outputs): + wait = True + else: + self.end_outputs.pop(self.activate_end) + self.activate_end = None + for node_id in data.keys(): + self._update_end_activate(node_id) + if message: + yield { + "event": "message", + "data": { + "chunk": message + } + } + logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} " f"- execution_id: {self.execution_id}") + result = graph.get_state(self.checkpoint_config).values + while self.activate_end: + message = '' + end_info = self.end_outputs[self.activate_end] + content = end_info.outputs[end_info.cursor] + if not content.is_variable: + message += content.literal + else: + node_outputs = result.get("runtime_vars", {}) + variables = result.get("variables", {}) + try: + chunk = evaluate_expression( + content.literal, + variables=variables, + node_outputs=node_outputs + ) + message += chunk + full_content += chunk + except ValueError: + pass + end_info.cursor += 1 + if end_info.cursor == len(end_info.outputs): + self.end_outputs.pop(self.activate_end) + self.activate_end = None + if self.end_outputs: + self.activate_end = list(self.end_outputs.keys())[0] + if message: + yield { + "event": "message", + "data": { + "chunk": message + } + } + # 计算耗时 end_time = datetime.datetime.now() elapsed_time = (end_time - start_time).total_seconds() result = graph.get_state(self.checkpoint_config).values + logger.info(result) + result["messages"].extend( + [ + { + "role": "user", + "content": input_data.get("message", '') + }, + { + "role": "assistant", + "content": full_content + } + ] + ) logger.info( f"Workflow execution completed (streaming), " f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s, execution_id: {self.execution_id}" @@ -392,7 +513,7 @@ class WorkflowExecutor: # 发送 workflow_end 事件 yield { "event": "workflow_end", - "data": self._build_final_output(result, elapsed_time) + "data": self._build_final_output(result, elapsed_time, full_content) } except Exception as e: @@ -414,31 +535,6 @@ class WorkflowExecutor: } } - @staticmethod - def _extract_final_output(node_outputs: dict[str, Any]) -> str | None: - """从节点输出中提取最终输出 - - 优先级： - 1. 最后一个执行的非 start/end 节点的 output - 2. 如果没有节点输出，返回 None - - Args: - node_outputs: 所有节点的输出 - - Returns: - 最终输出字符串或 None - """ - if not node_outputs: - return None - - # 获取最后一个节点的输出 - last_node_output = list(node_outputs.values())[-1] if node_outputs else None - - if last_node_output and isinstance(last_node_output, dict): - return last_node_output.get("output") - - return None - @staticmethod def _aggregate_token_usage(node_outputs: dict[str, Any]) -> dict[str, int] | None: """聚合所有节点的 token 使用情况 @@ -529,178 +625,3 @@ async def execute_workflow_stream( ) async for event in executor.execute_stream(input_data): yield event - -# ==================== 工具管理系统集成 ==================== - -# def get_workflow_tools(workspace_id: str, user_id: str) -> list: -# """获取工作流可用的工具列表 -# -# Args: -# workspace_id: 工作空间ID -# user_id: 用户ID -# -# Returns: -# 可用工具列表 -# """ -# if not TOOL_MANAGEMENT_AVAILABLE: -# logger.warning("工具管理系统不可用") -# return [] -# -# try: -# db = next(get_db()) -# -# # 创建工具注册表 -# registry = ToolRegistry(db) -# -# # 注册内置工具类 -# from app.core.tools.builtin import ( -# DateTimeTool, JsonTool, BaiduSearchTool, MinerUTool, TextInTool -# ) -# registry.register_tool_class(DateTimeTool) -# registry.register_tool_class(JsonTool) -# registry.register_tool_class(BaiduSearchTool) -# registry.register_tool_class(MinerUTool) -# registry.register_tool_class(TextInTool) -# -# # 获取活跃的工具 -# import uuid -# tools = registry.list_tools(workspace_id=uuid.UUID(workspace_id)) -# active_tools = [tool for tool in tools if tool.status.value == "active"] -# -# # 转换为Langchain工具 -# langchain_tools = [] -# for tool_info in active_tools: -# try: -# tool_instance = registry.get_tool(tool_info.id) -# if tool_instance: -# langchain_tool = LangchainAdapter.convert_tool(tool_instance) -# langchain_tools.append(langchain_tool) -# except Exception as e: -# logger.error(f"转换工具失败: {tool_info.name}, 错误: {e}") -# -# logger.info(f"为工作流获取了 {len(langchain_tools)} 个工具") -# return langchain_tools -# -# except Exception as e: -# logger.error(f"获取工作流工具失败: {e}") -# return [] -# -# -# class ToolWorkflowNode: -# """工具工作流节点 - 在工作流中执行工具""" -# -# def __init__(self, node_config: dict, workflow_config: dict): -# """初始化工具节点 -# -# Args: -# node_config: 节点配置 -# workflow_config: 工作流配置 -# """ -# self.node_config = node_config -# self.workflow_config = workflow_config -# self.tool_id = node_config.get("tool_id") -# self.tool_parameters = node_config.get("parameters", {}) -# -# async def run(self, state: WorkflowState) -> WorkflowState: -# """执行工具节点""" -# if not TOOL_MANAGEMENT_AVAILABLE: -# logger.error("工具管理系统不可用") -# state["error"] = "工具管理系统不可用" -# return state -# -# try: -# from sqlalchemy.orm import Session -# db = next(get_db()) -# -# # 创建工具执行器 -# registry = ToolRegistry(db) -# executor = ToolExecutor(db, registry) -# -# # 准备参数（支持变量替换） -# parameters = self._prepare_parameters(state) -# -# # 执行工具 -# result = await executor.execute_tool( -# tool_id=self.tool_id, -# parameters=parameters, -# user_id=uuid.UUID(state["user_id"]), -# workspace_id=uuid.UUID(state["workspace_id"]) -# ) -# -# # 更新状态 -# node_id = self.node_config.get("id") -# if result.success: -# state["node_outputs"][node_id] = { -# "type": "tool", -# "tool_id": self.tool_id, -# "output": result.data, -# "execution_time": result.execution_time, -# "token_usage": result.token_usage -# } -# -# # 更新运行时变量 -# if isinstance(result.data, dict): -# for key, value in result.data.items(): -# state["runtime_vars"][f"{node_id}.{key}"] = value -# else: -# state["runtime_vars"][f"{node_id}.result"] = result.data -# else: -# state["error"] = result.error -# state["error_node"] = node_id -# state["node_outputs"][node_id] = { -# "type": "tool", -# "tool_id": self.tool_id, -# "error": result.error, -# "execution_time": result.execution_time -# } -# -# return state -# -# except Exception as e: -# logger.error(f"工具节点执行失败: {e}") -# state["error"] = str(e) -# state["error_node"] = self.node_config.get("id") -# return state -# -# def _prepare_parameters(self, state: WorkflowState) -> dict: -# """准备工具参数（支持变量替换）""" -# parameters = {} -# -# for key, value in self.tool_parameters.items(): -# if isinstance(value, str) and value.startswith("${") and value.endswith("}"): -# # 变量替换 -# var_path = value[2:-1] -# -# # 支持多层级变量访问，如 ${sys.message} 或 ${node1.result} -# if "." in var_path: -# parts = var_path.split(".") -# current = state.get("variables", {}) -# -# for part in parts: -# if isinstance(current, dict) and part in current: -# current = current[part] -# else: -# # 尝试从运行时变量获取 -# runtime_key = ".".join(parts) -# current = state.get("runtime_vars", {}).get(runtime_key, value) -# break -# -# parameters[key] = current -# else: -# # 简单变量 -# variables = state.get("variables", {}) -# parameters[key] = variables.get(var_path, value) -# else: -# parameters[key] = value -# -# return parameters -# -# -# # 注册工具节点到NodeFactory（如果存在） -# try: -# from app.core.workflow.nodes import NodeFactory -# if hasattr(NodeFactory, 'register_node_type'): -# NodeFactory.register_node_type("tool", ToolWorkflowNode) -# logger.info("工具节点已注册到工作流系统") -# except Exception as e: -# logger.warning(f"注册工具节点失败: {e}") diff --git a/api/app/core/workflow/graph_builder.py b/api/app/core/workflow/graph_builder.py index 5b9388fc..a8ca19fe 100644 --- a/api/app/core/workflow/graph_builder.py +++ b/api/app/core/workflow/graph_builder.py @@ -1,12 +1,15 @@ import logging +import re import uuid from collections import defaultdict +from functools import lru_cache from typing import Any from langgraph.checkpoint.memory import InMemorySaver from langgraph.graph import START, END from langgraph.graph.state import CompiledStateGraph, StateGraph from langgraph.types import Send +from pydantic import BaseModel, Field from app.core.workflow.expression_evaluator import evaluate_condition from app.core.workflow.nodes import WorkflowState, NodeFactory @@ -15,6 +18,115 @@ from app.core.workflow.nodes.enums import NodeType, BRANCH_NODES logger = logging.getLogger(__name__) +class OutputContent(BaseModel): + """ + Represents a single output segment of an End node. + + An output segment can be either: + - literal text (static string) + - a variable placeholder (e.g. {{ node.field }}) + + Each segment has its own activation state, which is especially + important in stream mode. + """ + + literal: str = Field( + ..., + description="Raw output content. Can be literal text or a variable placeholder." + ) + + activate: bool = Field( + ..., + description=( + "Whether this output segment is currently active.\n" + "- True: allowed to be emitted/output\n" + "- False: blocked until activated by branch control" + ) + ) + + is_variable: bool = Field( + ..., + description=( + "Whether this segment represents a variable placeholder.\n" + "True -> variable (e.g. {{ node.field }})\n" + "False -> literal text" + ) + ) + + +class StreamOutputConfig(BaseModel): + """ + Streaming output configuration for an End node. + + This structure controls: + - whether the End node output is globally active + - which upstream branch nodes are responsible for activation + - how each output segment behaves in streaming mode + """ + + activate: bool = Field( + ..., + description=( + "Global activation state of the End node output.\n" + "If False, no output should be emitted until all control nodes are resolved." + ) + ) + + control_nodes: list[str] = Field( + ..., + description=( + "List of upstream branch node IDs that control this End node.\n" + "Each node must signal completion before output becomes active." + ) + ) + + outputs: list[OutputContent] = Field( + ..., + description="Ordered list of output segments parsed from the output template." + ) + + cursor: int = Field( + ..., + description=( + "Streaming cursor index.\n" + "Indicates how many output segments have already been emitted." + ) + ) + + def update_activate(self, node_id): + """ + Update activation state based on an upstream node completion. + + This method is typically called when a branch/control node finishes execution. + + Behavior: + 1. If the node is a control node: + - Remove it from `control_nodes` + - If all control nodes are resolved, activate the entire output + + 2. Activate variable output segments that depend on this node: + - If an output segment is a variable + - And its literal references the completed node_id + - Mark that segment as active + """ + + # Case 1: resolve control branch dependency + if node_id in self.control_nodes: + self.control_nodes.remove(node_id) + + # All branch constraints resolved → enable output + if not self.control_nodes: + self.activate = True + + # Case 2: activate variable segments related to this node + for i in range(len(self.outputs)): + if ( + self.outputs[i].is_variable + and node_id in self.outputs[i].literal + ): + self.outputs[i].activate = True + + class GraphBuilder: def __init__( self, @@ -29,6 +141,12 @@ class GraphBuilder: self.start_node_id = None self.end_node_ids = [] + self.node_map = {node["id"]: node for node in self.nodes} + self.end_node_map: dict[str, StreamOutputConfig] = {} + self._find_upstream_branch_node = lru_cache( + maxsize=len(self.nodes) * 2 + )(self._find_upstream_branche_node) + self._analyze_end_node_output() self.graph = StateGraph(WorkflowState) self.add_nodes() @@ -43,79 +161,182 @@ class GraphBuilder: def edges(self) -> list[dict[str, Any]]: return self.workflow_config.get("edges", []) - def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]: - """ - Analyze the prefix configuration for End nodes. + def get_node_type(self, node_id: str) -> str: + """Retrieve the type of node given its ID. - This function scans each End node's output template, identifies - references to its direct upstream nodes, and extracts the prefix - string appearing before the first reference. + Args: + node_id (str): The unique identifier of the node. Returns: - tuple: - - dict[str, str]: Mapping from upstream node ID to its End node prefix - - set[str]: Set of node IDs that are directly adjacent to End nodes and referenced + str: The type of the node. + + Raises: + RuntimeError: If no node with the given `node_id` exists. """ - import re + try: + return self.node_map[node_id]["type"] + except KeyError: + raise RuntimeError(f"Node not found: Id={node_id}") - prefixes = {} - adjacent_and_referenced = set() # Record nodes directly adjacent to End and referenced + def _find_upstream_branche_node(self, target_node: str) -> tuple[bool, tuple[str]]: + """Find upstream branch nodes for a given target node in the workflow graph. - # 找到所有 End 节点 + This method identifies all upstream control (branch) nodes that can affect + the execution of `target_node`. If `target_node` is reachable from a start + node (i.e., a node with no upstream nodes), the method returns an empty tuple. + + The function distinguishes between branch nodes (defined in `BRANCH_NODES`) + and non-branch nodes, recursively traversing upstream through non-branch + nodes. If any non-branch upstream path does not lead to a branch node, + the result will indicate that no valid upstream branch node exists. + + Args: + target_node (str): The identifier of the target node. + + Returns: + tuple[bool, tuple[str]]: + - has_branch (bool): True if all upstream non-branch paths lead to at least + one branch node; False if any path reaches a start node without a branch. + - branch_nodes (tuple[str]): A deduplicated tuple of upstream branch node IDs + affecting `target_node`. Returns an empty tuple if `has_branch` is False. + """ + source_nodes = [ + edge.get("source") + for edge in self.edges + if edge.get("target") == target_node + ] + if not source_nodes and self.get_node_type(target_node) in [NodeType.START, NodeType.CYCLE_START]: + return False, tuple() + + branch_nodes = [] + non_branch_nodes = [] + + for node_id in source_nodes: + if self.get_node_type(node_id) in BRANCH_NODES: + branch_nodes.append(node_id) + else: + non_branch_nodes.append(node_id) + + has_branch = True + for node_id in non_branch_nodes: + node_has_branch, nodes = self._find_upstream_branche_node(node_id) + has_branch = has_branch and node_has_branch + if not has_branch: + break + branch_nodes.extend(nodes) + if not has_branch: + branch_nodes = [] + + return has_branch, tuple(set(branch_nodes)) + + def _analyze_end_node_output(self): + """ + Analyze output templates of all End nodes and generate StreamOutputConfig. + + This method is responsible for parsing the `output` field of End nodes, + splitting literal text and variable placeholders (e.g. {{ node.field }}), + and determining whether each output segment should be activated immediately + or controlled by upstream branch nodes. + + In stream mode: + - If the End node is controlled by any upstream branch node, the output + will be initially inactive and controlled by those branch nodes. + - Otherwise, the output is activated immediately. + + In non-stream mode: + - All outputs are activated by default. + """ + + # Collect all End nodes in the workflow end_nodes = [node for node in self.nodes if node.get("type") == "end"] logger.info(f"[Prefix Analysis] Found {len(end_nodes)} End nodes") + # Iterate through each End node to analyze its output for end_node in end_nodes: end_node_id = end_node.get("id") - output_template = end_node.get("config", {}).get("output") + config = end_node.get("config", {}) + output = config.get("output") - logger.info(f"[Prefix Analysis] End node {end_node_id} template: {output_template}") - - if not output_template: + # Skip End nodes without output configuration + if not output: continue - # Find all node references in the template - # Matches {{node_id.xxx}} or {{ node_id.xxx }} format (allowing spaces) - pattern = r'\{\{\s*([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\s*\}\}' - matches = list(re.finditer(pattern, output_template)) + # Regex to split output into: + # - variable placeholders: {{ ... }} + # - normal literal text + # + # Example: + # "Hello {{user.name}}!" -> + # ["Hello ", "{{user.name}}", "!"] + pattern = r'\{\{.*?\}\}|[^{}]+' - logger.info(f"[Prefix Analysis] 模板中找到 {len(matches)} 个节点引用") + # Strict variable format: {{ node_id.field_name }} + variable_pattern_string = r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+\s*\}\}' + variable_pattern = re.compile(variable_pattern_string) - # Identify all direct upstream nodes connected to the End node - direct_upstream_nodes = [] - for edge in self.edges: - if edge.get("target") == end_node_id: - source_node_id = edge.get("source") - direct_upstream_nodes.append(source_node_id) + # Split output into ordered segments + output_template = list(re.findall(pattern, output)) - logger.info(f"[Prefix Analysis] Direct upstream nodes of End node: {direct_upstream_nodes}") + # Determine whether each segment is literal text + # True -> literal (can be directly output) + # False -> variable placeholder (needs runtime value) + output_flag = [ + not bool(variable_pattern.match(item)) + for item in output_template + ] - # 找到第一个直接上游节点的引用 - for match in matches: - referenced_node_id = match.group(1) - logger.info(f"[Prefix Analysis] Checking reference: {referenced_node_id}") + # Stream mode: output activation depends on upstream branch nodes + if self.stream: + # Find upstream branch nodes that can control this End node + has_branch, control_nodes = self._find_upstream_branche_node(end_node_id) - if referenced_node_id in direct_upstream_nodes: - # 这是直接上游节点的引用，提取前缀 - prefix = output_template[:match.start()] + # Build StreamOutputConfig for this End node + self.end_node_map[end_node_id] = StreamOutputConfig( + # If there is no upstream branch, output is active immediately + activate=not has_branch, - logger.info(f"[Prefix Analysis] " - f"✅ Found reference to direct upstream node {referenced_node_id}, prefix: '{prefix}'") + # Branch nodes that control activation of this End node + control_nodes=list(control_nodes), - # 标记这个节点为"相邻且被引用" - adjacent_and_referenced.add(referenced_node_id) + # Convert output segments into OutputContent objects + outputs=list( + [ + OutputContent( + literal=output_string, + # Literal text can be activated immediately unless blocked by branch + activate=activate, + # Variable segments are marked explicitly + is_variable=not activate + ) + for output_string, activate in zip(output_template, output_flag) + ] + ), + # Cursor for streaming output (initially 0) + cursor=0 + ) + logger.info(f"[Stream Analysis] end_id: {end_node_id}, " + f"activate: {not has_branch}, " + f"control_nodes: {control_nodes}," + f"output: {output_template}," + f"output_activate: {output_flag}") - if prefix: - prefixes[referenced_node_id] = prefix - logger.info(f"[Prefix Analysis] " - f"✅ Assign prefix for node {referenced_node_id}: '{prefix[:50]}...'") - - # 只处理第一个直接上游节点的引用 - break - - logger.info(f"[Prefix Analysis] Final prefixes: {prefixes}") - logger.info(f"[Prefix Analysis] Nodes adjacent to End and referenced: {adjacent_and_referenced}") - return prefixes, adjacent_and_referenced + # Non-stream mode: all outputs are activated by default + else: + self.end_node_map[end_node_id] = StreamOutputConfig( + activate=True, + control_nodes=[], + outputs=list( + [ + OutputContent( + literal=output_string, + activate=True, + is_variable=not activate + ) + for output_string, activate in zip(output_template, output_flag) + ] + ), + cursor=0 + ) def add_nodes(self): """Add all nodes from the workflow configuration to the state graph. @@ -135,9 +356,6 @@ class GraphBuilder: Returns: None """ - # Analyze End node prefixes if in stream mode - end_prefixes, adjacent_and_referenced = self._analyze_end_node_prefixes() if self.stream else ({}, set()) - for node in self.nodes: node_type = node.get("type") node_id = node.get("id") @@ -171,17 +389,6 @@ class GraphBuilder: related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'" if node_instance: - # Inject End node prefix configuration if in stream mode - if self.stream and node_id in end_prefixes: - node_instance._end_node_prefix = end_prefixes[node_id] - logger.info(f"Injected End prefix for node {node_id}") - - # Mark nodes as adjacent and referenced to End node in stream mode - if self.stream: - node_instance._is_adjacent_to_end = node_id in adjacent_and_referenced - if node_id in adjacent_and_referenced: - logger.info(f"Node {node_id} marked as adjacent and referenced to End node") - # Wrap node's run method to avoid closure issues if self.stream: # Stream mode: create an async generator function @@ -261,6 +468,7 @@ class GraphBuilder: for source_node, branches in conditional_edges.items(): def make_router(src, branch_list): """reate a router function for each source node that routes to a NOP node for later merging.""" + def make_branch_node(node_name, targets): def node(s): # NOTE: NOP NODE MUST NOT MODIFY STATE diff --git a/api/app/core/workflow/nodes/base_node.py b/api/app/core/workflow/nodes/base_node.py index 8a85bb0d..4dcdf2bb 100644 --- a/api/app/core/workflow/nodes/base_node.py +++ b/api/app/core/workflow/nodes/base_node.py @@ -67,10 +67,6 @@ class WorkflowState(TypedDict): error: str | None error_node: str | None - # Streaming buffer (stores real-time streaming output of nodes) - # Format: {node_id: {"chunks": [...], "full_content": "..."}} - streaming_buffer: Annotated[dict[str, Any], lambda x, y: {**x, **y}] - # node activate status activate: Annotated[dict[str, bool], merge_activate_state] @@ -300,7 +296,7 @@ class BaseNode(ABC): """ if not self.check_activate(state): yield self.trans_activate(state) - logger.info(f"跳过节点{self.node_id}") + logger.info(f"jump node: {self.node_id}") return import time @@ -313,19 +309,6 @@ class BaseNode(ABC): # Get LangGraph's stream writer for sending custom data writer = get_stream_writer() - # Check if this is an End node - # End nodes CAN send chunks (for suffix), but only after LLM content - is_end_node = self.node_type == "end" - - # Check if this node is adjacent to End node (for message type) - is_adjacent_to_end = getattr(self, '_is_adjacent_to_end', False) - - # Determine chunk type: "message" for End and adjacent nodes, "node_chunk" for others - chunk_type = "message" if (is_end_node or is_adjacent_to_end) else "node_chunk" - - logger.debug( - f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})") - # Accumulate complete result (for final wrapping) chunks = [] final_result = None @@ -340,66 +323,25 @@ class BaseNode(ABC): raise TimeoutError() # Check if it's a completion marker - if isinstance(item, dict) and item.get("__final__"): + if item.get("__final__"): final_result = item["result"] - elif isinstance(item, str): - # String is a chunk + else: chunk_count += 1 - chunks.append(item) - full_content = "".join(chunks) + content = str(item.get("chunk")) + done = item.get("done", False) + chunks.append(content) # Send chunks for all nodes (including End nodes for suffix) - logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {item[:50]}...") + logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {content[:50]}...") # 1. Send via stream writer (for real-time client updates) writer({ - "type": chunk_type, # "message" or "node_chunk" + "type": "node_chunk", "node_id": self.node_id, - "chunk": item, - "full_content": full_content, - "chunk_index": chunk_count + "chunk": content, + "done": done }) - # 2. Update streaming buffer in state (for downstream nodes) - # Only non-End nodes need streaming buffer - if not is_end_node: - yield { - "streaming_buffer": { - self.node_id: { - "full_content": full_content, - "chunk_count": chunk_count, - "is_complete": False - } - } - } - else: - # Other types are also treated as chunks - chunk_count += 1 - chunk_str = str(item) - chunks.append(chunk_str) - full_content = "".join(chunks) - - # Send chunks for all nodes - writer({ - "type": chunk_type, # "message" or "node_chunk" - "node_id": self.node_id, - "chunk": chunk_str, - "full_content": full_content, - "chunk_index": chunk_count - }) - - # Only non-End nodes need streaming buffer - if not is_end_node: - yield { - "streaming_buffer": { - self.node_id: { - "full_content": full_content, - "chunk_count": chunk_count, - "is_complete": False - } - } - } - elapsed_time = time.time() - start_time logger.info(f"节点 {self.node_id} 流式执行完成，耗时: {elapsed_time:.2f}s, chunks: {chunk_count}") @@ -426,16 +368,6 @@ class BaseNode(ABC): "looping": state["looping"] } - # Add streaming buffer for non-End nodes - if not is_end_node: - state_update["streaming_buffer"] = { - self.node_id: { - "full_content": "".join(chunks), - "chunk_count": chunk_count, - "is_complete": True # Mark as complete - } - } - # Finally yield state update # LangGraph will merge this into state yield state_update | self.trans_activate(state) diff --git a/api/app/core/workflow/nodes/end/node.py b/api/app/core/workflow/nodes/end/node.py index 0cbd9e8e..3a5153a9 100644 --- a/api/app/core/workflow/nodes/end/node.py +++ b/api/app/core/workflow/nodes/end/node.py @@ -5,10 +5,8 @@ End 节点实现 """ import logging -import re from app.core.workflow.nodes.base_node import BaseNode, WorkflowState -from app.core.workflow.nodes.enums import NodeType logger = logging.getLogger(__name__) @@ -37,24 +35,8 @@ class EndNode(BaseNode): # 如果配置了输出模板，使用模板渲染；否则使用默认输出 if output_template: output = self._render_template(output_template, state, strict=False) - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - { - "role": "assistant", - "content": output - } - ]) else: - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - ]) - output = "工作流已完成" + output = "" # 统计信息（用于日志） node_outputs = state.get("node_outputs", {}) @@ -63,274 +45,3 @@ class EndNode(BaseNode): logger.info(f"节点 {self.node_id} (End) 执行完成，共执行 {total_nodes} 个节点") return output - - def _extract_referenced_nodes(self, template: str) -> list[str]: - """从模板中提取引用的节点 ID - - 例如：'结果：{{llm_qa.output}}' -> ['llm_qa'] - - Args: - template: 模板字符串 - - Returns: - 引用的节点 ID 列表 - """ - # 匹配 {{node_id.xxx}} 格式 - pattern = r'\{\{([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\}\}' - matches = re.findall(pattern, template) - return list(set(matches)) # 去重 - - def _parse_template_parts(self, template: str, state: WorkflowState) -> list[dict]: - """解析模板，分离静态文本和动态引用 - - 例如：'你好 {{llm.output}}, 这是后缀' - 返回：[ - {"type": "static", "content": "你好 "}, - {"type": "dynamic", "node_id": "llm", "field": "output"}, - {"type": "static", "content": ", 这是后缀"} - ] - - Args: - template: 模板字符串 - state: 工作流状态 - - Returns: - 模板部分列表 - """ - import re - - parts = [] - last_end = 0 - - # 匹配 {{xxx}} 或 {{ xxx }} 格式（支持空格） - pattern = r'\{\{\s*([^}]+?)\s*\}\}' - - for match in re.finditer(pattern, template): - start, end = match.span() - - # 添加前面的静态文本 - if start > last_end: - static_text = template[last_end:start] - if static_text: - parts.append({"type": "static", "content": static_text}) - - # 解析动态引用 - ref = match.group(1).strip() - - # 检查是否是节点引用（如 llm.output 或 llm_qa.output） - if '.' in ref: - node_id, field = ref.split('.', 1) - parts.append({ - "type": "dynamic", - "node_id": node_id, - "field": field, - "raw": ref - }) - else: - # 其他引用（如 {{var.xxx}}），当作静态处理 - # 直接渲染这部分 - rendered = self._render_template(f"{{{{{ref}}}}}", state) - parts.append({"type": "static", "content": rendered}) - - last_end = end - - # 添加最后的静态文本 - if last_end < len(template): - static_text = template[last_end:] - if static_text: - parts.append({"type": "static", "content": static_text}) - - return parts - - async def execute_stream(self, state: WorkflowState): - """Execute End node business logic (streaming) - - Smart output strategy: - 1. Check if template references a direct upstream LLM node - 2. If yes, only output the part AFTER that reference (suffix) - 3. Prefix and LLM content have already been sent during LLM node streaming - - Note: Only LLM nodes get this special treatment. Other node types output normally. - - Example: '{{start.test}}hahaha {{ llm_qa.output }} lalalalala a' - - Direct upstream LLM node is llm_qa - - Prefix '{{start.test}}hahaha ' was sent before LLM node streaming - - LLM content was streamed during LLM node execution - - End node only outputs ' lalalalala a' (suffix, sent as one chunk) - - Args: - state: Workflow state - - Yields: - Completion marker - """ - logger.info(f"节点 {self.node_id} (End) 开始执行（流式）") - - # 获取配置的输出模板 - output_template = self.config.get("output") - - if not output_template: - output = "工作流已完成" - from langgraph.config import get_stream_writer - writer = get_stream_writer() - writer({ - "type": "message", # End node output uses message type - "node_id": self.node_id, - "chunk": "", - "full_content": output, - "chunk_index": 1, - "is_suffix": False - }) - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - } - ]) - yield {"__final__": True, "result": output} - return - - # Find direct upstream LLM nodes - direct_upstream_llm_nodes = [] - for edge in self.workflow_config.get("edges", []): - if edge.get("target") == self.node_id: - source_node_id = edge.get("source") - # Check if the source node is an LLM node - for node in self.workflow_config.get("nodes", []): - logger.info(f"节点 {self.node_id} 的类型 {node.get("type")}") - if node.get("id") == source_node_id and node.get("type") == NodeType.LLM: - direct_upstream_llm_nodes.append(source_node_id) - break - - logger.info(f"节点 {self.node_id} 的直接上游 LLM 节点: {direct_upstream_llm_nodes}") - - # Parse template parts - parts = self._parse_template_parts(output_template, state) - logger.info(f"节点 {self.node_id} 解析模板，共 {len(parts)} 个部分") - for i, part in enumerate(parts): - logger.info(f"[模板解析] part[{i}]: {part}") - - # Find the first reference to a direct upstream LLM node - upstream_llm_ref_index = None - for i, part in enumerate(parts): - if part["type"] == "dynamic" and part["node_id"] in direct_upstream_llm_nodes: - upstream_llm_ref_index = i - logger.info(f"节点 {self.node_id} 找到直接上游 LLM 节点 {part['node_id']} 的引用，索引: {i}") - break - - if upstream_llm_ref_index is None: - # No reference to direct upstream LLM node, output complete template content - output = self._render_template(output_template, state, strict=False) - logger.info(f"节点 {self.node_id} 没有引用直接上游 LLM 节点，输出完整内容: '{output[:50]}...'") - - # Send complete content via writer (as a single message chunk) - from langgraph.config import get_stream_writer - writer = get_stream_writer() - writer({ - "type": "message", # End node output uses message type - "node_id": self.node_id, - "chunk": output, - "full_content": output, - "chunk_index": 1, - "is_suffix": False - }) - logger.info(f"节点 {self.node_id} 已通过 writer 发送完整内容") - - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - { - "role": "assistant", - "content": output - } - ]) - - # yield completion marker - yield {"__final__": True, "result": output} - return - - # Has reference to direct upstream LLM node, only output the part after that reference (suffix) - logger.info( - f"节点 {self.node_id} 检测到直接上游 LLM 节点引用，只输出后缀部分（从索引 {upstream_llm_ref_index + 1} 开始）") - - # Collect suffix parts - suffix_parts = [] - logger.info(f"[后缀调试] 开始收集后缀，从索引 {upstream_llm_ref_index + 1} 到 {len(parts) - 1}") - for i in range(upstream_llm_ref_index + 1, len(parts)): - part = parts[i] - logger.info(f"[后缀调试] 处理 part[{i}]: {part}") - if part["type"] == "static": - # 静态文本 - logger.info(f"[后缀调试] 添加静态文本: '{part['content']}'") - suffix_parts.append(part["content"]) - - elif part["type"] == "dynamic": - # Other dynamic references (if there are multiple references) - node_id = part["node_id"] - field = part["field"] - - # Use VariablePool to get variable value - pool = self.get_variable_pool(state) - try: - # Try to get variable value with default empty string - content = pool.get([node_id, field], default="") - logger.info(f"[后缀调试] 获取变量 {node_id}.