569 lines
21 KiB
Python
569 lines
21 KiB
Python
"""
|
||
Memory Storage Service
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||
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Handles business logic for memory storage operations.
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||
"""
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||
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from typing import Dict, List, Optional, Any
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import os
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import json
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||
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from dotenv import load_dotenv
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||
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from app.core.logging_config import get_logger
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from app.schemas.memory_storage_schema import (
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ConfigFilter,
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ConfigPilotRun,
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ConfigParamsCreate,
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ConfigParamsDelete,
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ConfigUpdate,
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ConfigUpdateExtracted,
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ConfigUpdateForget,
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ConfigKey,
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)
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from app.repositories.data_config_repository import DataConfigRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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# TODO 后续更新
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# from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
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# from app.core.memory.analytics.memory_insight import MemoryInsight
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# from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats
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# from app.core.memory.analytics.user_summary import generate_user_summary
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from app.repositories.data_config_repository import DataConfigRepository
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logger = get_logger(__name__)
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# Load environment variables for Neo4j connector
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load_dotenv()
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_neo4j_connector = Neo4jConnector()
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class MemoryStorageService:
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"""Service for memory storage operations"""
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def __init__(self):
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logger.info("MemoryStorageService initialized")
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async def get_storage_info(self) -> dict:
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"""
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Example wrapper method - retrieves storage information
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Args:
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Returns:
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Storage information dictionary
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"""
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logger.info(f"Getting storage info ")
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# Empty wrapper - implement your logic here
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result = {
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"status": "active",
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"message": "This is an example wrapper"
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}
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return result
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class DataConfigService: # 数据配置服务类（PostgreSQL）
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"""Service layer for config params CRUD.
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The DB connection is optional; when absent, methods return a failure
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response containing an SQL preview to aid integration.
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"""
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def __init__(self, db_conn: Optional[Any] = None) -> None:
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self.db_conn = db_conn
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# --- Driver compatibility helpers ---
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@staticmethod
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def _is_pgsql_conn(conn: Any) -> bool: # 判断是否为 PostgreSQL 连接
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mod = type(conn).__module__
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return ("psycopg2" in mod) or ("psycopg" in mod)
