Fix/memory bug fix (#171)

api/app/controllers/emotion_config_controller.py

@@ -12,6 +12,7 @@ from fastapi import APIRouter, Depends, Query, HTTPException, status
 from pydantic import BaseModel, Field
 from typing import Optional
 from sqlalchemy.orm import Session
+from uuid import UUID
 
 from app.core.response_utils import success
 from app.dependencies import get_current_user
@@ -32,11 +33,11 @@ router = APIRouter(
 
 class EmotionConfigQuery(BaseModel):
     """情绪配置查询请求模型"""
-    config_id: int = Field(..., description="配置ID")
+    config_id: UUID = Field(..., description="配置ID")
 
 class EmotionConfigUpdate(BaseModel):
     """情绪配置更新请求模型"""
-    config_id: int = Field(..., description="配置ID")
+    config_id: UUID = Field(..., description="配置ID")
     emotion_enabled: bool = Field(..., description="是否启用情绪提取")
     emotion_model_id: Optional[str] = Field(None, description="情绪分析专用模型ID")
     emotion_extract_keywords: bool = Field(..., description="是否提取情绪关键词")
@@ -45,7 +46,7 @@ class EmotionConfigUpdate(BaseModel):
 
 @router.get("/read_config", response_model=ApiResponse)
 def get_emotion_config(
-    config_id: int = Query(..., description="配置ID"),
+    config_id: UUID = Query(..., description="配置ID"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):

api/app/controllers/emotion_controller.py

@@ -53,7 +53,7 @@ async def get_emotion_tags(
         api_logger.info(
             f"用户 {current_user.username} 请求获取情绪标签统计",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "emotion_type": request.emotion_type,
                 "start_date": request.start_date,
                 "end_date": request.end_date,
@@ -63,7 +63,7 @@ async def get_emotion_tags(
 
         # 调用服务层
         data = await emotion_service.get_emotion_tags(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             emotion_type=request.emotion_type,
             start_date=request.start_date,
             end_date=request.end_date,
@@ -73,7 +73,7 @@ async def get_emotion_tags(
         api_logger.info(
             "情绪标签统计获取成功",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "total_count": data.get("total_count", 0),
                 "tags_count": len(data.get("tags", []))
             }
@@ -84,7 +84,7 @@ async def get_emotion_tags(
     except Exception as e:
         api_logger.error(
             f"获取情绪标签统计失败: {str(e)}",
-            extra={"group_id": request.group_id},
+            extra={"end_user_id": request.end_user_id},
             exc_info=True
         )
         raise HTTPException(
@@ -105,7 +105,7 @@ async def get_emotion_wordcloud(
         api_logger.info(
             f"用户 {current_user.username} 请求获取情绪词云数据",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "emotion_type": request.emotion_type,
                 "limit": request.limit
             }
@@ -113,7 +113,7 @@ async def get_emotion_wordcloud(
 
         # 调用服务层
         data = await emotion_service.get_emotion_wordcloud(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             emotion_type=request.emotion_type,
             limit=request.limit
         )
@@ -121,7 +121,7 @@ async def get_emotion_wordcloud(
         api_logger.info(
             "情绪词云数据获取成功",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "total_keywords": data.get("total_keywords", 0)
             }
         )
@@ -131,7 +131,7 @@ async def get_emotion_wordcloud(
     except Exception as e:
         api_logger.error(
             f"获取情绪词云数据失败: {str(e)}",
-            extra={"group_id": request.group_id},
+            extra={"end_user_id": request.end_user_id},
             exc_info=True
         )
         raise HTTPException(
@@ -159,21 +159,21 @@ async def get_emotion_health(
         api_logger.info(
             f"用户 {current_user.username} 请求获取情绪健康指数",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "time_range": request.time_range
             }
         )
 
         # 调用服务层
         data = await emotion_service.calculate_emotion_health_index(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             time_range=request.time_range
         )
 
         api_logger.info(
             "情绪健康指数获取成功",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "health_score": data.get("health_score", 0),
                 "level": data.get("level", "未知")
             }
@@ -186,7 +186,7 @@ async def get_emotion_health(
     except Exception as e:
         api_logger.error(
             f"获取情绪健康指数失败: {str(e)}",
-            extra={"group_id": request.group_id},
+            extra={"end_user_id": request.end_user_id},
             exc_info=True
         )
         raise HTTPException(
@@ -206,7 +206,7 @@ async def get_emotion_suggestions(
     """获取个性化情绪建议（从缓存读取）
 
     Args:
-        request: 包含 group_id 和可选的 config_id
+        request: 包含 end_user_id 和可选的 config_id
         db: 数据库会话
         current_user: 当前用户
 
@@ -217,22 +217,22 @@ async def get_emotion_suggestions(
         api_logger.info(
             f"用户 {current_user.username} 请求获取个性化情绪建议（缓存）",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "config_id": request.config_id
             }
         )
 
         # 从缓存获取建议
         data = await emotion_service.get_cached_suggestions(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             db=db
         )
 
         if data is None:
             # 缓存不存在或已过期
             api_logger.info(
-                f"用户 {request.group_id} 的建议缓存不存在或已过期",
-                extra={"group_id": request.group_id}
+                f"用户 {request.end_user_id} 的建议缓存不存在或已过期",
+                extra={"end_user_id": request.end_user_id}
             )
             return fail(
                 BizCode.NOT_FOUND,
@@ -243,7 +243,7 @@ async def get_emotion_suggestions(
         api_logger.info(
             "个性化建议获取成功（缓存）",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "suggestions_count": len(data.get("suggestions", []))
             }
         )
@@ -253,7 +253,7 @@ async def get_emotion_suggestions(
     except Exception as e:
         api_logger.error(
             f"获取个性化建议失败: {str(e)}",
-            extra={"group_id": request.group_id},
+            extra={"end_user_id": request.end_user_id},
             exc_info=True
         )
         raise HTTPException(

api/app/controllers/implicit_memory_controller.py

@@ -122,10 +122,10 @@ def validate_confidence_threshold(threshold: float) -> None:
         raise ValueError("confidence_threshold must be between 0.0 and 1.0")
 
 
-@router.get("/preferences/{user_id}", response_model=ApiResponse)
+@router.get("/preferences/{end_user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_preference_tags(
-    user_id: str,
+    end_user_id: str,
     confidence_threshold: float = Query(0.5, ge=0.0, le=1.0, description="Minimum confidence threshold"),
     tag_category: Optional[str] = Query(None, description="Filter by tag category"),
     start_date: Optional[datetime] = Query(None, description="Filter start date"),
@@ -137,7 +137,7 @@ async def get_preference_tags(
     Get user preference tags from cache.
     
     Args:
-        user_id: Target user ID
+        end_user_id: Target end user ID
         confidence_threshold: Minimum confidence score (0.0-1.0)
         tag_category: Optional category filter
         start_date: Optional start date filter
@@ -146,20 +146,20 @@ async def get_preference_tags(
     Returns:
         List of preference tags from cache
     """
-    api_logger.info(f"Preference tags requested for user: {user_id} (from cache)")
+    api_logger.info(f"Preference tags requested for user: {end_user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(user_id)
+        validate_user_id(end_user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
             return fail(
                 BizCode.NOT_FOUND,
                 "画像缓存不存在或已过期，请右上角刷新生成新画像",
@@ -192,17 +192,17 @@ async def get_preference_tags(
             
             filtered_preferences.append(pref)
         
-        api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {user_id} (from cache)")
+        api_logger.info(f"Retrieved {len(filtered_preferences)} preference tags for user: {end_user_id} (from cache)")
         return success(data=filtered_preferences, msg="偏好标签获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "偏好标签获取", user_id)
+        return handle_implicit_memory_error(e, "偏好标签获取", end_user_id)
 
 
-@router.get("/portrait/{user_id}", response_model=ApiResponse)
+@router.get("/portrait/{end_user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_dimension_portrait(
-    user_id: str,
+    end_user_id: str,
     include_history: bool = Query(False, description="Include historical trends"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
@@ -211,26 +211,26 @@ async def get_dimension_portrait(
     Get user's four-dimension personality portrait from cache.
     
     Args:
-        user_id: Target user ID
+        end_user_id: Target end user ID
         include_history: Whether to include historical trend data (ignored for cached data)
         
     Returns:
         Four-dimension personality portrait from cache
     """
-    api_logger.info(f"Dimension portrait requested for user: {user_id} (from cache)")
+    api_logger.info(f"Dimension portrait requested for user: {end_user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(user_id)
+        validate_user_id(end_user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
             return fail(
                 BizCode.NOT_FOUND,
                 "画像缓存不存在或已过期，请右上角刷新生成新画像",
@@ -240,17 +240,17 @@ async def get_dimension_portrait(
         # Extract portrait from cache
         portrait = cached_profile.get("portrait", {})
         
-        api_logger.info(f"Dimension portrait retrieved for user: {user_id} (from cache)")
+        api_logger.info(f"Dimension portrait retrieved for user: {end_user_id} (from cache)")
         return success(data=portrait, msg="四维画像获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "四维画像获取", user_id)
+        return handle_implicit_memory_error(e, "四维画像获取", end_user_id)
 
 
-@router.get("/interest-areas/{user_id}", response_model=ApiResponse)
+@router.get("/interest-areas/{end_user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_interest_area_distribution(
-    user_id: str,
+    end_user_id: str,
     include_trends: bool = Query(False, description="Include trend analysis"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
@@ -259,26 +259,26 @@ async def get_interest_area_distribution(
     Get user's interest area distribution from cache.
     
     Args:
-        user_id: Target user ID
+        end_user_id: Target end user ID
         include_trends: Whether to include trend analysis data (ignored for cached data)
         
     Returns:
         Interest area distribution from cache
     """
-    api_logger.info(f"Interest area distribution requested for user: {user_id} (from cache)")
+    api_logger.info(f"Interest area distribution requested for user: {end_user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(user_id)
+        validate_user_id(end_user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
             return fail(
                 BizCode.NOT_FOUND,
                 "画像缓存不存在或已过期，请右上角刷新生成新画像",
@@ -288,17 +288,17 @@ async def get_interest_area_distribution(
         # Extract interest areas from cache
         interest_areas = cached_profile.get("interest_areas", {})
         
-        api_logger.info(f"Interest area distribution retrieved for user: {user_id} (from cache)")
+        api_logger.info(f"Interest area distribution retrieved for user: {end_user_id} (from cache)")
         return success(data=interest_areas, msg="兴趣领域分布获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "兴趣领域分布获取", user_id)
+        return handle_implicit_memory_error(e, "兴趣领域分布获取", end_user_id)
 
 
-@router.get("/habits/{user_id}", response_model=ApiResponse)
+@router.get("/habits/{end_user_id}", response_model=ApiResponse)
 @cur_workspace_access_guard()
 async def get_behavior_habits(
-    user_id: str,
+    end_user_id: str,
     confidence_level: Optional[str] = Query(None, regex="^(high|medium|low)$", description="Filter by confidence level"),
     frequency_pattern: Optional[str] = Query(None, regex="^(daily|weekly|monthly|seasonal|occasional|event_triggered)$", description="Filter by frequency pattern"),
     time_period: Optional[str] = Query(None, regex="^(current|past)$", description="Filter by time period"),
@@ -309,7 +309,7 @@ async def get_behavior_habits(
     Get user's behavioral habits from cache.
     
     Args:
-        user_id: Target user ID
+        end_user_id: Target end user ID
         confidence_level: Filter by confidence level (high, medium, low)
         frequency_pattern: Filter by frequency pattern (daily, weekly, monthly, seasonal, occasional, event_triggered)
         time_period: Filter by time period (current, past)
@@ -317,20 +317,20 @@ async def get_behavior_habits(
     Returns:
         List of behavioral habits from cache
     """
-    api_logger.info(f"Behavior habits requested for user: {user_id} (from cache)")
+    api_logger.info(f"Behavior habits requested for user: {end_user_id} (from cache)")
     
     try:
         # Validate inputs
-        validate_user_id(user_id)
+        validate_user_id(end_user_id)
         
         # Create service with user-specific config
-        service = ImplicitMemoryService(db=db, end_user_id=user_id)
+        service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
         
         # Get cached profile
-        cached_profile = await service.get_cached_profile(end_user_id=user_id, db=db)
+        cached_profile = await service.get_cached_profile(end_user_id=end_user_id, db=db)
         
         if cached_profile is None:
-            api_logger.info(f"用户 {user_id} 的画像缓存不存在或已过期")
+            api_logger.info(f"用户 {end_user_id} 的画像缓存不存在或已过期")
             return fail(
                 BizCode.NOT_FOUND,
                 "画像缓存不存在或已过期，请右上角刷新生成新画像",
@@ -368,11 +368,11 @@ async def get_behavior_habits(
             
             filtered_habits.append(habit)
         
-        api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {user_id} (from cache)")
+        api_logger.info(f"Retrieved {len(filtered_habits)} behavior habits for user: {end_user_id} (from cache)")
         return success(data=filtered_habits, msg="行为习惯获取成功（缓存）")
         
     except Exception as e:
-        return handle_implicit_memory_error(e, "行为习惯获取", user_id)
+        return handle_implicit_memory_error(e, "行为习惯获取", end_user_id)
 
 
 

api/app/controllers/memory_agent_controller.py

@@ -125,7 +125,7 @@ async def write_server(
     Write service endpoint - processes write operations synchronously
     
     Args:
-        user_input: Write request containing message and group_id
+        user_input: Write request containing message and end_user_id
     
     Returns:
         Response with write operation status
@@ -160,19 +160,18 @@ async def write_server(
             api_logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储")
             storage_type = 'neo4j'
     
-    api_logger.info(f"Write service requested for group {user_input.group_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}")
+    api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}")
     try:
-        # 获取标准化的消息列表
         messages_list = memory_agent_service.get_messages_list(user_input)
-        
         result = await memory_agent_service.write_memory(
-            user_input.group_id, 
-            messages_list,  # 传递结构化消息列表
+            user_input.end_user_id,
+            messages_list,
             config_id,
             db,
             storage_type, 
             user_rag_memory_id
         )
+
         return success(data=result, msg="写入成功")
     except BaseException as e:
         # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup
@@ -196,7 +195,7 @@ async def write_server_async(
     Async write service endpoint - enqueues write processing to Celery
     
     Args:
-        user_input: Write request containing message and group_id
+        user_input: Write request containing message and end_user_id
     
     Returns:
         Task ID for tracking async operation
@@ -226,10 +225,10 @@ async def write_server_async(
     try:
         # 获取标准化的消息列表
         messages_list = memory_agent_service.get_messages_list(user_input)
-        
+
         task = celery_app.send_task(
             "app.core.memory.agent.write_message",
-            args=[user_input.group_id, messages_list, config_id, storage_type, user_rag_memory_id]
+            args=[user_input.end_user_id, messages_list, config_id, storage_type, user_rag_memory_id]
         )
         api_logger.info(f"Write task queued: {task.id}")
         
@@ -255,7 +254,7 @@ async def read_server(
     - "2": Direct answer based on context
     
     Args:
-        user_input: Read request with message, history, search_switch, and group_id
+        user_input: Read request with message, history, search_switch, and end_user_id
     
     Returns:
         Response with query answer
@@ -277,12 +276,13 @@ async def read_server(
             name="USER_RAG_MERORY",
             workspace_id=workspace_id
         )
-        if knowledge: user_rag_memory_id = str(knowledge.id)
+        if knowledge:
+            user_rag_memory_id = str(knowledge.id)
     
-    api_logger.info(f"Read service: group={user_input.group_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
+    api_logger.info(f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
     try:
         result = await memory_agent_service.read_memory(
-            user_input.group_id,
+            user_input.end_user_id,
             user_input.message,
             user_input.history,
             user_input.search_switch,
@@ -293,12 +293,12 @@ async def read_server(
         )
         if str(user_input.search_switch) == "2":
             retrieve_info = result['answer']
-            history = await SessionService(store).get_history(user_input.group_id, user_input.group_id, user_input.group_id)
+            history = await SessionService(store).get_history(user_input.end_user_id, user_input.end_user_id, user_input.end_user_id)
             query = user_input.message
-            
+
             # 调用 memory_agent_service 的方法生成最终答案
             result['answer'] = await memory_agent_service.generate_summary_from_retrieve(
-                group_id=user_input.group_id,
+                end_user_id=user_input.end_user_id,
                 retrieve_info=retrieve_info,
                 history=history,
                 query=query,
@@ -404,7 +404,7 @@ async def read_server_async(
     try:
         task = celery_app.send_task(
             "app.core.memory.agent.read_message",
-            args=[user_input.group_id, user_input.message, user_input.history, user_input.search_switch,
+            args=[user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch,
                   config_id, storage_type, user_rag_memory_id]
         )
         api_logger.info(f"Read task queued: {task.id}")
@@ -448,7 +448,7 @@ async def get_read_task_result(
                 return success(
                     data={
                         "result": task_result.get("result"),
-                        "group_id": task_result.get("group_id"),
+                        "end_user_id": task_result.get("end_user_id"),
                         "elapsed_time": task_result.get("elapsed_time"),
                         "task_id": task_id
                     },
@@ -525,7 +525,7 @@ async def get_write_task_result(
                 return success(
                     data={
                         "result": task_result.get("result"),
-                        "group_id": task_result.get("group_id"),
+                        "end_user_id": task_result.get("end_user_id"),
                         "elapsed_time": task_result.get("elapsed_time"),
                         "task_id": task_id
                     },
@@ -579,16 +579,16 @@ async def status_type(
     Determine the type of user message (read or write)
     
     Args:
-        user_input: Request containing user message and group_id
+        user_input: Request containing user message and end_user_id
     
     Returns:
         Type classification result
     """
-    api_logger.info(f"Status type check requested for group {user_input.group_id}")
+    api_logger.info(f"Status type check requested for group {user_input.end_user_id}")
     try:
         # 获取标准化的消息列表
         messages_list = memory_agent_service.get_messages_list(user_input)
-        
+
         # 将消息列表转换为字符串用于分类
         # 只取最后一条用户消息进行分类
         last_user_message = ""
@@ -596,11 +596,11 @@ async def status_type(
             if msg.get('role') == 'user':
                 last_user_message = msg.get('content', '')
                 break
-        
+
         if not last_user_message:
             # 如果没有用户消息，使用所有消息的内容
             last_user_message = " ".join([msg.get('content', '') for msg in messages_list])
-        
+
         result = await memory_agent_service.classify_message_type(
             last_user_message,
             user_input.config_id,
@@ -625,7 +625,7 @@ async def get_knowledge_type_stats_api(
     会对缺失类型补 0，返回字典形式。
     可选按状态过滤。
     - 知识库类型根据当前用户的 current_workspace_id 过滤
-    - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (group_id) 过滤
+    - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤
     - 如果用户没有当前工作空间或未提供 end_user_id，对应的统计返回 0
     """
     api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}")
@@ -698,7 +698,7 @@ async def get_user_profile_api(
     current_user: User = Depends(get_current_user)
 ):
     """
-    获取工作空间下Popular Memory Tags，包含：
+    获取用户详情，包含：
     - name: 用户名字（直接使用 end_user_id）
     - tags: 3个用户特征标签（从语句和实体中LLM总结）
     - hot_tags: 4个热门记忆标签

api/app/controllers/memory_forget_controller.py

@@ -11,6 +11,7 @@
 """
 
 from typing import Optional
+from uuid import UUID
 
 from fastapi import APIRouter, Depends
 from sqlalchemy.orm import Session
@@ -106,7 +107,7 @@ async def trigger_forgetting_cycle(
         # 调用服务层执行遗忘周期
         report = await forget_service.trigger_forgetting_cycle(
             db=db,
-            group_id=end_user_id,  # 服务层方法的参数名是 group_id
+            end_user_id=end_user_id,  # 服务层方法的参数名是 end_user_id
             max_merge_batch_size=payload.max_merge_batch_size,
             min_days_since_access=payload.min_days_since_access,
             config_id=config_id
@@ -128,7 +129,7 @@ async def trigger_forgetting_cycle(
 
 @router.get("/read_config", response_model=ApiResponse)
 async def read_forgetting_config(
-    config_id: int,
+    config_id: UUID,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
@@ -236,7 +237,7 @@ async def update_forgetting_config(
 
 @router.get("/stats", response_model=ApiResponse)
 async def get_forgetting_stats(
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
@@ -246,7 +247,7 @@ async def get_forgetting_stats(
     返回知识层节点统计、激活值分布等信息。
     
     Args:
-        group_id: 组ID（即 end_user_id，可选）
+        end_user_id: 组ID（即 end_user_id，可选）
         current_user: 当前用户
         db: 数据库会话
     
@@ -260,20 +261,20 @@ async def get_forgetting_stats(
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
     
-    # 如果提供了 group_id，通过它获取 config_id
+    # 如果提供了 end_user_id，通过它获取 config_id
     config_id = None
-    if group_id:
+    if end_user_id:
         try:
             from app.services.memory_agent_service import get_end_user_connected_config
             
-            connected_config = get_end_user_connected_config(group_id, db)
+            connected_config = get_end_user_connected_config(end_user_id, db)
             config_id = connected_config.get("memory_config_id")
             
             if config_id is None:
-                api_logger.warning(f"终端用户 {group_id} 未关联记忆配置")
-                return fail(BizCode.INVALID_PARAMETER, f"终端用户 {group_id} 未关联记忆配置", "memory_config_id is None")
+                api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
+                return fail(BizCode.INVALID_PARAMETER, f"终端用户 {end_user_id} 未关联记忆配置", "memory_config_id is None")
             
-            api_logger.debug(f"通过 group_id={group_id} 获取到 config_id={config_id}")
+            api_logger.debug(f"通过 end_user_id={end_user_id} 获取到 config_id={config_id}")
         except ValueError as e:
             api_logger.warning(f"获取终端用户配置失败: {str(e)}")
             return fail(BizCode.INVALID_PARAMETER, str(e), "ValueError")
@@ -283,14 +284,14 @@ async def get_forgetting_stats(
     
     api_logger.info(
         f"用户 {current_user.username} 在工作空间 {workspace_id} 请求获取遗忘引擎统计: "
-        f"group_id={group_id}, config_id={config_id}"
+        f"end_user_id={end_user_id}, config_id={config_id}"
     )
     
     try:
         # 调用服务层获取统计信息
         stats = await forget_service.get_forgetting_stats(
             db=db,
-            group_id=group_id,
+            end_user_id=end_user_id,
             config_id=config_id
         )
         

api/app/controllers/memory_perceptual_controller.py

@@ -27,27 +27,27 @@ router = APIRouter(
 )
 
 
-@router.get("/{group_id}/count", response_model=ApiResponse)
+@router.get("/{end_user_id}/count", response_model=ApiResponse)
 def get_memory_count(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve perceptual memory statistics for a user group.
 
     Args:
-        group_id: ID of the user group (usually end_user_id in this context)
+        end_user_id: ID of the user group (usually end_user_id in this context)
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Response containing memory count statistics
     """
-    api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, group_id={group_id}")
+    api_logger.info(f"Fetching perceptual memory statistics: user={current_user.username}, end_user_id={end_user_id}")
 
     try:
         service = MemoryPerceptualService(db)
-        count_stats = service.get_memory_count(group_id)
+        count_stats = service.get_memory_count(end_user_id)
 
         api_logger.info(f"Memory statistics fetched successfully: total={count_stats.get('total', 0)}")
 
@@ -57,37 +57,37 @@ def get_memory_count(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch memory statistics: group_id={group_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch memory statistics: end_user_id={end_user_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch memory statistics",
         )
 
 
-@router.get("/{group_id}/last_visual", response_model=ApiResponse)
+@router.get("/{end_user_id}/last_visual", response_model=ApiResponse)
 def get_last_visual_memory(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve the most recent VISION-type memory for a user.
 
     Args:
-        group_id: ID of the user group
+        end_user_id: ID of the user group
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Metadata of the latest visual memory
     """
-    api_logger.info(f"Fetching latest visual memory: user={current_user.username}, group_id={group_id}")
+    api_logger.info(f"Fetching latest visual memory: user={current_user.username}, end_user_id={end_user_id}")
 
     try:
         service = MemoryPerceptualService(db)
-        visual_memory = service.get_latest_visual_memory(group_id)
+        visual_memory = service.get_latest_visual_memory(end_user_id)
 
         if visual_memory is None:
-            api_logger.info(f"No visual memory found: group_id={group_id}")
+            api_logger.info(f"No visual memory found: end_user_id={end_user_id}")
             return success(
                 data=None,
                 msg="No visual memory available"
@@ -101,37 +101,37 @@ def get_last_visual_memory(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch latest visual memory: group_id={group_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch latest visual memory: end_user_id={end_user_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch latest visual memory",
         )
 
 
-@router.get("/{group_id}/last_listen", response_model=ApiResponse)
+@router.get("/{end_user_id}/last_listen", response_model=ApiResponse)
 def get_last_memory_listen(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve the most recent AUDIO-type memory for a user.
 
     Args:
-        group_id: ID of the user group
+        end_user_id: ID of the user group
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Metadata of the latest audio memory
     """
-    api_logger.info(f"Fetching latest audio memory: user={current_user.username}, group_id={group_id}")
+    api_logger.info(f"Fetching latest audio memory: user={current_user.username}, end_user_id={end_user_id}")
 
     try:
         service = MemoryPerceptualService(db)
-        audio_memory = service.get_latest_audio_memory(group_id)
+        audio_memory = service.get_latest_audio_memory(end_user_id)
 
         if audio_memory is None:
-            api_logger.info(f"No audio memory found: group_id={group_id}")
+            api_logger.info(f"No audio memory found: end_user_id={end_user_id}")
             return success(
                 data=None,
                 msg="No audio memory available"
@@ -145,38 +145,38 @@ def get_last_memory_listen(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch latest audio memory: group_id={group_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch latest audio memory: end_user_id={end_user_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch latest audio memory",
         )
 
 
-@router.get("/{group_id}/last_text", response_model=ApiResponse)
+@router.get("/{end_user_id}/last_text", response_model=ApiResponse)
 def get_last_text_memory(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     """Retrieve the most recent TEXT-type memory for a user.
 
     Args:
-        group_id: ID of the user group
+        end_user_id: ID of the user group
         current_user: Current authenticated user
         db: Database session
 
     Returns:
         ApiResponse: Metadata of the latest text memory
     """
-    api_logger.info(f"Fetching latest text memory: user={current_user.username}, group_id={group_id}")
+    api_logger.info(f"Fetching latest text memory: user={current_user.username}, end_user_id={end_user_id}")
 
     try:
         # 调用服务层获取最近的文本记忆
         service = MemoryPerceptualService(db)
-        text_memory = service.get_latest_text_memory(group_id)
+        text_memory = service.get_latest_text_memory(end_user_id)
 
         if text_memory is None:
-            api_logger.info(f"No text memory found: group_id={group_id}")
+            api_logger.info(f"No text memory found: end_user_id={end_user_id}")
             return success(
                 data=None,
                 msg="No text memory available"
@@ -190,16 +190,16 @@ def get_last_text_memory(
         )
 
     except Exception as e:
-        api_logger.error(f"Failed to fetch latest text memory: group_id={group_id}, error={str(e)}")
+        api_logger.error(f"Failed to fetch latest text memory: end_user_id={end_user_id}, error={str(e)}")
         return fail(
             code=BizCode.INTERNAL_ERROR,
             msg="Failed to fetch latest text memory",
         )
 
 
-@router.get("/{group_id}/timeline", response_model=ApiResponse)
+@router.get("/{end_user_id}/timeline", response_model=ApiResponse)
 def get_memory_time_line(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         perceptual_type: Optional[PerceptualType] = Query(None, description="感知类型过滤"),
         page: int = Query(1, ge=1, description="页码"),
         page_size: int = Query(10, ge=1, le=100, description="每页大小"),
@@ -209,7 +209,7 @@ def get_memory_time_line(
     """Retrieve a timeline of perceptual memories for a user group.
 
     Args:
-        group_id: ID of the user group
+        end_user_id: ID of the user group
         perceptual_type: Optional filter for perceptual type
         page: Page number for pagination
         page_size: Number of items per page
@@ -221,7 +221,7 @@ def get_memory_time_line(
     """
     api_logger.info(
         f"Fetching perceptual memory timeline: user={current_user.username}, "
-        f"group_id={group_id}, type={perceptual_type}, page={page}"
+        f"end_user_id={end_user_id}, type={perceptual_type}, page={page}"
     )
 
     try:
@@ -232,7 +232,7 @@ def get_memory_time_line(
         )
 
         service = MemoryPerceptualService(db)
-        timeline_data = service.get_time_line(group_id, query)
+        timeline_data = service.get_time_line(end_user_id, query)
 
         api_logger.info(
             f"Perceptual memory timeline retrieved successfully: total={timeline_data.total}, "
@@ -246,7 +246,7 @@ def get_memory_time_line(
 
     except Exception as e:
         api_logger.error(
-            f"Failed to fetch perceptual memory timeline: group_id={group_id}, "
+            f"Failed to fetch perceptual memory timeline: end_user_id={end_user_id}, "
             f"error={str(e)}"
         )
         return fail(

api/app/controllers/memory_reflection_controller.py

@@ -1,6 +1,7 @@
 import asyncio
 import time
 import uuid
+from uuid import UUID
 
 from app.core.logging_config import get_api_logger
 from app.core.memory.storage_services.reflection_engine.self_reflexion import (
@@ -11,7 +12,7 @@ from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models.user_model import User
-from app.repositories.data_config_repository import DataConfigRepository
+from app.repositories.memory_config_repository import MemoryConfigRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_reflection_schemas import Memory_Reflection
 from app.services.memory_reflection_service import (
@@ -50,7 +51,7 @@ async def save_reflection_config(
 
         api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}")
 
-        data_config = DataConfigRepository.update_reflection_config(
+        memory_config = MemoryConfigRepository.update_reflection_config(
             db,
             config_id=config_id,
             enable_self_reflexion=request.reflection_enabled,
@@ -63,17 +64,17 @@ async def save_reflection_config(
         )
 
         db.commit()
-        db.refresh(data_config)
+        db.refresh(memory_config)
 
         reflection_result={
-                "config_id": data_config.config_id,
-                "enable_self_reflexion": data_config.enable_self_reflexion,
-                "iteration_period": data_config.iteration_period,
-                "reflexion_range": data_config.reflexion_range,
-                "baseline": data_config.baseline,
-                "reflection_model_id": data_config.reflection_model_id,
-                "memory_verify": data_config.memory_verify,
-                "quality_assessment": data_config.quality_assessment}
+                "config_id": memory_config.config_id,
+                "enable_self_reflexion": memory_config.enable_self_reflexion,
+                "iteration_period": memory_config.iteration_period,
+                "reflexion_range": memory_config.reflexion_range,
+                "baseline": memory_config.baseline,
+                "reflection_model_id": memory_config.reflection_model_id,
+                "memory_verify": memory_config.memory_verify,
+                "quality_assessment": memory_config.quality_assessment}
 
         return success(data=reflection_result, msg="反思配置成功")
         
@@ -111,14 +112,14 @@ async def start_workspace_reflection(
         reflection_results = []
         
         for data in result['apps_detailed_info']:
-            if data['data_configs'] == []: 
+            if data['memory_configs'] == []: 
                 continue
                 
             releases = data['releases']
-            data_configs = data['data_configs']
+            memory_configs = data['memory_configs']
             end_users = data['end_users']
             
-            for base, config, user in zip(releases, data_configs, end_users):
+            for base, config, user in zip(releases, memory_configs, end_users):
                 # 安全地转换为整数，处理空字符串和None的情况
                 print(base['config'])
                 try:
@@ -156,14 +157,14 @@ async def start_workspace_reflection(
 
 @router.get("/reflection/configs")
 async def start_reflection_configs(
-        config_id: int,
+        config_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db),
 ) -> dict:
-    """通过config_id查询data_config表中的反思配置信息"""
+    """通过config_id查询memory_config表中的反思配置信息"""
     try:
         api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-        result = DataConfigRepository.query_reflection_config_by_id(db, config_id)
+        result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
         # 构建返回数据
         reflection_config = {
             "config_id": result.config_id,
@@ -191,7 +192,7 @@ async def start_reflection_configs(
 
 @router.get("/reflection/run")
 async def reflection_run(
-    config_id: int,
+    config_id: UUID,
     language_type: str = Header(default="zh", alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
@@ -200,8 +201,8 @@ async def reflection_run(
 
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
 
-    # 使用DataConfigRepository查询反思配置
-    result = DataConfigRepository.query_reflection_config_by_id(db, config_id)
+    # 使用MemoryConfigRepository查询反思配置
+    result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
     if not result:
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,

api/app/controllers/memory_storage_controller.py

@@ -1,5 +1,6 @@
 import os
 from typing import Optional
+from uuid import UUID
 
 from app.core.error_codes import BizCode
 from app.core.logging_config import get_api_logger
@@ -160,7 +161,7 @@ def create_config(
 
 @router.delete("/delete_config", response_model=ApiResponse)  # 删除数据库中的内容（按配置名称）
 def delete_config(
-    config_id: str,
+    config_id: UUID,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
     ) -> dict:
@@ -232,7 +233,7 @@ def update_config_extracted(
 
 @router.get("/read_config_extracted", response_model=ApiResponse) # 通过查询参数读取某条配置（固定路径） 没有意义的话就删除
 def read_config_extracted(
-    config_id: str,
+    config_id: UUID,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
     ) -> dict:

api/app/controllers/memory_working_controller.py

@@ -20,18 +20,18 @@ router = APIRouter(
 )
 
 
-@router.get("/{group_id}/count", response_model=ApiResponse)
+@router.get("/{end_user_id}/count", response_model=ApiResponse)
 def get_memory_count(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
     pass
 
 
-@router.get("/{group_id}/conversations", response_model=ApiResponse)
+@router.get("/{end_user_id}/conversations", response_model=ApiResponse)
 def get_conversations(
-        group_id: uuid.UUID,
+        end_user_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
         db: Session = Depends(get_db)
 ):
@@ -39,7 +39,7 @@ def get_conversations(
     Retrieve all conversations for the current user in a specific group.
 
     Args:
-        group_id (UUID): The group identifier.
+        end_user_id (UUID): The group identifier.
         current_user (User, optional): The authenticated user.
         db (Session, optional): SQLAlchemy session.
 
