Merge branch 'release/v0.2.2'

# Conflicts:
#	api/app/repositories/memory_config_repository.py
#	api/app/services/emotion_analytics_service.py
#	api/app/utils/config_utils.py

.gitignore

@@ -35,3 +35,6 @@ nltk_data/
 tika-server*.jar*
 cl100k_base.tiktoken
 libssl*.deb
+
+sandbox/lib/seccomp_python/target
+sandbox/lib/seccomp_nodejs/target

api/app/controllers/app_controller.py

@@ -872,3 +872,44 @@ async def update_workflow_config(
     workspace_id = current_user.current_workspace_id
     cfg = app_service.update_workflow_config(db, app_id=app_id, data=payload, workspace_id=workspace_id)
     return success(data=WorkflowConfigSchema.model_validate(cfg))
+
+
+@router.get("/{app_id}/statistics", summary="应用统计数据")
+@cur_workspace_access_guard()
+def get_app_statistics(
+        app_id: uuid.UUID,
+        start_date: int,
+        end_date: int,
+        db: Session = Depends(get_db),
+        current_user=Depends(get_current_user),
+):
+    """获取应用统计数据
+    
+    Args:
+        app_id: 应用ID
+        start_date: 开始时间戳（毫秒）
+        end_date: 结束时间戳（毫秒）
+    
+    Returns:
+        - daily_conversations: 每日会话数统计
+        - total_conversations: 总会话数
+        - daily_new_users: 每日新增用户数
+        - total_new_users: 总新增用户数
+        - daily_api_calls: 每日API调用次数
+        - total_api_calls: 总API调用次数
+        - daily_tokens: 每日token消耗
+        - total_tokens: 总token消耗
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    from app.services.app_statistics_service import AppStatisticsService
+    stats_service = AppStatisticsService(db)
+    
+    result = stats_service.get_app_statistics(
+        app_id=app_id,
+        workspace_id=workspace_id,
+        start_date=start_date,
+        end_date=end_date
+    )
+    
+    return success(data=result)

api/app/controllers/emotion_config_controller.py

@@ -7,11 +7,13 @@ Routes:
     GET /memory/config/emotion - 获取情绪引擎配置
     POST /memory/config/emotion - 更新情绪引擎配置
 """
+import uuid
 
 from fastapi import APIRouter, Depends, Query, HTTPException, status
 from pydantic import BaseModel, Field
-from typing import Optional
+from typing import Optional, Union
 from sqlalchemy.orm import Session
+from uuid import UUID
 
 from app.core.response_utils import success
 from app.dependencies import get_current_user
@@ -20,6 +22,7 @@ from app.schemas.response_schema import ApiResponse
 from app.services.emotion_config_service import EmotionConfigService
 from app.core.logging_config import get_api_logger
 from app.db import get_db
+from app.utils.config_utils import resolve_config_id
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -32,11 +35,11 @@ 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: Union[uuid.UUID, int, str]= 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 +48,7 @@ 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|int = Query(..., description="配置ID"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
 ):
@@ -78,7 +81,7 @@ def get_emotion_config(
             f"用户 {current_user.username} 请求获取情绪配置",
             extra={"config_id": config_id}
         )
-        
+        config_id=resolve_config_id(config_id, db)
         # 初始化服务
         config_service = EmotionConfigService(db)
         
@@ -157,6 +160,7 @@ def update_emotion_config(
             }
         }
     """
+    config.config_id=resolve_config_id(config.config_id, db)
     try:
         api_logger.info(
             f"用户 {current_user.username} 请求更新情绪配置",

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",
+                f"用户 {request.end_user_id} 的建议缓存不存在或已过期",
-                extra={"group_id": request.group_id}
+                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/file_storage_controller.py

@@ -310,7 +310,7 @@ async def get_file_url(
     try:
         if permanent:
             # Generate permanent URL (no expiration check)
-            server_url = f"http://{settings.SERVER_IP}:8000/api"
+            server_url = settings.FILE_LOCAL_SERVER_URL
             url = f"{server_url}/storage/permanent/{file_id}"
             return success(
                 data={

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, 
+            user_input.end_user_id,
-            messages_list,  # 传递结构化消息列表
+            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}")
         
@@ -255,16 +254,14 @@ 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
     """
     config_id = user_input.config_id
     workspace_id = current_user.current_workspace_id
-    api_logger.info(f"Read service: workspace_id={workspace_id}, config_id={config_id}")
 
-    # 获取 storage_type，如果为 None 则使用默认值
     storage_type = workspace_service.get_workspace_storage_type(
         db=db,
         workspace_id=workspace_id,
@@ -279,12 +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,
@@ -295,17 +293,20 @@ 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(
+                end_user_id=user_input.end_user_id,
                 retrieve_info=retrieve_info,
                 history=history,
                 query=query,
                 config_id=config_id,
                 db=db
             )
+            if "信息不足，无法回答" in result['answer']:
+                result['answer']=retrieve_info
         return success(data=result, msg="回复对话消息成功")
     except BaseException as e:
         # Handle ExceptionGroup from TaskGroup (Python 3.11+) or BaseExceptionGroup
@@ -403,7 +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}")
@@ -447,7 +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
                     },
@@ -524,7 +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
                     },
@@ -578,16 +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 = ""
@@ -595,11 +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,
@@ -624,7 +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}")
@@ -697,7 +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_dashboard_controller.py

@@ -49,63 +49,134 @@ async def get_workspace_end_users(
     current_user: User = Depends(get_current_user),
 ):
     """
-    获取工作空间的宿主列表
+    获取工作空间的宿主列表（高性能优化版本 v2）
     
-    返回格式与原 memory_list 接口中的 end_users 字段相同，
+    优化策略：
-    并包含每个用户的记忆配置信息（memory_config_id 和 memory_config_name）
+    1. 批量查询 end_users（一次查询而非循环）
+    2. 并发查询所有用户的记忆数量（Neo4j）
+    3. RAG 模式使用批量查询（一次 SQL）
+    4. 只返回必要字段减少数据传输
+    5. 添加短期缓存减少重复查询
+    6. 并发执行配置查询和记忆数量查询
+    
+    返回格式：
+    {
+        "end_user": {"id": "uuid", "other_name": "名称"},
+        "memory_num": {"total": 数量},
+        "memory_config": {"memory_config_id": "id", "memory_config_name": "名称"}
+    }
     """
+    import asyncio
+    import json
+    from app.aioRedis import aio_redis_get, aio_redis_set
+    
     workspace_id = current_user.current_workspace_id
+    
+    # 尝试从缓存获取（30秒缓存）
+    cache_key = f"end_users:workspace:{workspace_id}"
+    try:
+        cached_data = await aio_redis_get(cache_key)
+        if cached_data:
+            api_logger.info(f"从缓存获取宿主列表: workspace_id={workspace_id}")
+            return success(data=json.loads(cached_data), msg="宿主列表获取成功")
+    except Exception as e:
+        api_logger.warning(f"Redis 缓存读取失败: {str(e)}")
+    
     # 获取当前空间类型
     current_workspace_type = memory_dashboard_service.get_current_workspace_type(db, workspace_id, current_user)
     api_logger.info(f"用户 {current_user.username} 请求获取工作空间 {workspace_id} 的宿主列表")
+    
+    # 获取 end_users（已优化为批量查询）
     end_users = memory_dashboard_service.get_workspace_end_users(
         db=db,
         workspace_id=workspace_id,
         current_user=current_user
     )
-    
+    if not end_users:
-    # 批量获取所有用户的记忆配置信息（优化：一次查询而非 N 次）
+        api_logger.info("工作空间下没有宿主")
-    end_user_ids = [str(user.id) for user in end_users]
+        # 缓存空结果，避免重复查询
-    memory_configs_map = {}
-    if end_user_ids:
         try:
-            memory_configs_map = get_end_users_connected_configs_batch(end_user_ids, db)
+            await aio_redis_set(cache_key, json.dumps([]), expire=30)
+        except Exception as e:
+            api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
+        return success(data=[], msg="宿主列表获取成功")
+    
+    end_user_ids = [str(user.id) for user in end_users]
+    
+    # 并发执行两个独立的查询任务
+    async def get_memory_configs():
+        """获取记忆配置（在线程池中执行同步查询）"""
+        try:
+            return await asyncio.to_thread(
+                get_end_users_connected_configs_batch,
+                end_user_ids, db
+            )
         except Exception as e:
             api_logger.error(f"批量获取记忆配置失败: {str(e)}")
-            # 失败时使用空字典，不影响其他数据返回
+            return {}
     
+    async def get_memory_nums():
+        """获取记忆数量"""
+        if current_workspace_type == "rag":
+            # RAG 模式：批量查询
+            try:
+                chunk_map = await asyncio.to_thread(
+                    memory_dashboard_service.get_users_total_chunk_batch,
+                    end_user_ids, db, current_user
+                )
+                return {uid: {"total": count} for uid, count in chunk_map.items()}
+            except Exception as e:
+                api_logger.error(f"批量获取 RAG chunk 数量失败: {str(e)}")
+                return {uid: {"total": 0} for uid in end_user_ids}
+        
+        elif current_workspace_type == "neo4j":
+            # Neo4j 模式：并发查询（带并发限制）
+            # 使用信号量限制并发数，避免大量用户时压垮 Neo4j
+            MAX_CONCURRENT_QUERIES = 10
+            semaphore = asyncio.Semaphore(MAX_CONCURRENT_QUERIES)
+            
+            async def get_neo4j_memory_num(end_user_id: str):
+                async with semaphore:
+                    try:
+                        return await memory_storage_service.search_all(end_user_id)
+                    except Exception as e:
+                        api_logger.error(f"获取用户 {end_user_id} Neo4j 记忆数量失败: {str(e)}")
+                        return {"total": 0}
+            
+            memory_nums_list = await asyncio.gather(*[get_neo4j_memory_num(uid) for uid in end_user_ids])
+            return {end_user_ids[i]: memory_nums_list[i] for i in range(len(end_user_ids))}
+        
+        return {uid: {"total": 0} for uid in end_user_ids}
+    
+    # 并发执行配置查询和记忆数量查询
+    memory_configs_map, memory_nums_map = await asyncio.gather(
+        get_memory_configs(),
+        get_memory_nums()
+    )
+    
+    # 构建结果（优化：使用列表推导式）
     result = []
     for end_user in end_users:
-        memory_num = {}
-        if current_workspace_type == "neo4j":
-            # EndUser 是 Pydantic 模型，直接访问属性而不是使用 .get()
-            memory_num = await memory_storage_service.search_all(str(end_user.id))
-        elif current_workspace_type == "rag":
-            memory_num = {
-                "total":memory_dashboard_service.get_current_user_total_chunk(str(end_user.id), db, current_user)
-            }
-        
-        # 从批量查询结果中获取配置信息
         user_id = str(end_user.id)
-        memory_config_info = memory_configs_map.get(user_id, {
+        config_info = memory_configs_map.get(user_id, {})
-            "memory_config_id": None,
+        result.append({
-            "memory_config_name": None
+            'end_user': {
-        })
+                'id': user_id,
-        
+                'other_name': end_user.other_name
-        # 只保留需要的字段，移除 error 字段（如果有）
+            },
-        memory_config = {
+            'memory_num': memory_nums_map.get(user_id, {"total": 0}),
-            "memory_config_id": memory_config_info.get("memory_config_id"),
+            'memory_config': {
-            "memory_config_name": memory_config_info.get("memory_config_name")
+                "memory_config_id": config_info.get("memory_config_id"),
-        }
+                "memory_config_name": config_info.get("memory_config_name")
-        
-        result.append(
-            {
-                'end_user': end_user,
-                'memory_num': memory_num,
-                'memory_config': memory_config
             }
-        )
+        })
-        
+    
+    # 写入缓存（30秒过期）
+    try:
+        await aio_redis_set(cache_key, json.dumps(result), expire=30)
+    except Exception as e:
+        api_logger.warning(f"Redis 缓存写入失败: {str(e)}")
+    
     api_logger.info(f"成功获取 {len(end_users)} 个宿主记录")
     return success(data=result, msg="宿主列表获取成功")
 

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
@@ -33,7 +34,7 @@ from app.schemas.memory_storage_schema import (
 )
 from app.schemas.response_schema import ApiResponse
 from app.services.memory_forget_service import MemoryForgetService
-
+from app.utils.config_utils import resolve_config_id
 
 # 获取API专用日志器
 api_logger = get_api_logger()
@@ -83,7 +84,8 @@ async def trigger_forgetting_cycle(
         
         connected_config = get_end_user_connected_config(end_user_id, db)
         config_id = connected_config.get("memory_config_id")
-        
+        config_id = resolve_config_id((config_id), db)
+
         if config_id is None:
             api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
             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")
@@ -106,7 +108,7 @@ 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 +130,7 @@ async def trigger_forgetting_cycle(
 
 @router.get("/read_config", response_model=ApiResponse)
 async def read_forgetting_config(
-    config_id: int,
+    config_id: UUID|int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db)
 ):
@@ -157,6 +159,7 @@ async def read_forgetting_config(
     )
     
     try:
+        config_id=resolve_config_id(config_id, db)
         # 调用服务层读取配置
         config = forget_service.read_forgetting_config(db=db, config_id=config_id)
         