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@staticmethod
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def _convert_timestamps_to_format(data_list: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""将 created_at 和 updated_at 字段从 datetime 对象转换为 YYYYMMDDHHmmss 格式"""
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from datetime import datetime
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for item in data_list:
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for field in ['created_at', 'updated_at']:
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if field in item and item[field] is not None:
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value = item[field]
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dt = None
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# 如果是 datetime 对象，直接使用
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if isinstance(value, datetime):
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dt = value
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# 如果是字符串，先解析
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elif isinstance(value, str):
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try:
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dt = datetime.fromisoformat(value.replace('Z', '+00:00'))
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except Exception:
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pass # 保持原值
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# 转换为 YYYYMMDDHHmmss 格式
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if dt:
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item[field] = dt.strftime('%Y%m%d%H%M%S')
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return data_list
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# --- Create ---
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def create(self, params: ConfigParamsCreate) -> Dict[str, Any]: # 创建配置参数（仅名称与描述）
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if self.db_conn is None:
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raise ConnectionError("数据库连接未配置")
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# 如果workspace_id存在且模型字段未全部指定，则自动获取
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if params.workspace_id and not all([params.llm_id, params.embedding_id, params.rerank_id]):
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configs = self._get_workspace_configs(params.workspace_id)
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if configs is None:
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raise ValueError(f"工作空间不存在: workspace_id={params.workspace_id}")
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# 只在未指定时填充（允许手动覆盖）
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if not params.llm_id:
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params.llm_id = configs.get('llm')
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if not params.embedding_id:
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params.embedding_id = configs.get('embedding')
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if not params.rerank_id:
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params.rerank_id = configs.get('rerank')
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query, qparams = DataConfigRepository.build_insert(params)
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cur = self.db_conn.cursor()
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# PostgreSQL 使用 psycopg2 的命名参数格式
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cur.execute(query, qparams)
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self.db_conn.commit()
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return {"affected": getattr(cur, "rowcount", None)}
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def _get_workspace_configs(self, workspace_id) -> Optional[Dict[str, Any]]:
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"""获取工作空间模型配置（内部方法，便于测试）"""
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from app.db import SessionLocal
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from app.repositories.workspace_repository import get_workspace_models_configs
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db_session = SessionLocal()
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try:
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return get_workspace_models_configs(db_session, workspace_id)
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finally:
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db_session.close()
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# --- Delete ---
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def delete(self, key: ConfigParamsDelete) -> Dict[str, Any]: # 删除配置参数（按配置名称）
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query, qparams = DataConfigRepository.build_delete(key)
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if self.db_conn is None:
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raise ConnectionError("数据库连接未配置")
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cur = self.db_conn.cursor()
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cur.execute(query, qparams)