@@ -53,7 +53,7 @@ def get_conversations(
     """
     conversation_service = ConversationService(db)
     conversations = conversation_service.get_user_conversations(
-        group_id
+        end_user_id
     )
     return success(data=[
         {
@@ -63,7 +63,7 @@ def get_conversations(
     ], msg="get conversations success")
 
 
-@router.get("/{group_id}/messages", response_model=ApiResponse)
+@router.get("/{end_user_id}/messages", response_model=ApiResponse)
 def get_messages(
         conversation_id: uuid.UUID,
         current_user: User = Depends(get_current_user),
@@ -100,7 +100,7 @@ def get_messages(
     return success(data=messages, msg="get conversation history success")
 
 
-@router.get("/{group_id}/detail", response_model=ApiResponse)
+@router.get("/{end_user_id}/detail", response_model=ApiResponse)
 async def get_conversation_detail(
         conversation_id: uuid.UUID,
         current_user: User = Depends(get_current_user),

api/app/controllers/service/memory_api_controller.py

@@ -39,7 +39,7 @@ async def write_memory_api_service(
     
     Stores memory content for the specified end user using the Memory API Service.
     """
-    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}")
+    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}")
     
     memory_api_service = MemoryAPIService(db)
     

api/app/controllers/user_memory_controllers.py

@@ -135,27 +135,27 @@ async def generate_cache_api(
         api_logger.warning(f"用户 {current_user.username} 尝试生成缓存但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
 
-    group_id = request.end_user_id
+    end_user_id = request.end_user_id
 
     api_logger.info(
         f"缓存生成请求: user={current_user.username}, workspace={workspace_id}, "
-        f"end_user_id={group_id if group_id else '全部用户'}"
+        f"end_user_id={end_user_id if end_user_id else '全部用户'}"
     )
 
     try:
-        if group_id:
+        if end_user_id:
             # 为单个用户生成
-            api_logger.info(f"开始为单个用户生成缓存: end_user_id={group_id}")
+            api_logger.info(f"开始为单个用户生成缓存: end_user_id={end_user_id}")
 
             # 生成记忆洞察
-            insight_result = await user_memory_service.generate_and_cache_insight(db, group_id, workspace_id)
+            insight_result = await user_memory_service.generate_and_cache_insight(db, end_user_id, workspace_id)
 
             # 生成用户摘要
-            summary_result = await user_memory_service.generate_and_cache_summary(db, group_id, workspace_id)
+            summary_result = await user_memory_service.generate_and_cache_summary(db, end_user_id, workspace_id)
 
             # 构建响应
             result = {
-                "end_user_id": group_id,
+                "end_user_id": end_user_id,
                 "insight_success": insight_result["success"],
                 "summary_success": summary_result["success"],
                 "errors": []
@@ -175,9 +175,9 @@ async def generate_cache_api(
 
             # 记录结果
             if result["insight_success"] and result["summary_success"]:
-                api_logger.info(f"成功为用户 {group_id} 生成缓存")
+                api_logger.info(f"成功为用户 {end_user_id} 生成缓存")
             else:
-                api_logger.warning(f"用户 {group_id} 的缓存生成部分失败: {result['errors']}")
+                api_logger.warning(f"用户 {end_user_id} 的缓存生成部分失败: {result['errors']}")
 
             return success(data=result, msg="生成完成")
 

api/app/core/agent/langchain_agent.py

@@ -155,13 +155,13 @@ class LangChainAgent:
     #                     userid=end_user_end,
     #                     messages=messages,
     #                     apply_id=end_user_end,
-    #                     group_id=end_user_end,
+    #                     end_user_id=end_user_end,
     #                     aimessages=aimessages
     #                 )
     #     store.delete_duplicate_sessions()
     #     # logger.info(f'Redis_Agent:{end_user_end};{session_id}')
     #     return session_id
-    
+
 # TODO 乐力齐 - 累积多组对话批量写入功能已禁用
     # async def term_memory_redis_read(self,end_user_end):
     #     end_user_end = f"Term_{end_user_end}"
@@ -179,7 +179,7 @@ class LangChainAgent:
     async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id):
         """
         写入记忆（支持结构化消息）
-        
+
         Args:
             storage_type: 存储类型 (neo4j/rag)
             end_user_id: 终端用户ID
@@ -188,7 +188,7 @@ class LangChainAgent:
             user_rag_memory_id: RAG 记忆ID
             actual_end_user_id: 实际用户ID
             actual_config_id: 配置ID
-            
+
         逻辑说明：
         - RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变
         - Neo4j 模式：使用结构化消息列表
@@ -204,20 +204,20 @@ class LangChainAgent:
         else:
             # Neo4j 模式：使用结构化消息列表
             structured_messages = []
-            
+
             # 始终添加用户消息（如果不为空）
             if user_message:
                 structured_messages.append({"role": "user", "content": user_message})
-            
+
             # 只有当 AI 回复不为空时才添加 assistant 消息
             if ai_message:
                 structured_messages.append({"role": "assistant", "content": ai_message})
-            
+
             # 如果没有消息，直接返回
             if not structured_messages:
                 logger.warning(f"No messages to write for user {actual_end_user_id}")
                 return
-            
+
             # 调用 Celery 任务，传递结构化消息列表
             # 数据流：
             # 1. structured_messages 传递给 write_message_task
@@ -228,7 +228,7 @@ class LangChainAgent:
             # 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段
             logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
             write_id = write_message_task.delay(
-                actual_end_user_id,  # group_id: 用户ID
+                actual_end_user_id,  # end_user_id: 用户ID
                 structured_messages,  # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
                 actual_config_id,    # config_id: 配置ID
                 storage_type,        # storage_type: "neo4j"

api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py

@@ -35,10 +35,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
     """问题分解节点"""
     # 从状态中获取数据
     content = state.get('data', '')
-    group_id = state.get('group_id', '')
+    end_user_id = state.get('end_user_id', '')
     memory_config = state.get('memory_config', None)
 
-    history = await SessionService(store).get_history(group_id, group_id, group_id)
+    history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
 
     # 生成 JSON schema 以指导 LLM 输出正确格式
     json_schema = ProblemExtensionResponse.model_json_schema()
@@ -140,7 +140,7 @@ async def Problem_Extension(state: ReadState) -> ReadState:
     start = time.time()
     content = state.get('data', '')
     data = state.get('spit_data', '')['context']
-    group_id = state.get('group_id', '')
+    end_user_id = state.get('end_user_id', '')
     storage_type = state.get('storage_type', '')
     user_rag_memory_id = state.get('user_rag_memory_id', '')
     memory_config = state.get('memory_config', None)
@@ -156,7 +156,7 @@ async def Problem_Extension(state: ReadState) -> ReadState:
         databasets = {}
         data = []
 
-    history = await SessionService(store).get_history(group_id, group_id, group_id)
+    history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
 
     # 生成 JSON schema 以指导 LLM 输出正确格式
     json_schema = ProblemExtensionResponse.model_json_schema()

api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py

@@ -52,9 +52,9 @@ async def rag_config(state):
     return kb_config
 async def rag_knowledge(state,question):
     kb_config = await rag_config(state)
-    group_id = state.get('group_id', '')
+    end_user_id = state.get('end_user_id', '')
     user_rag_memory_id=state.get("user_rag_memory_id",'')
-    retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(group_id)])
+    retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(end_user_id)])
     try:
         retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
         clean_content = '\n\n'.join(retrieval_knowledge)
@@ -159,7 +159,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
     problem_extension=state.get('problem_extension', '')['context']
     storage_type=state.get('storage_type', '')
     user_rag_memory_id=state.get('user_rag_memory_id', '')
-    group_id=state.get('group_id', '')
+    end_user_id=state.get('end_user_id', '')
     memory_config = state.get('memory_config', None)
     original=state.get('data', '')
     problem_list=[]
@@ -172,7 +172,7 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
         try:
             # Prepare search parameters based on storage type
             search_params = {
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "question": question,
                 "return_raw_results": True
             }
@@ -263,13 +263,13 @@ async def retrieve_nodes(state: ReadState) -> ReadState:
 
 
 async def retrieve(state: ReadState) -> ReadState:
-    # 从state中获取group_id
+    # 从state中获取end_user_id
     import time
     start=time.time()
     problem_extension = state.get('problem_extension', '')['context']
     storage_type = state.get('storage_type', '')
     user_rag_memory_id = state.get('user_rag_memory_id', '')
-    group_id = state.get('group_id', '')
+    end_user_id = state.get('end_user_id', '')
     memory_config = state.get('memory_config', None)
     original = state.get('data', '')
     problem_list = []
@@ -295,13 +295,13 @@ async def retrieve(state: ReadState) -> ReadState:
         temperature=0.2,
     )
 
-    time_retrieval_tool = create_time_retrieval_tool(group_id)
-    search_params = { "group_id": group_id, "return_raw_results": True }
+    time_retrieval_tool = create_time_retrieval_tool(end_user_id)
+    search_params = { "end_user_id": end_user_id, "return_raw_results": True }
     hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params)
     agent = create_agent(
         llm,
         tools=[time_retrieval_tool,hybrid_retrieval],
-        system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的group_id是: {group_id}"
+        system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的end_user_id是: {end_user_id}"
     )
 
     # 创建异步任务处理单个问题

api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py

@@ -34,8 +34,8 @@ class SummaryNodeService(LLMServiceMixin):
 summary_service = SummaryNodeService()
 
 async def summary_history(state: ReadState) -> ReadState:
-    group_id = state.get("group_id", '')
-    history = await SessionService(store).get_history(group_id, group_id, group_id)
+    end_user_id = state.get("end_user_id", '')
+    history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
     return history
 
 async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str:
@@ -122,12 +122,12 @@ async def summary_llm(state: ReadState, history, retrieve_info, template_name, o
 
 async def summary_redis_save(state: ReadState,aimessages) -> ReadState:
     data = state.get("data", '')
-    group_id = state.get("group_id", '')
+    end_user_id = state.get("end_user_id", '')
     await SessionService(store).save_session(
-        user_id=group_id,
+        user_id=end_user_id,
         query=data,
-        apply_id=group_id,
-        group_id=group_id,
+        apply_id=end_user_id,
+        end_user_id=end_user_id,
         ai_response=aimessages
     )
     await SessionService(store).cleanup_duplicates()
@@ -175,11 +175,11 @@ async def Input_Summary(state: ReadState) -> ReadState:
     memory_config = state.get('memory_config', None)
     user_rag_memory_id=state.get("user_rag_memory_id",'')
     data=state.get("data", '')
-    group_id=state.get("group_id", '')
+    end_user_id=state.get("end_user_id", '')
     logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
     history = await summary_history( state)
     search_params = {
-        "group_id": group_id,
+        "end_user_id": end_user_id,
         "question": data,
         "return_raw_results": True,
         "include": ["summaries"]  # Only search summary nodes for faster performance

api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py

@@ -62,12 +62,12 @@ async def Verify(state: ReadState):
     logger.info("=== Verify 节点开始执行 ===")
     try:
         content = state.get('data', '')
-        group_id = state.get('group_id', '')
+        end_user_id = state.get('end_user_id', '')
         memory_config = state.get('memory_config', None)
         
-        logger.info(f"Verify: content={content[:50] if content else 'empty'}..., group_id={group_id}")
+        logger.info(f"Verify: content={content[:50] if content else 'empty'}..., end_user_id={end_user_id}")
 
-        history = await SessionService(store).get_history(group_id, group_id, group_id)
+        history = await SessionService(store).get_history(end_user_id, end_user_id, end_user_id)
         logger.info(f"Verify: 获取历史记录完成，history length={len(history)}")
 
         retrieve = state.get("retrieve", {})

api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py

@@ -1,23 +1,24 @@
-
-from app.core.memory.agent.utils.llm_tools import  WriteState
+from app.core.memory.agent.utils.llm_tools import WriteState
 from app.core.memory.agent.utils.write_tools import write
 from app.core.logging_config import get_agent_logger
 
 logger = get_agent_logger(__name__)
+
+
 async def write_node(state: WriteState) -> WriteState:
     """
         Write data to the database/file system.
 
         Args:
-            state: WriteState containing messages, group_id, and memory_config
+            state: WriteState containing messages, end_user_id, and memory_config
 
         Returns:
             dict: Contains 'write_result' with status and data fields
         """
     messages = state.get('messages', [])
-    group_id = state.get('group_id', '')
+    end_user_id = state.get('end_user_id', '')
     memory_config = state.get('memory_config', '')
-    
+
     # Convert LangChain messages to structured format expected by write()
     structured_messages = []
     for msg in messages:
@@ -28,13 +29,11 @@ async def write_node(state: WriteState) -> WriteState:
                 "role": role,
                 "content": msg.content  # content is now guaranteed to be a string
             })
-    
+
     try:
         result = await write(
             messages=structured_messages,
-            user_id=group_id,
-            apply_id=group_id,
-            group_id=group_id,
+            end_user_id=end_user_id,
             memory_config=memory_config,
         )
         logger.info(f"Write completed successfully! Config: {memory_config.config_name}")

api/app/core/memory/agent/langgraph_graph/read_graph.py

@@ -79,7 +79,7 @@ async def make_read_graph():
 async def main():
     """主函数 - 运行工作流"""
     message = "昨天有什么好看的电影"
-    group_id = '88a459f5_text09'  # 组ID
+    end_user_id = '88a459f5_text09'  # 组ID
     storage_type = 'neo4j'  # 存储类型
     search_switch = '1'  # 搜索开关
     user_rag_memory_id = 'wwwwwwww'  # 用户RAG记忆ID
@@ -95,9 +95,9 @@ async def main():
     start=time.time()
     try:
         async with make_read_graph() as graph:
-            config = {"configurable": {"thread_id": group_id}}
+            config = {"configurable": {"thread_id": end_user_id}}
             # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"group_id":group_id
+            initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"end_user_id":end_user_id
                              ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config}
             # 获取节点更新信息
             _intermediate_outputs = []

api/app/core/memory/agent/langgraph_graph/tools/tool.py

@@ -48,11 +48,11 @@ def extract_tool_message_content(response):
 class TimeRetrievalInput(BaseModel):
     """时间检索工具的输入模式"""
     context: str = Field(description="用户输入的查询内容")
-    group_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果")
+    end_user_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果")
 
-def create_time_retrieval_tool(group_id: str):
+def create_time_retrieval_tool(end_user_id: str):
     """
-    创建一个带有特定group_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
+    创建一个带有特定end_user_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
     """
     
     def clean_temporal_result_fields(data):
@@ -93,26 +93,26 @@ def create_time_retrieval_tool(group_id: str):
             return data
     
     @tool
-    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, group_id_param: str = None, clean_output: bool = True) -> str:
+    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, end_user_id_param: str = None, clean_output: bool = True) -> str:
         """
         优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段
         显式接收参数：
         - context: 查询上下文内容
         - start_date: 开始时间（可选，格式：YYYY-MM-DD）
         - end_date: 结束时间（可选，格式：YYYY-MM-DD）
-        - group_id_param: 组ID（可选，用于覆盖默认组ID）
+        - end_user_id_param: 组ID（可选，用于覆盖默认组ID）
         - clean_output: 是否清理输出中的元数据字段
         -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
         """
         async def _async_search():
             # 使用传入的参数或默认值
-            actual_group_id = group_id_param or group_id
+            actual_end_user_id = end_user_id_param or end_user_id
             actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
             actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
             
             # 基本时间搜索
             results = await search_by_temporal(
-                group_id=actual_group_id,
+                end_user_id=actual_end_user_id,
                 start_date=actual_start_date,
                 end_date=actual_end_date,
                 limit=10
@@ -147,7 +147,7 @@ def create_time_retrieval_tool(group_id: str):
             # 关键词时间搜索
             results = await search_by_keyword_temporal(
                 query_text=context,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 start_date=actual_start_date,
                 end_date=actual_end_date,
                 limit=15
@@ -172,7 +172,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
     
     Args:
         memory_config: 内存配置对象
-        **search_params: 搜索参数，包含group_id, limit, include等
+        **search_params: 搜索参数，包含end_user_id, limit, include等
     """
     
     def clean_result_fields(data):
@@ -211,7 +211,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
         context: str, 
         search_type: str = "hybrid",
         limit: int = 10,
-        group_id: str = None,
+        end_user_id: str = None,
         rerank_alpha: float = 0.6,
         use_forgetting_rerank: bool = False,
         use_llm_rerank: bool = False,
@@ -224,7 +224,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
             context: 查询内容
             search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
             limit: 结果数量限制
-            group_id: 组ID，用于过滤搜索结果
+            end_user_id: 组ID，用于过滤搜索结果
             rerank_alpha: 重排序权重参数
             use_forgetting_rerank: 是否使用遗忘重排序
             use_llm_rerank: 是否使用LLM重排序
@@ -238,7 +238,7 @@ def create_hybrid_retrieval_tool_async(memory_config, **search_params):
             final_params = {
                 "query_text": context,
                 "search_type": search_type,
-                "group_id": group_id or search_params.get("group_id"),
+                "end_user_id": end_user_id or search_params.get("end_user_id"),
                 "limit": limit or search_params.get("limit", 10),
                 "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]),
                 "output_path": None,  # 不保存到文件
@@ -291,7 +291,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
         context: str, 
         search_type: str = "hybrid",
         limit: int = 10,
-        group_id: str = None,
+        end_user_id: str = None,
         clean_output: bool = True
     ) -> str:
         """
@@ -301,7 +301,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
             context: 查询内容
             search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
             limit: 结果数量限制
-            group_id: 组ID，用于过滤搜索结果
+            end_user_id: 组ID，用于过滤搜索结果
             clean_output: 是否清理输出中的元数据字段
         """
         async def _async_search():
@@ -311,7 +311,7 @@ def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
                 "context": context,
                 "search_type": search_type,
                 "limit": limit,
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "clean_output": clean_output
             })
         

api/app/core/memory/agent/langgraph_graph/write_graph.py

@@ -14,6 +14,7 @@ from app.db import get_db
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.llm_tools import WriteState
 from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
+from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_write
 from app.services.memory_config_service import MemoryConfigService
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
@@ -26,9 +27,21 @@ async def make_write_graph():
     """
     Create a write graph workflow for memory operations.
 
-    The workflow directly processes messages from the initial state
-    and saves them to Neo4j storage.
+    Args:
+        user_id: User identifier
+        tools: MCP tools loaded from session
+        apply_id: Application identifier
+        end_user_id: Group identifier
+        memory_config: MemoryConfig object containing all configuration
     """
+    # workflow = StateGraph(WriteState)
+    # workflow.add_node("content_input", content_input_write)
+    # workflow.add_node("save_neo4j", write_node)
+    # workflow.add_edge(START, "content_input")
+    # workflow.add_edge("content_input", "save_neo4j")
+    # workflow.add_edge("save_neo4j", END)
+    #
+    # graph = workflow.compile()
     workflow = StateGraph(WriteState)
     workflow.add_node("save_neo4j", write_node)
     workflow.add_edge(START, "save_neo4j")
@@ -42,7 +55,7 @@ async def make_write_graph():
 async def main():
     """主函数 - 运行工作流"""
     message = "今天周一"
-    group_id = 'new_2025test1103'  # 组ID
+    end_user_id = 'new_2025test1103'  # 组ID
 
 
     # 获取数据库会话
@@ -54,9 +67,9 @@ async def main():
     )
     try:
         async with make_write_graph() as graph:
-            config = {"configurable": {"thread_id": group_id}}
+            config = {"configurable": {"thread_id": end_user_id}}
             # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)],  "group_id": group_id, "memory_config": memory_config}
+            initial_state = {"messages": [HumanMessage(content=message)],  "end_user_id": end_user_id, "memory_config": memory_config}
 
             # 获取节点更新信息
             async for update_event in graph.astream(

api/app/core/memory/agent/services/parameter_builder.py

@@ -24,7 +24,7 @@ class ParameterBuilder:
         tool_call_id: str,
         search_switch: str,
         apply_id: str,
-        group_id: str,
+        end_user_id: str,
         storage_type: Optional[str] = None,
         user_rag_memory_id: Optional[str] = None
     ) -> Dict[str, Any]:
@@ -44,7 +44,7 @@ class ParameterBuilder:
             tool_call_id: Extracted tool call identifier
             search_switch: Search routing parameter
             apply_id: Application identifier
-            group_id: Group identifier
+            end_user_id: Group identifier
             storage_type: Storage type for the workspace (optional)
             user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional)
             
@@ -55,7 +55,7 @@ class ParameterBuilder:
         base_args = {
             "usermessages": tool_call_id,
             "apply_id": apply_id,
-            "group_id": group_id
+            "end_user_id": end_user_id
         }
         
         # Always add storage_type and user_rag_memory_id (with defaults if None)

api/app/core/memory/agent/services/search_service.py

@@ -91,7 +91,7 @@ class SearchService:
     
     async def execute_hybrid_search(
         self,
-        group_id: str,
+        end_user_id: str,
         question: str,
         limit: int = 5,
         search_type: str = "hybrid",
@@ -105,7 +105,7 @@ class SearchService:
         Execute hybrid search and return clean content.
         
         Args:
-            group_id: Group identifier for filtering results
+            end_user_id: Group identifier for filtering results
             question: Search query text
             limit: Maximum number of results to return (default: 5)
             search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid")
@@ -130,7 +130,7 @@ class SearchService:
             answer = await run_hybrid_search(
                 query_text=cleaned_query,
                 search_type=search_type,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=limit,
                 include=include,
                 output_path=output_path,
@@ -186,7 +186,7 @@ class SearchService:
             
         except Exception as e:
             logger.error(
-                f"Search failed for query '{question}' in group '{group_id}': {e}",
+                f"Search failed for query '{question}' in group '{end_user_id}': {e}",
                 exc_info=True
             )
             # Return empty results on failure

api/app/core/memory/agent/services/session_service.py

@@ -59,7 +59,7 @@ class SessionService:
         self,
         user_id: str,
         apply_id: str,
-        group_id: str
+        end_user_id: str
     ) -> List[dict]:
         """
         Retrieve conversation history from Redis.
@@ -67,20 +67,20 @@ class SessionService:
         Args:
             user_id: User identifier
             apply_id: Application identifier
-            group_id: Group identifier
+            end_user_id: Group identifier
             
         Returns:
             List of conversation history items with Query and Answer keys
             Returns empty list if no history found or on error
         """
         try:
-            history = self.store.find_user_apply_group(user_id, apply_id, group_id)
+            history = self.store.find_user_apply_group(user_id, apply_id, end_user_id)
             
             # Validate history structure
             if not isinstance(history, list):
                 logger.warning(
                     f"Invalid history format for user {user_id}, "
-                    f"apply {apply_id}, group {group_id}: expected list, got {type(history)}"
+                    f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}"
                 )
                 return []
             
@@ -89,7 +89,7 @@ class SessionService:
         except Exception as e:
             logger.error(
                 f"Failed to retrieve history for user {user_id}, "
-                f"apply {apply_id}, group {group_id}: {e}",
+                f"apply {apply_id}, group {end_user_id}: {e}",
                 exc_info=True
             )
             # Return empty list on error to allow execution to continue
@@ -100,7 +100,7 @@ class SessionService:
         user_id: str,
         query: str,
         apply_id: str,
-        group_id: str,
+        end_user_id: str,
         ai_response: str
     ) -> Optional[str]:
         """
@@ -110,7 +110,7 @@ class SessionService:
             user_id: User identifier
             query: User query/message
             apply_id: Application identifier
-            group_id: Group identifier
+            end_user_id: Group identifier
             ai_response: AI response/answer
             
         Returns:
@@ -131,7 +131,7 @@ class SessionService:
                 userid=user_id,
                 messages=query,
                 apply_id=apply_id,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 aimessages=ai_response
             )
             
@@ -152,7 +152,7 @@ class SessionService:
         Duplicates are identified by matching:
         - sessionid
         - user_id (id field)
-        - group_id
+        - end_user_id
         - messages
         - aimessages
         

api/app/core/memory/agent/utils/get_dialogs.py

@@ -9,9 +9,7 @@ from app.core.memory.models.message_models import DialogData, ConversationContex
 
 async def get_chunked_dialogs(
         chunker_strategy: str = "RecursiveChunker",
-        group_id: str = "group_1",
-        user_id: str = "user1",
-        apply_id: str = "applyid",
+        end_user_id: str = "group_1",
         messages: list = None,
         ref_id: str = "wyl_20251027",
         config_id: str = None
@@ -20,9 +18,7 @@ async def get_chunked_dialogs(
 
     Args:
         chunker_strategy: The chunking strategy to use (default: RecursiveChunker)
-        group_id: Group identifier
-        user_id: User identifier
-        apply_id: Application identifier
+        end_user_id: Group identifier
         messages: Structured message list [{"role": "user", "content": "..."}, ...]
         ref_id: Reference identifier
         config_id: Configuration ID for processing
@@ -32,42 +28,40 @@ async def get_chunked_dialogs(
     """
     from app.core.logging_config import get_agent_logger
     logger = get_agent_logger(__name__)
-    
+
     if not messages or not isinstance(messages, list) or len(messages) == 0:
         raise ValueError("messages parameter must be a non-empty list")
-    
+
     conversation_messages = []
-    
+
     for idx, msg in enumerate(messages):
         if not isinstance(msg, dict) or 'role' not in msg or 'content' not in msg:
             raise ValueError(f"Message {idx} format error: must contain 'role' and 'content' fields")
-        
+
         role = msg['role']
         content = msg['content']
-        
+
         if role not in ['user', 'assistant']:
             raise ValueError(f"Message {idx} role must be 'user' or 'assistant', got: {role}")
-        
+
         if content.strip():
             conversation_messages.append(ConversationMessage(role=role, msg=content.strip()))
-    
+
     if not conversation_messages:
         raise ValueError("Message list cannot be empty after filtering")
-                
+
     conversation_context = ConversationContext(msgs=conversation_messages)
     dialog_data = DialogData(
         context=conversation_context,
         ref_id=ref_id,
-        group_id=group_id,
-        user_id=user_id,
-        apply_id=apply_id,
+        end_user_id=end_user_id,
         config_id=config_id
     )
-    
+
     chunker = DialogueChunker(chunker_strategy)
     extracted_chunks = await chunker.process_dialogue(dialog_data)
     dialog_data.chunks = extracted_chunks
-    
+
     logger.info(f"DialogData created with {len(extracted_chunks)} chunks")
 
     return [dialog_data]

api/app/core/memory/agent/utils/llm_tools.py

@@ -13,13 +13,11 @@ class WriteState(TypedDict):
     Langgrapg Writing TypedDict
     '''
     messages: Annotated[list[AnyMessage], add_messages]
-    user_id:str
-    apply_id:str
-    group_id:str
+    end_user_id: str
     errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
     memory_config: object
     write_result: dict
-    data:str
+    data: str
 
 class ReadState(TypedDict):
     """
@@ -29,7 +27,7 @@ class ReadState(TypedDict):
         messages: 消息列表，支持自动追加
         loop_count: 遍历次数
         search_switch: 搜索类型开关
-        group_id: 组标识
+        end_user_id: 组标识
         config_id: 配置ID，用于过滤结果
         data: 从content_input_node传递的内容数据
         spit_data: 从Split_The_Problem传递的分解结果
@@ -40,7 +38,7 @@ class ReadState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]  # 消息追加模式
     loop_count: int
     search_switch: str
-    group_id: str
+    end_user_id: str
     config_id: str
     data: str  # 新增字段用于传递内容
     spit_data: dict  # 新增字段用于传递问题分解结果

api/app/core/memory/agent/utils/redis_tool.py

@@ -28,7 +28,7 @@ class RedisSessionStore:
             return text
 
     # 修改后的 save_session 方法
-    def save_session(self, userid, messages, aimessages, apply_id, group_id):
+    def save_session(self, userid, messages, aimessages, apply_id, end_user_id):
         """
         写入一条会话数据，返回 session_id
         优化版本：确保写入时间不超过1秒
@@ -46,7 +46,7 @@ class RedisSessionStore:
                 "id": self.uudi,
                 "sessionid": userid,
                 "apply_id": apply_id,
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "messages": messages,
                 "aimessages": aimessages,
                 "starttime": starttime
@@ -67,7 +67,7 @@ class RedisSessionStore:
     def save_sessions_batch(self, sessions_data):
         """
         批量写入多条会话数据，返回 session_id 列表
-        sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, group_id
+        sessions_data: list of dict, 每个 dict 包含 userid, messages, aimessages, apply_id, end_user_id
         优化版本：批量操作，大幅提升性能
         """
         try:
@@ -83,7 +83,7 @@ class RedisSessionStore:
                     "id": self.uudi,
                     "sessionid": session.get('userid'),
                     "apply_id": session.get('apply_id'),
-                    "group_id": session.get('group_id'),
+                    "end_user_id": session.get('end_user_id'),
                     "messages": session.get('messages'),
                     "aimessages": session.get('aimessages'),
                     "starttime": starttime
@@ -108,9 +108,9 @@ class RedisSessionStore:
         data = self.r.hgetall(key)
         return data if data else None
 
-    def get_session_apply_group(self, sessionid, apply_id, group_id):
+    def get_session_apply_group(self, sessionid, apply_id, end_user_id):
         """
-        根据 sessionid、apply_id 和 group_id 三个条件查询会话数据
+        根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据
         """
         result_items = []
 
@@ -124,7 +124,7 @@ class RedisSessionStore:
             # 检查三个条件是否都匹配
             if (data.get('sessionid') == sessionid and
                     data.get('apply_id') == apply_id and
-                    data.get('group_id') == group_id):
+                    data.get('end_user_id') == end_user_id):
                 result_items.append(data)
 
         return result_items
@@ -172,7 +172,7 @@ class RedisSessionStore:
     def delete_duplicate_sessions(self):
         """
         删除重复会话数据，条件：
-        "sessionid"、"user_id"、"group_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除
+        "sessionid"、"user_id"、"end_user_id"、"messages"、"aimessages" 五个字段都相同的只保留一个，其他删除
         优化版本：使用 pipeline 批量操作，确保在1秒内完成
         """
         import time
@@ -202,12 +202,12 @@ class RedisSessionStore:
             # 获取五个字段的值
             sessionid = data.get('sessionid', '')
             user_id = data.get('id', '')
-            group_id = data.get('group_id', '')
+            end_user_id = data.get('end_user_id', '')
             messages = data.get('messages', '')
             aimessages = data.get('aimessages', '')
 
             # 用五元组作为唯一标识
-            identifier = (sessionid, user_id, group_id, messages, aimessages)
+            identifier = (sessionid, user_id, end_user_id, messages, aimessages)
 
             if identifier in seen:
                 # 重复，标记为待删除
@@ -248,9 +248,9 @@ class RedisSessionStore:
             result_items = []
         return (result_items)
 
-    def find_user_apply_group(self, sessionid, apply_id, group_id):
+    def find_user_apply_group(self, sessionid, apply_id, end_user_id):
         """
-        根据 sessionid、apply_id 和 group_id 三个条件查询会话数据，返回最新的6条
+        根据 sessionid、apply_id 和 end_user_id 三个条件查询会话数据，返回最新的6条
         """
         import time
         start_time = time.time()
@@ -276,7 +276,7 @@ class RedisSessionStore:
             # 检查是否符合三个条件
 
             if (data.get('apply_id') == apply_id and
-                    data.get('group_id') == group_id):
+                    data.get('end_user_id') == end_user_id):
                 # 支持模糊匹配 sessionid 或者完全匹配
                 if sessionid in data.get('sessionid', '') or data.get('sessionid') == sessionid:
                     matched_items.append({

api/app/core/memory/agent/utils/session_tools.py

@@ -59,7 +59,7 @@ class SessionService:
         self,
         user_id: str,
         apply_id: str,
-        group_id: str
+        end_user_id: str
     ) -> List[dict]:
         """
         Retrieve conversation history from Redis.
@@ -67,20 +67,20 @@ class SessionService:
         Args:
             user_id: User identifier
             apply_id: Application identifier
-            group_id: Group identifier
+            end_user_id: Group identifier
             
         Returns:
             List of conversation history items with Query and Answer keys
             Returns empty list if no history found or on error
         """
         try:
-            history = self.store.find_user_apply_group(user_id, apply_id, group_id)
+            history = self.store.find_user_apply_group(user_id, apply_id, end_user_id)
             
             # Validate history structure
             if not isinstance(history, list):
                 logger.warning(
                     f"Invalid history format for user {user_id}, "
-                    f"apply {apply_id}, group {group_id}: expected list, got {type(history)}"
+                    f"apply {apply_id}, group {end_user_id}: expected list, got {type(history)}"
                 )
                 return []
             
@@ -89,7 +89,7 @@ class SessionService:
         except Exception as e:
             logger.error(
                 f"Failed to retrieve history for user {user_id}, "
-                f"apply {apply_id}, group {group_id}: {e}",
+                f"apply {apply_id}, group {end_user_id}: {e}",
                 exc_info=True
             )
             # Return empty list on error to allow execution to continue
@@ -100,7 +100,7 @@ class SessionService:
         user_id: str,
         query: str,
         apply_id: str,
-        group_id: str,
+        end_user_id: str,
         ai_response: str
     ) -> Optional[str]:
         """
@@ -110,7 +110,7 @@ class SessionService:
             user_id: User identifier
             query: User query/message
             apply_id: Application identifier
-            group_id: Group identifier
+            end_user_id: Group identifier
             ai_response: AI response/answer
             
         Returns:
@@ -131,7 +131,7 @@ class SessionService:
                 userid=user_id,
                 messages=query,
                 apply_id=apply_id,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 aimessages=ai_response
             )
             
@@ -152,7 +152,7 @@ class SessionService:
         Duplicates are identified by matching:
         - sessionid
         - user_id (id field)
-        - group_id
+        - end_user_id
         - messages
         - aimessages
         

api/app/core/memory/agent/utils/write_tools.py

@@ -29,20 +29,18 @@ logger = get_agent_logger(__name__)
 
 
 async def write(
-    user_id: str,
-    apply_id: str,
-    group_id: str,
+    end_user_id: str,
     memory_config: MemoryConfig,
     messages: list,
     ref_id: str = "wyl20251027",
 ) -> None:
     """
     Execute the complete knowledge extraction pipeline.
-    
+
     Args:
         user_id: User identifier
         apply_id: Application identifier
-        group_id: Group identifier
+        end_user_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
         messages: Structured message list [{"role": "user", "content": "..."}, ...]
         ref_id: Reference ID, defaults to "wyl20251027"
@@ -51,14 +49,14 @@ async def write(
     embedding_model_id = str(memory_config.embedding_model_id)
     chunker_strategy = memory_config.chunker_strategy
     config_id = str(memory_config.config_id)
-    
+
     logger.info("=== MemSci Knowledge Extraction Pipeline ===")
     logger.info(f"Config: {memory_config.config_name} (ID: {config_id})")
     logger.info(f"Workspace: {memory_config.workspace_name}")
     logger.info(f"LLM model: {memory_config.llm_model_name}")
     logger.info(f"Embedding model: {memory_config.embedding_model_name}")
     logger.info(f"Chunker strategy: {chunker_strategy}")
-    logger.info(f"Group ID: {group_id}")
+    logger.info(f"end_user_id ID: {end_user_id}")
 
     # Construct clients from memory_config using factory pattern with db session
     with get_db_context() as db:
@@ -83,9 +81,7 @@ async def write(
     step_start = time.time()
     chunked_dialogs = await get_chunked_dialogs(
         chunker_strategy=chunker_strategy,
-        group_id=group_id,
-        user_id=user_id,
-        apply_id=apply_id,
+        end_user_id=end_user_id,
         messages=messages,
         ref_id=ref_id,
         config_id=config_id,

api/app/core/memory/analytics/hot_memory_tags.py

@@ -16,13 +16,13 @@ class FilteredTags(BaseModel):
     """用于接收LLM筛选后的核心标签列表的模型。"""
     meaningful_tags: List[str] = Field(..., description="从原始列表中筛选出的具有核心代表意义的名词列表。")
 
-async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]:
+async def filter_tags_with_llm(tags: List[str], end_user_id: str) -> List[str]:
     """
     使用LLM筛选标签列表，仅保留具有代表性的核心名词。
     
     Args:
         tags: 原始标签列表
-        group_id: 用户组ID，用于获取配置
+        end_user_id: 用户组ID，用于获取配置
         
     Returns:
         筛选后的标签列表
@@ -37,12 +37,12 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]:
                 get_end_user_connected_config,
             )
             
-            connected_config = get_end_user_connected_config(group_id, db)
+            connected_config = get_end_user_connected_config(end_user_id, db)
             config_id = connected_config.get("memory_config_id")
             
             if not config_id:
                 raise ValueError(
-                    f"No memory_config_id found for group_id: {group_id}. "
+                    f"No memory_config_id found for end_user_id: {end_user_id}. "
                     "Please ensure the user has a valid memory configuration."
                 )
             
@@ -87,7 +87,7 @@ async def filter_tags_with_llm(tags: List[str], group_id: str) -> List[str]:
 
 async def get_raw_tags_from_db(
     connector: Neo4jConnector,
-    group_id: str,
+    end_user_id: str,
     limit: int,
     by_user: bool = False
 ) -> List[Tuple[str, int]]:
@@ -99,9 +99,9 @@ async def get_raw_tags_from_db(
 