@@ -194,6 +197,8 @@ async def update_forgetting_config(
         ApiResponse: 包含更新结果的响应
     """
     workspace_id = current_user.current_workspace_id
+    payload.config_id=resolve_config_id((payload.config_id), db)
+
     
     # 检查用户是否已选择工作空间
     if workspace_id is None:
@@ -236,7 +241,7 @@ 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 +251,7 @@ 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
     
@@ -254,26 +259,25 @@ async def get_forgetting_stats(
         ApiResponse: 包含统计信息的响应
     """
     workspace_id = current_user.current_workspace_id
-    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘引擎统计但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
-    
+    # 如果提供了 end_user_id，通过它获取 config_id
-    # 如果提供了 group_id，通过它获取 config_id
     config_id = None
-    if group_id:
+    if end_user_id:
         try:
             from app.services.memory_agent_service import get_end_user_connected_config
             
-            connected_config = get_end_user_connected_config(group_id, db)
+            connected_config = get_end_user_connected_config(end_user_id, db)
             config_id = connected_config.get("memory_config_id")
+            config_id = resolve_config_id(config_id, db)
             
             if config_id is None:
-                api_logger.warning(f"终端用户 {group_id} 未关联记忆配置")
+                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 {group_id} 未关联记忆配置", "memory_config_id is None")
+                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 +287,14 @@ 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
         )
         
@@ -324,7 +328,7 @@ async def get_forgetting_curve(
         ApiResponse: 包含遗忘曲线数据的响应
     """
     workspace_id = current_user.current_workspace_id
-    
+    request.config_id = resolve_config_id((request.config_id), db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间")

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 (
@@ -24,6 +25,8 @@ from fastapi import APIRouter, Depends, HTTPException, status,Header
 from sqlalchemy import text
 from sqlalchemy.orm import Session
 
+from app.utils.config_utils import resolve_config_id
+
 load_dotenv()
 api_logger = get_api_logger()
 
@@ -42,6 +45,7 @@ async def save_reflection_config(
     """Save reflection configuration to data_comfig table"""
     try:
         config_id = request.config_id
+        config_id = resolve_config_id(config_id, db)
         if not config_id:
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
@@ -50,7 +54,7 @@ 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 +67,17 @@ async def save_reflection_config(
         )
 
         db.commit()
-        db.refresh(data_config)
+        db.refresh(memory_config)
 
         reflection_result={
-                "config_id": data_config.config_id,
+                "config_id": memory_config.config_id,
-                "enable_self_reflexion": data_config.enable_self_reflexion,
+                "enable_self_reflexion": memory_config.enable_self_reflexion,
-                "iteration_period": data_config.iteration_period,
+                "iteration_period": memory_config.iteration_period,
-                "reflexion_range": data_config.reflexion_range,
+                "reflexion_range": memory_config.reflexion_range,
-                "baseline": data_config.baseline,
+                "baseline": memory_config.baseline,
-                "reflection_model_id": data_config.reflection_model_id,
+                "reflection_model_id": memory_config.reflection_model_id,
-                "memory_verify": data_config.memory_verify,
+                "memory_verify": memory_config.memory_verify,
-                "quality_assessment": data_config.quality_assessment}
+                "quality_assessment": memory_config.quality_assessment}
 
         return success(data=reflection_result, msg="反思配置成功")
         
@@ -111,14 +115,14 @@ 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:
@@ -156,17 +160,20 @@ async def start_workspace_reflection(
 
 @router.get("/reflection/configs")
 async def start_reflection_configs(
-        config_id: int,
+        config_id: uuid.UUID|int,
         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"""
+    config_id = resolve_config_id(config_id, db)
     try:
+        config_id=resolve_config_id(config_id,db)
         api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-        result = DataConfigRepository.query_reflection_config_by_id(db, config_id)
+        result = MemoryConfigRepository.query_reflection_config_by_id(db, config_id)
+        memory_config_id = resolve_config_id(result.config_id, db)
         # 构建返回数据
         reflection_config = {
-            "config_id": result.config_id,
+            "config_id": memory_config_id,
             "reflection_enabled": result.enable_self_reflexion,
             "reflection_period_in_hours": result.iteration_period,
             "reflexion_range": result.reflexion_range,
@@ -191,7 +198,7 @@ async def start_reflection_configs(
 
 @router.get("/reflection/run")
 async def reflection_run(
-    config_id: int,
+    config_id: UUID|int,
     language_type: str = Header(default="zh", alias="X-Language-Type"),
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
@@ -199,9 +206,9 @@ async def reflection_run(
     """Activate the reflection function for all matching applications in the workspace"""
 
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-
+    config_id = resolve_config_id(config_id, db)
-    # 使用DataConfigRepository查询反思配置
+    # 使用MemoryConfigRepository查询反思配置
-    result = DataConfigRepository.query_reflection_config_by_id(db, config_id)
+    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
@@ -34,6 +35,8 @@ from fastapi import APIRouter, Depends
 from fastapi.responses import StreamingResponse
 from sqlalchemy.orm import Session
 
+from app.utils.config_utils import resolve_config_id
+
 # Get API logger
 api_logger = get_api_logger()
 
@@ -140,7 +143,6 @@ def create_config(
     db: Session = Depends(get_db),
     ) -> dict:
     workspace_id = current_user.current_workspace_id
-    
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试创建配置但未选择工作空间")
@@ -160,12 +162,12 @@ 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|int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
     ) -> dict:
     workspace_id = current_user.current_workspace_id
-    
+    config_id=resolve_config_id(config_id, db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试删除配置但未选择工作空间")
@@ -187,7 +189,7 @@ def update_config(
     db: Session = Depends(get_db),
     ) -> dict:
     workspace_id = current_user.current_workspace_id
-    
+    payload.config_id = resolve_config_id(payload.config_id, db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新配置但未选择工作空间")
@@ -210,7 +212,7 @@ def update_config_extracted(
     db: Session = Depends(get_db),
     ) -> dict:
     workspace_id = current_user.current_workspace_id
-    
+    payload.config_id = resolve_config_id(payload.config_id, db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新提取配置但未选择工作空间")
@@ -232,12 +234,12 @@ 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 | int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
     ) -> dict:
     workspace_id = current_user.current_workspace_id
-    
+    config_id = resolve_config_id(config_id, db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试读取提取配置但未选择工作空间")
@@ -285,6 +287,7 @@ async def pilot_run(
         f"Pilot run requested: config_id={payload.config_id}, "
         f"dialogue_text_length={len(payload.dialogue_text)}"
     )
+    payload.config_id = resolve_config_id(payload.config_id, db)
     svc = DataConfigService(db)
     return StreamingResponse(
         svc.pilot_run_stream(payload),
@@ -420,15 +423,95 @@ async def get_hot_memory_tags_api(
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user),
     ) -> dict:
-    api_logger.info(f"Hot memory tags requested for current_user: {current_user.id}")
+    """
+    获取热门记忆标签（带Redis缓存）
+    
+    缓存策略：
+    - 缓存键：workspace_id + limit
+    - 过期时间：5分钟（300秒）
+    - 缓存命中：~50ms
+    - 缓存未命中：~600-800ms（取决于LLM速度）
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    # 构建缓存键
+    cache_key = f"hot_memory_tags:{workspace_id}:{limit}"
+    
+    api_logger.info(f"Hot memory tags requested for workspace: {workspace_id}, limit: {limit}")
+    
     try:
+        # 尝试从Redis缓存获取
+        from app.aioRedis import aio_redis_get, aio_redis_set
+        import json
+        
+        cached_result = await aio_redis_get(cache_key)
+        if cached_result:
+            api_logger.info(f"Cache hit for key: {cache_key}")
+            try:
+                data = json.loads(cached_result)
+                return success(data=data, msg="查询成功（缓存）")
+            except json.JSONDecodeError:
+                api_logger.warning(f"Failed to parse cached data, will refresh")
+        
+        # 缓存未命中，执行查询
+        api_logger.info(f"Cache miss for key: {cache_key}, executing query")
         result = await analytics_hot_memory_tags(db, current_user, limit)
+        
+        # 写入缓存（过期时间：5分钟）
+        # 注意：result是列表，需要转换为JSON字符串
+        try:
+            cache_data = json.dumps(result, ensure_ascii=False)
+            await aio_redis_set(cache_key, cache_data, expire=300)
+            api_logger.info(f"Cached result for key: {cache_key}")
+        except Exception as cache_error:
+            # 缓存写入失败不影响主流程
+            api_logger.warning(f"Failed to cache result: {str(cache_error)}")
+        
         return success(data=result, msg="查询成功")
+        
     except Exception as e:
         api_logger.error(f"Hot memory tags failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "热门标签查询失败", str(e))
 
 
+@router.delete("/analytics/hot_memory_tags/cache", response_model=ApiResponse)
+async def clear_hot_memory_tags_cache(
+    current_user: User = Depends(get_current_user),
+    ) -> dict:
+    """
+    清除热门标签缓存
+    
+    用于：
+    - 手动刷新数据
+    - 调试和测试
+    - 数据更新后立即生效
+    """
+    workspace_id = current_user.current_workspace_id
+    
+    api_logger.info(f"Clear hot memory tags cache requested for workspace: {workspace_id}")
+    
+    try:
+        from app.aioRedis import aio_redis_delete
+        
+        # 清除所有limit的缓存（常见的limit值）
+        cleared_count = 0
+        for limit in [5, 10, 15, 20, 30, 50]:
+            cache_key = f"hot_memory_tags:{workspace_id}:{limit}"
+            result = await aio_redis_delete(cache_key)
+            if result:
+                cleared_count += 1
+                api_logger.info(f"Cleared cache for key: {cache_key}")
+        
+        return success(
+            data={"cleared_count": cleared_count}, 
+            msg=f"成功清除 {cleared_count} 个缓存"
+        )
+        
+    except Exception as e:
+        api_logger.error(f"Clear cache failed: {str(e)}")
+        return fail(BizCode.INTERNAL_ERROR, "清除缓存失败", str(e))
+
+
 @router.get("/analytics/recent_activity_stats", response_model=ApiResponse)
 async def get_recent_activity_stats_api(
     current_user: User = Depends(get_current_user),

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/model_controller.py

@@ -3,15 +3,17 @@ from sqlalchemy.orm import Session
 from typing import Optional
 import uuid
 
-
+from app.core.error_codes import BizCode
+from app.core.exceptions import BusinessException
 from app.db import get_db
 from app.dependencies import get_current_user
-from app.models.models_model import ModelProvider, ModelType
+from app.models.models_model import ModelProvider, ModelType, LoadBalanceStrategy
 from app.models.user_model import User
+from app.repositories.model_repository import ModelConfigRepository
 from app.schemas import model_schema
 from app.core.response_utils import success
 from app.schemas.response_schema import ApiResponse, PageData
-from app.services.model_service import ModelConfigService, ModelApiKeyService
+from app.services.model_service import ModelConfigService, ModelApiKeyService, ModelBaseService
 from app.core.logging_config import get_api_logger
 
 # 获取API专用日志器
@@ -24,24 +26,83 @@ router = APIRouter(
 
 @router.get("/type", response_model=ApiResponse)
 def get_model_types():
-    
     return success(msg="获取模型类型成功", data=list(ModelType))
 
 
 @router.get("/provider", response_model=ApiResponse)
 def get_model_providers():
-    return success(msg="获取模型提供商成功", data=list(ModelProvider))
+    providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE]
+    return success(msg="获取模型提供商成功", data=providers)
+
+@router.get("/strategy", response_model=ApiResponse)
+def get_model_strategies():
+    return success(msg="获取模型策略成功", data=list(LoadBalanceStrategy))
 
