Merge branch 'develop' into fix/workflow
This commit is contained in:
Eternity
@@ -20,6 +20,8 @@ from . import (
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knowledgeshare_controller,
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memory_agent_controller,
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memory_dashboard_controller,
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memory_episodic_controller,
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memory_explicit_controller,
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memory_forget_controller,
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memory_reflection_controller,
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memory_short_term_controller,
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@@ -67,6 +69,8 @@ manager_router.include_router(memory_agent_controller.router)
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manager_router.include_router(memory_dashboard_controller.router)
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manager_router.include_router(memory_storage_controller.router)
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manager_router.include_router(user_memory_controllers.router)
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manager_router.include_router(memory_episodic_controller.router)
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manager_router.include_router(memory_explicit_controller.router)
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manager_router.include_router(api_key_controller.router)
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manager_router.include_router(release_share_controller.router)
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manager_router.include_router(public_share_controller.router) # 公开路由（无需认证）
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@@ -30,7 +30,7 @@ from sqlalchemy.orm import Session
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api_logger = get_api_logger()
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router = APIRouter(
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prefix="/memory/emotion",
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prefix="/memory/emotion-memory",
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tags=["Emotion Analysis"],
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dependencies=[Depends(get_current_user)] # 所有路由都需要认证
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)
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@@ -32,7 +32,6 @@ def get_workspace_list(
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@router.get("/version", response_model=ApiResponse)
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def get_system_version():
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"""获取系统版本号+说明"""
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return success(data={
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"version": settings.SYSTEM_VERSION,
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"introduction": settings.SYSTEM_INTRODUCTION
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}, msg="系统版本获取成功")
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current_version = settings.SYSTEM_VERSION
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version_introduction = HomePageService.load_version_introduction(current_version)
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return success(data={"version": current_version, "introduction": version_introduction}, msg="系统版本获取成功")
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@@ -1,18 +1,15 @@
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from fastapi import APIRouter, Depends, HTTPException, status, Query
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from sqlalchemy.orm import Session
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from typing import List, Optional
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import uuid
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from app.repositories.end_user_repository import update_end_user_other_name
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import uuid
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from typing import Optional
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from app.core.response_utils import success
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from app.db import get_db
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from app.dependencies import get_current_user
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from app.models.user_model import User
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from app.schemas.memory_agent_schema import End_User_Information
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from app.schemas.response_schema import ApiResponse
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from app.schemas.app_schema import App as AppSchema
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from app.services import memory_dashboard_service, memory_storage_service, workspace_service
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from app.services.memory_agent_service import get_end_users_connected_configs_batch
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from app.core.logging_config import get_api_logger
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# 获取API专用日志器
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@@ -102,7 +99,8 @@ async def get_workspace_end_users(
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"""
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获取工作空间的宿主列表
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返回格式与原 memory_list 接口中的 end_users 字段相同
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返回格式与原 memory_list 接口中的 end_users 字段相同，
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并包含每个用户的记忆配置信息（memory_config_id 和 memory_config_name）
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"""
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workspace_id = current_user.current_workspace_id
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# 获取当前空间类型
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@@ -113,6 +111,17 @@ async def get_workspace_end_users(
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workspace_id=workspace_id,
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current_user=current_user
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)
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# 批量获取所有用户的记忆配置信息（优化：一次查询而非 N 次）
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end_user_ids = [str(user.id) for user in end_users]
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memory_configs_map = {}
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if end_user_ids:
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try:
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memory_configs_map = get_end_users_connected_configs_batch(end_user_ids, db)
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except Exception as e:
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api_logger.error(f"批量获取记忆配置失败: {str(e)}")
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# 失败时使用空字典，不影响其他数据返回
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result = []
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for end_user in end_users:
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memory_num = {}
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@@ -123,10 +132,25 @@ async def get_workspace_end_users(
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memory_num = {
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"total":memory_dashboard_service.get_current_user_total_chunk(str(end_user.id), db, current_user)
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}
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# 从批量查询结果中获取配置信息
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user_id = str(end_user.id)
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memory_config_info = memory_configs_map.get(user_id, {
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"memory_config_id": None,
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"memory_config_name": None
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})
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# 只保留需要的字段，移除 error 字段（如果有）
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memory_config = {
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"memory_config_id": memory_config_info.get("memory_config_id"),
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"memory_config_name": memory_config_info.get("memory_config_name")
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}
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result.append(
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{
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'end_user':end_user,
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'memory_num':memory_num
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'end_user': end_user,
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'memory_num': memory_num,
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'memory_config': memory_config
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}
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)
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@@ -465,7 +489,6 @@ async def dashboard_data(
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if storage_type is None:
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storage_type = 'neo4j'
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user_rag_memory_id = None
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# 根据 storage_type 决定返回哪个数据对象
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# 如果是 'rag'，neo4j_data 为 null；否则 rag_data 为 null
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125
api/app/controllers/memory_episodic_controller.py
Normal file
125
api/app/controllers/memory_episodic_controller.py
Normal file
@@ -0,0 +1,125 @@
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"""
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情景记忆相关的控制器
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包含情景记忆总览和详情查询接口
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"""
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from fastapi import APIRouter, Depends
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from app.core.error_codes import BizCode
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from app.core.logging_config import get_api_logger
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from app.core.response_utils import fail, success
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from app.dependencies import get_current_user
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from app.models.user_model import User
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from app.schemas.response_schema import ApiResponse
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from app.schemas.memory_episodic_schema import (
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EpisodicMemoryOverviewRequest,
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EpisodicMemoryDetailsRequest,
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)
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from app.services.memory_episodic_service import memory_episodic_service
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# Get API logger
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api_logger = get_api_logger()
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router = APIRouter(
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prefix="/memory/episodic-memory",
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tags=["Episodic Memory"],
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)
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@router.post("/overview", response_model=ApiResponse)
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async def get_episodic_memory_overview_api(
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request: EpisodicMemoryOverviewRequest,
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current_user: User = Depends(get_current_user),
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) -> dict:
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"""
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获取情景记忆总览
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返回指定用户的所有情景记忆列表，包括标题和创建时间。
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支持通过时间范围、情景类型和标题关键词进行筛选。
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"""
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workspace_id = current_user.current_workspace_id
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# 检查用户是否已选择工作空间
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试查询情景记忆总览但未选择工作空间")
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return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
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# 验证参数
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valid_time_ranges = ["all", "today", "this_week", "this_month"]
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valid_episodic_types = ["all", "conversation", "project_work", "learning", "decision", "important_event"]
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if request.time_range not in valid_time_ranges:
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return fail(BizCode.INVALID_PARAMETER, f"无效的时间范围参数，可选值：{', '.join(valid_time_ranges)}")
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if request.episodic_type not in valid_episodic_types:
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return fail(BizCode.INVALID_PARAMETER, f"无效的情景类型参数，可选值：{', '.join(valid_episodic_types)}")
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# 处理 title_keyword（去除首尾空格）
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title_keyword = request.title_keyword.strip() if request.title_keyword else None
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api_logger.info(
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f"情景记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
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f"workspace={workspace_id}, time_range={request.time_range}, episodic_type={request.episodic_type}, "
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f"title_keyword={title_keyword}"
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)
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try:
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# 调用Service层方法
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result = await memory_episodic_service.get_episodic_memory_overview(
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request.end_user_id, request.time_range, request.episodic_type, title_keyword
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)
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api_logger.info(
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f"成功获取情景记忆总览: end_user_id={request.end_user_id}, "
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f"total={result['total']}"
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)