{field} 成功: '{content}'") - except Exception as e: - logger.warning(f"[后缀调试] 获取变量 {node_id}.{field} 失败: {e}") - content = "" - - # Convert to string if not None - suffix_parts.append(str(content) if content is not None else "") - - # 拼接后缀 - suffix = "".join(suffix_parts) - - # 构建完整输出（用于返回，包含前缀 + 动态内容 + 后缀） - full_output = self._render_template(output_template, state, strict=False) - - state['messages'].extend([ - { - "role": "user", - "content": self.get_variable("sys.message", state) - }, - { - "role": "assistant", - "content": full_output - } - ]) - - logger.info(f"[后缀调试] 节点 {self.node_id} 后缀部分数量: {len(suffix_parts)}") - logger.info(f"[后缀调试] 后缀内容: '{suffix}'") - logger.info(f"[后缀调试] 后缀长度: {len(suffix)}") - logger.info(f"[后缀调试] 后缀是否为空: {not suffix}") - - if suffix: - logger.info(f"节点 {self.node_id} 输出后缀: '{suffix}...' (长度: {len(suffix)})") - # 一次性输出后缀（作为单个 chunk） - # 注意：不要直接 yield 字符串，因为 base_node 会逐字符处理 - # 而是通过 writer 直接发送 - from langgraph.config import get_stream_writer - writer = get_stream_writer() - writer({ - "type": "message", # End 节点的输出使用 message 类型 - "node_id": self.node_id, - "chunk": suffix, - "full_content": full_output, # full_content 是完整的渲染结果（前缀+LLM+后缀） - "chunk_index": 1, - "is_suffix": True - }) - logger.info(f"节点 {self.node_id} 已通过 writer 发送后缀，full_content 长度: {len(full_output)}") - else: - logger.warning(f"[后缀调试] 节点 {self.node_id} 后缀为空，不发送！" - f"upstream_llm_ref_index={upstream_llm_ref_index}, parts数量={len(parts)}") - - # 统计信息 - node_outputs = state.get("node_outputs", {}) - total_nodes = len(node_outputs) - - logger.info(f"节点 {self.node_id} (End) 执行完成（流式），共执行了 {total_nodes} 个节点") - - # yield 完成标记（包含完整输出） - yield {"__final__": True, "result": full_output} diff --git a/api/app/core/workflow/nodes/llm/node.py b/api/app/core/workflow/nodes/llm/node.py index a74e0b60..f315b238 100644 --- a/api/app/core/workflow/nodes/llm/node.py +++ b/api/app/core/workflow/nodes/llm/node.py @@ -7,18 +7,18 @@ LLM 节点实现 import logging import re from typing import Any -from langchain_core.messages import AIMessage, SystemMessage, HumanMessage -from app.core.workflow.nodes.base_node import BaseNode, WorkflowState +from langchain_core.messages import AIMessage + +from app.core.error_codes import BizCode +from app.core.exceptions import BusinessException from app.core.models import RedBearLLM, RedBearModelConfig +from app.core.workflow.nodes.base_node import BaseNode, WorkflowState from app.core.workflow.nodes.llm.config import LLMNodeConfig from app.db import get_db_context from app.models import ModelType from app.services.model_service import ModelConfigService -from app.core.exceptions import BusinessException -from app.core.error_codes import BizCode - logger = logging.getLogger(__name__) @@ -231,42 +231,14 @@ class LLMNode(BaseNode): 文本片段（chunk）或完成标记 """ self.typed_config = LLMNodeConfig(**self.config) - from langgraph.config import get_stream_writer llm, prompt_or_messages = self._prepare_llm(state, True) logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（流式）") logger.debug(f"LLM 配置: streaming={getattr(llm._model, 'streaming', 'unknown')}") - # 检查是否有注入的 End 节点前缀配置 - writer = get_stream_writer() - end_prefix = getattr(self, '_end_node_prefix', None) - - logger.info(f"[LLM前缀] 节点 {self.node_id} 检查前缀配置: {end_prefix is not None}") - if end_prefix: - logger.info(f"[LLM前缀] 前缀内容: '{end_prefix}'") - - if end_prefix: - # 渲染前缀（可能包含其他变量） - try: - rendered_prefix = self._render_template(end_prefix, state) - logger.info(f"节点 {self.node_id} 提前发送 End 节点前缀: '{rendered_prefix[:50]}...'") - - # 提前发送 End 节点的前缀（使用 "message" 类型） - writer({ - "type": "message", # End 相关的内容都是 message 类型 - "node_id": "end", # 标记为 end 节点的输出 - "chunk": rendered_prefix, - "full_content": rendered_prefix, - "chunk_index": 0, - "is_prefix": True # 标记这是前缀 - }) - except Exception as e: - logger.warning(f"渲染/发送 End 节点前缀失败: {e}") - # 累积完整响应 full_response = "" - last_chunk = None chunk_count = 0 # 调用 LLM（流式，支持字符串或消息列表） @@ -284,12 +256,19 @@ class LLMNode(BaseNode): # 只有当内容不为空时才处理 if content: full_response += content - last_chunk = chunk chunk_count += 1 # 流式返回每个文本片段 - yield content + yield { + "__final__": False, + "chunk": content + } + yield { + "__final__": False, + "chunk": "", + "done": True + } logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(full_response)}, 总 chunks: {chunk_count}") # 构建完整的 AIMessage（包含元数据） diff --git a/api/docker-compose.yml b/api/docker-compose.yml index a7337689..f30220cb 100644 --- a/api/docker-compose.yml +++ b/api/docker-compose.yml @@ -15,6 +15,7 @@ services: networks: - default - celery + - sandbox depends_on: - worker-memory - worker-document @@ -63,5 +64,16 @@ services: depends_on: - worker-memory + sandbox: + image: redbear_sandbox:latest + container_name: sandbox + ports: + - "8194" + command: /code/.venv/bin/python main.py + restart: unless-stopped + networks: + - sandbox + networks: celery: + sandbox: From 166d05afe9ed142e3f2d03e347d6b9d4a74625e6 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 17:37:36 +0800 Subject: [PATCH 096/175] fix(workflow): fix function cache not taking effect and potential list index overflow --- api/app/core/workflow/executor.py | 4 +++- api/app/core/workflow/graph_builder.py | 8 ++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index a98a9c9a..11bb5bac 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -320,7 +320,9 @@ class WorkflowExecutor: if event_type == "node_chunk": node_id = data.get("node_id") if self.activate_end: - end_info = self.end_outputs[self.activate_end] + end_info = self.end_outputs.get(self.activate_end) + if not end_info or end_info.cursor >= len(end_info.outputs): + continue current_output = end_info.outputs[end_info.cursor] if current_output.is_variable and node_id in current_output.literal: if data.get("done"): diff --git a/api/app/core/workflow/graph_builder.py b/api/app/core/workflow/graph_builder.py index a8ca19fe..3576c576 100644 --- a/api/app/core/workflow/graph_builder.py +++ b/api/app/core/workflow/graph_builder.py @@ -145,7 +145,7 @@ class GraphBuilder: self.end_node_map: dict[str, StreamOutputConfig] = {} self._find_upstream_branch_node = lru_cache( maxsize=len(self.nodes) * 2 - )(self._find_upstream_branche_node) + )(self._find_upstream_branch_node) self._analyze_end_node_output() self.graph = StateGraph(WorkflowState) @@ -178,7 +178,7 @@ class GraphBuilder: except KeyError: raise RuntimeError(f"Node not found: Id={node_id}") - def _find_upstream_branche_node(self, target_node: str) -> tuple[bool, tuple[str]]: + def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[str]]: """Find upstream branch nodes for a given target node in the workflow graph. This method identifies all upstream control (branch) nodes that can affect @@ -219,7 +219,7 @@ class GraphBuilder: has_branch = True for node_id in non_branch_nodes: - node_has_branch, nodes = self._find_upstream_branche_node(node_id) + node_has_branch, nodes = self._find_upstream_branch_node(node_id) has_branch = has_branch and node_has_branch if not has_branch: break @@ -288,7 +288,7 @@ class GraphBuilder: # Stream mode: output activation depends on upstream branch nodes if self.stream: # Find upstream branch nodes that can control this End node - has_branch, control_nodes = self._find_upstream_branche_node(end_node_id) + has_branch, control_nodes = self._find_upstream_branch_node(end_node_id) # Build StreamOutputConfig for this End node self.end_node_map[end_node_id] = StreamOutputConfig( From 2a10e9f7eed723183a5b1f920c31942f5963ee52 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 17:51:27 +0800 Subject: [PATCH 097/175] style(workflow): enforce PEP8 style and remove redundant imports --- api/app/core/workflow/nodes/code/__init__.py | 2 +- api/app/core/workflow/nodes/code/node.py | 1 - .../workflow/nodes/cycle_graph/iteration.py | 1 - .../core/workflow/nodes/cycle_graph/node.py | 1 - api/app/core/workflow/nodes/if_else/node.py | 2 +- api/app/core/workflow/nodes/memory/config.py | 2 -- api/app/core/workflow/nodes/memory/node.py | 2 +- .../nodes/question_classifier/config.py | 3 ++- .../nodes/question_classifier/node.py | 19 ++++++++++--------- api/app/core/workflow/nodes/tool/__init__.py | 2 +- api/app/core/workflow/nodes/tool/node.py | 18 +++++++++--------- 11 files changed, 25 insertions(+), 28 deletions(-) diff --git a/api/app/core/workflow/nodes/code/__init__.py b/api/app/core/workflow/nodes/code/__init__.py index e42af93d..758ab3a5 100644 --- a/api/app/core/workflow/nodes/code/__init__.py +++ b/api/app/core/workflow/nodes/code/__init__.py @@ -1,3 +1,3 @@ from app.core.workflow.nodes.code.node import CodeNode -__all__ = ["CodeNode"] \ No newline at end of file +__all__ = ["CodeNode"] diff --git a/api/app/core/workflow/nodes/code/node.py b/api/app/core/workflow/nodes/code/node.py index 5262a7e2..b2a4da32 100644 --- a/api/app/core/workflow/nodes/code/node.py +++ b/api/app/core/workflow/nodes/code/node.py @@ -7,7 +7,6 @@ from textwrap import dedent from typing import Any import httpx -from sympy.physics.vector import vlatex from app.core.workflow.nodes import BaseNode, WorkflowState from app.core.workflow.nodes.base_config import VariableType diff --git a/api/app/core/workflow/nodes/cycle_graph/iteration.py b/api/app/core/workflow/nodes/cycle_graph/iteration.py index e9174df8..cd63d233 100644 --- a/api/app/core/workflow/nodes/cycle_graph/iteration.py +++ b/api/app/core/workflow/nodes/cycle_graph/iteration.py @@ -1,5 +1,4 @@ import asyncio -import copy import logging import re from typing import Any diff --git a/api/app/core/workflow/nodes/cycle_graph/node.py b/api/app/core/workflow/nodes/cycle_graph/node.py index 1f550b0b..82782658 100644 --- a/api/app/core/workflow/nodes/cycle_graph/node.py +++ b/api/app/core/workflow/nodes/cycle_graph/node.py @@ -6,7 +6,6 @@ from langgraph.graph.state import CompiledStateGraph from app.core.workflow.nodes import WorkflowState from app.core.workflow.nodes.base_node import BaseNode -from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig from app.core.workflow.nodes.cycle_graph.iteration import IterationRuntime from app.core.workflow.nodes.cycle_graph.loop import LoopRuntime from app.core.workflow.nodes.enums import NodeType diff --git a/api/app/core/workflow/nodes/if_else/node.py b/api/app/core/workflow/nodes/if_else/node.py index 41f1138b..cf5a1499 100644 --- a/api/app/core/workflow/nodes/if_else/node.py +++ b/api/app/core/workflow/nodes/if_else/node.py @@ -13,7 +13,7 @@ logger = logging.getLogger(__name__) class IfElseNode(BaseNode): def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) - self.typed_config: IfElseNodeConfig | None= None + self.typed_config: IfElseNodeConfig | None = None @staticmethod def _evaluate(operator, instance: CompareOperatorInstance) -> Any: diff --git a/api/app/core/workflow/nodes/memory/config.py b/api/app/core/workflow/nodes/memory/config.py index 4c8c43eb..853ba882 100644 --- a/api/app/core/workflow/nodes/memory/config.py +++ b/api/app/core/workflow/nodes/memory/config.py @@ -1,8 +1,6 @@ -import uuid from uuid import UUID from pydantic import Field -from typing import Literal from app.core.workflow.nodes.base_config import BaseNodeConfig diff --git a/api/app/core/workflow/nodes/memory/node.py b/api/app/core/workflow/nodes/memory/node.py index 0589cc82..f71c70ee 100644 --- a/api/app/core/workflow/nodes/memory/node.py +++ b/api/app/core/workflow/nodes/memory/node.py @@ -24,7 +24,7 @@ class MemoryReadNode(BaseNode): return await MemoryAgentService().read_memory( end_user_id=end_user_id, message=self._render_template(self.typed_config.message, state), - config_id=str(self.typed_config.config_id), + config_id=self.typed_config.config_id, search_switch=self.typed_config.search_switch, history=[], db=db, diff --git a/api/app/core/workflow/nodes/question_classifier/config.py b/api/app/core/workflow/nodes/question_classifier/config.py index 998e2fb4..2dd8d28a 100644 --- a/api/app/core/workflow/nodes/question_classifier/config.py +++ b/api/app/core/workflow/nodes/question_classifier/config.py @@ -5,6 +5,7 @@ from pydantic import Field, BaseModel from app.core.workflow.nodes.base_config import BaseNodeConfig + class ClassifierConfig(BaseModel): """分类器节点配置""" @@ -13,7 +14,7 @@ class ClassifierConfig(BaseModel): class QuestionClassifierNodeConfig(BaseNodeConfig): """问题分类器节点配置""" - + model_id: uuid.UUID = Field(..., description="LLM模型ID") input_variable: str = Field(default="{{sys.message}}", description="输入变量选择器(用户问题)") user_supplement_prompt: Optional[str] = Field(default=None, description="用户补充提示词，额外分类指令") diff --git a/api/app/core/workflow/nodes/question_classifier/node.py b/api/app/core/workflow/nodes/question_classifier/node.py index aee72eda..6df410cb 100644 --- a/api/app/core/workflow/nodes/question_classifier/node.py +++ b/api/app/core/workflow/nodes/question_classifier/node.py @@ -18,30 +18,30 @@ DEFAULT_EMPTY_QUESTION_CASE = f"{DEFAULT_CASE_PREFIX}1" class QuestionClassifierNode(BaseNode): """问题分类器节点""" - + def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) self.typed_config: QuestionClassifierNodeConfig | None = None self.category_to_case_map = {} - + def _get_llm_instance(self) -> RedBearLLM: """获取LLM实例""" with get_db_read() as db: config = ModelConfigService.get_model_by_id(db=db, model_id=self.typed_config.model_id) - + if not config: raise BusinessException("配置的模型不存在", BizCode.NOT_FOUND) - + if not config.api_keys or len(config.api_keys) == 0: raise BusinessException("模型配置缺少 API Key", BizCode.INVALID_PARAMETER) - + api_config = config.api_keys[0] model_name = api_config.model_name provider = api_config.provider api_key = api_config.api_key base_url = api_config.api_base model_type = config.type - + return RedBearLLM( RedBearModelConfig( model_name=model_name, @@ -64,7 +64,7 @@ class QuestionClassifierNode(BaseNode): case_tag = f"{DEFAULT_CASE_PREFIX}{idx}" category_map[category_name] = case_tag return category_map - + async def execute(self, state: WorkflowState) -> dict: """执行问题分类""" self.typed_config = QuestionClassifierNodeConfig(**self.config) @@ -74,11 +74,12 @@ class QuestionClassifierNode(BaseNode): categories = self.typed_config.categories or [] category_names = [class_item.class_name.strip() for class_item in categories] category_count = len(category_names) - + if not question: logger.warning( f"节点 {self.node_id} 未获取到输入问题，使用默认分支" - f"（默认分支：{DEFAULT_EMPTY_QUESTION_CASE}，分类总数：{category_count}）" + f"(默认分支:{DEFAULT_EMPTY_QUESTION_CASE}" + f"分类总数: {category_count})" ) # 若分类列表为空，返回默认unknown分支，否则返回CASE1 if category_count > 0: diff --git a/api/app/core/workflow/nodes/tool/__init__.py b/api/app/core/workflow/nodes/tool/__init__.py index 8392f05c..a311139e 100644 --- a/api/app/core/workflow/nodes/tool/__init__.py +++ b/api/app/core/workflow/nodes/tool/__init__.py @@ -1,4 +1,4 @@ from app.core.workflow.nodes.tool.config import ToolNodeConfig from app.core.workflow.nodes.tool.node import ToolNode -__all__ = ["ToolNode", "ToolNodeConfig"] \ No newline at end of file +__all__ = ["ToolNode", "ToolNodeConfig"] diff --git a/api/app/core/workflow/nodes/tool/node.py b/api/app/core/workflow/nodes/tool/node.py index 3e79b075..aba96303 100644 --- a/api/app/core/workflow/nodes/tool/node.py +++ b/api/app/core/workflow/nodes/tool/node.py @@ -16,11 +16,11 @@ TEMPLATE_PATTERN = re.compile(r"\{\{.*?\}\}") class ToolNode(BaseNode): """工具节点""" - + def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) self.typed_config: ToolNodeConfig | None = None - + async def execute(self, state: WorkflowState) -> dict[str, Any]: """执行工具""" self.typed_config = ToolNodeConfig(**self.config) @@ -28,21 +28,21 @@ class ToolNode(BaseNode): tenant_id = self.get_variable("sys.tenant_id", state) user_id = self.get_variable("sys.user_id", state) workspace_id = self.get_variable("sys.workspace_id", state) - + # 如果没有租户ID，尝试从工作流ID获取 if not tenant_id: if workspace_id: from app.repositories.tool_repository import ToolRepository with get_db_read() as db: tenant_id = ToolRepository.get_tenant_id_by_workspace_id(db, workspace_id) - + if not tenant_id: logger.error(f"节点 {self.node_id} 缺少租户ID") return { "success": False, "data": "缺少租户ID" } - + # 渲染工具参数 rendered_parameters = {} for param_name, param_template in self.typed_config.tool_parameters.items(): @@ -55,9 +55,9 @@ class ToolNode(BaseNode): # 非模板参数（数字/布尔/普通字符串）直接保留原值 rendered_value = param_template rendered_parameters[param_name] = rendered_value - + logger.info(f"节点 {self.node_id} 执行工具 {self.typed_config.tool_id}，参数: {rendered_parameters}") - + # 执行工具 with get_db_read() as db: tool_service = ToolService(db) @@ -79,7 +79,7 @@ class ToolNode(BaseNode): else: logger.error(f"节点 {self.node_id} 工具执行失败: {result.error}") return { - "data": result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False), + "data": result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False), "error_code": result.error_code, "execution_time": result.execution_time - } \ No newline at end of file + } From 2abbd5a7fb0565ca5169b18c150adbd61a774e7b Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 18:16:53 +0800 Subject: [PATCH 098/175] fix(workflow): fix streaming output error when variable is not a string --- api/app/core/workflow/executor.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index 11bb5bac..17adbdf2 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -163,6 +163,15 @@ class WorkflowExecutor: if self.end_outputs[node].activate and self.activate_end is None: self.activate_end = node + @staticmethod + def _trans_output_string(content): + if isinstance(content, str): + return content + elif isinstance(content, list): + return "\n".join(content) + else: + return str(content) + def build_graph(self, stream=False) -> CompiledStateGraph: """构建 LangGraph @@ -430,6 +439,7 @@ class WorkflowExecutor: variables={}, node_outputs=node_outputs ) + chunk = self._trans_output_string(chunk) message += chunk full_content += chunk except ValueError: @@ -472,6 +482,7 @@ class WorkflowExecutor: variables=variables, node_outputs=node_outputs ) + chunk = self._trans_output_string(chunk) message += chunk full_content += chunk except ValueError: From d93d52cf1071170c31b21d4343589d6d13ee4a33 Mon Sep 17 00:00:00 2001 From: lixiangcheng1 Date: Tue, 27 Jan 2026 18:30:27 +0800 Subject: [PATCH 099/175] [fix]remove aspose-slides --- api/app/core/rag/app/presentation.py | 165 --------------------------- api/pyproject.toml | 1 - api/requirements.txt | 1 - 3 files changed, 167 deletions(-) delete mode 100644 api/app/core/rag/app/presentation.py diff --git a/api/app/core/rag/app/presentation.py b/api/app/core/rag/app/presentation.py deleted file mode 100644 index d62e0096..00000000 --- a/api/app/core/rag/app/presentation.py +++ /dev/null @@ -1,165 +0,0 @@ -import copy -import re -from io import BytesIO -from PIL import Image - -from app.core.rag.nlp import tokenize, is_english -from app.core.rag.nlp import rag_tokenizer -from app.core.rag.deepdoc.parser import PdfParser, PlainParser -from app.core.rag.deepdoc.parser.ppt_parser import RAGPptParser as PptParser -from PyPDF2 import PdfReader as pdf2_read -from app.core.rag.app.naive import by_plaintext, PARSERS - -class Ppt(PptParser): - def __call__(self, fnm, from_page, to_page, callback=None): - txts = super().__call__(fnm, from_page, to_page) - - callback(0.5, "Text extraction finished.") - import aspose.slides as slides - import aspose.pydrawing as drawing - imgs = [] - with slides.Presentation(BytesIO(fnm)) as presentation: - for i, slide in enumerate(presentation.slides[from_page: to_page]): - try: - with BytesIO() as buffered: - slide.get_thumbnail( - 0.1, 0.1).save( - buffered, drawing.imaging.ImageFormat.jpeg) - buffered.seek(0) - imgs.append(Image.open(buffered).copy()) - except RuntimeError as e: - raise RuntimeError(f'ppt parse error at page {i+1}, original error: {str(e)}') from e - assert len(imgs) == len( - txts), "Slides text and image do not match: {} vs. {}".format(len(imgs), len(txts)) - callback(0.9, "Image extraction finished") - self.is_english = is_english(txts) - return [(txts[i], imgs[i]) for i in range(len(txts))] - -class Pdf(PdfParser): - def __init__(self): - super().__init__() - - def __garbage(self, txt): - txt = txt.lower().strip() - if re.match(r"[0-9\.,%/-]+$", txt): - return True - if len(txt) < 3: - return True - return False - - def __call__(self, filename, binary=None, from_page=0, - to_page=100000, zoomin=3, callback=None): - from timeit import default_timer as timer - start = timer() - callback(msg="OCR started") - self.__images__(filename if not binary else binary, - zoomin, from_page, to_page, callback) - callback(msg="Page {}~{}: OCR finished ({:.2f}s)".format(from_page, min(to_page, self.total_page), timer() - start)) - assert len(self.boxes) == len(self.page_images), "{} vs. {}".format( - len(self.boxes), len(self.page_images)) - res = [] - for i in range(len(self.boxes)): - lines = "\n".join([b["text"] for b in self.boxes[i] - if not self.__garbage(b["text"])]) - res.append((lines, self.page_images[i])) - callback(0.9, "Page {}~{}: Parsing finished".format( - from_page, min(to_page, self.total_page))) - return res, [] - - -class PlainPdf(PlainParser): - def __call__(self, filename, binary=None, from_page=0, - to_page=100000, callback=None, **kwargs): - self.pdf = pdf2_read(filename if not binary else BytesIO(binary)) - page_txt = [] - for page in self.pdf.pages[from_page: to_page]: - page_txt.append(page.extract_text()) - callback(0.9, "Parsing finished") - return [(txt, None) for txt in page_txt], [] - - -def chunk(filename, binary=None, from_page=0, to_page=100000, - lang="Chinese", callback=None, vision_model=None, parser_config=None, **kwargs): - """ - The supported file formats are pdf, pptx. - Every page will be treated as a chunk. And the thumbnail of every page will be stored. - PPT file will be parsed by using this method automatically, setting-up for every PPT file is not necessary. - """ - if parser_config is None: - parser_config = {} - eng = lang.lower() == "english" - doc = { - "docnm_kwd": filename, - "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) - } - doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) - res = [] - if re.search(r"\.pptx?$", filename, re.IGNORECASE): - if not binary: - with open(filename, "rb") as f: - binary = f.read() - ppt_parser = Ppt() - for pn, (txt, img) in enumerate(ppt_parser( - filename if not binary else binary, from_page, 1000000, callback)): - d = copy.deepcopy(doc) - pn += from_page - d["image"] = img - d["doc_type_kwd"] = "image" - d["page_num_int"] = [pn + 1] - d["top_int"] = [0] - d["position_int"] = [(pn + 1, 0, img.size[0], 0, img.size[1])] - tokenize(d, txt, eng) - res.append(d) - return res - elif re.search(r"\.pdf$", filename, re.IGNORECASE): - layout_recognizer = parser_config.get("layout_recognize", "DeepDOC") - - if isinstance(layout_recognizer, bool): - layout_recognizer = "DeepDOC" if layout_recognizer else "Plain Text" - - name = layout_recognizer.strip().lower() - parser = PARSERS.get(name, by_plaintext) - callback(0.1, "Start to parse.") - - sections, _, _ = parser( - filename=filename, - binary=binary, - from_page=from_page, - to_page=to_page, - lang=lang, - callback=callback, - vision_model=vision_model, - pdf_cls=Pdf, - **kwargs - ) - - if not sections: - return [] - - if name in ["tcadp", "docling", "mineru"]: - parser_config["chunk_token_num"] = 0 - - callback(0.8, "Finish parsing.") - - for pn, (txt, img) in enumerate(sections): - d = copy.deepcopy(doc) - pn += from_page - if img: - d["image"] = img - d["page_num_int"] = [pn + 1] - d["top_int"] = [0] - d["position_int"] = [(pn + 1, 0, img.size[0] if img else 0, 0, img.size[1] if img else 0)] - tokenize(d, txt, eng) - res.append(d) - return res - - raise NotImplementedError( - "file type not supported yet(pptx, pdf supported)") - - -if __name__ == "__main__": - import sys - - def dummy(a, b): - pass - chunk(sys.argv[1], callback=dummy) diff --git a/api/pyproject.toml b/api/pyproject.toml index 2dcc706d..e14495e2 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -88,7 +88,6 @@ dependencies = [ "cachetools==6.2.1", "ruamel.yaml==0.18.10", "strenum==0.4.15", - "aspose-slides==24.12.0", "opencv-python==4.10.0.84", "numpy>=1.26.0,<2.0.0", "huggingface-hub==0.25.2", diff --git a/api/requirements.txt b/api/requirements.txt index 99252e09..0fc47979 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -82,7 +82,6 @@ olefile==0.47 cachetools==6.2.1 ruamel.yaml==0.18.10 strenum==0.4.15 -aspose-slides==24.12.0 opencv-python==4.10.0.84 numpy>=1.26.0,<2.0.0 huggingface-hub==0.25.2 From c5a794f1b5cfd4c3317603e570e8d30b5fe73d17 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 18:39:47 +0800 Subject: [PATCH 100/175] perf(workflow): enhance streaming output node activation performance --- api/app/core/workflow/executor.py | 2 +- api/app/core/workflow/graph_builder.py | 40 +++++++++++++++++++++++++- 2 files changed, 40 insertions(+), 2 deletions(-) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index 17adbdf2..f0411ae3 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -333,7 +333,7 @@ class WorkflowExecutor: if not end_info or end_info.cursor >= len(end_info.outputs): continue current_output = end_info.outputs[end_info.cursor] - if current_output.is_variable and node_id in current_output.literal: + if current_output.is_variable and current_output.depends_on_node(node_id): if data.get("done"): end_info.cursor += 1 else: diff --git a/api/app/core/workflow/graph_builder.py b/api/app/core/workflow/graph_builder.py index 3576c576..9fa89fd2 100644 --- a/api/app/core/workflow/graph_builder.py +++ b/api/app/core/workflow/graph_builder.py @@ -53,6 +53,44 @@ class OutputContent(BaseModel): ) ) + def depends_on_node(self, node_id: str) -> bool: + """ + Check if this output segment depends on a specific node's variable. + + This method examines the `literal` of the output segment to see if it + contains a variable placeholder referencing the given node in the form: + + {{ node_id.field_name }} + + It uses a regular expression to match the exact node ID, avoiding + false positives from substring matches (e.g., 'node1' should not match 'node10'). + + Args: + node_id (str): The ID of the node to check for in this segment's variable placeholders. + + Returns: + bool: + - True if the segment contains a variable referencing the given node. + - False otherwise. + + Example: + literal = "{{node1.name}}" + + depends_on_node("node1") -> True + depends_on_node("node2") -> False + + Usage: + This method is primarily used in stream mode to determine whether + a particular variable output segment should be activated when a + specific upstream node completes execution. + """ + variable_pattern = rf"\{{\{{\s*{re.escape(node_id)}\.[a-zA-Z0-9_]+\s*\}}\}}" + pattern = re.compile(variable_pattern) + match = pattern.search(self.literal) + if match: + return True + return False + class StreamOutputConfig(BaseModel): """ @@ -122,7 +160,7 @@ class StreamOutputConfig(BaseModel): for i in range(len(self.outputs)): if ( self.outputs[i].is_variable - and node_id in self.outputs[i].literal + and self.outputs[i].depends_on_node(node_id) ): self.outputs[i].activate = True From 8fb9e779a647d753fecaf25a1b764853449712a2 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 18:52:51 +0800 Subject: [PATCH 101/175] feat(workflow): store token usage in message table --- api/app/services/workflow_service.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/api/app/services/workflow_service.py b/api/app/services/workflow_service.py index f9426c87..2958f4f9 100644 --- a/api/app/services/workflow_service.py +++ b/api/app/services/workflow_service.py @@ -528,7 +528,8 @@ class WorkflowService: self.conversation_service.add_message( conversation_id=conversation_id_uuid, role=message["role"], - content=message["content"] + content=message["content"], + meta_data=None if message["role"] == "user" else {"usage": token_usage} ) logger.info(f"Workflow Run Success, " f"execution_id: {execution.execution_id}, message count: {len(final_messages)}") @@ -678,7 +679,8 @@ class WorkflowService: self.conversation_service.add_message( conversation_id=conversation_id_uuid, role=message["role"], - content=message["content"] + content=message["content"], + meta_data=None if message["role"] == "user" else {"usage": token_usage} ) logger.info(f"Workflow Run Success, " f"execution_id: {execution.execution_id}, message count: {len(final_messages)}") From 3a0f07d36ff0fc9fe51fe5e4adf2ca6e25ecc85b Mon 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z34-o>1l_vd7n8cB9ds(C*@T|JaC6FN9xzeDfSwfP`q6A1IG*2daE9Srw|yOn187~? zFv368KN$~b>Ut@@J^2?