 
 @router.get("", response_model=ApiResponse)
 def get_model_list(
-    type: Optional[str] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
+        type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
-    provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"),
+        provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于API Key)"),
+        is_active: Optional[bool] = Query(None, description="激活状态筛选"),
+        is_public: Optional[bool] = Query(None, description="公开状态筛选"),
+        search: Optional[str] = Query(None, description="搜索关键词"),
+        page: int = Query(1, ge=1, description="页码"),
+        pagesize: int = Query(10, ge=1, le=100, description="每页数量"),
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
+):
+    """
+    获取模型配置列表
+
+    支持多个 type 参数：
+    - 单个：?type=LLM
+    - 多个（逗号分隔）：?type=LLM,EMBEDDING
+    - 多个（重复参数）：?type=LLM&type=EMBEDDING
+    """
+    api_logger.info(
+        f"获取模型配置列表请求: type={type}, provider={provider}, page={page}, pagesize={pagesize}, tenant_id={current_user.tenant_id}")
+
+    try:
+        # 解析 type 参数（支持逗号分隔）
+        type_list = []
+        if type is not None:
+            flat_type = []
+            for item in type:
+                split_items = [t.strip() for t in item.split(',') if t.strip()]
+                flat_type.extend(split_items)
+
+            unique_flat_type = list(dict.fromkeys(flat_type))
+            type_list = [ModelType(t.lower()) for t in unique_flat_type]
+
+        api_logger.error(f"获取模型type_list: {type_list}")
+        query = model_schema.ModelConfigQuery(
+            type=type_list,
+            provider=provider,
+            is_active=is_active,
+            is_public=is_public,
+            search=search,
+            page=page,
+            pagesize=pagesize
+        )
+
+        api_logger.debug(f"开始获取模型配置列表: {query.dict()}")
+        result_orm = ModelConfigService.get_model_list(db=db, query=query, tenant_id=current_user.tenant_id)
+        result = PageData.model_validate(result_orm)
+        api_logger.info(f"模型配置列表获取成功: 总数={result.page.total}, 当前页={len(result.items)}")
+        return success(data=result, msg="模型配置列表获取成功")
+    except Exception as e:
+        api_logger.error(f"获取模型配置列表失败: {str(e)}")
+        raise
+
+
+@router.get("/new", response_model=ApiResponse)
+def get_model_list_new(
+    type: Optional[list[str]] = Query(None, description="模型类型筛选（支持多个，如 ?type=LLM 或 ?type=LLM,EMBEDDING）"),
+    provider: Optional[model_schema.ModelProvider] = Query(None, description="提供商筛选(基于ModelConfig)"),
     is_active: Optional[bool] = Query(None, description="激活状态筛选"),
     is_public: Optional[bool] = Query(None, description="公开状态筛选"),
     search: Optional[str] = Query(None, description="搜索关键词"),
-    page: int = Query(1, ge=1, description="页码"),
+    is_composite: Optional[bool] = Query(None, description="组合模型筛选"),
-    pagesize: int = Query(10, ge=1, le=100, description="每页数量"),
     db: Session = Depends(get_db),
     current_user: User = Depends(get_current_user)
 ):
@@ -53,36 +114,127 @@ def get_model_list(
     - 多个（逗号分隔）：?type=LLM,EMBEDDING
     - 多个（重复参数）：?type=LLM&type=EMBEDDING
     """
-    api_logger.info(f"获取模型配置列表请求: type={type}, provider={provider}, page={page}, pagesize={pagesize}, tenant_id={current_user.tenant_id}")
+    api_logger.info(f"获取模型配置列表请求: type={type}, provider={provider}, tenant_id={current_user.tenant_id}")
     
     try:
         # 解析 type 参数（支持逗号分隔）
-        type_list = None
+        type_list = []
-        if type:
+        if type is not None:
-            type_values = [t.strip() for t in type.split(',')]
+            flat_type = []
-            type_list = [model_schema.ModelType(t.lower()) for t in type_values if t]
+            for item in type:
+                split_items = [t.strip() for t in item.split(',') if t.strip()]
+                flat_type.extend(split_items)
+
+            unique_flat_type = list(dict.fromkeys(flat_type))
+            type_list = [ModelType(t.lower()) for t in unique_flat_type]
         
-        api_logger.error(f"获取模型type_list: {type_list}")
+        api_logger.info(f"获取模型type_list: {type_list}")
-        query = model_schema.ModelConfigQuery(
+        query = model_schema.ModelConfigQueryNew(
             type=type_list,
             provider=provider,
             is_active=is_active,
             is_public=is_public,
-            search=search,
+            is_composite=is_composite,
-            page=page,
+            search=search
-            pagesize=pagesize
         )
         
-        api_logger.debug(f"开始获取模型配置列表: {query.dict()}")
+        api_logger.debug(f"开始获取模型配置列表: {query.model_dump()}")
-        result_orm = ModelConfigService.get_model_list(db=db, query=query, tenant_id=current_user.tenant_id)
+        result = ModelConfigService.get_model_list_new(db=db, query=query, tenant_id=current_user.tenant_id)
-        result = PageData.model_validate(result_orm)
+        api_logger.info(f"模型配置列表获取成功: 分组数={len(result)}, 总模型数={sum(len(item['models']) for item in result)}")
-        api_logger.info(f"模型配置列表获取成功: 总数={result.page.total}, 当前页={len(result.items)}")
         return success(data=result, msg="模型配置列表获取成功")
     except Exception as e:
         api_logger.error(f"获取模型配置列表失败: {str(e)}")
         raise
 
 
+@router.get("/model_plaza", response_model=ApiResponse)
+def get_model_plaza_list(
+    type: Optional[ModelType] = Query(None, description="模型类型"),
+    provider: Optional[ModelProvider] = Query(None, description="供应商"),
+    is_official: Optional[bool] = Query(None, description="是否官方模型"),
+    is_deprecated: Optional[bool] = Query(None, description="是否弃用"),
+    search: Optional[str] = Query(None, description="搜索关键词"),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """模型广场查询接口（按供应商分组）"""
+    
+    query = model_schema.ModelBaseQuery(
+        type=type,
+        provider=provider,
+        is_official=is_official,
+        is_deprecated=is_deprecated,
+        search=search
+    )
+    result = ModelBaseService.get_model_base_list(db=db, query=query, tenant_id=current_user.tenant_id)
+    return success(data=result, msg="模型广场列表获取成功")
+
+
+@router.get("/model_plaza/{model_base_id}", response_model=ApiResponse)
+def get_model_base_by_id(
+    model_base_id: uuid.UUID,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """获取基础模型详情"""
+    
+    result = ModelBaseService.get_model_base_by_id(db=db, model_base_id=model_base_id)
+    return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型获取成功")
+
+
+@router.post("/model_plaza", response_model=ApiResponse)
+def create_model_base(
+    data: model_schema.ModelBaseCreate,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """创建基础模型"""
+    
+    result = ModelBaseService.create_model_base(db=db, data=data)
+    return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型创建成功")
+
+
+@router.put("/model_plaza/{model_base_id}", response_model=ApiResponse)
+def update_model_base(
+    model_base_id: uuid.UUID,
+    data: model_schema.ModelBaseUpdate,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """更新基础模型"""
+    
+    # 不允许更改type类型
+    if data.type is not None or data.provider is not None:
+        raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER)
+    
+    result = ModelBaseService.update_model_base(db=db, model_base_id=model_base_id, data=data)
+    return success(data=model_schema.ModelBase.model_validate(result), msg="基础模型更新成功")
+
+
+@router.delete("/model_plaza/{model_base_id}", response_model=ApiResponse)
+def delete_model_base(
+    model_base_id: uuid.UUID,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """删除基础模型"""
+    
+    ModelBaseService.delete_model_base(db=db, model_base_id=model_base_id)
+    return success(msg="基础模型删除成功")
+
+
+@router.post("/model_plaza/{model_base_id}/add", response_model=ApiResponse)
+def add_model_from_plaza(
+    model_base_id: uuid.UUID,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """从模型广场添加模型到模型列表"""
+    
+    result = ModelBaseService.add_model_from_plaza(db=db, model_base_id=model_base_id, tenant_id=current_user.tenant_id)
+    return success(data=model_schema.ModelConfig.model_validate(result), msg="模型添加成功")
+
+
 @router.get("/{model_id}", response_model=ApiResponse)
 def get_model_by_id(
     model_id: uuid.UUID,
@@ -138,6 +290,73 @@ async def create_model(
         raise
 
 
+@router.post("/composite", response_model=ApiResponse)
+async def create_composite_model(
+    model_data: model_schema.CompositeModelCreate,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """
+    创建组合模型
+    
+    - 绑定一个或多个现有的 API Key
+    - 所有 API Key 必须来自非组合模型
+    - 所有 API Key 关联的模型类型必须与组合模型类型一致
+    """
+    api_logger.info(f"创建组合模型请求: {model_data.name}, 用户: {current_user.username}, tenant_id={current_user.tenant_id}")
+    
+    try:
+        result_orm = await ModelConfigService.create_composite_model(db=db, model_data=model_data, tenant_id=current_user.tenant_id)
+        api_logger.info(f"组合模型创建成功: {result_orm.name} (ID: {result_orm.id})")
+        
+        result = model_schema.ModelConfig.model_validate(result_orm)
+        return success(data=result, msg="组合模型创建成功")
+    except Exception as e:
+        api_logger.error(f"创建组合模型失败: {model_data.name} - {str(e)}")
+        raise
+
+
+@router.put("/composite/{model_id}", response_model=ApiResponse)
+async def update_composite_model(
+    model_id: uuid.UUID,
+    model_data: model_schema.CompositeModelCreate,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """更新组合模型"""
+    api_logger.info(f"更新组合模型请求: model_id={model_id}, 用户: {current_user.username}")
+    
+    try:
+        if model_data.type is not None:
+            raise BusinessException("不允许更改模型类型和供应商", BizCode.INVALID_PARAMETER)
+        result_orm = await ModelConfigService.update_composite_model(db=db, model_id=model_id, model_data=model_data, tenant_id=current_user.tenant_id)
+        api_logger.info(f"组合模型更新成功: {result_orm.name} (ID: {model_id})")
+        
+        result = model_schema.ModelConfig.model_validate(result_orm)
+        return success(data=result, msg="组合模型更新成功")
+    except Exception as e:
+        api_logger.error(f"更新组合模型失败: model_id={model_id} - {str(e)}")
+        raise
+
+
+@router.delete("/composite/{model_id}", response_model=ApiResponse)
+def delete_composite_model(
+    model_id: uuid.UUID,
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user)
+):
+    """删除组合模型"""
+    api_logger.info(f"删除组合模型请求: model_id={model_id}, 用户: {current_user.username}")
+    
+    try:
+        ModelConfigService.delete_model(db=db, model_id=model_id, tenant_id=current_user.tenant_id)
+        api_logger.info(f"组合模型删除成功: model_id={model_id}")
+        return success(msg="组合模型删除成功")
+    except Exception as e:
+        api_logger.error(f"删除组合模型失败: model_id={model_id} - {str(e)}")
+        raise
+
+
 @router.put("/{model_id}", response_model=ApiResponse)
 def update_model(
     model_id: uuid.UUID,
@@ -214,6 +433,53 @@ def get_model_api_keys(
         raise
 
 
+@router.post("/provider/apikeys", response_model=ApiResponse)
+async def create_model_api_key_by_provider(
+        api_key_data: model_schema.ModelApiKeyCreateByProvider,
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
+):
+    """
+    根据供应商为所有匹配的模型创建API Key
+    """
+    api_logger.info(f"创建API Key请求: provider={api_key_data.provider}, 用户: {current_user.username}")
+
+    try:
+        # 根据tenant_id和provider筛选model_config_id列表
+        model_config_ids = api_key_data.model_config_ids
+        if not model_config_ids:
+            model_config_ids = ModelConfigRepository.get_model_config_ids_by_provider(
+                db=db,
+                tenant_id=current_user.tenant_id,
+                provider=api_key_data.provider
+            )
+        
+        if not model_config_ids:
+            raise BusinessException(f"未找到供应商 {api_key_data.provider} 的模型配置", BizCode.MODEL_NOT_FOUND)
+        
+        # 构造schema并调用service
+        create_data = model_schema.ModelApiKeyCreateByProvider(
+            provider=api_key_data.provider,
+            api_key=api_key_data.api_key,
+            api_base=api_key_data.api_base,
+            description=api_key_data.description,
+            config=api_key_data.config,
+            is_active=api_key_data.is_active,
+            priority=api_key_data.priority,
+            model_config_ids=model_config_ids
+        )
+        created_keys, failed_models = await ModelApiKeyService.create_api_key_by_provider(db=db, data=create_data)
+        
+        api_logger.info(f"API Key创建成功: 关联{len(created_keys)}个模型")
+        # result_list = [model_schema.ModelApiKey.model_validate(key) for key in created_keys]
+        result = "API Key已存在" if len(created_keys) == 0 and len(failed_models) == 0 else \
+            f"成功为 {len(created_keys)} 个模型创建API Key, 失败模型列表{failed_models}"
+        return success(data=result, msg=f"成功为 {len(created_keys)} 个模型创建API Key")
+    except Exception as e:
+        api_logger.error(f"创建API Key失败: {str(e)}")
+        raise
+
+
 @router.post("/{model_id}/apikeys", response_model=ApiResponse, status_code=status.HTTP_201_CREATED)
 async def create_model_api_key(
     model_id: uuid.UUID,
@@ -228,11 +494,12 @@ async def create_model_api_key(
     
     try:
         # 设置模型配置ID
-        api_key_data.model_config_id = model_id
+        api_key_data.model_config_ids = [model_id]
         
         api_logger.debug(f"开始创建模型API Key: {api_key_data.model_name}")
-        result = await ModelApiKeyService.create_api_key(db=db, api_key_data=api_key_data)
+        result_orm = await ModelApiKeyService.create_api_key(db=db, api_key_data=api_key_data)
-        api_logger.info(f"模型API Key创建成功: {result.model_name} (ID: {result.id})")
+        api_logger.info(f"模型API Key创建成功: {result_orm.model_name} (ID: {result_orm.id})")
+        result = model_schema.ModelApiKey.model_validate(result_orm)
         return success(data=result, msg="模型API Key创建成功")
     except Exception as e:
         api_logger.error(f"创建模型API Key失败: {api_key_data.model_name} - {str(e)}")
@@ -334,5 +601,3 @@ async def validate_model_config(
     return success(data=model_schema.ModelValidateResponse(**result), msg="验证完成")
 