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return success(data=result, msg="查询成功")
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except Exception as e:
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api_logger.error(f"情景记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "情景记忆总览查询失败", str(e))
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@router.post("/details", response_model=ApiResponse)
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async def get_episodic_memory_details_api(
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request: EpisodicMemoryDetailsRequest,
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current_user: User = Depends(get_current_user),
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) -> dict:
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"""
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获取情景记忆详情
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返回指定情景记忆的详细信息，包括涉及对象、情景类型、内容记录和情绪。
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"""
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workspace_id = current_user.current_workspace_id
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# 检查用户是否已选择工作空间
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if workspace_id is None:
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api_logger.warning(f"用户 {current_user.username} 尝试查询情景记忆详情但未选择工作空间")
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return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
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api_logger.info(
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f"情景记忆详情查询请求: end_user_id={request.end_user_id}, summary_id={request.summary_id}, "
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f"user={current_user.username}, workspace={workspace_id}"
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)
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try:
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# 调用Service层方法
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result = await memory_episodic_service.get_episodic_memory_details(
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end_user_id=request.end_user_id,
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summary_id=request.summary_id
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)
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api_logger.info(
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f"成功获取情景记忆详情: end_user_id={request.end_user_id}, summary_id={request.summary_id}"
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)
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return success(data=result, msg="查询成功")
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except ValueError as e:
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# 处理情景记忆不存在的情况
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||||
api_logger.warning(f"情景记忆不存在: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
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return fail(BizCode.INVALID_PARAMETER, "情景记忆不存在", str(e))
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||||
except Exception as e:
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||||
api_logger.error(f"情景记忆详情查询失败: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
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return fail(BizCode.INTERNAL_ERROR, "情景记忆详情查询失败", str(e))
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115
api/app/controllers/memory_explicit_controller.py
Normal file
115
api/app/controllers/memory_explicit_controller.py
Normal file
@@ -0,0 +1,115 @@
|
||||
"""
|
||||
显性记忆控制器
|
||||
|
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处理显性记忆相关的API接口，包括情景记忆和语义记忆的查询。
|
||||
"""
|
||||
|
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from fastapi import APIRouter, Depends
|
||||
|
||||
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.services.memory_explicit_service import MemoryExplicitService
|
||||
from app.schemas.response_schema import ApiResponse
|
||||
from app.schemas.memory_explicit_schema import (
|
||||
ExplicitMemoryOverviewRequest,
|
||||
ExplicitMemoryDetailsRequest,
|
||||
)
|
||||
from app.dependencies import get_current_user
|
||||
from app.models.user_model import User
|
||||
|
||||
# Get API logger
|
||||
api_logger = get_api_logger()
|
||||
|
||||
# Initialize service
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||||
memory_explicit_service = MemoryExplicitService()
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/memory/explicit-memory",
|
||||
tags=["Explicit Memory"],
|
||||
)
|
||||
|
||||
|
||||
@router.post("/overview", response_model=ApiResponse)
|
||||
async def get_explicit_memory_overview_api(
|
||||
request: ExplicitMemoryOverviewRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
) -> dict:
|
||||
"""
|
||||
获取显性记忆总览
|
||||
|
||||
返回指定用户的所有显性记忆列表，包括标题、完整内容、创建时间和情绪信息。
|
||||
"""
|
||||
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} 尝试查询显性记忆总览但未选择工作空间")
|
||||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||||
|
||||
api_logger.info(
|
||||
f"显性记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
|
||||
f"workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await memory_explicit_service.get_explicit_memory_overview(
|
||||
request.end_user_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取显性记忆总览: end_user_id={request.end_user_id}, "
|
||||
f"total={result['total']}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except Exception as e:
|
||||
api_logger.error(f"显性记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "显性记忆总览查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/details", response_model=ApiResponse)
|
||||
async def get_explicit_memory_details_api(
|
||||
request: ExplicitMemoryDetailsRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
) -> dict:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 返回情景记忆或语义记忆的详细信息。
|
||||
- 情景记忆：包括标题、内容、情绪、创建时间
|
||||
- 语义记忆：包括名称、核心定义、详细笔记、创建时间
|
||||
"""
|
||||
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} 尝试查询显性记忆详情但未选择工作空间")
|
||||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||||
|
||||
api_logger.info(
|
||||
f"显性记忆详情查询请求: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
|
||||
f"user={current_user.username}, workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await memory_explicit_service.get_explicit_memory_details(
|
||||
end_user_id=request.end_user_id,
|
||||
memory_id=request.memory_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取显性记忆详情: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
|
||||
f"memory_type={result.get('memory_type')}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except ValueError as e:
|
||||
# 处理记忆不存在的情况
|
||||
api_logger.warning(f"显性记忆不存在: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
|
||||
return fail(BizCode.INVALID_PARAMETER, "显性记忆不存在", str(e))
|
||||
except Exception as e:
|
||||
api_logger.error(f"显性记忆详情查询失败: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "显性记忆详情查询失败", str(e))
|
||||
@@ -39,7 +39,7 @@ from app.services.memory_forget_service import MemoryForgetService
|
||||
api_logger = get_api_logger()
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/memory/forget",
|
||||
prefix="/memory/forget-memory",
|
||||
tags=["Memory Forgetting Engine"],
|
||||
dependencies=[Depends(get_current_user)] # 所有路由都需要认证
|
||||
)
|
||||
|
||||
@@ -20,12 +20,6 @@ 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.schemas.user_memory_schema import (
|
||||
EpisodicMemoryOverviewRequest,
|
||||
EpisodicMemoryDetailsRequest,
|
||||
ExplicitMemoryOverviewRequest,
|
||||
ExplicitMemoryDetailsRequest,
|
||||
)
|
||||
|
||||
from app.schemas.end_user_schema import (
|
||||
EndUserProfileResponse,
|
||||
@@ -440,195 +434,3 @@ async def memory_space_relationship_evolution(id: str, label: str,
|
||||
except Exception as e:
|
||||
api_logger.error(f"关系演变查询失败: id={id}, table={label}, error={str(e)}", exc_info=True)
|
||||
return fail(BizCode.INTERNAL_ERROR, "关系演变查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/episodic-memory", response_model=ApiResponse)
|
||||
async def get_episodic_memory_overview_api(
|
||||
request: EpisodicMemoryOverviewRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取情景记忆总览
|
||||
|
||||
返回指定用户的所有情景记忆列表，包括标题和创建时间。
|
||||
支持通过时间范围、情景类型和标题关键词进行筛选。
|
||||
|
||||
"""
|
||||
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} 尝试查询情景记忆总览但未选择工作空间")
|
||||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||||
|
||||
# 验证参数
|
||||
valid_time_ranges = ["all", "today", "this_week", "this_month"]
|
||||
valid_episodic_types = ["all", "conversation", "project_work", "learning", "decision", "important_event"]
|
||||
|
||||
if request.time_range not in valid_time_ranges:
|
||||
return fail(BizCode.INVALID_PARAMETER, f"无效的时间范围参数，可选值：{', '.join(valid_time_ranges)}")
|
||||
|
||||
if request.episodic_type not in valid_episodic_types:
|
||||
return fail(BizCode.INVALID_PARAMETER, f"无效的情景类型参数，可选值：{', '.join(valid_episodic_types)}")
|
||||
|
||||
# 处理 title_keyword（去除首尾空格）
|
||||
title_keyword = request.title_keyword.strip() if request.title_keyword else None
|
||||
|
||||
api_logger.info(
|
||||
f"情景记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
|
||||
f"workspace={workspace_id}, time_range={request.time_range}, episodic_type={request.episodic_type}, "
|
||||
f"title_keyword={title_keyword}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_episodic_memory_overview(
|
||||
db, request.end_user_id, request.time_range, request.episodic_type, title_keyword
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取情景记忆总览: end_user_id={request.end_user_id}, "
|
||||
f"total={result['total']}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except Exception as e:
|
||||
api_logger.error(f"情景记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "情景记忆总览查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/episodic-memory-details", response_model=ApiResponse)
|
||||
async def get_episodic_memory_details_api(
|
||||
request: EpisodicMemoryDetailsRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取情景记忆详情
|
||||
|
||||
返回指定情景记忆的详细信息，包括涉及对象、情景类型、内容记录和情绪。
|
||||
|
||||
"""
|
||||
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} 尝试查询情景记忆详情但未选择工作空间")
|
||||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||||
|
||||
api_logger.info(
|
||||
f"情景记忆详情查询请求: end_user_id={request.end_user_id}, summary_id={request.summary_id}, "
|
||||
f"user={current_user.username}, workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_episodic_memory_details(
|
||||
db=db,
|
||||
end_user_id=request.end_user_id,
|
||||
summary_id=request.summary_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取情景记忆详情: end_user_id={request.end_user_id}, summary_id={request.summary_id}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except ValueError as e:
|
||||
# 处理情景记忆不存在的情况
|
||||
api_logger.warning(f"情景记忆不存在: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
|
||||
return fail(BizCode.INVALID_PARAMETER, "情景记忆不存在", str(e))
|
||||
except Exception as e:
|
||||
api_logger.error(f"情景记忆详情查询失败: end_user_id={request.end_user_id}, summary_id={request.summary_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "情景记忆详情查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/explicit-memory", response_model=ApiResponse)
|
||||
async def get_explicit_memory_overview_api(
|
||||
request: ExplicitMemoryOverviewRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取显性记忆总览
|
||||
|
||||
返回指定用户的所有显性记忆列表，包括标题、完整内容、创建时间和情绪信息。
|
||||
"""
|
||||
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} 尝试查询显性记忆总览但未选择工作空间")
|
||||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||||
|
||||
api_logger.info(
|
||||
f"显性记忆总览查询请求: end_user_id={request.end_user_id}, user={current_user.username}, "
|
||||
f"workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_explicit_memory_overview(
|
||||
db, request.end_user_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取显性记忆总览: end_user_id={request.end_user_id}, "
|
||||
f"total={result['total']}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except Exception as e:
|
||||
api_logger.error(f"显性记忆总览查询失败: end_user_id={request.end_user_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "显性记忆总览查询失败", str(e))
|
||||
|
||||
|
||||
@router.post("/classifications/explicit-memory-details", response_model=ApiResponse)
|
||||
async def get_explicit_memory_details_api(
|
||||
request: ExplicitMemoryDetailsRequest,
|
||||
current_user: User = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> dict:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 返回情景记忆或语义记忆的详细信息。
|
||||
- 情景记忆：包括标题、内容、情绪、创建时间
|
||||
- 语义记忆：包括名称、核心定义、详细笔记、创建时间
|
||||
"""
|
||||
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} 尝试查询显性记忆详情但未选择工作空间")
|
||||
return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
|
||||
|
||||
api_logger.info(
|
||||
f"显性记忆详情查询请求: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
|
||||
f"user={current_user.username}, workspace={workspace_id}"
|
||||
)
|
||||
|
||||
try:
|
||||
# 调用Service层方法
|
||||
result = await user_memory_service.get_explicit_memory_details(
|
||||
db=db,
|
||||
end_user_id=request.end_user_id,
|
||||
memory_id=request.memory_id
|
||||
)
|
||||
|
||||
api_logger.info(
|
||||
f"成功获取显性记忆详情: end_user_id={request.end_user_id}, memory_id={request.memory_id}, "
|
||||
f"memory_type={result.get('memory_type')}"
|
||||
)
|
||||
return success(data=result, msg="查询成功")
|
||||
|
||||
except ValueError as e:
|
||||
# 处理记忆不存在的情况
|
||||
api_logger.warning(f"显性记忆不存在: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
|
||||
return fail(BizCode.INVALID_PARAMETER, "显性记忆不存在", str(e))
|
||||
except Exception as e:
|
||||
api_logger.error(f"显性记忆详情查询失败: end_user_id={request.end_user_id}, memory_id={request.memory_id}, error={str(e)}")
|
||||
return fail(BizCode.INTERNAL_ERROR, "显性记忆详情查询失败", str(e))
|
||||
|
||||
|
||||
|
||||
@@ -167,7 +167,6 @@ class Settings:
|
||||
|
||||
# official environment system version
|
||||
SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.0")
|
||||
SYSTEM_INTRODUCTION: str = os.getenv("SYSTEM_INTRODUCTION", "")
|
||||
|
||||
def get_memory_output_path(self, filename: str = "") -> str:
|
||||
"""
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import asyncio
|
||||
import json
|
||||
from datetime import datetime
|
||||
from typing import List, Optional
|
||||
from typing import List, Optional, Tuple
|
||||
from uuid import uuid4
|
||||
|
||||
from app.core.logging_config import get_memory_logger
|
||||
@@ -28,6 +29,118 @@ class MemorySummaryResponse(RobustLLMResponse):
|
||||
)
|
||||
|
||||
|
||||
async def generate_title_and_type_for_summary(
|
||||
content: str,
|
||||
llm_client
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
为MemorySummary生成标题和类型
|
||||
|
||||
此方法应该在创建MemorySummary节点时调用，生成title和type
|
||||
|
||||
Args:
|
||||
content: Summary的内容文本
|
||||
llm_client: LLM客户端实例
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组
|
||||
"""
|
||||
from app.core.memory.utils.prompt.prompt_utils import render_episodic_title_and_type_prompt
|
||||
|
||||
# 定义有效的类型集合
|
||||
VALID_TYPES = {
|
||||
"conversation", # 对话
|
||||
"project_work", # 项目/工作
|
||||
"learning", # 学习
|
||||
"decision", # 决策
|
||||