+GMvyCkZKIi0qSUCt417tZE{_x+w!T{L-VLp6~(T)DkFz711 z#anY2z`5<|6N|ln;yZ;9( ClSXa; literal 0 HcmV?d00001 diff --git a/web/src/components/Empty/PageEmpty.tsx b/web/src/components/Empty/PageEmpty.tsx new file mode 100644 index 00000000..17926fde --- /dev/null +++ b/web/src/components/Empty/PageEmpty.tsx @@ -0,0 +1,16 @@ +import { useTranslation } from 'react-i18next' +import pageEmptyIcon from '@/assets/images/empty/pageEmpty.png' +import Empty from './index' +const PageEmpty = ({ size = [240, 210] }: { size?: number | number[] }) => { + const { t } = useTranslation() + return ( + + ) +} +export default PageEmpty; \ No newline at end of file From 74f0018962bde6a6e5a9ffc9a6ffdb3e1e376bf7 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 27 Jan 2026 19:17:32 +0800 Subject: [PATCH 103/175] feat(web): add PageTabs component --- web/src/components/PageTabs/index.module.css | 13 +++++++++++++ web/src/components/PageTabs/index.tsx | 18 ++++++++++++++++++ 2 files changed, 31 insertions(+) create mode 100644 web/src/components/PageTabs/index.module.css create mode 100644 web/src/components/PageTabs/index.tsx diff --git a/web/src/components/PageTabs/index.module.css b/web/src/components/PageTabs/index.module.css new file mode 100644 index 00000000..6eab8a48 --- /dev/null +++ b/web/src/components/PageTabs/index.module.css @@ -0,0 +1,13 @@ +.page-tabs:global(.ant-segmented) { + background-color: rgba(91, 97, 103, 0.08); + padding: 4px; +} +.page-tabs:global(.ant-segmented .ant-segmented-item-label) { + line-height: 24px; + min-height: 24px; + padding: 0 12px; +} + +.page-tabs:global(.ant-segmented .ant-segmented-item-selected) { + box-shadow: 0px 2px 4px 0px rgba(33, 35, 50, 0.16); +} \ No newline at end of file diff --git a/web/src/components/PageTabs/index.tsx b/web/src/components/PageTabs/index.tsx new file mode 100644 index 00000000..33f02097 --- /dev/null +++ b/web/src/components/PageTabs/index.tsx @@ -0,0 +1,18 @@ +import { type FC } from 'react'; +import { Segmented, type SegmentedProps } from 'antd'; +import styles from './index.module.css'; + +const PageTabs: FC = ({ + value, + options, + onChange +}) => { + return ; +}; + +export default PageTabs; From c818ba7bc740c9390f004d2f90d8e5d7d2c18934 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Tue, 27 Jan 2026 19:26:50 +0800 Subject: [PATCH 104/175] perf(workflow): make memory configuration backward compatible --- api/app/core/workflow/nodes/memory/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/core/workflow/nodes/memory/config.py b/api/app/core/workflow/nodes/memory/config.py index 853ba882..57ee6dc2 100644 --- a/api/app/core/workflow/nodes/memory/config.py +++ b/api/app/core/workflow/nodes/memory/config.py @@ -10,7 +10,7 @@ class MemoryReadNodeConfig(BaseNodeConfig): ... ) - config_id: UUID = Field( + config_id: UUID | int = Field( ... ) From 75b3ea1f05c2229bcb4fd4c177fe367d6dfe87a0 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 27 Jan 2026 20:07:53 +0800 Subject: [PATCH 105/175] feat(web): update model management --- web/src/api/fileStorage.ts | 25 +++ web/src/api/models.ts | 71 +++++-- web/src/components/RbCard/Card.tsx | 6 +- web/src/components/Upload/UploadImages.tsx | 56 +++--- web/src/i18n/en.ts | 64 +++++- web/src/i18n/zh.ts | 65 +++++- web/src/styles/antdThemeConfig.ts | 5 +- web/src/utils/request.ts | 5 +- web/src/views/MemberManagement/index.tsx | 4 +- web/src/views/ModelManagement/Group.tsx | 97 +++++++++ web/src/views/ModelManagement/List.tsx | 83 ++++++++ web/src/views/ModelManagement/Square.tsx | 94 +++++++++ .../components/ConfigModal.tsx | 171 ---------------- .../components/CustomModelModal.tsx | 162 +++++++++++++++ .../components/GroupModelModal.tsx | 155 +++++++++++++++ .../components/KeyConfigModal.tsx | 92 +++++++++ .../ModelImplement/SubModelModal.tsx | 173 ++++++++++++++++ .../components/ModelImplement/index.tsx | 106 ++++++++++ .../components/ModelImplement/types.ts | 16 ++ .../components/ModelListDetail.tsx | 111 +++++++++++ .../components/ModelSquareDetail.tsx | 86 ++++++++ .../components/MultiKeyConfigModal.tsx | 121 ++++++++++++ web/src/views/ModelManagement/index.tsx | 174 +++++++++------- web/src/views/ModelManagement/types.ts | 186 ++++++++++++------ 24 files changed, 1768 insertions(+), 360 deletions(-) create mode 100644 web/src/api/fileStorage.ts create mode 100644 web/src/views/ModelManagement/Group.tsx create mode 100644 web/src/views/ModelManagement/List.tsx create mode 100644 web/src/views/ModelManagement/Square.tsx delete mode 100644 web/src/views/ModelManagement/components/ConfigModal.tsx create mode 100644 web/src/views/ModelManagement/components/CustomModelModal.tsx create mode 100644 web/src/views/ModelManagement/components/GroupModelModal.tsx create mode 100644 web/src/views/ModelManagement/components/KeyConfigModal.tsx create mode 100644 web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx create mode 100644 web/src/views/ModelManagement/components/ModelImplement/index.tsx create mode 100644 web/src/views/ModelManagement/components/ModelImplement/types.ts create mode 100644 web/src/views/ModelManagement/components/ModelListDetail.tsx create mode 100644 web/src/views/ModelManagement/components/ModelSquareDetail.tsx create mode 100644 web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx diff --git a/web/src/api/fileStorage.ts b/web/src/api/fileStorage.ts new file mode 100644 index 00000000..e7b476a3 --- /dev/null +++ b/web/src/api/fileStorage.ts @@ -0,0 +1,25 @@ +import { request, API_PREFIX } from '@/utils/request' + +// Upload file，file storage has expiration period +export const fileUploadUrl = `${API_PREFIX}/storage/files` +export const fileUpload = (formData?: unknown) => { + return request.uploadFile('/storage/files', formData) +} + +// Get file access URL (no token required) +export const getFileUrl = (file_id: string) => `/storage/files/${file_id}/url` +export const getFileLink = (fileId: string, data: { permanent?: boolean } = { permanent: true }) => { + return request.get(getFileUrl(fileId), data) +} + +// Get file internally +export const getInternalFileUrl = (file_id: string) => `/storage/files/${file_id}` +export const getInternalFile = (fileId: string) => { + return request.get(getInternalFileUrl(fileId)) +} + +// Delete file +export const deleteFileUrl = (file_id: string) => `/storage/files/${file_id}` +export const deleteFile = (fileId: string) => { + return request.delete(deleteFileUrl(fileId)) +} diff --git a/web/src/api/models.ts b/web/src/api/models.ts index 20fdf91a..f8619e43 100644 --- a/web/src/api/models.ts +++ b/web/src/api/models.ts @@ -1,23 +1,68 @@ import { request } from '@/utils/request' -import type { ModelFormData } from '@/views/ModelManagement/types' +import type { MultiKeyForm, Query, KeyConfigModalForm, CompositeModelForm, CustomModelForm } from '@/views/ModelManagement/types' -// 模型列表 +// Model list export const getModelListUrl = '/models' -export const getModelList = (data: { type: string; pagesize: number; page: number; }) => { +export const getModelList = (data: Query) => { return request.get(getModelListUrl, data) } -// 创建模型 -export const addModel = (data: ModelFormData) => { - return request.post('/models', data) -} -// 更新模型 -export const updateModel = (apiKeyId: string, data: ModelFormData) => { - return request.put(`/models/apikeys/${apiKeyId}`, data) -} -// 模型类型列表 +// Model type list export const modelTypeUrl = '/models/type' -// 模型供应商列表 +// Model provider list export const modelProviderUrl = '/models/provider' export const getModelProviderList = () => { return request.get(modelProviderUrl) +} +// New model list +export const getModelNewListUrl = '/models/new' +export const getModelNewList = (data: Query) => { + return request.get(getModelNewListUrl, data) +} +// Get model information +export const getModelInfo = (model_id: string) => { + return request.get(`/models/${model_id}`) +} +// Create composite model +export const addCompositeModel = (data: CompositeModelForm) => { + return request.post('/models/composite', data) +} +// Update composite model +export const updateCompositeModel = (model_id: string, data: CompositeModelForm) => { + return request.put(`/models/composite/${model_id}`, data) +} +// Delete composite model +export const deleteCompositeModel = (model_id: string) => { + return request.delete(`/models/composite/${model_id}`) +} +// Create API keys for all matching models by provider +export const updateProviderApiKeys = (data: KeyConfigModalForm) => { + return request.post('/models/provider/apikeys', data) +} +// Create model API key +export const addModelApiKey = (model_id: string, data: MultiKeyForm) => { + return request.post(`/models/${model_id}/apikeys`, data) +} +// Delete model API key +export const delteModelApiKey = (api_key_id: string) => { + return request.delete(`/models/apikeys/${api_key_id}`) +} +// Update model status +export const updateModelStatus = (model_id: string, data: { is_active: boolean; }) => { + return request.put(`/models/${model_id}`, data) +} +// Model plaza list +export const getModelPlaza = (data: { search?: string; provider?: string; }) => { + return request.get('/models/model_plaza', data) +} +// Add model to plaza +export const addModelPlaza = (model_base_id: string) => { + return request.post(`/models/model_plaza/${model_base_id}/add`) +} +// Create custom model +export const addCustomModel = (data: CustomModelForm) => { + return request.post('/models/model_plaza', data) +} +// Update custom model +export const updateCustomModel = (model_base_id: string, data: CustomModelForm) => { + return request.put(`/models/model_plaza/${model_base_id}`, data) } \ No newline at end of file diff --git a/web/src/components/RbCard/Card.tsx b/web/src/components/RbCard/Card.tsx index f86b1c60..eadd2916 100644 --- a/web/src/components/RbCard/Card.tsx +++ b/web/src/components/RbCard/Card.tsx @@ -1,5 +1,5 @@ import { type FC, type ReactNode } from 'react' -import { Card } from 'antd'; +import { Card, Tooltip } from 'antd'; import clsx from 'clsx'; interface RbCardProps { @@ -9,7 +9,7 @@ interface RbCardProps { extra?: ReactNode; children?: ReactNode; avatar?: ReactNode; - avatarUrl?: string; + avatarUrl?: string | null; bodyPadding?: string; bodyClassName?: string; headerType?: 'border' | 'borderless' | 'borderBL' | 'borderL'; @@ -63,7 +63,7 @@ const RbCard: FC = ({ } ) }> -
      {title}
      +
      {title}
       {subTitle &&
      {subTitle}
      }
    • : null diff --git a/web/src/components/Upload/UploadImages.tsx b/web/src/components/Upload/UploadImages.tsx index 2006ea09..77291e92 100644 --- a/web/src/components/Upload/UploadImages.tsx +++ b/web/src/components/Upload/UploadImages.tsx @@ -1,13 +1,12 @@ import { useState, useEffect, forwardRef, useImperativeHandle } from 'react'; -import { Upload, Modal, Image, App } from 'antd'; +import { Upload, Image, App } from 'antd'; import type { GetProp, UploadFile, UploadProps } from 'antd'; // import { UploadOutlined, } from '@ant-design/icons'; import type { UploadProps as RcUploadProps } from 'antd/es/upload/interface'; import { useTranslation } from 'react-i18next'; import PlusIcon from '@/assets/images/plus.svg' import { cookieUtils } from '@/utils/request' - -const { confirm } = Modal; +import { fileUploadUrl } from '@/api/fileStorage' interface UploadImagesProps extends Omit { /** 上传接口地址 */ @@ -17,7 +16,7 @@ interface UploadImagesProps extends Omit { /** 已上传的文件列表 */ fileList?: UploadFile[]; /** 文件列表变化回调 */ - onChange?: (fileList: UploadFile[]) => void; + onChange?: (fileList?: UploadFile[] | UploadFile) => void; /** 禁用上传 */ disabled?: boolean; /** 文件大小限制（MB） */ @@ -28,6 +27,7 @@ interface UploadImagesProps extends Omit { isAutoUpload?: boolean; /** 最大上传文件数 */ maxCount?: number; + className?: string; } const ALL_FILE_TYPE: { [key: string]: string; @@ -59,7 +59,7 @@ const getBase64 = (file: FileType): Promise => { * 支持单文件/多文件上传、拖拽上传、文件验证、预览等功能 */ const UploadImages = forwardRef(({ - action = '/api/upload', + action = fileUploadUrl, multiple = false, fileList: propFileList = [], onChange, @@ -68,27 +68,36 @@ const UploadImages = forwardRef(({ fileType = ['png', 'jpg', 'gif'], isAutoUpload = true, maxCount = 1, + className = 'rb:size-24! rb:leading-1!', ...props }, ref) => { const { t } = useTranslation(); - const { message } = App.useApp() + const { message, modal } = App.useApp() const [fileList, setFileList] = useState(propFileList); const [accept, setAccept] = useState(); // const [loading, setLoading] = useState(false); const [previewOpen, setPreviewOpen] = useState(false); const [previewImage, setPreviewImage] = useState(''); + const updateValue = (list: UploadFile[]) => { + if (maxCount === 1) { + onChange?.(list[0]) + } else { + onChange?.(list) + } + } + // 处理文件移除 const handleRemove = (file: UploadFile) => { - confirm({ - title: '确定要删除此文件吗？', - okText: '确定', + modal.confirm({ + title: t('common.confirmRemoveFile'), + okText: `${t('common.confirm')}`, okType: 'danger', - cancelText: '取消', + cancelText: `${t('common.cancel')}`, onOk: () => { const newFileList = fileList.filter((item) => item.uid !== file.uid); setFileList(newFileList); - onChange?.(newFileList); + updateValue(newFileList) }, }); return false; // 阻止默认删除行为，由confirm控制 @@ -100,7 +109,7 @@ const UploadImages = forwardRef(({ if (fileSize && file.size) { const isLtMaxSize = (file.size / 1024 / 1024) < fileSize; if (!isLtMaxSize) { - message.error(`文件大小不能超过 ${fileSize}MB`); + message.error(t('common.fileSizeTip', { size: fileSize })); return Upload.LIST_IGNORE; } } @@ -108,7 +117,7 @@ const UploadImages = forwardRef(({ if (accept && accept.length > 0 && file.type) { const isAccept = accept.includes(file.type); if (!isAccept) { - message.error(`不支持的文件类型: ${file.type}`); + message.error(`${t('common.fileAcceptTip')}${file.type}`); return Upload.LIST_IGNORE; } } @@ -119,7 +128,7 @@ const UploadImages = forwardRef(({ } const newFileList = [...fileList, file]; setFileList(newFileList); - onChange?.(newFileList); + updateValue(newFileList); return Upload.LIST_IGNORE; // 阻止自动上传 } @@ -129,17 +138,13 @@ const UploadImages = forwardRef(({ // 处理上传状态变化 const handleChange: UploadProps['onChange'] = ({ fileList: newFileList }) => { setFileList(newFileList); - if (onChange) { - onChange(newFileList); - } + updateValue(newFileList); }; // 清空已上传文件 const clearFiles = () => { setFileList([]); - if (onChange) { - onChange([]); - } + updateValue([]); } const handlePreview = async (file: UploadFile) => { @@ -167,7 +172,7 @@ const UploadImages = forwardRef(({ fileList, beforeUpload, headers: { - authorization: cookieUtils.get('authToken') || '', + authorization: `Bearer ${cookieUtils.get('authToken') }`, }, onPreview: handlePreview, onRemove: handleRemove, @@ -193,16 +198,9 @@ const UploadImages = forwardRef(({ <> {fileList.length < maxCount && ( -
    - -
    {t('common.clickUploadIcon')}
    -
    + )}     {previewImage && ( diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 1df2eb6d..aee68114 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -419,6 +419,9 @@ export const en = { statusEnabled: 'Available', statusDisabled: 'Unavailable', remove: 'Remove', + + fileSizeTip: 'File size cannot exceed {{size}}MB', + fileAcceptTip: 'Unsupported file type:' }, model: { searchPlaceholder: 'search model…', @@ -510,6 +513,59 @@ export const en = { gpustack: "Gpustack", bedrock: "Bedrock" }, + modelNew: { + group: 'Model Group', + list: 'Model List', + square: 'Model Plaza', + createGroupModel: 'Create Model Group', + groupSearchPlaceholder: 'Search model groups', + listSearchPlaceholder: 'Search available models', + squareSearchPlaceholder: 'Search platform models', + status: 'Model Status', + created_at: 'Created At', + configureBtn: 'Click to Configure', + showModel: 'Show Model', + keyConfig: 'Configure KEY', + + modelConfiguration: 'Model Configuration', + logo: 'Model LOGO', + name: 'Model Name', + type: 'Model Type', + modelImplement: 'Model Implementation', + addImplement: 'Add Implementation', + noAuth: 'Unauthorized (Limited to 1 implementation)', + implementConfig: 'Configure Model Implementation', + provider: 'Model Provider', + api_key_ids: 'Select Model', + viewAll: 'More', + modelCount: 'Total {{count}} models', + modelList: 'Model List', + added: ' Added', + addSuccess: 'Added successfully', + model_name: 'Model Name', + tags: 'Tags', + createCustomModel: 'Add Custom Model', + edit: 'Edit', + selectOneTip: 'Model API KEY not configured, please configure in Model Plaza first', + + api_key: 'API KEY', + api_base: 'API Base URL', + description: 'Description', + add: 'Add', + item: 'item', + apiKeyNum: ' API Keys', + + llm: 'LLM', + chat: 'Chat', + embedding: 'Embedding', + rerank: 'Rerank', + openai: "Openai", + dashscope: "Dashscope", + ollama: "Ollama", + xinference: "Xinference", + gpustack: "Gpustack", + bedrock: "Bedrock" + }, knowledgeBase: { pleaseUploadFileFirst: 'Please upload file first', shareSuccess: 'Share successfully', @@ -866,7 +922,7 @@ export const en = { minimumRetention: 'Minimum retention (λ_time)', minimumRetentionDesc: 'Controls the minimum retention threshold of memory retention', - forgettingRate: 'Forgetting rate (λ_mem)', + forgettingRate: 'Forgetting rate (λ_mem)', forgettingRateDesc: 'Control the speed of memory forgetting, the higher the value, the faster the forgetting', offset: 'Offset (offset)', offsetDesc: 'The offset of the minimum preservation degree', @@ -934,7 +990,7 @@ export const en = { number: 'Number', checkbox: 'Checkbox', apiVariable: 'API Variable', - + displayName: 'Display Name', maxLength: 'Max Length', required: 'Required', @@ -1534,7 +1590,9 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re noPermissionDesc: ' Please contact the administrator to grant permission', tableEmpty: 'No data available.', loadingEmpty: 'The content is loading…', - loadingEmptyDesc: 'Your content is on its way by rocket! It will soon land on your screen' + loadingEmptyDesc: 'Your content is on its way by rocket! It will soon land on your screen', + pageEmpty: 'Oops！ No search results available at the moment', + pageEmptyDesc: "Red Bear tilts its head and waits for you to change a new keyword, let's explore together.", }, apiKey: { name: 'Project Name', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 39908757..78fe948a 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -967,6 +967,9 @@ export const zh = { statusEnabled: '可用', statusDisabled: '不可用', remove: '删除', + + fileSizeTip: '文件大小不能超过 {{size}}MB', + fileAcceptTip: '不支持的文件类型:' }, product: { applicationManagement: '应用管理', @@ -1076,6 +1079,59 @@ export const zh = { gpustack: "Gpustack", bedrock: "Bedrock" }, + modelNew: { + group: '模型组合', + list: '模型列表', + square: '模型广场', + createGroupModel: '创建模型组合', + groupSearchPlaceholder: '搜索模型组合', + listSearchPlaceholder: '搜索可用模型', + squareSearchPlaceholder: '搜索平台模型', + status: '模型状态', + created_at: '创建时间', + configureBtn: '点击配置', + showModel: '显示模型', + keyConfig: '配置 KEY', + + modelConfiguration: '模型配置', + logo: '模型LOGO', + name: '模型名称', + type: '模型类型', + modelImplement: '模型实现', + addImplement: '添加实现', + noAuth: '未授权(限1个实现)', + implementConfig: '配置模型实现', + provider: '模型供应商', + api_key_ids: '选择模型', + viewAll: '更多', + modelCount: '共 {{count}} 个模型', + modelList: '模型列表', + added: ' 已添加', + addSuccess: '添加成功', + model_name: '模型名称', + tags: '标签', + createCustomModel: '添加自定义模型', + edit: '编辑', + selectOneTip: '模型未配置API KEY，请先在模型广场配置', + + api_key: 'API KEY', + api_base: 'API Base URL', + description: '描述', + add: '添加', + item: '个', + apiKeyNum: '个 API Key', + + llm: 'LLM', + chat: 'Chat', + embedding: 'Embedding', + rerank: 'Rerank', + openai: "Openai", + dashscope: "Dashscope", + ollama: "Ollama", + xinference: "Xinference", + gpustack: "Gpustack", + bedrock: "Bedrock" + }, timezones: { 'Asia/Shanghai': '中国标准时间 (UTC+8)', 'Asia/Kolkata': '印度标准时间 (UTC+5:30)', @@ -1607,13 +1663,10 @@ export const zh = { noPermissionDesc: '请联系管理员授予权限', tableEmpty: '目前没有数据', loadingEmpty: '内容正在加载中…', - loadingEmptyDesc: '您的内容正在火箭运输中！很快就会降落在您的屏幕上' + loadingEmptyDesc: '您的内容正在火箭运输中！很快就会降落在您的屏幕上', + pageEmpty: '哎呀！暂无搜索结果', + pageEmptyDesc: '红熊歪着头等待您更换新的关键词，让我们一起探索吧。', }, - count: '计数: {{count}}', - increment: '增加', - decrement: '减少', - reset: '重置', - switchLanguage: '切换语言', home: { title: '首页', diff --git a/web/src/styles/antdThemeConfig.ts b/web/src/styles/antdThemeConfig.ts index db1166fb..1d281730 100644 --- a/web/src/styles/antdThemeConfig.ts +++ b/web/src/styles/antdThemeConfig.ts @@ -22,7 +22,7 @@ export const lightTheme: ThemeConfig = { // colorBgContainer: '#FBFDFF', colorError: '#FF5D34', sizeSM: 12, - fontSizeSM: 12, + fontSizeSM: 12, }, components: { Layout: { @@ -105,6 +105,9 @@ export const lightTheme: ThemeConfig = { }, Select: { lineHeightSM: 26 + }, + Upload: { + pictureCardSize: 96, } } }; \ No newline at end of file diff --git a/web/src/utils/request.ts b/web/src/utils/request.ts index 479fc1f3..e7112ded 100644 --- a/web/src/utils/request.ts +++ b/web/src/utils/request.ts @@ -23,9 +23,10 @@ interface data { } +export const API_PREFIX = '/api' // 创建axios实例 const service = axios.create({ - baseURL: '/api', // 与vite.config.ts中的代理配置对应 + baseURL: API_PREFIX, // 与vite.config.ts中的代理配置对应 // timeout: 10000, // 请求超时时间 withCredentials: false, headers: { @@ -126,7 +127,7 @@ service.interceptors.response.use( if (axios.isCancel(error) || error.name === 'AbortError' || error.code === 'ERR_CANCELED') { return Promise.reject(error); } - + // 处理网络错误、超时等 let msg = error.response?.data?.error || error.response?.error; const status = error?.response ? error.response.status : error; diff --git a/web/src/views/MemberManagement/index.tsx b/web/src/views/MemberManagement/index.tsx index 8ce2fc62..68c90410 100644 --- a/web/src/views/MemberManagement/index.tsx +++ b/web/src/views/MemberManagement/index.tsx @@ -39,7 +39,7 @@ const MemberManagement: React.FC = () => { onOk: () => { deleteMember(member.id) .then(() => { - message.success(t('member.deleteSuccess')); + message.success(t('common.deleteSuccess')); refreshTable(); }) } @@ -93,7 +93,7 @@ const MemberManagement: React.FC = () => { return ( <> -
    +
    diff --git a/web/src/views/ModelManagement/Group.tsx b/web/src/views/ModelManagement/Group.tsx new file mode 100644 index 00000000..311455b4 --- /dev/null +++ b/web/src/views/ModelManagement/Group.tsx @@ -0,0 +1,97 @@ +import { useState, useEffect, forwardRef, useImperativeHandle } from 'react'; +import clsx from 'clsx' +import { Button } from 'antd' +import { useTranslation } from 'react-i18next'; + +import type { ProviderModelItem, ModelListItem, DescriptionItem, BaseRef } from './types' +import RbCard from '@/components/RbCard/Card' +import { getModelNewList } from '@/api/models' +import PageEmpty from '@/components/Empty/PageEmpty'; +import { formatDateTime } from '@/utils/format'; + +const Group = forwardRef void; }>(({ query, handleEdit }, ref) => { + const { t } = useTranslation(); + const [list, setList] = useState([]) + useEffect(() => { + getList() + }, [query]) + const getList = () => { + getModelNewList({ + ...query, + is_composite: true, + is_active: true, + }) + .then(res => { + const response = res as ProviderModelItem[] + setList(response[0]?.models || []) + }) + } + const formatData = (data: ModelListItem) => { + return [ + { + key: 'type', + label: t(`modelNew.type`), + children: data.type || '-', + }, + { + key: 'provider', + label: t(`modelNew.provider`), + children: data.provider || '-', + }, + { + key: 'is_active', + label: t(`modelNew.status`), + children: data.is_active ? t(`common.statusEnabled`) : t(`common.statusDisabled`), + }, + { + key: 'created_at', + label: t(`modelNew.created_at`), + children: data.created_at ? formatDateTime(data.created_at, 'YYYY-MM-DD HH:mm:ss') : '-', + }, + ] + } + + useImperativeHandle(ref, () => ({ + getList, + })); + + return ( + <> + {list.length === 0 + ? + :( +
    + {list.map(item => ( + + {item.name[0]} +
    + } + > + {formatData(item)?.map((description: DescriptionItem) => ( +
    + {(description.label as string)} + {(description.children as string)} +
    + ))} + + + ))} +
    + ) + } + + ) +}) + +export default Group \ No newline at end of file diff --git a/web/src/views/ModelManagement/List.tsx b/web/src/views/ModelManagement/List.tsx new file mode 100644 index 00000000..f1127623 --- /dev/null +++ b/web/src/views/ModelManagement/List.tsx @@ -0,0 +1,83 @@ +import { useRef, useState, useEffect, type FC } from 'react'; +import { Button, Space, Row, Col } from 'antd' +import { useTranslation } from 'react-i18next'; + +import type { ProviderModelItem, KeyConfigModalRef, ModelListDetailRef } from './types' +import RbCard from '@/components/RbCard/Card' +import { getModelNewList } from '@/api/models' +import PageEmpty from '@/components/Empty/PageEmpty'; +import Tag from '@/components/Tag'; +import KeyConfigModal from './components/KeyConfigModal' +import ModelListDetail from './components/ModelListDetail' + +const ModelList: FC<{ query: any }> = ({ query }) => { + const { t } = useTranslation(); + const keyConfigModalRef = useRef(null) + const modelListDetailRef = useRef(null) + const [list, setList] = useState([]) + useEffect(() => { + getList() + }, [query]) + const getList = () => { + getModelNewList({ + ...query, + is_composite: false, + is_active: true, + }) + .then(res => { + setList((res || []) as ProviderModelItem[]) + }) + } + + const handleShowModel = (vo: ProviderModelItem) => { + modelListDetailRef.current?.handleOpen(vo) + } + const handleKeyConfig = (vo: ProviderModelItem) => { + keyConfigModalRef.current?.handleOpen(vo) + } + + return ( + <> + {list.length === 0 + ? + :( +
    + {list.map(item => ( + + {item.provider[0]} +
    + } + > + {item.tags.map(tag => {t(`modelNew.${tag}`)})} + + + + + + + + + + ))} +
    + ) + } + + + + + ) +} + +export default ModelList \ No newline at end of file diff --git a/web/src/views/ModelManagement/Square.tsx b/web/src/views/ModelManagement/Square.tsx new file mode 100644 index 00000000..8b69140b --- /dev/null +++ b/web/src/views/ModelManagement/Square.tsx @@ -0,0 +1,94 @@ +import { useRef, useState, useEffect, forwardRef, useImperativeHandle } from 'react'; +import { Button, Space, App, Divider, Flex } from 'antd' +import { UsergroupAddOutlined } from '@ant-design/icons'; +import { useTranslation } from 'react-i18next'; + +import type { ModelPlaza, ModelPlazaItem, ModelSquareDetailRef, BaseRef } from './types' +import RbCard from '@/components/RbCard/Card' +import { getModelPlaza, addModelPlaza } from '@/api/models' +import PageEmpty from '@/components/Empty/PageEmpty'; +import Tag from '@/components/Tag'; +import ModelSquareDetail from './components/ModelSquareDetail' + +const ModelSquare = forwardRef void; }>(({ query, handleEdit }, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp() + const modelSquareDetailRef = useRef(null) + const [list, setList] = useState([]) + useEffect(() => { + getList() + }, [query]) + const getList = () => { + getModelPlaza(query) + .then(res => { + setList((res as ModelPlaza[]) || []) + }) + } + + const handleMore = (vo: ModelPlaza) => { + modelSquareDetailRef.current?.handleOpen(vo) + } + const handleAdd = (item: ModelPlazaItem) => { + addModelPlaza(item.id) + .then(() => { + message.success(`${item.name}${t('modelNew.addSuccess')}`) + getList() + }) + } + + useImperativeHandle(ref, () => ({ + getList, + })); + return ( + <> + {list.length === 0 + ? + : list.map(vo => ( +
    +
    +
    {vo.provider}
    + +
    + +
    + {vo.models.slice(0, 6).map(item => ( + + {item.name[0]} +
    + } + > + {t(`modelNew.${item.type}`)} +
    {item.description}
    + {item.tags.map((tag, tagIndex) => {tag})} + + + {item.add_count} + + {!item.is_official && } + {item.is_added + ? + : + } + + + + ))} +
    +