     Args:
         connector: Neo4j连接器实例
-        group_id: 如果by_user=False，则为group_id；如果by_user=True，则为user_id
+        end_user_id: 如果by_user=False，则为end_user_id；如果by_user=True，则为user_id
         limit: 返回的标签数量限制
-        by_user: 是否按user_id查询（默认False，按group_id查询）
+        by_user: 是否按user_id查询（默认False，按end_user_id查询）
         
     Returns:
         List[Tuple[str, int]]: 标签名称和频率的元组列表
@@ -119,7 +119,7 @@ async def get_raw_tags_from_db(
     else:
         query = (
             "MATCH (e:ExtractedEntity) "
-            "WHERE e.group_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude "
+            "WHERE e.end_user_id = $id AND e.entity_type <> '人物' AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude "
             "RETURN e.name AS name, count(e) AS frequency "
             "ORDER BY frequency DESC "
             "LIMIT $limit"
@@ -128,44 +128,44 @@ async def get_raw_tags_from_db(
     # 使用项目的Neo4jConnector执行查询
     results = await connector.execute_query(
         query,
-        id=group_id,
+        id=end_user_id,
         limit=limit,
         names_to_exclude=names_to_exclude
     )
     
     return [(record["name"], record["frequency"]) for record in results]
 
-async def get_hot_memory_tags(group_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]:
+async def get_hot_memory_tags(end_user_id: str, limit: int = 40, by_user: bool = False) -> List[Tuple[str, int]]:
     """
     获取原始标签，然后使用LLM进行筛选，返回最终的热门标签列表。
     查询更多的标签(limit=40)给LLM提供更丰富的上下文进行筛选。
 
     Args:
-        group_id: 必需参数。如果by_user=False，则为group_id；如果by_user=True，则为user_id
+        end_user_id: 必需参数。如果by_user=False，则为end_user_id；如果by_user=True，则为user_id
         limit: 返回的标签数量限制
-        by_user: 是否按user_id查询（默认False，按group_id查询）
+        by_user: 是否按user_id查询（默认False，按end_user_id查询）
         
     Raises:
-        ValueError: 如果group_id未提供或为空
+        ValueError: 如果end_user_id未提供或为空
     """
-    # 验证group_id必须提供且不为空
-    if not group_id or not group_id.strip():
+    # 验证end_user_id必须提供且不为空
+    if not end_user_id or not end_user_id.strip():
         raise ValueError(
-            "group_id is required. Please provide a valid group_id or user_id."
+            "end_user_id is required. Please provide a valid end_user_id or user_id."
         )
     
     # 使用项目的Neo4jConnector
     connector = Neo4jConnector()
     try:
         # 1. 从数据库获取原始排名靠前的标签
-        raw_tags_with_freq = await get_raw_tags_from_db(connector, group_id, limit, by_user=by_user)
+        raw_tags_with_freq = await get_raw_tags_from_db(connector, end_user_id, limit, by_user=by_user)
         if not raw_tags_with_freq:
             return []
 
         raw_tag_names = [tag for tag, freq in raw_tags_with_freq]
 
         # 2. 初始化LLM客户端并使用LLM筛选出有意义的标签
-        meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, group_id)
+        meaningful_tag_names = await filter_tags_with_llm(raw_tag_names, end_user_id)
 
         # 3. 根据LLM的筛选结果，构建最终的标签列表（保留原始频率和顺序）
         final_tags = []

api/app/core/memory/analytics/implicit_memory/data_source.py

@@ -75,8 +75,8 @@ class MemoryDataSource:
             start_date = time_range.start_date if time_range else None
             end_date = time_range.end_date if time_range else None
             
-            summary_dicts = await self.memory_summary_repo.find_by_group_id(
-                group_id=user_id,
+            summary_dicts = await self.memory_summary_repo.find_by_end_user_id(
+                end_user_id=user_id,
                 limit=limit,
                 start_date=start_date,
                 end_date=end_date

api/app/core/memory/evaluation/dialogue_queries.py

@@ -41,7 +41,7 @@ DIALOGUE_EMBEDDING_SEARCH = """
 WITH $embedding AS q
 MATCH (d:Dialogue)
 WHERE d.dialog_embedding IS NOT NULL
-  AND ($group_id IS NULL OR d.group_id = $group_id)
+  AND ($end_user_id IS NULL OR d.end_user_id = $end_user_id)
 WITH d, q, d.dialog_embedding AS v
 WITH d,
      reduce(dot = 0.0, i IN range(0, size(q)-1) | dot + toFloat(q[i]) * toFloat(v[i])) AS dot,
@@ -50,7 +50,7 @@ WITH d,
 WITH d, CASE WHEN qnorm = 0 OR vnorm = 0 THEN 0.0 ELSE dot / (qnorm * vnorm) END AS score
 WHERE score > $threshold
 RETURN d.id AS dialog_id,
-       d.group_id AS group_id,
+       d.end_user_id AS end_user_id,
        d.content AS content,
        d.created_at AS created_at,
        d.expired_at AS expired_at,

api/app/core/memory/evaluation/extraction_utils.py

@@ -36,7 +36,7 @@ from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 
 async def ingest_contexts_via_full_pipeline(
     contexts: List[str],
-    group_id: str,
+    end_user_id: str,
     chunker_strategy: str | None = None,
     embedding_name: str | None = None,
     save_chunk_output: bool = False,
@@ -48,7 +48,7 @@ async def ingest_contexts_via_full_pipeline(
     This function mirrors the steps in main(), but starts from raw text contexts.
     Args:
         contexts: List of dialogue texts, each containing lines like "role: message".
-        group_id: Group ID to assign to generated DialogData and graph nodes.
+        end_user_id: Group ID to assign to generated DialogData and graph nodes.
         chunker_strategy: Optional chunker strategy; defaults to SELECTED_CHUNKER_STRATEGY.
         embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID.
         save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging.
@@ -109,7 +109,7 @@ async def ingest_contexts_via_full_pipeline(
         dialog = DialogData(
             context=context_model,
             ref_id=f"pipeline_item_{idx}",
-            group_id=group_id,
+            end_user_id=end_user_id,
             user_id="default_user",
             apply_id="default_application",
         )
@@ -318,16 +318,16 @@ async def handle_context_processing(args):
         print("No contexts provided for processing.")
         return False
 
-    return await main_from_contexts(contexts, args.context_group_id)
+    return await main_from_contexts(contexts, args.context_end_user_id)
 
 
-async def main_from_contexts(contexts: List[str], group_id: str):
+async def main_from_contexts(contexts: List[str], end_user_id: str):
     """Run the pipeline from provided dialogue contexts instead of test data."""
     print("=== Running pipeline from provided contexts ===")
 
     success = await ingest_contexts_via_full_pipeline(
         contexts=contexts,
-        group_id=group_id,
+        end_user_id=end_user_id,
         chunker_strategy=SELECTED_CHUNKER_STRATEGY,
         embedding_name=SELECTED_EMBEDDING_ID,
         save_chunk_output=True

api/app/core/memory/evaluation/locomo/locomo_benchmark.py

@@ -47,7 +47,7 @@ from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
 from app.core.memory.utils.definitions import (
     PROJECT_ROOT,
     SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
+    SELECTED_end_user_id,
     SELECTED_LLM_ID,
 )
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -59,7 +59,7 @@ from app.services.memory_config_service import MemoryConfigService
 
 async def run_locomo_benchmark(
     sample_size: int = 20,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     search_type: str = "hybrid",
     search_limit: int = 12,
     context_char_budget: int = 8000,
@@ -85,7 +85,7 @@ async def run_locomo_benchmark(
     
     Args:
         sample_size: Number of QA pairs to evaluate (from first conversation)
-        group_id: Database group ID for retrieval (uses default if None)
+        end_user_id: Database group ID for retrieval (uses default if None)
         search_type: "keyword", "embedding", or "hybrid"
         search_limit: Max documents to retrieve per query
         context_char_budget: Max characters for context
@@ -96,8 +96,8 @@ async def run_locomo_benchmark(
     Returns:
         Dictionary with evaluation results including metrics, timing, and samples
     """
-    # Use default group_id if not provided
-    group_id = group_id or SELECTED_GROUP_ID
+    # Use default end_user_id if not provided
+    end_user_id = end_user_id or SELECTED_end_user_id
     
     # Determine data path
     data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json")
@@ -110,7 +110,7 @@ async def run_locomo_benchmark(
     print(f"{'='*60}")
     print("📊 Configuration:")
     print(f"   Sample size: {sample_size}")
-    print(f"   Group ID: {group_id}")
+    print(f"   Group ID: {end_user_id}")
     print(f"   Search type: {search_type}")
     print(f"   Search limit: {search_limit}")
     print(f"   Context budget: {context_char_budget} chars")
@@ -134,7 +134,7 @@ async def run_locomo_benchmark(
     # Step 2: Extract conversations and ingest if needed
     if skip_ingest:
         print("⏭️  Skipping data ingestion (using existing data in Neo4j)")
-        print(f"   Group ID: {group_id}\n")
+        print(f"   Group ID: {end_user_id}\n")
     else:
         print("💾 Checking database ingestion...")
         try:
@@ -142,10 +142,10 @@ async def run_locomo_benchmark(
             print(f"📝 Extracted {len(conversations)} conversations")
             
             # Always ingest for now (ingestion check not implemented)
-            print(f"🔄 Ingesting conversations into group '{group_id}'...")
+            print(f"🔄 Ingesting conversations into group '{end_user_id}'...")
             success = await ingest_conversations_if_needed(
                 conversations=conversations,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 reset=reset_group
             )
             
@@ -224,7 +224,7 @@ async def run_locomo_benchmark(
             try:
                 retrieved_info = await retrieve_relevant_information(
                     question=question,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     search_type=search_type,
                     search_limit=search_limit,
                     connector=connector,
@@ -409,7 +409,7 @@ async def run_locomo_benchmark(
         "sample_size": len(qa_items),
         "timestamp": datetime.now().isoformat(),
         "params": {
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "search_type": search_type,
             "search_limit": search_limit,
             "context_char_budget": context_char_budget,
@@ -467,7 +467,7 @@ def main():
         help="Number of QA pairs to evaluate"
     )
     parser.add_argument(
-        "--group_id",
+        "--end_user_id",
         type=str,
         default=None,
         help="Database group ID for retrieval (uses default if not specified)"
@@ -516,7 +516,7 @@ def main():
     # Run benchmark
     result = asyncio.run(run_locomo_benchmark(
         sample_size=args.sample_size,
-        group_id=args.group_id,
+        end_user_id=args.end_user_id,
         search_type=args.search_type,
         search_limit=args.search_limit,
         context_char_budget=args.context_char_budget,

api/app/core/memory/evaluation/locomo/locomo_test.py

@@ -556,7 +556,7 @@ async def run_enhanced_evaluation():
                 search_results = await run_hybrid_search(
                     query_text=q,
                     search_type="hybrid",
-                    group_id="locomo_sk",
+                    end_user_id="locomo_sk",
                     limit=20,
                     include=["statements", "chunks", "entities", "summaries"],
                     alpha=0.6,  # BM25权重

api/app/core/memory/evaluation/locomo/locomo_utils.py

@@ -348,7 +348,7 @@ def select_and_format_information(
 
 async def retrieve_relevant_information(
     question: str,
-    group_id: str,
+    end_user_id: str,
     search_type: str,
     search_limit: int,
     connector: Any,
@@ -368,7 +368,7 @@ async def retrieve_relevant_information(
     
     Args:
         question: Question to search for
-        group_id: Database group ID (identifies which conversation memory to search)
+        end_user_id: Database group ID (identifies which conversation memory to search)
         search_type: "keyword", "embedding", or "hybrid"
         search_limit: Max memory pieces to retrieve
         connector: Neo4j connector instance
@@ -396,7 +396,7 @@ async def retrieve_relevant_information(
                 connector=connector,
                 embedder_client=embedder,
                 query_text=question,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=search_limit,
                 include=["chunks", "statements", "entities", "summaries"],
             )
@@ -455,7 +455,7 @@ async def retrieve_relevant_information(
             search_results = await search_graph(
                 connector=connector,
                 q=question,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=search_limit
             )
             
@@ -491,7 +491,7 @@ async def retrieve_relevant_information(
                 search_results = await run_hybrid_search(
                     query_text=question,
                     search_type=search_type,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["chunks", "statements", "entities", "summaries"],
                     output_path=None,
@@ -524,7 +524,7 @@ async def retrieve_relevant_information(
                     connector=connector,
                     embedder_client=embedder,
                     query_text=question,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["chunks", "statements", "entities", "summaries"],
                 )
@@ -584,7 +584,7 @@ async def retrieve_relevant_information(
 
 async def ingest_conversations_if_needed(
     conversations: List[str],
-    group_id: str,
+    end_user_id: str,
     reset: bool = False
 ) -> bool:
     """
@@ -603,7 +603,7 @@ async def ingest_conversations_if_needed(
     Args:
         conversations: List of raw conversation texts from LoCoMo dataset
                       Example: ["User: I went to Paris. AI: When was that?", ...]
-        group_id: Target group ID for database storage
+        end_user_id: Target group ID for database storage
         reset: Whether to clear existing data first (not implemented in wrapper)
         
     Returns:
@@ -617,7 +617,7 @@ async def ingest_conversations_if_needed(
     try:
         success = await ingest_contexts_via_full_pipeline(
             contexts=conversations,
-            group_id=group_id,
+            end_user_id=end_user_id,
             save_chunk_output=True
         )
         return success

api/app/core/memory/evaluation/locomo/qwen_search_eval.py

@@ -249,7 +249,7 @@ def get_search_params_by_category(category: str):
 
 async def run_locomo_eval(
     sample_size: int = 1,
-    group_id: str | None = None,
+    end_user_id: str | None = None,
     search_limit: int = 8,
     context_char_budget: int = 4000,  # 保持默认值不变
     llm_temperature: float = 0.0,
@@ -262,7 +262,7 @@ async def run_locomo_eval(
 ) -> Dict[str, Any]:
 
     # 函数内部使用三路检索逻辑，但保持参数签名不变
-    group_id = group_id or SELECTED_GROUP_ID
+    end_user_id = end_user_id or SELECTED_end_user_id
     data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json")
     if not os.path.exists(data_path):
         data_path = os.path.join(os.getcwd(), "data", "locomo10.json")
@@ -340,7 +340,7 @@ async def run_locomo_eval(
 
     # 关键修复：强制重新摄入纯净的对话数据
     print("🔄 强制重新摄入纯净的对话数据...")
-    await ingest_contexts_via_full_pipeline(contents, group_id, save_chunk_output=True)
+    await ingest_contexts_via_full_pipeline(contents, end_user_id, save_chunk_output=True)
 
     # 使用异步LLM客户端
     with get_db_context() as db:
@@ -405,7 +405,7 @@ async def run_locomo_eval(
                         connector=connector,
                         embedder_client=embedder,
                         query_text=q,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=adjusted_limit,
                         include=["chunks", "statements", "entities", "summaries"],  # 修复：使用正确的类型
                     )
@@ -456,7 +456,7 @@ async def run_locomo_eval(
                     search_results = await search_graph(
                         connector=connector,
                         q=q,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=adjusted_limit
                     )
                     dialogs = search_results.get("dialogues", [])
@@ -486,7 +486,7 @@ async def run_locomo_eval(
                         search_results = await run_hybrid_search(
                             query_text=q,
                             search_type=search_type,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=adjusted_limit,
                             include=["chunks", "statements", "entities", "summaries"],
                             output_path=None,
@@ -524,7 +524,7 @@ async def run_locomo_eval(
                             connector=connector,
                             embedder_client=embedder,
                             query_text=q,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=adjusted_limit,
                             include=["chunks", "statements", "entities", "summaries"],
                         )
@@ -597,7 +597,7 @@ async def run_locomo_eval(
                         "dialogues": [
                             {
                                 "uuid": d.get("uuid", ""),
-                                "group_id": d.get("group_id", ""),
+                                "end_user_id": d.get("end_user_id", ""),
                                 "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""),
                                 "score": d.get("score", 0.0)
                             }
@@ -795,7 +795,7 @@ async def run_locomo_eval(
             },
             "samples": samples,
             "params": {
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "search_limit": search_limit,
                 "context_char_budget": context_char_budget,
                 "search_type": search_type,
@@ -825,7 +825,7 @@ async def run_locomo_eval(
 def main():
     parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search")
     parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate")
-    parser.add_argument("--group_id", type=str, default=None, help="Group ID for retrieval")
+    parser.add_argument("--end_user_id", type=str, default=None, help="Group ID for retrieval")
     parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query")
     parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context")
     parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature")
@@ -841,7 +841,7 @@ def main():
 
     result = asyncio.run(run_locomo_eval(
         sample_size=args.sample_size,
-        group_id=args.group_id,
+        end_user_id=args.end_user_id,
         search_limit=args.search_limit,
         context_char_budget=args.context_char_budget,
         llm_temperature=args.llm_temperature,

api/app/core/memory/evaluation/longmemeval/qwen_search_eval.py

@@ -524,11 +524,11 @@ def generate_query_keywords_cn(question: str) -> List[str]:
 
 
 # 通过别名匹配进行实体关键词检索（多token合并）
-async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]:
+async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]:
     results: List[Dict[str, Any]] = []
     try:
         for tok in tokens:
-            rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit)
+            rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit)
             if rows:
                 results.extend(rows)
     except Exception:
@@ -548,15 +548,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st
 # 通过对话/陈述中的entity_ids反查实体名称
 _FETCH_ENTITIES_BY_IDS = """
 MATCH (e:ExtractedEntity)
-WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id)
-RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type
+WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
+RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type
 """
 
-async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]:
+async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]:
     if not ids:
         return []
     try:
-        rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id)
+        rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id)
         return rows or []
     except Exception:
         return []
@@ -566,18 +566,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou
 _TIME_ENTITY_SEARCH = """
 MATCH (e:ExtractedEntity)
 WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern
-AND ($group_id IS NULL OR e.group_id = $group_id)
-RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type
+AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
+RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type
 LIMIT $limit
 """
 
-async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]:
+async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]:
     """专门搜索时间相关的实体"""
     try:
         date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*"
         rows = await connector.execute_query(_TIME_ENTITY_SEARCH,
                                            date_pattern=date_pattern,
-                                           group_id=group_id,
+                                           end_user_id=end_user_id,
                                            limit=limit)
         return rows or []
     except Exception:
@@ -624,7 +624,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str:
 
 async def run_longmemeval_test(
     sample_size: int = 3,
-    group_id: str = "longmemeval_zh_bak_3",
+    end_user_id: str = "longmemeval_zh_bak_3",
     search_limit: int = 8,
     context_char_budget: int = 4000,
     llm_temperature: float = 0.0,
@@ -678,13 +678,13 @@ async def run_longmemeval_test(
             contexts.extend(selected)
 
         print(f"📥 摄入 {len(contexts)} 个上下文到数据库")
-        if reset_group_before_ingest and group_id:
+        if reset_group_before_ingest and end_user_id:
             try:
                 _tmp_conn = Neo4jConnector()
-                await _tmp_conn.delete_group(group_id)
-                print(f"🧹 已清空组 {group_id} 的历史图数据")
+                await _tmp_conn.delete_group(end_user_id)
+                print(f"🧹 已清空组 {end_user_id} 的历史图数据")
             except Exception as _e:
-                print(f"⚠️ 清空组数据失败（忽略继续）: {group_id} - {_e}")
+                print(f"⚠️ 清空组数据失败（忽略继续）: {end_user_id} - {_e}")
             finally:
                 try:
                     await _tmp_conn.close()
@@ -696,7 +696,7 @@ async def run_longmemeval_test(
         else:
             await _ingest_fn(
                 contexts,
-                group_id,
+                end_user_id,
                 save_chunk_output=save_chunk_output,
                 save_chunk_output_path=save_chunk_output_path,
             )
@@ -751,7 +751,7 @@ async def run_longmemeval_test(
                         connector=connector,
                         embedder_client=embedder,
                         query_text=question,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=search_limit,
                         include=["chunks", "statements", "entities", "summaries"],
                     )
@@ -796,7 +796,7 @@ async def run_longmemeval_test(
                     search_results = await search_graph(
                         connector=connector,
                         q=question,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=search_limit,
                     )
                     chunks = search_results.get("chunks", [])
@@ -831,7 +831,7 @@ async def run_longmemeval_test(
                             connector=connector,
                             embedder_client=embedder,
                             query_text=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=search_limit,
                             include=["chunks", "statements", "entities", "summaries"],
                         )
@@ -849,7 +849,7 @@ async def run_longmemeval_test(
                         kw_res = await search_graph(
                             connector=connector,
                             q=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=search_limit,
                         )
                         if isinstance(kw_res, dict):
@@ -860,7 +860,7 @@ async def run_longmemeval_test(
                             # 时间推理问题的特殊处理
                             if is_temporal:
                                 # 专门搜索时间实体
-                                time_entities = await _search_time_entities(connector, group_id, search_limit//2)
+                                time_entities = await _search_time_entities(connector, end_user_id, search_limit//2)
                                 if time_entities:
                                     kw_entities.extend(time_entities)
                                 # 添加时间相关关键词检索
@@ -870,7 +870,7 @@ async def run_longmemeval_test(
                                         time_res = await search_graph(
                                             connector=connector,
                                             q=tk,
-                                            group_id=group_id,
+                                            end_user_id=end_user_id,
                                             limit=2,
                                         )
                                         if isinstance(time_res, dict):
@@ -881,7 +881,7 @@ async def run_longmemeval_test(
 
                             # 中文关键词拆分后做别名匹配
                             cn_tokens = _extract_cn_tokens(question)
-                            alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit)
+                            alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit)
                             if alias_entities:
                                 kw_entities.extend(alias_entities)
 
@@ -895,7 +895,7 @@ async def run_longmemeval_test(
                             except Exception:
                                 pass
                             if ids:
-                                id_entities = await _fetch_entities_by_ids(connector, ids, group_id)
+                                id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id)
                                 if id_entities:
                                     kw_entities.extend(id_entities)
 
@@ -909,7 +909,7 @@ async def run_longmemeval_test(
                                     sub_res = await search_graph(
                                         connector=connector,
                                         q=str(kw),
-                                        group_id=group_id,
+                                        end_user_id=end_user_id,
                                         limit=max(3, search_limit // 2),
                                     )
                                     if isinstance(sub_res, dict):
@@ -928,7 +928,7 @@ async def run_longmemeval_test(
                                     opt_res = await search_graph(
                                         connector=connector,
                                         q=str(opt),
-                                        group_id=group_id,
+                                        end_user_id=end_user_id,
                                         limit=max(3, search_limit // 2),
                                     )
                                     if isinstance(opt_res, dict):
@@ -1010,7 +1010,7 @@ async def run_longmemeval_test(
                         kw_fallback = await search_graph(
                             connector=connector,
                             q=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=max(search_limit, 5),
                         )
                         fb_dialogs = kw_fallback.get("dialogues", []) or []
@@ -1224,7 +1224,7 @@ async def run_longmemeval_test(
                 "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0,
             },
             "params": {
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "search_limit": search_limit,
                 "context_char_budget": context_char_budget,
                 "search_type": search_type,
@@ -1307,7 +1307,7 @@ def main():
     result = asyncio.run(
         run_longmemeval_test(
             sample_size=sample_size,
-            group_id=args.group_id,
+            end_user_id=args.end_user_id,
             search_limit=args.search_limit,
             context_char_budget=args.context_char_budget,
             llm_temperature=args.llm_temperature,

api/app/core/memory/evaluation/longmemeval/test_eval.py

@@ -498,11 +498,11 @@ def smart_context_selection(contexts: List[str], question: str, max_chars: int =
 
 
 # 通过别名匹配进行实体关键词检索（多token合并）
-async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]:
+async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]:
     results: List[Dict[str, Any]] = []
     try:
         for tok in tokens:
-            rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit)
+            rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit)
             if rows:
                 results.extend(rows)
     except Exception:
@@ -522,15 +522,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st
 # 通过对话/陈述中的entity_ids反查实体名称
 _FETCH_ENTITIES_BY_IDS = """
 MATCH (e:ExtractedEntity)
-WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id)
-RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type
+WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
+RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type
 """
 
-async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]:
+async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]:
     if not ids:
         return []
     try:
-        rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id)
+        rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id)
         return rows or []
     except Exception:
         return []
@@ -540,18 +540,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou
 _TIME_ENTITY_SEARCH = """
 MATCH (e:ExtractedEntity)
 WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern
-AND ($group_id IS NULL OR e.group_id = $group_id)
-RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type
+AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
+RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type
 LIMIT $limit
 """
 
-async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]:
+async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]:
     """专门搜索时间相关的实体"""
     try:
         date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*"
         rows = await connector.execute_query(_TIME_ENTITY_SEARCH,
                                            date_pattern=date_pattern,
-                                           group_id=group_id,
+                                           end_user_id=end_user_id,
                                            limit=limit)
         return rows or []
     except Exception:
@@ -559,25 +559,25 @@ async def _search_time_entities(connector: Neo4jConnector, group_id: str | None,
 
 
 # 技术术语专门检索
-async def _search_tech_terms(connector: Neo4jConnector, question: str, group_id: str | None, limit: int = 3) -> List[Dict[str, Any]]:
+async def _search_tech_terms(connector: Neo4jConnector, question: str, end_user_id: str | None, limit: int = 3) -> List[Dict[str, Any]]:
     """专门搜索技术术语相关的实体"""
     tech_entities = []
     try:
         # GPS相关
         if any(term in question for term in ["GPS", "导航", "定位系统"]):
-            gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", group_id=group_id, limit=limit)
+            gps_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="GPS", end_user_id=end_user_id, limit=limit)
             if gps_rows:
                 tech_entities.extend(gps_rows)
 
         # 活动相关
         if any(term in question for term in ["工作坊", "研讨会", "网络研讨会"]):
-            workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", group_id=group_id, limit=limit)
+            workshop_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="工作坊", end_user_id=end_user_id, limit=limit)
             if workshop_rows:
                 tech_entities.extend(workshop_rows)
 
         # 时间顺序相关
         if any(term in question for term in ["先", "后", "第一个"]):
-            time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", group_id=group_id, limit=limit)
+            time_rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q="第一次", end_user_id=end_user_id, limit=limit)
             if time_rows:
                 tech_entities.extend(time_rows)
 
@@ -627,7 +627,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str:
 
 async def run_longmemeval_test(
     sample_size: int = 3,
-    group_id: str = "longmemeval_zh_bak_2",
+    end_user_id: str = "longmemeval_zh_bak_2",
     search_limit: int = 8,
     context_char_budget: int = 4000,
     llm_temperature: float = 0.0,
@@ -707,7 +707,7 @@ async def run_longmemeval_test(
                         connector=connector,
                         embedder_client=embedder,
                         query_text=question,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=search_limit,
                         include=["dialogues", "statements", "entities"],
                     )
@@ -746,7 +746,7 @@ async def run_longmemeval_test(
                     search_results = await search_graph(
                         connector=connector,
                         q=question,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=search_limit,
                     )
                     dialogs = search_results.get("dialogues", [])
@@ -776,7 +776,7 @@ async def run_longmemeval_test(
                             connector=connector,
                             embedder_client=embedder,
                             query_text=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=search_limit,
                             include=["dialogues", "statements", "entities"],
                         )
@@ -792,7 +792,7 @@ async def run_longmemeval_test(
                         kw_res = await search_graph(
                             connector=connector,
                             q=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=search_limit,
                         )
                         if isinstance(kw_res, dict):
@@ -801,14 +801,14 @@ async def run_longmemeval_test(
                             kw_entities = kw_res.get("entities", []) or []
 
                             # 技术术语专门检索
-                            tech_entities = await _search_tech_terms(connector, question, group_id, search_limit//2)
+                            tech_entities = await _search_tech_terms(connector, question, end_user_id, search_limit//2)
                             if tech_entities:
                                 kw_entities.extend(tech_entities)
 
                             # 时间推理问题的特殊处理
                             if is_temporal:
                                 # 专门搜索时间实体
-                                time_entities = await _search_time_entities(connector, group_id, search_limit//2)
+                                time_entities = await _search_time_entities(connector, end_user_id, search_limit//2)
                                 if time_entities:
                                     kw_entities.extend(time_entities)
                                 # 添加时间相关关键词检索
@@ -818,7 +818,7 @@ async def run_longmemeval_test(
                                         time_res = await search_graph(
                                             connector=connector,
                                             q=tk,
-                                            group_id=group_id,
+                                            end_user_id=end_user_id,
                                             limit=2,
                                         )
                                         if isinstance(time_res, dict):
@@ -829,7 +829,7 @@ async def run_longmemeval_test(
 
                             # 中文关键词拆分后做别名匹配
                             cn_tokens = generate_query_keywords_cn(question)  # 使用增强版关键词提取
-                            alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit)
+                            alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit)
                             if alias_entities:
                                 kw_entities.extend(alias_entities)
 
@@ -843,7 +843,7 @@ async def run_longmemeval_test(
                             except Exception:
                                 pass
                             if ids:
-                                id_entities = await _fetch_entities_by_ids(connector, ids, group_id)
+                                id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id)
                                 if id_entities:
                                     kw_entities.extend(id_entities)
 
@@ -857,7 +857,7 @@ async def run_longmemeval_test(
                                     sub_res = await search_graph(
                                         connector=connector,
                                         q=str(kw),
-                                        group_id=group_id,
+                                        end_user_id=end_user_id,
                                         limit=max(3, search_limit // 2),
                                     )
                                     if isinstance(sub_res, dict):
@@ -876,7 +876,7 @@ async def run_longmemeval_test(
                                     opt_res = await search_graph(
                                         connector=connector,
                                         q=str(opt),
-                                        group_id=group_id,
+                                        end_user_id=end_user_id,
                                         limit=max(3, search_limit // 2),
                                     )
                                     if isinstance(opt_res, dict):
@@ -971,7 +971,7 @@ async def run_longmemeval_test(
                         kw_fallback = await search_graph(
                             connector=connector,
                             q=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=max(search_limit, 5),
                         )
                         fb_dialogs = kw_fallback.get("dialogues", []) or []
@@ -1199,7 +1199,7 @@ async def run_longmemeval_test(
                 "count_avg": statistics.mean(per_query_context_counts) if per_query_context_counts else 0.0,
             },
             "params": {
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "search_limit": search_limit,
                 "context_char_budget": context_char_budget,
                 "search_type": search_type,
@@ -1278,7 +1278,7 @@ def main():
     result = asyncio.run(
         run_longmemeval_test(
             sample_size=sample_size,
-            group_id=args.group_id,
+            end_user_id=args.end_user_id,
             search_limit=args.search_limit,
             context_char_budget=args.context_char_budget,
             llm_temperature=args.llm_temperature,

api/app/core/memory/evaluation/memsciqa/evaluate_qa.py

@@ -135,8 +135,8 @@ def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any
     return merged
 
 
-async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]:
-    group_id = group_id or SELECTED_GROUP_ID
+async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]:
+    end_user_id = end_user_id or SELECTED_GROUP_ID
     # Load data
     data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl")
     if not os.path.exists(data_path):
@@ -147,7 +147,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s
     # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入
     # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略
     contexts: List[str] = [build_context_from_dialog(item) for item in items]
-    await ingest_contexts_via_full_pipeline(contexts, group_id)
+    await ingest_contexts_via_full_pipeline(contexts, end_user_id)
 
     # LLM client (使用异步调用)
     with get_db_context() as db:
@@ -173,7 +173,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s
                 results = await run_hybrid_search(
                     query_text=question,
                     search_type=search_type,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["dialogues", "statements", "entities"],
                     output_path=None,
@@ -298,7 +298,7 @@ def main():
     load_dotenv()
     parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen")
     parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量")
-    parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json")
+    parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json")
     parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数")
     parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算")
     parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度")
@@ -309,7 +309,7 @@ def main():
     result = asyncio.run(
         run_memsciqa_eval(
             sample_size=args.sample_size,
-            group_id=args.group_id,
+            end_user_id=args.end_user_id,
             search_limit=args.search_limit,
             context_char_budget=args.context_char_budget,
             llm_temperature=args.llm_temperature,

api/app/core/memory/evaluation/memsciqa/memsciqa-test.py

@@ -199,7 +199,7 @@ def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]:
 
 async def run_memsciqa_test(
     sample_size: int = 3,
-    group_id: str | None = None,
+    end_user_id: str | None = None,
     search_limit: int = 8,
     context_char_budget: int = 4000,
     llm_temperature: float = 0.0,
@@ -217,7 +217,7 @@ async def run_memsciqa_test(
     """
 
     # 默认使用指定的 memsci 组 ID
-    group_id = group_id or "group_memsci"
+    end_user_id = end_user_id or "group_memsci"
 
     # 数据路径解析（项目根与当前工作目录兜底）
     if not data_path:
@@ -283,7 +283,7 @@ async def run_memsciqa_test(
                     connector=connector,
                     embedder_client=embedder,
                     query_text=question,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["chunks", "statements", "entities", "summaries"],  # 使用 chunks 而不是 dialogues
                 )
@@ -292,7 +292,7 @@ async def run_memsciqa_test(
                 results = await search_graph(
                     connector=connector,
                     q=question,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["chunks", "statements", "entities", "summaries"],  # 使用 chunks 而不是 dialogues
                 )
@@ -500,7 +500,7 @@ async def run_memsciqa_test(
         },
         "samples": samples,
         "params": {
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "search_limit": search_limit,
             "context_char_budget": context_char_budget,
             "llm_temperature": llm_temperature,
@@ -543,7 +543,7 @@ def main():
     result = asyncio.run(
         run_memsciqa_test(
             sample_size=sample_size,
-            group_id=args.group_id,
+            end_user_id=args.end_user_id,
             search_limit=args.search_limit,
             context_char_budget=args.context_char_budget,
             llm_temperature=args.llm_temperature,

api/app/core/memory/evaluation/run_eval.py

@@ -26,7 +26,7 @@ async def run(
     dataset: str,
     sample_size: int,
     reset_group: bool,
-    group_id: str | None,
+    end_user_id: str | None,
     judge_model: str | None = None,
     search_limit: int | None = None,
     context_char_budget: int | None = None,
@@ -37,17 +37,17 @@ async def run(
     max_contexts_per_item: int | None = None,
 ) -> Dict[str, Any]:
     # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认
-    group_id = group_id or SELECTED_GROUP_ID
+    end_user_id = end_user_id or SELECTED_GROUP_ID
 
     if reset_group:
         connector = Neo4jConnector()
         try:
-            await connector.delete_group(group_id)
+            await connector.delete_group(end_user_id)
         finally:
             await connector.close()
 
     if dataset == "locomo":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
+        kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id}
         if search_limit is not None:
             kwargs["search_limit"] = search_limit
         if context_char_budget is not None:
@@ -61,7 +61,7 @@ async def run(
         return await run_locomo_eval(**kwargs)
 
     if dataset == "memsciqa":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
+        kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id}
         if search_limit is not None:
             kwargs["search_limit"] = search_limit
         if context_char_budget is not None:
@@ -75,7 +75,7 @@ async def run(
         return await run_memsciqa_eval(**kwargs)
 
     if dataset == "longmemeval":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
+        kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id}
         if search_limit is not None:
             kwargs["search_limit"] = search_limit
         if context_char_budget is not None:
@@ -99,8 +99,8 @@ def main():
     parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo")
     parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True)
     parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通")
-    parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 group_id 的图数据")
-    parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json")
+    parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 end_user_id 的图数据")
+    parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json")
     parser.add_argument("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名")
     parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）")
     parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）")
@@ -117,7 +117,7 @@ def main():
         args.dataset,
         args.sample_size,
         args.reset_group,
-        args.group_id,
+        args.end_user_id,
         args.judge_model,
         args.search_limit,
         args.context_char_budget,

api/app/core/memory/llm_tools/chunker_client.py

@@ -187,11 +187,11 @@ class ChunkerClient:
     async def generate_chunks(self, dialogue: DialogData):
         """
         Generate chunks following 1 Message = 1 Chunk strategy.
-        
+
         Each message creates one chunk, directly inheriting role information.
         If a message is too long, it will be split into multiple sub-chunks,
         each maintaining the same speaker.
-        
+
         Raises:
             ValueError: If dialogue has no messages or chunking fails
         """
@@ -201,9 +201,9 @@ class ChunkerClient:
                 f"Dialogue {dialogue.ref_id} has no messages. "
                 f"Cannot generate chunks from empty dialogue."
             )
-        
+
         dialogue.chunks = []
-        
+
         # 按消息分块：每个消息创建一个或多个 chunk，直接继承角色
         for msg_idx, msg in enumerate(dialogue.context.msgs):
             # Validate message has required attributes
@@ -212,13 +212,13 @@ class ChunkerClient:
                     f"Message {msg_idx} in dialogue {dialogue.ref_id} "
                     f"missing 'role' or 'msg' attribute"
                 )
-            
+
             msg_content = msg.msg.strip()
-            
+
             # Skip empty messages
             if not msg_content:
                 continue
-            
+
             # 如果消息太长，可以进一步分块
             if len(msg_content) > self.chunk_size:
                 # 对单个消息的内容进行分块
@@ -228,14 +228,14 @@ class ChunkerClient:
                     raise ValueError(
                         f"Failed to chunk long message {msg_idx} in dialogue {dialogue.ref_id}: {e}"
                     )
-                
+
                 for idx, sub_chunk in enumerate(sub_chunks):
                     sub_chunk_text = sub_chunk.text if hasattr(sub_chunk, 'text') else str(sub_chunk)
                     sub_chunk_text = sub_chunk_text.strip()
-                    
+
                     if len(sub_chunk_text) < (self.min_characters_per_chunk or 50):
                         continue
-                    
+
                     chunk = Chunk(
                         content=f"{msg.role}: {sub_chunk_text}",
                         speaker=msg.role,  # 直接继承角色
@@ -260,7 +260,7 @@ class ChunkerClient:
                     },
                 )
                 dialogue.chunks.append(chunk)
-        
+
         # Validate we generated at least one chunk
         if not dialogue.chunks:
             raise ValueError(
@@ -268,7 +268,7 @@ class ChunkerClient:
                 f"All messages were either empty or too short. "
                 f"Messages count: {len(dialogue.context.msgs)}"
             )
-        
+
         return dialogue
 
     def evaluate_chunking(self, dialogue: DialogData) -> dict:

api/app/core/memory/models/config_models.py

@@ -72,7 +72,7 @@ class TemporalSearchParams(BaseModel):
     """Parameters for temporal search queries in the knowledge graph.
 