 
-
-

api/app/controllers/public_share_controller.py

@@ -317,9 +317,12 @@ async def chat(
         appid = share.app_id
         """获取存储类型和工作空间的ID"""
 
-        # 直接通过 SQLAlchemy 查询 app
+        # 直接通过 SQLAlchemy 查询 app（仅查询未删除的应用）
         from app.models.app_model import App
-        app = db.query(App).filter(App.id == appid).first()
+        app = db.query(App).filter(
+            App.id == appid,
+            App.is_active.is_(True)
+        ).first()
         if not app:
             raise BusinessException("应用不存在", BizCode.APP_NOT_FOUND)
 

api/app/controllers/service/app_api_controller.py

@@ -235,11 +235,11 @@ async def chat(
 
                     message=payload.message,
                     conversation_id=conversation.id,  # 使用已创建的会话 ID
-                    user_id=new_end_user.id,  # 转换为字符串
+                    user_id=end_user_id,  # 转换为字符串
                     variables=payload.variables,
                     config=config,
-                    web_search=payload.web_search,
+                    web_search=web_search,
-                    memory=payload.memory,
+                    memory=memory,
                     storage_type=storage_type,
                     user_rag_memory_id=user_rag_memory_id,
                     app_id=app.id,
@@ -268,11 +268,11 @@ async def chat(
 
             message=payload.message,
             conversation_id=conversation.id,  # 使用已创建的会话 ID
-            user_id=new_end_user.id,  # 转换为字符串
+            user_id=end_user_id,  # 转换为字符串
             variables=payload.variables,
             config=config,
-            web_search=payload.web_search,
+            web_search=web_search,
-            memory=payload.memory,
+            memory=memory,
             storage_type=storage_type,
             user_rag_memory_id=user_rag_memory_id,
             app_id=app.id,

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/controllers/workflow_controller.py

@@ -54,7 +54,7 @@ async def create_workflow_config(
         app = db.query(App).filter(
             App.id == app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:
@@ -214,7 +214,7 @@ async def delete_workflow_config(
         app = db.query(App).filter(
             App.id == app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:
@@ -259,7 +259,7 @@ async def validate_workflow_config(
         app = db.query(App).filter(
             App.id == app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:
@@ -329,7 +329,7 @@ async def get_workflow_executions(
         app = db.query(App).filter(
             App.id == app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:
@@ -389,7 +389,7 @@ async def get_workflow_execution(
         app = db.query(App).filter(
             App.id == execution.app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:
@@ -440,7 +440,7 @@ async def run_workflow(
         app = db.query(App).filter(
             App.id == app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:
@@ -578,7 +578,7 @@ async def cancel_workflow_execution(
         app = db.query(App).filter(
             App.id == execution.app_id,
             App.workspace_id == current_user.current_workspace_id,
-            App.is_active == True
+            App.is_active.is_(True)
         ).first()
 
         if not app:

api/app/core/agent/langchain_agent.py

@@ -28,6 +28,8 @@ from langchain.agents import create_agent
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
 from langchain_core.tools import BaseTool
 
+from app.utils.config_utils import resolve_config_id
+
 logger = get_business_logger()
 
 
@@ -155,13 +157,13 @@ 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}"
@@ -175,11 +177,10 @@ class LangChainAgent:
     #         messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
     #         retrieved_content.append({query: aimessages})
     #     return messagss_list,retrieved_content
-
     async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id):
         """
         写入记忆（支持结构化消息）
-        
+
         Args:
             storage_type: 存储类型 (neo4j/rag)
             end_user_id: 终端用户ID
@@ -188,7 +189,7 @@ 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
@@ -196,48 +197,54 @@ class LangChainAgent:
           2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景）
           3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段
         """
-        if storage_type == "rag":
-            # RAG 模式：组合消息为字符串格式（保持原有逻辑）
-            combined_message = f"user: {user_message}\nassistant: {ai_message}"
-            await write_rag(end_user_id, combined_message, user_rag_memory_id)
-            logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
-        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
-            # 2. write_message_task 调用 memory_agent_service.write_memory
-            # 3. write_memory 调用 write_tools.write，传递 messages 参数
-            # 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数
-            # 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段
-            # 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
-                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"
-                user_rag_memory_id   # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
-            )
-            logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
-            write_status = get_task_memory_write_result(str(write_id))
-            logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
 
+        db = next(get_db())
+        try:
+            actual_config_id=resolve_config_id(actual_config_id, db)
+
+            if storage_type == "rag":
+                # RAG 模式：组合消息为字符串格式（保持原有逻辑）
+                combined_message = f"user: {user_message}\nassistant: {ai_message}"
+                await write_rag(end_user_id, combined_message, user_rag_memory_id)
+                logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
+            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
+                # 2. write_message_task 调用 memory_agent_service.write_memory
+                # 3. write_memory 调用 write_tools.write，传递 messages 参数
+                # 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数
+                # 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段
+                # 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,  # 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"
+                    user_rag_memory_id   # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
+                )
+                logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
+                write_status = get_task_memory_write_result(str(write_id))
+                logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
+        finally:
+            db.close()
     async def chat(
             self,
             message: str,

api/app/core/config.py

@@ -9,6 +9,25 @@ load_dotenv()
 
 
 class Settings:
+    # ========================================================================
+    # Deployment Mode Configuration
+    # ========================================================================
+    # community: 社区版（开源，功能受限）
+    # cloud: SaaS 云服务版（全功能，按量计费）
+    # enterprise: 企业私有化版（License 控制）
+    DEPLOYMENT_MODE: str = os.getenv("DEPLOYMENT_MODE", "community")
+    
+    # License 配置（企业版）
+    LICENSE_FILE: str = os.getenv("LICENSE_FILE", "/etc/app/license.json")
+    LICENSE_SERVER_URL: str = os.getenv("LICENSE_SERVER_URL", "https://license.yourcompany.com")
+    
+    # 计费服务配置（SaaS 版）
+    BILLING_SERVICE_URL: str = os.getenv("BILLING_SERVICE_URL", "")
+    
+    # 基础 URL（用于 SSO 回调等）
+    BASE_URL: str = os.getenv("BASE_URL", "http://localhost:8000")
+    FRONTEND_URL: str = os.getenv("FRONTEND_URL", "http://localhost:3000")
+    
     ENABLE_SINGLE_WORKSPACE: bool = os.getenv("ENABLE_SINGLE_WORKSPACE", "true").lower() == "true"
     # API Keys Configuration
     OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
@@ -72,6 +91,10 @@ class Settings:
 
     # Single Sign-On configuration
     ENABLE_SINGLE_SESSION: bool = os.getenv("ENABLE_SINGLE_SESSION", "false").lower() == "true"
+    
+    # SSO 免登配置
+    SSO_TOKEN_EXPIRE_SECONDS: int = int(os.getenv("SSO_TOKEN_EXPIRE_SECONDS", "300"))
+    SSO_TRUSTED_SOURCES_CONFIG: str = os.getenv("SSO_TRUSTED_SOURCES_CONFIG", "{}")
 
     # File Upload
     MAX_FILE_SIZE: int = int(os.getenv("MAX_FILE_SIZE", "52428800"))
@@ -107,6 +130,7 @@ class Settings:
 
     # Server Configuration
     SERVER_IP: str = os.getenv("SERVER_IP", "127.0.0.1")
+    FILE_LOCAL_SERVER_URL : str = os.getenv("FILE_LOCAL_SERVER_URL", "http://localhost:8000/api")
 
     # ========================================================================
     # Internal Configuration (not in .env, used by application code)
@@ -184,7 +208,7 @@ class Settings:
     ENABLE_TOOL_MANAGEMENT: bool = os.getenv("ENABLE_TOOL_MANAGEMENT", "true").lower() == "true"
 
     # official environment system version
-    SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.0")
+    SYSTEM_VERSION: str = os.getenv("SYSTEM_VERSION", "v0.2.1")
 
     # workflow config
     WORKFLOW_NODE_TIMEOUT: int = int(os.getenv("WORKFLOW_NODE_TIMEOUT", 600))

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

@@ -14,7 +14,7 @@ from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.memory.agent.utils.template_tools import TemplateService
 from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
 
-template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
+template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
 db_session = next(get_db())
 logger = get_agent_logger(__name__)
 
@@ -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)
+    time_retrieval_tool = create_time_retrieval_tool(end_user_id)
-    search_params = { "group_id": group_id, "return_raw_results": True }
+    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

@@ -19,7 +19,7 @@ from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.memory.agent.utils.template_tools import TemplateService
 from app.db import get_db
 
-template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
+template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
 logger = get_agent_logger(__name__)
 db_session = next(get_db())
 
@@ -34,8 +34,8 @@ class SummaryNodeService(LLMServiceMixin):
 summary_service = SummaryNodeService()
 
 async def summary_history(state: ReadState) -> ReadState:
-    group_id = state.get("group_id", '')
+    end_user_id = state.get("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)
     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,
+        apply_id=end_user_id,
-        group_id=group_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
@@ -236,7 +236,7 @@ async def Retrieve_Summary(state: ReadState)-> ReadState:
     retrieve_info_str='\n'.join(retrieve_info_str)
 
     aimessages=await  summary_llm(state,history,retrieve_info_str,
-                                  'Retrieve_Summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1")
+                                  'direct_summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1")
     if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
         await summary_redis_save(state, aimessages)
     if aimessages == '':
@@ -276,7 +276,6 @@ async def Summary(state: ReadState)-> ReadState:
     aimessages=await  summary_llm(state,history,data,
                                   'summary_prompt.jinja2','summary',SummaryResponse,0)
 
-
     if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
         await summary_redis_save(state, aimessages)
     if aimessages == '':
@@ -295,9 +294,26 @@ async def Summary(state: ReadState)-> ReadState:
 async def Summary_fails(state: ReadState)-> ReadState:
     storage_type=state.get("storage_type", '')
     user_rag_memory_id=state.get("user_rag_memory_id", '')
+    history = await summary_history(state)
+    query = state.get("data", '')
+    verify = state.get("verify", '')
+    verify_expansion_issue = verify.get("verified_data", '')
+    retrieve_info_str = ''
+    for data in verify_expansion_issue:
+        for key, value in data.items():
+            if key == 'answer_small':
+                for i in value:
+                    retrieve_info_str += i + '\n'
+    data = {
+        "query": query,
+        "history": history,
+        "retrieve_info": retrieve_info_str
+    }
+    aimessages = await  summary_llm(state, history, data,
+                                 'fail_summary_prompt.jinja2', 'summary', SummaryResponse, 0)
     result= {
         "status": "success",
-        "summary_result": "没有相关数据",
+        "summary_result": aimessages,
         "storage_type": storage_type,
         "user_rag_memory_id": user_rag_memory_id
     }

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

@@ -12,7 +12,7 @@ from app.core.memory.agent.utils.session_tools import SessionService
 from app.core.memory.agent.utils.template_tools import TemplateService
 from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
 
-template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
+template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
 db_session = next(get_db())
 logger = get_agent_logger(__name__)
 
@@ -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,
+            end_user_id=end_user_id,
-            apply_id=group_id,
-            group_id=group_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
+    Args:
-    and saves them to Neo4j storage.
+        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",
+        end_user_id: str = "group_1",
-        user_id: str = "user1",
-        apply_id: str = "applyid",
         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
+        end_user_id: Group identifier
-        user_id: User identifier
-        apply_id: Application 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,
+        end_user_id=end_user_id,
-        user_id=user_id,
-        apply_id=apply_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

@@ -1,24 +1,23 @@
 import os
 from collections import defaultdict
+from pathlib import Path
 from typing import Annotated, TypedDict
 
 from langchain_core.messages import AnyMessage
 from langgraph.graph import add_messages
 
-PROJECT_ROOT_ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+PROJECT_ROOT_ = str(Path(__file__).resolve().parents[3])
 
 class WriteState(TypedDict):
     '''
     Langgrapg Writing TypedDict
     '''
     messages: Annotated[list[AnyMessage], add_messages]
-    user_id:str
+    end_user_id: str
-    apply_id:str
-    group_id:str
     errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
     memory_config: object
     write_result: dict
-    data:str
+    data: str
 
 class ReadState(TypedDict):
     """
@@ -28,7 +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
@@ -39,7 +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/prompt/direct_summary_prompt.jinja2