    + )) + } + + + + ) +}) + +export default ModelSquare \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/ConfigModal.tsx b/web/src/views/ModelManagement/components/ConfigModal.tsx deleted file mode 100644 index e4bdf84c..00000000 --- a/web/src/views/ModelManagement/components/ConfigModal.tsx +++ /dev/null @@ -1,171 +0,0 @@ -import { forwardRef, useImperativeHandle, useState } from 'react'; -import { Form, Input, App } from 'antd'; -import { useTranslation } from 'react-i18next'; -import type { ModelFormData, Model, ConfigModalRef, ConfigModalProps } from '../types'; -import RbModal from '@/components/RbModal' -import CustomSelect from '@/components/CustomSelect' -import { updateModel, addModel, modelTypeUrl, modelProviderUrl } from '@/api/models' - -const ConfigModal = forwardRef(({ - refresh -}, ref) => { - const { t } = useTranslation(); - const { message } = App.useApp(); - const [visible, setVisible] = useState(false); - const [model, setModel] = useState({} as Model); - const [isEdit, setIsEdit] = useState(false); - const [form] = Form.useForm(); - const [loading, setLoading] = useState(false) - - const values = Form.useWatch([], form); - - // 封装取消方法，添加关闭弹窗逻辑 - const handleClose = () => { - setModel({} as Model); - form.resetFields(); - setLoading(false) - setVisible(false); - }; - - const handleOpen = (model?: Model) => { - if (model) { - setIsEdit(true); - setModel(model); - // 设置表单值 - const apiKeyInfo = model.api_keys[0] - form.setFieldsValue({ - provider: apiKeyInfo.provider, - model_name: apiKeyInfo.model_name, - api_key: apiKeyInfo.api_key, - api_base: apiKeyInfo.api_base - }); - } else { - setIsEdit(false); - form.resetFields(); - } - setVisible(true); - }; - // 封装保存方法，添加提交逻辑 - const handleSave = () => { - form - .validateFields() - .then(() => { - const data = { - name: values.name, - type: values.type, - api_keys: { - provider: values.provider, - model_name: values.model_name, - api_key: values.api_key, - api_base: values.api_base - }, - } - setLoading(true) - const res = isEdit - ? updateModel(model.api_keys[0].id, { - provider: values.provider, - model_name: values.model_name, - api_key: values.api_key, - api_base: values.api_base - } as ModelFormData) - : addModel(data as ModelFormData) - - res.then(() => { - if (refresh) { - refresh(); - } - handleClose() - message.success(isEdit ? t('common.updateSuccess') : t('common.createSuccess')) - }) - .catch(() => { - setLoading(false) - }); - }) - .catch((err) => { - console.log('err', err) - }); - } - - // 暴露给父组件的方法 - useImperativeHandle(ref, () => ({ - handleOpen, - handleClose - })); - - return ( - - - {!isEdit && ( - <> - - - - - items.map((item) => ({ label: t(`model.${item}`), value: item }))} - /> - - - )} - - - - items.map((item) => ({ label: t(`model.${item}`), value: item }))} - /> - - - - - - - - - - - - - - - ); -}); - -export default ConfigModal; \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/CustomModelModal.tsx b/web/src/views/ModelManagement/components/CustomModelModal.tsx new file mode 100644 index 00000000..df6693f7 --- /dev/null +++ b/web/src/views/ModelManagement/components/CustomModelModal.tsx @@ -0,0 +1,162 @@ +import { forwardRef, useImperativeHandle, useState } from 'react'; +import { Form, Input, App, Select } from 'antd'; +import { useTranslation } from 'react-i18next'; + +import type { CustomModelForm, ModelPlazaItem, CustomModelModalRef, CustomModelModalProps } from '../types'; +import RbModal from '@/components/RbModal' +import CustomSelect from '@/components/CustomSelect' +import UploadImages from '@/components/Upload/UploadImages' +import { updateCustomModel, addCustomModel, modelTypeUrl, modelProviderUrl } from '@/api/models' +import { getFileLink } from '@/api/fileStorage' + +const CustomModelModal = forwardRef(({ + refresh +}, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp(); + const [visible, setVisible] = useState(false); + const [model, setModel] = useState({} as ModelPlazaItem); + const [isEdit, setIsEdit] = useState(false); + const [form] = Form.useForm(); + const [loading, setLoading] = useState(false) + const formValues = Form.useWatch([], form) + + const handleClose = () => { + setModel({} as ModelPlazaItem); + form.resetFields(); + setLoading(false) + setVisible(false); + }; + + const handleOpen = (model?: ModelPlazaItem) => { + if (model) { + setIsEdit(true); + setModel(model); + form.setFieldsValue({ + ...model, + }); + } else { + setIsEdit(false); + form.resetFields(); + } + setVisible(true); + }; + const handleSave = () => { + form + .validateFields() + .then((values) => { + setLoading(true) + values.is_official = false; + const logo = values.logo as any; + if (typeof logo === 'object') { + getFileLink(logo?.response?.data.file_id).then(res => { + const logoRes = res as { url: string } + values.logo = logoRes.url.replace('http://127.0.0.1:8000', 'https://devmemorybear.redbearai.com') + addCustomModel(values).then(() => { + if (refresh) { + refresh(); + } + handleClose() + message.success(isEdit ? t('common.updateSuccess') : t('common.createSuccess')) + }) + .catch(() => { + setLoading(false) + }); + }) + } else { + updateCustomModel(model.id, values).then(() => { + if (refresh) { + refresh(); + } + handleClose() + message.success(isEdit ? t('common.updateSuccess') : t('common.createSuccess')) + }) + .catch(() => { + setLoading(false) + }); + } + }) + .catch((err) => { + console.log('err', err) + }); + } + + useImperativeHandle(ref, () => ({ + handleOpen, + })); + + console.log('formValues', formValues) + + return ( + +
    + {!isEdit && + + } + + + + + + items.map((item) => ({ label: t(`modelNew.${item}`), value: String(item) }))} + /> + + + + items.map((item) => ({ label: t(`modelNew.${item}`), value: String(item) }))} + /> + + + + + + + + + + + items.map((item) => ({ + label: t(`modelNew.${typeof item === 'object' ? item.value : item}`), + value: typeof item === 'object' ? item.value : item + }))} + disabled={isEdit} + /> + + + + + + + + + +
    +     + ); +}); + +export default GroupModelModal; \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/KeyConfigModal.tsx b/web/src/views/ModelManagement/components/KeyConfigModal.tsx new file mode 100644 index 00000000..d157dde7 --- /dev/null +++ b/web/src/views/ModelManagement/components/KeyConfigModal.tsx @@ -0,0 +1,92 @@ +import { forwardRef, useImperativeHandle, useState } from 'react'; +import { Form, Input, App } from 'antd'; +import { useTranslation } from 'react-i18next'; +import type { KeyConfigModalForm, ProviderModelItem, KeyConfigModalRef, KeyConfigModalProps } from '../types'; +import RbModal from '@/components/RbModal' +import { updateProviderApiKeys } from '@/api/models' + +const KeyConfigModal = forwardRef(({ + refresh +}, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp(); + const [visible, setVisible] = useState(false); + const [model, setModel] = useState({} as ProviderModelItem); + const [form] = Form.useForm(); + const [loading, setLoading] = useState(false) + + const handleClose = () => { + setModel({} as ProviderModelItem); + form.resetFields(); + setLoading(false) + setVisible(false); + }; + + const handleOpen = (vo: ProviderModelItem) => { + setVisible(true); + setModel(vo); + }; + const handleSave = () => { + form + .validateFields() + .then((values) => { + setLoading(true) + + updateProviderApiKeys({ + ...values, + provider: model.provider + }).then(() => { + if (refresh) { + refresh(); + } + handleClose() + message.success(t('common.updateSuccess')) + }) + .catch(() => { + setLoading(false) + }); + }) + .catch((err) => { + console.log('err', err) + }); + } + + useImperativeHandle(ref, () => ({ + handleOpen, + handleClose + })); + + return ( + +
    + + + + + + + +
    +     + ); +}); + +export default KeyConfigModal; \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx b/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx new file mode 100644 index 00000000..cfe3f090 --- /dev/null +++ b/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx @@ -0,0 +1,173 @@ +import { forwardRef, useImperativeHandle, useState } from 'react'; +import { Form, Cascader, App } from 'antd'; +import { useTranslation } from 'react-i18next'; + +import type { SubModelModalForm, SubModelModalRef, SubModelModalProps, ModelList } from './types'; +import RbModal from '@/components/RbModal' +import CustomSelect from '@/components/CustomSelect' +import { modelProviderUrl, getModelNewList } from '@/api/models' +import type { ProviderModelItem } from '../../types' + +const { SHOW_CHILD } = Cascader; + +interface Option { + value: string | number; + label: string; + children?: Option[]; + [key: string]: any; +} +const SubModelModal = forwardRef(({ + refresh, + type +}, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp() + const [visible, setVisible] = useState(false); + const [form] = Form.useForm(); + const [loading, setLoading] = useState(false) + const [selecteds, setSelecteds] = useState([]) + const [modelList, setModelList] = useState([]) + + // 封装取消方法，添加关闭弹窗逻辑 + const handleClose = () => { + form.resetFields(); + setLoading(false) + setVisible(false); + setSelecteds([]) + }; + + const handleOpen = (list?: ModelList[], provider?: string) => { + if (list?.length && provider) { + const initialValue: SubModelModalForm = { + provider, + api_key_ids: list.map(vo => { + return [vo.model_config_ids[0], vo.id] + }) + } + + form.setFieldsValue(initialValue); + handleChangeProvider(provider, initialValue.api_key_ids) + } else { + form.resetFields() + } + setVisible(true); + }; + // 封装保存方法，添加提交逻辑 + const handleSave = () => { + form + .validateFields() + .then((values) => { + console.log('SubModelModal values', values, selecteds, selecteds.map(vo => ({ + ...vo[0], + model_name: vo[0].name, + model_config_ids: [vo[0].id], + id: vo[1].value + }))) + refresh?.(selecteds.map(vo => ({ + ...vo[0], + model_name: vo[0].name, + model_config_ids: [vo[0].id], + id: vo[1].value + }))) + handleClose() + }) + } + const handleChange = (value: (string | number)[][], selectedOptions: Option[][]) => { + const filterList = selectedOptions.filter(vo => vo.length === 1).map(item => item[0]) + const lastFilterLit = value.filter(vo => vo.length !== 1) + console.log('onchange', value, lastFilterLit, selectedOptions, filterList) + if (filterList.length) { + message.warning(`【${filterList.map(vo => vo.label)}】${t('modelNew.selectOneTip')}`) + form.setFieldValue('api_key_ids', lastFilterLit) + } + setSelecteds(selectedOptions) + } + + const handleChangeProvider = (provider: string, api_key_ids?: any[]) => { + form.setFieldValue('api_key_ids', undefined) + getModelNewList({ + provider: provider, + is_composite: false, + is_active: true, + type + }) + .then(res => { + const response = res as ProviderModelItem[] + const list = response[0]?.models || [] + setModelList(list.map(vo => { + const children = vo.api_keys.map(item => ({ + label: item.api_key, + value: item.id, + })) + return { + ...vo, + label: vo.name, + value: vo.id, + children: children + } + })) + + if (api_key_ids?.length) { + form.setFieldsValue({ + api_key_ids: api_key_ids + }) + } + }) + } + + // 暴露给父组件的方法 + useImperativeHandle(ref, () => ({ + handleOpen, + })); + + return ( + +
    + + items.map((item) => ({ + label: t(`modelNew.${typeof item === 'object' ? item.value : item}`), + value: typeof item === 'object' ? item.value : item + }))} + onChange={(value) => handleChangeProvider(value)} + /> + + + + +
    +     + ); +}); + +export default SubModelModal; \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/ModelImplement/index.tsx b/web/src/views/ModelManagement/components/ModelImplement/index.tsx new file mode 100644 index 00000000..a876587d --- /dev/null +++ b/web/src/views/ModelManagement/components/ModelImplement/index.tsx @@ -0,0 +1,106 @@ +import { type FC, useRef } from "react"; +import { useTranslation } from 'react-i18next'; +import { Flex, Button, Space, App } from 'antd' + +import type { SubModelModalRef, ModelList } from './types' +import SubModelModal from './SubModelModal' +import Empty from '@/components/Empty' +import Tag from '@/components/Tag' + +interface ModelImplementProps { + type?: string; + value?: any; + onChange?: (value: any) => void; +} +const ModelImplement: FC = ({ type, value, onChange }) => { + const { t } = useTranslation(); + const { modal, message } = App.useApp(); + const subModelModalRef = useRef(null) + + const handleAdd = () => { + if (!type || type.trim() === '') { + message.warning(t('common.selectPlaceholder', { title: t('modelNew.type') })) + return + } + subModelModalRef.current?.handleOpen() + } + const handleEdit = (list: ModelList[], provider: string ) => { + subModelModalRef.current?.handleOpen(list, provider) + } + const handleDelete = (provider: string) => { + modal.confirm({ + title: t('common.confirmDeleteDesc', { name: provider }), + content: t('application.apiKeyDeleteContent'), + okText: t('common.delete'), + cancelText: t('common.cancel'), + okType: 'danger', + onOk: () => { + onChange?.(value?.filter((item: any) => item.provider !== provider)) + } + }) + } + const handleRefresh = (list: ModelList[]) => { + const existingModels = value || []; + let updatedModels = [...existingModels]; + + const provider = list[0].provider + + updatedModels = updatedModels.filter(item => item.provider !== provider) + updatedModels = [...updatedModels, ...list] + + onChange?.([...updatedModels]); + } + + const groupedByProvider: Record = (value || []).reduce((acc: Record, item: ModelList) => { + const provider = item.provider || 'unknown'; + if (!acc[provider]) acc[provider] = []; + acc[provider].push(item); + return acc; + }, {} as Record); + + return ( +
    + + {t('modelNew.modelImplement')} + + + + + + + + +
    + {!value || value.length === 0 + ? + : Object.entries(groupedByProvider).map(([provider, items]: [string, ModelList[]]) => { + return ( +
    + +
    {[...new Set(items?.map((vo) => vo.model_name))].join(', ')}
    + +
    handleEdit(items, provider)} + >
    +
    handleDelete(provider)} + >
    +     +     + {provider} +
    + ) + })} +
    + +
    + ) +} + +export default ModelImplement \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/ModelImplement/types.ts b/web/src/views/ModelManagement/components/ModelImplement/types.ts new file mode 100644 index 00000000..c6d2f6d6 --- /dev/null +++ b/web/src/views/ModelManagement/components/ModelImplement/types.ts @@ -0,0 +1,16 @@ +import type { ModelListItem } from '../../types' + +export interface ModelList extends ModelListItem { + api_key_id: string; +} +export interface SubModelModalForm { + provider: string; + api_key_ids: string[][]; +} +export interface SubModelModalRef { + handleOpen: (list?: ModelList[], provider?: string) => void; +} +export interface SubModelModalProps { + type?: string; + refresh?: (vo: ModelList[]) => void; +} \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/ModelListDetail.tsx b/web/src/views/ModelManagement/components/ModelListDetail.tsx new file mode 100644 index 00000000..48abd953 --- /dev/null +++ b/web/src/views/ModelManagement/components/ModelListDetail.tsx @@ -0,0 +1,111 @@ +import { useState, useImperativeHandle, forwardRef, useRef } from 'react'; +import { useTranslation } from 'react-i18next'; +import { Button, Switch, Row, Col, Space } from 'antd' + +import type { ProviderModelItem, ModelListItem, ModelListDetailRef, MultiKeyConfigModalRef } from '../types'; +import RbDrawer from '@/components/RbDrawer'; +import RbCard from '@/components/RbCard/Card' +import Tag from '@/components/Tag'; +import PageEmpty from '@/components/Empty/PageEmpty'; +import MultiKeyConfigModal from './MultiKeyConfigModal' +import { getModelNewList, updateModelStatus } from '@/api/models' + +interface ModelListDetailProps { + refresh?: () => void; +} + +const ModelListDetail = forwardRef(({ refresh }, ref) => { + const { t } = useTranslation(); + const [open, setOpen] = useState(false); + const [data, setData] = useState({} as ProviderModelItem) + const [list, setList] = useState([]) + const multiKeyConfigModalRef = useRef(null) + const [loading, setLoading] = useState(false) + + const handleOpen = (vo: ProviderModelItem) => { + setOpen(true) + getData(vo) + } + + const getData = (vo: ProviderModelItem) => { + if (!vo.provider) return + + getModelNewList({ + provider: vo.provider + }) + .then(res => { + const response = res as ProviderModelItem[] + setData(response[0]) + setList(response[0].models) + }) + } + const handleKeyConfig = (vo: ModelListItem) => { + multiKeyConfigModalRef.current?.handleOpen(vo, data.provider) + } + const handleChange = (vo: ModelListItem) => { + setLoading(true) + updateModelStatus(vo.id, { is_active: !vo.is_active }) + .finally(() => { + getData(data) + setLoading(false) + }) + } + + const handleClose = () => { + setOpen(false) + refresh?.() + } + const handleRefresh = () => { + getData(data) + } + + useImperativeHandle(ref, () => ({ + handleOpen, + })); + + return ( + {data.provider} {t('modelNew.modelList')} ({list.length}{t('modelNew.item')})} + open={open} + onClose={handleClose} + > + {list.length === 0 + ? + :
    + {list.map(item => ( + + {t(`modelNew.${item.type}`)} + {item.api_keys.length}{t('modelNew.apiKeyNum')} + } + avatarUrl={item.logo} + avatar={ +
    + {item.name[0]} +
    + } + extra={ handleChange(item)} />} + > + +
    {item.description}
    + + + + + +     + ))} +
    + } + + +     + ); +}); + +export default ModelListDetail; \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx new file mode 100644 index 00000000..6d437729 --- /dev/null +++ b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx @@ -0,0 +1,86 @@ +import { useState, useImperativeHandle, forwardRef } from 'react'; +import { useTranslation } from 'react-i18next'; +import { Button, Space, App } from 'antd' + +import type { ModelPlaza, ModelPlazaItem, ModelSquareDetailRef } from '../types'; +import RbDrawer from '@/components/RbDrawer'; +import { getModelPlaza, addModelPlaza } from '@/api/models' +import RbCard from '@/components/RbCard/Card' +import Tag from '@/components/Tag'; +import PageEmpty from '@/components/Empty/PageEmpty'; + +interface ModelSquareDetailProps { + refresh: () => void; +} +const ModelSquareDetail = forwardRef(({ refresh }, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp() + const [model, setModel] = useState({} as ModelPlaza) + const [open, setOpen] = useState(false); + + const [list, setList] = useState([]) + + const handleOpen = (vo: ModelPlaza) => { + setModel(vo) + setOpen(true) + getList(vo) + } + const handleClose = () => { + setOpen(false) + refresh() + } + const getList = (vo: ModelPlaza) => { + getModelPlaza({ provider: vo.provider }) + .then(res => { + const response = res as ModelPlaza[] + setList(response.length > 0 ? response[0].models : []) + }) + } + const handleAdd = (item: ModelPlazaItem) => { + addModelPlaza(item.id) + .then(() => { + message.success(`${item.name}${t('modelNew.addSuccess')}`) + getList(model) + }) + } + + useImperativeHandle(ref, () => ({ + handleOpen, + })); + + return ( + {model.provider} {t('modelNew.modelList')} ({list.length}{t('modelNew.item')})} + open={open} + onClose={handleClose} + > + {list.length === 0 + ? + :
    + {list.map(item => ( + + {item.name[0]} +
    + } + > + {t(`modelNew.${item.type}`)} +
    {item.description}
    + {item.tags.map((tag, tagIndex) => {tag})} + {item.is_added + ? + : + } +     + ))} + + } + + ); +}); + +export default ModelSquareDetail; \ No newline at end of file diff --git a/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx b/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx new file mode 100644 index 00000000..860efc3f --- /dev/null +++ b/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx @@ -0,0 +1,121 @@ +import { forwardRef, useImperativeHandle, useState } from 'react'; +import { Form, Input, App, Button } from 'antd'; +import { useTranslation } from 'react-i18next'; +import type { ModelListItem, MultiKeyForm, MultiKeyConfigModalRef, MultiKeyConfigModalProps } from '../types'; +import RbModal from '@/components/RbModal' +import { addModelApiKey, delteModelApiKey, getModelInfo } from '@/api/models' + +const MultiKeyConfigModal = forwardRef(({ refresh }, ref) => { + const { t } = useTranslation(); + const { message } = App.useApp(); + const [visible, setVisible] = useState(false); + const [model, setModel] = useState({} as ModelListItem); + const [form] = Form.useForm(); + const [loading, setLoading] = useState(false) + + const handleClose = () => { + setModel({} as ModelListItem); + refresh?.() + + form.resetFields(); + setLoading(false) + setVisible(false); + }; + + const handleOpen = (vo: ModelListItem) => { + setVisible(true); + getData(vo) + }; + + const getData = (vo: ModelListItem) => { + if (!vo.id) return + + getModelInfo(vo?.id) + .then(res => { + setModel(res as ModelListItem) + }) + } + const handleSave = () => { + form + .validateFields() + .then((values) => { + addModelApiKey(model.id, { + ...values, + model_config_id: model.id, + model_name: model.name, + provider: model.provider, + }).then(() => { + message.success(t('common.saveSuccess')) + form.resetFields(); + getData(model) + }) + .catch(() => { + setLoading(false) + }); + }) + .catch((err) => { + console.log('err', err) + }); + } + const handleDelete = (api_key_id: string) => { + delteModelApiKey(api_key_id) + .then(() => { + message.success(t('common.deleteSuccess')) + getData(model) + }) + } + + useImperativeHandle(ref, () => ({ + handleOpen, + })); + + return ( + + {model.api_keys && model.api_keys.length > 0 && ( +
    + {model.api_keys.map((key) => ( +
    +
    +
    {key.api_key}
    +
    {key.api_base}
    +
    + +
    + ))} +
    + )} +
    + + + + + + + + + + + +
    +     + ); +}); + +export default MultiKeyConfigModal; \ No newline at end of file diff --git a/web/src/views/ModelManagement/index.tsx b/web/src/views/ModelManagement/index.tsx index 930a18e6..35f4c887 100644 --- a/web/src/views/ModelManagement/index.tsx +++ b/web/src/views/ModelManagement/index.tsx @@ -1,99 +1,123 @@ import { useState, useRef, type FC } from 'react'; -import { Row, Col, Button } from 'antd' +import { Button, Flex, Space, type SegmentedProps } from 'antd' import { useTranslation } from 'react-i18next'; -import clsx from 'clsx'; -import ConfigModal from './components/ConfigModal' -import type { Model, DescriptionItem, ConfigModalRef } from './types' -import RbCard from '@/components/RbCard/Card' +import GroupModelModal from './components/GroupModelModal' +import type { ModelListItem, GroupModelModalRef, CustomModelModalRef, ModelPlazaItem, BaseRef } from './types' import SearchInput from '@/components/SearchInput' -import PageScrollList, { type PageScrollListRef } from '@/components/PageScrollList' -import { getModelListUrl } from '@/api/models' -import { formatDateTime } from '@/utils/format'; +import PageTabs from '@/components/PageTabs' +import GroupModel from './Group' +import ModelList from './List' +import ModelSquare from './Square' +import CustomModelModal from './components/CustomModelModal' +import CustomSelect from '@/components/CustomSelect' +import { modelTypeUrl, modelProviderUrl } from '@/api/models' +const tabKeys = ['group', 'list', 'square'] const ModelManagement: FC = () => { const { t } = useTranslation(); + const [activeTab, setActiveTab] = useState('group'); const [query, setQuery] = useState({}) - const configModalRef = useRef(null) - const scrollListRef = useRef(null) + const configModalRef = useRef(null) + const customModelModalRef = useRef(null) + const groupRef = useRef(null) + const squareRef = useRef(null) - const formatData = (data: Model) => { - return [ - { - key: 'type', - label: t(`model.type`), - children: data.type || '-', - }, - { - key: 'provider', - label: t(`model.provider`), - children: data.api_keys[0].provider || '-', - }, - { - key: 'is_active', - label: t(`model.status`), - children: data.is_active ? t(`common.statusEnabled`) : t(`common.statusDisabled`), - }, - { - key: 'created', - label: t(`model.created`), - children: data.created_at ? formatDateTime(data.created_at, 'YYYY-MM-DD HH:mm:ss') : '-', - }, - ] + const formatTabItems = () => { + return tabKeys.map(value => ({ + value, + label: t(`modelNew.${value}`), + })) + } + const handleChangeTab = (value: SegmentedProps['value']) => { + setActiveTab(value as string); + setQuery({}) } - const handleEdit = (model?: Model) => { - configModalRef?.current?.handleOpen(model) + const handleEdit = (vo?: ModelListItem | ModelPlazaItem) => { + switch(activeTab) { + case 'group': + configModalRef?.current?.handleOpen(vo as ModelListItem) + break + case 'square': + customModelModalRef?.current?.handleOpen(vo as ModelPlazaItem) + break + } + } + const handleRefresh = () => { + switch (activeTab) { + case 'group': + groupRef.current?.getList() + break + case 'square': + squareRef.current?.getList() + break + } } const handleSearch = (value?: string) => { setQuery({ search: value }) } + const handleTypeChange = (value: string) => { + setQuery(pre => ({ ...pre, type: value })) + } + const handleProviderChange = (value: string) => { + setQuery(pre => ({ ...pre, provider: value })) + } return ( -
    - - - + + + + + {activeTab === 'list' ? <> + items.map((item) => ({ label: t(`modelNew.${item}`), value: String(item) }))} + onChange={handleTypeChange} + className="rb:w-30" + allowClear={true} + placeholder={t('modelNew.type')} + /> + items.map((item) => ({ label: t(`modelNew.${item}`), value: String(item) }))} + onChange={handleProviderChange} + className="rb:w-30" + allowClear={true} + placeholder={t('modelNew.provider')} + /> + + : - - - - - + className="rb:w-70!" + />} + {activeTab === 'group' && } + {activeTab === 'square' && } + + - ( - - {formatData(item)?.map((description: DescriptionItem) => ( -
    - {(description.label as string)} - {(description.children as string)} -
    - ))} - -     - )} - /> - - + {activeTab === 'group' && } + {activeTab === 'list' && } + {activeTab === 'square' && } +
    + scrollListRef?.current?.refresh()} + refresh={handleRefresh} /> - + + ) } diff --git a/web/src/views/ModelManagement/types.ts b/web/src/views/ModelManagement/types.ts index 215e0d9f..b8d830cc 100644 --- a/web/src/views/ModelManagement/types.ts +++ b/web/src/views/ModelManagement/types.ts @@ -1,70 +1,146 @@ -// 模型表单数据类型 -export interface ModelFormData extends ApiKey { - name: string; - type: string; - api_keys: ApiKey; +export interface Query { + type?: string; + provider?: string; + is_active?: boolean; + is_public?: boolean; + is_composite?: boolean; + search?: string; } - export interface DescriptionItem { key: string; label: string; children: string; } +export interface CompositeModelForm { + logo?: any; + name: string; + type: string; + description: string; + api_key_ids: ModelApiKey[] | string[]; +} +export interface GroupModelModalRef { + handleOpen: (model?: ModelListItem) => void; +} +export interface GroupModelModalProps { + refresh?: () => void; +} +export interface SubModelModalForm { + provider: string; + model_ids: string[] +} +export interface SubModelModalRef { + handleOpen: (model?: SubModelModalForm) => void; +} +export interface SubModelModalProps { + refresh?: () => void; +} +export interface ModelListDetailRef { + handleOpen: (vo: ProviderModelItem) => void; +} -// 模型类型定义 -export interface Model { + +export interface ModelApiKey { + model_name: string; + description: string | null; + provider: string; + api_key: string; + api_base: string; + config: any; + is_active: boolean; + priority: string; + id: string; + usage_count: string; + last_used_at: number; + created_at: number; + updated_at: number; + model_config_ids: string[]; +} +export interface ModelListItem { + model_name?: string; + model_config_ids: string[]; + name: string; + type: string; + logo: string; + description: string; + provider: string; + config: any; + is_active: boolean; + is_public: boolean; + id: string; + created_at: number; + updated_at: number; + api_keys: ModelApiKey[] +} +export interface ProviderModelItem { + provider: string; + logo?: string; + tags: string[]; + models: ModelListItem[]; +} +export interface KeyConfigModalForm { + provider: string; + api_key: string; + api_base: string; +} +export interface KeyConfigModalRef { + handleOpen: (vo: ProviderModelItem) => void; +} +export interface KeyConfigModalProps { + refresh?: () => void; +} +export interface MultiKeyForm { + model_config_id?: string; + model_name: string; + provider: string; + api_key: string; + api_base: string; +} + +export interface MultiKeyConfigModalRef { + handleOpen: (vo: ModelListItem, provider?: string) => void; +} +export interface MultiKeyConfigModalProps { + refresh?: () => void; +} + + +export interface ModelPlaza { + provider: string; + models: ModelPlazaItem[]; +} +export interface ModelPlazaItem { id: string; name: string; type: string; - description?: string; - config: Record; - is_active: boolean; - is_public: boolean; - created_at: string | number; - updated_at: string | number; - api_keys: ApiKey[]; - - // provider: string; - // temperature: number, - // topP: number, - // status: string; - // vectorDimension: number; - // batchSize: number; - // truncateStrategy: string; - // created: string; - // updatedAt: string; - // descriptionItems?: Record[]; - // basicParameters?: string; - // normalization?: string; - // maxInputLength?: number; - // encodingFormat?: string; - // enablePooling?: boolean; - // poolingStrategy?: string; - // apiKey?: string; - // apiEndpoint?: string; - // timeout?: number; - // autoRetry?: boolean; - // retryCount?: number; -} -interface ApiKey { - model_name?: string; provider: string; - api_key?: string; - api_base?: string; - config?: Record; - is_active?: boolean; - priority?: string; - id: string; - model_config_id?: string; - usage_count?: string; - last_used_at?: string | null; - created_at?: string; - updated_at?: string; + logo: string; + description: string; + is_deprecated: boolean; + is_official: boolean; + tags: string[]; + add_count: number; + is_added: boolean; } -// 定义组件暴露的方法接口 -export interface ConfigModalRef { - handleOpen: (model?: Model) => void; +export interface ModelSquareDetailRef { + handleOpen: (vo: ModelPlaza) => void; } -export interface ConfigModalProps { +export interface CustomModelForm { + name: string; + type: string; + provider: string; + logo: string; + description: string; + is_official: boolean; + tags: string[]; +} +export interface CustomModelModalRef { + handleOpen: (vo?: ModelPlazaItem) => void; +} +export interface CustomModelModalProps { refresh?: () => void; +} + + +export interface BaseRef { + getList: () => void; } \ No newline at end of file From f821893653f633d344e718f0573c91d63e38c53a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 20:22:14 +0800 Subject: [PATCH 106/175] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 4 +-- api/app/services/memory_config_service.py | 30 ++++++++++++++++++++--- 2 files changed, 28 insertions(+), 6 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 09a14c32..c05ae027 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -265,7 +265,7 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id @@ -374,7 +374,7 @@ class MemoryAgentService: message: str, history: List[Dict], search_switch: str, - config_id: Optional[UUID], + config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str) -> Dict: diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index e901d65d..f423e31e 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -7,7 +7,8 @@ This service eliminates code duplication between MemoryAgentService and MemorySt import time from datetime import datetime - +from app.models.memory_config_model import MemoryConfig as MemoryConfigModel +from sqlalchemy import select from app.core.logging_config import get_config_logger, get_logger from app.core.validators.memory_config_validators import ( validate_and_resolve_model_id, @@ -29,7 +30,7 @@ logger = get_logger(__name__) config_logger = get_config_logger() import uuid -def _validate_config_id(config_id): +def _validate_config_id(config_id, db: Session = None): """Validate configuration ID format (supports both UUID and integer).""" if isinstance(config_id, uuid.UUID): return config_id @@ -48,6 +49,16 @@ def _validate_config_id(config_id): field_name="config_id", invalid_value=config_id, ) + + # 如果提供了数据库会话，尝试通过 user_id 查询 config_id + if db is not None: + # 查询 user_id 匹配的记录 + stmt = select(MemoryConfigModel).where(MemoryConfigModel.config_id_old == str(config_id)) + result = db.execute(stmt).scalars().first() + if result: + logger.info(f"Found config_id {result.config_id} for user_id {config_id}") + return result.config_id + return config_id if isinstance(config_id, str): @@ -61,13 +72,24 @@ def _validate_config_id(config_id): # Fall back to integer parsing try: - parsed_id = config_id_stripped + parsed_id = int(config_id_stripped) if parsed_id <= 0: raise InvalidConfigError( f"Configuration ID must be positive: {parsed_id}", field_name="config_id", invalid_value=config_id, ) + + # 如果提供了数据库会话，尝试通过 user_id 查询 config_id + if db is not None: + # 查询 user_id 匹配的记录 + stmt = select(MemoryConfigModel).where(MemoryConfigModel.user_id == str(parsed_id)) + result = db.execute(stmt).scalars().first() + + if result: + logger.info(f"Found config_id {result.config_id} for user_id {parsed_id}") + return result.config_id + return parsed_id except ValueError: raise InvalidConfigError( @@ -136,7 +158,7 @@ class MemoryConfigService: logger.info(f"Loading memory configuration from database: config_id={config_id}") try: - validated_config_id = _validate_config_id(config_id) + validated_config_id = _validate_config_id(config_id, self.db) # Step 1: Get config and workspace db_query_start = time.time() From 46abb23ee81a0de280162cca989167fe4cc9dc6b Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 20:24:05 +0800 Subject: [PATCH 107/175] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_config_service.