"important_event" # 重要事件
|
||||
}
|
||||
DEFAULT_TYPE = "conversation" # 默认类型
|
||||
|
||||
try:
|
||||
if not content:
|
||||
logger.warning("content为空，无法生成标题和类型")
|
||||
return ("空内容", DEFAULT_TYPE)
|
||||
|
||||
# 1. 渲染Jinja2提示词模板
|
||||
prompt = await render_episodic_title_and_type_prompt(content)
|
||||
|
||||
# 2. 调用LLM生成标题和类型
|
||||
messages = [
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
response = await llm_client.chat(messages=messages)
|
||||
|
||||
# 3. 解析LLM响应
|
||||
content_response = response.content
|
||||
if isinstance(content_response, list):
|
||||
if len(content_response) > 0:
|
||||
if isinstance(content_response[0], dict):
|
||||
text = content_response[0].get('text', content_response[0].get('content', str(content_response[0])))
|
||||
full_response = str(text)
|
||||
else:
|
||||
full_response = str(content_response[0])
|
||||
else:
|
||||
full_response = ""
|
||||
elif isinstance(content_response, dict):
|
||||
full_response = str(content_response.get('text', content_response.get('content', str(content_response))))
|
||||
else:
|
||||
full_response = str(content_response) if content_response is not None else ""
|
||||
|
||||
# 4. 解析JSON响应
|
||||
try:
|
||||
# 尝试从响应中提取JSON
|
||||
# 移除可能的markdown代码块标记
|
||||
json_str = full_response.strip()
|
||||
if json_str.startswith("```json"):
|
||||
json_str = json_str[7:]
|
||||
if json_str.startswith("```"):
|
||||
json_str = json_str[3:]
|
||||
if json_str.endswith("```"):
|
||||
json_str = json_str[:-3]
|
||||
json_str = json_str.strip()
|
||||
|
||||
result_data = json.loads(json_str)
|
||||
title = result_data.get("title", "未知标题")
|
||||
episodic_type_raw = result_data.get("type", DEFAULT_TYPE)
|
||||
|
||||
# 5. 校验和归一化类型
|
||||
# 将类型转换为小写并去除空格
|
||||
episodic_type_normalized = str(episodic_type_raw).lower().strip()
|
||||
|
||||
# 检查是否在有效类型集合中
|
||||
if episodic_type_normalized in VALID_TYPES:
|
||||
episodic_type = episodic_type_normalized
|
||||
else:
|
||||
# 尝试映射常见的中文类型到英文
|
||||
type_mapping = {
|
||||
"对话": "conversation",
|
||||
"项目": "project_work",
|
||||
"工作": "project_work",
|
||||
"项目/工作": "project_work",
|
||||
"学习": "learning",
|
||||
"决策": "decision",
|
||||
"重要事件": "important_event",
|
||||
"事件": "important_event"
|
||||
}
|
||||
episodic_type = type_mapping.get(episodic_type_raw, DEFAULT_TYPE)
|
||||
logger.warning(
|
||||
f"LLM返回的类型 '{episodic_type_raw}' 不在有效集合中，"
|
||||
f"已归一化为 '{episodic_type}'"
|
||||
)
|
||||
|
||||
logger.info(f"成功生成标题和类型: title={title}, type={episodic_type}")
|
||||
return (title, episodic_type)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
logger.error(f"无法解析LLM响应为JSON: {full_response}")
|
||||
return ("解析失败", DEFAULT_TYPE)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", DEFAULT_TYPE)
|
||||
|
||||
async def _process_chunk_summary(
|
||||
dialog: DialogData,
|
||||
chunk,
|
||||
@@ -63,10 +176,9 @@ async def _process_chunk_summary(
|
||||
title = None
|
||||
episodic_type = None
|
||||
try:
|
||||
from app.services.user_memory_service import UserMemoryService
|
||||
title, episodic_type = await UserMemoryService.generate_title_and_type_for_summary(
|
||||
title, episodic_type = await generate_title_and_type_for_summary(
|
||||
content=summary_text,
|
||||
end_user_id=dialog.group_id
|
||||
llm_client=llm_client
|
||||
)
|
||||
logger.info(f"Generated title and type for MemorySummary: title={title}, type={episodic_type}")
|
||||
except Exception as e:
|
||||
|
||||
@@ -260,17 +260,32 @@ class ForgettingStrategy:
|
||||
)
|
||||
|
||||
# 生成标题和类型（使用LLM）
|
||||
from app.services.user_memory_service import UserMemoryService
|
||||
from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import generate_title_and_type_for_summary
|
||||
|
||||
# 获取 LLM 客户端
|
||||
llm_client = None
|
||||
if config_id is not None and db is not None:
|
||||
try:
|
||||
llm_client = await self._get_llm_client(db, config_id)
|
||||
except Exception as e:
|
||||
logger.warning(f"获取 LLM 客户端失败: {str(e)}")
|
||||
|
||||
# 生成标题和类型
|
||||
try:
|
||||
title, episodic_type = await UserMemoryService.generate_title_and_type_for_summary(
|
||||
content=summary_text,
|
||||
end_user_id=group_id
|
||||
)
|
||||
logger.info(f"成功为MemorySummary生成标题和类型: title={title}, type={episodic_type}")
|
||||
if llm_client is not None:
|
||||
title, episodic_type = await generate_title_and_type_for_summary(
|
||||
content=summary_text,
|
||||
llm_client=llm_client
|
||||
)
|
||||
logger.info(f"成功为MemorySummary生成标题和类型: title={title}, type={episodic_type}")
|
||||
else:
|
||||
logger.warning("LLM 客户端不可用，使用默认标题和类型")
|
||||
title = "未命名"
|
||||
episodic_type = "conversation"
|
||||
except Exception as e:
|
||||
logger.error(f"生成标题和类型失败，使用默认值: {str(e)}")
|
||||
title = "未命名"
|
||||
episodic_type = "其他"
|
||||
episodic_type = "conversation"
|
||||
|
||||
# 计算继承的激活值和重要性（取较高值）
|
||||
inherited_activation = max(statement_activation, entity_activation)
|
||||
|
||||
@@ -110,7 +110,7 @@ class BaiduSearchTool(BuiltinTool):
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
return ToolResult.success_result(
|
||||
data=result,
|
||||
data=result["results"],
|
||||
execution_time=execution_time
|
||||
)
|
||||
|
||||
|
||||
@@ -95,7 +95,7 @@ class DateTimeTool(BuiltinTool):
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
return ToolResult.success_result(
|
||||
data=result,
|
||||
data=result["result_data"],
|
||||
execution_time=execution_time
|
||||
)
|
||||
|
||||
@@ -123,12 +123,14 @@ class DateTimeTool(BuiltinTool):
|
||||
utc_now = datetime.now(timezone.utc)
|
||||
|
||||
return {
|
||||
"datetime": now.strftime(output_format),
|
||||
"timestamp": int(now.timestamp()),
|
||||
"timezone": timezone_str,
|
||||
"iso_format": now.isoformat(),
|
||||
"timestamp_ms": int(now.timestamp() * 1000),
|
||||
"utc_datetime": utc_now.strftime(output_format)
|
||||
"result_data": {
|
||||
"datetime": now.strftime(output_format),
|
||||
"timestamp": int(now.timestamp()),
|
||||
"timestamp_ms": int(now.timestamp() * 1000),
|
||||
"utc_datetime": utc_now.strftime(output_format),
|
||||
}
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
@@ -148,7 +150,8 @@ class DateTimeTool(BuiltinTool):
|
||||
"original": input_value,
|
||||
"formatted": dt.strftime(output_format),
|
||||
"timestamp": int(dt.timestamp()),
|
||||
"iso_format": dt.isoformat()
|
||||
"iso_format": dt.isoformat(),
|
||||
"result_data": dt.strftime(output_format)
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
@@ -189,7 +192,8 @@ class DateTimeTool(BuiltinTool):
|
||||
"original_timezone": from_timezone,
|
||||
"converted": converted_dt.strftime(output_format),
|
||||
"converted_timezone": to_timezone,
|
||||
"timestamp": int(converted_dt.timestamp())
|
||||
"timestamp": int(converted_dt.timestamp()),
|
||||
"result_data": converted_dt.strftime(output_format)
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
@@ -219,7 +223,8 @@ class DateTimeTool(BuiltinTool):
|
||||
"timestamp": timestamp,
|
||||
"datetime": dt.strftime(output_format),
|
||||
"timezone": timezone_str,
|
||||
"iso_format": dt.isoformat()
|
||||
"iso_format": dt.isoformat(),
|
||||
"result_data": dt.strftime(output_format)
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
@@ -249,7 +254,8 @@ class DateTimeTool(BuiltinTool):
|
||||
"datetime": input_value,
|
||||
"timezone": timezone_str,
|
||||
"timestamp": int(dt.timestamp()),
|
||||
"iso_format": dt.isoformat()
|
||||
"iso_format": dt.isoformat(),
|
||||
"result_data": int(dt.timestamp())
|
||||
}
|
||||
|
||||
def _calculate_datetime(self, kwargs) -> dict:
|
||||
@@ -287,7 +293,8 @@ class DateTimeTool(BuiltinTool):
|
||||
"calculation": calculation,
|
||||
"result": calculated_dt.strftime(output_format),
|
||||
"timezone": timezone_str,
|
||||
"timestamp": int(calculated_dt.timestamp())
|
||||
"timestamp": int(calculated_dt.timestamp()),
|
||||
"result_data": calculated_dt.strftime(output_format)
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
|
||||
@@ -69,7 +69,7 @@ class JsonTool(BuiltinTool):
|
||||
ToolParameter(
|
||||
name="json_path",
|
||||
type=ParameterType.STRING,
|
||||
description="JSON路径表达式（用于extract、insert、replace、delete、parse操作，如：$.user.name或users[0].name）",
|
||||
description="JSON路径表达式（用于insert、replace、delete、parse操作，如：$.user.name或users[0].name）",
|
||||
required=False
|
||||
),
|
||||
ToolParameter(
|
||||
@@ -136,7 +136,7 @@ class JsonTool(BuiltinTool):
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
return ToolResult.success_result(
|
||||
data=result,
|
||||
data=result["result_data"],
|
||||
execution_time=execution_time
|
||||
)
|
||||
|
||||
@@ -671,7 +671,8 @@ class JsonTool(BuiltinTool):
|
||||
"success": True,
|
||||
"value": current,
|
||||
"value_type": type(current).__name__,
|
||||
"value_json": json.dumps(current, indent=2, ensure_ascii=False) if isinstance(current, (dict, list)) else str(current)
|
||||
"value_json": json.dumps(current, indent=2, ensure_ascii=False) if isinstance(current, (dict, list)) else str(current),
|
||||
"result_data": json.dumps(current, indent=2, ensure_ascii=False) if isinstance(current, (dict, list)) else str(current)
|
||||
}
|
||||
|
||||
except (KeyError, IndexError, TypeError) as e:
|
||||
@@ -680,7 +681,8 @@ class JsonTool(BuiltinTool):
|
||||
"json_path": json_path,
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"value": None
|
||||
"value": None,
|
||||
"result_data": None
|
||||
}
|
||||
|
||||
def _analyze_json_structure(self, data: Any, depth: int = 0) -> Dict[str, Any]:
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
|
||||
@@ -25,10 +27,10 @@ class ToolNode(BaseNode):
|
||||
# 获取租户ID和用户ID
|
||||
tenant_id = self.get_variable("sys.tenant_id", state)
|
||||
user_id = self.get_variable("sys.user_id", state)
|
||||
workspace_id = self.get_variable("sys.workspace_id", state)
|
||||
|
||||
# 如果没有租户ID，尝试从工作流ID获取
|
||||
if not tenant_id:
|
||||
workspace_id = self.get_variable("sys.workspace_id", state)
|
||||
if workspace_id:
|
||||
from app.repositories.tool_repository import ToolRepository
|
||||
with get_db_read() as db:
|
||||
@@ -63,21 +65,21 @@ class ToolNode(BaseNode):
|
||||
tool_id=self.typed_config.tool_id,
|
||||
parameters=rendered_parameters,
|
||||
tenant_id=tenant_id,
|
||||
user_id=user_id
|
||||
user_id=uuid.UUID(user_id),
|
||||
workspace_id=uuid.UUID(workspace_id)
|
||||
)
|
||||
|
||||
if result.success:
|
||||
logger.info(f"节点 {self.node_id} 工具执行成功")
|
||||
return {
|
||||
"success": True,
|
||||
"data": result.data,
|
||||
"data": result.data if isinstance(result.data, str) else json.dumps(result.data, ensure_ascii=False),
|
||||
"error_code": "",
|
||||
"execution_time": result.execution_time
|
||||
}
|
||||
else:
|
||||
logger.error(f"节点 {self.node_id} 工具执行失败: {result.error}")
|
||||
return {
|
||||
"success": False,
|
||||
"data": result.error,
|
||||
"data": result.error if isinstance(result.error, str) else json.dumps(result.error, ensure_ascii=False),
|
||||
"error_code": result.error_code,
|
||||
"execution_time": result.execution_time
|
||||
}
|
||||
@@ -211,12 +211,11 @@ class ToolExecution(Base):
|
||||
token_usage = Column(JSON)
|
||||
|
||||
# 用户信息
|
||||
user_id = Column(UUID(as_uuid=True), ForeignKey("users.id"), index=True)
|
||||
user_id = Column(UUID(as_uuid=True), index=True, nullable=True)
|
||||
workspace_id = Column(UUID(as_uuid=True), ForeignKey("workspaces.id"), nullable=False, index=True)
|
||||
|
||||
# 关联关系
|
||||
tool_config = relationship("ToolConfig", back_populates="executions")
|
||||
user = relationship("User")
|
||||
workspace = relationship("Workspace")
|
||||
|
||||
def __repr__(self):
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
"""
|
||||
用户记忆相关的请求和响应模型
|
||||
情景记忆的请求和响应模型
|
||||
"""
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Optional
|
||||
@@ -28,16 +28,3 @@ 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")
|
||||
|
||||
|
||||
class ExplicitMemoryOverviewRequest(BaseModel):
|
||||
"""显性记忆总览查询请求"""
|
||||
|
||||
end_user_id: str = Field(..., description="终端用户ID")
|
||||
|
||||
|
||||
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）")
|
||||
15
api/app/schemas/memory_explicit_schema.py
Normal file
15
api/app/schemas/memory_explicit_schema.py
Normal file
@@ -0,0 +1,15 @@
|
||||
"""
|
||||
显性记忆的请求和响应模型
|
||||
"""
|
||||
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")
|
||||
|
||||
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）")
|
||||
@@ -55,7 +55,7 @@ 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",'17')
|
||||
config_id= memory_config.get("memory_content",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:
|
||||
@@ -94,7 +94,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
|
||||
group_id=end_user_id,
|
||||
message=question,
|
||||
history=[],
|
||||
search_switch="1",
|
||||
search_switch="2",
|
||||
config_id=config_id,
|
||||
db=db,
|
||||
storage_type=storage_type,
|
||||
|
||||
@@ -1,6 +1,11 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from fastapi import HTTPException
|
||||
from sqlalchemy.orm import Session
|
||||
from uuid import UUID
|
||||
from typing import Dict, Any
|
||||
|
||||
from app.repositories.home_page_repository import HomePageRepository
|
||||
from app.schemas.home_page_schema import HomeStatistics, WorkspaceInfo
|
||||
@@ -68,4 +73,69 @@ class HomePageService:
|
||||
)
|
||||
workspace_list.append(workspace_info)
|
||||
|
||||
return workspace_list
|
||||
return workspace_list
|
||||
|
||||
@staticmethod
|
||||
def load_version_introduction(version: str) -> Dict[str, Any]:
|
||||
"""
|
||||
从 JSON 文件加载对应版本的介绍
|
||||
:param version: 系统版本号（如 "0.2.0"）
|
||||
:return: 对应版本的详细介绍
|
||||
"""
|
||||
# 1. 定义 JSON 文件路径（使用 Path 处理跨平台路径问题）
|
||||
json_file_path = Path(__file__).parent.parent / "version_info.json"
|
||||
# 转换为绝对路径，便于调试
|
||||
json_abs_path = json_file_path.resolve()
|
||||
|
||||
try:
|
||||
# 2. 读取 JSON 文件
|
||||
if not json_abs_path.exists():
|
||||
return {
|
||||
"message": f"版本介绍文件不存在：{json_abs_path}",
|
||||
"codeName": "",
|
||||
"releaseDate": "",
|
||||
"upgradePosition": "",
|
||||
"coreUpgrades": []
|
||||
}
|
||||
|
||||
with open(json_abs_path, "r", encoding="utf-8") as f:
|
||||
changelogs = json.load(f)
|
||||
|
||||
# 3. 匹配对应版本的介绍，若版本不存在返回默认提示
|
||||
if version not in changelogs:
|
||||
return {
|
||||
"message": f"暂未查询到 {version} 版本的详细介绍",
|
||||
"codeName": "",
|
||||
"releaseDate": "",
|
||||
"upgradePosition": "",
|
||||
"coreUpgrades": []
|
||||
}
|
||||
return changelogs[version]
|
||||
|
||||
except FileNotFoundError as e:
|
||||
# 处理文件不存在异常
|
||||
return {
|
||||
"message": f"系统内部错误：{str(e)}",
|
||||
"codeName": "",
|
||||
"releaseDate": "",
|
||||
"upgradePosition": "",
|
||||
"coreUpgrades": []
|
||||
}
|
||||
except json.JSONDecodeError:
|
||||
# 处理 JSON 格式错误
|
||||
return {
|
||||
"message": "版本介绍文件格式错误，无法解析 JSON",
|
||||
"codeName": "",
|
||||
"releaseDate": "",
|
||||
"upgradePosition": "",
|
||||
"coreUpgrades": []
|
||||
}
|
||||
except Exception as e:
|
||||
# 处理其他未知异常
|
||||
return {
|
||||
"message": f"加载版本介绍失败：{str(e)}",
|
||||
"codeName": "",
|
||||
"releaseDate": "",
|
||||
"upgradePosition": "",
|
||||
"coreUpgrades": []
|
||||
}
|
||||
@@ -4,7 +4,6 @@ Memory Agent Service
|
||||
Handles business logic for memory agent operations including read/write services,
|
||||
health checks, and message type classification.