@@ -0,0 +1,61 @@
+# 角色
+你是一个智能问答助手，基于检索信息和历史对话回答用户问题。
+# 任务
+根据提供的上下文信息回答用户的问题。
+# 输入信息
+- 历史对话：{{history}}
+- 检索信息：{{retrieve_info}}
+# 用户问题
+{{query}}
+# 回答指南
+## 1. 仔细阅读检索信息
+- 答案可能直接或间接地出现在检索信息中
+- 如果检索信息中提到"小曼会使用Python"，说明用户名是"小曼"
+- 第三人称描述的偏好、行为通常指用户本人
+
+## 2. 判断信息相关性
+**情况A：信息匹配问题**
+- 直接回答，像自然对话一样
+- 例：检索到"小曼会使用Python" → 问"我叫什么" → 答"你叫小曼"
+
+**情况B：信息部分相关**
+- 先回答已知部分，再自然地询问更多信息
+- 例：检索到"用户去过上海的面包店" → 问"我吃过哪家面包" → 答"我记得你去过上海的面包店，但具体是哪家我不太清楚，是哪家呢？"
+
+**情况C：信息完全不相关**
+- 自然地表达不知道，但可以提及检索到的相关信息，让对话更连贯
+- 使用友好的表达：
+  - "你好像没和我说过...，但是我知道你[检索到的相关信息]"
+  - "关于这个我不太清楚，不过我记得你[检索到的相关信息]，能告诉我更多吗？"
+  - "我不记得你提到过...，但你[检索到的相关信息]"
+- 即使检索信息不直接回答问题，也可以自然地融入对话中
+- 避免僵硬的"信息不足，无法回答"
+## 3. 回答要求
+- 像人类对话一样自然流畅
+- 不要提及"检索信息"、"搜索结果"、"根据资料"等技术术语
+- 不要解释推理过程或引用信息来源
+- 保持友好、乐于助人的语气
+- 使用与问题相同的语言回答
+# 关键示例
+**示例1 - 直接匹配：**
+- 检索信息："小曼会使用Python..."
+- 问题："我叫什么"
+- ✓ 正确："你叫小曼"
+- ✗ 错误："你没有告诉我你的名字"
+**示例2 - 间接匹配：**
+- 检索信息："用户很喜欢吃星巴克的甜品"
+- 问题："我喜欢什么"
+- ✓ 正确："你很喜欢吃星巴克的甜品"
+- ✗ 错误："信息不足"
+**示例3 - 信息不匹配（推荐做法）：**
+- 检索信息："用户只喝拿铁咖啡，认为美式咖啡太苦"
+- 问题："我吃过哪家面包"
+- ✓ 最佳："你好像没和我说过吃过哪家面包，但是我知道你喜欢喝拿铁，能跟我分享一下吗？"
+- ✓ 可以："你好像没和我说过吃过哪家面包，能跟我分享一下吗？"
+- ✗ 错误："用户只喝拿铁咖啡，认为美式咖啡太苦。"（答非所问）
+- ✗ 错误："信息不足，无法回答。"（太僵硬）
+# 重要提醒
+- 检索信息中描述用户行为/偏好时提到的名字，就是用户的名字
+- 信息不匹配时，不要强行回答无关内容，但可以自然地提及检索到的信息，让对话更有温度
+- 用对话式语言表达"不知道"，而非机械模板
+- 检索信息代表你对用户的了解，即使不直接回答问题，也能体现你对用户的记忆

api/app/core/memory/agent/utils/prompt/fail_summary_prompt.jinja2

@@ -0,0 +1,43 @@
+{# 角色定义 #}
+你是专业的问题解答专家+引导学者
+
+{# 输入数据展示 #}
+{% if data %}
+## 输入数据
+上下文信息:
+{% for item in data.history %}
+- {{ item }}
+{% endfor %}
+检索到的所有信息:
+{% for item in data.retrieve_info %}
+- {{ item }}
+{% endfor %}
+{% endif %}
+
+## User Query
+{{ query }}
+
+{# 问题回答标准 #}
+## 问题回答核心标准
+根据上下文信息(history)和检索到的所有信息(retrieve_info)准确回答用户的问题(query)。
+注意，仔细阅读检索信息，答案可能直接或间接地出现在检索信息中或者历史上下文消息中，同时需要 判断信息相关性
+**情况A：信息匹配问题**
+- 直接回答，像自然对话一样
+- 例：检索到"小曼会使用Python" → 问"我叫什么" → 答"你叫小曼"
+
+**情况B：信息部分相关**
+- 先回答已知部分，再自然地询问更多信息
+- 例：检索到"用户去过上海的面包店" → 问"我吃过哪家面包" → 答"我记得你去过上海的面包店，但具体是哪家我不太清楚，是哪家呢？"
+
+**情况C：信息完全不相关**
+- 自然地表达不知道，但可以提及检索到的相关信息，让对话更连贯
+- 使用友好的表达：
+  - "你好像没和我说过...，但是我知道你[检索到的相关信息]"
+  - "关于这个我不太清楚，不过我记得你[检索到的相关信息]，能告诉我更多吗？"
+  - "我不记得你提到过...，但你[检索到的相关信息]"
+- 即使检索信息不直接回答问题，也可以自然地融入对话中
+- 避免僵硬的"信息不足，无法回答"
+
+{# 重要提醒 #}
+当检索以及上下文的历史信息都无法回答的时候，可引导对方进行提问/回答，或者进行其他引导
+当检索或者上下文中出现了，相似的问题，可以委婉，提醒对方，我记得刚刚提过这个问题，但是我自己不记得了，能在描述一次吗～以此为例

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,
+    end_user_id: str,
-    apply_id: str,
-    group_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,
+        end_user_id=end_user_id,
-        user_id=user_id,
-        apply_id=apply_id,
         messages=messages,
         ref_id=ref_id,
         config_id=config_id,

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

@@ -139,7 +139,8 @@ def parse_api_docs(file_path: str) -> Dict[str, Any]:
 
 
 def get_default_docs_path() -> str:
-    project_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
+    from pathlib import Path
+    project_root = str(Path(__file__).resolve().parents[2])
     return os.path.join(project_root, "src", "analytics", "API接口.md")
 
 

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
+    # 验证end_user_id必须提供且不为空
-    if not group_id or not group_id.strip():
+    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(
+            summary_dicts = await self.memory_summary_repo.find_by_end_user_id(
-                group_id=user_id,
+                end_user_id=user_id,
                 limit=limit,
                 start_date=start_date,
                 end_date=end_date

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

@@ -2,13 +2,16 @@ import os
 import re
 import glob
 import json
+from pathlib import Path
 from typing import Tuple
 
 try:
     from app.core.memory.utils.config.definitions import PROJECT_ROOT
 except Exception:
     # Fallback: derive project root from this file location
-    PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    # 当前文件在 api/app/core/memory/analytics/recent_activity_stats.py
+    # 需要向上 5 级到达 api/ 目录
+    PROJECT_ROOT = str(Path(__file__).resolve().parents[4])
 
 
 def _get_latest_prompt_log_path() -> str | None:
@@ -67,44 +70,43 @@ def parse_stats_from_log(log_path: str) -> dict:
     triplet_relations_count = 0
     temporal_count = 0
 
-    # Patterns
+    # 正则表达式模式 - 匹配当前日志格式
     pat_chunk_render = re.compile(r"===\s*RENDERED\s*STATEMENT\s*EXTRACTION\s*PROMPT\s*===")
-    pat_triplet_start = re.compile(r"\[Triplet\].*statements_to_process\s*=\s*(\d+)")
+    pat_triplet_started = re.compile(r"\[Triplet\]\s+Started\s+-\s+statement_id=")
-    pat_triplet_done = re.compile(
+    pat_triplet_completed = re.compile(
-        r"\[Triplet\].*completed,\s*total_triplets\s*=\s*(\d+),\s*total_entities\s*=\s*(\d+)"
+        r"\[Triplet\]\s+Completed\s+-\s+statement_id=[^,]+,\s+triplets=(\d+),\s+entities=(\d+)"
     )
-    pat_temporal_done = re.compile(
+    pat_temporal_completed = re.compile(
-        r"\[Temporal\].*completed,\s*extracted_valid_ranges\s*=\s*(\d+)"
+        r"\[Temporal\]\s+Completed\s+-\s+statement_id=[^,]+,\s+valid_ranges=(\d+)"
     )
 
     with open(log_path, "r", encoding="utf-8", errors="ignore") as f:
         for line in f:
-            # Chunk prompts count (each chunk triggers one statement-extraction prompt render)
+            # 文本块数量（每个块触发一次陈述提取提示）
             if pat_chunk_render.search(line):
                 chunk_count += 1
                 continue
 
-            m1 = pat_triplet_start.search(line)
+            # 陈述数量（每个 Triplet Started 代表一个陈述被处理）
-            if m1:
+            if pat_triplet_started.search(line):
+                statements_count += 1
+                continue
+
+            # 三元组完成：[Triplet] Completed - statement_id=xxx, triplets=X, entities=Y
+            m_triplet = pat_triplet_completed.search(line)
+            if m_triplet:
                 try:
-                    statements_count += int(m1.group(1))
+                    triplet_relations_count += int(m_triplet.group(1))
+                    triplet_entities_count += int(m_triplet.group(2))
                 except Exception:
                     pass
                 continue
 
-            m2 = pat_triplet_done.search(line)
+            # 时间信息完成：[Temporal] Completed - statement_id=xxx, valid_ranges=X
-            if m2:
+            m_temporal = pat_temporal_completed.search(line)
+            if m_temporal:
                 try:
-                    triplet_relations_count += int(m2.group(1))
+                    temporal_count += int(m_temporal.group(1))
-                    triplet_entities_count += int(m2.group(2))
-                except Exception:
-                    pass
-                continue
-
-            m3 = pat_temporal_done.search(line)
-            if m3:
-                try:
-                    temporal_count += int(m3.group(1))
                 except Exception:
                     pass
                 continue
@@ -120,15 +122,20 @@ def parse_stats_from_log(log_path: str) -> dict:
 
 
 def get_recent_activity_stats() -> Tuple[dict, str]:
-    """Get aggregated stats from all prompt logs in logs/.
+    """Get stats from the latest prompt log file only.
 
     Returns (stats_dict, message).
     """
-    all_logs = _get_all_prompt_logs()
+    # 获取最新的日志文件
-    # Fallback to recursive search if none found in logs/
+    latest_log = _get_latest_prompt_log_path()
-    if not all_logs:
+    
+    # 如果没有找到，尝试递归搜索
+    if not latest_log:
         all_logs = _get_any_logs_recursive()
-    if not all_logs:
+        if all_logs:
+            latest_log = all_logs[-1]  # 取最新的
+    
+    if not latest_log:
         return (
             {
                 "chunk_count": 0,
@@ -141,24 +148,13 @@ def get_recent_activity_stats() -> Tuple[dict, str]:
             "未找到日志文件，请确认已运行过提取流程。",
         )
 
-    agg = {
+    # 只解析最新的日志文件
-        "chunk_count": 0,
+    stats = parse_stats_from_log(latest_log)
-        "statements_count": 0,
+    
-        "triplet_entities_count": 0,
+    # 添加日志文件路径信息
-        "triplet_relations_count": 0,
+    stats["log_path"] = f"最新：{latest_log}"
-        "temporal_count": 0,
+    
-    }
+    return stats, "成功读取最近一次记忆活动统计。"
-    for path in all_logs:
-        s = parse_stats_from_log(path)
-        agg["chunk_count"] += s.get("chunk_count", 0)
-        agg["statements_count"] += s.get("statements_count", 0)
-        agg["triplet_entities_count"] += s.get("triplet_entities_count", 0)
-        agg["triplet_relations_count"] += s.get("triplet_relations_count", 0)
-        agg["temporal_count"] += s.get("temporal_count", 0)
-
-    # Attach a summary of files combined
-    agg["log_path"] = f"{len(all_logs)} 个日志文件，最新：{all_logs[-1]}"
-    return agg, "成功汇总 logs 目录中所有提示日志。"
 
 
 def _format_summary(stats: dict) -> str:

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

@@ -1 +0,0 @@
-"""Evaluation package with dataset-specific pipelines and a unified runner."""