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index f423e31e..26b86b71 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -49,7 +49,6 @@ def _validate_config_id(config_id, db: Session = None): field_name="config_id", invalid_value=config_id, ) - # 如果提供了数据库会话，尝试通过 user_id 查询 config_id if db is not None: # 查询 user_id 匹配的记录 From 375660f2321e946ef2e54068a9762aed7a1015d8 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Tue, 27 Jan 2026 20:26:14 +0800 Subject: [PATCH 108/175] Fix/memory bug fix (#211) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * 检查需要更改的格式问题 * 修复宿主列表获取memory_config_idBUG * config_id做映射 * config_id做映射 --------- Co-authored-by: lanceyq <1982376970@qq.com> --- api/app/services/memory_agent_service.py | 4 ++-- api/app/services/memory_config_service.py | 29 +++++++++++++++++++---- 2 files changed, 27 insertions(+), 6 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 09a14c32..c05ae027 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -265,7 +265,7 @@ class MemoryAgentService: logger.info("Log streaming completed, cleaning up resources") # LogStreamer uses context manager for file handling, so cleanup is automatic - async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID], db: Session, storage_type: str, user_rag_memory_id: str) -> str: + async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str) -> str: """ Process write operation with config_id @@ -374,7 +374,7 @@ class MemoryAgentService: message: str, history: List[Dict], search_switch: str, - config_id: Optional[UUID], + config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str) -> Dict: diff --git a/api/app/services/memory_config_service.py b/api/app/services/memory_config_service.py index e901d65d..26b86b71 100644 --- a/api/app/services/memory_config_service.py +++ b/api/app/services/memory_config_service.py @@ -7,7 +7,8 @@ This service eliminates code duplication between MemoryAgentService and MemorySt import time from datetime import datetime - +from app.models.memory_config_model import MemoryConfig as MemoryConfigModel +from sqlalchemy import select from app.core.logging_config import get_config_logger, get_logger from app.core.validators.memory_config_validators import ( validate_and_resolve_model_id, @@ -29,7 +30,7 @@ logger = get_logger(__name__) config_logger = get_config_logger() import uuid -def _validate_config_id(config_id): +def _validate_config_id(config_id, db: Session = None): """Validate configuration ID format (supports both UUID and integer).""" if isinstance(config_id, uuid.UUID): return config_id @@ -48,6 +49,15 @@ def _validate_config_id(config_id): field_name="config_id", invalid_value=config_id, ) + # 如果提供了数据库会话，尝试通过 user_id 查询 config_id + if db is not None: + # 查询 user_id 匹配的记录 + stmt = select(MemoryConfigModel).where(MemoryConfigModel.config_id_old == str(config_id)) + result = db.execute(stmt).scalars().first() + if result: + logger.info(f"Found config_id {result.config_id} for user_id {config_id}") + return result.config_id + return config_id if isinstance(config_id, str): @@ -61,13 +71,24 @@ def _validate_config_id(config_id): # Fall back to integer parsing try: - parsed_id = config_id_stripped + parsed_id = int(config_id_stripped) if parsed_id <= 0: raise InvalidConfigError( f"Configuration ID must be positive: {parsed_id}", field_name="config_id", invalid_value=config_id, ) + + # 如果提供了数据库会话，尝试通过 user_id 查询 config_id + if db is not None: + # 查询 user_id 匹配的记录 + stmt = select(MemoryConfigModel).where(MemoryConfigModel.user_id == str(parsed_id)) + result = db.execute(stmt).scalars().first() + + if result: + logger.info(f"Found config_id {result.config_id} for user_id {parsed_id}") + return result.config_id + return parsed_id except ValueError: raise InvalidConfigError( @@ -136,7 +157,7 @@ class MemoryConfigService: logger.info(f"Loading memory configuration from database: config_id={config_id}") try: - validated_config_id = _validate_config_id(config_id) + validated_config_id = _validate_config_id(config_id, self.db) # Step 1: Get config and workspace db_query_start = time.time() From 1e481a311afb3ec34cdc5235f60a61f945c4ae24 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Tue, 27 Jan 2026 20:33:23 +0800 Subject: [PATCH 109/175] feat(web): getModelListUrl add is_active param --- web/src/api/knowledgeBase.ts | 2 +- web/src/views/ApplicationConfig/Agent.tsx | 2 +- web/src/views/ApplicationConfig/Cluster.tsx | 2 +- .../ApplicationConfig/components/AiPromptModal.tsx | 2 +- .../Knowledge/KnowledgeGlobalConfigModal.tsx | 2 +- web/src/views/EmotionEngine/index.tsx | 2 +- web/src/views/MemoryExtractionEngine/index.tsx | 10 +++++----- web/src/views/ModelManagement/types.ts | 3 +++ web/src/views/SelfReflectionEngine/index.tsx | 2 +- web/src/views/SpaceConfig/index.tsx | 6 +++--- .../views/SpaceManagement/components/SpaceModal.tsx | 12 ++++++------ .../Knowledge/KnowledgeGlobalConfigModal.tsx | 2 +- web/src/views/Workflow/constant.ts | 6 +++--- 13 files changed, 28 insertions(+), 25 deletions(-) diff --git a/web/src/api/knowledgeBase.ts b/web/src/api/knowledgeBase.ts index 5f171a72..38a0d40d 100644 --- a/web/src/api/knowledgeBase.ts +++ b/web/src/api/knowledgeBase.ts @@ -65,7 +65,7 @@ export const getModelTypeList = async () => { }; // 获取模型列表 export const getModelList = async (pageInfo: PageRequest) => { - const response = await request.get(`${apiPrefix}/models`, pageInfo); + const response = await request.get(`${apiPrefix}/models`, { ...pageInfo, is_active: true }); return response as any; }; //获取模型提供者 diff --git a/web/src/views/ApplicationConfig/Agent.tsx b/web/src/views/ApplicationConfig/Agent.tsx index 77e90440..279ac18c 100644 --- a/web/src/views/ApplicationConfig/Agent.tsx +++ b/web/src/views/ApplicationConfig/Agent.tsx @@ -237,7 +237,7 @@ const Agent = forwardRef((_props, ref) => { }) } const getModels = () => { - getModelList({ type: 'llm,chat', pagesize: 100, page: 1 }) + getModelList({ type: 'llm,chat', pagesize: 100, page: 1, is_active: true }) .then(res => { const response = res as { items: Model[] } setModelList(response.items) diff --git a/web/src/views/ApplicationConfig/Cluster.tsx b/web/src/views/ApplicationConfig/Cluster.tsx index 3081aa04..aa4a5d98 100644 --- a/web/src/views/ApplicationConfig/Cluster.tsx +++ b/web/src/views/ApplicationConfig/Cluster.tsx @@ -225,7 +225,7 @@ const Cluster = forwardRef((_props, ref) => { (({ > { const values = Form.useWatch([], form) const [loading, setLoading] = useState(false) const [iterationPeriodDisabled, setIterationPeriodDisabled] = useState(false) - const [modelList, setModelList] = useState([]) + const [modelList, setModelList] = useState([]) useEffect(() => { if (values?.reflexion_range === 'database') { @@ -55,9 +55,9 @@ const MemoryExtractionEngine: FC = () => { }, [values]) const getModels = () => { - getModelList({ type: 'llm,chat', pagesize: 100, page: 1 }) + getModelList({ type: 'llm,chat', pagesize: 100, page: 1, is_active: true }) .then(res => { - const response = res as { items: Model[] } + const response = res as { items: ModelListItem[] } setModelList(response.items) }) } diff --git a/web/src/views/ModelManagement/types.ts b/web/src/views/ModelManagement/types.ts index b8d830cc..ad0dca26 100644 --- a/web/src/views/ModelManagement/types.ts +++ b/web/src/views/ModelManagement/types.ts @@ -5,6 +5,9 @@ export interface Query { is_public?: boolean; is_composite?: boolean; search?: string; + + pagesize?: number; + page?: number; } export interface DescriptionItem { key: string; diff --git a/web/src/views/SelfReflectionEngine/index.tsx b/web/src/views/SelfReflectionEngine/index.tsx index 784f066c..30117bed 100644 --- a/web/src/views/SelfReflectionEngine/index.tsx +++ b/web/src/views/SelfReflectionEngine/index.tsx @@ -24,7 +24,7 @@ const configList = [ key: 'reflection_model_id', type: 'customSelect', url: getModelListUrl, - params: { type: 'chat,llm', page: 1, pagesize: 100 }, // chat,llm + params: { type: 'chat,llm', page: 1, pagesize: 100, is_active: true }, // chat,llm }, // 迭代周期 { diff --git a/web/src/views/SpaceConfig/index.tsx b/web/src/views/SpaceConfig/index.tsx index ad99e220..25490e91 100644 --- a/web/src/views/SpaceConfig/index.tsx +++ b/web/src/views/SpaceConfig/index.tsx @@ -66,7 +66,7 @@ const SpaceConfig: FC = () => { > { > { > (({ const [form] = Form.useForm(); const [loading, setLoading] = useState(false) const [editVo, setEditVo] = useState(null) - const [modelList, setModelList] = useState([]) + const [modelList, setModelList] = useState([]) const values = Form.useWatch([], form); @@ -80,9 +80,9 @@ const SpaceModal = forwardRef(({ }, []) const getModels = () => { - getModelList({ type: 'llm,chat', pagesize: 100, page: 1 }) + getModelList({ type: 'llm,chat', pagesize: 100, page: 1, is_active: true }) .then(res => { - const response = res as { items: Model[] } + const response = res as { items: ModelListItem[] } setModelList(response.items) }) } @@ -134,7 +134,7 @@ const SpaceModal = forwardRef(({ > (({ > Date: Tue, 27 Jan 2026 21:09:06 +0800 Subject: [PATCH 110/175] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84+?= =?UTF-8?q?1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 38 ++++++++++++++++++++---- 1 file changed, 33 insertions(+), 5 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index c05ae027..a56ffa68 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1283,6 +1283,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) # 3. 收集所有 memory_config_id 并批量查询配置名称 memory_config_ids = [] + old_config_ids = [] # 存储旧的整数ID + for end_user_id, app_id in user_to_app.items(): release = app_to_release.get(app_id) if release: @@ -1290,13 +1292,39 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None if memory_config_id: - memory_config_ids.append(memory_config_id) - + # 判断是否为UUID格式 + try: + # 尝试将其解析为UUID + uuid.UUID(str(memory_config_id)) + memory_config_ids.append(memory_config_id) + except (ValueError, AttributeError): + # 如果不是UUID，则是旧的整数ID + old_config_ids.append(str(memory_config_id)) + # 批量查询 memory_config_name config_id_to_name = {} + + # 记录分类结果 + if memory_config_ids or old_config_ids: + logger.info(f"Collected {len(memory_config_ids)} UUID config_ids and {len(old_config_ids)} old integer config_ids") + if old_config_ids: + logger.debug(f"Old config IDs: {old_config_ids}") + + # 查询新的UUID格式的config_id if memory_config_ids: memory_configs = db.query(MemoryConfig).filter(MemoryConfig.config_id.in_(memory_config_ids)).all() - config_id_to_name = {str(mc.config_id): mc.config_name for mc in memory_configs} + config_id_to_name.update({str(mc.config_id): mc.config_name for mc in memory_configs}) + + # 查询旧的整数ID（通过config_id_old字段） + if old_config_ids: + old_memory_configs = db.query(MemoryConfig).filter(MemoryConfig.config_id_old.in_(old_config_ids)).all() + # 使用config_id_old作为key，这样后面查找时能匹配上 + config_id_to_name.update({str(mc.config_id_old): mc.config_name for mc in old_memory_configs}) + # 同时也添加config_id作为key，方便后续使用 + for mc in old_memory_configs: + if mc.config_id_old: + config_id_to_name[str(mc.config_id)] = mc.config_name + logger.info(f"Found {len(old_memory_configs)} configs for old IDs") # 4. 构建最终结果 for end_user_id, app_id in user_to_app.items(): @@ -1312,8 +1340,8 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_obj = config.get('memory', {}) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None - # 获取配置名称 - memory_config_name = config_id_to_name.get(memory_config_id) if memory_config_id else None + # 获取配置名称（使用字符串形式的ID进行查找，兼容新旧格式） + memory_config_name = config_id_to_name.get(str(memory_config_id)) if memory_config_id else None result[end_user_id] = { "memory_config_id": memory_config_id, From d373f924f63a39652ed9ae05cc2c160d0f4702bb Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 21:10:32 +0800 Subject: [PATCH 111/175] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84+?= =?UTF-8?q?1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_dashboard_controller.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/controllers/memory_dashboard_controller.py b/api/app/controllers/memory_dashboard_controller.py index 6181c319..88684a39 100644 --- a/api/app/controllers/memory_dashboard_controller.py +++ b/api/app/controllers/memory_dashboard_controller.py @@ -92,7 +92,6 @@ async def get_workspace_end_users( workspace_id=workspace_id, current_user=current_user ) - if not end_users: api_logger.info("工作空间下没有宿主") # 缓存空结果，避免重复查询 From 2a23082203703c8dd143257fab845ea38a3043e2 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 21:15:38 +0800 Subject: [PATCH 112/175] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84+?= =?UTF-8?q?1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_agent_service.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index a56ffa68..2378da51 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -1293,12 +1293,10 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None if memory_config_id: # 判断是否为UUID格式 - try: - # 尝试将其解析为UUID + if len(str(memory_config_id))>=5: uuid.UUID(str(memory_config_id)) memory_config_ids.append(memory_config_id) - except (ValueError, AttributeError): - # 如果不是UUID，则是旧的整数ID + else: old_config_ids.append(str(memory_config_id)) # 批量查询 memory_config_name From ce01e588c9f6b2327c6612880ab6b9b37f7591f8 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 09:55:20 +0800 Subject: [PATCH 113/175] feat(web): remove file url replace --- web/src/views/ModelManagement/components/CustomModelModal.tsx | 4 +--- web/src/views/ModelManagement/components/GroupModelModal.tsx | 4 ++-- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/web/src/views/ModelManagement/components/CustomModelModal.tsx b/web/src/views/ModelManagement/components/CustomModelModal.tsx index df6693f7..ebc5a5ce 100644 --- a/web/src/views/ModelManagement/components/CustomModelModal.tsx +++ b/web/src/views/ModelManagement/components/CustomModelModal.tsx @@ -51,7 +51,7 @@ const CustomModelModal = forwardRef( if (typeof logo === 'object') { getFileLink(logo?.response?.data.file_id).then(res => { const logoRes = res as { url: string } - values.logo = logoRes.url.replace('http://127.0.0.1:8000', 'https://devmemorybear.redbearai.com') + values.logo = logoRes.url addCustomModel(values).then(() => { if (refresh) { refresh(); @@ -143,14 +143,12 @@ const CustomModelModal = forwardRef( diff --git a/web/src/views/ModelManagement/components/GroupModelModal.tsx b/web/src/views/ModelManagement/components/GroupModelModal.tsx index 9d8132d9..53a80c61 100644 --- a/web/src/views/ModelManagement/components/GroupModelModal.tsx +++ b/web/src/views/ModelManagement/components/GroupModelModal.tsx @@ -36,7 +36,7 @@ const GroupModelModal = forwardRef(({ form.setFieldsValue({ ...model, api_key_ids: model.api_keys, - logo: model.logo ? [{url: model.logo, uid: model.logo, status: 'done', name: 'logo'}] : undefined + logo: model.logo ? { url: model.logo, uid: model.logo, status: 'done', name: 'logo' } : undefined }) } else { setIsEdit(false); @@ -48,7 +48,7 @@ const GroupModelModal = forwardRef(({ form .validateFields() .then((values) => { - const { api_key_ids, logo, ...rest } = values + const { api_key_ids = [], logo, ...rest } = values const formData: CompositeModelForm = { ...rest, @@ -62,6 +62,7 @@ const GroupModelModal = forwardRef(({ handleUpdate(formData) }) } else { + formData.logo = typeof logo === 'string' ? logo : logo.url handleUpdate(formData) } }) diff --git a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx index 6d437729..d7a5f807 100644 --- a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx +++ b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx @@ -1,6 +1,7 @@ import { useState, useImperativeHandle, forwardRef } from 'react'; import { useTranslation } from 'react-i18next'; -import { Button, Space, App } from 'antd' +import { Button, Space, App, Flex } from 'antd' +import { UsergroupAddOutlined } from '@ant-design/icons'; import type { ModelPlaza, ModelPlazaItem, ModelSquareDetailRef } from '../types'; import RbDrawer from '@/components/RbDrawer'; @@ -11,8 +12,9 @@ import PageEmpty from '@/components/Empty/PageEmpty'; interface ModelSquareDetailProps { refresh: () => void; + handleEdit: (vo: ModelPlazaItem) => void; } -const ModelSquareDetail = forwardRef(({ refresh }, ref) => { +const ModelSquareDetail = forwardRef(({ refresh, handleEdit }, ref) => { const { t } = useTranslation(); const { message } = App.useApp() const [model, setModel] = useState({} as ModelPlaza) @@ -71,10 +73,17 @@ const ModelSquareDetail = forwardRef{t(`modelNew.${item.type}`)}
    {item.description}
    {item.tags.map((tag, tagIndex) => {tag})} - {item.is_added - ? - : - } + + + {item.add_count} + + {!item.is_official && } + {item.is_added + ? + : + } + + ))} diff --git a/web/src/views/ModelManagement/types.ts b/web/src/views/ModelManagement/types.ts index ad0dca26..1967f393 100644 --- a/web/src/views/ModelManagement/types.ts +++ b/web/src/views/ModelManagement/types.ts @@ -27,16 +27,6 @@ export interface GroupModelModalRef { export interface GroupModelModalProps { refresh?: () => void; } -export interface SubModelModalForm { - provider: string; - model_ids: string[] -} -export interface SubModelModalRef { - handleOpen: (model?: SubModelModalForm) => void; -} -export interface SubModelModalProps { - refresh?: () => void; -} export interface ModelListDetailRef { handleOpen: (vo: ProviderModelItem) => void; } @@ -131,7 +121,7 @@ export interface CustomModelForm { name: string; type: string; provider: string; - logo: string; + logo?: any; description: string; is_official: boolean; tags: string[]; From d34ad7343947c5867b5d3c191f796cdc208477df Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 10:56:41 +0800 Subject: [PATCH 117/175] =?UTF-8?q?=E5=BA=94=E7=94=A8=E5=B1=82memory=5Fcon?= =?UTF-8?q?tent->memory=5Fconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/app_schema.py | 2 +- api/app/services/draft_run_service.py | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index 09410091..b350f17c 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -51,7 +51,7 @@ class ToolOldConfig(BaseModel): class MemoryConfig(BaseModel): """记忆配置""" enabled: bool = Field(default=True, description="是否启用对话历史记忆") - memory_content: Optional[str] = Field(default=None, description="选择记忆的内容类型") + memory_config: Optional[str] = Field(default=None, description="选择记忆的内容类型") max_history: int = Field(default=10, ge=0, le=100, description="最大保留的历史对话轮数") diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 9766eec0..0d1f51a4 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -56,7 +56,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str 长期记忆工具 """ # search_switch = memory_config.get("search_switch", "2") - config_id= memory_config.get("memory_content",None) + config_id= memory_config.get("memory_content") or memory_config.get("memory_config",None) logger.info(f"创建长期记忆工具，配置: end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") @tool(args_schema=LongTermMemoryInput) def long_term_memory(question: str) -> str: @@ -106,9 +106,9 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str "app.core.memory.agent.read_message", args=[end_user_id, question, [], "1", config_id, storage_type, user_rag_memory_id] ) - # result = task_service.get_task_memory_read_result(task.id) - # status = result.get("status") - # logger.info(f"读取任务状态：{status}") + result = task_service.get_task_memory_read_result(task.id) + status = result.get("status") + logger.info(f"读取任务状态：{status}") finally: db.close() @@ -418,7 +418,7 @@ class DraftRunService: ) memory_config_= agent_config.memory - config_id = memory_config_.get("memory_content") + config_id = memory_config_.get("memory_content") or memory_config_.get("memory_config",None) # 7. 调用 Agent result = await agent.chat( @@ -644,7 +644,7 @@ class DraftRunService: }) memory_config_ = agent_config.memory - config_id = memory_config_.get("memory_content") + config_id = memory_config_.get("memory_content") or memory_config_.get("memory_config",None) # 9. 流式调用 Agent full_content = "" From 462c3b069654edddabeccf057633ba980fca0ea8 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 10:57:45 +0800 Subject: [PATCH 118/175] fix(web): correct spelling --- web/src/api/models.ts | 2 +- .../views/ModelManagement/components/MultiKeyConfigModal.tsx | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/web/src/api/models.ts b/web/src/api/models.ts index f8619e43..e5d0f339 100644 --- a/web/src/api/models.ts +++ b/web/src/api/models.ts @@ -43,7 +43,7 @@ export const addModelApiKey = (model_id: string, data: MultiKeyForm) => { return request.post(`/models/${model_id}/apikeys`, data) } // Delete model API key -export const delteModelApiKey = (api_key_id: string) => { +export const deleteModelApiKey = (api_key_id: string) => { return request.delete(`/models/apikeys/${api_key_id}`) } // Update model status diff --git a/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx b/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx index 860efc3f..c76e195d 100644 --- a/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx +++ b/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx @@ -3,7 +3,7 @@ import { Form, Input, App, Button } from 'antd'; import { useTranslation } from 'react-i18next'; import type { ModelListItem, MultiKeyForm, MultiKeyConfigModalRef, MultiKeyConfigModalProps } from '../types'; import RbModal from '@/components/RbModal' -import { addModelApiKey, delteModelApiKey, getModelInfo } from '@/api/models' +import { addModelApiKey, deleteModelApiKey, getModelInfo } from '@/api/models' const MultiKeyConfigModal = forwardRef(({ refresh }, ref) => { const { t } = useTranslation(); @@ -58,7 +58,7 @@ const MultiKeyConfigModal = forwardRef { - delteModelApiKey(api_key_id) + deleteModelApiKey(api_key_id) .then(() => { message.success(t('common.deleteSuccess')) getData(model) From d2a67a53b592d5c6430605f6aa3aa12a5aff8b65 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 10:58:46 +0800 Subject: [PATCH 119/175] =?UTF-8?q?=E5=BA=94=E7=94=A8=E5=B1=82memory=5Fcon?= =?UTF-8?q?tent->memory=5Fconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/draft_run_service.py | 1 + 1 file changed, 1 insertion(+) diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 0d1f51a4..88a2f26a 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -86,6 +86,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str 检索到的历史记忆内容 """ logger.info(f" 长期记忆工具被调用！question={question}, user={end_user_id}") + try: from app.db import get_db db = next(get_db()) From b58d97fad373a4a693c325c9ad023695924a4032 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 10:59:38 +0800 Subject: [PATCH 120/175] =?UTF-8?q?=E5=BA=94=E7=94=A8=E5=B1=82memory=5Fcon?= =?UTF-8?q?tent->memory=5Fconfig?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/draft_run_service.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 88a2f26a..0d1f51a4 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -86,7 +86,6 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str 检索到的历史记忆内容 """ logger.info(f" 长期记忆工具被调用！question={question}, user={end_user_id}") - try: from app.db import get_db db = next(get_db()) From 7ba443afa558126e947d02e191a963c09ca4b3e0 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Wed, 28 Jan 2026 11:01:58 +0800 Subject: [PATCH 121/175] Fix/memory bug fix (#215) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * 检查需要更改的格式问题 * 修复宿主列表获取memory_config_idBUG * config_id做映射 * config_id做映射 * config_id做映射+1 * config_id做映射+1 * config_id做映射+1 * 应用层memory_content->memory_config * 应用层memory_content->memory_config * 应用层memory_content->memory_config --------- Co-authored-by: lanceyq <1982376970@qq.com> --- api/app/schemas/app_schema.py | 2 +- api/app/services/draft_run_service.py | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index 09410091..b350f17c 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -51,7 +51,7 @@ class ToolOldConfig(BaseModel): class MemoryConfig(BaseModel): """记忆配置""" enabled: bool = Field(default=True, description="是否启用对话历史记忆") - memory_content: Optional[str] = Field(default=None, description="选择记忆的内容类型") + memory_config: Optional[str] = Field(default=None, description="选择记忆的内容类型") max_history: int = Field(default=10, ge=0, le=100, description="最大保留的历史对话轮数") diff --git a/api/app/services/draft_run_service.py b/api/app/services/draft_run_service.py index 9766eec0..0d1f51a4 100644 --- a/api/app/services/draft_run_service.py +++ b/api/app/services/draft_run_service.py @@ -56,7 +56,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str 长期记忆工具 """ # search_switch = memory_config.get("search_switch", "2") - config_id= memory_config.get("memory_content",None) + config_id= memory_config.get("memory_content") or memory_config.get("memory_config",None) logger.info(f"创建长期记忆工具，配置: end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}") @tool(args_schema=LongTermMemoryInput) def long_term_memory(question: str) -> str: @@ -106,9 +106,9 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str "app.core.memory.agent.read_message", args=[end_user_id, question, [], "1", config_id, storage_type, user_rag_memory_id] ) - # result = task_service.get_task_memory_read_result(task.id) - # status = result.get("status") - # logger.info(f"读取任务状态：{status}") + result = task_service.get_task_memory_read_result(task.id) + status = result.get("status") + logger.info(f"读取任务状态：{status}") finally: db.close() @@ -418,7 +418,7 @@ class DraftRunService: ) memory_config_= agent_config.memory - config_id = memory_config_.get("memory_content") + config_id = memory_config_.get("memory_content") or memory_config_.get("memory_config",None) # 7. 调用 Agent result = await agent.chat( @@ -644,7 +644,7 @@ class DraftRunService: }) memory_config_ = agent_config.memory - config_id = memory_config_.get("memory_content") + config_id = memory_config_.get("memory_content") or memory_config_.get("memory_config",None) # 9. 流式调用 Agent full_content = "" From e5e914903cf0c6c5783b0f23d5d5dbb1c3f400ab Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Wed, 28 Jan 2026 11:04:46 +0800 Subject: [PATCH 122/175] feat(model and app statistic): 1. Optimize the model list; 2. Increase the model combination; 3. Add a model square; 4. Add application management statistics --- api/app/models/models_model.py | 2 +- api/app/schemas/model_schema.py | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/api/app/models/models_model.py b/api/app/models/models_model.py index a2bfa284..a8918c7c 100644 --- a/api/app/models/models_model.py +++ b/api/app/models/models_model.py @@ -149,7 +149,7 @@ class ModelBase(Base): description = Column(Text, comment="模型描述") is_deprecated = Column(Boolean, default=False, nullable=False, comment="是否弃用") is_official = Column(Boolean, default=True, comment="是否供应商官方模型（区分自定义）") - tags = Column(ARRAY(String), default=[], nullable=False, comment="模型标签（如['聊天', '创作']）") + tags = Column(ARRAY(String), default=list, nullable=False, comment="模型标签（如['聊天', '创作']）") add_count = Column(Integer, default=0, nullable=False, comment="模型被用户添加的次数") # 关联关系 diff --git a/api/app/schemas/model_schema.py b/api/app/schemas/model_schema.py index b83107ef..ce1b36bb 100644 --- a/api/app/schemas/model_schema.py +++ b/api/app/schemas/model_schema.py @@ -4,6 +4,10 @@ import datetime import uuid from app.models.models_model import ModelProvider, ModelType +from app.core.logging_config import get_business_logger + +schema_logger = get_business_logger() + @@ -164,7 +168,7 @@ class ModelApiKey(ModelApiKeyBase): and getattr(mc, 'name', None) == self.model_name)) ] except Exception as e: - print(f"提取 model_config_ids 失败：{e}") + schema_logger.warning(f"提取 model_config_ids 失败：{e}") self.model_config_ids = [] model_config = ConfigDict( From 18204bc1f7dddfaa2491722b44bc622d07d32d04 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 11:11:28 +0800 Subject: [PATCH 123/175] fix(web): model loading update --- .../components/CustomModelModal.tsx | 63 ++++++++++--------- .../components/GroupModelModal.tsx | 2 + .../ModelImplement/SubModelModal.tsx | 11 +--- .../components/MultiKeyConfigModal.tsx | 1 + 4 files changed, 37 insertions(+), 40 deletions(-) diff --git a/web/src/views/ModelManagement/components/CustomModelModal.tsx b/web/src/views/ModelManagement/components/CustomModelModal.tsx index edeb732f..d22fbcdd 100644 --- a/web/src/views/ModelManagement/components/CustomModelModal.tsx +++ b/web/src/views/ModelManagement/components/CustomModelModal.tsx @@ -42,40 +42,43 @@ const CustomModelModal = forwardRef( } setVisible(true); }; + const handleUpdate = (data: CustomModelForm) => { + setLoading(true) + const res = isEdit ? updateCustomModel(model.id, data) : addCustomModel(data) + + res.then(() => { + refresh && refresh() + handleClose() + message.success(isEdit ? t('common.updateSuccess') : t('common.createSuccess')) + }) + .catch(() => { + setLoading(false) + }); + } const handleSave = () => { form .validateFields() .then((values) => { setLoading(true) - values.is_official = false; - const logo = values.logo as any; + const { logo, ...rest } = values; + let formData: CustomModelForm = { + ...rest + } + formData.is_official = false; + if (typeof logo === 'object' && logo?.response?.data.file_id) { - getFileLink(logo?.response?.data.file_id).then(res => { - const logoRes = res as { url: string } - values.logo = logoRes.url - addCustomModel(values).then(() => { - if (refresh) { - refresh(); - } - handleClose() - message.success(isEdit ? t('common.updateSuccess') : t('common.createSuccess')) + getFileLink(logo?.response?.data.file_id) + .then(res => { + const logoRes = res as { url: string } + formData.logo = logoRes.url + handleUpdate(formData) }) - .catch(() => { - setLoading(false) - }); - }) - } else { - values.logo = typeof logo === 'string' ? logo : logo.url - updateCustomModel(model.id, values).then(() => { - if (refresh) { - refresh(); - } - handleClose() - message.success(isEdit ? t('common.updateSuccess') : t('common.createSuccess')) - }) .catch(() => { - setLoading(false) - }); + handleUpdate(formData) + }) + } else { + formData.logo = typeof logo === 'string' ? logo : logo.url + handleUpdate(formData) } }) .catch((err) => { @@ -102,18 +105,18 @@ const CustomModelModal = forwardRef( form={form} layout="vertical" > - {!isEdit && - } +     diff --git a/web/src/views/ModelManagement/components/GroupModelModal.tsx b/web/src/views/ModelManagement/components/GroupModelModal.tsx index 53a80c61..6ae54d4c 100644 --- a/web/src/views/ModelManagement/components/GroupModelModal.tsx +++ b/web/src/views/ModelManagement/components/GroupModelModal.tsx @@ -60,6 +60,8 @@ const GroupModelModal = forwardRef(({ const logoRes = res as { url: string } formData.logo = logoRes.url handleUpdate(formData) + }).catch(() => { + handleUpdate(formData) }) } else { formData.logo = typeof logo === 'string' ? logo : logo.url diff --git a/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx b/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx index cfe3f090..069f785d 100644 --- a/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx +++ b/web/src/views/ModelManagement/components/ModelImplement/SubModelModal.tsx @@ -24,14 +24,12 @@ const SubModelModal = forwardRef(({ const { message } = App.useApp() const [visible, setVisible] = useState(false); const [form] = Form.useForm(); - const [loading, setLoading] = useState(false) const [selecteds, setSelecteds] = useState([]) const [modelList, setModelList] = useState([]) // 封装取消方法，添加关闭弹窗逻辑 const handleClose = () => { form.resetFields(); - setLoading(false) setVisible(false); setSelecteds([]) }; @@ -56,13 +54,7 @@ const SubModelModal = forwardRef(({ const handleSave = () => { form .validateFields() - .then((values) => { - console.log('SubModelModal values', values, selecteds, selecteds.map(vo => ({ - ...vo[0], - model_name: vo[0].name, - model_config_ids: [vo[0].id], - id: vo[1].value - }))) + .then(() => { refresh?.(selecteds.map(vo => ({ ...vo[0], model_name: vo[0].name, @@ -127,7 +119,6 @@ const SubModelModal = forwardRef(({ onCancel={handleClose} okText={t('common.save')} onOk={handleSave} - confirmLoading={loading} >