|
||||
"""
|
||||
import datetime
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
@@ -27,7 +26,7 @@ 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_config_schema import ConfigurationError, MemoryConfig
|
||||
from app.schemas.memory_config_schema import ConfigurationError
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
from app.services.memory_konwledges_server import (
|
||||
write_rag,
|
||||
@@ -610,7 +609,7 @@ class MemoryAgentService:
|
||||
reranked_results=raw_results.get('reranked_results',[])
|
||||
try:
|
||||
statements=[statement['statement'] for statement in reranked_results.get('statements', [])]
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
statements=[]
|
||||
statements=list(set(statements))
|
||||
retrieved_content.append({query:statements})
|
||||
@@ -832,7 +831,6 @@ class MemoryAgentService:
|
||||
# 获取当前空间下的所有宿主
|
||||
from app.repositories import app_repository, end_user_repository
|
||||
from app.schemas.app_schema import App as AppSchema
|
||||
from app.schemas.end_user_schema import EndUser as EndUserSchema
|
||||
|
||||
# 查询应用并转换为 Pydantic 模型
|
||||
apps_orm = app_repository.get_apps_by_workspace_id(db, current_workspace_id)
|
||||
@@ -1175,19 +1173,21 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
|
||||
1. 根据 end_user_id 获取用户的 app_id
|
||||
2. 获取该应用的最新发布版本
|
||||
3. 从发布版本的 config 字段中提取 memory_config_id
|
||||
4. 根据 memory_config_id 查询配置名称
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
db: 数据库会话
|
||||
|
||||
Returns:
|
||||
包含 memory_config_id 和相关信息的字典
|
||||
包含 memory_config_id、config_name 和相关信息的字典
|
||||
|
||||
Raises:
|
||||
ValueError: 当终端用户不存在或应用未发布时
|
||||
"""
|
||||
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 sqlalchemy import select
|
||||
|
||||
logger.info(f"Getting connected config for end_user: {end_user_id}")
|
||||
@@ -1220,13 +1220,158 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
|
||||
memory_obj = config.get('memory', {})
|
||||
memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
|
||||
|
||||
# 4. 根据 memory_config_id 查询配置名称
|
||||
config_name = None
|
||||
if memory_config_id:
|
||||
try:
|
||||
# memory_config_id 可能是整数或字符串，需要转换
|
||||
config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
|
||||
data_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
|
||||
if data_config:
|
||||
config_name = data_config.config_name
|
||||
logger.debug(f"Found config_name: {config_name} for config_id: {config_id}")
|
||||
else:
|
||||
logger.warning(f"DataConfig not found for config_id: {config_id}")
|
||||
except (ValueError, TypeError) as e:
|
||||
logger.warning(f"Invalid memory_config_id format: {memory_config_id}, error: {str(e)}")
|
||||
|
||||
result = {
|
||||
"end_user_id": str(end_user_id),
|
||||
"app_id": str(app_id),
|
||||
"release_id": str(latest_release.id),
|
||||
"release_version": latest_release.version,
|
||||
"memory_config_id": memory_config_id
|
||||
"memory_config_id": memory_config_id,
|
||||
"memory_config_name": config_name
|
||||
}
|
||||
|
||||
logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}")
|
||||
logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}, config_name={config_name}")
|
||||
return result
|
||||
|
||||
|
||||
def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) -> Dict[str, Dict[str, Any]]:
|
||||
"""
|
||||
批量获取多个终端用户关联的记忆配置
|
||||
|
||||
通过优化的查询减少数据库往返次数：
|
||||
1. 一次性查询所有 end_user 及其 app_id
|
||||
2. 批量查询所有相关的 app_release
|
||||
3. 批量查询所有相关的 data_config
|
||||
|
||||
Args:
|
||||
end_user_ids: 终端用户ID列表
|
||||
db: 数据库会话
|
||||
|
||||
Returns:
|
||||
字典，key 为 end_user_id，value 为配置信息字典
|
||||
对于查询失败的用户，value 包含 error 字段
|
||||
"""
|
||||
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 sqlalchemy import select
|
||||
|
||||
logger.info(f"Batch getting connected configs for {len(end_user_ids)} end users")
|
||||
|
||||
result = {}
|
||||
|
||||
# 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()
|
||||
|
||||
# 构建 end_user_id -> end_user 的映射
|
||||
end_user_map = {str(user.id): user for user in end_users}
|
||||
|
||||
# 记录不存在的用户
|
||||
for user_id in end_user_ids:
|
||||
if user_id not in end_user_map:
|
||||
result[user_id] = {
|
||||
"end_user_id": user_id,
|
||||
"memory_config_id": None,
|
||||
"memory_config_name": None,
|
||||
"error": f"终端用户不存在: {user_id}"
|
||||
}
|
||||
|
||||
if not end_users:
|
||||
logger.warning("No valid end users found")
|
||||
return result
|
||||
|
||||
# 2. 批量查询所有相关应用的最新发布版本
|
||||
app_ids = [user.app_id for user in end_users]
|
||||
|
||||
# 使用子查询找到每个 app 的最新版本
|
||||
from sqlalchemy import and_
|
||||
|
||||
# 查询所有相关的活跃发布版本
|
||||
releases = db.query(AppRelease).filter(
|
||||
and_(
|
||||
AppRelease.app_id.in_(app_ids),
|
||||
AppRelease.is_active.is_(True)
|
||||
)
|
||||
).order_by(AppRelease.app_id, AppRelease.version.desc()).all()
|
||||
|
||||
# 构建 app_id -> latest_release 的映射（每个 app 只保留最新版本）
|
||||
app_release_map = {}
|
||||
for release in releases:
|
||||
app_id_str = str(release.app_id)
|
||||
if app_id_str not in app_release_map:
|
||||
app_release_map[app_id_str] = release
|
||||
|
||||
# 3. 收集所有 memory_config_id
|
||||
memory_config_ids = []
|
||||
for release in app_release_map.values():
|
||||
config = release.config or {}
|
||||
memory_obj = config.get('memory', {})
|
||||
memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
|
||||
if memory_config_id:
|
||||
try:
|
||||
config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
|
||||
memory_config_ids.append(config_id)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
# 4. 批量查询所有 data_config
|
||||
config_name_map = {}
|
||||
if memory_config_ids:
|
||||
data_configs = db.query(DataConfig).filter(
|
||||
DataConfig.config_id.in_(memory_config_ids)
|
||||
).all()
|
||||
config_name_map = {config.config_id: config.config_name for config in data_configs}
|
||||
|
||||
# 5. 组装结果
|
||||
for user in end_users:
|
||||
user_id = str(user.id)
|
||||
app_id = str(user.app_id)
|
||||
|
||||
# 检查是否有发布版本
|
||||
if app_id not in app_release_map:
|
||||
result[user_id] = {
|
||||
"end_user_id": user_id,
|
||||
"memory_config_id": None,
|
||||
"memory_config_name": None,
|
||||
"error": f"应用未发布: {app_id}"
|
||||
}
|
||||
continue
|
||||
|
||||
release = app_release_map[app_id]
|
||||
|
||||
# 提取 memory_config_id
|
||||
config = release.config or {}
|
||||
memory_obj = config.get('memory', {})
|
||||
memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
|
||||
|
||||
# 获取 config_name
|
||||
config_name = None
|
||||
if memory_config_id:
|
||||
try:
|
||||
config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
|
||||
config_name = config_name_map.get(config_id)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
result[user_id] = {
|
||||
"end_user_id": user_id,
|
||||
"memory_config_id": memory_config_id,
|
||||
"memory_config_name": config_name
|
||||
}
|
||||
|
||||
logger.info(f"Successfully retrieved batch configs: total={len(result)}, with_config={sum(1 for v in result.values() if v.get('memory_config_id'))}")
|
||||
return result
|
||||
297
api/app/services/memory_base_service.py
Normal file
297
api/app/services/memory_base_service.py
Normal file
@@ -0,0 +1,297 @@
|
||||
"""
|
||||
Memory Base Service
|
||||
|
||||
提供记忆服务的基础功能和共享辅助方法。
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from app.core.logging_config import get_logger
|
||||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||||
from app.services.emotion_analytics_service import EmotionAnalyticsService
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryBaseService:
|
||||
"""记忆服务基类，提供共享的辅助方法"""
|
||||
|
||||
def __init__(self):
|
||||
self.neo4j_connector = Neo4jConnector()
|
||||
|
||||
@staticmethod
|
||||
def parse_timestamp(timestamp_value) -> Optional[int]:
|
||||
"""
|
||||
将时间戳转换为毫秒级时间戳
|
||||
|
||||
支持多种输入格式：
|
||||
- Neo4j DateTime 对象
|
||||
- ISO格式的时间戳字符串
|
||||
- Python datetime 对象
|
||||
|
||||
Args:
|
||||
timestamp_value: 时间戳值（可以是多种类型）
|
||||
|
||||
Returns:
|
||||
毫秒级时间戳，如果解析失败则返回None
|
||||
"""
|
||||
if not timestamp_value:
|
||||
return None
|
||||
|
||||
try:
|
||||
# 处理 Neo4j DateTime 对象
|
||||
if hasattr(timestamp_value, 'to_native'):
|
||||
dt_object = timestamp_value.to_native()
|
||||
return int(dt_object.timestamp() * 1000)
|
||||
|
||||
# 处理 Python datetime 对象
|
||||
if isinstance(timestamp_value, datetime):
|
||||
return int(timestamp_value.timestamp() * 1000)
|
||||
|
||||
# 处理字符串格式
|
||||
if isinstance(timestamp_value, str):
|
||||
dt_object = datetime.fromisoformat(timestamp_value.replace("Z", "+00:00"))
|
||||
return int(dt_object.timestamp() * 1000)
|
||||
|
||||
# 其他情况尝试转换为字符串再解析
|
||||
dt_object = datetime.fromisoformat(str(timestamp_value).replace("Z", "+00:00"))
|
||||
return int(dt_object.timestamp() * 1000)
|
||||
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {timestamp_value}, error={str(e)}")
|
||||
return None
|
||||
|
||||
async def extract_episodic_emotion(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
提取情景记忆的主要情绪
|
||||
|
||||
查询MemorySummary节点关联的Statement节点，
|
||||
返回emotion_intensity最大的emotion_type。
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
最大emotion_intensity对应的emotion_type，如果没有则返回None
|
||||
"""
|
||||
try:
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.emotion_type IS NOT NULL
|
||||
AND stmt.emotion_intensity IS NOT NULL
|
||||
RETURN stmt.emotion_type AS emotion_type,
|
||||
stmt.emotion_intensity AS emotion_intensity
|
||||
ORDER BY emotion_intensity DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if result and len(result) > 0:
|
||||
emotion_type = result[0].get("emotion_type")
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的情绪: {emotion_type}")
|
||||
return emotion_type
|
||||
else:
|
||||
logger.info(f"summary_id={summary_id} 没有情绪信息")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取情景记忆情绪时出错: {str(e)}", exc_info=True)
|
||||
return None
|
||||
|
||||
async def get_episodic_memory_count(
|
||||
self,
|
||||
end_user_id: Optional[str] = None
|
||||
) -> int:
|
||||
"""
|
||||
获取情景记忆数量
|
||||
|
||||
查询 MemorySummary 节点的数量。
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID，用于过滤特定用户的节点
|
||||
|
||||
Returns:
|
||||
情景记忆的数量
|
||||
"""
|
||||
try:
|
||||
if end_user_id:
|
||||
query = """
|
||||
MATCH (n:MemorySummary)
|
||||
WHERE n.group_id = $group_id
|
||||
RETURN count(n) as count
|
||||
"""
|
||||
result = await self.neo4j_connector.execute_query(query, group_id=end_user_id)
|
||||
else:
|
||||
query = """
|
||||
MATCH (n:MemorySummary)
|
||||
RETURN count(n) as count
|
||||
"""
|
||||
result = await self.neo4j_connector.execute_query(query)
|
||||
|
||||
count = result[0]["count"] if result and len(result) > 0 else 0
|
||||
logger.debug(f"情景记忆数量: {count} (end_user_id={end_user_id})")
|
||||
return count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆数量时出错: {str(e)}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def get_explicit_memory_count(
|
||||
self,
|
||||
end_user_id: Optional[str] = None
|
||||
) -> int:
|
||||
"""
|
||||
获取显性记忆数量
|
||||
|
||||
显性记忆 = 情景记忆（MemorySummary）+ 语义记忆（ExtractedEntity with is_explicit_memory=true）
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID，用于过滤特定用户的节点
|
||||
|
||||
Returns:
|
||||
显性记忆的数量
|
||||
"""
|
||||
try:
|
||||