api/app/core/memory/evaluation/benchmark.md

@@ -1,30 +0,0 @@
-⏬数据集下载地址：
-    Locomo10.json：https://github.com/snap-research/locomo/tree/main/data
-    LongMemEval_oracle.json：https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned
-    msc_self_instruct.jsonl:https://huggingface.co/datasets/MemGPT/MSC-Self-Instruct
-    上方数据集下载好后全部放入app/core/memory/data文件夹中
-
-全流程基准测试运行：
-    locomo：
-        python -m app.core.memory.evaluation.run_eval --dataset locomo --sample-size 1 --reset-group --group-id yyw1 --search-type hybrid --search-limit 8 --context-char-budget 12000 --llm-max-tokens 32
-    LongMemEval：
-        python -m app.core.memory.evaluation.run_eval --dataset longmemeval --sample-size 10 --start-index 0 --group-id longmemeval_zh_bak_2 --search-limit 8 --context-char-budget 4000 --search-type hybrid --max-contexts-per-item 2 --reset-group
-    memsciqa：
-        python -m app.core.memory.evaluation.run_eval --dataset memsciqa --sample-size 10 --reset-group --group-id group_memsci
-
-单独检索评估运行命令：
-    python -m app.core.memory.evaluation.locomo.locomo_test
-    python -m app.core.memory.evaluation.longmemeval.test_eval
-    python -m app.core.memory.evaluation.memsciqa.memsciqa-test
-    需要先在项目中修改需要检测评估的group_id。
-
-参数及解释：
-    ● --dataset longmemeval - 指定数据集
-    ● --sample-size 10 - 评估10个样本
-    ● --start-index 0 - 从第0个样本开始
-    ● --group-id longmemeval_zh_bak_2 - 使用指定的组ID
-    ● --search-limit 8 - 检索限制8条
-    ● --context-char-budget 4000 - 上下文字符预算4000
-    ● --search-type hybrid - 使用混合检索
-    ● --max-contexts-per-item 2 - 每个样本最多摄入2个上下文
-    ● --reset-group - 运行前清空组数据

api/app/core/memory/evaluation/common/metrics.py

@@ -1,100 +0,0 @@
-import math
-import re
-from typing import List, Dict
-
-
-def _normalize(text: str) -> List[str]:
-    """Lowercase, strip punctuation, and split into tokens."""
-    text = text.lower().strip()
-    # Python's re doesn't support \p classes; use a simple non-word filter
-    text = re.sub(r"[^\w\s]", " ", text)
-    tokens = [t for t in text.split() if t]
-    return tokens
-
-
-def exact_match(pred: str, ref: str) -> float:
-    return float(_normalize(pred) == _normalize(ref))
-
-
-def jaccard(pred: str, ref: str) -> float:
-    p = set(_normalize(pred))
-    r = set(_normalize(ref))
-    if not p and not r:
-        return 1.0
-    if not p or not r:
-        return 0.0
-    return len(p & r) / len(p | r)
-
-
-def f1_score(pred: str, ref: str) -> float:
-    p_tokens = _normalize(pred)
-    r_tokens = _normalize(ref)
-    if not p_tokens and not r_tokens:
-        return 1.0
-    if not p_tokens or not r_tokens:
-        return 0.0
-    p_set = set(p_tokens)
-    r_set = set(r_tokens)
-    tp = len(p_set & r_set)
-    precision = tp / len(p_set) if p_set else 0.0
-    recall = tp / len(r_set) if r_set else 0.0
-    if precision + recall == 0:
-        return 0.0
-    return 2 * precision * recall / (precision + recall)
-
-
-def bleu1(pred: str, ref: str) -> float:
-    """Unigram BLEU (BLEU-1) with clipping and brevity penalty."""
-    p_tokens = _normalize(pred)
-    r_tokens = _normalize(ref)
-    if not p_tokens:
-        return 0.0
-    # Clipped count
-    r_counts: Dict[str, int] = {}
-    for t in r_tokens:
-        r_counts[t] = r_counts.get(t, 0) + 1
-    clipped = 0
-    p_counts: Dict[str, int] = {}
-    for t in p_tokens:
-        p_counts[t] = p_counts.get(t, 0) + 1
-    for t, c in p_counts.items():
-        clipped += min(c, r_counts.get(t, 0))
-    precision = clipped / max(len(p_tokens), 1)
-    # Brevity penalty
-    ref_len = len(r_tokens)
-    pred_len = len(p_tokens)
-    if pred_len > ref_len or pred_len == 0:
-        bp = 1.0
-    else:
-        bp = math.exp(1 - ref_len / max(pred_len, 1))
-    return bp * precision
-
-
-def percentile(values: List[float], p: float) -> float:
-    if not values:
-        return 0.0
-    vals = sorted(values)
-    k = (len(vals) - 1) * p
-    f = math.floor(k)
-    c = math.ceil(k)
-    if f == c:
-        return vals[int(k)]
-    return vals[f] + (k - f) * (vals[c] - vals[f])
-
-
-def latency_stats(latencies_ms: List[float]) -> Dict[str, float]:
-    """Return basic latency stats: mean, p50, p95, iqr (p75-p25)."""
-    if not latencies_ms:
-        return {"mean": 0.0, "p50": 0.0, "p95": 0.0, "iqr": 0.0}
-    p25 = percentile(latencies_ms, 0.25)
-    p50 = percentile(latencies_ms, 0.50)
-    p75 = percentile(latencies_ms, 0.75)
-    p95 = percentile(latencies_ms, 0.95)
-    mean = sum(latencies_ms) / max(len(latencies_ms), 1)
-    return {"mean": mean, "p50": p50, "p95": p95, "iqr": p75 - p25}
-
-
-def avg_context_tokens(contexts: List[str]) -> float:
-    if not contexts:
-        return 0.0
-    return sum(len(_normalize(c)) for c in contexts) / len(contexts)

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

@@ -1,60 +0,0 @@
-"""
-Dialogue search queries for evaluation purposes.
-This file contains Cypher queries for searching dialogues, entities, and chunks.
-Placed in evaluation directory to avoid circular imports with src modules.
-"""
-
-# Entity search queries
-SEARCH_ENTITIES_BY_NAME = """
-MATCH (e:Entity)
-WHERE e.name = $name
-RETURN e
-"""
-
-SEARCH_ENTITIES_BY_NAME_FALLBACK = """
-MATCH (e:Entity)
-WHERE e.name CONTAINS $name
-RETURN e
-"""
-
-# Chunk search queries
-SEARCH_CHUNKS_BY_CONTENT = """
-MATCH (c:Chunk)
-WHERE c.content CONTAINS $content
-RETURN c
-"""
-
-# Dialogue search queries
-SEARCH_DIALOGUE_BY_DIALOG_ID = """
-MATCH (d:Dialogue)
-WHERE d.dialog_id = $dialog_id
-RETURN d
-"""
-
-SEARCH_DIALOGUES_BY_CONTENT = """
-MATCH (d:Dialogue)
-WHERE d.content CONTAINS $q
-RETURN d
-"""
-
-DIALOGUE_EMBEDDING_SEARCH = """
-WITH $embedding AS q
-MATCH (d:Dialogue)
-WHERE d.dialog_embedding IS NOT NULL
-  AND ($group_id IS NULL OR d.group_id = $group_id)
-WITH d, q, d.dialog_embedding AS v
-WITH d,
-     reduce(dot = 0.0, i IN range(0, size(q)-1) | dot + toFloat(q[i]) * toFloat(v[i])) AS dot,
-     sqrt(reduce(qs = 0.0, i IN range(0, size(q)-1) | qs + toFloat(q[i]) * toFloat(q[i]))) AS qnorm,
-     sqrt(reduce(vs = 0.0, i IN range(0, size(v)-1) | vs + toFloat(v[i]) * toFloat(v[i]))) AS vnorm
-WITH d, CASE WHEN qnorm = 0 OR vnorm = 0 THEN 0.0 ELSE dot / (qnorm * vnorm) END AS score
-WHERE score > $threshold
-RETURN d.id AS dialog_id,
-       d.group_id AS group_id,
-       d.content AS content,
-       d.created_at AS created_at,
-       d.expired_at AS expired_at,
-       score
-ORDER BY score DESC
-LIMIT $limit
-"""

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

@@ -1,341 +0,0 @@
-import asyncio
-import json
-import os
-import re
-from datetime import datetime
-from typing import Any, Dict, List, Optional
-
-from app.core.memory.llm_tools.openai_client import LLMClient
-from app.core.memory.models.message_models import (
-    ConversationContext,
-    ConversationMessage,
-    DialogData,
-)
-
-# 使用新的模块化架构
-from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import (
-    ExtractionOrchestrator,
-)
-from app.core.memory.storage_services.extraction_engine.knowledge_extraction.chunk_extraction import (
-    DialogueChunker,
-)
-from app.core.memory.utils.config.definitions import (
-    SELECTED_CHUNKER_STRATEGY,
-    SELECTED_EMBEDDING_ID,
-)
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.db import get_db_context
-
-# Import from database module
-from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-
-# Cypher queries for evaluation
-# Note: Entity, chunk, and dialogue search queries have been moved to evaluation/dialogue_queries.py
-
-
-async def ingest_contexts_via_full_pipeline(
-    contexts: List[str],
-    group_id: str,
-    chunker_strategy: str | None = None,
-    embedding_name: str | None = None,
-    save_chunk_output: bool = False,
-    save_chunk_output_path: str | None = None,
-) -> bool:
-    """DEPRECATED: 此函数使用旧的流水线架构，建议使用新的 ExtractionOrchestrator
-    
-    Run the full extraction pipeline on provided dialogue contexts and save to Neo4j.
-    This function mirrors the steps in main(), but starts from raw text contexts.
-    Args:
-        contexts: List of dialogue texts, each containing lines like "role: message".
-        group_id: Group ID to assign to generated DialogData and graph nodes.
-        chunker_strategy: Optional chunker strategy; defaults to SELECTED_CHUNKER_STRATEGY.
-        embedding_name: Optional embedding model ID; defaults to SELECTED_EMBEDDING_ID.
-        save_chunk_output: If True, write chunked DialogData list to a JSON file for debugging.
-        save_chunk_output_path: Optional output path; defaults to src/chunker_test_output.txt.
-    Returns:
-        True if data saved successfully, False otherwise.
-    """
-    chunker_strategy = chunker_strategy or SELECTED_CHUNKER_STRATEGY
-    embedding_name = embedding_name or SELECTED_EMBEDDING_ID
-
-    # Initialize llm client with graceful fallback
-    llm_client = None
-    llm_available = True
-    try:
-        from app.core.memory.utils.config import definitions as config_defs
-        with get_db_context() as db:
-            factory = MemoryClientFactory(db)
-            llm_client = factory.get_llm_client(config_defs.SELECTED_LLM_ID)
-    except Exception as e:
-        print(f"[Ingestion] LLM client unavailable, will skip LLM-dependent steps: {e}")
-        llm_available = False
-
-    # Step A: Build DialogData list from contexts with robust parsing
-    chunker = DialogueChunker(chunker_strategy)
-    dialog_data_list: List[DialogData] = []
-
-    for idx, ctx in enumerate(contexts):
-        messages: List[ConversationMessage] = []
-
-        # Improved parsing: capture multi-line message blocks, normalize roles
-        pattern = r"^\s*(用户|AI|assistant|user)\s*[：:]\s*(.+?)(?=\n\s*(?:用户|AI|assistant|user)\s*[：:]|\Z)"
-        matches = list(re.finditer(pattern, ctx, flags=re.MULTILINE | re.DOTALL))
-
-        if matches:
-            for m in matches:
-                raw_role = m.group(1).strip()
-                content = m.group(2).strip()
-                norm_role = "AI" if raw_role.lower() in ("ai", "assistant") else "用户"