    { + setLoading(true) addModelApiKey(model.id, { ...values, model_config_id: model.id, From 511e16f1d332bafd50d3d90da13ee0520cd1cf7e Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:18:11 +0800 Subject: [PATCH 124/175] =?UTF-8?q?=E7=BB=9F=E4=B8=80=E5=AD=97=E6=AE=B5?= =?UTF-8?q?=E4=B8=BAconfig=5Fid=5Fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 784288de..ab910536 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -186,7 +186,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） config_id_old = None if config.user_id: try: - config_id_old = int(config.user_id) + config_id_old = int(config.config_id_old) except (ValueError, TypeError): config_id_old = None From 4786b0c5d4480e9978c47a5daa9814f9fed2ddaa Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:19:24 +0800 Subject: [PATCH 125/175] =?UTF-8?q?=E7=BB=9F=E4=B8=80=E5=AD=97=E6=AE=B5?= =?UTF-8?q?=E4=B8=BAconfig=5Fid=5Fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index ab910536..87352f35 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -178,7 +178,6 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Read All --- def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数 configs = MemoryConfigRepository.get_all(self.db, workspace_id) - # 将 ORM 对象转换为字典列表 data_list = [] for config in configs: From 9a4b1f093789e8fb26eae3e5cbe1dea2b3c91fce Mon Sep 17 00:00:00 2001 From: Timebomb2018 <18868801967@163.com> Date: Wed, 28 Jan 2026 11:42:45 +0800 Subject: [PATCH 126/175] feat(model and app statistic): 1. Optimize the model list; 2. Increase the model combination; 3. Add a model square; 4. Add application management statistics --- api/app/controllers/model_controller.py | 30 +++++++++++++++---------- 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/api/app/controllers/model_controller.py b/api/app/controllers/model_controller.py index 481c520e..509f7cad 100644 --- a/api/app/controllers/model_controller.py +++ b/api/app/controllers/model_controller.py @@ -36,7 +36,7 @@ def get_model_providers(): @router.get("", response_model=ApiResponse) def get_model_list( - type: Optional[str | list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), + type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"), is_active: Optional[bool] = Query(None, description="激活状态筛选"), is_public: Optional[bool] = Query(None, description="公开状态筛选"), @@ -60,11 +60,14 @@ def get_model_list( try: # 解析 type 参数（支持逗号分隔） type_list = [] - if isinstance(type, str): - type_values = [t.strip() for t in type.split(',')] - type_list = [model_schema.ModelType(t.lower()) for t in type_values if t] - elif isinstance(type, list): - type_list = type + if type is not None: + flat_type = [] + for item in type: + split_items = [t.strip() for t in item.split(',') if t.strip()] + flat_type.extend(split_items) + + unique_flat_type = list(dict.fromkeys(flat_type)) + type_list = [ModelType(t.lower()) for t in unique_flat_type] api_logger.error(f"获取模型type_list: {type_list}") query = model_schema.ModelConfigQuery( @@ -89,7 +92,7 @@ def get_model_list( @router.get("/new", response_model=ApiResponse) def get_model_list( - type: Optional[str | list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), + type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"), provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于ModelConfig)"), is_active: Optional[bool] = Query(None, description="激活状态筛选"), is_public: Optional[bool] = Query(None, description="公开状态筛选"), @@ -111,11 +114,14 @@ def get_model_list( try: # 解析 type 参数（支持逗号分隔） type_list = [] - if isinstance(type, str): - type_values = [t.strip() for t in type.split(',')] - type_list = [model_schema.ModelType(t.lower()) for t in type_values if t] - elif isinstance(type, list): - type_list = type + if type is not None: + flat_type = [] + for item in type: + split_items = [t.strip() for t in item.split(',') if t.strip()] + flat_type.extend(split_items) + + unique_flat_type = list(dict.fromkeys(flat_type)) + type_list = [ModelType(t.lower()) for t in unique_flat_type] api_logger.info(f"获取模型type_list: {type_list}") query = model_schema.ModelConfigQueryNew( From fbc7bedb6c2d8c7797ed34c8a9fda1effc3224e1 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:45:51 +0800 Subject: [PATCH 127/175] =?UTF-8?q?=E7=BB=9F=E4=B8=80=E5=AD=97=E6=AE=B5?= =?UTF-8?q?=E4=B8=BAconfig=5Fid=5Fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 87352f35..db1c6415 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -178,6 +178,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） # --- Read All --- def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数 configs = MemoryConfigRepository.get_all(self.db, workspace_id) + # 将 ORM 对象转换为字典列表 data_list = [] for config in configs: @@ -185,12 +186,17 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） config_id_old = None if config.user_id: try: - config_id_old = int(config.config_id_old) + config_id_old = int(config.user_id) except (ValueError, TypeError): config_id_old = None - + + + if config_id_old: + memory_config=config_id_old + else: + memory_config=config.config_id config_dict = { - "config_id": config.config_id, + "config_id": memory_config, "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, From 981d78c8ba951950e527d02605459c97bab73c13 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:47:52 +0800 Subject: [PATCH 128/175] =?UTF-8?q?=E7=BB=9F=E4=B8=80=E5=AD=97=E6=AE=B5?= =?UTF-8?q?=E4=B8=BAconfig=5Fid=5Fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index db1c6415..573e2ca7 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -186,7 +186,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） config_id_old = None if config.user_id: try: - config_id_old = int(config.user_id) + config_id_old = int(config.config_id_old) except (ValueError, TypeError): config_id_old = None From 1be6de30d78dfb75ecc08c87bea49b07a7e08407 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:54:07 +0800 Subject: [PATCH 129/175] =?UTF-8?q?memory=5Fcontent=E6=9A=82=E6=97=B6?= =?UTF-8?q?=E4=B8=8D=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/schemas/app_schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index b350f17c..09410091 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -51,7 +51,7 @@ class ToolOldConfig(BaseModel): class MemoryConfig(BaseModel): """记忆配置""" enabled: bool = Field(default=True, description="是否启用对话历史记忆") - memory_config: Optional[str] = Field(default=None, description="选择记忆的内容类型") + memory_content: Optional[str] = Field(default=None, description="选择记忆的内容类型") max_history: int = Field(default=10, ge=0, le=100, description="最大保留的历史对话轮数") From 45833542a77a435b007a31c2de89def0f8c895ba Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:57:17 +0800 Subject: [PATCH 130/175] =?UTF-8?q?memory=5Fcontent=E6=9A=82=E6=97=B6?= =?UTF-8?q?=E4=B8=8D=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/memory_storage_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 573e2ca7..eec1007b 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -184,7 +184,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） for config in configs: # 安全地转换 user_id 为 int config_id_old = None - if config.user_id: + if config.config_id_old: try: config_id_old = int(config.config_id_old) except (ValueError, TypeError): From 2e7f6afe3f9a8d9cef03df08a2dfae4bf83cb96f Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Wed, 28 Jan 2026 11:58:10 +0800 Subject: [PATCH 131/175] Fix/memory bug fix (#217) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 图谱数据量限制数量去掉 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 用户详情优化 * 读取的接口，去掉全局锁 * 输出数组 * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化1.0（优化隐私输出、时间检索） * 反思优化测试接口 * 反思优化测试接口 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 读取接口内层嵌套BUG修复 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察) * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 新增中翻英功能（记忆时间线）(用户摘要)(兴趣分布接口)(查询核心档案)(记忆洞察)-接口添加翻译字段 * 把group_id替换end_user_id * 把group_id替换end_user_id_ * 把group_id替换end_user_id_ * config_config替换成memory_config * config_config替换成memory_config * [fix]Fix the memory interface to use end_user_id. * config_config替换成memory_config * config_config替换成memory_config * config_config替换成memory_config * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID * config_id字段改成UUID，与develop校对恢复 * 检查项目，修复group_id的遗留问题 * 检查项目，修复group_id的遗留问题 * 解决冲突 * 解决冲突 * end_user_id清理干净 * end_user_id清理干净 * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 修复遗留合并BUG * 感知meta_data字段BUG修复 * user_id->现实为config_id_old * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * user_id->显示为config_id_old传输 * 检查需要更改的格式问题 * 修复宿主列表获取memory_config_idBUG * config_id做映射 * config_id做映射 * config_id做映射+1 * config_id做映射+1 * config_id做映射+1 * 应用层memory_content->memory_config * 应用层memory_content->memory_config * 应用层memory_content->memory_config * 统一字段为config_id_old * 统一字段为config_id_old * 统一字段为config_id_old * 统一字段为config_id_old * memory_content暂时不修改 * memory_content暂时不修改 --------- Co-authored-by: lanceyq <1982376970@qq.com> --- api/app/schemas/app_schema.py | 2 +- api/app/services/memory_storage_service.py | 13 +++++++++---- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/api/app/schemas/app_schema.py b/api/app/schemas/app_schema.py index b350f17c..09410091 100644 --- a/api/app/schemas/app_schema.py +++ b/api/app/schemas/app_schema.py @@ -51,7 +51,7 @@ class ToolOldConfig(BaseModel): class MemoryConfig(BaseModel): """记忆配置""" enabled: bool = Field(default=True, description="是否启用对话历史记忆") - memory_config: Optional[str] = Field(default=None, description="选择记忆的内容类型") + memory_content: Optional[str] = Field(default=None, description="选择记忆的内容类型") max_history: int = Field(default=10, ge=0, le=100, description="最大保留的历史对话轮数") diff --git a/api/app/services/memory_storage_service.py b/api/app/services/memory_storage_service.py index 784288de..eec1007b 100644 --- a/api/app/services/memory_storage_service.py +++ b/api/app/services/memory_storage_service.py @@ -184,14 +184,19 @@ class DataConfigService: # 数据配置服务类（PostgreSQL） for config in configs: # 安全地转换 user_id 为 int config_id_old = None - if config.user_id: + if config.config_id_old: try: - config_id_old = int(config.user_id) + config_id_old = int(config.config_id_old) except (ValueError, TypeError): config_id_old = None - + + + if config_id_old: + memory_config=config_id_old + else: + memory_config=config.config_id config_dict = { - "config_id": config.config_id, + "config_id": memory_config, "config_name": config.config_name, "config_desc": config.config_desc, "workspace_id": str(config.workspace_id) if config.workspace_id else None, From 9e4a5276758c261e1efa4305386d3296fca986cc Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 11:59:37 +0800 Subject: [PATCH 132/175] feat(web): add app statistics --- web/src/i18n/en.ts | 6 + web/src/i18n/zh.ts | 8 +- .../views/ApplicationConfig/Statistics.tsx | 86 ++++++++++++ .../components/ConfigHeader.tsx | 2 +- .../ApplicationConfig/components/LineCard.tsx | 127 ++++++++++++++++++ web/src/views/ApplicationConfig/index.tsx | 2 + web/src/views/ApplicationConfig/types.ts | 15 +++ 7 files changed, 244 insertions(+), 2 deletions(-) create mode 100644 web/src/views/ApplicationConfig/Statistics.tsx create mode 100644 web/src/views/ApplicationConfig/components/LineCard.tsx diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index 1df2eb6d..595337ba 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -1175,6 +1175,12 @@ export const en = { priority: 'Structured Integration', addTool: 'Add Tool', tool: 'Tool', + + statistics: 'Data Statistics', + daily_conversations: 'Daily Conversations', + daily_new_users: 'Daily New Users', + daily_api_calls: 'Daily API Calls', + daily_tokens: 'Token Consumption', }, userMemory: { userMemory: 'User Memory', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 39908757..daf5977c 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -658,7 +658,13 @@ export const zh = { priority: '结构化整合', addTool: '添加工具', tool: '工具', - variableConfig: '配置变量' + variableConfig: '配置变量', + + statistics: '数据统计', + daily_conversations: '消息会话数', + daily_new_users: '新增用户数', + daily_api_calls: '调用次数', + daily_tokens: 'Token消耗', }, role: { roleManagement: '角色管理', diff --git a/web/src/views/ApplicationConfig/Statistics.tsx b/web/src/views/ApplicationConfig/Statistics.tsx new file mode 100644 index 00000000..8a76ab06 --- /dev/null +++ b/web/src/views/ApplicationConfig/Statistics.tsx @@ -0,0 +1,86 @@ +import { type FC, useState, useEffect } from 'react'; +import { Row, Col, Flex, DatePicker } from 'antd'; +import type { Dayjs } from 'dayjs' +import dayjs from 'dayjs'; + +const { RangePicker } = DatePicker; + +import type { Application } from '@/views/ApplicationManagement/types' +import { getAppStatistics } from '@/api/application'; +import LineCard from './components/LineCard' +import type { StatisticsData, StatisticsItem } from './types' + +const TotalObj: Record = { + daily_conversations: 'total_conversations', + daily_new_users: 'total_new_users', + daily_api_calls: 'total_api_calls', + daily_tokens: 'total_tokens', +} +const Statistics: FC<{ application: Application | null }> = ({ application }) => { + const [data, setData] = useState({ + daily_conversations: [], + total_conversations: 0, + daily_new_users: [], + total_new_users: 0, + daily_api_calls: [], + total_api_calls: 0, + daily_tokens: [], + total_tokens: 0 + }) + const [query, setQuery] = useState({ + start_date: dayjs().subtract(6, 'd'), + end_date: dayjs().subtract(0, 'd'), + }) + + useEffect(() => { + getData() + }, [application, query]) + const getData = () => { + if (!application?.id) { + return + } + const params = { + start_date: query.start_date.startOf('d').valueOf(), + end_date: query.end_date.endOf('d').valueOf(), + } + + getAppStatistics(application.id, params) + .then(res => { + setData(res as StatisticsData) + }) + } + const handleChange = (date: [Dayjs | null, Dayjs | null] | null) => { + if (!date || !date[0] || !date[1]) return + setQuery({ + start_date: date[0], + end_date: date[1], + }) + } + return ( +
    + + + + + + + {Object.entries(data).map(([key, value]) => { + if (key.includes('total')) { + return null + } + const totalKey = TotalObj[key]; + return ( + + + + ) + })} + +
    + ); +} +export default Statistics; \ No newline at end of file diff --git a/web/src/views/ApplicationConfig/components/ConfigHeader.tsx b/web/src/views/ApplicationConfig/components/ConfigHeader.tsx index 94ef0ef7..db1e0fa5 100644 --- a/web/src/views/ApplicationConfig/components/ConfigHeader.tsx +++ b/web/src/views/ApplicationConfig/components/ConfigHeader.tsx @@ -17,7 +17,7 @@ import CopyModal from './CopyModal' const { Header } = Layout; -const tabKeys = ['arrangement', 'api', 'release'] +const tabKeys = ['arrangement', 'api', 'release', 'statistics'] const menuIcons: Record = { edit: editIcon, copy: copyIcon, diff --git a/web/src/views/ApplicationConfig/components/LineCard.tsx b/web/src/views/ApplicationConfig/components/LineCard.tsx new file mode 100644 index 00000000..0cfc3f0e --- /dev/null +++ b/web/src/views/ApplicationConfig/components/LineCard.tsx @@ -0,0 +1,127 @@ +import { type FC, useEffect, useRef } from 'react' +import { useTranslation } from 'react-i18next' +import ReactEcharts from 'echarts-for-react'; +import * as echarts from 'echarts'; +import Empty from '@/components/Empty' + +import Card from './Card' +import type { StatisticsItem } from '../types' + +interface LineCardProps { + chartData: StatisticsItem[]; + type: string; + total: number; +} + +const SeriesConfig = { + type: 'line', + stack: 'Total', + smooth: true, + lineStyle: { + width: 3 + }, + showSymbol: true, + label: { + show: false, + position: 'top' + }, + emphasis: { + focus: 'series' + }, +} + +const ColorObj: Record = { + daily_conversations: '#FFB048', + daily_new_users: '#4DA8FF', + daily_api_calls: '#155EEF', + daily_tokens: '#AD88FF' +} + +const LineCard: FC = ({ chartData, type, total }) => { + const { t } = useTranslation() + const chartRef = useRef(null); + + useEffect(() => { + + }, [chartData]) + + const getSeries = () => { + return [{ + ...SeriesConfig, + name: t(`application.${type}`), + data: chartData.map(vo => vo.count), + areaStyle: { + opacity: 0.8, + color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [ + { offset: 0, color: ColorObj[type] }, + { offset: 1, color: '#FFFFFF' } + ]) + }, + }] + } + + return ( + {t(`application.${type}`)} {total}} + > + {chartData && chartData.length > 0 ? ( + item.date), + boundaryGap: false, + }, + yAxis: { + type: 'value', + axisLabel: { + color: '#A8A9AA', + fontFamily: 'PingFangSC, PingFang SC', + align: 'right', + lineHeight: 17, + }, + axisLine: { + lineStyle: { + color: '#EBEBEB', + } + }, + }, + series: getSeries() + }} + style={{ height: '265px', width: '100%', minWidth: '100%', boxSizing: 'border-box' }} + opts={{ renderer: 'canvas' }} + notMerge={true} + lazyUpdate={true} + /> + ) : } + + ) +} + +export default LineCard diff --git a/web/src/views/ApplicationConfig/index.tsx b/web/src/views/ApplicationConfig/index.tsx index 7d5d5950..4dd9231a 100644 --- a/web/src/views/ApplicationConfig/index.tsx +++ b/web/src/views/ApplicationConfig/index.tsx @@ -9,6 +9,7 @@ import ReleasePage from './ReleasePage' import Cluster from './Cluster' import { getApplication } from '@/api/application' import Workflow from '@/views/Workflow'; +import Statistics from './Statistics' const ApplicationConfig: React.FC = () => { const { id } = useParams(); @@ -68,6 +69,7 @@ const ApplicationConfig: React.FC = () => { {activeTab === 'arrangement' && application?.type === 'workflow' && } {activeTab === 'api' && } {activeTab === 'release' && } + {activeTab === 'statistics' && } ); }; diff --git a/web/src/views/ApplicationConfig/types.ts b/web/src/views/ApplicationConfig/types.ts index 6f641ebb..9df6e04a 100644 --- a/web/src/views/ApplicationConfig/types.ts +++ b/web/src/views/ApplicationConfig/types.ts @@ -150,4 +150,19 @@ export interface AiPromptForm { } export interface ChatVariableConfigModalRef { handleOpen: (values: Variable[]) => void; +} + +export interface StatisticsItem { + count: number; + date: string; +} +export interface StatisticsData { + daily_conversations: StatisticsItem[]; + daily_new_users: StatisticsItem[]; + daily_api_calls: StatisticsItem[]; + daily_tokens: StatisticsItem[]; + total_conversations: number; + total_new_users: number; + total_api_calls: number; + total_tokens: number; } \ No newline at end of file From dbc4ba84c2a6086a3855b9597b57696493b462d6 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Wed, 28 Jan 2026 13:02:50 +0800 Subject: [PATCH 133/175] fix(workflow): fix streaming output issues with multi-output End nodes End nodes with multiple output segments could cause cursor errors or leave some segments inactive, resulting in incorrect final outputs. Unified _emit_active_chunks and _update_scope_activate to ensure all segments are activated in order and streamed correctly. --- api/app/core/workflow/executor.py | 257 ++++++++++++++++--------- api/app/core/workflow/graph_builder.py | 183 ++++++++++-------- 2 files changed, 272 insertions(+), 168 deletions(-) diff --git a/api/app/core/workflow/executor.py b/api/app/core/workflow/executor.py index f0411ae3..b7abf659 100644 --- a/api/app/core/workflow/executor.py +++ b/api/app/core/workflow/executor.py @@ -16,7 +16,6 @@ from app.core.workflow.graph_builder import GraphBuilder, StreamOutputConfig from app.core.workflow.nodes import WorkflowState from app.core.workflow.nodes.base_config import VariableType from app.core.workflow.nodes.enums import NodeType -from app.core.workflow.template_renderer import render_template logger = logging.getLogger(__name__) @@ -157,12 +156,137 @@ class WorkflowExecutor: "error": result.get("error"), } - def _update_end_activate(self, node_id): + def _update_scope_activate(self, scope, status=None): + """ + Update the activation state of all End nodes based on a completed scope (node or variable). + + Iterates over all End nodes in `self.end_outputs` and calls + `update_activate` on each, which may: + - Activate variable segments that depend on the completed node/scope. + - Activate the entire End node output if all control conditions are met. + + If any End node becomes active and `self.activate_end` is not yet set, + this node will be marked as the currently active End node. + + Args: + scope (str): The node ID or scope that has completed execution. + status (str | None): Optional status of the node (used for branch/control nodes). + """ for node in self.end_outputs.keys(): - self.end_outputs[node].update_activate(node_id) + self.end_outputs[node].update_activate(scope, status) if self.end_outputs[node].activate and self.activate_end is None: self.activate_end = node + def _update_stream_output_status(self, activate, data): + """ + Update the stream output state of End nodes based on workflow state updates. + + This method checks which nodes/scopes are activated and propagates + activation to End nodes accordingly. + + Args: + activate (dict): Mapping of node_id -> bool indicating which nodes/scopes are activated. + data (dict): Mapping of node_id -> node runtime data, including outputs. + + Behavior: + For each node in `data`: + 1. If the node is activated (`activate[node_id]` is True), + retrieve its output status from `runtime_vars`. + 2. Call `_update_scope_activate` to propagate the activation + to all relevant End nodes and update `self.activate_end`. + """ + for node_id in data.keys(): + if activate.get(node_id): + node_output_status = ( + data[node_id] + .get('runtime_vars', {}) + .get(node_id) + .get("output") + ) + self._update_scope_activate(node_id, status=node_output_status) + + async def _emit_active_chunks( + self, + node_outputs: dict, + variables: dict, + force=False + ): + """ + Process and yield all currently active output segments for the currently active End node. + + This method handles stream-mode output for an End node by iterating through its output segments + (`OutputContent`). Only segments marked as active (`activate=True`) are processed, unless + `force=True`, which allows all segments to be processed regardless of their activation state. + + Behavior: + 1. Iterates from the current `cursor` position to the end of the outputs list. + 2. For each segment: + - If the segment is literal text (`is_variable=False`), append it directly. + - If the segment is a variable (`is_variable=True`), evaluate it using + `evaluate_expression` with the given `node_outputs` and `variables`, + then transform the result with `_trans_output_string`. + 3. Yield a stream event of type "message" containing the processed chunk. + 4. Move the `cursor` forward after processing each segment. + 5. When all segments have been processed, remove this End node from `end_outputs` + and reset `activate_end` to None. + + Args: + node_outputs (dict): Current runtime node outputs, used for variable evaluation. + variables (dict): Current runtime variables, used for variable evaluation. + force (bool, default=False): If True, process segments even if `activate=False`. + + Yields: + dict: A stream event of type "message" containing the processed chunk. + + Notes: + - Segments that fail evaluation (ValueError) are skipped with a warning logged. + - This method only processes the currently active End node (`self.activate_end`). + - Use `force=True` for final emission regardless of activation state. + """ + + end_info = self.end_outputs[self.activate_end] + + while end_info.cursor < len(end_info.outputs): + final_chunk = '' + current_segment = end_info.outputs[end_info.cursor] + + if not current_segment.activate and not force: + # Stop processing until this segment becomes active + break + + # Literal segment + if not current_segment.is_variable: + final_chunk += current_segment.literal + else: + # Variable segment: evaluate and transform + try: + chunk = evaluate_expression( + current_segment.literal, + variables=variables, + node_outputs=node_outputs + ) + chunk = self._trans_output_string(chunk) + final_chunk += chunk + except ValueError: + # Log failed evaluation but continue streaming + logger.warning(f"[STREAM] Failed to evaluate segment: {current_segment.literal}") + + if final_chunk: + yield { + "event": "message", + "data": { + "chunk": final_chunk + } + } + + # Advance cursor after processing + end_info.cursor += 1 + + # Remove End node from active tracking if all segments have been processed + if end_info.cursor >= len(end_info.outputs): + self.end_outputs.pop(self.activate_end) + self.activate_end = None + @staticmethod def _trans_output_string(content): if isinstance(content, str): @@ -218,14 +342,8 @@ class WorkflowExecutor: result = await graph.ainvoke(initial_state, config=self.checkpoint_config) full_content = '' - for end_info in self.end_outputs.values(): - output_template = "".join([output.literal for output in end_info.outputs]) - full_content += render_template( - output_template, - result.get("variables", {}), - result.get("runtime_vars", {}), - strict=False - ) + for end_id in self.end_outputs.keys(): + full_content += result.get('runtime_vars', {}).get(end_id, {}).get('output', '') result["messages"].extend( [ { @@ -306,7 +424,7 @@ class WorkflowExecutor: try: chunk_count = 0 full_content = '' - + self._update_scope_activate("sys") async for event in graph.astream( initial_state, stream_mode=["updates", "debug", "custom"], # Use updates + debug + custom mode @@ -333,9 +451,12 @@ class WorkflowExecutor: if not end_info or end_info.cursor >= len(end_info.outputs): continue current_output = end_info.outputs[end_info.cursor] - if current_output.is_variable and current_output.depends_on_node(node_id): + if current_output.is_variable and current_output.depends_on_scope(node_id): if data.get("done"): end_info.cursor += 1 + if end_info.cursor >= len(end_info.outputs): + self.end_outputs.pop(self.activate_end) + self.activate_end = None else: full_content += data.get("chunk") yield { @@ -415,91 +536,53 @@ class WorkflowExecutor: elif mode == "updates": # Handle state updates - store final state - for node_id in data.keys(): - self._update_end_activate(node_id) - wait = False - state = graph.get_state(config=self.checkpoint_config) - node_outputs = state.values.get("runtime_vars", {}) - for _ in data.keys(): - node_outputs = node_outputs | data.get(_).get("runtime_vars", {}) + state = graph.get_state(config=self.checkpoint_config).values + node_outputs = state.get("runtime_vars", {}) + variables = state.get("variables", {}) + activate = state.get("activate", {}) + for _, node_data in data.items(): + node_outputs |= node_data.get("runtime_vars", {}) + variables |= node_data.get("variables", {}) + self._update_stream_output_status(activate, data) + wait = False while self.activate_end and not wait: - message = '' - logger.info(self.activate_end) - end_info = self.end_outputs[self.activate_end] - content = end_info.outputs[end_info.cursor] - while content.activate: - if not content.is_variable: - full_content += content.literal - message += content.literal - else: - try: - chunk = evaluate_expression( - content.literal, - variables={}, - node_outputs=node_outputs - ) - chunk = self._trans_output_string(chunk) - message += chunk - full_content += chunk - except ValueError: - pass - end_info.cursor += 1 - if end_info.cursor == len(end_info.outputs): - break - content = end_info.outputs[end_info.cursor] - if end_info.cursor != len(end_info.outputs): + async for msg_event in self._emit_active_chunks( + node_outputs=node_outputs, + variables=variables + ): + full_content += msg_event["data"]['chunk'] + yield msg_event + + if self.activate_end: wait = True else: - self.end_outputs.pop(self.activate_end) - self.activate_end = None - for node_id in data.keys(): - self._update_end_activate(node_id) - if message: - yield { - "event": "message", - "data": { - "chunk": message - } - } + self._update_stream_output_status(activate, data) logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} " f"- execution_id: {self.execution_id}") result = graph.get_state(self.checkpoint_config).values - while self.activate_end: - message = '' - end_info = self.end_outputs[self.activate_end] - content = end_info.outputs[end_info.cursor] - if not content.is_variable: - message += content.literal - else: - node_outputs = result.get("runtime_vars", {}) - variables = result.get("variables", {}) - try: - chunk = evaluate_expression( - content.literal, + node_outputs = result.get("runtime_vars", {}) + variables = result.get("variables", {}) + self.end_outputs = { + node_id: node_info + for node_id, node_info in self.end_outputs.items() + if node_info.activate + } + + if self.end_outputs or self.activate_end: + while self.activate_end: + async for msg_event in self._emit_active_chunks( + node_outputs=node_outputs, variables=variables, - node_outputs=node_outputs - ) - chunk = self._trans_output_string(chunk) - message += chunk - full_content += chunk - except ValueError: - pass - end_info.cursor += 1 - if end_info.cursor == len(end_info.outputs): - self.end_outputs.pop(self.activate_end) - self.activate_end = None - if self.end_outputs: + force=True + ): + full_content += msg_event["data"]['chunk'] + yield msg_event + + if not self.activate_end and self.end_outputs: self.activate_end = list(self.end_outputs.keys())[0] - if message: - yield { - "event": "message", - "data": { - "chunk": message - } - } # 计算耗时 end_time = datetime.datetime.now() diff --git a/api/app/core/workflow/graph_builder.py b/api/app/core/workflow/graph_builder.py index 9fa89fd2..b1d43e08 100644 --- a/api/app/core/workflow/graph_builder.py +++ b/api/app/core/workflow/graph_builder.py @@ -53,114 +53,110 @@ class OutputContent(BaseModel): ) ) - def depends_on_node(self, node_id: str) -> bool: + def depends_on_scope(self, scope: str) -> bool: """ - Check if this output segment depends on a specific node's variable. - - This method examines the `literal` of the output segment to see if it - contains a variable placeholder referencing the given node in the form: - - {{ node_id.field_name }} - - It uses a regular expression to match the exact node ID, avoiding - false positives from substring matches (e.g., 'node1' should not match 'node10'). + Check if this segment depends on a given scope. Args: - node_id (str): The ID of the node to check for in this segment's variable placeholders. + scope (str): Node ID or special variable prefix (e.g., "sys"). Returns: - bool: - - True if the segment contains a variable referencing the given node. - - False otherwise. - - Example: - literal = "{{node1.name}}" - - depends_on_node("node1") -> True - depends_on_node("node2") -> False - - Usage: - This method is primarily used in stream mode to determine whether - a particular variable output segment should be activated when a - specific upstream node completes execution. + bool: True if this segment references the given scope. """ - variable_pattern = rf"\{{\{{\s*{re.escape(node_id)}\.