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affected = getattr(cur, "rowcount", None)
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self.db_conn.commit()
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# 如果没有任何行被删除，抛出异常
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if not affected:
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raise ValueError("未找到配置")
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return {"affected": affected}
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# --- Update ---
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def update(self, update: ConfigUpdate) -> Dict[str, Any]: # 部分更新配置参数
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query, qparams = DataConfigRepository.build_update(update)
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if self.db_conn is None:
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raise ConnectionError("数据库连接未配置")
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cur = self.db_conn.cursor()
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cur.execute(query, qparams)
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affected = getattr(cur, "rowcount", None)
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self.db_conn.commit()
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if not affected:
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raise ValueError("未找到配置")
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return {"affected": affected}
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||
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||
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def update_extracted(self, update: ConfigUpdateExtracted) -> Dict[str, Any]: # 更新记忆萃取引擎配置参数
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query, qparams = DataConfigRepository.build_update_extracted(update)
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if self.db_conn is None:
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raise ConnectionError("数据库连接未配置")
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cur = self.db_conn.cursor()
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cur.execute(query, qparams)
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affected = getattr(cur, "rowcount", None)
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self.db_conn.commit()
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if not affected:
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raise ValueError("未找到配置")
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||
return {"affected": affected}
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||
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||
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||
# --- Forget config params ---
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def update_forget(self, update: ConfigUpdateForget) -> Dict[str, Any]: # 保存遗忘引擎的配置
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query, qparams = DataConfigRepository.build_update_forget(update)
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||
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||
if self.db_conn is None:
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||
raise ConnectionError("数据库连接未配置")
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||
|
||
cur = self.db_conn.cursor()
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cur.execute(query, qparams)
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||
affected = getattr(cur, "rowcount", None)
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self.db_conn.commit()
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||
if not affected:
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||
raise ValueError("未找到配置")
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||
return {"affected": affected}
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||
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# --- Read ---
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def get_extracted(self, key: ConfigKey) -> Dict[str, Any]: # 获取配置参数
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query, qparams = DataConfigRepository.build_select_extracted(key)
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if self.db_conn is None:
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raise ConnectionError("数据库连接未配置")
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||
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||
cur = self.db_conn.cursor()
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||
cur.execute(query, qparams)
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||
row = cur.fetchone()
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if not row:
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||
raise ValueError("未找到配置")
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||
# Map row to dict (DB-API cursor description available for many drivers)
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||
columns = [desc[0] for desc in cur.description]
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raw = {columns[i]: row[i] for i in range(len(columns))}
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# 将 created_at 和 updated_at 转换为 YYYYMMDDHHmmss 格式
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||