# 1. 获取情景记忆数量
|
||||
episodic_count = await self.get_episodic_memory_count(end_user_id)
|
||||
|
||||
# 2. 获取语义记忆数量（ExtractedEntity 且 is_explicit_memory = true）
|
||||
if end_user_id:
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE e.group_id = $group_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
|
||||
)
|
||||
else:
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE e.is_explicit_memory = true
|
||||
RETURN count(e) as count
|
||||
"""
|
||||
semantic_result = await self.neo4j_connector.execute_query(semantic_query)
|
||||
|
||||
semantic_count = semantic_result[0]["count"] if semantic_result and len(semantic_result) > 0 else 0
|
||||
|
||||
# 3. 计算总数
|
||||
explicit_count = episodic_count + semantic_count
|
||||
logger.debug(
|
||||
f"显性记忆数量: {explicit_count} "
|
||||
f"(情景={episodic_count}, 语义={semantic_count}, end_user_id={end_user_id})"
|
||||
)
|
||||
return explicit_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆数量时出错: {str(e)}", exc_info=True)
|
||||
return 0
|
||||
|
||||
async def get_emotional_memory_count(
|
||||
self,
|
||||
end_user_id: Optional[str] = None,
|
||||
statement_count_fallback: int = 0
|
||||
) -> int:
|
||||
"""
|
||||
获取情绪记忆数量
|
||||
|
||||
通过 EmotionAnalyticsService 获取情绪标签统计总数。
|
||||
如果获取失败或没有指定 end_user_id，使用 statement_count_fallback 作为后备。
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID
|
||||
statement_count_fallback: 后备方案的数量（通常是 statement 节点数量）
|
||||
|
||||
Returns:
|
||||
情绪记忆的数量
|
||||
"""
|
||||
try:
|
||||
if end_user_id:
|
||||
emotion_service = EmotionAnalyticsService()
|
||||
|
||||
emotion_data = await emotion_service.get_emotion_tags(
|
||||
end_user_id=end_user_id,
|
||||
emotion_type=None,
|
||||
start_date=None,
|
||||
end_date=None,
|
||||
limit=10
|
||||
)
|
||||
emotion_count = emotion_data.get("total_count", 0)
|
||||
logger.debug(f"情绪记忆数量: {emotion_count} (end_user_id={end_user_id})")
|
||||
return emotion_count
|
||||
else:
|
||||
# 如果没有指定 end_user_id，使用后备方案
|
||||
logger.debug(f"情绪记忆数量: {statement_count_fallback} (使用后备方案)")
|
||||
return statement_count_fallback
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"获取情绪记忆数量失败，使用后备方案: {str(e)}")
|
||||
return statement_count_fallback
|
||||
|
||||
async def get_forget_memory_count(
|
||||
self,
|
||||
end_user_id: Optional[str] = None,
|
||||
forgetting_threshold: float = 0.3
|
||||
) -> int:
|
||||
"""
|
||||
获取遗忘记忆数量
|
||||
|
||||
统计激活值低于遗忘阈值的节点数量（low_activation_nodes）。
|
||||
查询范围包括：Statement、ExtractedEntity、MemorySummary、Chunk 节点。
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID，用于过滤特定用户的节点
|
||||
forgetting_threshold: 遗忘阈值，默认 0.3
|
||||
|
||||
Returns:
|
||||
遗忘记忆的数量（激活值低于阈值的节点数）
|
||||
"""
|
||||
try:
|
||||
# 构建查询语句
|
||||
query = """
|
||||
MATCH (n)
|
||||
WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
|
||||
"""
|
||||
|
||||
if end_user_id:
|
||||
query += " AND n.group_id = $group_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
|
||||
"""
|
||||
|
||||
# 设置查询参数
|
||||
params = {'threshold': forgetting_threshold}
|
||||
if end_user_id:
|
||||
params['group_id'] = end_user_id
|
||||
|
||||
# 执行查询
|
||||
result = await self.neo4j_connector.execute_query(query, **params)
|
||||
|
||||
# 提取结果
|
||||
forget_count = result[0]['low_activation_nodes'] if result and len(result) > 0 else 0
|
||||
forget_count = forget_count or 0 # 处理 None 值
|
||||
|
||||
logger.debug(
|
||||
f"遗忘记忆数量: {forget_count} "
|
||||
f"(threshold={forgetting_threshold}, end_user_id={end_user_id})"
|
||||
)
|
||||
return forget_count
|
||||
|
||||
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
|
||||
405
api/app/services/memory_episodic_service.py
Normal file
405
api/app/services/memory_episodic_service.py
Normal file
@@ -0,0 +1,405 @@
|
||||
"""
|
||||
Episodic Memory Service
|
||||
|
||||
处理情景记忆相关的业务逻辑，包括情景记忆总览、详情查询等。
|
||||
"""
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import pytz
|
||||
from app.core.logging_config import get_logger
|
||||
from app.services.memory_base_service import MemoryBaseService
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryEpisodicService(MemoryBaseService):
|
||||
"""情景记忆服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
logger.info("MemoryEpisodicService initialized")
|
||||
|
||||
async def _get_title_and_type(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
读取情景记忆的标题(title)和类型(type)
|
||||
|
||||
仅负责读取已存在的title和type，不进行生成
|
||||
title从name属性读取，type从memory_type属性读取
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组，如果不存在则返回默认值
|
||||
"""
|
||||
try:
|
||||
# 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
|
||||
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
|
||||
)
|
||||
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的节点")
|
||||
return ("未知标题", "其他")
|
||||
|
||||
record = result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
episodic_type = record.get("type") or "其他"
|
||||
|
||||
return (title, episodic_type)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"读取标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", "其他")
|
||||
|
||||
async def _extract_involved_objects(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆涉及的前3个最重要实体
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
前3个实体的name属性列表
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的Statement节点,再查询Statement指向的ExtractedEntity节点
|
||||
# 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
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity)
|
||||
WHERE entity.activation_value IS NOT NULL
|
||||
RETURN DISTINCT entity.name AS name, entity.activation_value AS activation
|
||||
ORDER BY activation DESC
|
||||
LIMIT 3
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取实体名称
|
||||
involved_objects = [record["name"] for record in result if record.get("name")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的涉及对象: {involved_objects}")
|
||||
|
||||
return involved_objects
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取涉及对象时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def _extract_content_records(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆的内容记录
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
所有Statement节点的statement属性内容列表
|
||||
"""
|
||||
try:
|
||||
# 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
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.statement IS NOT NULL AND stmt.statement <> ''
|
||||
RETURN stmt.statement AS statement
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取statement内容
|
||||
content_records = [record["statement"] for record in result if record.get("statement")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的内容记录,共 {len(content_records)} 条")
|
||||
|
||||
return content_records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取内容记录时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
def _calculate_time_filter(self, time_range: str) -> Optional[str]:
|
||||
"""
|
||||
根据时间范围计算过滤的起始时间
|
||||
|
||||
Args:
|
||||
time_range: 时间范围 (all/today/this_week/this_month)
|
||||
|
||||
Returns:
|
||||
ISO格式的时间字符串，如果是"all"则返回None
|
||||
"""
|
||||
if time_range == "all":
|
||||
return None
|
||||
|
||||
# 获取当前时间（UTC）
|
||||
now = datetime.now(pytz.UTC)
|
||||
|
||||
if time_range == "today":
|
||||
# 今天的开始时间（00:00:00）
|
||||
start_time = now.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
elif time_range == "this_week":
|
||||
# 本周的开始时间（周一00:00:00）
|
||||
days_since_monday = now.weekday()
|
||||
start_time = (now - timedelta(days=days_since_monday)).replace(
|
||||
hour=0, minute=0, second=0, microsecond=0
|
||||
)
|
||||
elif time_range == "this_month":
|
||||
# 本月的开始时间（1号00:00:00）
|
||||
start_time = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
|
||||
else:
|
||||
return None
|
||||
|
||||
# 返回ISO格式字符串
|
||||
return start_time.isoformat()
|
||||
|
||||
async def get_episodic_memory_overview(
|
||||
self,
|
||||
end_user_id: str,
|
||||
time_range: str = "all",
|
||||
episodic_type: str = "all",
|
||||
title_keyword: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取情景记忆总览信息
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
time_range: 时间范围筛选
|
||||
episodic_type: 情景类型筛选
|
||||
title_keyword: 标题关键词（可选，用于模糊搜索）
|
||||
"""
|
||||
try:
|
||||
logger.info(
|
||||
f"开始查询 end_user_id={end_user_id} 的情景记忆总览, "
|
||||
f"time_range={time_range}, episodic_type={episodic_type}, title_keyword={title_keyword}"
|
||||
)
|
||||
|
||||
# 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
|
||||
RETURN count(s) AS total_all
|
||||
"""
|
||||
total_all_result = await self.neo4j_connector.execute_query(
|
||||
total_all_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
total_all = total_all_result[0]["total_all"] if total_all_result else 0
|
||||
|
||||
# 2. 计算时间范围的起始时间戳
|
||||
time_filter = self._calculate_time_filter(time_range)
|
||||
|
||||
# 3. 构建Cypher查询
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
"""
|
||||
|
||||
# 添加时间范围过滤
|
||||
if time_filter:
|
||||
query += " AND s.created_at >= $time_filter"
|
||||
|
||||
# 添加标题关键词过滤（如果提供了title_keyword）
|
||||
if title_keyword:
|
||||
query += " AND toLower(s.name) CONTAINS toLower($title_keyword)"
|
||||
|
||||
query += """
|
||||
RETURN elementId(s) AS id,
|
||||
s.created_at AS created_at,
|
||||
s.memory_type AS type,
|
||||
s.name AS title
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
params = {"group_id": end_user_id}
|
||||
if time_filter:
|
||||
params["time_filter"] = time_filter
|
||||
if title_keyword:
|
||||
params["title_keyword"] = title_keyword
|
||||
|
||||
result = await self.neo4j_connector.execute_query(query, **params)
|
||||
|
||||
# 4. 如果没有数据，返回空列表
|
||||
if not result:
|
||||
logger.info(f"end_user_id={end_user_id} 没有情景记忆数据")
|
||||
return {
|
||||
"total": 0,
|
||||
"total_all": total_all,
|
||||
"episodic_memories": []
|
||||
}
|
||||
|
||||
# 5. 对每个节点读取标题和类型，并应用类型筛选
|
||||
episodic_memories = []
|
||||
for record in result:
|
||||
summary_id = record["id"]
|
||||
created_at_str = record.get("created_at")
|
||||
memory_type = record.get("type", "其他")
|
||||
title = record.get("title") or "未命名" # 直接从查询结果获取标题
|
||||
|
||||
# 应用情景类型筛选
|
||||
if episodic_type != "all":
|
||||
# 检查类型是否匹配
|
||||
# 注意：Neo4j 中存储的 memory_type 现在应该是英文格式（如 "conversation", "project_work"）
|
||||
# 但为了兼容旧数据，我们也支持中文格式的匹配
|
||||
type_mapping = {
|
||||
"conversation": "对话",
|
||||
"project_work": "项目/工作",
|
||||
"learning": "学习",
|
||||
"decision": "决策",
|
||||
"important_event": "重要事件"
|
||||
}
|
||||
|
||||
# 获取对应的中文类型（用于兼容旧数据）
|
||||
chinese_type = type_mapping.get(episodic_type)
|
||||
|
||||
# 检查类型是否匹配（支持新的英文格式和旧的中文格式）
|
||||
if memory_type != episodic_type and memory_type != chinese_type:
|
||||
continue
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"type": memory_type,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的情景记忆总览,"
|
||||
f"筛选后 {len(episodic_memories)} 条，总共 {total_all} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": len(episodic_memories),
|
||||
"total_all": total_all,
|
||||
"episodic_memories": episodic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_episodic_memory_details(
|
||||
self,
|
||||
end_user_id: str,
|
||||
summary_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取单个情景记忆的详细信息
|
||||