[a-zA-Z0-9_]+\s*\}}\}}" - pattern = re.compile(variable_pattern) - match = pattern.search(self.literal) - if match: - return True - return False + pattern = rf"\{{\{{\s*{re.escape(scope)}\.[a-zA-Z0-9_]+\s*\}}\}}" + return bool(re.search(pattern, self.literal)) class StreamOutputConfig(BaseModel): """ Streaming output configuration for an End node. - This structure controls: - - whether the End node output is globally active - - which upstream branch nodes are responsible for activation - - how each output segment behaves in streaming mode + This configuration describes how the End node output behaves in streaming mode, + including: + - whether output emission is globally activated + - which upstream branch/control nodes gate the activation + - how each parsed output segment is streamed and activated """ activate: bool = Field( ..., description=( - "Global activation state of the End node output.\n" - "If False, no output should be emitted until all control nodes are resolved." + "Global activation flag for the End node output.\n" + "When False, output segments should not be emitted even if available.\n" + "This flag typically becomes True once required control branch conditions " + "are satisfied." ) ) - control_nodes: list[str] = Field( + control_nodes: dict[str, str] = Field( ..., description=( - "List of upstream branch node IDs that control this End node.\n" - "Each node must signal completion before output becomes active." + "Control branch conditions for this End node output.\n" + "Mapping of `branch_node_id -> expected_branch_label`.\n" + "The End node output becomes globally active when a controlling branch node " + "reports a matching completion status." ) ) outputs: list[OutputContent] = Field( ..., - description="Ordered list of output segments parsed from the output template." + description=( + "Ordered list of output segments parsed from the output template.\n" + "Each segment represents either a literal text block or a variable placeholder " + "that may be activated independently." + ) ) cursor: int = Field( ..., description=( "Streaming cursor index.\n" - "Indicates how many output segments have already been emitted." + "Indicates the next output segment index to be emitted.\n" + "Segments with index < cursor are considered already streamed." ) ) - def update_activate(self, node_id): + def update_activate(self, scope: str, status=None): """ - Update activation state based on an upstream node completion. + Update streaming activation state based on an upstream node or special variable. - This method is typically called when a branch/control node finishes execution. + Args: + scope (str): + Identifier of the completed upstream entity. + - If a control branch node, it should match a key in `control_nodes`. + - If a variable placeholder (e.g., "sys.xxx"), it may appear in output segments. + status (optional): + Completion status of the control branch node. + Required when `scope` refers to a control node. Behavior: - 1. If the node is a control node: - - Remove it from `control_nodes` - - If all control nodes are resolved, activate the entire output + 1. Control branch nodes: + - If `scope` matches a key in `control_nodes` and `status` matches the expected + branch label, the End node output becomes globally active (`activate = True`). - 2. Activate variable output segments that depend on this node: - - If an output segment is a variable - - And its literal references the completed node_id - - Mark that segment as active + 2. Variable output segments: + - For each segment that is a variable (`is_variable=True`): + - If the segment literal references `scope`, mark the segment as active. + - This applies both to regular node variables (e.g., "node_id.field") + and special system variables (e.g., "sys.xxx"). + + Notes: + - This method does not emit output or advance the streaming cursor. + - It only updates activation flags based on upstream events or special variables. """ # Case 1: resolve control branch dependency - if node_id in self.control_nodes: - self.control_nodes.remove(node_id) - - # All branch constraints resolved → enable output - if not self.control_nodes: + if scope in self.control_nodes.keys(): + if status is None: + raise RuntimeError("[Stream Output] Control node activation status not provided") + if status == self.control_nodes[scope]: self.activate = True # Case 2: activate variable segments related to this node for i in range(len(self.outputs)): if ( self.outputs[i].is_variable - and self.outputs[i].depends_on_node(node_id) + and self.outputs[i].depends_on_scope(scope) ): self.outputs[i].activate = True @@ -184,11 +180,11 @@ class GraphBuilder: self._find_upstream_branch_node = lru_cache( maxsize=len(self.nodes) * 2 )(self._find_upstream_branch_node) - self._analyze_end_node_output() self.graph = StateGraph(WorkflowState) self.add_nodes() self.add_edges() + self._analyze_end_node_output() # EDGES MUST BE ADDED AFTER NODES ARE ADDED. @property @@ -216,30 +212,53 @@ class GraphBuilder: except KeyError: raise RuntimeError(f"Node not found: Id={node_id}") - def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[str]]: - """Find upstream branch nodes for a given target node in the workflow graph. + def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[tuple[str, str]]]: + """ + Recursively find all upstream branch (control) nodes that influence the execution + of the given target node. - This method identifies all upstream control (branch) nodes that can affect - the execution of `target_node`. If `target_node` is reachable from a start - node (i.e., a node with no upstream nodes), the method returns an empty tuple. + This method walks upstream along the workflow graph starting from `target_node`. + It distinguishes between: + - branch nodes (node types listed in `BRANCH_NODES`) + - non-branch nodes (ordinary processing nodes) - The function distinguishes between branch nodes (defined in `BRANCH_NODES`) - and non-branch nodes, recursively traversing upstream through non-branch - nodes. If any non-branch upstream path does not lead to a branch node, - the result will indicate that no valid upstream branch node exists. + Traversal rules: + 1. For each immediate upstream node: + - If it is a branch node, it is recorded as an affecting control node. + - If it is a non-branch node, the traversal continues recursively upstream. + 2. If ANY upstream path reaches a START / CYCLE_START node without encountering + a branch node, the traversal is considered invalid: + - `has_branch` will be False + - no branch nodes are returned. + 3. Only when ALL upstream non-branch paths eventually lead to at least one + branch node will `has_branch` be True. + + Special case: + - If `target_node` has no upstream nodes AND its type is START or CYCLE_START, + it is considered directly reachable from the workflow entry, and therefore + has no controlling branch nodes. Args: - target_node (str): The identifier of the target node. + target_node (str): + The identifier of the node whose upstream control branches + are to be resolved. Returns: - tuple[bool, tuple[str]]: - - has_branch (bool): True if all upstream non-branch paths lead to at least - one branch node; False if any path reaches a start node without a branch. - - branch_nodes (tuple[str]): A deduplicated tuple of upstream branch node IDs - affecting `target_node`. Returns an empty tuple if `has_branch` is False. + tuple[bool, tuple[tuple[str, str]]]: + - has_branch (bool): + True if every upstream path from `target_node` encounters + at least one branch node. + False if any path reaches a start node without a branch. + - branch_nodes (tuple[tuple[str, str]]): + A deduplicated tuple of `(branch_node_id, branch_label)` pairs + representing all branch nodes that can influence `target_node`. + Returns an empty tuple if `has_branch` is False. """ source_nodes = [ - edge.get("source") + { + "id": edge.get("source"), + "branch": edge.get("label") + } for edge in self.edges if edge.get("target") == target_node ] @@ -249,11 +268,13 @@ class GraphBuilder: branch_nodes = [] non_branch_nodes = [] - for node_id in source_nodes: - if self.get_node_type(node_id) in BRANCH_NODES: - branch_nodes.append(node_id) + for node_info in source_nodes: + if self.get_node_type(node_info["id"]) in BRANCH_NODES: + branch_nodes.append( + (node_info["id"], node_info["branch"]) + ) else: - non_branch_nodes.append(node_id) + non_branch_nodes.append(node_info["id"]) has_branch = True for node_id in non_branch_nodes: @@ -334,7 +355,7 @@ class GraphBuilder: activate=not has_branch, # Branch nodes that control activation of this End node - control_nodes=list(control_nodes), + control_nodes=dict(control_nodes), # Convert output segments into OutputContent objects outputs=list( @@ -362,7 +383,7 @@ class GraphBuilder: else: self.end_node_map[end_node_id] = StreamOutputConfig( activate=True, - control_nodes=[], + control_nodes={}, outputs=list( [ OutputContent( From 8984ba7aeff5a69e4ab7152e5869a1c03285bc60 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 14:49:30 +0800 Subject: [PATCH 134/175] feat(web): add apps statistics api --- web/src/api/application.ts | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/web/src/api/application.ts b/web/src/api/application.ts index 69d27d44..1f20282e 100644 --- a/web/src/api/application.ts +++ b/web/src/api/application.ts @@ -108,4 +108,8 @@ export const getShareToken = (share_token: string, user_id: string) => { // 复制应用 export const copyApplication = (app_id: string, new_name: string) => { return request.post(`/apps/${app_id}/copy?new_name=${new_name}`) -} \ No newline at end of file +} +// 数据统计 +export const getAppStatistics = (app_id: string, data: { start_date: number; end_date: number; }) => { + return request.get(`/apps/${app_id}/statistics`, data) +} From 35a10e86b5b49e92e9058c13f92c21b1018372dc Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 15:05:12 +0800 Subject: [PATCH 135/175] fix(web): agent's knowledge_bases bugfix --- web/src/views/ApplicationConfig/Agent.tsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/web/src/views/ApplicationConfig/Agent.tsx b/web/src/views/ApplicationConfig/Agent.tsx index 77e90440..82a3f177 100644 --- a/web/src/views/ApplicationConfig/Agent.tsx +++ b/web/src/views/ApplicationConfig/Agent.tsx @@ -212,7 +212,7 @@ const Agent = forwardRef((_props, ref) => { ...data.knowledge_retrieval, ...knowledgeRest, knowledge_bases: knowledge_bases.map(item => ({ - kb_id: item.id, + kb_id: item.kb_id || item.id, ...(item.config || {}) })) } as KnowledgeConfig : null, From 102712a16e4efc76e11bcf8f9f316d737fe45eeb Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 15:20:31 +0800 Subject: [PATCH 136/175] Revert "feat(web): update read_all_config select valueKey" This reverts commit 46f0f3cee90f7cf852bf5bcf89866b57448f1ffa. --- web/src/components/CustomSelect/index.tsx | 19 ++++++------------- web/src/views/ApplicationConfig/Agent.tsx | 6 ++---- web/src/views/Workflow/constant.ts | 4 ++-- web/src/views/Workflow/types.ts | 2 +- 4 files changed, 11 insertions(+), 20 deletions(-) diff --git a/web/src/components/CustomSelect/index.tsx b/web/src/components/CustomSelect/index.tsx index 6153a76d..1887d635 100644 --- a/web/src/components/CustomSelect/index.tsx +++ b/web/src/components/CustomSelect/index.tsx @@ -15,7 +15,7 @@ interface ApiResponse { interface CustomSelectProps extends Omit { url: string; params?: Record; - valueKey?: string | string[]; + valueKey?: string; labelKey?: string; placeholder?: string; hasAll?: boolean; @@ -66,18 +66,11 @@ const CustomSelect: FC = ({ {...props} > {hasAll && {allTitle || t('common.all')}} - {displayOptions.map((option) => { - const getValue = () => { - if (typeof valueKey === 'string') return option[valueKey]; - return valueKey.find(key => option[key] != null) ? option[valueKey.find(key => option[key] != null)!] : undefined; - }; - const value = getValue(); - return ( - - {String(option[labelKey])} - - ); - })} + {displayOptions.map((option) => ( + + {String(option[labelKey])} + + ))} ); }; diff --git a/web/src/views/ApplicationConfig/Agent.tsx b/web/src/views/ApplicationConfig/Agent.tsx index 97a622d1..77e90440 100644 --- a/web/src/views/ApplicationConfig/Agent.tsx +++ b/web/src/views/ApplicationConfig/Agent.tsx @@ -79,7 +79,7 @@ const SelectWrapper: FC<{ title: string, desc: string, name: string | string[], placeholder={t('common.pleaseSelect')} url={url} hasAll={false} - valueKey={['config_id_old', 'config_id']} + valueKey='config_id' labelKey="config_name" /> @@ -126,14 +126,12 @@ const Agent = forwardRef((_props, ref) => { getApplicationConfig(id as string).then(res => { const response = res as Config let allTools = Array.isArray(response.tools) ? response.tools : [] - const memoryContent = response.memory?.memory_content - const convertedMemoryContent = memoryContent && !isNaN(Number(memoryContent)) ? Number(memoryContent) : memoryContent form.setFieldsValue({ ...response, tools: allTools, memory: { ...response.memory, - memory_content: convertedMemoryContent + memory_content: response.memory?.memory_content ? Number(response.memory?.memory_content) : undefined } }) setData({ diff --git a/web/src/views/Workflow/constant.ts b/web/src/views/Workflow/constant.ts index aab8be7d..e250e184 100644 --- a/web/src/views/Workflow/constant.ts +++ b/web/src/views/Workflow/constant.ts @@ -200,7 +200,7 @@ export const nodeLibrary: NodeLibrary[] = [ config_id: { type: 'customSelect', url: memoryConfigListUrl, - valueKey: ['config_id_old', 'config_id'], + valueKey: 'config_id', labelKey: 'config_name' }, search_switch: { @@ -223,7 +223,7 @@ export const nodeLibrary: NodeLibrary[] = [ config_id: { type: 'customSelect', url: memoryConfigListUrl, - valueKey: ['config_id_old', 'config_id'], + valueKey: 'config_id', labelKey: 'config_name' } } diff --git a/web/src/views/Workflow/types.ts b/web/src/views/Workflow/types.ts index 31d1f512..909c30e4 100644 --- a/web/src/views/Workflow/types.ts +++ b/web/src/views/Workflow/types.ts @@ -14,7 +14,7 @@ export interface NodeConfig { url?: string; params?: { [key: string]: unknown; } - valueKey?: string | string[]; + valueKey?: string; labelKey?: string; defaultValue?: any; From 44bf1eeae2d88383cca334a464e33c4d3aac3bad Mon Sep 17 00:00:00 2001 From: Mark Date: Wed, 28 Jan 2026 15:24:55 +0800 Subject: [PATCH 137/175] [add] migrations script --- .../versions/915bed077f8d_202601281340.py | 224 ++++++++++++++++++ 1 file changed, 224 insertions(+) create mode 100644 api/migrations/versions/915bed077f8d_202601281340.py diff --git a/api/migrations/versions/915bed077f8d_202601281340.py b/api/migrations/versions/915bed077f8d_202601281340.py new file mode 100644 index 00000000..022f0d25 --- /dev/null +++ b/api/migrations/versions/915bed077f8d_202601281340.py @@ -0,0 +1,224 @@ +"""202601281340 + +Revision ID: 915bed077f8d +Revises: 75f0ec80e50b +Create Date: 2026-01-28 13:38:49.471560 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '915bed077f8d' +down_revision: Union[str, None] = '75f0ec80e50b' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + +BACKUP_TABLE_NAME = 'model_api_keys_backup_20260123' + +def get_temp_models(): + """创建临时模型，用于迁移过程中查询数据""" + metadata = sa.MetaData() + + # 临时ModelApiKey表（仅包含需要的字段） + ModelApiKey = sa.Table( + 'model_api_keys', metadata, + sa.Column('id', sa.UUID(), primary_key=True), + sa.Column('model_config_id', sa.UUID(), nullable=True), + ) + + # 临时关联表（和升级脚本创建的表结构一致） + ModelConfigApiKeyAssociation = sa.Table( + 'model_config_api_key_association', metadata, + sa.Column('model_config_id', sa.UUID(), nullable=False), + sa.Column('api_key_id', sa.UUID(), nullable=False), + sa.Column('created_at', sa.DateTime(), nullable=True), + ) + + ModelApiKeyBackup = sa.Table( + BACKUP_TABLE_NAME, metadata, + sa.Column('id', sa.UUID(), primary_key=True), + sa.Column('model_name', sa.String(), nullable=False), + sa.Column('description', sa.String(), nullable=True), + sa.Column('provider', sa.String(), nullable=False), + sa.Column('api_key', sa.String(), nullable=False), + sa.Column('api_base', sa.String(), nullable=True), + sa.Column('config', sa.JSON(), nullable=True), + sa.Column('usage_count', sa.String(), default="0"), + sa.Column('last_used_at', sa.DateTime(), nullable=True), + sa.Column('priority', sa.String(), default="1"), + sa.Column('model_config_id', sa.UUID(), nullable=True), + sa.Column('created_at', sa.DateTime(), nullable=True), + sa.Column('updated_at', sa.DateTime(), nullable=True), + sa.Column('is_active', sa.Boolean(), default=True), + ) + + return ModelApiKey, ModelConfigApiKeyAssociation, ModelApiKeyBackup + + +def backup_model_api_keys(): + """备份model_api_keys表的结构和数据""" + connection = op.get_bind() + + # 检查备份表是否已存在 + result = connection.execute(sa.text(f""" + SELECT EXISTS ( + SELECT FROM information_schema.tables + WHERE table_name = '{BACKUP_TABLE_NAME}' + ); + """)).scalar() + + if result: + # 备份表已存在，先删除再重建（确保结构一致） + op.execute(f"DROP TABLE IF EXISTS {BACKUP_TABLE_NAME};") + + # 直接复制表结构和数据（PostgreSQL专用，一步完成） + op.execute(f""" + CREATE TABLE {BACKUP_TABLE_NAME} AS + SELECT * FROM model_api_keys; + """) + + # 统计行数 + backup_count = connection.execute(sa.text(f"SELECT COUNT(*) FROM {BACKUP_TABLE_NAME}")).scalar() + original_count = connection.execute(sa.text("SELECT COUNT(*) FROM model_api_keys")).scalar() + + print( + f"已备份model_api_keys表到 {BACKUP_TABLE_NAME} \n" + f" 原表数据行数：{original_count} | 备份表数据行数：{backup_count}" + ) + +# def restore_model_api_keys_from_backup(): +# """从备份表恢复model_api_keys数据（可选，用于回滚失败时手动恢复）""" +# # 1. 清空原表（谨慎使用！） +# # op.execute("TRUNCATE TABLE model_api_keys;") +# +# # 2. 从备份表恢复数据 +# op.execute(f""" +# INSERT INTO model_api_keys +# SELECT * FROM {BACKUP_TABLE_NAME} +# ON CONFLICT (id) DO UPDATE SET +# model_name = EXCLUDED.model_name, +# description = EXCLUDED.description, +# provider = EXCLUDED.provider, +# api_key = EXCLUDED.api_key, +# api_base = EXCLUDED.api_base, +# config = EXCLUDED.config, +# usage_count = EXCLUDED.usage_count, +# last_used_at = EXCLUDED.last_used_at, +# priority = EXCLUDED.priority, +# model_config_id = EXCLUDED.model_config_id, +# created_at = EXCLUDED.created_at, +# updated_at = EXCLUDED.updated_at, +# is_active = EXCLUDED.is_active; +# """) +# print(f"✅ 已从 {BACKUP_TABLE_NAME} 恢复model_api_keys表数据") + +def upgrade() -> None: + backup_model_api_keys() + # ### commands auto generated by Alembic - please adjust! ### + op.create_table('model_bases', + sa.Column('id', sa.UUID(), nullable=False), + sa.Column('logo', sa.String(length=255), nullable=True, comment='模型logo图片URL'), + sa.Column('name', sa.String(), nullable=False, comment='模型唯一标识（如gpt-3.5-turbo）'), + sa.Column('type', sa.String(), nullable=False, comment='模型类型'), + sa.Column('provider', sa.String(), nullable=False), + sa.Column('description', sa.Text(), nullable=True, comment='模型描述'), + sa.Column('is_deprecated', sa.Boolean(), nullable=False, comment='是否弃用'), + sa.Column('is_official', sa.Boolean(), nullable=True, comment='是否供应商官方模型（区分自定义）'), + sa.Column('tags', sa.ARRAY(sa.String()), nullable=False, comment="模型标签（如['聊天', '创作']）"), + sa.Column('add_count', sa.Integer(), nullable=False, comment='模型被用户添加的次数'), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('name', 'provider', name='uk_model_name_provider') + ) + op.create_index(op.f('ix_model_bases_id'), 'model_bases', ['id'], unique=False) + op.create_index(op.f('ix_model_bases_provider'), 'model_bases', ['provider'], unique=False) + op.create_index(op.f('ix_model_bases_type'), 'model_bases', ['type'], unique=False) + op.create_table('model_config_api_key_association', + sa.Column('model_config_id', sa.UUID(), nullable=False), + sa.Column('api_key_id', sa.UUID(), nullable=False), + sa.Column('created_at', sa.DateTime(), nullable=True), + sa.ForeignKeyConstraint(['api_key_id'], ['model_api_keys.id'], ), + sa.ForeignKeyConstraint(['model_config_id'], ['model_configs.id'], ), + sa.PrimaryKeyConstraint('model_config_id', 'api_key_id') + ) + op.add_column('model_api_keys', sa.Column('description', sa.String(), nullable=True, comment='备注')) + op.add_column('model_configs', sa.Column('model_id', sa.UUID(), nullable=True, comment='基础模型ID')) + op.add_column('model_configs', sa.Column('logo', sa.String(length=255), nullable=True, comment='模型logo图片URL')) + op.add_column('model_configs', sa.Column('provider', sa.String(), server_default='composite', nullable=False, comment='供应商')) + op.add_column('model_configs', sa.Column('is_composite', sa.Boolean(), server_default='true', nullable=False, comment='是否为组合模型')) + op.add_column('model_configs', sa.Column('load_balance_strategy', sa.String(), nullable=True, comment='负载均衡策略')) + op.create_index(op.f('ix_model_configs_model_id'), 'model_configs', ['model_id'], unique=False) + op.create_foreign_key("model_configs_model_id_fkey", 'model_configs', 'model_bases', ['model_id'], ['id']) + connection = op.get_bind() + ModelApiKey, ModelConfigApiKeyAssociation, _ = get_temp_models() + + # 查询所有有model_config_id的API Key + api_keys = connection.execute( + sa.select(ModelApiKey.c.id, ModelApiKey.c.model_config_id) + .where(ModelApiKey.c.model_config_id.isnot(None)) + ).fetchall() + + # 批量插入到多对多表 + if api_keys: + association_data = [ + { + 'model_config_id': row.model_config_id, + 'api_key_id': row.id + } + for row in api_keys + ] + connection.execute(ModelConfigApiKeyAssociation.insert(), association_data) + op.drop_constraint(op.f('model_api_keys_model_config_id_fkey'), 'model_api_keys', type_='foreignkey') + op.drop_column('model_api_keys', 'model_config_id') + # ### end Alembic commands ### + + +def downgrade() -> None: + # ### commands auto generated by Alembic - please adjust! ### + op.drop_constraint("model_configs_model_id_fkey", 'model_configs', type_='foreignkey') + op.drop_index(op.f('ix_model_configs_model_id'), table_name='model_configs') + op.drop_column('model_configs', 'load_balance_strategy') + op.drop_column('model_configs', 'is_composite') + op.drop_column('model_configs', 'provider') + op.drop_column('model_configs', 'logo') + op.drop_column('model_configs', 'model_id') + op.add_column('model_api_keys', sa.Column('model_config_id', sa.UUID(), autoincrement=False, nullable=True, comment='模型配置ID')) + connection = op.get_bind() + ModelApiKey, ModelConfigApiKeyAssociation, _ = get_temp_models() + + # 查询多对多表中的关联数据（取每个API Key的第一个关联的model_config_id） + association_data = connection.execute( + sa.select( + ModelConfigApiKeyAssociation.c.api_key_id, + ModelConfigApiKeyAssociation.c.model_config_id + ).distinct(ModelConfigApiKeyAssociation.c.api_key_id) + ).fetchall() + + # 批量更新model_api_keys表 + if association_data: + for api_key_id, model_config_id in association_data: + connection.execute( + sa.update(ModelApiKey) + .where(ModelApiKey.c.id == api_key_id) + .values(model_config_id=model_config_id) + ) + + op.execute( + "UPDATE model_api_keys SET model_config_id = '00000000-0000-0000-0000-000000000000' WHERE model_config_id IS NULL") + op.alter_column('model_api_keys', 'model_config_id', nullable=False) + op.create_foreign_key(op.f('model_api_keys_model_config_id_fkey'), 'model_api_keys', 'model_configs', ['model_config_id'], ['id']) + op.drop_column('model_api_keys', 'description') + op.drop_table('model_config_api_key_association') + # ### 可选：回滚时恢复备份（如需）### + # restore_model_api_keys_from_backup() + + print( + f"回滚完成！备份表 {BACKUP_TABLE_NAME} 仍保留，如需手动恢复可执行 restore_model_api_keys_from_backup() 函数") + op.drop_index(op.f('ix_model_bases_type'), table_name='model_bases') + op.drop_index(op.f('ix_model_bases_provider'), table_name='model_bases') + op.drop_index(op.f('ix_model_bases_id'), table_name='model_bases') + op.drop_table('model_bases') + # ### end Alembic commands ### From 1748a390ecaa81efc84254fb43cb28b7c2cc66d8 Mon Sep 17 00:00:00 2001 From: Eternity <1533512157@qq.com> Date: Wed, 28 Jan 2026 15:30:36 +0800 Subject: [PATCH 138/175] perf(workflow): make memory write node backward-compatible and defer config validation --- api/app/core/workflow/nodes/memory/config.py | 2 +- api/app/core/workflow/nodes/memory/node.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/api/app/core/workflow/nodes/memory/config.py b/api/app/core/workflow/nodes/memory/config.py index 57ee6dc2..31881e24 100644 --- a/api/app/core/workflow/nodes/memory/config.py +++ b/api/app/core/workflow/nodes/memory/config.py @@ -25,6 +25,6 @@ class MemoryWriteNodeConfig(BaseNodeConfig): ... ) - config_id: UUID = Field( + config_id: UUID | int = Field( ... ) diff --git a/api/app/core/workflow/nodes/memory/node.py b/api/app/core/workflow/nodes/memory/node.py index f71c70ee..13860bec 100644 --- a/api/app/core/workflow/nodes/memory/node.py +++ b/api/app/core/workflow/nodes/memory/node.py @@ -36,9 +36,10 @@ class MemoryReadNode(BaseNode): class MemoryWriteNode(BaseNode): def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]): super().__init__(node_config, workflow_config) - self.typed_config = MemoryWriteNodeConfig(**self.config) + self.typed_config: MemoryWriteNodeConfig | None = None async def execute(self, state: WorkflowState) -> Any: + self.typed_config = MemoryWriteNodeConfig(**self.config) end_user_id = self.get_variable("sys.user_id", state) if not end_user_id: From 00c4a524b7689e1c5590fb607ab186d7b24c1558 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 16:04:38 +0800 Subject: [PATCH 139/175] =?UTF-8?q?=E6=97=A7=E6=95=B0=E6=8D=AE=E5=85=BC?= =?UTF-8?q?