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 01/76] =?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 02/76] =?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 03/76] =?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 04/76] =?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 05/76] =?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 06/76] =?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 07/76] =?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 08/76] =?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 09/76] =?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 10/76] =?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 11/76] =?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 12/76] =?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 13/76] =?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 14/76] =?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 15/76] =?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 16/76] =?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 17/76] =?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 18/76] =?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 19/76] =?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 20/76] =?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 21/76] =?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 22/76] =?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 23/76] =?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 24/76] =?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 25/76] =?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 26/76] =?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 27/76] =?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 28/76] =?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 29/76] =?UTF-8?q?=E6=8A=8Agroup=5Fid=E6=9B=BF=E6=8D=A2end?= =?UTF-8?q?=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 30/76] =?UTF-8?q?=E6=8A=8Agroup=5Fid=E6=9B=BF=E6=8D=A2end?= =?UTF-8?q?=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 31/76] =?UTF-8?q?=E6=8A=8Agroup=5Fid=E6=9B=BF=E6=8D=A2end?= =?UTF-8?q?=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 32/76] =?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 33/76] =?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 35/76] =?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 36/76] 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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 38/76] =?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 39/76] =?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 40/76] =?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 41/76] =?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 42/76] =?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 43/76] =?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 1aabaff1f2c696274bd90d513a2e015768d5ef89 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 15:00:09 +0800 Subject: [PATCH 44/76] =?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 45/76] =?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 af6e1e2b99272cbeefd49fa4abf06c4be456a2e2 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Fri, 23 Jan 2026 17:20:07 +0800 Subject: [PATCH 46/76] =?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 47/76] =?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 48/76] =?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 49/76] =?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 50/76] =?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 51/76] =?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 9de6b4f151223888d296fdccff539a433488e2e7 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 11:06:49 +0800 Subject: [PATCH 52/76] =?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 399357f75293e9bbd24aa7cf8591ddb1d629156e Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:06:55 +0800 Subject: [PATCH 53/76] =?UTF-8?q?user=5Fid->=E7=8E=B0=E5=AE=9E=E4=B8=BAcon?= =?UTF-8?q?fig=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 54/76] =?UTF-8?q?user=5Fid->=E6=98=BE=E7=A4=BA=E4=B8=BAcon?= =?UTF-8?q?fig=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 e1f56078366ea06fa330853ca2bb0ddfd6d2eef2 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:37:40 +0800 Subject: [PATCH 55/76] =?UTF-8?q?user=5Fid->=E6=98=BE=E7=A4=BA=E4=B8=BAcon?= =?UTF-8?q?fig=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 722746c78b7c95a2d72e11ac637945fa65daac5a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Mon, 26 Jan 2026 17:47:05 +0800 Subject: [PATCH 56/76] =?UTF-8?q?user=5Fid->=E6=98=BE=E7=A4=BA=E4=B8=BAcon?= =?UTF-8?q?fig=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 a53be317656c1c2f17db96e143be4ea29bba9a31 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 11:41:16 +0800 Subject: [PATCH 57/76] =?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 a047cf2e91d3973442b67ae285730e1040d2a146 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 14:32:48 +0800 Subject: [PATCH 58/76] =?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 f821893653f633d344e718f0573c91d63e38c53a Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 20:22:14 +0800 Subject: [PATCH 59/76] =?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 60/76] =?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 d51355a0ad44fbc5d3a9dcbf20a2c9023f88e912 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Tue, 27 Jan 2026 21:09:06 +0800 Subject: [PATCH 61/76] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84+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 62/76] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84+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 63/76] =?UTF-8?q?config=5Fid=E5=81=9A=E6=98=A0=E5=B0=84+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 d34ad7343947c5867b5d3c191f796cdc208477df Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 10:56:41 +0800 Subject: [PATCH 64/76] =?UTF-8?q?=E5=BA=94=E7=94=A8=E5=B1=82memory=5Fconte?= =?UTF-8?q?nt->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 d2a67a53b592d5c6430605f6aa3aa12a5aff8b65 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 10:58:46 +0800 Subject: [PATCH 65/76] =?UTF-8?q?=E5=BA=94=E7=94=A8=E5=B1=82memory=5Fconte?= =?UTF-8?q?nt->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 66/76] =?UTF-8?q?=E5=BA=94=E7=94=A8=E5=B1=82memory=5Fconte?= =?UTF-8?q?nt->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 511e16f1d332bafd50d3d90da13ee0520cd1cf7e Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:18:11 +0800 Subject: [PATCH 67/76] =?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 68/76] =?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 fbc7bedb6c2d8c7797ed34c8a9fda1effc3224e1 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 11:45:51 +0800 Subject: [PATCH 69/76] =?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 70/76] =?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 71/76] =?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 72/76] =?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 00c4a524b7689e1c5590fb607ab186d7b24c1558 Mon Sep 17 00:00:00 2001 From: lixinyue <2569494688@qq.com> Date: Wed, 28 Jan 2026 16:04:38 +0800 Subject: [PATCH 73/76] =?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 74/76] =?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 75/76] =?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 76/76] =?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