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id}, summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
# 1. 查询指定的MemorySummary节点
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_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
|
||||
)
|
||||
|
||||
# 2. 如果节点不存在，返回错误
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的情景记忆")
|
||||
raise ValueError(f"情景记忆不存在: summary_id={summary_id}")
|
||||
|
||||
# 3. 获取基本信息
|
||||
record = result[0]
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
# 4. 调用_get_title_and_type读取标题和类型
|
||||
title, episodic_type = await self._get_title_and_type(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 5. 调用_extract_involved_objects提取涉及对象
|
||||
involved_objects = await self._extract_involved_objects(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 6. 调用_extract_content_records提取内容记录
|
||||
content_records = await self._extract_content_records(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 7. 使用基类方法提取情绪
|
||||
emotion = await self.extract_episodic_emotion(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 8. 返回完整的详情信息
|
||||
details = {
|
||||
"id": summary_id,
|
||||
"created_at": created_at_timestamp,
|
||||
"involved_objects": involved_objects,
|
||||
"episodic_type": episodic_type,
|
||||
"content_records": content_records,
|
||||
"emotion": emotion
|
||||
}
|
||||
|
||||
logger.info(f"成功获取 summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
return details
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出ValueError，让Controller层处理
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
# 创建全局服务实例（供控制器层使用）
|
||||
memory_episodic_service = MemoryEpisodicService()
|
||||
274
api/app/services/memory_explicit_service.py
Normal file
274
api/app/services/memory_explicit_service.py
Normal file
@@ -0,0 +1,274 @@
|
||||
"""
|
||||
显性记忆服务
|
||||
|
||||
处理显性记忆相关的业务逻辑，包括情景记忆和语义记忆的查询。
|
||||
"""
|
||||
|
||||
from typing import Any, Dict
|
||||
|
||||
from app.core.logging_config import get_logger
|
||||
from app.services.memory_base_service import MemoryBaseService
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class MemoryExplicitService(MemoryBaseService):
|
||||
"""显性记忆服务类"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
logger.info("MemoryExplicitService initialized")
|
||||
|
||||
async def get_explicit_memory_overview(
|
||||
self,
|
||||
end_user_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆总览信息
|
||||
|
||||
返回两部分：
|
||||
1. 情景记忆（episodic_memories）- 来自MemorySummary节点
|
||||
2. 语义记忆（semantic_memories）- 来自ExtractedEntity节点（is_explicit_memory=true）
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
|
||||
Returns:
|
||||
{
|
||||
"total": int,
|
||||
"episodic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"title": str,
|
||||
"content": str,
|
||||
"created_at": int
|
||||
}
|
||||
],
|
||||
"semantic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"name": str,
|
||||
"entity_type": str,
|
||||
"core_definition": str
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id} 的显性记忆总览（情景记忆+语义记忆）")
|
||||
|
||||
# ========== 1. 查询情景记忆（MemorySummary节点） ==========
|
||||
episodic_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
RETURN elementId(s) AS id,
|
||||
s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理情景记忆数据
|
||||
episodic_memories = []
|
||||
if episodic_result:
|
||||
for record in episodic_result:
|
||||
summary_id = record["id"]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
# 注意：总览接口不返回 emotion 字段
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"content": content,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
# ========== 2. 查询语义记忆（ExtractedEntity节点） ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN elementId(e) AS id,
|
||||
e.name AS name,
|
||||
e.entity_type AS entity_type,
|
||||
e.description AS core_definition
|
||||
ORDER BY e.name ASC
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理语义记忆数据
|
||||
semantic_memories = []
|
||||
if semantic_result:
|
||||
for record in semantic_result:
|
||||
entity_id = record["id"]
|
||||
name = record.get("name") or "未命名"
|
||||
entity_type = record.get("entity_type") or "未分类"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
|
||||
# 注意：总览接口不返回 detailed_notes 和 created_at 字段
|
||||
semantic_memories.append({
|
||||
"id": entity_id,
|
||||
"name": name,
|
||||
"entity_type": entity_type,
|
||||
"core_definition": core_definition
|
||||
})
|
||||
|
||||
# ========== 3. 返回结果 ==========
|
||||
total_count = len(episodic_memories) + len(semantic_memories)
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的显性记忆总览，"
|
||||
f"情景记忆={len(episodic_memories)} 条，语义记忆={len(semantic_memories)} 条，"
|
||||
f"总计 {total_count} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": total_count,
|
||||
"episodic_memories": episodic_memories,
|
||||
"semantic_memories": semantic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_explicit_memory_details(
|
||||
self,
|
||||
end_user_id: str,
|
||||
memory_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 查询情景记忆或语义记忆的详细信息。
|
||||
先尝试查询情景记忆，如果找不到再查询语义记忆。
|
||||
|
||||
Args:
|
||||
end_user_id: 终端用户ID
|
||||
memory_id: 记忆ID（可以是情景记忆或语义记忆的ID）
|
||||
|
||||
Returns:
|
||||
情景记忆返回：
|
||||
{
|
||||
"memory_type": "episodic",
|
||||
"title": str,
|
||||
"content": str,
|
||||
"emotion": Dict,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
语义记忆返回：
|
||||
{
|
||||
"memory_type": "semantic",
|
||||
"name": str,
|
||||
"core_definition": str,
|
||||
"detailed_notes": str,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
Raises:
|
||||
ValueError: 当记忆不存在时
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询显性记忆详情: end_user_id={end_user_id}, memory_id={memory_id}")
|
||||
|
||||
# ========== 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
|
||||
RETURN s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if episodic_result and len(episodic_result) > 0:
|
||||
record = episodic_result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
# 使用基类方法获取情绪信息
|
||||
emotion = await self.extract_episodic_emotion(
|
||||
summary_id=memory_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
logger.info(f"成功获取情景记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "episodic",
|
||||
"title": title,
|
||||
"content": content,
|
||||
"emotion": emotion,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 2. 如果不是情景记忆，尝试查询语义记忆 ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE elementId(e) = $memory_id
|
||||
AND e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN e.name AS name,
|
||||
e.description AS core_definition,
|
||||
e.example AS detailed_notes,
|
||||
e.created_at AS created_at
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if semantic_result and len(semantic_result) > 0:
|
||||
record = semantic_result[0]
|
||||
name = record.get("name") or "未命名"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
detailed_notes = record.get("detailed_notes") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 使用基类方法转换时间戳
|
||||
created_at_timestamp = self.parse_timestamp(created_at_str)
|
||||
|
||||
logger.info(f"成功获取语义记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "semantic",
|
||||
"name": name,
|
||||
"core_definition": core_definition,
|
||||
"detailed_notes": detailed_notes,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 3. 两种记忆都找不到 ==========
|
||||
logger.warning(f"记忆不存在: memory_id={memory_id}, end_user_id={end_user_id}")
|
||||
raise ValueError(f"记忆不存在: memory_id={memory_id}")
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出 ValueError（记忆不存在）
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
@@ -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.services.memory_base_service import MemoryBaseService
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy.orm import Session
|
||||
@@ -883,866 +884,6 @@ class UserMemoryService:
|
||||
"failed": failed,
|
||||
"errors": errors + [{"error": f"批量处理失败: {str(e)}"}]
|
||||
}
|
||||
|
||||
async def _get_title_and_type(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
读取情景记忆的标题(title)和类型(type)
|
||||
|
||||
仅负责读取已存在的title和type，不进行生成
|
||||
title从name属性读取，type从memory_type属性读取
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组，如果不存在则返回默认值
|
||||
"""
|
||||
try:
|
||||
# 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
|
||||
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
|
||||
)
|
||||
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的节点")
|
||||
return ("未知标题", "其他")
|
||||
|
||||
record = result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
episodic_type = record.get("type") or "其他"
|
||||
|
||||
return (title, episodic_type)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"读取标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", "其他")
|
||||
|
||||
@staticmethod
|
||||
async def generate_title_and_type_for_summary(
|
||||
content: str,
|
||||
end_user_id: str
|
||||
) -> Tuple[str, str]:
|
||||
"""
|
||||
为MemorySummary生成标题和类型（静态方法，用于创建节点时调用）
|
||||
|
||||
此方法应该在创建MemorySummary节点时调用，生成title和type
|
||||
|
||||
Args:
|
||||
content: Summary的内容文本
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
(标题, 类型)元组
|
||||
"""
|
||||
from app.core.memory.utils.prompt.prompt_utils import render_episodic_title_and_type_prompt
|
||||
import json
|
||||
|
||||
# 定义有效的类型集合
|
||||
VALID_TYPES = {
|
||||
"conversation", # 对话
|
||||
"project_work", # 项目/工作
|
||||
"learning", # 学习
|
||||
"decision", # 决策
|
||||
"important_event" # 重要事件
|
||||
}
|
||||
DEFAULT_TYPE = "conversation" # 默认类型
|
||||
|
||||
try:
|
||||
if not content:
|
||||
logger.warning("content为空，无法生成标题和类型")
|
||||
return ("空内容", DEFAULT_TYPE)
|
||||
|
||||
# 1. 渲染Jinja2提示词模板
|
||||
prompt = await render_episodic_title_and_type_prompt(content)
|
||||
|
||||
# 2. 调用LLM生成标题和类型
|
||||
llm_client = _get_llm_client_for_user(end_user_id)
|
||||
messages = [
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
response = await llm_client.chat(messages=messages)
|
||||
|
||||
# 3. 解析LLM响应
|
||||
content_response = response.content
|
||||
if isinstance(content_response, list):
|
||||
if len(content_response) > 0:
|
||||
if isinstance(content_response[0], dict):
|
||||
text = content_response[0].get('text', content_response[0].get('content', str(content_response[0])))
|
||||
full_response = str(text)
|
||||
else:
|
||||
full_response = str(content_response[0])
|
||||
else:
|
||||
full_response = ""
|
||||
elif isinstance(content_response, dict):
|
||||
full_response = str(content_response.get('text', content_response.get('content', str(content_response))))
|
||||
else:
|
||||
full_response = str(content_response) if content_response is not None else ""
|
||||
|
||||
# 4. 解析JSON响应
|
||||
try:
|
||||
# 尝试从响应中提取JSON
|
||||
# 移除可能的markdown代码块标记
|
||||
json_str = full_response.strip()
|
||||
if json_str.startswith("```json"):
|
||||
json_str = json_str[7:]
|
||||
if json_str.startswith("```"):
|
||||
json_str = json_str[3:]
|
||||
if json_str.endswith("```"):
|
||||
json_str = json_str[:-3]
|
||||
json_str = json_str.strip()
|
||||
|
||||
result_data = json.loads(json_str)
|
||||
title = result_data.get("title", "未知标题")
|
||||
episodic_type_raw = result_data.get("type", DEFAULT_TYPE)
|
||||
|
||||
# 5. 校验和归一化类型
|
||||
# 将类型转换为小写并去除空格
|
||||
episodic_type_normalized = str(episodic_type_raw).lower().strip()