     Attributes:
-        group_id: Group ID to filter search results (default: 'test')
+        end_user_id: Group ID to filter search results (default: 'test')
         apply_id: Application ID to filter search results
         user_id: User ID to filter search results
         start_date: Start date for temporal filtering (format: 'YYYY-MM-DD')
@@ -81,7 +81,7 @@ class TemporalSearchParams(BaseModel):
         invalid_date: Date when memory should be invalid (format: 'YYYY-MM-DD')
         limit: Maximum number of results to return (default: 3)
     """
-    group_id: Optional[str] = Field("test", description="The group ID to filter the search.")
+    end_user_id: Optional[str] = Field("test", description="The group ID to filter the search.")
     apply_id: Optional[str] = Field(None, description="The apply ID to filter the search.")
     user_id: Optional[str] = Field(None, description="The user ID to filter the search.")
     start_date: Optional[str] = Field(None, description="The start date for the search.")

api/app/core/memory/models/graph_models.py

@@ -103,9 +103,7 @@ class Edge(BaseModel):
         id: Unique identifier for the edge
         source: ID of the source node
         target: ID of the target node
-        group_id: Group ID for multi-tenancy
-        user_id: User ID for user-specific data
-        apply_id: Application ID for application-specific data
+        end_user_id: End user ID for multi-tenancy
         run_id: Unique identifier for the pipeline run that created this edge
         created_at: Timestamp when the edge was created (system perspective)
         expired_at: Optional timestamp when the edge expires (system perspective)
@@ -113,9 +111,7 @@ class Edge(BaseModel):
     id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the edge.")
     source: str = Field(..., description="The ID of the source node.")
     target: str = Field(..., description="The ID of the target node.")
-    group_id: str = Field(..., description="The group ID of the edge.")
-    user_id: str = Field(..., description="The user ID of the edge.")
-    apply_id: str = Field(..., description="The apply ID of the edge.")
+    end_user_id: str = Field(..., description="The end user ID of the edge.")
     run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.")
     created_at: datetime = Field(..., description="The valid time of the edge from system perspective.")
     expired_at: Optional[datetime] = Field(None, description="The expired time of the edge from system perspective.")
@@ -185,18 +181,14 @@ class Node(BaseModel):
     Attributes:
         id: Unique identifier for the node
         name: Name of the node
-        group_id: Group ID for multi-tenancy
-        user_id: User ID for user-specific data
-        apply_id: Application ID for application-specific data
+        end_user_id: End user ID for multi-tenancy
         run_id: Unique identifier for the pipeline run that created this node
         created_at: Timestamp when the node was created (system perspective)
         expired_at: Optional timestamp when the node expires (system perspective)
     """
     id: str = Field(..., description="The unique identifier for the node.")
     name: str = Field(..., description="The name of the node.")
-    group_id: str = Field(..., description="The group ID of the node.")
-    user_id: str = Field(..., description="The user ID of the edge.")
-    apply_id: str = Field(..., description="The apply ID of the edge.")
+    end_user_id: str = Field(..., description="The end user ID of the node.")
     run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.")
     created_at: datetime = Field(..., description="The valid time of the node from system perspective.")
     expired_at: Optional[datetime] = Field(None, description="The expired time of the node from system perspective.")

api/app/core/memory/models/message_models.py

@@ -55,7 +55,7 @@ class Statement(BaseModel):
     Attributes:
         id: Unique identifier for the statement
         chunk_id: ID of the parent chunk this statement belongs to
-        group_id: Optional group ID for multi-tenancy
+        end_user_id: Optional group ID for multi-tenancy
         statement: The actual statement text content
         speaker: Optional speaker identifier ('用户' for user, 'AI' for AI responses)
         statement_embedding: Optional embedding vector for the statement
@@ -73,7 +73,7 @@ class Statement(BaseModel):
     """
     id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the statement.")
     chunk_id: str = Field(..., description="ID of the parent chunk this statement belongs to.")
-    group_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.")
+    end_user_id: Optional[str] = Field(None, description="ID of the group this statement belongs to.")
     statement: str = Field(..., description="The text content of the statement.")
     speaker: Optional[str] = Field(None, description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses")
     statement_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the statement.")
@@ -159,9 +159,7 @@ class DialogData(BaseModel):
         context: Full conversation context
         dialog_embedding: Optional embedding vector for the entire dialog
         ref_id: Reference ID linking to external dialog system
-        group_id: Group ID for multi-tenancy
-        user_id: User ID for user-specific data
-        apply_id: Application ID for application-specific data
+        end_user_id: End user ID for multi-tenancy
         created_at: Timestamp when the dialog was created
         expired_at: Timestamp when the dialog expires (default: far future)
         metadata: Additional metadata as key-value pairs
@@ -175,9 +173,7 @@ class DialogData(BaseModel):
     context: ConversationContext = Field(..., description="The full conversation context as a single string.")
     dialog_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the dialog.")
     ref_id: str = Field(..., description="Refer to external dialog id. This is used to link to the original dialog.")
-    group_id: str = Field(default=..., description="Group ID of dialogue data")
-    user_id: str = Field(..., description="USER ID of dialogue data")
-    apply_id: str = Field(..., description="APPLY ID of dialogue data")
+    end_user_id: str = Field(default=..., description="End user ID of dialogue data")
     run_id: str = Field(default_factory=lambda: uuid4().hex, description="Unique identifier for this pipeline run.")
     created_at: datetime = Field(default_factory=datetime.now, description="The timestamp when the dialog was created.")
     expired_at: datetime = Field(default_factory=lambda: datetime(9999, 12, 31), description="The timestamp when the dialog expires.")
@@ -250,11 +246,11 @@ class DialogData(BaseModel):
         return []
 
     def assign_group_id_to_statements(self) -> None:
-        """Assign this dialog's group_id to all statements in all chunks.
+        """Assign this dialog's end_user_id to all statements in all chunks.
 
-        This method updates statements that don't have a group_id set.
+        This method updates statements that don't have a end_user_id set.
         """
         for chunk in self.chunks:
             for statement in chunk.statements:
-                if statement.group_id is None:
-                    statement.group_id = self.group_id
+                if statement.end_user_id is None:
+                    statement.end_user_id = self.end_user_id

api/app/core/memory/src/search.py

@@ -6,6 +6,7 @@ import os
 import time
 from datetime import datetime
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from uuid import UUID
 
 if TYPE_CHECKING:
     from app.schemas.memory_config_schema import MemoryConfig
@@ -396,13 +397,13 @@ def rerank_with_activation(
     return reranked
 
 
-def log_search_query(query_text: str, search_type: str, group_id: str | None, limit: int, include: List[str], log_file: str = None):
+def log_search_query(query_text: str, search_type: str, end_user_id: str | None, limit: int, include: List[str], log_file: str = None):
     """Log search query information using the logger.
     
     Args:
         query_text: The search query text
         search_type: Type of search (keyword, embedding, hybrid)
-        group_id: Group identifier for filtering
+        end_user_id: Group identifier for filtering
         limit: Maximum number of results
         include: List of result types to include
         log_file: Deprecated parameter, kept for backward compatibility
@@ -413,7 +414,7 @@ def log_search_query(query_text: str, search_type: str, group_id: str | None, li
     # Log using the standard logger
     logger.info(
         f"Search query: query='{cleaned_query}', type={search_type}, "
-        f"group_id={group_id}, limit={limit}, include={include}"
+        f"end_user_id={end_user_id}, limit={limit}, include={include}"
     )
 
 
@@ -672,7 +673,7 @@ def apply_reranker_placeholder(
 async def run_hybrid_search(
     query_text: str,
     search_type: str,
-    group_id: str | None,
+    end_user_id: str | None,
     limit: int,
     include: List[str],
     output_path: str | None,
@@ -715,7 +716,7 @@ async def run_hybrid_search(
         }
     
     # Log the search query
-    log_search_query(query_text, search_type, group_id, limit, include)
+    log_search_query(query_text, search_type, end_user_id, limit, include)
 
     connector = Neo4jConnector()
     results = {}
@@ -732,7 +733,7 @@ async def run_hybrid_search(
                 search_graph(
                     connector=connector,
                     q=query_text,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=limit,
                     include=include
                 )
@@ -769,7 +770,7 @@ async def run_hybrid_search(
                     connector=connector,
                     embedder_client=embedder,
                     query_text=query_text,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=limit,
                     include=include,
                 )
@@ -916,9 +917,7 @@ async def run_hybrid_search(
 
 
 async def search_by_temporal(
-    group_id: Optional[str] = "test",
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = "test",
     start_date: Optional[str] = None,
     end_date: Optional[str] = None,
     valid_date: Optional[str] = None,
@@ -929,7 +928,7 @@ async def search_by_temporal(
     Temporal search across Statements.
 
     - Matches statements created between start_date and end_date
-    - Optionally filters by group_id
+    - Optionally filters by end_user_id
     - Returns up to 'limit' statements
     """
     connector = Neo4jConnector()
@@ -939,9 +938,7 @@ async def search_by_temporal(
         end_date = normalize_date_safe(end_date)
 
     params = TemporalSearchParams.model_validate({
-        "group_id": group_id,
-        "apply_id": apply_id,
-        "user_id": user_id,
+        "end_user_id": end_user_id,
         "start_date": start_date,
         "end_date": end_date,
         "valid_date": valid_date,
@@ -950,9 +947,7 @@ async def search_by_temporal(
     })
     statements = await search_graph_by_temporal(
         connector=connector,
-        group_id=params.group_id,
-        apply_id=params.apply_id,
-        user_id=params.user_id,
+        end_user_id=params.end_user_id,
         start_date=params.start_date,
         end_date=params.end_date,
         valid_date=params.valid_date,
@@ -964,9 +959,7 @@ async def search_by_temporal(
 
 async def search_by_keyword_temporal(
     query_text: str,
-    group_id: Optional[str] = "test",
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = "test",
     start_date: Optional[str] = None,
     end_date: Optional[str] = None,
     valid_date: Optional[str] = None,
@@ -987,9 +980,7 @@ async def search_by_keyword_temporal(
         invalid_date = normalize_date_safe(invalid_date)
 
     params = TemporalSearchParams.model_validate({
-        "group_id": group_id,
-        "apply_id": apply_id,
-        "user_id": user_id,
+        "end_user_id": end_user_id,
         "start_date": start_date,
         "end_date": end_date,
         "valid_date": valid_date,
@@ -999,9 +990,7 @@ async def search_by_keyword_temporal(
     statements = await search_graph_by_keyword_temporal(
         connector=connector,
         query_text=query_text,
-        group_id=params.group_id,
-        apply_id=params.apply_id,
-        user_id=params.user_id,
+        end_user_id=params.end_user_id,
         start_date=params.start_date,
         end_date=params.end_date,
         valid_date=params.valid_date,
@@ -1013,7 +1002,7 @@ async def search_by_keyword_temporal(
 
 async def search_chunk_by_chunk_id(
     chunk_id: str,
-    group_id: Optional[str] = "test",
+    end_user_id: Optional[str] = "test",
     limit: int = 1,
 ):
     """
@@ -1023,7 +1012,7 @@ async def search_chunk_by_chunk_id(
     chunks = await search_graph_by_chunk_id(
         connector=connector,
         chunk_id=chunk_id,
-        group_id=group_id,
+        end_user_id=end_user_id,
         limit=limit
     )
     return {"chunks": chunks}

api/app/core/memory/storage_services/extraction_engine/data_preprocessing/data_preprocessor.py

@@ -555,8 +555,8 @@ class DataPreprocessor:
                 dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}')))
 
 
-                # 获取group_id，如果不存在则生成默认值
-                group_id = item.get('group_id', f'group_default_{i}')
+                # 获取end_user_id，如果不存在则生成默认值
+                end_user_id = item.get('end_user_id', f'group_default_{i}')
                 user_id = item.get('user_id', f'user_default_{i}')
                 apply_id = item.get('apply_id', f'apply_default_{i}')
 
@@ -574,7 +574,7 @@ class DataPreprocessor:
                 dialog_data = DialogData(
                     context=context,
                     ref_id=dialog_id,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     user_id=user_id,
                     apply_id=apply_id,
                     metadata=metadata
@@ -644,7 +644,7 @@ class DataPreprocessor:
                 
                 context = ConversationContext(msgs=messages)
                 dialog_id = item.get('dialog_id', item.get('ref_id', item.get('id', f'dialog_{i}')))
-                group_id = item.get('group_id', f'group_default_{i}')
+                end_user_id = item.get('end_user_id', f'group_default_{i}')
                 user_id = item.get('user_id', f'user_default_{i}')
                 apply_id = item.get('apply_id', f'apply_default_{i}')
                 
@@ -657,7 +657,7 @@ class DataPreprocessor:
                 dialog_data = DialogData(
                     context=context,
                     ref_id=dialog_id,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     user_id=user_id,
                     apply_id=apply_id,
                     metadata=metadata

api/app/core/memory/storage_services/extraction_engine/deduplication/deduped_and_disamb.py

@@ -199,7 +199,7 @@ def accurate_match(
     entity_nodes: List[ExtractedEntityNode]
 ) -> Tuple[List[ExtractedEntityNode], Dict[str, str], Dict[str, Dict]]:
     """
-    精确匹配：按 (group_id, name, entity_type) 合并实体并建立重定向与合并记录。
+    精确匹配：按 (end_user_id, name, entity_type) 合并实体并建立重定向与合并记录。
     返回: (deduped_entities, id_redirect, exact_merge_map)
     """
     exact_merge_map: Dict[str, Dict] = {}
@@ -210,8 +210,8 @@ def accurate_match(
     for ent in entity_nodes:
         name_norm = (getattr(ent, "name", "") or "").strip()
         type_norm = (getattr(ent, "entity_type", "") or "").strip()
-        key = f"{getattr(ent, 'group_id', None)}|{name_norm}|{type_norm}"
-        # 为避免跨业务组误并，明确以 group_id 为范围边界
+        key = f"{getattr(ent, 'end_user_id', None)}|{name_norm}|{type_norm}"
+        # 为避免跨业务组误并，明确以 end_user_id 为范围边界
         if key not in canonical_map:
             canonical_map[key] = ent
             id_redirect[ent.id] = ent.id
@@ -223,11 +223,11 @@ def accurate_match(
         id_redirect[ent.id] = canonical.id
         # 记录精确匹配的合并项（使用规范化键，避免外层变量误用）
         try:
-            k = f"{canonical.group_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}"
+            k = f"{canonical.end_user_id}|{(canonical.name or '').strip()}|{(canonical.entity_type or '').strip()}"
             if k not in exact_merge_map:
                 exact_merge_map[k] = {
                     "canonical_id": canonical.id,
-                    "group_id": canonical.group_id,
+                    "end_user_id": canonical.end_user_id,
                     "name": canonical.name,
                     "entity_type": canonical.entity_type,
                     "merged_ids": set(),
@@ -596,7 +596,7 @@ def fuzzy_match(
             b = deduped_entities[j]
             
             # 跳过不同业务组的实体
-            if getattr(a, "group_id", None) != getattr(b, "group_id", None):
+            if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None):
                 j += 1
                 continue
             
@@ -671,7 +671,7 @@ def fuzzy_match(
                     merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]"
                     merge_reason = "[别名匹配]" if alias_match_merge else "[模糊]"
                     fuzzy_merge_records.append(
-                        f"{merge_reason} 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type}) | "
+                        f"{merge_reason} 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type}) | "
                         f"s_name={s_name:.3f}, s_type={s_type:.3f}, overall={overall:.3f}, exact_alias={has_exact_match}"
                     )
                 except Exception:
@@ -779,7 +779,7 @@ async def LLM_decision(  # 决策中包含去重和消歧的功能
             # 记录 LLM 融合日志
             try:
                 llm_records.append(
-                    f"[LLM融合] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})"
+                    f"[LLM融合] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})"
                 )
                 # 详细的“同类名称相似”记录改由 LLM 去重模块统一生成以携带 conf/reason
             except Exception:
@@ -847,7 +847,7 @@ async def LLM_disamb_decision(
                         id_redirect[k] = a.id
                 try:
                     disamb_records.append(
-                        f"[DISAMB合并应用] 规范实体 {a.id} ({a.group_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.group_id}|{b.name}|{b.entity_type})"
+                        f"[DISAMB合并应用] 规范实体 {a.id} ({a.end_user_id}|{a.name}|{a.entity_type}) <- 合并实体 {b.id} ({b.end_user_id}|{b.name}|{b.entity_type})"
                     )
                 except Exception:
                     pass

api/app/core/memory/storage_services/extraction_engine/deduplication/entity_dedup_llm.py

@@ -174,7 +174,7 @@ async def _judge_pair(
         pass
 # 3. 构建LLM判断的“上下文信息”（规则层计算的所有特征）  判断上下文特征有助于实体消歧首先判断的类型关系
     ctx = {
-        "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None),
+        "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None),
         "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)),
         "type_similarity": _type_similarity(getattr(a, "entity_type", None), getattr(b, "entity_type", None)),
         "name_text_sim": name_text_sim,
@@ -235,7 +235,7 @@ async def _judge_pair_disamb(
     except Exception:
         pass
     ctx = {
-        "same_group": getattr(a, "group_id", None) == getattr(b, "group_id", None),
+        "same_group": getattr(a, "end_user_id", None) == getattr(b, "end_user_id", None),
         "type_ok": _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)),
         "name_text_sim": name_text_sim,
         "name_embed_sim": name_embed_sim,
@@ -317,8 +317,8 @@ async def llm_dedup_entities(  # 保留对偶判断作为子流程，是为了
         a = entity_nodes[i]
         for j in range(i + 1, len(entity_nodes)):
             b = entity_nodes[j]
-            # 规则1：必须属于同一组（group_id相同，不同组的实体不重复）
-            if getattr(a, "group_id", None) != getattr(b, "group_id", None):
+            # 规则1：必须属于同一组（end_user_id相同，不同组的实体不重复）
+            if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None):
                 continue
             # 规则2：类型必须兼容（调用_simple_type_ok判断）
             if not _simple_type_ok(getattr(a, "entity_type", None), getattr(b, "entity_type", None)):
@@ -474,7 +474,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重
     - max_rounds: upper bound for iterative passes (default 3)
     - auto_merge_threshold: decision confidence for auto-merge when no co-occurrence (default 0.90)
     - co_ctx_threshold: lower threshold when co-occurrence is detected (default 0.83)
-    - shuffle_each_round: whether to shuffle entities within group_id each round to vary block composition
+    - shuffle_each_round: whether to shuffle entities within end_user_id each round to vary block composition
 
     Returns:
     - global_redirect: dict losing_id -> canonical_id accumulated across rounds
@@ -509,7 +509,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重
 
     def _partition_blocks(nodes: List[ExtractedEntityNode]) -> List[List[ExtractedEntityNode]]:
         """
-        按 group_id 分块，避免跨组实体在同一块，减少无效候选对
+        按 end_user_id 分块，避免跨组实体在同一块，减少无效候选对
 
         Args:
             nodes: 实体节点列表
@@ -519,7 +519,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重
         """
         groups: Dict[str, List[ExtractedEntityNode]] = {}
         for e in nodes:
-            gid = getattr(e, "group_id", None)
+            gid = getattr(e, "end_user_id", None)
             groups.setdefault(str(gid), []).append(e)
         blocks: List[List[ExtractedEntityNode]] = []
         for gid, arr in groups.items():
@@ -559,7 +559,7 @@ async def llm_dedup_entities_iterative_blocks( # 迭代分块并发 LLM 去重
         # Collapse nodes to canonical reps before each round to avoid redundant comparisons
         # 步骤1：折叠实体（合并已确定的重复实体，减少后续计算量）
         current_nodes = _collapse_nodes(current_nodes)
-        # 步骤2：分块（按group_id分块，避免跨组处理）
+        # 步骤2：分块（按end_user_id分块，避免跨组处理）
         blocks = _partition_blocks(current_nodes)
         if not blocks: # 无块可处理（实体已全部折叠），退出循环
             break
@@ -645,7 +645,7 @@ async def llm_disambiguate_pairs_iterative(
             a = entity_nodes[i]
             b = entity_nodes[j]
             # 必须同组
-            if getattr(a, "group_id", None) != getattr(b, "group_id", None):
+            if getattr(a, "end_user_id", None) != getattr(b, "end_user_id", None):
                 continue
             ta = getattr(a, "entity_type", None)
             tb = getattr(b, "entity_type", None)

api/app/core/memory/storage_services/extraction_engine/deduplication/second_layer_dedup.py

@@ -61,7 +61,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode:
     return ExtractedEntityNode(
         id=row.get("id"),
         name=row.get("name") or "",
-        group_id=row.get("group_id") or "",
+        end_user_id=row.get("end_user_id") or "",
         user_id=row.get("user_id") or "",
         apply_id=row.get("apply_id") or "",
         created_at=_parse_dt(row.get("created_at")),
@@ -79,7 +79,7 @@ def _row_to_entity(row: Dict[str, Any]) -> ExtractedEntityNode:
 
 async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑，与 Neo4j 中同组实体联合去重
     connector: Neo4jConnector,
-    group_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重
+    end_user_id: str, # 用于定位neo4j中同一组的实体，确保只在同组内去重
     entity_nodes: List[ExtractedEntityNode], # 输入的实体节点列表，包含待去重的实体
     statement_entity_edges: List[StatementEntityEdge], # 输入的语句实体边列表，用于处理实体之间的关系
     entity_entity_edges: List[EntityEntityEdge], # 输入的实体实体边列表，用于处理实体之间的关系
@@ -88,7 +88,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑
 ) -> Tuple[List[ExtractedEntityNode], List[StatementEntityEdge], List[EntityEntityEdge]]:
     """
     第二层去重消歧：
-    - 以第一层结果为索引，检索相同 group_id 下的 DB 候选实体
+    - 以第一层结果为索引，检索相同 end_user_id 下的 DB 候选实体
     - 将 DB 候选与当前实体集合联合，按既有精确/模糊/LLM 决策进行融合
     - 返回融合后的实体与重定向后的边（边已指向规范 ID，优先 DB ID）
     """
@@ -102,7 +102,7 @@ async def second_layer_dedup_and_merge_with_neo4j( # 二层去重的核心逻辑
 
     ]
     candidates_map = await get_dedup_candidates_for_entities( # 从 Neo4j 中查询候选实体，并将结果赋值给candidates_map（等待异步操作完成）。
-        connector=connector, group_id=group_id,
+        connector=connector, end_user_id=end_user_id,
         entities=incoming_rows,  # 传入参数：第一层实体的核心信息（作为查询索引）
         use_contains_fallback=True # 传入参数：启用 “包含关系” 作为匹配失败的降级策略（若精确匹配无结果，用包含关系召回候选），与src\database\cypher_queries.py的307产生联动
     )

api/app/core/memory/storage_services/extraction_engine/deduplication/two_stage_dedup.py

@@ -57,11 +57,11 @@ async def dedup_layers_and_merge_and_return(
     if pipeline_config is None:
         raise ValueError("pipeline_config is required for dedup_layers_and_merge_and_return")
 
-    # 先探测 group_id，决定报告写入策略
-    group_id: Optional[str] = None
+    # 先探测 end_user_id，决定报告写入策略
+    end_user_id: Optional[str] = None
     for dd in dialog_data_list:
-        group_id = getattr(dd, "group_id", None)
-        if group_id:
+        end_user_id = getattr(dd, "end_user_id", None)
+        if end_user_id:
             break
 
     # 第一层去重消歧
@@ -82,11 +82,11 @@ async def dedup_layers_and_merge_and_return(
 
     # 第二层去重消歧：与 Neo4j 中同组实体联合融合
     try:
-        if group_id:
+        if end_user_id:
             if connector:
                 fused_entity_nodes, fused_statement_entity_edges, fused_entity_entity_edges = await second_layer_dedup_and_merge_with_neo4j(
                     connector=connector,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     entity_nodes=dedup_entity_nodes,
                     statement_entity_edges=dedup_statement_entity_edges,
                     entity_entity_edges=dedup_entity_entity_edges,
@@ -96,7 +96,7 @@ async def dedup_layers_and_merge_and_return(
             else:
                 print("Skip second-layer dedup: missing connector")
         else:
-            print("Skip second-layer dedup: missing group_id")
+            print("Skip second-layer dedup: missing end_user_id")
     except Exception as e:
         print(f"Second-layer dedup failed: {e}")
 

api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py

@@ -287,7 +287,7 @@ class ExtractionOrchestrator:
         for d_idx, dialog in enumerate(dialog_data_list):
             dialogue_content = dialog.content if self.config.statement_extraction.include_dialogue_context else None
             for c_idx, chunk in enumerate(dialog.chunks):
-                all_chunks.append((chunk, dialog.group_id, dialogue_content))
+                all_chunks.append((chunk, dialog.end_user_id, dialogue_content))
                 chunk_metadata.append((d_idx, c_idx))
 
         logger.info(f"收集到 {len(all_chunks)} 个分块，开始全局并行提取")
@@ -299,9 +299,9 @@ class ExtractionOrchestrator:
         # 全局并行处理所有分块
         async def extract_for_chunk(chunk_data, chunk_index):
             nonlocal completed_chunks
-            chunk, group_id, dialogue_content = chunk_data
+            chunk, end_user_id, dialogue_content = chunk_data
             try:
-                statements = await self.statement_extractor._extract_statements(chunk, group_id, dialogue_content)
+                statements = await self.statement_extractor._extract_statements(chunk, end_user_id, dialogue_content)
                 
                 #  流式输出：每提取完一个分块的陈述句，立即发送进度
                 # 注意：只在试运行模式下发送陈述句详情，正式模式不发送
@@ -569,32 +569,32 @@ class ExtractionOrchestrator:
         if dialog_data_list and hasattr(dialog_data_list[0], 'config_id'):
             config_id = dialog_data_list[0].config_id
         
-        # 加载DataConfig
-        data_config = None
+        # 加载MemoryConfig
+        memory_config = None
         if config_id:
             try:
                 from app.db import SessionLocal
-                from app.repositories.data_config_repository import DataConfigRepository
+                from app.repositories.memory_config_repository import MemoryConfigRepository
                 
                 db = SessionLocal()
                 try:
-                    data_config = DataConfigRepository.get_by_id(db, config_id)
+                    memory_config = MemoryConfigRepository.get_by_id(db, config_id)
                 finally:
                     db.close()
                     
-                if data_config and not data_config.emotion_enabled:
+                if memory_config and not memory_config.emotion_enabled:
                     logger.info("情绪提取已在配置中禁用，跳过情绪提取")
                     return [{} for _ in dialog_data_list]
                     
             except Exception as e:
-                logger.warning(f"加载DataConfig失败: {e}，将跳过情绪提取")
+                logger.warning(f"加载MemoryConfig失败: {e}，将跳过情绪提取")
                 return [{} for _ in dialog_data_list]
         else:
             logger.info("未找到config_id，跳过情绪提取")
             return [{} for _ in dialog_data_list]
         
         # 如果配置未启用情绪提取，直接返回空映射
-        if not data_config or not data_config.emotion_enabled:
+        if not memory_config or not memory_config.emotion_enabled:
             logger.info("情绪提取未启用，跳过")
             return [{} for _ in dialog_data_list]
 
@@ -608,7 +608,7 @@ class ExtractionOrchestrator:
                     total_statements += 1
                     # 只处理用户的陈述句 (role 为 "user")
                     if hasattr(statement, 'speaker') and statement.speaker == "user":
-                        all_statements.append((statement, data_config))
+                        all_statements.append((statement, memory_config))
                         statement_metadata.append((d_idx, statement.id))
                         filtered_statements += 1
 
@@ -617,7 +617,7 @@ class ExtractionOrchestrator:
         # 初始化情绪提取服务
         from app.services.emotion_extraction_service import EmotionExtractionService
         emotion_service = EmotionExtractionService(
-            llm_id=data_config.emotion_model_id if data_config.emotion_model_id else None
+            llm_id=memory_config.emotion_model_id if memory_config.emotion_model_id else None
         )
 
         # 全局并行处理所有陈述句
@@ -992,9 +992,7 @@ class ExtractionOrchestrator:
                 id=dialog_data.id,
                 name=f"Dialog_{dialog_data.id}",  # 添加必需的 name 字段
                 ref_id=dialog_data.ref_id,
-                group_id=dialog_data.group_id,
-                user_id=dialog_data.user_id,
-                apply_id=dialog_data.apply_id,
+                end_user_id=dialog_data.end_user_id,
                 run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                 content=dialog_data.context.content if dialog_data.context else "",
                 dialog_embedding=dialog_data.dialog_embedding if hasattr(dialog_data, 'dialog_embedding') else None,
@@ -1012,9 +1010,7 @@ class ExtractionOrchestrator:
                     id=chunk.id,
                     name=f"Chunk_{chunk.id}",  # 添加必需的 name 字段
                     dialog_id=dialog_data.id,
-                    group_id=dialog_data.group_id,
-                    user_id=dialog_data.user_id,
-                    apply_id=dialog_data.apply_id,
+                    end_user_id=dialog_data.end_user_id,
                     run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                     content=chunk.content,
                     chunk_embedding=chunk.chunk_embedding,
@@ -1035,9 +1031,7 @@ class ExtractionOrchestrator:
                         stmt_type=getattr(statement, 'stmt_type', 'general'),  # 添加必需的 stmt_type 字段
                         temporal_info=getattr(statement, 'temporal_info', TemporalInfo.ATEMPORAL),  # 添加必需的 temporal_info 字段
                         connect_strength=statement.connect_strength if statement.connect_strength is not None else 'Strong',  # 添加必需的 connect_strength 字段
-                        group_id=dialog_data.group_id,
-                        user_id=dialog_data.user_id,
-                        apply_id=dialog_data.apply_id,
+                        end_user_id=dialog_data.end_user_id,
                         run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                         statement=statement.statement,
                         speaker=getattr(statement, 'speaker', None),  # 添加 speaker 字段
@@ -1060,9 +1054,7 @@ class ExtractionOrchestrator:
                     statement_chunk_edge = StatementChunkEdge(
                         source=statement.id,
                         target=chunk.id,
-                        group_id=dialog_data.group_id,
-                        user_id=dialog_data.user_id,
-                        apply_id=dialog_data.apply_id,
+                        end_user_id=dialog_data.end_user_id,
                         run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                         created_at=dialog_data.created_at,
                     )
@@ -1095,9 +1087,7 @@ class ExtractionOrchestrator:
                                     aliases=getattr(entity, 'aliases', []) or [],  # 传递从三元组提取阶段获取的aliases
                                     name_embedding=getattr(entity, 'name_embedding', None),
                                     is_explicit_memory=getattr(entity, 'is_explicit_memory', False),  # 新增：传递语义记忆标记
-                                    group_id=dialog_data.group_id,
-                                    user_id=dialog_data.user_id,
-                                    apply_id=dialog_data.apply_id,
+                                    end_user_id=dialog_data.end_user_id,
                                     run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                                     created_at=dialog_data.created_at,
                                     expired_at=dialog_data.expired_at,
@@ -1112,9 +1102,7 @@ class ExtractionOrchestrator:
                                 source=statement.id,
                                 target=entity.id,
                                 connect_strength=entity_connect_strength if entity_connect_strength is not None else 'Strong',
-                                group_id=dialog_data.group_id,
-                                user_id=dialog_data.user_id,
-                                apply_id=dialog_data.apply_id,
+                                end_user_id=dialog_data.end_user_id,
                                 run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                                 created_at=dialog_data.created_at,
                             )
@@ -1134,9 +1122,7 @@ class ExtractionOrchestrator:
                                     relation_type=triplet.predicate,
                                     statement=statement.statement,
                                     source_statement_id=statement.id,
-                                    group_id=dialog_data.group_id,
-                                    user_id=dialog_data.user_id,
-                                    apply_id=dialog_data.apply_id,
+                                    end_user_id=dialog_data.end_user_id,
                                     run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                                     created_at=dialog_data.created_at,
                                     expired_at=dialog_data.expired_at,
@@ -1763,14 +1749,14 @@ class ExtractionOrchestrator:
 
 async def get_chunked_dialogs(
     chunker_strategy: str = "RecursiveChunker",
-    group_id: str = "group_1",
+    end_user_id: str = "group_1",
     indices: Optional[List[int]] = None,
 ) -> List[DialogData]:
     """从测试数据生成分块对话
     
     Args:
         chunker_strategy: 分块策略（默认: RecursiveChunker）
-        group_id: 组ID
+        end_user_id: 组ID
         indices: 要处理的数据索引列表（可选）
         
     Returns:
@@ -1834,7 +1820,7 @@ async def get_chunked_dialogs(
         dialog_data = DialogData(
             context=conversation_context,
             ref_id=data['id'],
-            group_id=group_id,
+            end_user_id=end_user_id,
             metadata=dialog_metadata,
         )
         