-                messages.append(ConversationMessage(role=norm_role, msg=content))
-        else:
-            # Fallback: line-by-line parsing
-            for raw in ctx.split("\n"):
-                line = raw.strip()
-                if not line:
-                    continue
-                m = re.match(r'^\s*([^:：]+)\s*[：:]\s*(.+)$', line)
-                if m:
-                    role = m.group(1).strip()
-                    msg = m.group(2).strip()
-                    norm_role = "AI" if role.lower() in ("ai", "assistant") else "用户"
-                    messages.append(ConversationMessage(role=norm_role, msg=msg))
-                else:
-                    # Final fallback: treat as user message
-                    default_role = "AI" if re.match(r'^\s*(assistant|AI)\b', line, flags=re.IGNORECASE) else "用户"
-                    messages.append(ConversationMessage(role=default_role, msg=line))
-
-        context_model = ConversationContext(msgs=messages)
-        dialog = DialogData(
-            context=context_model,
-            ref_id=f"pipeline_item_{idx}",
-            group_id=group_id,
-            user_id="default_user",
-            apply_id="default_application",
-        )
-        # Generate chunks
-        dialog.chunks = await chunker.process_dialogue(dialog)
-        dialog_data_list.append(dialog)
-
-    if not dialog_data_list:
-        print("No dialogs to process for ingestion.")
-        return False
-
-    # Optionally save chunking outputs for debugging
-    if save_chunk_output:
-        try:
-            def _serialize_datetime(obj):
-                if isinstance(obj, datetime):
-                    return obj.isoformat()
-                raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
-
-            from app.core.config import settings
-            settings.ensure_memory_output_dir()
-            default_path = settings.get_memory_output_path("chunker_test_output.txt")
-            out_path = save_chunk_output_path or default_path
-
-            combined_output = [dd.model_dump() for dd in dialog_data_list]
-            with open(out_path, "w", encoding="utf-8") as f:
-                json.dump(combined_output, f, ensure_ascii=False, indent=4, default=_serialize_datetime)
-            print(f"Saved chunking results to: {out_path}")
-        except Exception as e:
-            print(f"Failed to save chunking results: {e}")
-
-    # Step B-G: 使用新的 ExtractionOrchestrator 执行完整的提取流水线
-    if not llm_available:
-        print("[Ingestion] Skipping extraction pipeline (no LLM).")
-        return False
-    
-    # 初始化 embedder 客户端
-    from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
-    from app.core.models.base import RedBearModelConfig
-    from app.services.memory_config_service import MemoryConfigService
-    
-    try:
-        with get_db_context() as db:
-            embedder_config_dict = MemoryConfigService(db).get_embedder_config(embedding_name or SELECTED_EMBEDDING_ID)
-        embedder_config = RedBearModelConfig(**embedder_config_dict)
-        embedder_client = OpenAIEmbedderClient(embedder_config)
-    except Exception as e:
-        print(f"[Ingestion] Failed to initialize embedder client: {e}")
-        print("[Ingestion] Skipping extraction pipeline (embedder initialization failed).")
-        return False
-    
-    connector = Neo4jConnector()
-    
-    # 初始化并运行 ExtractionOrchestrator
-    from app.core.memory.utils.config.config_utils import get_pipeline_config
-    config = get_pipeline_config()
-    
-    orchestrator = ExtractionOrchestrator(
-        llm_client=llm_client,
-        embedder_client=embedder_client,
-        connector=connector,
-        config=config,
-    )
-    
-    # 创建一个包装的 orchestrator 来修复时间提取器的输出
-    # 保存原始的 _assign_extracted_data 方法
-    original_assign = orchestrator._assign_extracted_data
-    
-    def clean_temporal_value(value):
-        """清理 temporal_validity 字段的值，将无效值转换为 None"""
-        if value is None:
-            return None
-        if isinstance(value, str):
-            # 处理字符串形式的 'null', 'None', 空字符串等
-            if value.lower() in ('null', 'none', '') or value.strip() == '':
-                return None
-        return value
-    
-    async def patched_assign_extracted_data(*args, **kwargs):
-        """包装方法：在赋值后清理 temporal_validity 中的无效字符串"""
-        result = await original_assign(*args, **kwargs)
-        
-        # 清理返回的 dialog_data_list 中的 temporal_validity
-        for dialog in result:
-            if hasattr(dialog, 'chunks') and dialog.chunks:
-                for chunk in dialog.chunks:
-                    if hasattr(chunk, 'statements') and chunk.statements:
-                        for statement in chunk.statements:
-                            if hasattr(statement, 'temporal_validity') and statement.temporal_validity:
-                                tv = statement.temporal_validity
-                                # 清理 valid_at 和 invalid_at
-                                if hasattr(tv, 'valid_at'):
-                                    tv.valid_at = clean_temporal_value(tv.valid_at)
-                                if hasattr(tv, 'invalid_at'):
-                                    tv.invalid_at = clean_temporal_value(tv.invalid_at)
-        return result
-    
-    # 替换方法
-    orchestrator._assign_extracted_data = patched_assign_extracted_data
-    
-    # 同时包装 _create_nodes_and_edges 方法，在创建节点前再次清理
-    original_create = orchestrator._create_nodes_and_edges
-    
-    async def patched_create_nodes_and_edges(dialog_data_list_arg):
-        """包装方法：在创建节点前再次清理 temporal_validity"""
-        # 最后一次清理，确保万无一失
-        for dialog in dialog_data_list_arg:
-            if hasattr(dialog, 'chunks') and dialog.chunks:
-                for chunk in dialog.chunks:
-                    if hasattr(chunk, 'statements') and chunk.statements:
-                        for statement in chunk.statements:
-                            if hasattr(statement, 'temporal_validity') and statement.temporal_validity:
-                                tv = statement.temporal_validity
-                                if hasattr(tv, 'valid_at'):
-                                    tv.valid_at = clean_temporal_value(tv.valid_at)
-                                if hasattr(tv, 'invalid_at'):
-                                    tv.invalid_at = clean_temporal_value(tv.invalid_at)
-        
-        return await original_create(dialog_data_list_arg)
-    
-    orchestrator._create_nodes_and_edges = patched_create_nodes_and_edges
-    
-    # 运行完整的提取流水线
-    # orchestrator.run 返回 7 个元素的元组
-    result = await orchestrator.run(dialog_data_list, is_pilot_run=False)
-    (
-        dialogue_nodes,
-        chunk_nodes,
-        statement_nodes,
-        entity_nodes,
-        statement_chunk_edges,
-        statement_entity_edges,
-        entity_entity_edges,
-    ) = result
-    
-    # statement_chunk_edges 已经由 orchestrator 创建，无需重复创建
-
-    # Step G: 生成记忆摘要
-    print("[Ingestion] Generating memory summaries...")
-    try:
-        from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import (
-            memory_summary_generation,
-        )
-        from app.repositories.neo4j.add_edges import add_memory_summary_statement_edges
-        from app.repositories.neo4j.add_nodes import add_memory_summary_nodes
-        
-        summaries = await memory_summary_generation(
-            chunked_dialogs=dialog_data_list,
-            llm_client=llm_client,
-            embedder_client=embedder_client
-        )
-        print(f"[Ingestion] Generated {len(summaries)} memory summaries")
-    except Exception as e:
-        print(f"[Ingestion] Warning: Failed to generate memory summaries: {e}")
-        summaries = []
-
-    # Step H: Save to Neo4j
-    try:
-        success = await save_dialog_and_statements_to_neo4j(
-            dialogue_nodes=dialogue_nodes,
-            chunk_nodes=chunk_nodes,
-            statement_nodes=statement_nodes,
-            entity_nodes=entity_nodes,
-            entity_edges=entity_entity_edges,
-            statement_chunk_edges=statement_chunk_edges,
-            statement_entity_edges=statement_entity_edges,
-            connector=connector
-        )
-        
-        # Save memory summaries separately
-        if summaries:
-            try:
-                await add_memory_summary_nodes(summaries, connector)
-                await add_memory_summary_statement_edges(summaries, connector)
-                print(f"Successfully saved {len(summaries)} memory summary nodes to Neo4j")
-            except Exception as e:
-                print(f"Warning: Failed to save summary nodes: {e}")
-        
-        await connector.close()
-        if success:
-            print("Successfully saved extracted data to Neo4j!")
-        else:
-            print("Failed to save data to Neo4j")
-        return success
-    except Exception as e:
-        print(f"Failed to save data to Neo4j: {e}")
-        return False
-
-
-async def handle_context_processing(args):
-    """Handle context-based processing from command line arguments."""
-    contexts = []
-
-    if args.contexts:
-        contexts.extend(args.contexts)
-
-    if args.context_file:
-        try:
-            with open(args.context_file, 'r', encoding='utf-8') as f:
-                contexts.extend(line.strip() for line in f if line.strip())
-        except Exception as e:
-            print(f"Error reading context file: {e}")
-            return False
-
-    if not contexts:
-        print("No contexts provided for processing.")
-        return False
-
-    return await main_from_contexts(contexts, args.context_group_id)
-
-
-async def main_from_contexts(contexts: List[str], group_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,
-        chunker_strategy=SELECTED_CHUNKER_STRATEGY,
-        embedding_name=SELECTED_EMBEDDING_ID,
-        save_chunk_output=True
-    )
-
-    if success:
-        print("Successfully processed and saved contexts to Neo4j!")
-    else:
-        print("Failed to process contexts.")
-
-    return success