|
||||
|
||||
# 检查是否在有效类型集合中
|
||||
if episodic_type_normalized in VALID_TYPES:
|
||||
episodic_type = episodic_type_normalized
|
||||
else:
|
||||
# 尝试映射常见的中文类型到英文
|
||||
type_mapping = {
|
||||
"对话": "conversation",
|
||||
"项目": "project_work",
|
||||
"工作": "project_work",
|
||||
"项目/工作": "project_work",
|
||||
"学习": "learning",
|
||||
"决策": "decision",
|
||||
"重要事件": "important_event",
|
||||
"事件": "important_event"
|
||||
}
|
||||
episodic_type = type_mapping.get(episodic_type_raw, DEFAULT_TYPE)
|
||||
logger.warning(
|
||||
f"LLM返回的类型 '{episodic_type_raw}' 不在有效集合中，"
|
||||
f"已归一化为 '{episodic_type}'"
|
||||
)
|
||||
|
||||
logger.info(f"成功生成标题和类型: title={title}, type={episodic_type}")
|
||||
return (title, episodic_type)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
logger.error(f"无法解析LLM响应为JSON: {full_response}")
|
||||
return ("解析失败", DEFAULT_TYPE)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成标题和类型时出错: {str(e)}", exc_info=True)
|
||||
return ("错误", DEFAULT_TYPE)
|
||||
|
||||
async def _extract_involved_objects(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆涉及的前3个最重要实体
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
前3个实体的name属性列表
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的Statement节点,再查询Statement指向的ExtractedEntity节点
|
||||
# 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
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity)
|
||||
WHERE entity.activation_value IS NOT NULL
|
||||
RETURN DISTINCT entity.name AS name, entity.activation_value AS activation
|
||||
ORDER BY activation DESC
|
||||
LIMIT 3
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取实体名称
|
||||
involved_objects = [record["name"] for record in result if record.get("name")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的涉及对象: {involved_objects}")
|
||||
|
||||
return involved_objects
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取涉及对象时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def _extract_content_records(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> List[str]:
|
||||
"""
|
||||
提取情景记忆的内容记录
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
所有Statement节点的statement属性内容列表
|
||||
"""
|
||||
try:
|
||||
# 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
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.statement IS NOT NULL AND stmt.statement <> ''
|
||||
RETURN stmt.statement AS statement
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取statement内容
|
||||
content_records = [record["statement"] for record in result if record.get("statement")]
|
||||
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的内容记录,共 {len(content_records)} 条")
|
||||
|
||||
return content_records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取内容记录时出错: {str(e)}", exc_info=True)
|
||||
return []
|
||||
|
||||
async def _extract_episodic_emotion(
|
||||
self,
|
||||
summary_id: str,
|
||||
end_user_id: str
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
提取情景记忆的主要情绪
|
||||
|
||||
Args:
|
||||
summary_id: Summary节点的ID
|
||||
end_user_id: 终端用户ID (group_id)
|
||||
|
||||
Returns:
|
||||
最大emotion_intensity对应的emotion_type,如果没有则返回None
|
||||
"""
|
||||
try:
|
||||
# 查询Summary节点指向的所有Statement节点
|
||||
# 筛选具有emotion_type属性的节点
|
||||
# 按emotion_intensity降序排序,返回第一个
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_id
|
||||
MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
|
||||
WHERE stmt.emotion_type IS NOT NULL
|
||||
AND stmt.emotion_intensity IS NOT NULL
|
||||
RETURN stmt.emotion_type AS emotion_type,
|
||||
stmt.emotion_intensity AS emotion_intensity
|
||||
ORDER BY emotion_intensity DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
result = await self.neo4j_connector.execute_query(
|
||||
query,
|
||||
summary_id=summary_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 提取emotion_type
|
||||
if result and len(result) > 0:
|
||||
emotion_type = result[0].get("emotion_type")
|
||||
logger.info(f"成功提取 summary_id={summary_id} 的情绪: {emotion_type}")
|
||||
return emotion_type
|
||||
else:
|
||||
logger.info(f"summary_id={summary_id} 没有情绪信息")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"提取情景记忆情绪时出错: {str(e)}", exc_info=True)
|
||||
return None
|
||||
|
||||
async def get_episodic_memory_overview(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
time_range: str = "all",
|
||||
episodic_type: str = "all",
|
||||
title_keyword: Optional[str] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取情景记忆总览信息
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
time_range: 时间范围筛选
|
||||
episodic_type: 情景类型筛选
|
||||
title_keyword: 标题关键词（可选，用于模糊搜索）
|
||||
"""
|
||||
try:
|
||||
logger.info(
|
||||
f"开始查询 end_user_id={end_user_id} 的情景记忆总览, "
|
||||
f"time_range={time_range}, episodic_type={episodic_type}, title_keyword={title_keyword}"
|
||||
)
|
||||
|
||||
# 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
|
||||
RETURN count(s) AS total_all
|
||||
"""
|
||||
total_all_result = await self.neo4j_connector.execute_query(
|
||||
total_all_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
total_all = total_all_result[0]["total_all"] if total_all_result else 0
|
||||
|
||||
# 2. 计算时间范围的起始时间戳
|
||||
time_filter = self._calculate_time_filter(time_range)
|
||||
|
||||
# 3. 构建Cypher查询
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
"""
|
||||
|
||||
# 添加时间范围过滤
|
||||
if time_filter:
|
||||
query += " AND s.created_at >= $time_filter"
|
||||
|
||||
# 添加标题关键词过滤（如果提供了title_keyword）
|
||||
if title_keyword:
|
||||
query += " AND toLower(s.name) CONTAINS toLower($title_keyword)"
|
||||
|
||||
query += """
|
||||
RETURN elementId(s) AS id,
|
||||
s.created_at AS created_at,
|
||||
s.memory_type AS type,
|
||||
s.name AS title
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
params = {"group_id": end_user_id}
|
||||
if time_filter:
|
||||
params["time_filter"] = time_filter
|
||||
if title_keyword:
|
||||
params["title_keyword"] = title_keyword
|
||||
|
||||
result = await self.neo4j_connector.execute_query(query, **params)
|
||||
|
||||
# 4. 如果没有数据，返回空列表
|
||||
if not result:
|
||||
logger.info(f"end_user_id={end_user_id} 没有情景记忆数据")
|
||||
return {
|
||||
"total": 0,
|
||||
"total_all": total_all,
|
||||
"episodic_memories": []
|
||||
}
|
||||
|
||||
# 5. 对每个节点读取标题和类型，并应用类型筛选
|
||||
episodic_memories = []
|
||||
for record in result:
|
||||
summary_id = record["id"]
|
||||
created_at_str = record.get("created_at")
|
||||
memory_type = record.get("type", "其他")
|
||||
title = record.get("title") or "未命名" # 直接从查询结果获取标题
|
||||
|
||||
# 应用情景类型筛选
|
||||
if episodic_type != "all":
|
||||
# 检查类型是否匹配
|
||||
# 注意：Neo4j 中存储的 memory_type 现在应该是英文格式（如 "conversation", "project_work"）
|
||||
# 但为了兼容旧数据，我们也支持中文格式的匹配
|
||||
type_mapping = {
|
||||
"conversation": "对话",
|
||||
"project_work": "项目/工作",
|
||||
"learning": "学习",
|
||||
"decision": "决策",
|
||||
"important_event": "重要事件"
|
||||
}
|
||||
|
||||
# 获取对应的中文类型（用于兼容旧数据）
|
||||
chinese_type = type_mapping.get(episodic_type)
|
||||
|
||||
# 检查类型是否匹配（支持新的英文格式和旧的中文格式）
|
||||
if memory_type != episodic_type and memory_type != chinese_type:
|
||||
continue
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"type": memory_type,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的情景记忆总览,"
|
||||
f"筛选后 {len(episodic_memories)} 条，总共 {total_all} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": len(episodic_memories),
|
||||
"total_all": total_all,
|
||||
"episodic_memories": episodic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
def _calculate_time_filter(self, time_range: str) -> Optional[str]:
|
||||
"""
|
||||
根据时间范围计算过滤的起始时间
|
||||
|
||||
Args:
|
||||
time_range: 时间范围 (all/today/this_week/this_month)
|
||||
|
||||
Returns:
|
||||
ISO格式的时间字符串，如果是"all"则返回None
|
||||
"""
|
||||
from datetime import datetime, timedelta
|
||||
import pytz
|
||||
|
||||
if time_range == "all":
|
||||
return None
|
||||
|
||||
# 获取当前时间（UTC）
|
||||
now = datetime.now(pytz.UTC)
|
||||
|
||||
if time_range == "today":
|
||||
# 今天的开始时间（00:00:00）
|
||||
start_time = now.replace(hour=0, minute=0, second=0, microsecond=0)
|
||||
elif time_range == "this_week":
|
||||
# 本周的开始时间（周一00:00:00）
|
||||
days_since_monday = now.weekday()
|
||||
start_time = (now - timedelta(days=days_since_monday)).replace(
|
||||
hour=0, minute=0, second=0, microsecond=0
|
||||
)
|
||||
elif time_range == "this_month":
|
||||
# 本月的开始时间（1号00:00:00）
|
||||
start_time = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
|
||||
else:
|
||||
return None
|
||||
|
||||
# 返回ISO格式字符串
|
||||
return start_time.isoformat()
|
||||
|
||||
async def get_episodic_memory_details(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
summary_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取单个情景记忆的详细信息
|
||||
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id}, summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
# 1. 查询指定的MemorySummary节点
|
||||
query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE elementId(s) = $summary_id AND s.group_id = $group_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
|
||||
)
|
||||
|
||||
# 2. 如果节点不存在，返回错误
|
||||
if not result or len(result) == 0:
|
||||
logger.warning(f"未找到 summary_id={summary_id} 的情景记忆")
|
||||
raise ValueError(f"情景记忆不存在: summary_id={summary_id}")
|
||||
|
||||
# 3. 获取基本信息
|
||||
record = result[0]
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 4. 调用_get_title_and_type读取标题和类型
|
||||
title, episodic_type = await self._get_title_and_type(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 5. 调用_extract_involved_objects提取涉及对象
|
||||
involved_objects = await self._extract_involved_objects(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 6. 调用_extract_content_records提取内容记录
|
||||
content_records = await self._extract_content_records(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 7. 调用_extract_episodic_emotion提取情绪
|
||||
emotion = await self._extract_episodic_emotion(
|
||||
summary_id=summary_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
# 8. 返回完整的详情信息
|
||||
details = {
|
||||
"id": summary_id,
|
||||
"created_at": created_at_timestamp,
|
||||
"involved_objects": involved_objects,
|
||||
"episodic_type": episodic_type,
|
||||
"content_records": content_records,
|
||||
"emotion": emotion
|
||||
}
|
||||
|
||||
logger.info(f"成功获取 summary_id={summary_id} 的情景记忆详情")
|
||||
|
||||
return details
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出ValueError，让Controller层处理
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取情景记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_explicit_memory_overview(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆总览信息
|
||||
|
||||
返回两部分：
|
||||
1. 情景记忆（episodic_memories）- 来自MemorySummary节点
|
||||
2. 语义记忆（semantic_memories）- 来自ExtractedEntity节点（is_explicit_memory=true）
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
|
||||
Returns:
|
||||
{
|
||||
"total": int,
|
||||
"episodic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"title": str,
|
||||
"content": str,
|
||||
"created_at": int,
|
||||
"emotion": Dict
|
||||
}
|
||||
],
|
||||
"semantic_memories": [
|
||||
{
|
||||
"id": str,
|
||||
"name": str,
|
||||
"entity_type": str,
|
||||
"core_definition": str,
|
||||
"detailed_notes": str,
|
||||
"created_at": int