=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../controllers/emotion_config_controller.py | 5 +-- .../controllers/memory_forget_controller.py | 13 +++++-- .../memory_reflection_controller.py | 12 ++++--- .../repositories/memory_config_repository.py | 6 ++-- api/app/schemas/memory_storage_schema.py | 8 ++--- api/app/services/emotion_config_service.py | 28 +++++++++++++++ api/app/services/memory_agent_service.py | 4 ++- api/app/utils/config_utils.py | 35 +++++++++++++++++++ 8 files changed, 95 insertions(+), 16 deletions(-) create mode 100644 api/app/utils/config_utils.py diff --git a/api/app/controllers/emotion_config_controller.py b/api/app/controllers/emotion_config_controller.py index b0015bc2..752f4c49 100644 --- a/api/app/controllers/emotion_config_controller.py +++ b/api/app/controllers/emotion_config_controller.py @@ -21,6 +21,7 @@ from app.schemas.response_schema import ApiResponse from app.services.emotion_config_service import EmotionConfigService from app.core.logging_config import get_api_logger from app.db import get_db +from app.utils.config_utils import resolve_config_id # 获取API专用日志器 api_logger = get_api_logger() @@ -46,7 +47,7 @@ class EmotionConfigUpdate(BaseModel): @router.get("/read_config", response_model=ApiResponse) def get_emotion_config( - config_id: UUID = Query(..., description="配置ID"), + config_id: UUID|int = Query(..., description="配置ID"), db: Session = Depends(get_db), current_user: User = Depends(get_current_user), ): @@ -79,7 +80,7 @@ def get_emotion_config( f"用户 {current_user.username} 请求获取情绪配置", extra={"config_id": config_id} ) - + config_id=resolve_config_id(config_id, db) # 初始化服务 config_service = EmotionConfigService(db) diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index a6b6028f..b1f0ccc1 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -34,7 +34,7 @@ from app.schemas.memory_storage_schema import ( ) from app.schemas.response_schema import ApiResponse from app.services.memory_forget_service import MemoryForgetService - +from app.utils.config_utils import resolve_config_id # 获取API专用日志器 api_logger = get_api_logger() @@ -84,6 +84,9 @@ async def trigger_forgetting_cycle( connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") + config_id = resolve_config_id(int(config_id), db) + + if config_id is None: api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") @@ -129,7 +132,7 @@ async def trigger_forgetting_cycle( @router.get("/read_config", response_model=ApiResponse) async def read_forgetting_config( - config_id: UUID, + config_id: UUID|int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db) ): @@ -158,6 +161,7 @@ async def read_forgetting_config( ) try: + config_id=resolve_config_id(config_id, db) # 调用服务层读取配置 config = forget_service.read_forgetting_config(db=db, config_id=config_id) @@ -195,6 +199,8 @@ async def update_forgetting_config( ApiResponse: 包含更新结果的响应 """ workspace_id = current_user.current_workspace_id + payload.config_id=resolve_config_id(int(payload.config_id), db) + # 检查用户是否已选择工作空间 if workspace_id is None: @@ -269,6 +275,7 @@ async def get_forgetting_stats( connected_config = get_end_user_connected_config(end_user_id, db) config_id = connected_config.get("memory_config_id") + config_id = resolve_config_id(config_id, db) if config_id is None: api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置") @@ -325,7 +332,7 @@ async def get_forgetting_curve( ApiResponse: 包含遗忘曲线数据的响应 """ workspace_id = current_user.current_workspace_id - + request.config_id = resolve_config_id(int(request.config_id), db) # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间") diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py index ccf9485f..dbf3bf16 100644 --- a/api/app/controllers/memory_reflection_controller.py +++ b/api/app/controllers/memory_reflection_controller.py @@ -25,6 +25,8 @@ from fastapi import APIRouter, Depends, HTTPException, status,Header from sqlalchemy import text from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + load_dotenv() api_logger = get_api_logger() @@ -157,17 +159,19 @@ async def start_workspace_reflection( @router.get("/reflection/configs") async def start_reflection_configs( - config_id: uuid.UUID, + config_id: uuid.UUID|int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: """通过config_id查询memory_config表中的反思配置信息""" try: + config_id=resolve_config_id(config_id,db) api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) + memory_config_id = resolve_config_id(result.config_id, db) # 构建返回数据 reflection_config = { - "config_id": result.config_id, + "config_id": memory_config_id, "reflection_enabled": result.enable_self_reflexion, "reflection_period_in_hours": result.iteration_period, "reflexion_range": result.reflexion_range, @@ -192,7 +196,7 @@ async def start_reflection_configs( @router.get("/reflection/run") async def reflection_run( - config_id: UUID, + config_id: UUID|int, language_type: str = Header(default="zh", alias="X-Language-Type"), current_user: User = Depends(get_current_user), db: Session = Depends(get_db), @@ -200,7 +204,7 @@ async def reflection_run( """Activate the reflection function for all matching applications in the workspace""" api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}") - + config_id = resolve_config_id(config_id, db) # 使用MemoryConfigRepository查询反思配置 result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id) if not result: diff --git a/api/app/repositories/memory_config_repository.py b/api/app/repositories/memory_config_repository.py index 12e564e2..fbc04f2e 100644 --- a/api/app/repositories/memory_config_repository.py +++ b/api/app/repositories/memory_config_repository.py @@ -24,6 +24,8 @@ from app.schemas.memory_storage_schema import ( from sqlalchemy import desc, select from sqlalchemy.orm import Session +from app.utils.config_utils import resolve_config_id + # 获取数据库专用日志器 db_logger = get_db_logger() # 获取配置专用日志器 @@ -410,7 +412,7 @@ class MemoryConfigRepository: raise @staticmethod - def get_extracted_config(db: Session, config_id: UUID) -> Optional[Dict]: + def get_extracted_config(db: Session, config_id: UUID |int) -> Optional[Dict]: """获取萃取配置，通过主键查询某条配置 Args: @@ -420,8 +422,8 @@ class MemoryConfigRepository: Returns: Optional[Dict]: 萃取配置字典，不存在则返回None """ + config_id=resolve_config_id(config_id,db) db_logger.debug(f"查询萃取配置: config_id={config_id}") - try: db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first() if not db_config: diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py index d9c04f8f..b855d57d 100644 --- a/api/app/schemas/memory_storage_schema.py +++ b/api/app/schemas/memory_storage_schema.py @@ -147,7 +147,7 @@ class ReflexionResultSchema(BaseModel): # Composite key identifying a config row class ConfigKey(BaseModel): # 配置参数键模型 model_config = ConfigDict(populate_by_name=True, extra="forbid") - config_id: uuid.UUID = Field("config_id", description="配置唯一标识（UUID）") + config_id: Union[uuid.UUID, int] = Field(..., description="配置唯一标识（UUID或int)") user_id: str = Field("user_id", description="用户标识（字符串）") apply_id: str = Field("apply_id", description="应用或场景标识（字符串）") @@ -423,8 +423,8 @@ class ForgettingConfigResponse(BaseModel): class ForgettingConfigUpdateRequest(BaseModel): """遗忘引擎配置更新请求模型""" model_config = ConfigDict(populate_by_name=True, extra="forbid") - - config_id: uuid.UUID = Field(..., description="配置ID") + + config_id: Union[uuid.UUID, int,str] = Field(..., description="配置唯一标识（UUID或int)") decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d") lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数") lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数") @@ -499,7 +499,7 @@ class ForgettingCurveRequest(BaseModel): importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）") days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）") - config_id: Optional[uuid.UUID] = Field(None, description="配置ID（可选，如果为None则使用默认配置）") + config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)") class ForgettingCurveResponse(BaseModel): diff --git a/api/app/services/emotion_config_service.py b/api/app/services/emotion_config_service.py index 9880d4e1..bab6d7a8 100644 --- a/api/app/services/emotion_config_service.py +++ b/api/app/services/emotion_config_service.py @@ -212,3 +212,31 @@ class EmotionConfigService: self.db.rollback() logger.error(f"更新情绪配置失败: {str(e)}", exc_info=True) raise + + +def resolve_config_id(config_id: UUID | int, db: Session) -> UUID: + """ + 解析 config_id，如果是整数则通过 config_id_old 查找对应的 UUID + + Args: + config_id: 配置ID（UUID 或整数） + db: 数据库会话 + + Returns: + UUID: 解析后的配置ID + + Raises: + ValueError: 当找不到对应的配置时 + """ + if isinstance(config_id, int): + from app.models.memory_config_model import MemoryConfig + memory_config = db.query(MemoryConfig).filter( + MemoryConfig.config_id_old == config_id + ).first() + + if not memory_config: + raise ValueError(f"未找到 config_id_old={config_id} 对应的配置") + + return memory_config.config_id + + return config_id diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py index 2378da51..823d5d43 100644 --- a/api/app/services/memory_agent_service.py +++ b/api/app/services/memory_agent_service.py @@ -334,7 +334,9 @@ class MemoryAgentService: langchain_messages.append(HumanMessage(content=msg['content'])) elif msg['role'] == 'assistant': langchain_messages.append(AIMessage(content=msg['content'])) - + print(100*'-') + print(langchain_messages) + print(100*'-') # 初始状态 - 包含所有必要字段 initial_state = { "messages": langchain_messages, diff --git a/api/app/utils/config_utils.py b/api/app/utils/config_utils.py new file mode 100644 index 00000000..f75b0aab --- /dev/null +++ b/api/app/utils/config_utils.py @@ -0,0 +1,35 @@ +""" +Configuration utility functions + +Shared utilities for configuration handling to avoid circular imports. +""" +from uuid import UUID +from sqlalchemy.orm import Session + + +def resolve_config_id(config_id: UUID | int, db: Session) -> UUID: + """ + 解析 config_id，如果是整数则通过 config_id_old 查找对应的 UUID + + Args: + config_id: 配置ID（UUID 或整数） + db: 数据库会话 + + Returns: + UUID: 解析后的配置ID + + Raises: + ValueError: 当找不到对应的配置时 + """ + if isinstance(config_id, int): + from app.models.memory_config_model import MemoryConfig + memory_config = db.query(MemoryConfig).filter( + MemoryConfig.config_id_old == config_id + ).first() + + if not memory_config: + raise ValueError(f"未找到 config_id_old={config_id} 对应的配置") + + return memory_config.config_id + + return config_id From 7207d7c8474eea5b9cf73016141883a694684816 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 16:05:35 +0800 Subject: [PATCH 140/175] =?UTF-8?q?=E6=97=A7=E6=95=B0=E6=8D=AE=E5=85=BC?= =?UTF-8?q?=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_forget_controller.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index b1f0ccc1..27ca54e2 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -261,7 +261,6 @@ async def get_forgetting_stats( ApiResponse: 包含统计信息的响应 """ workspace_id = current_user.current_workspace_id - # 检查用户是否已选择工作空间 if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间") From 95f15b77a3f293124b69676c73014c2da478a079 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 16:05:54 +0800 Subject: [PATCH 141/175] =?UTF-8?q?=E6=97=A7=E6=95=B0=E6=8D=AE=E5=85=BC?= =?UTF-8?q?=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_forget_controller.py | 1 - 1 file changed, 1 deletion(-) diff --git a/api/app/controllers/memory_forget_controller.py b/api/app/controllers/memory_forget_controller.py index 27ca54e2..ea55ea26 100644 --- a/api/app/controllers/memory_forget_controller.py +++ b/api/app/controllers/memory_forget_controller.py @@ -265,7 +265,6 @@ async def get_forgetting_stats( if workspace_id is None: api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间") return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None") - # 如果提供了 end_user_id，通过它获取 config_id config_id = None if end_user_id: From 0f1eed0b1e9a87425497a0fa8ba0b772f55b980b Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 16:07:53 +0800 Subject: [PATCH 142/175] =?UTF-8?q?=E6=97=A7=E6=95=B0=E6=8D=AE=E5=85=BC?= =?UTF-8?q?=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/services/emotion_config_service.py | 28 ---------------------- 1 file changed, 28 deletions(-) diff --git a/api/app/services/emotion_config_service.py b/api/app/services/emotion_config_service.py index bab6d7a8..9880d4e1 100644 --- a/api/app/services/emotion_config_service.py +++ b/api/app/services/emotion_config_service.py @@ -212,31 +212,3 @@ class EmotionConfigService: self.db.rollback() logger.error(f"更新情绪配置失败: {str(e)}", exc_info=True) raise - - -def resolve_config_id(config_id: UUID | int, db: Session) -> UUID: - """ - 解析 config_id，如果是整数则通过 config_id_old 查找对应的 UUID - - Args: - config_id: 配置ID（UUID 或整数） - db: 数据库会话 - - Returns: - UUID: 解析后的配置ID - - Raises: - ValueError: 当找不到对应的配置时 - """ - if isinstance(config_id, int): - from app.models.memory_config_model import MemoryConfig - memory_config = db.query(MemoryConfig).filter( - MemoryConfig.config_id_old == config_id - ).first() - - if not memory_config: - raise ValueError(f"未找到 config_id_old={config_id} 对应的配置") - - return memory_config.config_id - - return config_id From e89c23da4d20d5139352e30c1b00fd0b2e986415 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 18:41:56 +0800 Subject: [PATCH 143/175] fix(web): model bugfix --- web/src/assets/images/model/bedrock.svg | 15 ++++ web/src/assets/images/model/dashscope.png | Bin 0 -> 2835 bytes web/src/assets/images/model/gpustack.png | Bin 0 -> 57988 bytes web/src/assets/images/model/ollama.svg | 15 ++++ web/src/assets/images/model/openai.svg | 4 + web/src/assets/images/model/xinference.svg | 24 ++++++ web/src/views/ModelManagement/List.tsx | 26 +++--- web/src/views/ModelManagement/Square.tsx | 40 ++++++---- .../components/ModelListDetail.tsx | 23 +++--- .../components/ModelSquareDetail.tsx | 74 ++++++++++-------- web/src/views/ModelManagement/utils.ts | 26 ++++++ 11 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/dev/null +++ b/web/src/assets/images/model/openai.svg @@ -0,0 +1,4 @@ + + + + diff --git a/web/src/assets/images/model/xinference.svg b/web/src/assets/images/model/xinference.svg new file mode 100644 index 00000000..f5c5f75e --- /dev/null +++ b/web/src/assets/images/model/xinference.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/web/src/views/ModelManagement/List.tsx b/web/src/views/ModelManagement/List.tsx index f1127623..ac85125a 100644 --- a/web/src/views/ModelManagement/List.tsx +++ b/web/src/views/ModelManagement/List.tsx @@ -1,5 +1,5 @@ import { useRef, useState, useEffect, type FC } from 'react'; -import { Button, Space, Row, Col } from 'antd' +import { Button, Flex, Row, Col } from 'antd' import { useTranslation } from 'react-i18next'; import type { ProviderModelItem, KeyConfigModalRef, ModelListDetailRef } from './types' @@ -9,6 +9,7 @@ import PageEmpty from '@/components/Empty/PageEmpty'; import Tag from '@/components/Tag'; import KeyConfigModal from './components/KeyConfigModal' import ModelListDetail from './components/ModelListDetail' +import { getLogoUrl } from './utils' const ModelList: FC<{ query: any }> = ({ query }) => { const { t } = useTranslation(); @@ -46,22 +47,25 @@ const ModelList: FC<{ query: any }> = ({ query }) => { {item.provider[0]} } + bodyClassName="rb:relative rb:pb-[64px]! rb:h-[calc(100%-64px)]!" > - {item.tags.map(tag => {t(`modelNew.${tag}`)})} - - - - - - - - + {item.tags.map(tag => {t(`modelNew.${tag}`)})} +

    + + + + + + + + +
    ))} diff --git a/web/src/views/ModelManagement/Square.tsx b/web/src/views/ModelManagement/Square.tsx index 7ecd838c..af72700f 100644 --- a/web/src/views/ModelManagement/Square.tsx +++ b/web/src/views/ModelManagement/Square.tsx @@ -1,5 +1,5 @@ import { useRef, useState, useEffect, forwardRef, useImperativeHandle } from 'react'; -import { Button, Space, App, Divider, Flex } from 'antd' +import { Button, Space, App, Divider, Flex, Tooltip } from 'antd' import { UsergroupAddOutlined } from '@ant-design/icons'; import { useTranslation } from 'react-i18next'; @@ -9,6 +9,7 @@ import { getModelPlaza, addModelPlaza } from '@/api/models' import PageEmpty from '@/components/Empty/PageEmpty'; import Tag from '@/components/Tag'; import ModelSquareDetail from './components/ModelSquareDetail' +import { getLogoUrl } from './utils' const ModelSquare = forwardRef void; }>(({ query, handleEdit }, ref) => { const { t } = useTranslation(); @@ -46,7 +47,7 @@ const ModelSquare = forwardRef (
    -
    {vo.provider}
    +
    {t(`modelNew.${vo.provider}`)}
    @@ -55,27 +56,32 @@ const ModelSquare = forwardRef {t(`modelNew.${item.type}`)}} + avatarUrl={getLogoUrl(item.logo)} avatar={
    {item.name[0]}
    } + bodyClassName="rb:relative rb:pb-[80px]! rb:h-[calc(100%-64px)]!" > - {t(`modelNew.${item.type}`)} -
    {item.description}
    - {item.tags.map((tag, tagIndex) => {tag})} - - - {item.add_count} - - {!item.is_official && } - {item.is_added - ? - : - } - - + +
    {item.description}
    +     + {item.tags.map((tag, tagIndex) => {tag})} +
    + + + {item.add_count} + + {!item.is_official && } + {item.is_added + ? + : + } + + +
    ))}
    diff --git a/web/src/views/ModelManagement/components/ModelListDetail.tsx b/web/src/views/ModelManagement/components/ModelListDetail.tsx index 48abd953..9d4303f8 100644 --- a/web/src/views/ModelManagement/components/ModelListDetail.tsx +++ b/web/src/views/ModelManagement/components/ModelListDetail.tsx @@ -1,6 +1,6 @@ import { useState, useImperativeHandle, forwardRef, useRef } from 'react'; import { useTranslation } from 'react-i18next'; -import { Button, Switch, Row, Col, Space } from 'antd' +import { Button, Switch, Row, Col, Space, Tooltip } from 'antd' import type { ProviderModelItem, ModelListItem, ModelListDetailRef, MultiKeyConfigModalRef } from '../types'; import RbDrawer from '@/components/RbDrawer'; @@ -9,6 +9,7 @@ import Tag from '@/components/Tag'; import PageEmpty from '@/components/Empty/PageEmpty'; import MultiKeyConfigModal from './MultiKeyConfigModal' import { getModelNewList, updateModelStatus } from '@/api/models' +import { getLogoUrl } from '../utils' interface ModelListDetailProps { refresh?: () => void; @@ -80,21 +81,25 @@ const ModelListDetail = forwardRef(({ {t(`modelNew.${item.type}`)} {item.api_keys.length}{t('modelNew.apiKeyNum')} } - avatarUrl={item.logo} + avatarUrl={getLogoUrl(item.logo)} avatar={
    {item.name[0]}
    } extra={ handleChange(item)} />} + bodyClassName="rb:relative rb:pb-[64px]! rb:h-[calc(100%-64px)]!" > - -
    {item.description}
    - - - - - + +
    {item.description}
    +     +
    + + + + + +
    ))} diff --git a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx index d7a5f807..844339fb 100644 --- a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx +++ b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx @@ -1,6 +1,6 @@ import { useState, useImperativeHandle, forwardRef } from 'react'; import { useTranslation } from 'react-i18next'; -import { Button, Space, App, Flex } from 'antd' +import { Button, Space, App, Flex, Tooltip, Divider } from 'antd' import { UsergroupAddOutlined } from '@ant-design/icons'; import type { ModelPlaza, ModelPlazaItem, ModelSquareDetailRef } from '../types'; @@ -9,6 +9,7 @@ import { getModelPlaza, addModelPlaza } from '@/api/models' import RbCard from '@/components/RbCard/Card' import Tag from '@/components/Tag'; import PageEmpty from '@/components/Empty/PageEmpty'; +import { getLogoUrl } from '../utils' interface ModelSquareDetailProps { refresh: () => void; @@ -52,42 +53,49 @@ const ModelSquareDetail = forwardRef{model.provider} {t('modelNew.modelList')} ({list.length}{t('modelNew.item')})} + title={<>{t(`modelNew.${model.provider}`)} {t('modelNew.modelList')} ({list.length}{t('modelNew.item')})} open={open} onClose={handleClose} > - {list.length === 0 - ? - :
    - {list.map(item => ( - - {item.name[0]} +
    + {list.length === 0 + ? + :
    + {list.map(item => ( + {t(`modelNew.${item.type}`)}} + avatarUrl={getLogoUrl(item.logo)} + avatar={ +
    + {item.name[0]} +
    + } + bodyClassName="rb:relative rb:pb-[80px]! rb:h-[calc(100%-64px)]!" + > + +
    {item.description}
    +     + {item.tags.map((tag, tagIndex) => {tag})} +
    + + + {item.add_count} + + {!item.is_official && } + {item.is_added + ? + : + } + +
    - } - > - {t(`modelNew.${item.type}`)} -
    {item.description}
    - {item.tags.map((tag, tagIndex) => {tag})} - - - {item.add_count} - - {!item.is_official && } - {item.is_added - ? - : - } - - -     - ))} -
    - } + + ))} +
    + } +
    ); }); diff --git a/web/src/views/ModelManagement/utils.ts b/web/src/views/ModelManagement/utils.ts new file mode 100644 index 00000000..c753a8b6 --- /dev/null +++ b/web/src/views/ModelManagement/utils.ts @@ -0,0 +1,26 @@ +import bedrockIcon from '@/assets/images/model/bedrock.svg' +import dashscopeIcon from '@/assets/images/model/dashscope.png' +import gpustackIcon from '@/assets/images/model/gpustack.png' +import ollamaIcon from '@/assets/images/model/ollama.svg' +import openaiIcon from '@/assets/images/model/openai.svg' +import xinferenceIcon from '@/assets/images/model/xinference.svg' + +export const ICONS = { + bedrock: bedrockIcon, + dashscope: dashscopeIcon, + gpustack: gpustackIcon, + ollama: ollamaIcon, + openai: openaiIcon, + xinference: xinferenceIcon +} + +export const getLogoUrl = (logo?: string) => { + if (!logo) { + return undefined + } + if (logo.startsWith('http')) { + return logo + } + + return ICONS[logo as keyof typeof ICONS] || undefined +} \ No newline at end of file From 0eb335d1122c7116951cb8d613a16391d15aeec8 Mon Sep 17 00:00:00 2001 From: zhaoying Date: Wed, 28 Jan 2026 19:58:33 +0800 Subject: [PATCH 144/175] fix(web): model bugfix --- web/src/components/RbCard/Card.tsx | 4 ++-- web/src/views/ModelManagement/Group.tsx | 7 +------ web/src/views/ModelManagement/List.tsx | 4 ++-- web/src/views/ModelManagement/Square.tsx | 2 +- .../ModelManagement/components/ModelImplement/index.tsx | 2 +- .../views/ModelManagement/components/ModelListDetail.tsx | 4 ++-- .../views/ModelManagement/components/ModelSquareDetail.tsx | 2 +- .../ModelManagement/components/MultiKeyConfigModal.tsx | 2 +- 8 files changed, 11 insertions(+), 16 deletions(-) diff --git a/web/src/components/RbCard/Card.tsx b/web/src/components/RbCard/Card.tsx index eadd2916..7ed81160 100644 --- a/web/src/components/RbCard/Card.tsx +++ b/web/src/components/RbCard/Card.tsx @@ -50,7 +50,7 @@ const RbCard: FC = ({ +
    {avatarUrl ? : avatar ? avatar : null @@ -59,7 +59,7 @@ const RbCard: FC = ({ clsx( { 'rb:max-w-full': !avatarUrl && !avatar, - 'rb:max-w-[calc(100%-60px)]': avatarUrl || avatar, + 'rb:max-w-[calc(100%-80px)]': avatarUrl || avatar, } ) }> diff --git a/web/src/views/ModelManagement/Group.tsx b/web/src/views/ModelManagement/Group.tsx index 311455b4..398bd60b 100644 --- a/web/src/views/ModelManagement/Group.tsx +++ b/web/src/views/ModelManagement/Group.tsx @@ -31,12 +31,7 @@ const Group = forwardRef = ({ query }) => { {list.map(item => ( - {item.provider[0]} + {item.provider[0].toUpperCase()}
    } bodyClassName="rb:relative rb:pb-[64px]! rb:h-[calc(100%-64px)]!" diff --git a/web/src/views/ModelManagement/Square.tsx b/web/src/views/ModelManagement/Square.tsx index af72700f..ad5d2441 100644 --- a/web/src/views/ModelManagement/Square.tsx +++ b/web/src/views/ModelManagement/Square.tsx @@ -56,7 +56,7 @@ const ModelSquare = forwardRef {t(`modelNew.${item.type}`)}} + subTitle={{t(`modelNew.${item.type}`)}} avatarUrl={getLogoUrl(item.logo)} avatar={
    diff --git a/web/src/views/ModelManagement/components/ModelImplement/index.tsx b/web/src/views/ModelManagement/components/ModelImplement/index.tsx index a876587d..e2d7aa79 100644 --- a/web/src/views/ModelManagement/components/ModelImplement/index.tsx +++ b/web/src/views/ModelManagement/components/ModelImplement/index.tsx @@ -89,7 +89,7 @@ const ModelImplement: FC = ({ type, value, onChange }) => { >
    - {provider} + {t(`modelNew.${provider}`)} ) })} diff --git a/web/src/views/ModelManagement/components/ModelListDetail.tsx b/web/src/views/ModelManagement/components/ModelListDetail.tsx index 9d4303f8..61951dc9 100644 --- a/web/src/views/ModelManagement/components/ModelListDetail.tsx +++ b/web/src/views/ModelManagement/components/ModelListDetail.tsx @@ -66,7 +66,7 @@ const ModelListDetail = forwardRef(({ return ( {data.provider} {t('modelNew.modelList')} ({list.length}{t('modelNew.item')})} + title={<>{t(`modelNew.${data.provider}`)} {t('modelNew.modelList')} ({list.length}{t('modelNew.item')})} open={open} onClose={handleClose} > @@ -77,7 +77,7 @@ const ModelListDetail = forwardRef(({ + subTitle={ {t(`modelNew.${item.type}`)} {item.api_keys.length}{t('modelNew.apiKeyNum')} } diff --git a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx index 844339fb..55d4ff63 100644 --- a/web/src/views/ModelManagement/components/ModelSquareDetail.tsx +++ b/web/src/views/ModelManagement/components/ModelSquareDetail.tsx @@ -65,7 +65,7 @@ const ModelSquareDetail = forwardRef{t(`modelNew.${item.type}`)}} + subTitle={{t(`modelNew.${item.type}`)}} avatarUrl={getLogoUrl(item.logo)} avatar={
    diff --git a/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx b/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx index 334badc8..2638f10c 100644 --- a/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx +++ b/web/src/views/ModelManagement/components/MultiKeyConfigModal.tsx @@ -50,7 +50,7 @@ const MultiKeyConfigModal = forwardRef { + .finally(() => { setLoading(false) }); }) From 4d80e119f74cad53eecf6c3794e6e2f4174d2974 Mon Sep 17 00:00:00 2001 From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com> Date: Thu, 29 Jan 2026 10:13:55 +0800 Subject: [PATCH 145/175] =?UTF-8?q?=E6=8F=90=E4=BA=A4=E9=81=97=E6=BC=8F=20?= =?UTF-8?q?(#228)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- api/app/controllers/memory_storage_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/controllers/memory_storage_controller.py b/api/app/controllers/memory_storage_controller.py index fb0ebc14..f24d2f70 100644 --- a/api/app/controllers/memory_storage_controller.py +++ b/api/app/controllers/memory_storage_controller.py @@ -233,7 +233,7 @@ def update_config_extracted( @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除 def read_config_extracted( - config_id: UUID, + config_id: UUID | int, current_user: User = Depends(get_current_user), db: Session = Depends(get_db), ) -> dict: From 808961243dc4ef72823020aa31005e533c43986c Mon Sep 17 00:00:00 2001 From: Mark Date: Thu, 29 Jan 2026 11:47:39 +0800 Subject: [PATCH 146/175] [fix] chat api for workflow --- api/app/controllers/service/app_api_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/controllers/service/app_api_controller.py b/api/app/controllers/service/app_api_controller.py index 677e1623..326649c6 100644 --- a/api/app/controllers/service/app_api_controller.py +++ b/api/app/controllers/service/app_api_controller.py @@ -238,7 +238,7 @@ async def chat( user_id=new_end_user.id, # 转换为字符串 variables=payload.variables, config=config, - web_search=payload.web_search, + web_search=web_search, memory=payload.memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, From cd4c93a5cb2a6269989624ad343bff01b02a6650 Mon Sep 17 00:00:00 2001 From: Mark Date: Thu, 29 Jan 2026 11:52:59 +0800 Subject: [PATCH 147/175] [fix] web search set for v1 api --- api/app/controllers/service/app_api_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/app/controllers/service/app_api_controller.py b/api/app/controllers/service/app_api_controller.py index 326649c6..94d7b60b 100644 --- a/api/app/controllers/service/app_api_controller.py +++ b/api/app/controllers/service/app_api_controller.py @@ -271,7 +271,7 @@ async def chat( user_id=new_end_user.id, # 转换为字符串 variables=payload.variables, config=config, - web_search=payload.web_search, + web_search=web_search, memory=payload.memory, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, From 1e16b06a24bcf25ab183aa5cd184a11013c8e66e Mon Sep 17 00:00:00 2001 From: zhaoying Date: Thu, 29 Jan 2026 12:10:19 +0800 Subject: [PATCH 148/175] fix(web): model bugfix --- web/src/i18n/en.ts | 5 +- web/src/i18n/zh.ts | 5 +- .../components/CustomModelModal.tsx | 2 + .../components/GroupModelModal.tsx | 16 ++- .../components/KeyConfigModal.tsx | 4 +- .../ModelImplement/SubModelModal.tsx | 106 ++++++++++-------- .../components/ModelImplement/index.tsx | 19 +--- .../components/ModelImplement/types.ts | 3 +- .../components/ModelListDetail.tsx | 36 +++++- web/src/views/ModelManagement/index.tsx | 85 +++++++------- web/src/views/Workflow/constant.ts | 6 +- 11 files changed, 173 insertions(+), 114 deletions(-) diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts index aee68114..e986562c 100644 --- a/web/src/i18n/en.ts +++ b/web/src/i18n/en.ts @@ -546,7 +546,10 @@ export const en = { tags: 'Tags', createCustomModel: 'Add Custom Model', edit: 'Edit', - selectOneTip: 'Model API KEY not configured, please configure in Model Plaza first', + selectOneTip: 'Model API KEY not configured, please configure it in the model list first', + load_balance_strategy: 'Concurrency Strategy', + round_robin: 'Sequential Execution - Call each model in order', + none: 'None', api_key: 'API KEY', api_base: 'API Base URL', diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts index 78fe948a..87f96cf0 100644 --- a/web/src/i18n/zh.ts +++ b/web/src/i18n/zh.ts @@ -1112,7 +1112,10 @@ export const zh = { tags: '标签', createCustomModel: '添加自定义模型', edit: '编辑', - selectOneTip: '模型未配置API KEY，请先在模型广场配置', + selectOneTip: '模型未配置API KEY，请先在模型列表配置', + load_balance_strategy: '并发策略', + round_robin: '顺序执行 - 按顺序依次调用每个模型', + none: '无', api_key: 'API KEY', api_base: 'API Base URL', diff --git a/web/src/views/ModelManagement/components/CustomModelModal.tsx b/web/src/views/ModelManagement/components/CustomModelModal.tsx index d22fbcdd..47928d87 100644 --- a/web/src/views/ModelManagement/components/CustomModelModal.tsx +++ b/web/src/views/ModelManagement/components/CustomModelModal.tsx @@ -129,6 +129,7 @@ const CustomModelModal = forwardRef( items.map((item) => ({ label: t(`modelNew.${item}`), value: String(item) }))} /> @@ -141,6 +142,7 @@ const CustomModelModal = forwardRef( items.map((item) => ({ label: t(`modelNew.${item}`), value: String(item) }))} /> diff --git a/web/src/views/ModelManagement/components/GroupModelModal.tsx b/web/src/views/ModelManagement/components/GroupModelModal.tsx index 6ae54d4c..c66288e5 100644 --- a/web/src/views/ModelManagement/components/GroupModelModal.tsx +++ b/web/src/views/ModelManagement/components/GroupModelModal.tsx @@ -1,5 +1,5 @@ import { forwardRef, useImperativeHandle, useState } from 'react'; -import { Form, Input, App } from 'antd'; +import { Form, Input, App, Select } from 'antd'; import { useTranslation } from 'react-i18next'; import type { ModelListItem, CompositeModelForm, GroupModelModalRef, GroupModelModalProps, ModelApiKey } from '../types'; @@ -106,6 +106,7 @@ const GroupModelModal = forwardRef(({ (({ + +