data_list = self._convert_timestamps_to_format([raw])
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return data_list[0] if data_list else raw
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||
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||
def get_forget(self, key: ConfigKey) -> Dict[str, Any]: # 获取配置参数
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||
query, qparams = DataConfigRepository.build_select_forget(key)
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||
if self.db_conn is None:
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||
raise ConnectionError("数据库连接未配置")
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||
|
||
cur = self.db_conn.cursor()
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||
cur.execute(query, qparams)
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||
row = cur.fetchone()
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||
if not row:
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||
raise ValueError("未找到配置")
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||
# Map row to dict (DB-API cursor description available for many drivers)
|
||
columns = [desc[0] for desc in cur.description]
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||
raw = {columns[i]: row[i] for i in range(len(columns))}
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||
# 将 created_at 和 updated_at 转换为 YYYYMMDDHHmmss 格式
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||
data_list = self._convert_timestamps_to_format([raw])
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||
return data_list[0] if data_list else raw
|
||
|
||
# --- Read All ---
|
||
def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数
|
||
query, qparams = DataConfigRepository.build_select_all(workspace_id)
|
||
if self.db_conn is None:
|
||
raise ConnectionError("数据库连接未配置")
|
||
|
||
cur = self.db_conn.cursor()
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||
cur.execute(query, qparams)
|
||
rows = cur.fetchall()
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||
# 如果没有查询到任何配置，返回空列表（这是正常情况，不应抛出异常）
|
||
if not rows:
|
||
return []
|
||
# Map rows to list of dicts
|
||
columns = [desc[0] for desc in cur.description]
|
||
data_list = [dict(zip(columns, row)) for row in rows]
|
||
# 将 UUID 转换为字符串，将 created_at 和 updated_at 转换为 YYYYMMDDHHmmss 格式
|
||
for item in data_list:
|
||
if 'workspace_id' in item and item['workspace_id'] is not None:
|
||
item['workspace_id'] = str(item['workspace_id'])
|
||
return self._convert_timestamps_to_format(data_list)
|
||
|
||
|
||
async def pilot_run(self, payload: ConfigPilotRun) -> Dict[str, Any]:
|
||
"""
|
||
选择策略与内存覆写与同步版保持一致：优先 payload.config_id，其次 dbrun.json；两者皆无时报错。
|
||
支持 dialogue_text 参数用于试运行模式。
|
||
"""
|
||
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||
dbrun_path = os.path.join(project_root, "app", "core", "memory", "dbrun.json")
|
||
|
||
payload_cid = str(getattr(payload, "config_id", "") or "").strip()
|
||
cid: Optional[str] = payload_cid if payload_cid else None
|
||
|
||
if not cid and os.path.isfile(dbrun_path):
|
||
try:
|
||
with open(dbrun_path, "r", encoding="utf-8") as f:
|
||
dbrun = json.load(f)
|
||
if isinstance(dbrun, dict):
|
||
sel = dbrun.get("selections", {})
|
||
if isinstance(sel, dict):
|
||
fallback_cid = str(sel.get("config_id") or "").strip()
|
||
cid = fallback_cid or None
|
||
except Exception:
|
||
cid = None
|
||
|
||
if not cid:
|
||
raise ValueError("未提供 payload.config_id，且 dbrun.json 未设置 selections.config_id，禁止启动试运行")
|
||
|
||
# 验证 dialogue_text 必须提供
|
||
dialogue_text = payload.dialogue_text.strip() if payload.dialogue_text else ""
|
||
logger.info(f"[PILOT_RUN] Received dialogue_text length: {len(dialogue_text)}, preview: {dialogue_text[:100]}")
|
||
if not dialogue_text:
|
||
raise ValueError("试运行模式必须提供 dialogue_text 参数")
|
||
|
||
# 应用内存覆写并刷新常量（在导入主管线前）
|
||
# 注意：仅在内存中覆写配置，不修改 runtime.json 文件
|
||
from app.core.memory.utils.config.definitions import reload_configuration_from_database
|
||
|
||
ok_override = reload_configuration_from_database(cid)
|
||
if not ok_override:
|
||
raise RuntimeError("运行时覆写失败，config_id 无效或刷新常量失败")
|
||
|
||
# 导入并 await 主管线（使用当前 ASGI 事件循环）
|
||
from app.core.memory.main import main as pipeline_main
|
||
from app.core.memory.utils.self_reflexion_utils import reflexion
|
||
|
||
logger.info(f"[PILOT_RUN] Calling pipeline_main with dialogue_text length: {len(dialogue_text)}, is_pilot_run=True")
|
||
await pipeline_main(dialogue_text=dialogue_text, is_pilot_run=True)
|
||
logger.info("[PILOT_RUN] pipeline_main completed")
|
||
|
||
# 调用自我反思
|
||
# data = [
|
||
# {
|
||
# "data": {
|
||
# "id": "1",
|
||
# "statement": "张明现在在谷歌工作。",
|
||
# "group_id": "1",
|
||
# "chunk_id": "10",
|
||
# "created_at": "2023-01-01",
|
||
# "expired_at": "2023-01-02",
|
||
# "valid_at": "2023-01-01",
|
||
# "invalid_at": "2023-01-02",
|
||
# "entity_ids": []
|
||
# },
|
||
# "conflict": True,
|
||
# "conflict_memory": {
|
||
# "id": "1",
|
||
# "statement": "张明现在在清华大学当讲师。",
|
||
# "group_id": "1",
|
||
# "chunk_id": "1",
|
||
# "created_at": "2019-12-01T19:15:05.213210",
|
||
# "expired_at": None,
|
||
# "valid_at": None,
|
||
# "invalid_at": None,
|
||
# "entity_ids": []
|
||
# }
|
||
# }
|
||
# ]
|
||