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询 end_user_id={end_user_id} 的显性记忆总览（情景记忆+语义记忆）")
|
||||
|
||||
# ========== 1. 查询情景记忆（MemorySummary节点） ==========
|
||||
episodic_query = """
|
||||
MATCH (s:MemorySummary)
|
||||
WHERE s.group_id = $group_id
|
||||
RETURN elementId(s) AS id,
|
||||
s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
ORDER BY s.created_at DESC
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理情景记忆数据
|
||||
episodic_memories = []
|
||||
if episodic_result:
|
||||
for record in episodic_result:
|
||||
summary_id = record["id"]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 注意：总览接口不返回 emotion 字段
|
||||
episodic_memories.append({
|
||||
"id": summary_id,
|
||||
"title": title,
|
||||
"content": content,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
# ========== 2. 查询语义记忆（ExtractedEntity节点） ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN elementId(e) AS id,
|
||||
e.name AS name,
|
||||
e.entity_type AS entity_type,
|
||||
e.description AS core_definition,
|
||||
e.example AS detailed_notes,
|
||||
e.created_at AS created_at
|
||||
ORDER BY e.created_at DESC
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
# 处理语义记忆数据
|
||||
semantic_memories = []
|
||||
if semantic_result:
|
||||
for record in semantic_result:
|
||||
entity_id = record["id"]
|
||||
name = record.get("name") or "未命名"
|
||||
entity_type = record.get("entity_type") or "未分类"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 注意：总览接口不返回 detailed_notes 字段
|
||||
semantic_memories.append({
|
||||
"id": entity_id,
|
||||
"name": name,
|
||||
"entity_type": entity_type,
|
||||
"core_definition": core_definition,
|
||||
"created_at": created_at_timestamp
|
||||
})
|
||||
|
||||
# ========== 3. 返回结果 ==========
|
||||
total_count = len(episodic_memories) + len(semantic_memories)
|
||||
|
||||
logger.info(
|
||||
f"成功获取 end_user_id={end_user_id} 的显性记忆总览，"
|
||||
f"情景记忆={len(episodic_memories)} 条，语义记忆={len(semantic_memories)} 条，"
|
||||
f"总计 {total_count} 条"
|
||||
)
|
||||
|
||||
return {
|
||||
"total": total_count,
|
||||
"episodic_memories": episodic_memories,
|
||||
"semantic_memories": semantic_memories
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆总览时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def get_explicit_memory_details(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
memory_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取显性记忆详情
|
||||
|
||||
根据 memory_id 查询情景记忆或语义记忆的详细信息。
|
||||
先尝试查询情景记忆，如果找不到再查询语义记忆。
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID
|
||||
memory_id: 记忆ID（可以是情景记忆或语义记忆的ID）
|
||||
|
||||
Returns:
|
||||
情景记忆返回：
|
||||
{
|
||||
"memory_type": "episodic",
|
||||
"title": str,
|
||||
"content": str,
|
||||
"emotion": Dict,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
语义记忆返回：
|
||||
{
|
||||
"memory_type": "semantic",
|
||||
"name": str,
|
||||
"core_definition": str,
|
||||
"detailed_notes": str,
|
||||
"created_at": int
|
||||
}
|
||||
|
||||
Raises:
|
||||
ValueError: 当记忆不存在时
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始查询显性记忆详情: end_user_id={end_user_id}, memory_id={memory_id}")
|
||||
|
||||
# ========== 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
|
||||
RETURN s.name AS title,
|
||||
s.content AS content,
|
||||
s.created_at AS created_at
|
||||
"""
|
||||
|
||||
episodic_result = await self.neo4j_connector.execute_query(
|
||||
episodic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if episodic_result and len(episodic_result) > 0:
|
||||
record = episodic_result[0]
|
||||
title = record.get("title") or "未命名"
|
||||
content = record.get("content") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
# 获取情绪信息
|
||||
emotion = await self._extract_episodic_emotion(
|
||||
summary_id=memory_id,
|
||||
end_user_id=end_user_id
|
||||
)
|
||||
|
||||
logger.info(f"成功获取情景记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "episodic",
|
||||
"title": title,
|
||||
"content": content,
|
||||
"emotion": emotion,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 2. 如果不是情景记忆，尝试查询语义记忆 ==========
|
||||
semantic_query = """
|
||||
MATCH (e:ExtractedEntity)
|
||||
WHERE elementId(e) = $memory_id
|
||||
AND e.group_id = $group_id
|
||||
AND e.is_explicit_memory = true
|
||||
RETURN e.name AS name,
|
||||
e.description AS core_definition,
|
||||
e.example AS detailed_notes,
|
||||
e.created_at AS created_at
|
||||
"""
|
||||
|
||||
semantic_result = await self.neo4j_connector.execute_query(
|
||||
semantic_query,
|
||||
memory_id=memory_id,
|
||||
group_id=end_user_id
|
||||
)
|
||||
|
||||
if semantic_result and len(semantic_result) > 0:
|
||||
record = semantic_result[0]
|
||||
name = record.get("name") or "未命名"
|
||||
core_definition = record.get("core_definition") or ""
|
||||
detailed_notes = record.get("detailed_notes") or ""
|
||||
created_at_str = record.get("created_at")
|
||||
|
||||
# 转换时间戳
|
||||
created_at_timestamp = None
|
||||
if created_at_str:
|
||||
try:
|
||||
from datetime import datetime
|
||||
dt_object = datetime.fromisoformat(created_at_str.replace("Z", "+00:00"))
|
||||
created_at_timestamp = int(dt_object.timestamp() * 1000)
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
logger.warning(f"无法解析时间戳: {created_at_str}, error={str(e)}")
|
||||
|
||||
logger.info(f"成功获取语义记忆详情: memory_id={memory_id}")
|
||||
return {
|
||||
"memory_type": "semantic",
|
||||
"name": name,
|
||||
"core_definition": core_definition,
|
||||
"detailed_notes": detailed_notes,
|
||||
"created_at": created_at_timestamp
|
||||
}
|
||||
|
||||
# ========== 3. 两种记忆都找不到 ==========
|
||||
logger.warning(f"记忆不存在: memory_id={memory_id}, end_user_id={end_user_id}")
|
||||
raise ValueError(f"记忆不存在: memory_id={memory_id}")
|
||||
|
||||
except ValueError:
|
||||
# 重新抛出 ValueError（记忆不存在）
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"获取显性记忆详情时出错: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
# 独立的分析函数
|
||||
@@ -2055,17 +1196,18 @@ async def analytics_memory_types(
|
||||
end_user_id: Optional[str] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
统计8种记忆类型的数量和百分比
|
||||
统计9种记忆类型的数量和百分比
|
||||
|
||||
计算规则：
|
||||
1. 感知记忆 (PERCEPTUAL_MEMORY) = statement + entity
|
||||
2. 工作记忆 (WORKING_MEMORY) = chunk + entity
|
||||
3. 短期记忆 (SHORT_TERM_MEMORY) = chunk
|
||||
4. 长期记忆 (LONG_TERM_MEMORY) = entity
|
||||
5. 显性记忆 (EXPLICIT_MEMORY) = 1/2 * entity
|
||||
5. 显性记忆 (EXPLICIT_MEMORY) = 情景记忆 + 语义记忆（通过 MemoryBaseService.get_explicit_memory_count 获取）
|
||||
6. 隐性记忆 (IMPLICIT_MEMORY) = 1/3 * entity
|
||||
7. 情绪记忆 (EMOTIONAL_MEMORY) = statement
|
||||
8. 情景记忆 (EPISODIC_MEMORY) = memory_summary
|
||||
7. 情绪记忆 (EMOTIONAL_MEMORY) = 情绪标签统计总数（通过 MemoryBaseService.get_emotional_memory_count 获取）
|
||||
8. 情景记忆 (EPISODIC_MEMORY) = memory_summary（通过 MemoryBaseService.get_episodic_memory_count 获取）
|
||||
9. 遗忘记忆 (FORGET_MEMORY) = 激活值低于阈值的节点数（通过 MemoryBaseService.get_forget_memory_count 获取）
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
@@ -2090,13 +1232,16 @@ async def analytics_memory_types(
|
||||
- IMPLICIT_MEMORY: 隐性记忆
|
||||
- EMOTIONAL_MEMORY: 情绪记忆
|
||||
- EPISODIC_MEMORY: 情景记忆
|
||||
- FORGET_MEMORY: 遗忘记忆
|
||||
"""
|
||||
# 定义需要查询的节点类型
|
||||
# 初始化基础服务
|
||||
base_service = MemoryBaseService()
|
||||
|
||||
# 定义需要查询的基础节点类型
|
||||
node_types = {
|
||||
"Statement": "Statement",
|
||||
"Entity": "ExtractedEntity",
|
||||
"Chunk": "Chunk",
|
||||
"MemorySummary": "MemorySummary"
|
||||
"Chunk": "Chunk"
|
||||
}
|
||||
|
||||
# 存储每种节点类型的计数
|
||||
@@ -2126,18 +1271,45 @@ async def analytics_memory_types(
|
||||
statement_count = node_counts.get("Statement", 0)
|
||||
entity_count = node_counts.get("Entity", 0)
|
||||
chunk_count = node_counts.get("Chunk", 0)
|
||||
memory_summary_count = node_counts.get("MemorySummary", 0)
|
||||
|
||||
# 按规则计算8种记忆类型的数量（使用英文枚举作为key）
|
||||
# 获取用户的遗忘阈值配置
|
||||
forgetting_threshold = 0.3 # 默认值
|
||||
if end_user_id:
|
||||
try:
|
||||
from app.services.memory_agent_service import get_end_user_connected_config
|
||||
from app.core.memory.storage_services.forgetting_engine.config_utils import load_actr_config_from_db
|
||||
|
||||
# 获取用户关联的 config_id
|
||||
connected_config = get_end_user_connected_config(end_user_id, db)
|
||||
config_id = connected_config.get('memory_config_id')
|
||||
|
||||
if config_id:
|
||||
# 从数据库加载配置
|
||||
config = load_actr_config_from_db(db, config_id)
|
||||
forgetting_threshold = config.get('forgetting_threshold', 0.3)
|
||||
logger.debug(f"使用用户配置的遗忘阈值: {forgetting_threshold} (end_user_id={end_user_id}, config_id={config_id})")
|
||||
else:
|
||||
logger.debug(f"用户未关联配置，使用默认遗忘阈值: {forgetting_threshold} (end_user_id={end_user_id})")
|
||||
except Exception as e:
|
||||
logger.warning(f"获取用户遗忘阈值配置失败，使用默认值 {forgetting_threshold}: {str(e)}")
|
||||
|
||||
# 使用 MemoryBaseService 的共享方法获取特殊记忆类型的数量
|
||||
episodic_count = await base_service.get_episodic_memory_count(end_user_id)
|
||||
explicit_count = await base_service.get_explicit_memory_count(end_user_id)
|
||||
emotion_count = await base_service.get_emotional_memory_count(end_user_id, statement_count)
|
||||
forget_count = await base_service.get_forget_memory_count(end_user_id, forgetting_threshold)
|
||||
|
||||
# 按规则计算9种记忆类型的数量（使用英文枚举作为key）
|
||||
memory_counts = {
|
||||
"PERCEPTUAL_MEMORY": statement_count + entity_count, # 感知记忆
|
||||
"WORKING_MEMORY": chunk_count + entity_count, # 工作记忆
|
||||
"SHORT_TERM_MEMORY": chunk_count, # 短期记忆
|
||||
"LONG_TERM_MEMORY": entity_count, # 长期记忆
|
||||
"EXPLICIT_MEMORY": entity_count // 2, # 显性记忆 (1/2 entity)
|
||||
"EXPLICIT_MEMORY": explicit_count, # 显性记忆（情景记忆 + 语义记忆）
|
||||
"IMPLICIT_MEMORY": entity_count // 3, # 隐性记忆 (1/3 entity)
|
||||
"EMOTIONAL_MEMORY": statement_count, # 情绪记忆
|
||||
"EPISODIC_MEMORY": memory_summary_count # 情景记忆
|
||||
"EMOTIONAL_MEMORY": emotion_count, # 情绪记忆（使用情绪标签统计）
|
||||
"EPISODIC_MEMORY": episodic_count, # 情景记忆
|
||||
"FORGET_MEMORY": forget_count # 遗忘记忆（激活值低于阈值）
|
||||
}
|
||||
|
||||
# 计算总数
|
||||
|
||||
33
api/app/version_info.json
Normal file
33
api/app/version_info.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"v0.2.0": {
|
||||
"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。"
|
||||
]
|
||||
},
|
||||
"v0.1.0": {
|
||||
"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！"
|
||||
]
|
||||
}
|
||||
}
|
||||
30
api/migrations/versions/9ab9b6393f32_20261511.py
Normal file
30
api/migrations/versions/9ab9b6393f32_20261511.py
Normal file
@@ -0,0 +1,30 @@
|
||||
"""20261511
|
||||
|
||||
Revision ID: 9ab9b6393f32
|
||||
Revises: 793c31683aa5
|
||||
Create Date: 2026-01-13 15:14:54.708405
|
||||
|
||||
"""
|
||||
from typing import Sequence, Union
|
||||
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = '9ab9b6393f32'
|
||||
down_revision: Union[str, None] = '793c31683aa5'
|
||||
branch_labels: Union[str, Sequence[str], None] = None
|
||||
depends_on: Union[str, Sequence[str], None] = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.drop_constraint(op.f('tool_executions_user_id_fkey'), 'tool_executions', type_='foreignkey')
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.create_foreign_key(op.f('tool_executions_user_id_fkey'), 'tool_executions', 'users', ['user_id'], ['id'])
|
||||
# ### end Alembic commands ###
|
||||
Reference in New Issue
Block a user