@@ -1936,7 +1922,7 @@ async def get_chunked_dialogs_from_preprocessed(
 
 async def get_chunked_dialogs_with_preprocessing(
     chunker_strategy: str = "RecursiveChunker",
-    group_id: str = "default",
+    end_user_id: str = "default",
     user_id: str = "default",
     apply_id: str = "default",
     indices: Optional[List[int]] = None,
@@ -1948,7 +1934,7 @@ async def get_chunked_dialogs_with_preprocessing(
     
     Args:
         chunker_strategy: 分块策略
-        group_id: 组ID
+        end_user_id: 组ID
         user_id: 用户ID
         apply_id: 应用ID
         indices: 要处理的数据索引列表
@@ -1976,11 +1962,9 @@ async def get_chunked_dialogs_with_preprocessing(
         indices=indices,
     )
             
-    # 设置 group_id, user_id, apply_id
+    # 设置 end_user_id
     for dd in preprocessed_data:
-        dd.group_id = group_id
-        dd.user_id = user_id
-        dd.apply_id = apply_id
+        dd.end_user_id = end_user_id
         
     # 步骤2: 语义剪枝
     try:

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/memory_summary.py

@@ -193,9 +193,9 @@ async def _process_chunk_summary(
         node = MemorySummaryNode(
             id=uuid4().hex,
             name=title if title else f"MemorySummaryChunk_{chunk.id}",
-            group_id=dialog.group_id,
-            user_id=dialog.user_id,
-            apply_id=dialog.apply_id,
+            end_user_id=dialog.end_user_id,
+            user_id=dialog.end_user_id,
+            apply_id=dialog.end_user_id,
             run_id=dialog.run_id,  # 使用 dialog 的 run_id
             created_at=datetime.now(),
             expired_at=datetime(9999, 12, 31),

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/statement_extraction.py

@@ -82,12 +82,12 @@ class StatementExtractor:
         logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty")
         return None
 
-    async def _extract_statements(self, chunk, group_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]:
+    async def _extract_statements(self, chunk, end_user_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]:
         """Process a single chunk and return extracted statements
 
         Args:
             chunk: Chunk object to process
-            group_id: Group ID to assign to all statements in this chunk
+            end_user_id: Group ID to assign to all statements in this chunk
             dialogue_content: Full dialogue content to provide as context
 
         Returns:
@@ -158,7 +158,7 @@ class StatementExtractor:
                     temporal_info=temporal_type,
                     relevence_info=relevence_info,
                     chunk_id=chunk.id,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     speaker=chunk_speaker,
                 )
                 
@@ -184,10 +184,10 @@ class StatementExtractor:
 
         logger.info(f"Processing {len(chunks_to_process)} chunks for statement extraction")
 
-        # Process all chunks concurrently, passing the group_id and dialogue content from dialog_data
+        # Process all chunks concurrently, passing the end_user_id and dialogue content from dialog_data
         dialogue_content = dialog_data.content if self.config.include_dialogue_context else None
         results = await asyncio.gather(
-            *[self._extract_statements(chunk, dialog_data.group_id, dialogue_content) for chunk in chunks_to_process],
+            *[self._extract_statements(chunk, dialog_data.end_user_id, dialogue_content) for chunk in chunks_to_process],
             return_exceptions=True
         )
 
@@ -225,7 +225,7 @@ class StatementExtractor:
             for i, statement in enumerate(statements, 1):
                 f.write(f"Statement {i}:\n")
                 f.write(f"Id: {statement.id}\n")
-                f.write(f"Group Id: {statement.group_id}\n")
+                f.write(f"Group Id: {statement.end_user_id}\n")
                 f.write(f"Content: {statement.statement}\n")
                 f.write(f"Type: {statement.stmt_type.value}\n")
                 f.write(f"Temporal Info: {statement.temporal_info.value}\n")
@@ -298,7 +298,7 @@ class StatementExtractor:
 
             dialog_sections.append({
                 "dialog_id": dialog.ref_id,
-                "group_id": dialog.group_id,
+                "end_user_id": dialog.end_user_id,
                 "content": dialog.content if getattr(dialog, "content", None) else "",
                 "strong": strong_relations,
                 "weak": weak_relations,
@@ -312,7 +312,7 @@ class StatementExtractor:
                 for idx, section in enumerate(dialog_sections, 1):
                     f.write(f"Dialog {idx}:\n")
                     f.write(f"Dialog ID: {section.get('dialog_id', '')}\n")
-                    f.write(f"Group ID: {section.get('group_id', '')}\n")
+                    f.write(f"Group ID: {section.get('end_user_id', '')}\n")
                     f.write("Content:\n")
                     f.write(f"{section.get('content', '')}\n")
                     f.write("-" * 40 + "\n\n")

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/temporal_extraction.py

@@ -132,7 +132,7 @@ class TemporalExtractor:
             prompt_logger.info("")
             prompt_logger.info("=== TEMPORAL EXTRACTION RESULTS ===")
             prompt_logger.info(
-                f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}"
+                f"[Temporal] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}"
             )
         except Exception:
             pass

api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/triplet_extraction.py

@@ -116,7 +116,7 @@ class TripletExtractor:
         logger.info(f"Processing {len(all_statements)} statements for triplet extraction...")
         try:
             prompt_logger.info(
-                f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, group_id={getattr(dialog_data, 'group_id', None)}, statements_to_process={len(all_statements)}"
+                f"[Triplet] Dialog ref_id={getattr(dialog_data, 'ref_id', None)}, end_user_id={getattr(dialog_data, 'end_user_id', None)}, statements_to_process={len(all_statements)}"
             )
         except Exception:
             pass

api/app/core/memory/storage_services/forgetting_engine/access_history_manager.py

@@ -75,7 +75,7 @@ class AccessHistoryManager:
         self,
         node_id: str,
         node_label: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         current_time: Optional[datetime] = None
     ) -> Dict[str, Any]:
         """
@@ -91,7 +91,7 @@ class AccessHistoryManager:
         Args:
             node_id: 节点ID
             node_label: 节点标签（Statement, ExtractedEntity, MemorySummary）
-            group_id: 组ID（可选，用于过滤）
+            end_user_id: 组ID（可选，用于过滤）
             current_time: 当前时间（可选，默认使用系统时间）
         
         Returns:
@@ -123,7 +123,7 @@ class AccessHistoryManager:
         for attempt in range(self.max_retries):
             try:
                 # 步骤1：读取当前节点状态
-                node_data = await self._fetch_node(node_id, node_label, group_id)
+                node_data = await self._fetch_node(node_id, node_label, end_user_id)
                 
                 if not node_data:
                     raise ValueError(
@@ -142,7 +142,7 @@ class AccessHistoryManager:
                     node_id=node_id,
                     node_label=node_label,
                     update_data=update_data,
-                    group_id=group_id
+                    end_user_id=end_user_id
                 )
                 
                 logger.info(
@@ -172,7 +172,7 @@ class AccessHistoryManager:
         self,
         node_ids: List[str],
         node_label: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         current_time: Optional[datetime] = None
     ) -> List[Dict[str, Any]]:
         """
@@ -184,7 +184,7 @@ class AccessHistoryManager:
         Args:
             node_ids: 节点ID列表
             node_label: 节点标签（所有节点必须是同一类型）
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
             current_time: 当前时间（可选）
         
         Returns:
@@ -202,7 +202,7 @@ class AccessHistoryManager:
             task = self.record_access(
                 node_id=node_id,
                 node_label=node_label,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 current_time=current_time
             )
             tasks.append(task)
@@ -235,7 +235,7 @@ class AccessHistoryManager:
         self,
         node_id: str,
         node_label: str,
-        group_id: Optional[str] = None
+        end_user_id: Optional[str] = None
     ) -> Tuple[ConsistencyCheckResult, Optional[str]]:
         """
         检查节点数据的一致性
@@ -249,14 +249,14 @@ class AccessHistoryManager:
         Args:
             node_id: 节点ID
             node_label: 节点标签
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
         
         Returns:
             Tuple[ConsistencyCheckResult, Optional[str]]: 
                 - 一致性检查结果枚举
                 - 错误描述（如果不一致）
         """
-        node_data = await self._fetch_node(node_id, node_label, group_id)
+        node_data = await self._fetch_node(node_id, node_label, end_user_id)
         
         if not node_data:
             return ConsistencyCheckResult.CONSISTENT, None
@@ -305,7 +305,7 @@ class AccessHistoryManager:
     async def check_batch_consistency(
         self,
         node_label: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         limit: int = 1000
     ) -> Dict[str, Any]:
         """
@@ -313,7 +313,7 @@ class AccessHistoryManager:
         
         Args:
             node_label: 节点标签
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
             limit: 检查的最大节点数
         
         Returns:
@@ -329,16 +329,16 @@ class AccessHistoryManager:
         MATCH (n:{node_label})
         WHERE n.access_history IS NOT NULL
         """
-        if group_id:
-            query += " AND n.group_id = $group_id"
+        if end_user_id:
+            query += " AND n.end_user_id = $end_user_id"
         query += """
         RETURN n.id as id
         LIMIT $limit
         """
         
         params = {"limit": limit}
-        if group_id:
-            params["group_id"] = group_id
+        if end_user_id:
+            params["end_user_id"] = end_user_id
         
         results = await self.connector.execute_query(query, **params)
         node_ids = [r['id'] for r in results]
@@ -351,7 +351,7 @@ class AccessHistoryManager:
             result, message = await self.check_consistency(
                 node_id=node_id,
                 node_label=node_label,
-                group_id=group_id
+                end_user_id=end_user_id
             )
             
             if result == ConsistencyCheckResult.CONSISTENT:
@@ -387,7 +387,7 @@ class AccessHistoryManager:
         self,
         node_id: str,
         node_label: str,
-        group_id: Optional[str] = None
+        end_user_id: Optional[str] = None
     ) -> bool:
         """
         自动修复节点的数据不一致问题
@@ -401,7 +401,7 @@ class AccessHistoryManager:
         Args:
             node_id: 节点ID
             node_label: 节点标签
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
         
         Returns:
             bool: 修复成功返回True，否则返回False
@@ -411,7 +411,7 @@ class AccessHistoryManager:
             result, message = await self.check_consistency(
                 node_id=node_id,
                 node_label=node_label,
-                group_id=group_id
+                end_user_id=end_user_id
             )
             
             if result == ConsistencyCheckResult.CONSISTENT:
@@ -419,7 +419,7 @@ class AccessHistoryManager:
                 return True
             
             # 获取节点数据
-            node_data = await self._fetch_node(node_id, node_label, group_id)
+            node_data = await self._fetch_node(node_id, node_label, end_user_id)
             if not node_data:
                 logger.error(f"节点不存在，无法修复: {node_label}[{node_id}]")
                 return False
@@ -457,8 +457,8 @@ class AccessHistoryManager:
             query = f"""
             MATCH (n:{node_label} {{id: $node_id}})
             """
-            if group_id:
-                query += " WHERE n.group_id = $group_id"
+            if end_user_id:
+                query += " WHERE n.end_user_id = $end_user_id"
             query += """
             SET n += $repair_data
             RETURN n
@@ -468,8 +468,8 @@ class AccessHistoryManager:
                 'node_id': node_id,
                 'repair_data': repair_data
             }
-            if group_id:
-                params['group_id'] = group_id
+            if end_user_id:
+                params['end_user_id'] = end_user_id
             
             await self.connector.execute_query(query, **params)
             
@@ -491,7 +491,7 @@ class AccessHistoryManager:
         self,
         node_id: str,
         node_label: str,
-        group_id: Optional[str] = None
+        end_user_id: Optional[str] = None
     ) -> Optional[Dict[str, Any]]:
         """
         获取节点数据
@@ -499,7 +499,7 @@ class AccessHistoryManager:
         Args:
             node_id: 节点ID
             node_label: 节点标签
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
         
         Returns:
             Optional[Dict[str, Any]]: 节点数据，如果不存在返回None
@@ -507,8 +507,8 @@ class AccessHistoryManager:
         query = f"""
         MATCH (n:{node_label} {{id: $node_id}})
         """
-        if group_id:
-            query += " WHERE n.group_id = $group_id"
+        if end_user_id:
+            query += " WHERE n.end_user_id = $end_user_id"
         query += """
         RETURN n.id as id,
                n.importance_score as importance_score,
@@ -519,8 +519,8 @@ class AccessHistoryManager:
         """
         
         params = {'node_id': node_id}
-        if group_id:
-            params['group_id'] = group_id
+        if end_user_id:
+            params['end_user_id'] = end_user_id
         
         results = await self.connector.execute_query(query, **params)
         
@@ -585,7 +585,7 @@ class AccessHistoryManager:
         node_id: str,
         node_label: str,
         update_data: Dict[str, Any],
-        group_id: Optional[str] = None
+        end_user_id: Optional[str] = None
     ) -> Dict[str, Any]:
         """
         原子性更新节点（使用乐观锁）
@@ -597,7 +597,7 @@ class AccessHistoryManager:
             node_id: 节点ID
             node_label: 节点标签
             update_data: 更新数据
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
         
         Returns:
             Dict[str, Any]: 更新后的节点数据
@@ -606,13 +606,13 @@ class AccessHistoryManager:
             RuntimeError: 如果更新失败或发生版本冲突
         """
         # 定义事务函数
-        async def update_transaction(tx, node_id, node_label, update_data, group_id):
+        async def update_transaction(tx, node_id, node_label, update_data, end_user_id):
             # 步骤1：读取当前节点并获取版本号
             read_query = f"""
             MATCH (n:{node_label} {{id: $node_id}})
             """
-            if group_id:
-                read_query += " WHERE n.group_id = $group_id"
+            if end_user_id:
+                read_query += " WHERE n.end_user_id = $end_user_id"
             read_query += """
             RETURN n.id as id,
                    n.version as version,
@@ -624,8 +624,8 @@ class AccessHistoryManager:
             """
             
             read_params = {'node_id': node_id}
-            if group_id:
-                read_params['group_id'] = group_id
+            if end_user_id:
+                read_params['end_user_id'] = end_user_id
             
             read_result = await tx.run(read_query, **read_params)
             current_node = await read_result.single()
@@ -656,8 +656,8 @@ class AccessHistoryManager:
             
             # 构建 WHERE 子句
             where_conditions = []
-            if group_id:
-                where_conditions.append("n.group_id = $group_id")
+            if end_user_id:
+                where_conditions.append("n.end_user_id = $end_user_id")
             
             # 添加版本检查
             if current_version > 0:
@@ -695,8 +695,8 @@ class AccessHistoryManager:
                 'last_access_time': update_data['last_access_time'],
                 'access_count': update_data['access_count']
             }
-            if group_id:
-                update_params['group_id'] = group_id
+            if end_user_id:
+                update_params['end_user_id'] = end_user_id
             
             update_result = await tx.run(update_query, **update_params)
             updated_node = await update_result.single()
@@ -720,7 +720,7 @@ class AccessHistoryManager:
                 node_id=node_id,
                 node_label=node_label,
                 update_data=update_data,
-                group_id=group_id
+                end_user_id=end_user_id
             )
             return result
         except Exception as e:

api/app/core/memory/storage_services/forgetting_engine/config_utils.py

@@ -11,9 +11,10 @@ Functions:
 
 import logging
 from typing import Optional, Dict, Any
+from uuid import UUID
 from sqlalchemy.orm import Session
 
-from app.repositories.data_config_repository import DataConfigRepository
+from app.repositories.memory_config_repository import MemoryConfigRepository
 from app.core.memory.storage_services.forgetting_engine.actr_calculator import ACTRCalculator
 
 
@@ -61,12 +62,12 @@ def calculate_forgetting_rate(lambda_time: float, lambda_mem: float) -> float:
 
 def load_actr_config_from_db(
     db: Session,
-    config_id: Optional[int] = None
+    config_id: Optional[UUID] = None
 ) -> Dict[str, Any]:
     """
     从数据库加载 ACT-R 配置参数
     
-    从 PostgreSQL 的 data_config 表读取配置参数，
+    从 PostgreSQL 的 memory_config 表读取配置参数，
     并计算派生参数（如 forgetting_rate）。
     
     Args:
@@ -99,7 +100,7 @@ def load_actr_config_from_db(
     
     # 从数据库加载配置
     try:
-        repository = DataConfigRepository()
+        repository = MemoryConfigRepository()
         db_config = repository.get_by_id(db, config_id)
         
         if db_config is None:
@@ -150,7 +151,7 @@ def load_actr_config_from_db(
 
 def create_actr_calculator_from_config(
     db: Session,
-    config_id: Optional[int] = None
+    config_id: Optional[UUID] = None
 ) -> ACTRCalculator:
     """
     从数据库配置创建 ACTRCalculator 实例
@@ -168,11 +169,6 @@ def create_actr_calculator_from_config(
         ValueError: 如果指定的 config_id 不存在
     
     Examples:
-        >>> from sqlalchemy.orm import Session
-        >>> db = Session()
-        >>> calculator = create_actr_calculator_from_config(db, config_id=1)
-        >>> # 使用计算器
-        >>> activation = calculator.calculate_memory_activation(...)
     """
     # 加载配置
     config = load_actr_config_from_db(db, config_id)

api/app/core/memory/storage_services/forgetting_engine/forgetting_scheduler.py

@@ -16,6 +16,7 @@ Classes:
 
 import logging
 from typing import Dict, Any, Optional
+from uuid import UUID
 from datetime import datetime
 
 from app.core.memory.storage_services.forgetting_engine.forgetting_strategy import ForgettingStrategy
@@ -66,10 +67,10 @@ class ForgettingScheduler:
     
     async def run_forgetting_cycle(
         self,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         max_merge_batch_size: int = 100,
         min_days_since_access: int = 30,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         db = None
     ) -> Dict[str, Any]:
         """
@@ -77,7 +78,7 @@ class ForgettingScheduler:
         
         
         Args:
-            group_id: 组 ID（可选，用于过滤特定组的节点）
+            end_user_id: 组 ID（可选，用于过滤特定组的节点）
             max_merge_batch_size: 单次最大融合节点对数（默认 100）
             min_days_since_access: 最小未访问天数（默认 30 天）
             config_id: 配置ID（可选，用于获取 llm_id）
@@ -107,19 +108,19 @@ class ForgettingScheduler:
         start_time_iso = start_time.isoformat()
         
         logger.info(
-            f"开始遗忘周期: group_id={group_id}, "
+            f"开始遗忘周期: end_user_id={end_user_id}, "
             f"max_batch={max_merge_batch_size}, "
             f"min_days={min_days_since_access}"
         )
         
         try:
             # 步骤1：统计遗忘前的节点数量
-            nodes_before = await self._count_knowledge_nodes(group_id)
+            nodes_before = await self._count_knowledge_nodes(end_user_id)
             logger.info(f"遗忘前节点总数: {nodes_before}")
             
             # 步骤2：识别可遗忘的节点对
             forgettable_pairs = await self.forgetting_strategy.find_forgettable_nodes(
-                group_id=group_id,
+                end_user_id=end_user_id,
                 min_days_since_access=min_days_since_access
             )
             
@@ -213,7 +214,7 @@ class ForgettingScheduler:
                         'statement_text': pair['statement_text'],
                         'statement_activation': pair['statement_activation'],
                         'statement_importance': pair['statement_importance'],
-                        'group_id': group_id
+                        'end_user_id': end_user_id
                     }
                     
                     entity_node = {
@@ -222,7 +223,7 @@ class ForgettingScheduler:
                         'entity_type': pair['entity_type'],
                         'entity_activation': pair['entity_activation'],
                         'entity_importance': pair['entity_importance'],
-                        'group_id': group_id
+                        'end_user_id': end_user_id
                     }
                     
                     # 融合节点
@@ -262,7 +263,7 @@ class ForgettingScheduler:
                     continue
             
             # 步骤6：统计遗忘后的节点数量
-            nodes_after = await self._count_knowledge_nodes(group_id)
+            nodes_after = await self._count_knowledge_nodes(end_user_id)
             logger.info(f"遗忘后节点总数: {nodes_after}")
             
             # 步骤7：生成遗忘报告
@@ -315,7 +316,7 @@ class ForgettingScheduler:
     
     async def _count_knowledge_nodes(
         self,
-        group_id: Optional[str] = None
+        end_user_id: Optional[str] = None
     ) -> int:
         """
         统计知识层节点总数
@@ -323,7 +324,7 @@ class ForgettingScheduler:
         统计 Statement、ExtractedEntity 和 MemorySummary 节点的总数。
         
         Args:
-            group_id: 组 ID（可选，用于过滤特定组的节点）
+            end_user_id: 组 ID（可选，用于过滤特定组的节点）
         
         Returns:
             int: 知识层节点总数
@@ -333,16 +334,16 @@ class ForgettingScheduler:
         WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
         """
         
-        if group_id:
-            query += " AND n.group_id = $group_id"
+        if end_user_id:
+            query += " AND n.end_user_id = $end_user_id"
         
         query += """
         RETURN count(n) as total
         """
         
         params = {}
-        if group_id:
-            params['group_id'] = group_id
+        if end_user_id:
+            end_user_id['end_user_id'] = end_user_id
         
         results = await self.connector.execute_query(query, **params)
         

api/app/core/memory/storage_services/forgetting_engine/forgetting_strategy.py

@@ -13,6 +13,7 @@ Classes:
 
 import logging
 from typing import List, Dict, Any, Optional
+from uuid import UUID
 from datetime import datetime, timedelta
 
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
@@ -90,7 +91,7 @@ class ForgettingStrategy:
     
     async def find_forgettable_nodes(
         self,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         min_days_since_access: int = 30
     ) -> List[Dict[str, Any]]:
         """
@@ -102,7 +103,7 @@ class ForgettingStrategy:
         3. Statement 和 Entity 之间存在关系边
         
         Args:
-            group_id: 组 ID（可选，用于过滤特定组的节点）
+            end_user_id: 组 ID（可选，用于过滤特定组的节点）
             min_days_since_access: 最小未访问天数（默认 30 天）
         
         Returns:
@@ -136,8 +137,8 @@ class ForgettingStrategy:
           AND (e.entity_type IS NULL OR e.entity_type <> 'Person')
         """
         
-        if group_id:
-            query += " AND s.group_id = $group_id AND e.group_id = $group_id"
+        if end_user_id:
+            query += " AND s.end_user_id = $end_user_id AND e.end_user_id = $end_user_id"
         
         query += """
         RETURN s.id as statement_id,
@@ -159,8 +160,8 @@ class ForgettingStrategy:
             'threshold': self.forgetting_threshold,
             'cutoff_time': cutoff_time_iso
         }
-        if group_id:
-            params['group_id'] = group_id
+        if end_user_id:
+            params['end_user_id'] = end_user_id
         
         results = await self.connector.execute_query(query, **params)
         
@@ -176,7 +177,7 @@ class ForgettingStrategy:
         self,
         statement_node: Dict[str, Any],
         entity_node: Dict[str, Any],
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         db = None
     ) -> str:
         """
@@ -247,8 +248,8 @@ class ForgettingStrategy:
         entity_activation = entity_node['entity_activation']
         entity_importance = entity_node['entity_importance']
         
-        # 获取 group_id（从 statement 或 entity 节点）
-        group_id = statement_node.get('group_id') or entity_node.get('group_id')
+        # 获取 end_user_id（从 statement 或 entity 节点）
+        end_user_id = statement_node.get('end_user_id') or entity_node.get('end_user_id')
         
         # 生成摘要内容
         summary_text = await self._generate_summary(
@@ -325,7 +326,7 @@ class ForgettingStrategy:
                 last_access_time: $current_time,
                 access_count: 1,
                 version: 1,
-                group_id: $group_id,
+                end_user_id: $end_user_id,
                 created_at: datetime($current_time),
                 merged_at: datetime($current_time)
             })
@@ -423,7 +424,7 @@ class ForgettingStrategy:
             'inherited_activation': inherited_activation,
             'inherited_importance': inherited_importance,
             'current_time': current_time_iso,
-            'group_id': group_id
+            'end_user_id': end_user_id
         }
         
         try:
@@ -462,7 +463,7 @@ class ForgettingStrategy:
         statement_text: str,
         entity_name: str,
         entity_type: str,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         db = None
     ) -> str:
         """
@@ -527,7 +528,7 @@ class ForgettingStrategy:
                 statement_text, entity_name, entity_type
             )
     
-    async def _get_llm_client(self, db, config_id: int):
+    async def _get_llm_client(self, db, config_id: UUID):
         """
         从数据库获取 LLM 客户端
         
@@ -539,11 +540,11 @@ class ForgettingStrategy:
             LLM 客户端实例，如果无法获取则返回 None
         """
         try:
-            from app.repositories.data_config_repository import DataConfigRepository
+            from app.repositories.memory_config_repository import MemoryConfigRepository
             from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
             
             # 从数据库读取配置
-            repository = DataConfigRepository()
+            repository = MemoryConfigRepository()
             db_config = repository.get_by_id(db, config_id)
             
             if db_config is None or db_config.llm_id is None:

api/app/core/memory/storage_services/search/__init__.py

@@ -37,7 +37,7 @@ __all__ = [
 async def run_hybrid_search(
     query_text: str,
     search_type: str = "hybrid",
-    group_id: str | None = None,
+    end_user_id: str | None = None,
     apply_id: str | None = None,
     user_id: str | None = None,
     limit: int = 50,
@@ -54,7 +54,7 @@ async def run_hybrid_search(
     Args:
         query_text: 查询文本
         search_type: 搜索类型（"hybrid", "keyword", "semantic"）
-        group_id: 组ID过滤
+        end_user_id: 组ID过滤
         apply_id: 应用ID过滤
         user_id: 用户ID过滤
         limit: 每个类别的最大结果数
@@ -104,7 +104,7 @@ async def run_hybrid_search(
         # 执行搜索
         result = await strategy.search(
             query_text=query_text,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
             include=include,
             alpha=alpha,

api/app/core/memory/storage_services/search/hybrid_search.py

@@ -77,7 +77,7 @@
 #     async def search(
 #         self,
 #         query_text: str,
-#         group_id: Optional[str] = None,
+#         end_user_id: Optional[str] = None,
 #         limit: int = 50,
 #         include: Optional[List[str]] = None,
 #         **kwargs
@@ -86,7 +86,7 @@
 
 #         Args:
 #             query_text: 查询文本
-#             group_id: 可选的组ID过滤
+#             end_user_id: 可选的组ID过滤
 #             limit: 每个类别的最大结果数
 #             include: 要包含的搜索类别列表
 #             **kwargs: 其他搜索参数（如alpha, use_forgetting_curve）
@@ -94,7 +94,7 @@
 #         Returns:
 #             SearchResult: 搜索结果对象
 #         """
-#         logger.info(f"执行混合搜索: query='{query_text}', group_id={group_id}, limit={limit}")
+#         logger.info(f"执行混合搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}")
 
 #         # 从kwargs中获取参数
 #         alpha = kwargs.get("alpha", self.alpha)
@@ -107,14 +107,14 @@
 #             # 并行执行关键词搜索和语义搜索
 #             keyword_result = await self.keyword_strategy.search(
 #                 query_text=query_text,
-#                 group_id=group_id,
+#                 end_user_id=end_user_id,
 #                 limit=limit,
 #                 include=include_list
 #             )
 
 #             semantic_result = await self.semantic_strategy.search(
 #                 query_text=query_text,
-#                 group_id=group_id,
+#                 end_user_id=end_user_id,
 #                 limit=limit,
 #                 include=include_list
 #             )
@@ -139,7 +139,7 @@
 #             metadata = self._create_metadata(
 #                 query_text=query_text,
 #                 search_type="hybrid",
-#                 group_id=group_id,
+#                 end_user_id=end_user_id,
 #                 limit=limit,
 #                 include=include_list,
 #                 alpha=alpha,
@@ -165,7 +165,7 @@
 #                 metadata=self._create_metadata(
 #                     query_text=query_text,
 #                     search_type="hybrid",
-#                     group_id=group_id,
+#                     end_user_id=end_user_id,
 #                     limit=limit,
 #                     error=str(e)
 #                 )

api/app/core/memory/storage_services/search/keyword_search.py

@@ -44,7 +44,7 @@ class KeywordSearchStrategy(SearchStrategy):
     async def search(
         self,
         query_text: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         limit: int = 50,
         include: Optional[List[str]] = None,
         **kwargs
@@ -53,7 +53,7 @@ class KeywordSearchStrategy(SearchStrategy):
 
         Args:
             query_text: 查询文本
-            group_id: 可选的组ID过滤
+            end_user_id: 可选的组ID过滤
             limit: 每个类别的最大结果数
             include: 要包含的搜索类别列表
             **kwargs: 其他搜索参数
@@ -61,7 +61,7 @@ class KeywordSearchStrategy(SearchStrategy):
         Returns:
             SearchResult: 搜索结果对象
         """
-        logger.info(f"执行关键词搜索: query='{query_text}', group_id={group_id}, limit={limit}")
+        logger.info(f"执行关键词搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}")
 
         # 获取有效的搜索类别
         include_list = self._get_include_list(include)
@@ -75,7 +75,7 @@ class KeywordSearchStrategy(SearchStrategy):
             results_dict = await search_graph(
                 connector=self.connector,
                 q=query_text,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=limit,
                 include=include_list
             )
@@ -84,7 +84,7 @@ class KeywordSearchStrategy(SearchStrategy):
             metadata = self._create_metadata(
                 query_text=query_text,
                 search_type="keyword",
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=limit,
                 include=include_list
             )
@@ -115,7 +115,7 @@ class KeywordSearchStrategy(SearchStrategy):
                 metadata=self._create_metadata(
                     query_text=query_text,
                     search_type="keyword",
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=limit,
                     error=str(e)
                 )

api/app/core/memory/storage_services/search/search_strategy.py

@@ -58,7 +58,7 @@ class SearchStrategy(ABC):
     async def search(
         self,
         query_text: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         limit: int = 50,
         include: Optional[List[str]] = None,
         **kwargs
@@ -67,7 +67,7 @@ class SearchStrategy(ABC):
 
         Args:
             query_text: 查询文本
-            group_id: 可选的组ID过滤
+            end_user_id: 可选的组ID过滤
             limit: 每个类别的最大结果数
             include: 要包含的搜索类别列表（statements, chunks, entities, summaries）
             **kwargs: 其他搜索参数
@@ -81,7 +81,7 @@ class SearchStrategy(ABC):
         self,
         query_text: str,
         search_type: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         limit: int = 50,
         **kwargs
     ) -> Dict[str, Any]:
@@ -90,7 +90,7 @@ class SearchStrategy(ABC):
         Args:
             query_text: 查询文本
             search_type: 搜索类型
-            group_id: 组ID
+            end_user_id: 组ID
             limit: 结果限制
             **kwargs: 其他元数据
 
@@ -100,7 +100,7 @@ class SearchStrategy(ABC):
         metadata = {
             "query": query_text,
             "search_type": search_type,
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "limit": limit,
             "timestamp": datetime.now().isoformat()
         }

api/app/core/memory/storage_services/search/semantic_search.py

@@ -85,7 +85,7 @@ class SemanticSearchStrategy(SearchStrategy):
     async def search(
         self,
         query_text: str,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         limit: int = 50,
         include: Optional[List[str]] = None,
         **kwargs
@@ -94,7 +94,7 @@ class SemanticSearchStrategy(SearchStrategy):
 
         Args:
             query_text: 查询文本
-            group_id: 可选的组ID过滤
+            end_user_id: 可选的组ID过滤
             limit: 每个类别的最大结果数
             include: 要包含的搜索类别列表
             **kwargs: 其他搜索参数
@@ -102,7 +102,7 @@ class SemanticSearchStrategy(SearchStrategy):
         Returns:
             SearchResult: 搜索结果对象
         """
-        logger.info(f"执行语义搜索: query='{query_text}', group_id={group_id}, limit={limit}")
+        logger.info(f"执行语义搜索: query='{query_text}', end_user_id={end_user_id}, limit={limit}")
 
         # 获取有效的搜索类别
         include_list = self._get_include_list(include)
@@ -119,7 +119,7 @@ class SemanticSearchStrategy(SearchStrategy):
                 connector=self.connector,
                 embedder_client=self.embedder_client,
                 query_text=query_text,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=limit,
                 include=include_list
             )
@@ -128,7 +128,7 @@ class SemanticSearchStrategy(SearchStrategy):
             metadata = self._create_metadata(
                 query_text=query_text,
                 search_type="semantic",
-                group_id=group_id,
+                end_user_id=end_user_id,
                 limit=limit,
                 include=include_list
             )
@@ -159,7 +159,7 @@ class SemanticSearchStrategy(SearchStrategy):
                 metadata=self._create_metadata(
                     query_text=query_text,
                     search_type="semantic",
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=limit,
                     error=str(e)
                 )

api/app/core/memory/utils/config/get_data.py

@@ -23,7 +23,7 @@ async def _load_(data: List[Any]) -> List[Dict]:
     target_keys = [
         "id",
         "statement",
-        "group_id",
+        "end_user_id",
         "chunk_id",
         "created_at",
         "expired_at",
@@ -75,7 +75,7 @@ async def get_data(result):
     """
     EXCLUDE_FIELDS = {
         "user_id",
-        "group_id",
+        "end_user_id",
         "entity_type",
         "connect_strength",
         "relationship_type",

api/app/core/memory/utils/log/audit_logger.py

@@ -62,7 +62,7 @@ class ConfigAuditLogger:
         self,
         config_id: str,
         user_id: Optional[str] = None,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         success: bool = True,
         details: Optional[Dict[str, Any]] = None
     ):
@@ -72,14 +72,14 @@ class ConfigAuditLogger:
         Args:
             config_id: 配置 ID
             user_id: 用户 ID（可选）
-            group_id: 组 ID（可选）
+            end_user_id: 组 ID（可选）
             success: 是否成功
             details: 详细信息（可选）
         """
         result = "SUCCESS" if success else "FAILED"
         msg = (
             f"CONFIG_LOAD config_id={config_id} "
-            f"user={user_id or 'N/A'} group={group_id or 'N/A'} "
+            f"user={user_id or 'N/A'} group={end_user_id or 'N/A'} "
             f"result={result}"
         )
         if details:
@@ -121,7 +121,7 @@ class ConfigAuditLogger:
         self,
         operation: str,
         config_id: str,
-        group_id: str,
+        end_user_id: str,
         success: bool = True,
         duration: Optional[float] = None,
         error: Optional[str] = None,
@@ -133,7 +133,7 @@ class ConfigAuditLogger:
         Args:
             operation: 操作类型（WRITE, READ 等）
             config_id: 配置 ID
-            group_id: 组 ID
+            end_user_id: 组 ID
             success: 是否成功
             duration: 操作耗时（秒）
             error: 错误信息（可选）
@@ -142,7 +142,7 @@ class ConfigAuditLogger:
         result = "SUCCESS" if success else "FAILED"
         msg = (
             f"{operation.upper()} config_id={config_id} "
-            f"group={group_id} result={result}"
+            f"group={end_user_id} result={result}"
         )
         if duration is not None:
             msg += f" duration={duration:.2f}s"

api/app/core/rag/vdb/field.py

@@ -4,7 +4,7 @@ from enum import StrEnum, auto
 class Field(StrEnum):
     CONTENT_KEY = "page_content"
     METADATA_KEY = "metadata"
-    GROUP_KEY = "group_id"
+    GROUP_KEY = "end_user_id"
     VECTOR = auto()
     # Sparse Vector aims to support full text search
     SPARSE_VECTOR = auto()

api/app/core/validators/memory_config_validators.py

@@ -26,7 +26,7 @@ logger = get_config_logger()
 
 
 def _parse_model_id(model_id: Union[str, UUID, None], model_type: str,
-                    config_id: Optional[int] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]:
+                    config_id: Optional[UUID] = None, workspace_id: Optional[UUID] = None) -> Optional[UUID]:
     """Parse model ID from string or UUID."""
     if model_id is None:
         return None
@@ -59,7 +59,7 @@ def validate_model_exists_and_active(
     model_type: str,
     db: Session,
     tenant_id: Optional[UUID] = None,
-    config_id: Optional[int] = None,
+    config_id: Optional[UUID] = None,
     workspace_id: Optional[UUID] = None
 ) -> tuple[str, bool]:
     """Validate that a model exists and is active.
@@ -166,7 +166,7 @@ def validate_and_resolve_model_id(
     db: Session,
     tenant_id: Optional[UUID] = None,
     required: bool = False,
-    config_id: Optional[int] = None,
+    config_id: Optional[UUID] = None,
     workspace_id: Optional[UUID] = None
 ) -> tuple[Optional[UUID], Optional[str]]:
     """Validate and resolve a model ID, checking existence and active status.
@@ -204,7 +204,7 @@ def validate_and_resolve_model_id(
 
 
 def validate_embedding_model(
-    config_id: int,
+    config_id: UUID,
     embedding_id: Union[str, UUID, None],
     db: Session,
     tenant_id: Optional[UUID] = None,
@@ -256,7 +256,7 @@ def validate_embedding_model(
 
 
 def validate_llm_model(
-    config_id: int,
+    config_id: UUID,
     llm_id: Union[str, UUID, None],
     db: Session,
     tenant_id: Optional[UUID] = None,

api/app/core/workflow/nodes/memory/config.py

@@ -1,4 +1,5 @@
 import uuid
+from uuid import UUID
 
 from pydantic import Field
 from typing import Literal
@@ -11,7 +12,7 @@ class MemoryReadNodeConfig(BaseNodeConfig):
         ...
     )
 