from app.core.memory.utils.config.get_example_data import get_example_data
|
||
data = get_example_data()
|
||
reflexion_result = await reflexion(data)
|
||
|
||
# 读取输出，使用全局配置路径
|
||
from app.core.config import settings
|
||
result_path = settings.get_memory_output_path("extracted_result.json")
|
||
if not os.path.isfile(result_path):
|
||
raise FileNotFoundError(f"试运行完成，但未找到提取结果文件: {result_path}")
|
||
|
||
with open(result_path, "r", encoding="utf-8") as rf:
|
||
extracted_result = json.load(rf)
|
||
|
||
extracted_result["self_reflexion"] = reflexion_result if reflexion_result else None
|
||
return {
|
||
"config_id": cid,
|
||
"time_log": os.path.join(project_root, "time.log"),
|
||
"extracted_result": extracted_result,
|
||
}
|
||
|
||
|
||
# -------------------- Neo4j Search & Analytics (fused from data_search_service.py) --------------------
|
||
# Ensure env for connector (e.g., NEO4J_PASSWORD)
|
||
load_dotenv()
|
||
_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,
|
||
)
|
||
data = {"search_for": "dialogue", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
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,
|
||
)
|
||
data = {"search_for": "chunk", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
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,
|
||
)
|
||
data = {"search_for": "statement", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
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,
|
||
)
|
||
data = {"search_for": "entity", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
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,
|
||
)
|
||
|
||
# 检查结果是否为空或长度不足
|
||
if not result or len(result) < 4:
|
||
data = {
|
||
"total": 0,
|
||
"counts": {
|
||
"dialogue": 0,
|
||
"chunk": 0,
|
||
"statement": 0,
|
||
"entity": 0,
|
||
},
|
||
}
|
||
return data
|
||
|
||
data = {
|
||
"total": result[-1]["Count"],
|
||
"counts": {
|
||
"dialogue": result[0]["Count"],
|
||
"chunk": result[1]["Count"],
|
||
"statement": result[2]["Count"],
|
||
"entity": result[3]["Count"],
|
||
},
|
||
}
|
||
return data
|
||
|
||
|
||
async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
"""统一知识库类型分布接口。
|
||
|
||
聚合 dialogue/chunk/statement/entity 四类计数，返回统一的分布结构，便于前端一次性消费。
|
||
"""
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_ALL,
|
||
group_id=end_user_id,
|
||
)
|
||
|
||
# 检查结果是否为空或长度不足
|
||
if not result or len(result) < 4:
|
||
data = {
|
||
"total": 0,
|
||
"distribution": [
|
||
{"type": "dialogue", "count": 0},
|
||
{"type": "chunk", "count": 0},
|
||
{"type": "statement", "count": 0},
|
||
{"type": "entity", "count": 0},
|
||
]
|
||
}
|
||
return data
|
||
|
||
total = result[-1]["Count"]
|
||
distribution = [
|
||
{"type": "dialogue", "count": result[0]["Count"]},
|
||
{"type": "chunk", "count": result[1]["Count"]},
|
||
{"type": "statement", "count": result[2]["Count"]},
|
||
{"type": "entity", "count": result[3]["Count"]},
|
||
]
|
||
|
||
data = {"total": total, "distribution": distribution}
|
||
return data
|
||
|
||
|
||
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,
|
||
)
|
||
return result
|
||
|
||
|
||
async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_EDGES,
|
||
group_id=end_user_id,
|
||
)
|
||
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,
|
||
group_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(end_user_id: Optional[str] = None, limit: int = 10) -> List[Dict[str, Any]]:
|
||
"""
|
||
获取热门记忆标签，按数量排序并返回前N个
|
||
"""
|
||
# 获取更多标签供LLM筛选（获取limit*4个标签）
|
||
raw_limit = limit * 4
|
||
tags = await get_hot_memory_tags(end_user_id, limit=raw_limit)
|
||
|
||
# 按频率降序排序（虽然数据库已经排序，但为了确保正确性再次排序）
|
||
sorted_tags = sorted(tags, key=lambda x: x[1], reverse=True)
|
||
|
||
# 只返回前limit个
|
||
top_tags = sorted_tags[:limit]
|
||
|
||
return [{"name": t, "frequency": f} for t, f in top_tags]
|
||
|
||
|
||
async def analytics_memory_insight_report(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
insight = MemoryInsight(end_user_id)
|
||
report = await insight.generate_insight_report()
|
||
await insight.close()
|
||
data = {"report": report}
|
||
return data
|
||
|
||
|
||
async def analytics_recent_activity_stats() -> Dict[str, Any]:
|
||
stats, _msg = get_recent_activity_stats()
|
||
total = (
|
||
stats.get("chunk_count", 0)
|
||
+ stats.get("statements_count", 0)
|
||
+ stats.get("triplet_entities_count", 0)
|
||
+ stats.get("triplet_relations_count", 0)
|
||
+ stats.get("temporal_count", 0)
|
||
)
|
||
# 精简：仅提供“最新一次活动多久前”
|
||
latest_relative = None
|
||
try:
|
||
info = stats.get("log_path", "")
|
||
idx = info.rfind("最新：")
|
||
if idx != -1:
|
||
latest_path = info[idx + 3 :].strip()
|
||
if latest_path and os.path.exists(latest_path):
|
||
import time
|
||
diff = max(0.0, time.time() - os.path.getmtime(latest_path))
|
||
m = int(diff // 60)
|
||
if m < 1:
|
||
latest_relative = "刚刚"
|
||
elif m < 60:
|
||
latest_relative = f"{m}分钟前"
|
||
else:
|
||
h = int(m // 60)
|
||
latest_relative = f"{h}小时前" if h < 24 else f"{int(h // 24)}天前"
|
||
except Exception:
|
||
pass
|
||
|
||
data = {"total": total, "stats": stats, "latest_relative": latest_relative}
|
||
return data
|
||
|
||
|
||
async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
summary = await generate_user_summary(end_user_id)
|
||
data = {"summary": summary}
|
||
return data |