-    config_id: int = Field(
+    config_id: UUID = Field(
         ...
     )
 
@@ -26,6 +27,6 @@ class MemoryWriteNodeConfig(BaseNodeConfig):
         ...
     )
 
-    config_id: int = Field(
+    config_id: UUID = Field(
         ...
     )

api/app/core/workflow/nodes/memory/node.py

@@ -22,7 +22,7 @@ class MemoryReadNode(BaseNode):
                 raise RuntimeError("End user id is required")
 
             return await MemoryAgentService().read_memory(
-                group_id=end_user_id,
+                end_user_id=end_user_id,
                 message=self._render_template(self.typed_config.message, state),
                 config_id=str(self.typed_config.config_id),
                 search_switch=self.typed_config.search_switch,

api/app/models/__init__.py

@@ -18,7 +18,7 @@ from .appshare_model import AppShare
 from .release_share_model import ReleaseShare
 from .conversation_model import Conversation, Message
 from .api_key_model import ApiKey, ApiKeyLog, ApiKeyType
-from .data_config_model import DataConfig
+from .memory_config_model import MemoryConfig
 from .multi_agent_model import MultiAgentConfig, AgentInvocation
 from .workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution
 from .retrieval_info import RetrievalInfo
@@ -57,7 +57,7 @@ __all__ = [
     "ApiKey",
     "ApiKeyLog",
     "ApiKeyType",
-    "DataConfig",
+    "MemoryConfig",
     "MultiAgentConfig",
     "AgentInvocation",
     "WorkflowConfig",

api/app/models/data_config_model.py

@@ -1,88 +0,0 @@
-import datetime
-from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float
-from sqlalchemy.dialects.postgresql import UUID
-from app.db import Base
-
-
-class DataConfig(Base):
-    """数据配置表 - 用于存储记忆系统的配置参数"""
-    __tablename__ = "data_config"
-
-    # 主键
-    config_id = Column(Integer, primary_key=True, autoincrement=True, comment="配置ID")
-
-    # 基本信息
-    config_name = Column(String, nullable=False, comment="配置名称")
-    config_desc = Column(String, nullable=True, comment="配置描述")
-
-    # 组织信息
-    workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID")
-    group_id = Column(String, nullable=True, comment="组ID")
-    user_id = Column(String, nullable=True, comment="用户ID")
-    apply_id = Column(String, nullable=True, comment="应用ID")
-
-    # 模型选择（从workspace继承）
-    llm_id = Column(String, nullable=True, comment="LLM模型配置ID")
-    embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID")
-    rerank_id = Column(String, nullable=True, comment="重排序模型配置ID")
-
-    # 记忆萃取引擎配置
-    enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重")
-    enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧")
-    deep_retrieval = Column(Boolean, default=True, comment="深度检索开关")
-
-    # 阈值配置 (0-1 之间的浮点数)
-    t_type_strict = Column(Float, default=0.8, comment="类型严格阈值")
-    t_name_strict = Column(Float, default=0.8, comment="名称严格阈值")
-    t_overall = Column(Float, default=0.8, comment="综合阈值")
-
-    # 状态配置
-    state = Column(Boolean, default=False, comment="配置使用状态")
-
-    # 分块策略
-    chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略")
-
-    # 剪枝配置
-    pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝")
-    pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound")
-    pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）")
-
-    # 自我反思配置
-    enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思")
-    iteration_period = Column(String, default="3", comment="反思迭代周期")
-    reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部")
-    baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实")
-    reflection_model_id = Column(String, nullable=True, comment="反思模型ID")
-    memory_verify = Column(Boolean, default=True, comment="记忆验证")
-    quality_assessment = Column(Boolean, default=True, comment="质量评估")
-
-    # 遗忘引擎配置
-    statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3")
-    include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文")
-    max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量")
-    lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数")
-    lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数")
-    offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数")
-    
-    # ACT-R 遗忘引擎配置
-    decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5")
-    forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3")
-    forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24")
-    enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True")
-    max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100")
-    max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100")
-    min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30")
-    
-    # 情绪引擎配置
-    emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取")
-    emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID")
-    emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词")
-    emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值")
-    emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类")
-    
-    # 时间戳
-    created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间")
-    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间")
-
-    def __repr__(self):
-        return f"<DataConfig(config_id={self.config_id}, config_name={self.config_name})>"

api/app/models/memory_config_model.py

@@ -1,39 +1,88 @@
-# -*- coding: utf-8 -*-
-"""Memory Configuration Model - Backward Compatibility
+import datetime
+from sqlalchemy import Column, String, Boolean, DateTime, Integer, Float
+from sqlalchemy.dialects.postgresql import UUID
+from app.db import Base
 
-This module provides backward compatibility for imports.
-All classes have been moved to app.schemas.memory_config_schema.
 
-DEPRECATED: Import from app.schemas.memory_config_schema instead.
-"""
+class MemoryConfig(Base):
+    """记忆配置表 - 用于存储记忆系统的配置参数"""
+    __tablename__ = "memory_config"
 
-# Re-export for backward compatibility
-from app.schemas.memory_config_schema import (
-    ConfigurationError,
-    InvalidConfigError,
-    MemoryConfig,
-    MemoryConfigValidation,
-    ModelInactiveError,
-    ModelNotFoundError,
-    ModelValidation,
-    WorkspaceNotFoundError,
-    WorkspaceValidation,
-    validate_memory_config_data,
-    validate_model_data,
-    validate_workspace_data,
-)
+    # 主键
+    config_id = Column(UUID(as_uuid=True), primary_key=True,  comment="配置ID")
 
-__all__ = [
-    "ConfigurationError",
-    "InvalidConfigError",
-    "MemoryConfig",
-    "MemoryConfigValidation",
-    "ModelInactiveError",
-    "ModelNotFoundError",
-    "ModelValidation",
-    "WorkspaceNotFoundError",
-    "WorkspaceValidation",
-    "validate_memory_config_data",
-    "validate_model_data",
-    "validate_workspace_data",
-]
+    # 基本信息
+    config_name = Column(String, nullable=False, comment="配置名称")
+    config_desc = Column(String, nullable=True, comment="配置描述")
+
+    # 组织信息
+    workspace_id = Column(UUID(as_uuid=True), nullable=True, comment="工作空间ID")
+    end_user_id = Column(String, nullable=True, comment="组ID")
+    user_id = Column(String, nullable=True, comment="用户ID")
+    apply_id = Column(String, nullable=True, comment="应用ID")
+
+    # 模型选择（从workspace继承）
+    llm_id = Column(String, nullable=True, comment="LLM模型配置ID")
+    embedding_id = Column(String, nullable=True, comment="嵌入模型配置ID")
+    rerank_id = Column(String, nullable=True, comment="重排序模型配置ID")
+
+    # 记忆萃取引擎配置
+    enable_llm_dedup_blockwise = Column(Boolean, default=True, comment="启用LLM决策去重")
+    enable_llm_disambiguation = Column(Boolean, default=True, comment="启用LLM决策消歧")
+    deep_retrieval = Column(Boolean, default=True, comment="深度检索开关")
+
+    # 阈值配置 (0-1 之间的浮点数)
+    t_type_strict = Column(Float, default=0.8, comment="类型严格阈值")
+    t_name_strict = Column(Float, default=0.8, comment="名称严格阈值")
+    t_overall = Column(Float, default=0.8, comment="综合阈值")
+
+    # 状态配置
+    state = Column(Boolean, default=False, comment="配置使用状态")
+
+    # 分块策略
+    chunker_strategy = Column(String, default="RecursiveChunker", comment="分块策略")
+
+    # 剪枝配置
+    pruning_enabled = Column(Boolean, default=False, comment="是否启动智能语义剪枝")
+    pruning_scene = Column(String, nullable=True, comment="智能剪枝场景：education/online_service/outbound")
+    pruning_threshold = Column(Float, nullable=True, comment="智能语义剪枝阈值（0-0.9）")
+
+    # 自我反思配置
+    enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思")
+    iteration_period = Column(String, default="3", comment="反思迭代周期")
+    reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部")
+    baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实")
+    reflection_model_id = Column(String, nullable=True, comment="反思模型ID")
+    memory_verify = Column(Boolean, default=True, comment="记忆验证")
+    quality_assessment = Column(Boolean, default=True, comment="质量评估")
+
+    # 遗忘引擎配置
+    statement_granularity = Column(Integer, default=2, comment="陈述提取颗粒度，挡位 1/2/3")
+    include_dialogue_context = Column(Boolean, default=False, comment="是否包含对话上下文")
+    max_context = Column(Integer, default=1000, comment="对话语境中包含字符的最大数量")
+    lambda_time = Column("lambda_time", Float, default=0.5, comment="最低保持度，0-1 小数")
+    lambda_mem = Column("lambda_mem", Float, default=0.5, comment="遗忘率，0-1 小数")
+    offset = Column("offset", Float, default=0.0, comment="偏移度，0-1 小数")
+    
+    # ACT-R 遗忘引擎配置
+    decay_constant = Column(Float, default=0.5, comment="ACT-R衰减常数d，默认0.5")
+    forgetting_threshold = Column(Float, default=0.3, comment="遗忘阈值，默认0.3")
+    forgetting_interval_hours = Column(Integer, default=24, comment="遗忘周期间隔（小时），默认24")
+    enable_llm_summary = Column(Boolean, default=True, comment="是否使用LLM生成摘要，默认True")
+    max_merge_batch_size = Column(Integer, default=100, comment="单次最大融合节点对数，默认100")
+    max_history_length = Column(Integer, default=100, comment="访问历史最大长度，默认100")
+    min_days_since_access = Column(Integer, default=30, comment="最小未访问天数，默认30")
+    
+    # 情绪引擎配置
+    emotion_enabled = Column(Boolean, default=True, comment="是否启用情绪提取")
+    emotion_model_id = Column(String, nullable=True, comment="情绪分析专用模型ID")
+    emotion_extract_keywords = Column(Boolean, default=True, comment="是否提取情绪关键词")
+    emotion_min_intensity = Column(Float, default=0.1, comment="最小情绪强度阈值")
+    emotion_enable_subject = Column(Boolean, default=True, comment="是否启用主体分类")
+    
+    # 时间戳
+    created_at = Column(DateTime, default=datetime.datetime.now, comment="创建时间")
+    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="更新时间")
+
+    def __repr__(self):
+        return f"<MemoryConfig(config_id={self.config_id}, config_name={self.config_name})>"

api/app/models/memory_perceptual_model.py

@@ -16,7 +16,7 @@ class PerceptualType(IntEnum):
     CONVERSATION = 4
 
 
-class FileStorageType(IntEnum):
+class FileStorageService(IntEnum):
     LOCAL = 1
     REMOTE = 2
 

api/app/repositories/memory_config_repository.py

@@ -1,18 +1,19 @@
 # -*- coding: utf-8 -*-
-"""数据配置Repository模块
+"""记忆配置Repository模块
 
-本模块提供data_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。
+本模块提供memory_config表的数据访问层，使用SQLAlchemy ORM进行数据库操作。
 包括CRUD操作和Neo4j Cypher查询常量。
 
 Classes:
-    DataConfigRepository: 数据配置仓储类，提供CRUD操作
+    MemoryConfigRepository: 记忆配置仓储类，提供CRUD操作
 """
 
 import uuid
+from uuid import UUID
 from typing import Dict, List, Optional, Tuple
 from app.core.exceptions import BusinessException
 from app.core.logging_config import get_config_logger, get_db_logger
-from app.models.data_config_model import DataConfig
+from app.models.memory_config_model import MemoryConfig
 from app.schemas.memory_storage_schema import (
     ConfigKey,
     ConfigParamsCreate,
@@ -28,11 +29,11 @@ db_logger = get_db_logger()
 # 获取配置专用日志器
 config_logger = get_config_logger()
 
-TABLE_NAME = "data_config"
-class DataConfigRepository:
-    """数据配置Repository
+TABLE_NAME = "memory_config"
+class MemoryConfigRepository:
+    """记忆配置Repository
 
-    提供data_config表的数据访问方法，包括：
+    提供memory_config表的数据访问方法，包括：
     - SQLAlchemy ORM 数据库操作
     - Neo4j Cypher查询常量
     """
@@ -41,48 +42,48 @@ class DataConfigRepository:
 
     # Dialogue count by group
     SEARCH_FOR_DIALOGUE = """
-    MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN COUNT(n) AS num
+    MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num
     """
 
     # Chunk count by group
     SEARCH_FOR_CHUNK = """
-    MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN COUNT(n) AS num
+    MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num
     """
 
     # Statement count by group
     SEARCH_FOR_STATEMENT = """
-    MATCH (n:Statement) WHERE n.group_id = $group_id RETURN COUNT(n) AS num
+    MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num
     """
 
     # ExtractedEntity count by group
     SEARCH_FOR_ENTITY = """
-    MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN COUNT(n) AS num
+    MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN COUNT(n) AS num
     """
 
     # All counts by label and total
     SEARCH_FOR_ALL = """
-    OPTIONAL MATCH (n:Dialogue) WHERE n.group_id = $group_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count
+    OPTIONAL MATCH (n:Dialogue) WHERE n.end_user_id = $end_user_id RETURN 'Dialogue' AS Label, COUNT(n) AS Count
     UNION ALL
-    OPTIONAL MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN 'Chunk' AS Label, COUNT(n) AS Count
+    OPTIONAL MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN 'Chunk' AS Label, COUNT(n) AS Count
     UNION ALL
-    OPTIONAL MATCH (n:Statement) WHERE n.group_id = $group_id RETURN 'Statement' AS Label, COUNT(n) AS Count
+    OPTIONAL MATCH (n:Statement) WHERE n.end_user_id = $end_user_id RETURN 'Statement' AS Label, COUNT(n) AS Count
     UNION ALL
-    OPTIONAL MATCH (n:ExtractedEntity) WHERE n.group_id = $group_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count
+    OPTIONAL MATCH (n:ExtractedEntity) WHERE n.end_user_id = $end_user_id RETURN 'ExtractedEntity' AS Label, COUNT(n) AS Count
     UNION ALL
-    OPTIONAL MATCH (n) WHERE n.group_id = $group_id RETURN 'ALL' AS Label, COUNT(n) AS Count
+    OPTIONAL MATCH (n) WHERE n.end_user_id = $end_user_id RETURN 'ALL' AS Label, COUNT(n) AS Count
     """
 
     # Extracted entity details within group/app/user
     SEARCH_FOR_DETIALS = """
     MATCH (n:ExtractedEntity)
-    WHERE n.group_id = $group_id
+    WHERE n.end_user_id = $end_user_id
     RETURN n.entity_idx AS entity_idx, 
         n.connect_strength AS connect_strength, 
         n.description AS description, 
         n.entity_type AS entity_type, 
         n.name AS name,
         COALESCE(n.fact_summary, '') AS fact_summary,
-        n.group_id AS group_id,
+        n.end_user_id AS end_user_id,
         n.apply_id AS apply_id,
         n.user_id AS user_id,
         n.id AS id
@@ -91,9 +92,9 @@ class DataConfigRepository:
     # Edges between extracted entities within group/app/user
     SEARCH_FOR_EDGES = """
     MATCH (n:ExtractedEntity)-[r]->(m:ExtractedEntity)
-    WHERE n.group_id = $group_id
+    WHERE n.end_user_id = $end_user_id
     RETURN
-      r.group_id AS group_id,
+      r.end_user_id AS end_user_id,
       r.apply_id AS apply_id,
       r.user_id AS user_id,
       elementId(r) AS rel_id,
@@ -107,7 +108,7 @@ class DataConfigRepository:
     @staticmethod
     def update_reflection_config(
             db: Session,
-            config_id: int,
+            config_id: uuid.UUID,
             enable_self_reflexion: bool,
             iteration_period: str,
             reflexion_range: str,
@@ -115,7 +116,7 @@ class DataConfigRepository:
             reflection_model_id: str,
             memory_verify: bool,
             quality_assessment: bool
-    ) -> DataConfig:
+    ) -> MemoryConfig:
         """构建反思配置更新语句（SQLAlchemy text() 命名参数）
 
         Args:
@@ -130,28 +131,28 @@ class DataConfigRepository:
             config_id: 配置ID
 
         Returns:
-            Data
+            MemoryConfig
 
         Raises:
             ValueError: 没有字段需要更新时抛出
         """
         db_logger.debug(f"构建反思配置更新语句: config_id={config_id}")
-        stmt = select(DataConfig).where(DataConfig.config_id == config_id)
-        data_config_obj = db.scalars(stmt).first()
-        if not data_config_obj:
+        stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id)
+        memory_config_obj = db.scalars(stmt).first()
+        if not memory_config_obj:
             raise BusinessException
-        data_config_obj.enable_self_reflexion = enable_self_reflexion
-        data_config_obj.iteration_period = iteration_period
-        data_config_obj.reflexion_range = reflexion_range
-        data_config_obj.baseline = baseline
-        data_config_obj.reflection_model_id = reflection_model_id
-        data_config_obj.memory_verify = memory_verify
-        data_config_obj.quality_assessment = quality_assessment
+        memory_config_obj.enable_self_reflexion = enable_self_reflexion
+        memory_config_obj.iteration_period = iteration_period
+        memory_config_obj.reflexion_range = reflexion_range
+        memory_config_obj.baseline = baseline
+        memory_config_obj.reflection_model_id = reflection_model_id
+        memory_config_obj.memory_verify = memory_verify
+        memory_config_obj.quality_assessment = quality_assessment
 
-        return data_config_obj
+        return memory_config_obj
 
     @staticmethod
-    def query_reflection_config_by_id(db: Session, config_id: int) -> DataConfig:
+    def query_reflection_config_by_id(db: Session, config_id: uuid.UUID) -> MemoryConfig:
         """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数）
 
         Args:
@@ -162,13 +163,13 @@ class DataConfigRepository:
             Tuple[str, Dict]: (SQL查询字符串, 参数字典)
         """
         db_logger.debug(f"构建反思配置查询语句: config_id={config_id}")
-        stmt = select(DataConfig).where(DataConfig.config_id == config_id)
-        data_config = db.scalars(stmt).first()
-        if not data_config:
+        stmt = select(MemoryConfig).where(MemoryConfig.config_id == config_id)
+        memory_config = db.scalars(stmt).first()
+        if not memory_config:
             raise RuntimeError("reflection config not found")
-        return data_config
+        return memory_config
     @staticmethod
-    def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> DataConfig:
+    def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> MemoryConfig:
         """构建查询所有配置的语句（SQLAlchemy text() 命名参数）
 
         Args:
@@ -180,11 +181,11 @@ class DataConfigRepository:
         """
         db_logger.debug(f"构建查询所有配置语句: workspace_id={workspace_id}")
 
-        stmt = select(DataConfig).where(DataConfig.workspace_id == workspace_id)
-        data_config = db.scalars(stmt).first()
-        if not data_config:
+        stmt = select(MemoryConfig).where(MemoryConfig.workspace_id == workspace_id)
+        memory_config = db.scalars(stmt).first()
+        if not memory_config:
             raise RuntimeError("reflection config not found")
-        return data_config
+        return memory_config
 
 
     @staticmethod
@@ -208,20 +209,21 @@ class DataConfigRepository:
         return query, params
 
     @staticmethod
-    def create(db: Session, params: ConfigParamsCreate) -> DataConfig:
-        """创建数据配置
+    def create(db: Session, params: ConfigParamsCreate) -> MemoryConfig:
+        """创建记忆配置
 
         Args:
             db: 数据库会话
             params: 配置参数创建模型
 
         Returns:
-            DataConfig: 创建的配置对象
+            MemoryConfig: 创建的配置对象
         """
-        db_logger.debug(f"创建数据配置: config_name={params.config_name}, workspace_id={params.workspace_id}")
+        db_logger.debug(f"创建记忆配置: config_name={params.config_name}, workspace_id={params.workspace_id}")
 
         try:
-            db_config = DataConfig(
+            db_config = MemoryConfig(
+                config_id=uuid.uuid4(),
                 config_name=params.config_name,
                 config_desc=params.config_desc,
                 workspace_id=params.workspace_id,
@@ -232,16 +234,16 @@ class DataConfigRepository:
             db.add(db_config)
             db.flush()  # 获取自增ID但不提交事务
 
-            db_logger.info(f"数据配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})")
+            db_logger.info(f"记忆配置已添加到会话: {db_config.config_name} (ID: {db_config.config_id})")
             return db_config
 
         except Exception as e:
             db.rollback()
-            db_logger.error(f"创建数据配置失败: {params.config_name} - {str(e)}")
+            db_logger.error(f"创建记忆配置失败: {params.config_name} - {str(e)}")
             raise
 
     @staticmethod
-    def update(db: Session, update: ConfigUpdate) -> Optional[DataConfig]:
+    def update(db: Session, update: ConfigUpdate) -> Optional[MemoryConfig]:
         """更新基础配置
 
         Args:
@@ -249,17 +251,17 @@ class DataConfigRepository:
             update: 配置更新模型
 
         Returns:
-            Optional[DataConfig]: 更新后的配置对象，不存在则返回None
+            Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None
 
         Raises:
             ValueError: 没有字段需要更新时抛出
         """
-        db_logger.debug(f"更新数据配置: config_id={update.config_id}")
+        db_logger.debug(f"更新记忆配置: config_id={update.config_id}")
 
         try:
-            db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first()
+            db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first()
             if not db_config:
-                db_logger.warning(f"数据配置不存在: config_id={update.config_id}")
+                db_logger.warning(f"记忆配置不存在: config_id={update.config_id}")
                 return None
 
             # 更新字段
@@ -277,17 +279,17 @@ class DataConfigRepository:
             db.commit()
             db.refresh(db_config)
 
-            db_logger.info(f"数据配置更新成功: {db_config.config_name} (ID: {update.config_id})")
+            db_logger.info(f"记忆配置更新成功: {db_config.config_name} (ID: {update.config_id})")
             return db_config
 
         except Exception as e:
             db.rollback()
-            db_logger.error(f"更新数据配置失败: config_id={update.config_id} - {str(e)}")
+            db_logger.error(f"更新记忆配置失败: config_id={update.config_id} - {str(e)}")
             raise
 
 
     @staticmethod
-    def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[DataConfig]:
+    def update_extracted(db: Session, update: ConfigUpdateExtracted) -> Optional[MemoryConfig]:
         """更新记忆萃取引擎配置
 
         Args:
@@ -295,7 +297,7 @@ class DataConfigRepository:
             update: 萃取配置更新模型
 
         Returns:
-            Optional[DataConfig]: 更新后的配置对象，不存在则返回None
+            Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None
 
         Raises:
             ValueError: 没有字段需要更新时抛出
@@ -303,9 +305,9 @@ class DataConfigRepository:
         db_logger.debug(f"更新萃取配置: config_id={update.config_id}")
 
         try:
-            db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first()
+            db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first()
             if not db_config:
-                db_logger.warning(f"数据配置不存在: config_id={update.config_id}")
+                db_logger.warning(f"记忆配置不存在: config_id={update.config_id}")
                 return None
 
             # 更新字段映射
@@ -360,7 +362,7 @@ class DataConfigRepository:
             raise
 
     @staticmethod
-    def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[DataConfig]:
+    def update_forget(db: Session, update: ConfigUpdateForget) -> Optional[MemoryConfig]:
         """更新遗忘引擎配置
 
         Args:
@@ -368,7 +370,7 @@ class DataConfigRepository:
             update: 遗忘配置更新模型
 
         Returns:
-            Optional[DataConfig]: 更新后的配置对象，不存在则返回None
+            Optional[MemoryConfig]: 更新后的配置对象，不存在则返回None
 
         Raises:
             ValueError: 没有字段需要更新时抛出
@@ -376,9 +378,9 @@ class DataConfigRepository:
         db_logger.debug(f"更新遗忘配置: config_id={update.config_id}")
 
         try:
-            db_config = db.query(DataConfig).filter(DataConfig.config_id == update.config_id).first()
+            db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == update.config_id).first()
             if not db_config:
-                db_logger.warning(f"数据配置不存在: config_id={update.config_id}")
+                db_logger.warning(f"记忆配置不存在: config_id={update.config_id}")
                 return None
 
             # 更新字段
@@ -408,7 +410,7 @@ class DataConfigRepository:
             raise
 
     @staticmethod
-    def get_extracted_config(db: Session, config_id: int) -> Optional[Dict]:
+    def get_extracted_config(db: Session, config_id: UUID) -> Optional[Dict]:
         """获取萃取配置，通过主键查询某条配置
 
         Args:
@@ -421,7 +423,7 @@ class DataConfigRepository:
         db_logger.debug(f"查询萃取配置: config_id={config_id}")
 
         try:
-            db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
+            db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
             if not db_config:
                 db_logger.debug(f"萃取配置不存在: config_id={config_id}")
                 return None
@@ -457,7 +459,7 @@ class DataConfigRepository:
             raise
 
     @staticmethod
-    def get_forget_config(db: Session, config_id: int) -> Optional[Dict]:
+    def get_forget_config(db: Session, config_id: UUID) -> Optional[Dict]:
         """获取遗忘配置，通过主键查询某条配置
 
         Args:
@@ -470,7 +472,7 @@ class DataConfigRepository:
         db_logger.debug(f"查询遗忘配置: config_id={config_id}")
 
         try:
-            db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
+            db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
             if not db_config:
                 db_logger.debug(f"遗忘配置不存在: config_id={config_id}")
                 return None
@@ -489,39 +491,39 @@ class DataConfigRepository:
             raise
 
     @staticmethod
-    def get_by_id(db: Session, config_id: int) -> Optional[DataConfig]:
-        """根据ID获取数据配置
+    def get_by_id(db: Session, config_id: uuid.UUID) -> Optional[MemoryConfig]:
+        """根据ID获取记忆配置
 
         Args:
             db: 数据库会话
             config_id: 配置ID
 
         Returns:
-            Optional[DataConfig]: 配置对象，不存在则返回None
+            Optional[MemoryConfig]: 配置对象，不存在则返回None
         """
-        db_logger.debug(f"根据ID查询数据配置: config_id={config_id}")
+        db_logger.debug(f"根据ID查询记忆配置: config_id={config_id}")
 
         try:
-            config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
+            config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
 
             if config:
-                db_logger.debug(f"数据配置查询成功: {config.config_name} (ID: {config_id})")
+                db_logger.debug(f"记忆配置查询成功: {config.config_name} (ID: {config_id})")
             else:
-                db_logger.debug(f"数据配置不存在: config_id={config_id}")
+                db_logger.debug(f"记忆配置不存在: config_id={config_id}")
             return config
         except Exception as e:
-            db_logger.error(f"根据ID查询数据配置失败: config_id={config_id} - {str(e)}")
+            db_logger.error(f"根据ID查询记忆配置失败: config_id={config_id} - {str(e)}")
             raise
     @staticmethod
-    def get_config_with_workspace(db: Session, config_id: int) -> Optional[tuple]:
-        """Get data config and its associated workspace information
+    def get_config_with_workspace(db: Session, config_id: uuid.UUID) -> Optional[tuple]:
+        """Get memory config and its associated workspace information
         
         Args:
             db: Database session
             config_id: Configuration ID
             
         Returns:
-            Optional[tuple]: (DataConfig, Workspace) tuple, None if not found
+            Optional[tuple]: (MemoryConfig, Workspace) tuple, None if not found
             
         Raises:
             ValueError: Raised when config exists but workspace doesn't
@@ -541,19 +543,19 @@ class DataConfigRepository:
             }
         )
         
-        db_logger.debug(f"Querying data config and workspace: config_id={config_id}")
+        db_logger.debug(f"Querying memory config and workspace: config_id={config_id}")
         
         try:
             # Use join query to get both config and workspace
-            result = db.query(DataConfig, Workspace).join(
-                Workspace, DataConfig.workspace_id == Workspace.id
-            ).filter(DataConfig.config_id == config_id).first()
+            result = db.query(MemoryConfig, Workspace).join(
+                Workspace, MemoryConfig.workspace_id == Workspace.id
+            ).filter(MemoryConfig.config_id == config_id).first()
             
             elapsed_ms = (time.time() - start_time) * 1000
             
             if not result:
                 # Check if config exists but workspace is missing
-                config_only = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
+                config_only = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
                 if config_only:
                     if config_only.workspace_id is None:
                         config_logger.error(
@@ -566,7 +568,7 @@ class DataConfigRepository:
                                 "elapsed_ms": elapsed_ms
                             }
                         )
-                        db_logger.error(f"Data config {config_id} has no associated workspace ID")
+                        db_logger.error(f"Memory config {config_id} has no associated workspace ID")
                         raise ValueError(f"Configuration {config_id} has no associated workspace")
                     else:
                         config_logger.error(
@@ -579,7 +581,7 @@ class DataConfigRepository:
                                 "elapsed_ms": elapsed_ms
                             }
                         )
-                        db_logger.error(f"Data config {config_id} references non-existent workspace {config_only.workspace_id}")
+                        db_logger.error(f"Memory config {config_id} references non-existent workspace {config_only.workspace_id}")
                         raise ValueError(f"Workspace {config_only.workspace_id} not found for configuration {config_id}")
                 
                 config_logger.debug(
@@ -591,7 +593,7 @@ class DataConfigRepository:
                         "elapsed_ms": elapsed_ms
                     }
                 )
-                db_logger.debug(f"Data config not found: config_id={config_id}")
+                db_logger.debug(f"Memory config not found: config_id={config_id}")
                 return None
             
             config, workspace = result
@@ -611,7 +613,7 @@ class DataConfigRepository:
                 }
             )
             
-            db_logger.debug(f"Data config and workspace query successful: config={config.config_name}, workspace={workspace.name}")
+            db_logger.debug(f"Memory config and workspace query successful: config={config.config_name}, workspace={workspace.name}")
             return (config, workspace)
             
         except ValueError:
@@ -633,10 +635,10 @@ class DataConfigRepository:
                 exc_info=True
             )
             
-            db_logger.error(f"Failed to query data config and workspace: config_id={config_id} - {str(e)}")
+            db_logger.error(f"Failed to query memory config and workspace: config_id={config_id} - {str(e)}")
             raise
     @staticmethod
-    def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[DataConfig]:
+    def get_all(db: Session, workspace_id: Optional[uuid.UUID] = None) -> List[MemoryConfig]:
         """获取所有配置参数
 
         Args:
@@ -644,17 +646,17 @@ class DataConfigRepository:
             workspace_id: 工作空间ID，用于过滤查询结果
 
         Returns:
-            List[DataConfig]: 配置列表
+            List[MemoryConfig]: 配置列表
         """
         db_logger.debug(f"查询所有配置: workspace_id={workspace_id}")
 
         try:
-            query = db.query(DataConfig)
+            query = db.query(MemoryConfig)
 
             if workspace_id:
-                query = query.filter(DataConfig.workspace_id == workspace_id)
+                query = query.filter(MemoryConfig.workspace_id == workspace_id)
 
-            configs = query.order_by(desc(DataConfig.updated_at)).all()
+            configs = query.order_by(desc(MemoryConfig.updated_at)).all()
 
             db_logger.debug(f"配置列表查询成功: 数量={len(configs)}")
             return configs
@@ -664,8 +666,8 @@ class DataConfigRepository:
             raise
 
     @staticmethod
-    def delete(db: Session, config_id: int) -> bool:
-        """删除数据配置
+    def delete(db: Session, config_id: uuid.UUID) -> bool:
+        """删除记忆配置
 
         Args:
             db: 数据库会话
@@ -674,22 +676,22 @@ class DataConfigRepository:
         Returns:
             bool: 删除成功返回True，配置不存在返回False
         """
-        db_logger.debug(f"删除数据配置: config_id={config_id}")
+        db_logger.debug(f"删除记忆配置: config_id={config_id}")
 
         try:
-            db_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
+            db_config = db.query(MemoryConfig).filter(MemoryConfig.config_id == config_id).first()
             if not db_config:
-                db_logger.warning(f"数据配置不存在: config_id={config_id}")
+                db_logger.warning(f"记忆配置不存在: config_id={config_id}")
                 return False
 
             db.delete(db_config)
             db.commit()
 
-            db_logger.info(f"数据配置删除成功: config_id={config_id}")
+            db_logger.info(f"记忆配置删除成功: config_id={config_id}")
             return True
 
         except Exception as e:
             db.rollback()
-            db_logger.error(f"删除数据配置失败: config_id={config_id} - {str(e)}")
+            db_logger.error(f"删除记忆配置失败: config_id={config_id} - {str(e)}")
             raise
 

api/app/repositories/memory_perceptual_repository.py

@@ -6,7 +6,7 @@ from sqlalchemy import and_, desc
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_db_logger
-from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageType
+from app.models.memory_perceptual_model import MemoryPerceptualModel, PerceptualType, FileStorageService
 from app.schemas.memory_perceptual_schema import PerceptualQuerySchema
 
 db_logger = get_db_logger()
@@ -28,7 +28,7 @@ class MemoryPerceptualRepository:
             file_ext: str,
             summary: Optional[str] = None,
             meta_data: Optional[dict] = None,
-            storage_service: FileStorageType = FileStorageType.LOCAL
+            storage_service: FileStorageService = FileStorageService.LOCAL
 
     ) -> MemoryPerceptualModel:
 

api/app/repositories/neo4j/add_edges.py

@@ -32,7 +32,7 @@ async def add_chunk_statement_edges(chunks: List[Chunk], connector: Neo4jConnect
                     "id": stable_edge_id,
                     "source": chunk.id,
                     "target": stmt.id,
-                    "group_id": getattr(stmt, 'group_id', None),
+                    "end_user_id": getattr(stmt, 'end_user_id', None),
                     "user_id":getattr(stmt, 'user_id', None),
                     "apply_id": getattr(stmt, 'apply_id', None),
                     "run_id": getattr(stmt, 'run_id', None) or getattr(chunk, 'run_id', None),
@@ -83,7 +83,7 @@ async def add_memory_summary_statement_edges(summaries: List[MemorySummaryNode],
                 edges.append({
                     "summary_id": s.id,
                     "chunk_id": chunk_id,
-                    "group_id": s.group_id,
+                    "end_user_id": s.end_user_id,
                     "run_id": s.run_id,
                     "created_at": s.created_at.isoformat() if s.created_at else None,
                     "expired_at": s.expired_at.isoformat() if s.expired_at else None,

api/app/repositories/neo4j/add_nodes.py

@@ -6,10 +6,10 @@ from app.core.memory.models.graph_models import DialogueNode, StatementNode, Chu
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 
 
-async def delete_all_nodes(group_id: str, connector: Neo4jConnector):
+async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector):
     """Delete all nodes in the database."""
-    result = await connector.execute_query(f"MATCH (n {{group_id: '{group_id}'}}) DETACH DELETE n")
-    print(f"All group_id: {group_id} node and edge deleted successfully")
+    result = await connector.execute_query(f"MATCH (n {{end_user_id: '{end_user_id}'}}) DETACH DELETE n")
+    print(f"All end_user_id: {end_user_id} node and edge deleted successfully")
     return result
 
 async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConnector) -> Optional[List[str]]:
@@ -32,9 +32,7 @@ async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConn
         for dialogue in dialogues:
             flattened_dialogues.append({
                 "id": dialogue.id,
-                "group_id": dialogue.group_id,
-                "user_id": dialogue.user_id,
-                "apply_id": dialogue.apply_id,
+                "end_user_id": dialogue.end_user_id,
                 "run_id": dialogue.run_id,
                 "ref_id": dialogue.ref_id,
                 "name": dialogue.name,
@@ -79,9 +77,7 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC
             flattened_statement = {
                 "id": statement.id,
                 "name": statement.name,
-                "group_id": statement.group_id,
-                "user_id": statement.user_id,
-                "apply_id": statement.apply_id,
+                "end_user_id": statement.end_user_id,
                 "run_id": statement.run_id,
                 "chunk_id": statement.chunk_id,
                 # "created_at": statement.created_at.isoformat(),
@@ -154,9 +150,7 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) ->
             flattened_chunk = {
                 "id": chunk.id,
                 "name": chunk.name,
-                "group_id": chunk.group_id,
-                "user_id": chunk.user_id,
-                "apply_id": chunk.apply_id,
+                "end_user_id": chunk.end_user_id,
                 "run_id": chunk.run_id,
                 "created_at": chunk.created_at.isoformat() if chunk.created_at else None,
                 "expired_at": chunk.expired_at.isoformat() if chunk.expired_at else None,
@@ -206,9 +200,7 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector
             flattened.append({
                 "id": s.id,
                 "name": s.name,
-                "group_id": s.group_id,
-                "user_id": s.user_id,
-                "apply_id": s.apply_id,
+                "end_user_id": s.end_user_id,
                 "run_id": s.run_id,
                 "created_at": s.created_at.isoformat() if s.created_at else None,
                 "expired_at": s.expired_at.isoformat() if s.expired_at else None,

api/app/repositories/neo4j/base_neo4j_repository.py

@@ -152,7 +152,7 @@ class BaseNeo4jRepository(BaseRepository[T]):
             
         Example:
             >>> results = await repository.find(
-            ...     {"group_id": "group_123", "user_id": "user_456"},
+            ...     {"end_user_id": "group_123", "user_id": "user_456"},
             ...     limit=50
             ... )
         """

api/app/repositories/neo4j/cypher_queries.py

@@ -3,9 +3,7 @@ DIALOGUE_NODE_SAVE = """
     UNWIND $dialogues AS dialogue
     MERGE (n:Dialogue {id: dialogue.id})
     SET n.uuid = coalesce(n.uuid, dialogue.id),
-        n.group_id = dialogue.group_id,
-        n.user_id = dialogue.user_id,
-        n.apply_id = dialogue.apply_id,
+        n.end_user_id = dialogue.end_user_id,
         n.run_id = dialogue.run_id,
         n.ref_id = dialogue.ref_id,
         n.created_at = dialogue.created_at,
@@ -22,9 +20,7 @@ SET s += {
     id: statement.id,
     run_id: statement.run_id,
     chunk_id: statement.chunk_id,
-    group_id: statement.group_id,
-    user_id: statement.user_id,
-    apply_id: statement.apply_id,
+    end_user_id: statement.end_user_id,
     stmt_type: statement.stmt_type,
     statement: statement.statement,
     emotion_intensity: statement.emotion_intensity,
@@ -54,9 +50,7 @@ MERGE (c:Chunk {id: chunk.id})
 SET c += {
     id: chunk.id,
     name: chunk.name,
-    group_id: chunk.group_id,
-    user_id: chunk.user_id,
-    apply_id: chunk.apply_id,
+    end_user_id: chunk.end_user_id,
     run_id: chunk.run_id,
     created_at: chunk.created_at,
     expired_at: chunk.expired_at,
@@ -76,9 +70,7 @@ EXTRACTED_ENTITY_NODE_SAVE = """
 UNWIND $entities AS entity
 MERGE (e:ExtractedEntity {id: entity.id})
 SET e.name = CASE WHEN entity.name IS NOT NULL AND entity.name <> '' THEN entity.name ELSE e.name END,
-    e.group_id = CASE WHEN entity.group_id IS NOT NULL AND entity.group_id <> '' THEN entity.group_id ELSE e.group_id END,
-    e.user_id = CASE WHEN entity.user_id IS NOT NULL AND entity.user_id <> '' THEN entity.user_id ELSE e.user_id END,
-    e.apply_id = CASE WHEN entity.apply_id IS NOT NULL AND entity.apply_id <> '' THEN entity.apply_id ELSE e.apply_id END,
+    e.end_user_id = CASE WHEN entity.end_user_id IS NOT NULL AND entity.end_user_id <> '' THEN entity.end_user_id ELSE e.end_user_id END,
     e.run_id = CASE WHEN entity.run_id IS NOT NULL AND entity.run_id <> '' THEN entity.run_id ELSE e.run_id END,
     e.created_at = CASE
         WHEN entity.created_at IS NOT NULL AND (e.created_at IS NULL OR entity.created_at < e.created_at)
@@ -134,9 +126,9 @@ RETURN e.id AS uuid
 # Add back ENTITY_RELATIONSHIP_SAVE to be used by graph_saver.save_entities_and_relationships
 ENTITY_RELATIONSHIP_SAVE = """
 UNWIND $relationships AS rel
-// Match entities by stable id within group, do not constrain by run_id
-MATCH (subject:ExtractedEntity {id: rel.source_id, group_id: rel.group_id})
-MATCH (object:ExtractedEntity {id: rel.target_id, group_id: rel.group_id})
+// Match entities by stable id within end_user_id, do not constrain by run_id
+MATCH (subject:ExtractedEntity {id: rel.source_id, end_user_id: rel.end_user_id})
+MATCH (object:ExtractedEntity {id: rel.target_id, end_user_id: rel.end_user_id})
 // Avoid duplicate edges across runs for the same endpoints
 MERGE (subject)-[r:EXTRACTED_RELATIONSHIP]->(object)
 SET r.predicate = rel.predicate,
@@ -148,7 +140,7 @@ SET r.predicate = rel.predicate,
     r.created_at = rel.created_at,
     r.expired_at = rel.expired_at,
     r.run_id = rel.run_id,
-    r.group_id = rel.group_id
+    r.end_user_id = rel.end_user_id
 RETURN elementId(r) AS uuid
 """
 
@@ -160,7 +152,7 @@ UNWIND $weak_entities AS entity
 MERGE (e:ExtractedEntity {id: entity.id, run_id: entity.run_id})
 SET e += {
     name: entity.name,
-    group_id: entity.group_id,
+    end_user_id: entity.end_user_id,
     run_id: entity.run_id,
     description: entity.description,
     chunk_id: entity.chunk_id,
@@ -175,11 +167,11 @@ RETURN e.id AS id
 SAVE_STRONG_TRIPLE_ENTITIES = """
 UNWIND $items AS item
 MERGE (s:ExtractedEntity {id: item.source_id, run_id: item.run_id})
-SET s += {name: item.subject, group_id: item.group_id, run_id: item.run_id}
+SET s += {name: item.subject, end_user_id: item.end_user_id, run_id: item.run_id}
 // Independent strong flag
 SET s.is_strong = true
 MERGE (o:ExtractedEntity {id: item.target_id, run_id: item.run_id})
-SET o += {name: item.object, group_id: item.group_id, run_id: item.run_id}
+SET o += {name: item.object, end_user_id: item.end_user_id, run_id: item.run_id}
 // Independent strong flag
 SET o.is_strong = true
 """
@@ -194,7 +186,7 @@ DIALOGUE_STATEMENT_EDGE_SAVE = """
     // 仅按端点去重，关系属性可更新
     MERGE (dialogue)-[e:MENTIONS]->(statement)
     SET e.uuid = edge.id,
-        e.group_id = edge.group_id,
+        e.end_user_id = edge.end_user_id,
         e.created_at = edge.created_at,
         e.expired_at = edge.expired_at
     RETURN e.uuid AS uuid
@@ -208,7 +200,7 @@ CHUNK_STATEMENT_EDGE_SAVE = """
     MATCH (statement:Statement {id: edge.source, run_id: edge.run_id})
     MATCH (chunk:Chunk {id: edge.target, run_id: edge.run_id})
     MERGE (chunk)-[e:CONTAINS {id: edge.id}]->(statement)
-    SET e.group_id = edge.group_id,
+    SET e.end_user_id = edge.end_user_id,
         e.run_id = edge.run_id,
         e.created_at = edge.created_at,
         e.expired_at = edge.expired_at
@@ -218,13 +210,12 @@ CHUNK_STATEMENT_EDGE_SAVE = """
 STATEMENT_ENTITY_EDGE_SAVE = """
 UNWIND $relationships AS rel
 // Statement nodes are per-run; keep run_id constraint on statements
-// Statement nodes are per-run; keep run_id constraint on statements
 MATCH (statement:Statement {id: rel.source, run_id: rel.run_id})
-// Entities are shared across runs within a group; do not constrain by run_id
-MATCH (entity:ExtractedEntity {id: rel.target, group_id: rel.group_id})
+// Entities are shared across runs within end_user_id; do not constrain by run_id
+MATCH (entity:ExtractedEntity {id: rel.target, end_user_id: rel.end_user_id})
 // Avoid duplicate edges across runs for same endpoints
 MERGE (statement)-[r:REFERENCES_ENTITY]->(entity)
-SET r.group_id = rel.group_id,
+SET r.end_user_id = rel.end_user_id,
     r.run_id = rel.run_id,
     r.created_at = rel.created_at,
     r.expired_at = rel.expired_at,
@@ -236,10 +227,10 @@ ENTITY_EMBEDDING_SEARCH = """
 CALL db.index.vector.queryNodes('entity_embedding_index', $limit * 100, $embedding)
 YIELD node AS e, score
 WHERE e.name_embedding IS NOT NULL
-  AND ($group_id IS NULL OR e.group_id = $group_id)
+  AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
 RETURN e.id AS id,
        e.name AS name,
-       e.group_id AS group_id,
+       e.end_user_id AS end_user_id,
        e.entity_type AS entity_type,
        COALESCE(e.activation_value, e.importance_score, 0.5) AS activation_value,
        COALESCE(e.importance_score, 0.5) AS importance_score,
@@ -254,10 +245,10 @@ STATEMENT_EMBEDDING_SEARCH = """
 CALL db.index.vector.queryNodes('statement_embedding_index', $limit * 100, $embedding)
 YIELD node AS s, score
 WHERE s.statement_embedding IS NOT NULL
-  AND ($group_id IS NULL OR s.group_id = $group_id)
+  AND ($end_user_id IS NULL OR s.end_user_id = $end_user_id)
 RETURN s.id AS id,
        s.statement AS statement,
-       s.group_id AS group_id,
+       s.end_user_id AS end_user_id,
        s.chunk_id AS chunk_id,
        s.created_at AS created_at,
        s.expired_at AS expired_at,
@@ -277,9 +268,9 @@ CHUNK_EMBEDDING_SEARCH = """
 CALL db.index.vector.queryNodes('chunk_embedding_index', $limit * 100, $embedding)
 YIELD node AS c, score
 WHERE c.chunk_embedding IS NOT NULL
-  AND ($group_id IS NULL OR c.group_id = $group_id)
+  AND ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
 RETURN c.id AS chunk_id,
-       c.group_id AS group_id,
+       c.end_user_id AS end_user_id,
        c.content AS content,
        c.dialog_id AS dialog_id,
        COALESCE(c.activation_value, 0.5) AS activation_value,
@@ -292,12 +283,12 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_BY_KEYWORD = """
 CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score
-WHERE ($group_id IS NULL OR s.group_id = $group_id)
+WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id)
 OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s)
 OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity)
 RETURN s.id AS id,
        s.statement AS statement,
-       s.group_id AS group_id,
+       s.end_user_id AS end_user_id,
        s.chunk_id AS chunk_id,
        s.created_at AS created_at,
        s.expired_at AS expired_at,
@@ -316,15 +307,13 @@ LIMIT $limit
 # 查询实体名称包含指定字符串的实体
 SEARCH_ENTITIES_BY_NAME = """
 CALL db.index.fulltext.queryNodes("entitiesFulltext", $q) YIELD node AS e, score
-WHERE ($group_id IS NULL OR e.group_id = $group_id)
+WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
 OPTIONAL MATCH (s:Statement)-[:REFERENCES_ENTITY]->(e)
 OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s)
 RETURN e.id AS id,
        e.name AS name,
-       e.group_id AS group_id,
+       e.end_user_id AS end_user_id,
        e.entity_type AS entity_type,
-       e.apply_id AS apply_id,
-       e.user_id AS user_id,
        e.created_at AS created_at,
        e.expired_at AS expired_at,
        e.entity_idx AS entity_idx,
@@ -347,11 +336,11 @@ LIMIT $limit
 
 SEARCH_CHUNKS_BY_CONTENT = """
 CALL db.index.fulltext.queryNodes("chunksFulltext", $q) YIELD node AS c, score
-WHERE ($group_id IS NULL OR c.group_id = $group_id)
+WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
 OPTIONAL MATCH (c)-[:CONTAINS]->(s:Statement)
 OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity)
 RETURN c.id AS chunk_id,
-       c.group_id AS group_id,
+       c.end_user_id AS end_user_id,
        c.content AS content,
        c.dialog_id AS dialog_id,
        c.sequence_number AS sequence_number,
@@ -413,10 +402,10 @@ LIMIT $limit
 
 SEARCH_DIALOGUE_BY_DIALOG_ID = """
 MATCH (d:Dialogue)
-WHERE ($group_id IS NULL OR d.group_id = $group_id)
+WHERE ($end_user_id IS NULL OR d.end_user_id = $end_user_id)
   AND d.id = $dialog_id
 RETURN d.id AS dialog_id,
-       d.group_id AS group_id,
+       d.end_user_id AS end_user_id,
        d.content AS content,
        d.created_at AS created_at,
        d.expired_at AS expired_at
@@ -426,10 +415,10 @@ LIMIT $limit
 
 SEARCH_CHUNK_BY_CHUNK_ID = """
 MATCH (c:Chunk)
-WHERE ($group_id IS NULL OR c.group_id = $group_id)
+WHERE ($end_user_id IS NULL OR c.end_user_id = $end_user_id)
   AND c.id = $chunk_id
 RETURN c.id AS chunk_id,
-       c.group_id AS group_id,
+       c.end_user_id AS end_user_id,
        c.content AS content,
        c.dialog_id AS dialog_id,
        c.created_at AS created_at,
@@ -441,18 +430,14 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_BY_TEMPORAL = """
 MATCH (s:Statement)
-WHERE ($group_id IS NULL OR s.group_id = $group_id)
-  AND ($apply_id IS NULL OR s.apply_id = $apply_id)
-  AND ($user_id IS NULL OR s.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id)
   AND ((($start_date IS NULL OR datetime(s.created_at) >= datetime($start_date))
   AND ($end_date IS NULL OR datetime(s.created_at) <= datetime($end_date)))
   OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date)))
   AND ($invalid_date IS NULL OR (s.invalid_at IS NOT NULL AND datetime(s.invalid_at) <= datetime($invalid_date)))))
 RETURN s.id AS id,
        s.statement AS statement,
-       s.group_id AS group_id,
-       s.apply_id AS apply_id,
-       s.user_id AS user_id,
+       s.end_user_id AS end_user_id,
        s.chunk_id AS chunk_id,
        s.created_at AS created_at,
        s.valid_at AS valid_at,
@@ -468,9 +453,7 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL = """
 CALL db.index.fulltext.queryNodes("statementsFulltext", $q) YIELD node AS s, score
-WHERE ($group_id IS NULL OR s.group_id = $group_id)
-  AND ($apply_id IS NULL OR s.apply_id = $apply_id)
-  AND ($user_id IS NULL OR s.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id)
   AND ((($start_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) >= datetime($start_date)))
   AND ($end_date IS NULL OR (s.created_at IS NOT NULL AND datetime(s.created_at) <= datetime($end_date))))
   OR (($valid_date IS NULL OR (s.valid_at IS NOT NULL AND datetime(s.valid_at) >= datetime($valid_date)))
@@ -479,9 +462,7 @@ OPTIONAL MATCH (c:Chunk)-[:CONTAINS]->(s)
 OPTIONAL MATCH (s)-[:REFERENCES_ENTITY]->(e:ExtractedEntity)
 RETURN s.id AS id,
        s.statement AS statement,
-       s.group_id AS group_id,
-       s.apply_id AS apply_id,
-       s.user_id AS user_id,
+       s.end_user_id AS end_user_id,
        s.chunk_id AS chunk_id,
        s.created_at AS created_at,
        s.valid_at AS valid_at,
@@ -499,15 +480,11 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_BY_CREATED_AT = """
 MATCH (n:Statement)
-WHERE ($group_id IS NULL OR n.group_id = $group_id)
-  AND ($apply_id IS NULL OR n.apply_id = $apply_id)
-  AND ($user_id IS NULL OR n.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id)
   AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 10)) = date($created_at))
 RETURN n.id AS id,
        n.statement AS statement,
-       n.group_id AS group_id,
-       n.apply_id AS apply_id,
-       n.user_id AS user_id,
+       n.end_user_id AS end_user_id,
        n.chunk_id AS chunk_id,
        n.created_at AS created_at,
        n.valid_at AS valid_at,
@@ -519,15 +496,11 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_BY_VALID_AT = """
 MATCH (n:Statement)
-WHERE ($group_id IS NULL OR n.group_id = $group_id)
-  AND ($apply_id IS NULL OR n.apply_id = $apply_id)
-  AND ($user_id IS NULL OR n.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id)
   AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) = date($valid_at))
 RETURN n.id AS id,
        n.statement AS statement,
-       n.group_id AS group_id,
-       n.apply_id AS apply_id,
-       n.user_id AS user_id,
+       n.end_user_id AS end_user_id,
        n.chunk_id AS chunk_id,
        n.created_at AS created_at,
        n.valid_at AS valid_at,
@@ -539,15 +512,11 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_G_CREATED_AT = """
 MATCH (n:Statement)
-WHERE ($group_id IS NULL OR n.group_id = $group_id)
-  AND ($apply_id IS NULL OR n.apply_id = $apply_id)
-  AND ($user_id IS NULL OR n.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id)
   AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) = date($created_at))
 RETURN n.id AS id,
        n.statement AS statement,
-       n.group_id AS group_id,
-       n.apply_id AS apply_id,
-       n.user_id AS user_id,
+       n.end_user_id AS end_user_id,
        n.chunk_id AS chunk_id,
        n.created_at AS created_at,
        n.valid_at AS valid_at,
@@ -559,15 +528,11 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_L_CREATED_AT = """
 MATCH (n:Statement)
-WHERE ($group_id IS NULL OR n.group_id = $group_id)
-  AND ($apply_id IS NULL OR n.apply_id = $apply_id)
-  AND ($user_id IS NULL OR n.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id)
   AND ($created_at IS NOT NULL AND date(substring(n.created_at, 0, 19)) < date($created_at))
 RETURN n.id AS id,
        n.statement AS statement,
-       n.group_id AS group_id,
-       n.apply_id AS apply_id,
-       n.user_id AS user_id,
+       n.end_user_id AS end_user_id,
        n.chunk_id AS chunk_id,
        n.created_at AS created_at,
        n.valid_at AS valid_at,
@@ -579,15 +544,11 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_G_VALID_AT = """
 MATCH (n:Statement)
-WHERE ($group_id IS NULL OR n.group_id = $group_id)
-  AND ($apply_id IS NULL OR n.apply_id = $apply_id)
-  AND ($user_id IS NULL OR n.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id)
   AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) > date($valid_at))
 RETURN n.id AS id,
        n.statement AS statement,
-       n.group_id AS group_id,
-       n.apply_id AS apply_id,
-       n.user_id AS user_id,
+       n.end_user_id AS end_user_id,
        n.chunk_id AS chunk_id,
        n.created_at AS created_at,
        n.valid_at AS valid_at,
@@ -599,15 +560,11 @@ LIMIT $limit
 
 SEARCH_STATEMENTS_L_VALID_AT = """
 MATCH (n:Statement)
-WHERE ($group_id IS NULL OR n.group_id = $group_id)
-  AND ($apply_id IS NULL OR n.apply_id = $apply_id)
-  AND ($user_id IS NULL OR n.user_id = $user_id)
+WHERE ($end_user_id IS NULL OR n.end_user_id = $end_user_id)
   AND ($valid_at IS NOT NULL AND date(substring(n.valid_at, 0, 10)) < date($valid_at))
 RETURN n.id AS id,
        n.statement AS statement,
-       n.group_id AS group_id,
-       n.apply_id AS apply_id,
-       n.user_id AS user_id,
+       n.end_user_id AS end_user_id,
        n.chunk_id AS chunk_id,
        n.created_at AS created_at,
        n.valid_at AS valid_at,
@@ -665,18 +622,18 @@ LIMIT $limit
 
 # 根据id修改句子的invalid_at的值
 UPDATE_STATEMENT_INVALID_AT = """
-MATCH (n:Statement {group_id: $group_id, id: $id})
+MATCH (n:Statement {end_user_id: $end_user_id, id: $id})
 SET n.invalid_at = $new_invalid_at
 """
 
 # MemorySummary keyword search using fulltext index
 SEARCH_MEMORY_SUMMARIES_BY_KEYWORD = """
 CALL db.index.fulltext.queryNodes("summariesFulltext", $q) YIELD node AS m, score
-WHERE ($group_id IS NULL OR m.group_id = $group_id)
+WHERE ($end_user_id IS NULL OR m.end_user_id = $end_user_id)
 OPTIONAL MATCH (m)-[:DERIVED_FROM_STATEMENT]->(s:Statement)
 RETURN m.id AS id,
        m.name AS name,
-       m.group_id AS group_id,
+       m.end_user_id AS end_user_id,
        m.dialog_id AS dialog_id,
        m.chunk_ids AS chunk_ids,
        m.content AS content,
@@ -695,10 +652,10 @@ MEMORY_SUMMARY_EMBEDDING_SEARCH = """
 CALL db.index.vector.queryNodes('summary_embedding_index', $limit * 100, $embedding)
 YIELD node AS m, score
 WHERE m.summary_embedding IS NOT NULL
-  AND ($group_id IS NULL OR m.group_id = $group_id)
+  AND ($end_user_id IS NULL OR m.end_user_id = $end_user_id)
 RETURN m.id AS id,
        m.name AS name,
-       m.group_id AS group_id,
+       m.end_user_id AS end_user_id,
        m.dialog_id AS dialog_id,
        m.chunk_ids AS chunk_ids,
        m.content AS content,
@@ -718,9 +675,7 @@ MERGE (m:MemorySummary {id: summary.id})
 SET m += {
     id: summary.id,
     name: summary.name,
-    group_id: summary.group_id,
-    user_id: summary.user_id,
-    apply_id: summary.apply_id,
+    end_user_id: summary.end_user_id,
     run_id: summary.run_id,
     created_at: summary.created_at,
     expired_at: summary.expired_at,
@@ -745,7 +700,7 @@ MATCH (ms:MemorySummary {id: e.summary_id, run_id: e.run_id})
 MATCH (c:Chunk {id: e.chunk_id, run_id: e.run_id})
 MATCH (c)-[:CONTAINS]->(s:Statement {run_id: e.run_id})
 MERGE (ms)-[r:DERIVED_FROM_STATEMENT]->(s)
-SET r.group_id = e.group_id,
+SET r.end_user_id = e.end_user_id,
     r.run_id = e.run_id,
     r.created_at = e.created_at,
     r.expired_at = e.expired_at
@@ -774,7 +729,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END |
         source_statement_id: rel.source_statement_id,
         valid_at: rel.valid_at,
         invalid_at: rel.invalid_at,
-        group_id: rel.group_id,
+        end_user_id: rel.end_user_id,
         user_id: rel.user_id,
         apply_id: rel.apply_id,
         run_id: rel.run_id,
@@ -796,7 +751,7 @@ FOREACH (rel IN CASE WHEN r IS NOT NULL THEN [r] ELSE [] END |
         source_statement_id: rel.source_statement_id,
         valid_at: rel.valid_at,
         invalid_at: rel.invalid_at,
-        group_id: rel.group_id,
+        end_user_id: rel.end_user_id,
         user_id: rel.user_id,
         apply_id: rel.apply_id,
         run_id: rel.run_id,
@@ -814,7 +769,7 @@ RETURN count(losing) as deleted
 
 neo4j_statement_part = '''
 MATCH (n:Statement)
-WHERE n.group_id = "{}" 
+WHERE n.end_user_id = "{}" 
   AND datetime(n.created_at) >= datetime() - duration('P3D')
 RETURN 
   n.statement as statement_name,
@@ -824,7 +779,7 @@ RETURN
 '''
 neo4j_statement_all = '''
 MATCH (n:Statement)
-WHERE n.group_id = "{}" 
+WHERE n.end_user_id = "{}" 
 RETURN 
   n.statement as statement_name,
   n.id as statement_id
@@ -832,7 +787,7 @@ RETURN
 '''
 neo4j_query_part = """
             MATCH (n)-[r]-(m:ExtractedEntity)
-            WHERE n.group_id = "{}" 
+            WHERE n.end_user_id = "{}" 
             AND datetime(n.created_at) >= datetime() - duration('P3D')
             WITH DISTINCT m
             OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity)
@@ -853,7 +808,7 @@ neo4j_query_part = """
                           """
 neo4j_query_all = """
                 MATCH (n)-[r]-(m:ExtractedEntity)
-                WHERE n.group_id = "{}" 
+                WHERE n.end_user_id = "{}" 
                 WITH DISTINCT m
                 OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity)
                 RETURN 
@@ -1027,14 +982,14 @@ RETURN DISTINCT
 
 Memory_Space_User="""
 MATCH (n)-[r]->(m)
-WHERE n.group_id = $group_id  AND m.name="用户" 
+WHERE n.end_user_id = $end_user_id  AND m.name="用户" 
 return DISTINCT elementId(m) as id
 """
 Memory_Space_Entity="""
 MATCH (n)-[]-(m)
 WHERE elementId(m) = $id AND  m.entity_type = "Person"
 RETURN
-DISTINCT m.name as name,m.group_id as group_id
+DISTINCT m.name as name,m.end_user_id as end_user_id
 """
 Memory_Space_Associative="""
 MATCH (u)-[]-(x)-[]-(h)

api/app/repositories/neo4j/dialog_repository.py

@@ -19,7 +19,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
     """对话仓储
     
     管理对话节点的创建、查询、更新和删除操作。
-    提供按group_id、user_id、ref_id等条件查询对话的方法。
+    提供按end_user_id、user_id、ref_id等条件查询对话的方法。
     
     Attributes:
         connector: Neo4j连接器实例
@@ -54,17 +54,17 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
         
         return DialogueNode(**n)
     
-    async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[DialogueNode]:
-        """根据group_id查询对话
+    async def find_by_end_user_id(self, end_user_id: str, limit: int = 100) -> List[DialogueNode]:
+        """根据end_user_id查询对话
         
         Args:
-            group_id: 组ID
+            end_user_id: 组ID
             limit: 返回结果的最大数量
             
         Returns:
             List[DialogueNode]: 对话列表
         """
-        return await self.find({"group_id": group_id}, limit=limit)
+        return await self.find({"end_user_id": end_user_id}, limit=limit)
     
     async def find_by_user_id(self, user_id: str, limit: int = 100) -> List[DialogueNode]:
         """根据user_id查询对话
@@ -94,14 +94,14 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
     
     async def find_by_group_and_user(
         self,
-        group_id: str,
+        end_user_id: str,
         user_id: str,
         limit: int = 100
     ) -> List[DialogueNode]:
-        """根据group_id和user_id查询对话
+        """根据end_user_id和user_id查询对话
         
         Args:
-            group_id: 组ID
+            end_user_id: 组ID
             user_id: 用户ID
             limit: 返回结果的最大数量
             
@@ -109,20 +109,20 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
             List[DialogueNode]: 对话列表
         """
         return await self.find(
-            {"group_id": group_id, "user_id": user_id},
+            {"end_user_id": end_user_id, "user_id": user_id},
             limit=limit
         )
     
     async def find_recent_dialogs(
         self,
-        group_id: str,
+        end_user_id: str,
         days: int = 7,
         limit: int = 100
     ) -> List[DialogueNode]:
         """查询最近的对话
         
         Args:
-            group_id: 组ID
+            end_user_id: 组ID
             days: 查询最近多少天的对话
             limit: 返回结果的最大数量
             
@@ -131,7 +131,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         AND n.created_at >= datetime() - duration({{days: $days}})
         RETURN n
         ORDER BY n.created_at DESC
@@ -139,7 +139,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
         """
         results = await self.connector.execute_query(
             query,
-            group_id=group_id,
+            end_user_id=end_user_id,
             days=days,
             limit=limit
         )
@@ -164,22 +164,22 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
     async def find_by_config_and_group(
         self,
         config_id: str,
-        group_id: str,
+        end_user_id: str,
         limit: int = 100
     ) -> List[DialogueNode]:
-        """根据config_id和group_id查询对话
+        """根据config_id和end_user_id查询对话
         
         支持按配置ID和组ID同时过滤,确保只返回使用特定配置处理的对话。
         
         Args:
             config_id: 配置ID
-            group_id: 组ID
+            end_user_id: 组ID
             limit: 返回结果的最大数量
             
         Returns:
             List[DialogueNode]: 对话列表
         """
         return await self.find(
-            {"config_id": config_id, "group_id": group_id},
+            {"config_id": config_id, "end_user_id": end_user_id},
             limit=limit
         )

api/app/repositories/neo4j/emotion_repository.py

@@ -40,7 +40,7 @@ class EmotionRepository:
     
     async def get_emotion_tags(
         self,
-        group_id: str,
+        end_user_id: str,
         emotion_type: Optional[str] = None,
         start_date: Optional[str] = None,
         end_date: Optional[str] = None,
@@ -51,7 +51,7 @@ class EmotionRepository:
         查询指定用户的情绪类型分布，包括计数、百分比和平均强度。
         
         Args:
-            group_id: 用户组ID（宿主ID）
+            end_user_id: 用户组ID（宿主ID）
             emotion_type: 可选的情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）
             start_date: 可选的开始日期（ISO格式字符串）
             end_date: 可选的结束日期（ISO格式字符串）
@@ -65,8 +65,8 @@ class EmotionRepository:
                 - avg_intensity: 平均强度
         """
         # 构建查询条件
-        where_clauses = ["s.group_id = $group_id", "s.emotion_type IS NOT NULL"]
-        params = {"group_id": group_id, "limit": limit}
+        where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_type IS NOT NULL"]
+        params = {"end_user_id": end_user_id, "limit": limit}
         
         if emotion_type:
             where_clauses.append("s.emotion_type = $emotion_type")
@@ -119,7 +119,7 @@ class EmotionRepository:
     
     async def get_emotion_wordcloud(
         self,
-        group_id: str,
+        end_user_id: str,
         emotion_type: Optional[str] = None,
         limit: int = 50
     ) -> List[Dict[str, Any]]:
@@ -128,7 +128,7 @@ class EmotionRepository:
         查询情绪关键词及其频率，用于生成词云可视化。
         
         Args:
-            group_id: 用户组ID（宿主ID）
+            end_user_id: 用户组ID（宿主ID）
             emotion_type: 可选的情绪类型过滤
             limit: 返回关键词的最大数量
             
@@ -140,8 +140,8 @@ class EmotionRepository:
                 - avg_intensity: 平均强度
         """
         # 构建查询条件
-        where_clauses = ["s.group_id = $group_id", "s.emotion_keywords IS NOT NULL"]
-        params = {"group_id": group_id, "limit": limit}
+        where_clauses = ["s.end_user_id = $end_user_id", "s.emotion_keywords IS NOT NULL"]
+        params = {"end_user_id": end_user_id, "limit": limit}
         
         if emotion_type:
             where_clauses.append("s.emotion_type = $emotion_type")
@@ -186,7 +186,7 @@ class EmotionRepository:
     
     async def get_emotions_in_range(
         self,
-        group_id: str,
+        end_user_id: str,
         time_range: str = "30d"
     ) -> List[Dict[str, Any]]:
         """获取时间范围内的情绪数据
@@ -194,7 +194,7 @@ class EmotionRepository:
         查询指定时间范围内的所有情绪数据，用于健康指数计算。
         
         Args:
-            group_id: 用户组ID（宿主ID）
+            end_user_id: 用户组ID（宿主ID）
             time_range: 时间范围（7d/30d/90d）
             
         Returns:
@@ -214,7 +214,7 @@ class EmotionRepository:
         # 优化的 Cypher 查询：使用字符串比较避免时区问题
         query = """
         MATCH (s:Statement)
-        WHERE s.group_id = $group_id
+        WHERE s.end_user_id = $end_user_id
           AND s.emotion_type IS NOT NULL
           AND s.created_at >= $start_date
         RETURN s.id as statement_id,
@@ -227,7 +227,7 @@ class EmotionRepository:
         try:
             results = await self.connector.execute_query(
                 query,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 start_date=start_date
             )
             formatted_results = [

api/app/repositories/neo4j/graph_saver.py

@@ -44,9 +44,7 @@ async def save_entities_and_relationships(
             'created_at': edge.created_at.isoformat(),
             'expired_at': edge.expired_at.isoformat(),
             'run_id': edge.run_id,
-            'group_id': edge.group_id,
-            'user_id': edge.user_id,
-            'apply_id': edge.apply_id,
+            'end_user_id': edge.end_user_id,
         }
         all_relationships.append(relationship)
 
@@ -101,9 +99,7 @@ async def save_statement_chunk_edges(
             "id": edge.id,
             "source": edge.source,
             "target": edge.target,
-            "group_id": edge.group_id,
-            "user_id": edge.user_id,
-            "apply_id": edge.apply_id,
+            "end_user_id": edge.end_user_id,
             "run_id": edge.run_id,
             "created_at": edge.created_at.isoformat() if edge.created_at else None,
             "expired_at": edge.expired_at.isoformat() if edge.expired_at else None,
@@ -132,9 +128,7 @@ async def save_statement_entity_edges(
         edge_data = {
             "source": edge.source,
             "target": edge.target,
-            "group_id": edge.group_id,
-            "user_id": edge.user_id,
-            "apply_id": edge.apply_id,
+            "end_user_id": edge.end_user_id,
             "run_id": edge.run_id,
             "connect_strength": edge.connect_strength,
             "created_at": edge.created_at.isoformat() if edge.created_at else None,

api/app/repositories/neo4j/graph_search.py

@@ -33,7 +33,7 @@ async def _update_activation_values_batch(
     connector: Neo4jConnector,
     nodes: List[Dict[str, Any]],
     node_label: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     max_retries: int = 3
 ) -> List[Dict[str, Any]]:
     """
@@ -46,7 +46,7 @@ async def _update_activation_values_batch(
         connector: Neo4j连接器
         nodes: 节点列表，每个节点必须包含 'id' 字段
         node_label: 节点标签（Statement, ExtractedEntity, MemorySummary）
-        group_id: 组ID（可选）
+        end_user_id: 组ID（可选）
         max_retries: 最大重试次数
     
     Returns:
@@ -97,7 +97,7 @@ async def _update_activation_values_batch(
         updated_nodes = await access_manager.record_batch_access(
             node_ids=unique_node_ids,
             node_label=node_label,
-            group_id=group_id
+            end_user_id=end_user_id
         )
         
         logger.info(
@@ -118,7 +118,7 @@ async def _update_activation_values_batch(
 async def _update_search_results_activation(
     connector: Neo4jConnector,
     results: Dict[str, List[Dict[str, Any]]],
-    group_id: Optional[str] = None
+    end_user_id: Optional[str] = None
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     更新搜索结果中所有知识节点的激活值
@@ -129,7 +129,7 @@ async def _update_search_results_activation(
     Args:
         connector: Neo4j连接器
         results: 搜索结果字典，包含不同类型节点的列表
-        group_id: 组ID（可选）
+        end_user_id: 组ID（可选）
     
     Returns:
         Dict[str, List[Dict[str, Any]]]: 更新后的搜索结果
@@ -152,7 +152,7 @@ async def _update_search_results_activation(
                     connector=connector,
                     nodes=results[key],
                     node_label=label,
-                    group_id=group_id
+                    end_user_id=end_user_id
                 )
             )
             update_keys.append(key)
@@ -218,7 +218,7 @@ async def _update_search_results_activation(
 async def search_graph(
     connector: Neo4jConnector,
     q: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 50,
     include: List[str] = None,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -236,7 +236,7 @@ async def search_graph(
     Args:
         connector: Neo4j connector
         q: Query text
-        group_id: Optional group filter
+        end_user_id: Optional group filter
         limit: Max results per category
         include: List of categories to search (default: all)
 
@@ -254,7 +254,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_STATEMENTS_BY_KEYWORD,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("statements")
@@ -263,7 +263,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_ENTITIES_BY_NAME,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("entities")
@@ -272,7 +272,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_CHUNKS_BY_CONTENT,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("chunks")
@@ -281,7 +281,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_MEMORY_SUMMARIES_BY_KEYWORD,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("summaries")
@@ -310,12 +310,12 @@ async def search_graph(
         key in include and key in results and results[key]
         for key in ['statements', 'entities', 'chunks']
     )
-    
+
     if needs_activation_update:
         results = await _update_search_results_activation(
             connector=connector,
             results=results,
-            group_id=group_id
+            end_user_id=end_user_id
         )
     
     return results
@@ -325,7 +325,7 @@ async def search_graph_by_embedding(
     connector: Neo4jConnector,
     embedder_client,
     query_text: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 50,
     include: List[str] = ["statements", "chunks", "entities","summaries"],
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -337,7 +337,7 @@ async def search_graph_by_embedding(
 
     - Computes query embedding with the provided embedder_client
     - Ranks by cosine similarity in Cypher
-    - Filters by group_id if provided
+    - Filters by end_user_id if provided
     - Returns up to 'limit' per included type
     """
     import time
@@ -346,7 +346,7 @@ async def search_graph_by_embedding(
     embed_start = time.time()
     embeddings = await embedder_client.response([query_text])
     embed_time = time.time() - embed_start
-    logger.info(f"[PERF] Embedding generation took: {embed_time:.4f}s")
+    print(f"[PERF] Embedding generation took: {embed_time:.4f}s")
     
     if not embeddings or not embeddings[0]:
         return {"statements": [], "chunks": [], "entities": [], "summaries": []}
@@ -361,7 +361,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             STATEMENT_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("statements")
@@ -371,7 +371,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             CHUNK_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("chunks")
@@ -381,7 +381,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             ENTITY_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("entities")
@@ -391,7 +391,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             MEMORY_SUMMARY_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("summaries")
@@ -400,7 +400,7 @@ async def search_graph_by_embedding(
     query_start = time.time()
     task_results = await asyncio.gather(*tasks, return_exceptions=True)
     query_time = time.time() - query_start
-    logger.info(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s")
+    print(f"[PERF] Neo4j queries (parallel) took: {query_time:.4f}s")
     
     # Build results dictionary
     results: Dict[str, List[Dict[str, Any]]] = {
@@ -429,13 +429,13 @@ async def search_graph_by_embedding(
         key in include and key in results and results[key]
         for key in ['statements', 'entities', 'chunks']
     )
-    
+
     if needs_activation_update:
         update_start = time.time()
         results = await _update_search_results_activation(
             connector=connector,
             results=results,
-            group_id=group_id
+            end_user_id=end_user_id
         )
         update_time = time.time() - update_start
         logger.info(f"[PERF] Activation value updates took: {update_time:.4f}s")
@@ -445,7 +445,7 @@ async def search_graph_by_embedding(
     return results
 async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全文索引按名称检索候选实体
     connector: Neo4jConnector,
-    group_id: str,
+    end_user_id: str,
     entities: List[Dict[str, Any]],
     use_contains_fallback: bool = True,
     batch_size: int = 500,
@@ -453,7 +453,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     为第二层去重消歧批量检索候选实体（适配新版 cypher_queries）：
-    - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (group_id, name) 检索候选；
+    - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (end_user_id, name) 检索候选；
     - 保留并发控制与返回结构（incoming_id -> [db_entity_props...]）；
     - 若提供 `entity_type`，在本地对返回结果做类型过滤；
     - `use_contains_fallback` 保留形参以兼容，必要时可扩展二次查询策略。
@@ -477,7 +477,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
                 rows = await connector.execute_query(
                     SEARCH_ENTITIES_BY_NAME,
                     q=name,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=100,
                 )
             except Exception:
@@ -501,7 +501,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
                     rows = await connector.execute_query(
                         SEARCH_ENTITIES_BY_NAME,
                         q=name.lower(),
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=100,
                     )
                     for r in rows:
@@ -532,9 +532,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
 async def search_graph_by_keyword_temporal(
     connector: Neo4jConnector,
     query_text: str,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     start_date: Optional[str] = None,
     end_date: Optional[str] = None,
     valid_date: Optional[str] = None,
@@ -547,32 +545,30 @@ async def search_graph_by_keyword_temporal(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements containing query_text created between start_date and end_date
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     if not query_text:
-        logger.warning(f"query_text cannot be empty")
+        print(f"query_text不能为空")
         return {"statements": []}
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL,
         q=query_text,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
         start_date=start_date,
         end_date=end_date,
         valid_date=valid_date,
         invalid_date=invalid_date,
         limit=limit,
     )
-    logger.debug(f"Temporal keyword search results: {len(statements)} statements found")
+    print(f"查询结果为：\n{statements}")
 
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
 
     return results
@@ -580,9 +576,7 @@ async def search_graph_by_keyword_temporal(
 
 async def search_graph_by_temporal(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     start_date: Optional[str] = None,
     end_date: Optional[str] = None,
     valid_date: Optional[str] = None,
@@ -595,14 +589,12 @@ async def search_graph_by_temporal(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created between start_date and end_date
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_TEMPORAL,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
         start_date=start_date,
         end_date=end_date,
         valid_date=valid_date,
@@ -610,16 +602,16 @@ async def search_graph_by_temporal(
         limit=limit,
     )
 
-    logger.debug(f"Temporal search query: {SEARCH_STATEMENTS_BY_TEMPORAL}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, start_date={start_date}, end_date={end_date}, valid_date={valid_date}, invalid_date={invalid_date}, limit={limit}")
-    logger.debug(f"Temporal search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_TEMPORAL}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
@@ -628,23 +620,23 @@ async def search_graph_by_temporal(
 async def search_graph_by_dialog_id(
     connector: Neo4jConnector,
     dialog_id: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Dialogues.
 
     - Matches dialogues with dialog_id
-    - Optionally filters by group_id
+    - Optionally filters by end_user_id
     - Returns up to 'limit' dialogues
     """
     if not dialog_id:
-        logger.warning(f"dialog_id cannot be empty")
+        print(f"dialog_id不能为空")
         return {"dialogues": []}
 
     dialogues = await connector.execute_query(
         SEARCH_DIALOGUE_BY_DIALOG_ID,
-        group_id=group_id,
+        end_user_id=end_user_id,
         dialog_id=dialog_id,
         limit=limit,
     )
@@ -654,15 +646,15 @@ async def search_graph_by_dialog_id(
 async def search_graph_by_chunk_id(
     connector: Neo4jConnector,
     chunk_id : str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
     if not chunk_id:
-        logger.warning(f"chunk_id cannot be empty")
+        print(f"chunk_id不能为空")
         return {"chunks": []}
     chunks = await connector.execute_query(
         SEARCH_CHUNK_BY_CHUNK_ID,
-        group_id=group_id,
+        end_user_id=end_user_id,
         chunk_id=chunk_id,
         limit=limit,
     )
@@ -671,9 +663,9 @@ async def search_graph_by_chunk_id(
 
 async def search_graph_by_created_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
+    
+    
     created_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -683,37 +675,37 @@ async def search_graph_by_created_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_CREATED_AT,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
+        
+        
         created_at=created_at,
         limit=limit,
     )
 
-    logger.debug(f"Search by created_at query: {SEARCH_STATEMENTS_BY_CREATED_AT}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}")
-    logger.debug(f"Search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_CREATED_AT}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id} created_at: {created_at}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_by_valid_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
+    
+    
     valid_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -723,37 +715,37 @@ async def search_graph_by_valid_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_VALID_AT,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
+        
+        
         valid_at=valid_at,
         limit=limit,
     )
 
-    logger.debug(f"Search by valid_at query: {SEARCH_STATEMENTS_BY_VALID_AT}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}")
-    logger.debug(f"Search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_VALID_AT}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}， valid_at: {valid_at}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_g_created_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
+    
+    
     created_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -763,37 +755,37 @@ async def search_graph_g_created_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_G_CREATED_AT,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
+        
+        
         created_at=created_at,
         limit=limit,
     )
 
-    logger.debug(f"Search greater than created_at query: {SEARCH_STATEMENTS_G_CREATED_AT}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}")
-    logger.debug(f"Search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_G_CREATED_AT}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_g_valid_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
+    
+    
     valid_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -803,37 +795,37 @@ async def search_graph_g_valid_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_G_VALID_AT,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
+        
+        
         valid_at=valid_at,
         limit=limit,
     )
 
-    logger.debug(f"Search greater than valid_at query: {SEARCH_STATEMENTS_G_VALID_AT}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}")
-    logger.debug(f"Search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_G_VALID_AT}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_l_created_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
+    
+    
     created_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -843,37 +835,37 @@ async def search_graph_l_created_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_L_CREATED_AT,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
+        
+        
         created_at=created_at,
         limit=limit,
     )
 
-    logger.debug(f"Search less than created_at query: {SEARCH_STATEMENTS_L_CREATED_AT}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, created_at={created_at}, limit={limit}")
-    logger.debug(f"Search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_L_CREATED_AT}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_l_valid_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
+    
+    
     valid_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -883,28 +875,28 @@ async def search_graph_l_valid_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_L_VALID_AT,
-        group_id=group_id,
-        apply_id=apply_id,
-        user_id=user_id,
+        end_user_id=end_user_id,
+        
+        
         valid_at=valid_at,
         limit=limit,
     )
 
-    logger.debug(f"Search less than valid_at query: {SEARCH_STATEMENTS_L_VALID_AT}")
-    logger.debug(f"Query params: group_id={group_id}, apply_id={apply_id}, user_id={user_id}, valid_at={valid_at}, limit={limit}")
-    logger.debug(f"Search results: {len(statements)} statements found")
+    print(f"查询语句为：\n{SEARCH_STATEMENTS_L_VALID_AT}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id},  valid_at: {valid_at}, limit: {limit}}}")
+    print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
     results = {"statements": statements}
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results

api/app/repositories/neo4j/memory_summary_repository.py

@@ -18,7 +18,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     """Memory Summary Repository
     
     Manages CRUD operations for MemorySummary nodes.
-    Provides methods to query summaries by group_id, user_id, and time ranges.
+    Provides methods to query summaries by end_user_id, user_id, and time ranges.
     
     Attributes:
         connector: Neo4j connector instance
@@ -51,17 +51,17 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         
         return dict(n)
     
-    async def find_by_group_id(
+    async def find_by_end_user_id(
         self, 
-        group_id: str, 
+        end_user_id: str,
         limit: int = 1000,
         start_date: Optional[datetime] = None,
         end_date: Optional[datetime] = None
     ) -> List[Dict[str, Any]]:
-        """Query memory summaries by group_id
+        """Query memory summaries by end_user_id
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             limit: Maximum number of results to return
             start_date: Optional start date filter
             end_date: Optional end date filter
@@ -71,10 +71,10 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         """
         
-        params = {"group_id": group_id, "limit": limit}
+        params = {"end_user_id": end_user_id, "limit": limit}
         
         # Add date range filters if provided
         if start_date:
@@ -139,16 +139,16 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     
     async def find_by_group_and_user(
         self,
-        group_id: str,
+        end_user_id: str,
         user_id: str,
         limit: int = 1000,
         start_date: Optional[datetime] = None,
         end_date: Optional[datetime] = None
     ) -> List[Dict[str, Any]]:
-        """Query memory summaries by both group_id and user_id
+        """Query memory summaries by both end_user_id and user_id
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             user_id: User ID to filter by
             limit: Maximum number of results to return
             start_date: Optional start date filter
@@ -159,10 +159,10 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id AND n.user_id = $user_id
+        WHERE n.end_user_id = $end_user_id AND n.user_id = $user_id
         """
         
-        params = {"group_id": group_id, "user_id": user_id, "limit": limit}
+        params = {"end_user_id": end_user_id, "user_id": user_id, "limit": limit}
         
         # Add date range filters if provided
         if start_date:
@@ -184,14 +184,14 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     
     async def find_recent_summaries(
         self,
-        group_id: str,
+        end_user_id: str,
         days: int = 7,
         limit: int = 1000
     ) -> List[Dict[str, Any]]:
         """Query recent memory summaries
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             days: Number of recent days to query
             limit: Maximum number of results to return
             
@@ -200,7 +200,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         AND n.created_at >= datetime() - duration({{days: $days}})
         RETURN n
         ORDER BY n.created_at DESC
@@ -209,7 +209,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         
         results = await self.connector.execute_query(
             query,
-            group_id=group_id,
+            end_user_id=end_user_id,
             days=days,
             limit=limit
         )
@@ -217,14 +217,14 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     
     async def find_by_content_keywords(
         self,
-        group_id: str,
+        end_user_id: str,
         keywords: List[str],
         limit: int = 100
     ) -> List[Dict[str, Any]]:
         """Query memory summaries by content keywords
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             keywords: List of keywords to search for in content
             limit: Maximum number of results to return
             
@@ -233,7 +233,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         # Build keyword search conditions
         keyword_conditions = []
-        params = {"group_id": group_id, "limit": limit}
+        params = {"end_user_id": end_user_id, "limit": limit}
         
         for i, keyword in enumerate(keywords):
             keyword_conditions.append(f"toLower(n.content) CONTAINS toLower($keyword_{i})")
@@ -243,7 +243,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         AND ({keyword_filter})
         RETURN n
         ORDER BY n.created_at DESC
@@ -253,21 +253,21 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         results = await self.connector.execute_query(query, **params)
         return [self._map_to_dict(r) for r in results]
     
-    async def get_summary_count_by_group(self, group_id: str) -> int:
+    async def get_summary_count_by_group(self, end_user_id: str) -> int:
         """Get count of memory summaries for a group
         
         Args:
-            group_id: Group ID to count summaries for
+            end_user_id: Group ID to count summaries for
             
         Returns:
             int: Number of memory summaries
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         RETURN count(n) as count
         """
         
-        results = await self.connector.execute_query(query, group_id=group_id)
+        results = await self.connector.execute_query(query, end_user_id=end_user_id)
         return results[0]['count'] if results else 0
     

api/app/repositories/neo4j/neo4j_connector.py

@@ -70,11 +70,7 @@ class Neo4jConnector:
             List[Dict[str, Any]]: 查询结果列表，每个元素是一个字典
             
         Example:
-            >>> connector = Neo4jConnector()
-            >>> results = await connector.execute_query(
-            ...     "MATCH (n:Person {name: $name}) RETURN n",
-            ...     name="Alice"
-            ... )
+
         """
         result = await self.driver.execute_query(
             query,
@@ -98,17 +94,7 @@ class Neo4jConnector:
             Any: 事务函数的返回值
             
         Example:
-            >>> async def create_node(tx, name):
-            ...     result = await tx.run(
-            ...         "CREATE (n:Person {name: $name}) RETURN n",
-            ...         name=name
-            ...     )
-            ...     return await result.single()
-            >>> 
-            >>> connector = Neo4jConnector()
-            >>> result = await connector.execute_write_transaction(
-            ...     create_node, name="Alice"
-            ... )
+
         """
         async with self.driver.session(database="neo4j") as session:
             return await session.execute_write(transaction_func, **kwargs)
@@ -126,45 +112,33 @@ class Neo4jConnector:
             Any: 事务函数的返回值
             
         Example:
-            >>> async def get_node(tx, name):
-            ...     result = await tx.run(
-            ...         "MATCH (n:Person {name: $name}) RETURN n",
-            ...         name=name
-            ...     )
-            ...     return await result.single()
-            >>> 
-            >>> connector = Neo4jConnector()
-            >>> result = await connector.execute_read_transaction(
-            ...     get_node, name="Alice"
-            ... )
+
         """
         async with self.driver.session(database="neo4j") as session:
             return await session.execute_read(transaction_func, **kwargs)
     
-    async def delete_group(self, group_id: str):
+    async def delete_group(self, end_user_id: str):
         """删除指定组的所有数据
         
-        删除所有属于指定group_id的节点和边。
+        删除所有属于指定end_user_id的节点和边。
         这是一个危险操作，会永久删除数据。
         
         Args:
-            group_id: 要删除的组ID
+            end_user_id: 要删除的组ID
             
         Example:
-            >>> connector = Neo4jConnector()
-            >>> await connector.delete_group("group_123")
             Group group_123 deleted.
         """
         # 删除节点（DETACH DELETE会同时删除相关的边）
         await self.driver.execute_query(
-            "MATCH (n) WHERE n.group_id = $group_id DETACH DELETE n",
+            "MATCH (n) WHERE n.end_user_id = $end_user_id DETACH DELETE n",
             database="neo4j",
-            group_id=group_id
+            end_user_id=end_user_id
         )
         # 删除独立的边（如果有的话）
         await self.driver.execute_query(
-            "MATCH ()-[r]->() WHERE r.group_id = $group_id DELETE r",
+            "MATCH ()-[r]->() WHERE r.end_user_id = $end_user_id DELETE r",
             database="neo4j",
-            group_id=group_id
+            end_user_id=end_user_id
         )
-        print(f"Group {group_id} deleted.")
+        print(f"Group {end_user_id} deleted.")

api/app/repositories/neo4j/statement_repository.py

@@ -20,7 +20,7 @@ class StatementRepository(BaseNeo4jRepository[StatementNode]):
     """陈述句仓储
     
     管理陈述句节点的创建、查询、更新和删除操作。
-    提供按chunk_id、group_id、向量相似度等条件查询陈述句的方法。
+    提供按chunk_id、end_user_id、向量相似度等条件查询陈述句的方法。
     
     Attributes:
         connector: Neo4j连接器实例

api/app/schemas/app_schema.py

@@ -299,6 +299,18 @@ class AppRelease(BaseModel):
     created_at: datetime.datetime
     updated_at: datetime.datetime
 
+    @field_validator("config", mode="before")
+    @classmethod
+    def parse_config(cls, v):
+        """处理 config 字段，如果是字符串则解析为字典"""
+        if isinstance(v, str):
+            import json
+            try:
+                return json.loads(v)
+            except json.JSONDecodeError:
+                return {}
+        return v if v is not None else {}
+
     @field_serializer("created_at", when_used="json")
     def _serialize_created_at(self, dt: datetime.datetime):
         return int(dt.timestamp() * 1000) if dt else None

api/app/schemas/emotion_schema.py

@@ -1,11 +1,12 @@
 """情绪分析相关的请求和响应模型"""
 
 from typing import Optional
+from uuid import UUID
 from pydantic import BaseModel, Field
 
 class EmotionTagsRequest(BaseModel):
     """获取情绪标签统计请求"""
-    group_id: str = Field(..., description="组ID")
+    end_user_id: str = Field(..., description="组ID")
     emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）")
     start_date: Optional[str] = Field(None, description="开始日期（ISO格式，如：2024-01-01）")
     end_date: Optional[str] = Field(None, description="结束日期（ISO格式，如：2024-12-31）")
@@ -14,14 +15,14 @@ class EmotionTagsRequest(BaseModel):
 
 class EmotionWordcloudRequest(BaseModel):
     """获取情绪词云数据请求"""
-    group_id: str = Field(..., description="组ID")
+    end_user_id: str = Field(..., description="组ID")
     emotion_type: Optional[str] = Field(None, description="情绪类型过滤（joy/sadness/anger/fear/surprise/neutral）")
     limit: int = Field(50, ge=1, le=200, description="返回词语数量")
 
 
 class EmotionHealthRequest(BaseModel):
     """获取情绪健康指数请求"""
-    group_id: str = Field(..., description="组ID")
+    end_user_id: str = Field(..., description="组ID")
     time_range: str = Field("30d", description="时间范围（7d/30d/90d）")
 
 
@@ -29,8 +30,8 @@ class EmotionHealthRequest(BaseModel):
 
 class EmotionSuggestionsRequest(BaseModel):
     """获取个性化情绪建议请求"""
-    group_id: str = Field(..., description="组ID")
-    config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）")
+    end_user_id: str = Field(..., description="组ID")
+    config_id: Optional[UUID] = Field(None, description="配置ID（用于指定LLM模型）")
 
 
 class EmotionGenerateSuggestionsRequest(BaseModel):

api/app/schemas/memory_agent_schema.py

@@ -7,11 +7,11 @@ class UserInput(BaseModel):
     message: str
     history: list[dict]
     search_switch: str
-    group_id: str
+    end_user_id: str
     config_id: Optional[str] = None
 
 
 class Write_UserInput(BaseModel):
     messages: list[dict]
-    group_id: str
-    config_id: Optional[str] = None
+    end_user_id: str
+    config_id: Optional[str] = None

api/app/schemas/memory_config_schema.py

@@ -35,7 +35,7 @@ class ConfigurationError(Exception):
     def __init__(
         self,
         message: str,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         workspace_id: Optional[UUID] = None,
         context: Optional[Dict[str, Any]] = None,
     ):
@@ -72,7 +72,7 @@ class WorkspaceNotFoundError(ConfigurationError):
     def __init__(
         self,
         workspace_id: UUID,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         message: Optional[str] = None,
     ):
         if message is None:
@@ -89,7 +89,7 @@ class ModelNotFoundError(ConfigurationError):
         self,
         model_id: Union[str, UUID],
         model_type: str,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         workspace_id: Optional[UUID] = None,
         message: Optional[str] = None,
     ):
@@ -112,7 +112,7 @@ class ModelInactiveError(ConfigurationError):
         model_id: Union[str, UUID],
         model_name: str,
         model_type: str,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         workspace_id: Optional[UUID] = None,
         message: Optional[str] = None,
     ):
@@ -136,7 +136,7 @@ class InvalidConfigError(ConfigurationError):
         message: str,
         field_name: Optional[str] = None,
         invalid_value: Optional[Any] = None,
-        config_id: Optional[int] = None,
+        config_id: Optional[UUID] = None,
         workspace_id: Optional[UUID] = None,
     ):
         context = {}
@@ -155,7 +155,7 @@ class InvalidConfigError(ConfigurationError):
 class MemoryConfigValidation(BaseModel):
     """Pydantic model for validating memory configuration data from database."""
     
-    config_id: int = Field(..., gt=0, description="Configuration ID must be positive")
+    config_id: UUID = Field(..., description="Configuration ID (UUID)")
     config_name: str = Field(..., min_length=1, max_length=255)
     workspace_id: UUID = Field(..., description="Workspace UUID")
     workspace_name: str = Field(..., min_length=1, max_length=255)
@@ -275,7 +275,7 @@ class ModelValidation(BaseModel):
 
 
 def validate_memory_config_data(
-    config_data: Dict[str, Any], config_id: Optional[int] = None
+    config_data: Dict[str, Any], config_id: Optional[UUID] = None
 ) -> MemoryConfigValidation:
     """Validate memory configuration data using Pydantic model."""
     try:
@@ -302,7 +302,7 @@ def validate_memory_config_data(
 
 
 def validate_workspace_data(
-    workspace_data: Dict[str, Any], config_id: Optional[int] = None
+    workspace_data: Dict[str, Any], config_id: Optional[UUID] = None
 ) -> WorkspaceValidation:
     """Validate workspace data using Pydantic model."""
     try:
@@ -331,7 +331,7 @@ def validate_workspace_data(
 
 
 def validate_model_data(
-    model_data: Dict[str, Any], config_id: Optional[int] = None
+    model_data: Dict[str, Any], config_id: Optional[UUID] = None
 ) -> ModelValidation:
     """Validate model data using Pydantic model."""
     try:
@@ -364,7 +364,7 @@ def validate_model_data(
 class MemoryConfig:
     """Immutable memory configuration loaded from database."""
     
-    config_id: int
+    config_id: UUID
     config_name: str
     workspace_id: UUID
     workspace_name: str

api/app/schemas/memory_perceptual_schema.py

@@ -4,7 +4,7 @@ from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from app.models.memory_perceptual_model import PerceptualType, FileStorageType
+from app.models.memory_perceptual_model import PerceptualType, FileStorageService
 
 
 class PerceptualFilter(BaseModel):
@@ -38,12 +38,14 @@ class PerceptualMemoryItem(BaseModel):
     """感知记忆项"""
     id: uuid.UUID = Field(..., description="Unique memory ID")
     perceptual_type: PerceptualType = Field(..., description="Type of perception, e.g., text, audio, or video")
+    storage_service: FileStorageService = Field(..., description="Storage service for file")
     file_path: str = Field(..., description="File path in the storage service")
-    file_ext: str = Field(..., description="File extension")
     file_name: str = Field(..., description="File name")
+    file_ext: str = Field(..., description="File extension")
     summary: Optional[str] = Field(None, description="summary")
-    storage_type: FileStorageType = Field(..., description="Storage type for file")
+    meta_data: Optional[dict] = Field(None, description="Metadata information")
     created_time: int = Field(..., description="create time")
+
     topic: str = Field(..., description="topic")
     domain: str = Field(..., description="domain")
     keywords: list[str] = Field(..., description="keywords")

api/app/schemas/memory_reflection_schemas.py

@@ -1,5 +1,6 @@
 from pydantic import BaseModel, Field
 from typing import Optional
+from uuid import UUID
 from enum import Enum
 
 
@@ -9,7 +10,7 @@ class OptimizationStrategy(str, Enum):
     ACCURACY_FIRST = "accuracy_first"
     BALANCED = "balanced"
 class Memory_Reflection(BaseModel):
-    config_id: Optional[int] = None
+    config_id: Optional[UUID] = None
     reflection_enabled: bool
     reflection_period_in_hours: str
     reflexion_range: Optional[str] = "partial"

api/app/schemas/memory_storage_schema.py

@@ -1,5 +1,5 @@
 """
-所有的内容是放错误地方了，应该放在models
+
 """
 
 from typing import Any, Optional, List, Dict, Literal, Union
@@ -8,20 +8,8 @@ import uuid
 from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator
 
 
-# ============================================================================
-# 原 UserInput 相关 Schema (保留原有功能)
-# ============================================================================
-class UserInput(BaseModel):
-    message: str
-    history: list[dict]
-    search_switch: str
-    group_id: str
 
 
-class Write_UserInput(BaseModel):
-    message: str
-    group_id: str
-
 
 # ============================================================================
 # 从 json_schema.py 迁移的 Schema
@@ -159,7 +147,7 @@ class ReflexionResultSchema(BaseModel):
 # Composite key identifying a config row
 class ConfigKey(BaseModel):  # 配置参数键模型
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
-    config_id: int = Field("config_id", description="配置唯一标识（字符串）")
+    config_id: uuid.UUID = Field("config_id", description="配置唯一标识（UUID）")
     user_id: str = Field("user_id", description="用户标识（字符串）")
     apply_id: str = Field("apply_id", description="应用或场景标识（字符串）")
 
@@ -250,17 +238,17 @@ class ConfigParamsCreate(BaseModel):  # 创建配置参数模型（仅 body，
 class ConfigParamsDelete(BaseModel):  # 删除配置参数模型（请求体）
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     # config_name: str = Field("配置名称", description="配置名称（字符串）")
-    config_id: int = Field("配置ID", description="配置ID（字符串）")
+    config_id: uuid.UUID = Field("配置ID", description="配置ID（UUID）")
 
 
 class ConfigUpdate(BaseModel):  # 更新记忆萃取引擎配置参数时使用的模型
-    config_id: Optional[int] = None
+    config_id: Optional[uuid.UUID] = None
     config_name: str = Field("配置名称", description="配置名称（字符串）")
     config_desc: str = Field("配置描述", description="配置描述（字符串）")
 
 
 class ConfigUpdateExtracted(BaseModel):  # 更新记忆萃取引擎配置参数时使用的模型
-    config_id: Optional[int] = None
+    config_id: Optional[uuid.UUID] = None
     llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
     embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
     rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
@@ -327,14 +315,14 @@ class ConfigUpdateExtracted(BaseModel):  # 更新记忆萃取引擎配置参数
 
 class ConfigUpdateForget(BaseModel):  # 更新遗忘引擎配置参数时使用的模型
     # 遗忘引擎配置参数更新模型
-    config_id: Optional[int] = None
+    config_id: Optional[uuid.UUID] = None
     lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5")
     lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5")
     offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0")
 
 
 class ConfigPilotRun(BaseModel):  # 试运行触发请求模型
-    config_id: int = Field(..., description="配置ID（唯一）")
+    config_id: uuid.UUID = Field(..., description="配置ID（唯一）")
     dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填")
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
 
@@ -342,7 +330,7 @@ class ConfigPilotRun(BaseModel):  # 试运行触发请求模型
 class ConfigFilter(BaseModel):  # 查询配置参数时使用的模型
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
 
-    config_id: Optional[int] = None
+    config_id: Optional[uuid.UUID] = None
     user_id: Optional[str] = None
     apply_id: Optional[str] = None
 
@@ -418,7 +406,7 @@ class ForgettingConfigResponse(BaseModel):
     """遗忘引擎配置响应模型"""
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     
-    config_id: int = Field(..., description="配置ID")
+    config_id: uuid.UUID = Field(..., description="配置ID")
     decay_constant: float = Field(..., description="衰减常数 d")
     lambda_time: float = Field(..., description="时间衰减参数")
     lambda_mem: float = Field(..., description="记忆衰减参数")
@@ -436,7 +424,7 @@ class ForgettingConfigUpdateRequest(BaseModel):
     """遗忘引擎配置更新请求模型"""
     model_config = ConfigDict(populate_by_name=True, extra="forbid")
     
-    config_id: int = Field(..., description="配置ID")
+    config_id: uuid.UUID = Field(..., description="配置ID")
     decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d")
     lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数")
     lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数")
@@ -511,7 +499,7 @@ class ForgettingCurveRequest(BaseModel):
     
     importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）")
     days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）")
-    config_id: Optional[int] = Field(None, description="配置ID（可选，如果为None则使用默认配置）")
+    config_id: Optional[uuid.UUID] = Field(None, description="配置ID（可选，如果为None则使用默认配置）")
 
 
 class ForgettingCurveResponse(BaseModel):

api/app/schemas/model_schema.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, field_serializer, ConfigDict
+from pydantic import BaseModel, Field, field_serializer, field_validator, ConfigDict
 from typing import Optional, List, Dict, Any
 import datetime
 import uuid
@@ -91,6 +91,18 @@ class ModelApiKey(ModelApiKeyBase):
     created_at: datetime.datetime
     updated_at: datetime.datetime
 
+    @field_validator("config", mode="before")
+    @classmethod
+    def parse_config(cls, v):
+        """处理 config 字段，如果是字符串则解析为字典"""
+        if isinstance(v, str):
+            import json
+            try:
+                return json.loads(v)
+            except json.JSONDecodeError:
+                return {}
+        return v
+
     @field_serializer("created_at", when_used="json")
     def _serialize_created_at(self, dt: datetime.datetime):
         return int(dt.timestamp() * 1000) if dt else None

api/app/schemas/release_share_schema.py

@@ -1,7 +1,7 @@
 import uuid
 import datetime
 from typing import Optional, List, Dict, Any
-from pydantic import BaseModel, Field, ConfigDict, field_serializer
+from pydantic import BaseModel, Field, ConfigDict, field_serializer, field_validator
 
 
 # ---------- Input Schemas ----------
@@ -88,6 +88,18 @@ class SharedReleaseInfo(BaseModel):
     # 嵌入配置
     allow_embed: bool
 
+    @field_validator("config", mode="before")
+    @classmethod
+    def parse_config(cls, v):
+        """处理 config 字段，如果是字符串则解析为字典"""
+        if isinstance(v, str):
+            import json
+            try:
+                return json.loads(v)
+            except json.JSONDecodeError:
+                return {}
+        return v if v is not None else {}
+
 
 class EmbedCode(BaseModel):
     """嵌入代码"""

api/app/services/draft_run_service.py

@@ -92,7 +92,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
             try:
                 memory_content = asyncio.run(
                     MemoryAgentService().read_memory(
-                        group_id=end_user_id,
+                        end_user_id=end_user_id,
                         message=question,
                         history=[],
                         search_switch="2",

api/app/services/emotion_analytics_service.py

@@ -75,7 +75,7 @@ class EmotionAnalyticsService:
             
             # 调用仓储层查询
             tags = await self.emotion_repo.get_emotion_tags(
-                group_id=end_user_id,
+                end_user_id=end_user_id,
                 emotion_type=emotion_type,
                 start_date=start_date,
                 end_date=end_date,
@@ -157,7 +157,7 @@ class EmotionAnalyticsService:
             
             # 调用仓储层查询
             keywords = await self.emotion_repo.get_emotion_wordcloud(
-                group_id=end_user_id,
+                end_user_id=end_user_id,
                 emotion_type=emotion_type,
                 limit=limit
             )
@@ -339,7 +339,7 @@ class EmotionAnalyticsService:
             
             # 获取时间范围内的情绪数据
             emotions = await self.emotion_repo.get_emotions_in_range(
-                group_id=end_user_id,
+                end_user_id=end_user_id,
                 time_range=time_range
             )
             
@@ -505,7 +505,7 @@ class EmotionAnalyticsService:
                     )
                     config_service = MemoryConfigService(db)
                     memory_config = config_service.load_memory_config(
-                        config_id=int(config_id),
+                        config_id=(config_id),
                         service_name="EmotionAnalyticsService.generate_emotion_suggestions"
                     )
                     from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -519,7 +519,7 @@ class EmotionAnalyticsService:
             
             # 3. 获取情绪数据用于模式分析
             emotions = await self.emotion_repo.get_emotions_in_range(
-                group_id=end_user_id,
+                end_user_id=end_user_id,
                 time_range="30d"
             )
             
@@ -598,13 +598,13 @@ class EmotionAnalyticsService:
             # 查询用户的实体和标签
             query = """
             MATCH (e:Entity)
-            WHERE e.group_id = $group_id
+            WHERE e.end_user_id = $end_user_id
             RETURN e.name as name, e.type as type
             ORDER BY e.created_at DESC
             LIMIT 20
             """
             
-            entities = await connector.execute_query(query, group_id=end_user_id)
+            entities = await connector.execute_query(query, end_user_id=end_user_id)
             
             # 提取兴趣标签
             interests = [e["name"] for e in entities if e.get("type") in ["INTEREST", "HOBBY"]][:5]