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

@@ -1,575 +0,0 @@
-"""
-LoCoMo Benchmark Script
-
-This module provides the main entry point for running LoCoMo benchmark evaluations.
-It orchestrates data loading, ingestion, retrieval, LLM inference, and metric calculation
-in a clean, maintainable way.
-
-Usage:
-    python locomo_benchmark.py --sample_size 20 --search_type hybrid
-"""
-
-import argparse
-import asyncio
-import json
-import os
-import time
-from datetime import datetime
-from typing import Any, Dict, List, Optional
-
-try:
-    from dotenv import load_dotenv
-except ImportError:
-    def load_dotenv():
-        pass
-
-from app.core.memory.evaluation.common.metrics import (
-    avg_context_tokens,
-    bleu1,
-    f1_score,
-    jaccard,
-    latency_stats,
-)
-from app.core.memory.evaluation.locomo.locomo_metrics import (
-    get_category_name,
-    locomo_f1_score,
-    locomo_multi_f1,
-)
-from app.core.memory.evaluation.locomo.locomo_utils import (
-    extract_conversations,
-    ingest_conversations_if_needed,
-    load_locomo_data,
-    resolve_temporal_references,
-    retrieve_relevant_information,
-    select_and_format_information,
-)
-from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
-from app.core.memory.utils.definitions import (
-    PROJECT_ROOT,
-    SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
-    SELECTED_LLM_ID,
-)
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.core.models.base import RedBearModelConfig
-from app.db import get_db_context
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-from app.services.memory_config_service import MemoryConfigService
-
-
-async def run_locomo_benchmark(
-    sample_size: int = 20,
-    group_id: Optional[str] = None,
-    search_type: str = "hybrid",
-    search_limit: int = 12,
-    context_char_budget: int = 8000,
-    reset_group: bool = False,
-    skip_ingest: bool = False,
-    output_dir: Optional[str] = None
-) -> Dict[str, Any]:
-    """
-    Run LoCoMo benchmark evaluation.
-    
-    This function orchestrates the complete evaluation pipeline:
-    1. Load LoCoMo dataset (only QA pairs from first conversation)
-    2. Check/ingest conversations into database (only first conversation, unless skip_ingest=True)
-    3. For each question:
-       - Retrieve relevant information
-       - Generate answer using LLM
-       - Calculate metrics
-    4. Aggregate results and save to file
-    
-    Note: By default, only the first conversation is ingested into the database,
-    and only QA pairs from that conversation are evaluated. This ensures that
-    all questions have corresponding memory in the database for retrieval.
-    
-    Args:
-        sample_size: Number of QA pairs to evaluate (from first conversation)
-        group_id: Database group ID for retrieval (uses default if None)
-        search_type: "keyword", "embedding", or "hybrid"
-        search_limit: Max documents to retrieve per query
-        context_char_budget: Max characters for context
-        reset_group: Whether to clear and re-ingest data (not implemented)
-        skip_ingest: If True, skip data ingestion and use existing data in Neo4j
-        output_dir: Directory to save results (uses default if None)
-        
-    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
-    
-    # Determine data path
-    data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json")
-    if not os.path.exists(data_path):
-        # Fallback to current directory
-        data_path = os.path.join(os.getcwd(), "data", "locomo10.json")
-    
-    print(f"\n{'='*60}")
-    print("🚀 Starting LoCoMo Benchmark Evaluation")
-    print(f"{'='*60}")
-    print("📊 Configuration:")
-    print(f"   Sample size: {sample_size}")
-    print(f"   Group ID: {group_id}")
-    print(f"   Search type: {search_type}")
-    print(f"   Search limit: {search_limit}")
-    print(f"   Context budget: {context_char_budget} chars")
-    print(f"   Data path: {data_path}")
-    print(f"{'='*60}\n")
-    
-    # Step 1: Load LoCoMo data
-    print("📂 Loading LoCoMo dataset...")
-    try:
-        # Only load QA pairs from the first conversation (index 0)
-        # since we only ingest the first conversation into the database
-        qa_items = load_locomo_data(data_path, sample_size, conversation_index=0)
-        print(f"✅ Loaded {len(qa_items)} QA pairs from conversation 0\n")
-    except Exception as e:
-        print(f"❌ Failed to load data: {e}")
-        return {
-            "error": f"Data loading failed: {e}",
-            "timestamp": datetime.now().isoformat()
-        }
-    
-    # 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")
-    else:
-        print("💾 Checking database ingestion...")
-        try:
-            conversations = extract_conversations(data_path, max_dialogues=1)
-            print(f"📝 Extracted {len(conversations)} conversations")
-            
-            # Always ingest for now (ingestion check not implemented)
-            print(f"🔄 Ingesting conversations into group '{group_id}'...")
-            success = await ingest_conversations_if_needed(
-                conversations=conversations,
-                group_id=group_id,
-                reset=reset_group
-            )
-            
-            if success:
-                print("✅ Ingestion completed successfully\n")
-            else:
-                print("⚠️  Ingestion may have failed, continuing anyway\n")
-        
-        except Exception as e:
-            print(f"❌ Ingestion failed: {e}")
-            print("⚠️  Continuing with evaluation (database may be empty)\n")
-    
-    # Step 3: Initialize clients
-    print("🔧 Initializing clients...")
-    connector = Neo4jConnector()
-    
-    # Initialize LLM client with database context
-    with get_db_context() as db:
-        factory = MemoryClientFactory(db)
-        llm_client = factory.get_llm_client(SELECTED_LLM_ID)
-    
-    # Initialize embedder
-    with get_db_context() as db:
-        config_service = MemoryConfigService(db)
-        cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID)
-    embedder = OpenAIEmbedderClient(
-        model_config=RedBearModelConfig.model_validate(cfg_dict)
-    )
-    print("✅ Clients initialized\n")
-    
-    # Step 4: Process questions
-    print(f"🔍 Processing {len(qa_items)} questions...")
-    print(f"{'='*60}\n")
-    
-    # Tracking variables
-    latencies_search: List[float] = []
-    latencies_llm: List[float] = []
-    context_counts: List[int] = []
-    context_chars: List[int] = []
-    context_tokens: List[int] = []
-    
-    # Metric lists
-    f1_scores: List[float] = []
-    bleu1_scores: List[float] = []
-    jaccard_scores: List[float] = []
-    locomo_f1_scores: List[float] = []
-    
-    # Per-category tracking
-    category_counts: Dict[str, int] = {}
-    category_f1: Dict[str, List[float]] = {}
-    category_bleu1: Dict[str, List[float]] = {}
-    category_jaccard: Dict[str, List[float]] = {}
-    category_locomo_f1: Dict[str, List[float]] = {}
-    
-    # Detailed samples
-    samples: List[Dict[str, Any]] = []
-    
-    # Fixed anchor date for temporal resolution
-    anchor_date = datetime(2023, 5, 8)
-    
-    try:
-        for idx, item in enumerate(qa_items, 1):
-            question = item.get("question", "")
-            ground_truth = item.get("answer", "")
-            category = get_category_name(item)
-            
-            # Ensure ground truth is a string
-            ground_truth_str = str(ground_truth) if ground_truth is not None else ""
-            
-            print(f"[{idx}/{len(qa_items)}] Category: {category}")
-            print(f"❓ Question: {question}")
-            print(f"✅ Ground Truth: {ground_truth_str}")
-            
-            # Step 4a: Retrieve relevant information
-            t_search_start = time.time()
-            try:
-                retrieved_info = await retrieve_relevant_information(
-                    question=question,
-                    group_id=group_id,
-                    search_type=search_type,
-                    search_limit=search_limit,
-                    connector=connector,
-                    embedder=embedder
-                )
-                t_search_end = time.time()
-                search_latency = (t_search_end - t_search_start) * 1000
-                latencies_search.append(search_latency)
-                
-                print(f"🔍 Retrieved {len(retrieved_info)} documents ({search_latency:.1f}ms)")
-                
-            except Exception as e:
-                print(f"❌ Retrieval failed: {e}")
-                retrieved_info = []
-                search_latency = 0.0
-                latencies_search.append(search_latency)
-            
-            # Step 4b: Select and format context
-            context_text = select_and_format_information(
-                retrieved_info=retrieved_info,
-                question=question,
-                max_chars=context_char_budget
-            )
-            
-            # Resolve temporal references
-            context_text = resolve_temporal_references(context_text, anchor_date)
-            
-            # Add reference date to context
-            if context_text:
-                context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n{context_text}"
-            else:
-                context_text = "No relevant context found."
-            
-            # Track context statistics
-            context_counts.append(len(retrieved_info))
-            context_chars.append(len(context_text))
-            context_tokens.append(len(context_text.split()))
-            
-            print(f"📝 Context: {len(context_text)} chars, {len(retrieved_info)} docs")
-            
-            # Step 4c: Generate answer with LLM
-            messages = [
-                {
-                    "role": "system",
-                    "content": (
-                        "You are a precise QA assistant. Answer following these rules:\n"
-                        "1) Extract the EXACT information mentioned in the context\n"
-                        "2) For time questions: calculate actual dates from relative times\n"
-                        "3) Return ONLY the answer text in simplest form\n"
-                        "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n"
-                        "5) If no clear answer found, respond with 'Unknown'"
-                    )
-                },
-                {
-                    "role": "user",
-                    "content": f"Question: {question}\n\nContext:\n{context_text}"
-                }
-            ]
-            
-            t_llm_start = time.time()
-            try:
-                response = await llm_client.chat(messages=messages)
-                t_llm_end = time.time()
-                llm_latency = (t_llm_end - t_llm_start) * 1000
-                latencies_llm.append(llm_latency)
-                
-                # Extract prediction from response
-                if hasattr(response, 'content'):
-                    prediction = response.content.strip()
-                elif isinstance(response, dict):
-                    prediction = response["choices"][0]["message"]["content"].strip()
-                else:
-                    prediction = "Unknown"
-                
-                print(f"🤖 Prediction: {prediction} ({llm_latency:.1f}ms)")
-                
-            except Exception as e:
-                print(f"❌ LLM failed: {e}")
-                prediction = "Unknown"
-                llm_latency = 0.0
-                latencies_llm.append(llm_latency)
-            
-            # Step 4d: Calculate metrics
-            f1_val = f1_score(prediction, ground_truth_str)
-            bleu1_val = bleu1(prediction, ground_truth_str)
-            jaccard_val = jaccard(prediction, ground_truth_str)
-            
-            # LoCoMo-specific F1: use multi-answer for category 1 (Multi-Hop)
-            if item.get("category") == 1:
-                locomo_f1_val = locomo_multi_f1(prediction, ground_truth_str)
-            else:
-                locomo_f1_val = locomo_f1_score(prediction, ground_truth_str)
-            
-            # Accumulate metrics
-            f1_scores.append(f1_val)
-            bleu1_scores.append(bleu1_val)
-            jaccard_scores.append(jaccard_val)
-            locomo_f1_scores.append(locomo_f1_val)
-            
-            # Track by category
-            category_counts[category] = category_counts.get(category, 0) + 1
-            category_f1.setdefault(category, []).append(f1_val)
-            category_bleu1.setdefault(category, []).append(bleu1_val)
-            category_jaccard.setdefault(category, []).append(jaccard_val)
-            category_locomo_f1.setdefault(category, []).append(locomo_f1_val)
-            
-            print(f"📊 Metrics - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, "
-                  f"Jaccard: {jaccard_val:.3f}, LoCoMo F1: {locomo_f1_val:.3f}")
-            print()
-            
-            # Save sample details
-            samples.append({
-                "question": question,
-                "ground_truth": ground_truth_str,
-                "prediction": prediction,
-                "category": category,
-                "metrics": {
-                    "f1": f1_val,
-                    "bleu1": bleu1_val,
-                    "jaccard": jaccard_val,
-                    "locomo_f1": locomo_f1_val
-                },
-                "retrieval": {
-                    "num_docs": len(retrieved_info),
-                    "context_length": len(context_text)
-                },
-                "timing": {
-                    "search_ms": search_latency,
-                    "llm_ms": llm_latency
-                }
-            })
-    
-    finally:
-        # Close connector
-        await connector.close()
-    
-    # Step 5: Aggregate results
-    print(f"\n{'='*60}")
-    print("📊 Aggregating Results")
-    print(f"{'='*60}\n")
-    
-    # Overall metrics
-    overall_metrics = {
-        "f1": sum(f1_scores) / max(len(f1_scores), 1) if f1_scores else 0.0,
-        "bleu1": sum(bleu1_scores) / max(len(bleu1_scores), 1) if bleu1_scores else 0.0,
-        "jaccard": sum(jaccard_scores) / max(len(jaccard_scores), 1) if jaccard_scores else 0.0,
-        "locomo_f1": sum(locomo_f1_scores) / max(len(locomo_f1_scores), 1) if locomo_f1_scores else 0.0
-    }
-    
-    # Per-category metrics
-    by_category: Dict[str, Dict[str, Any]] = {}
-    for cat in category_counts:
-        f1_list = category_f1.get(cat, [])
-        b1_list = category_bleu1.get(cat, [])
-        j_list = category_jaccard.get(cat, [])
-        lf_list = category_locomo_f1.get(cat, [])
-        
-        by_category[cat] = {
-            "count": category_counts[cat],
-            "f1": sum(f1_list) / max(len(f1_list), 1) if f1_list else 0.0,
-            "bleu1": sum(b1_list) / max(len(b1_list), 1) if b1_list else 0.0,
-            "jaccard": sum(j_list) / max(len(j_list), 1) if j_list else 0.0,
-            "locomo_f1": sum(lf_list) / max(len(lf_list), 1) if lf_list else 0.0
-        }
-    
-    # Latency statistics
-    latency = {
-        "search": latency_stats(latencies_search),
-        "llm": latency_stats(latencies_llm)
-    }
-    
-    # Context statistics
-    context_stats = {
-        "avg_retrieved_docs": sum(context_counts) / max(len(context_counts), 1) if context_counts else 0.0,
-        "avg_context_chars": sum(context_chars) / max(len(context_chars), 1) if context_chars else 0.0,
-        "avg_context_tokens": sum(context_tokens) / max(len(context_tokens), 1) if context_tokens else 0.0
-    }
-    
-    # Build result dictionary
-    result = {
-        "dataset": "locomo",
-        "sample_size": len(qa_items),
-        "timestamp": datetime.now().isoformat(),
-        "params": {
-            "group_id": group_id,
-            "search_type": search_type,
-            "search_limit": search_limit,
-            "context_char_budget": context_char_budget,
-            "llm_id": SELECTED_LLM_ID,
-            "embedding_id": SELECTED_EMBEDDING_ID
-        },
-        "overall_metrics": overall_metrics,
-        "by_category": by_category,
-        "latency": latency,
-        "context_stats": context_stats,
-        "samples": samples
-    }
-    
-    # Step 6: Save results
-    if output_dir is None:
-        output_dir = os.path.join(
-            os.path.dirname(__file__),
-            "results"
-        )
-    
-    os.makedirs(output_dir, exist_ok=True)
-    
-    # Generate timestamped filename
-    timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S")
-    output_path = os.path.join(output_dir, f"locomo_{timestamp_str}.json")
-    
-    try:
-        with open(output_path, "w", encoding="utf-8") as f:
-            json.dump(result, f, ensure_ascii=False, indent=2)
-        print(f"✅ Results saved to: {output_path}\n")
-    except Exception as e:
-        print(f"❌ Failed to save results: {e}")
-        print("📊 Printing results to console instead:\n")
-        print(json.dumps(result, ensure_ascii=False, indent=2))
-    
-    return result
-
-
-def main():
-    """
-    Parse command-line arguments and run benchmark.
-    
-    This function provides a CLI interface for running LoCoMo benchmarks
-    with configurable parameters.
-    """
-    parser = argparse.ArgumentParser(
-        description="Run LoCoMo benchmark evaluation",
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-    
-    parser.add_argument(
-        "--sample_size",
-        type=int,
-        default=20,
-        help="Number of QA pairs to evaluate"
-    )
-    parser.add_argument(
-        "--group_id",
-        type=str,
-        default=None,
-        help="Database group ID for retrieval (uses default if not specified)"
-    )
-    parser.add_argument(
-        "--search_type",
-        type=str,
-        default="hybrid",
-        choices=["keyword", "embedding", "hybrid"],
-        help="Search strategy to use"
-    )
-    parser.add_argument(
-        "--search_limit",
-        type=int,
-        default=12,
-        help="Maximum number of documents to retrieve per query"
-    )
-    parser.add_argument(
-        "--context_char_budget",
-        type=int,
-        default=8000,
-        help="Maximum characters for context"
-    )
-    parser.add_argument(
-        "--reset_group",
-        action="store_true",
-        help="Clear and re-ingest data (not implemented)"
-    )
-    parser.add_argument(
-        "--skip_ingest",
-        action="store_true",
-        help="Skip data ingestion and use existing data in Neo4j"
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default=None,
-        help="Directory to save results (uses default if not specified)"
-    )
-    
-    args = parser.parse_args()
-    
-    # Load environment variables
-    load_dotenv()
-    
-    # Run benchmark
-    result = asyncio.run(run_locomo_benchmark(
-        sample_size=args.sample_size,
-        group_id=args.group_id,
-        search_type=args.search_type,
-        search_limit=args.search_limit,
-        context_char_budget=args.context_char_budget,
-        reset_group=args.reset_group,
-        skip_ingest=args.skip_ingest,
-        output_dir=args.output_dir
-    ))
-    
-    # Print summary
-    print(f"\n{'='*60}")
-    
-    # Check if there was an error
-    if 'error' in result:
-        print("❌ Benchmark Failed!")
-        print(f"{'='*60}")
-        print(f"Error: {result['error']}")
-        return
-    
-    print("🎉 Benchmark Complete!")
-    print(f"{'='*60}")
-    print("📊 Final Results:")
-    print(f"   Sample size: {result.get('sample_size', 0)}")
-    print(f"   F1: {result['overall_metrics']['f1']:.3f}")
-    print(f"   BLEU-1: {result['overall_metrics']['bleu1']:.3f}")
-    print(f"   Jaccard: {result['overall_metrics']['jaccard']:.3f}")
-    print(f"   LoCoMo F1: {result['overall_metrics']['locomo_f1']:.3f}")
-    
-    if result.get('context_stats'):
-        print("\n📈 Context Statistics:")
-        print(f"   Avg retrieved docs: {result['context_stats']['avg_retrieved_docs']:.1f}")
-        print(f"   Avg context chars: {result['context_stats']['avg_context_chars']:.0f}")
-        print(f"   Avg context tokens: {result['context_stats']['avg_context_tokens']:.0f}")
-    
-    if result.get('latency'):
-        print("\n⏱️  Latency Statistics:")
-        print(f"   Search - Mean: {result['latency']['search']['mean']:.1f}ms, "
-              f"P50: {result['latency']['search']['p50']:.1f}ms, "
-              f"P95: {result['latency']['search']['p95']:.1f}ms")
-        print(f"   LLM - Mean: {result['latency']['llm']['mean']:.1f}ms, "
-              f"P50: {result['latency']['llm']['p50']:.1f}ms, "
-              f"P95: {result['latency']['llm']['p95']:.1f}ms")
-    
-    if result.get('by_category'):
-        print("\n📂 Results by Category:")
-        for cat, metrics in result['by_category'].items():
-            print(f"   {cat}:")
-            print(f"     Count: {metrics['count']}")
-            print(f"     F1: {metrics['f1']:.3f}")
-            print(f"     LoCoMo F1: {metrics['locomo_f1']:.3f}")
-            print(f"     Jaccard: {metrics['jaccard']:.3f}")
-    
-    print(f"\n{'='*60}\n")
-
-
-if __name__ == "__main__":
-    main()

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

@@ -1,225 +0,0 @@
-"""
-LoCoMo-specific metric calculations.
-
-This module provides clean, simplified implementations of metrics used for
-LoCoMo benchmark evaluation, including text normalization and F1 score variants.
-"""
-
-import re
-from typing import Dict, Any
-
-
-def normalize_text(text: str) -> str:
-    """
-    Normalize text for LoCoMo evaluation.
-    
-    Normalization steps:
-    - Convert to lowercase
-    - Remove commas
-    - Remove stop words (a, an, the, and)
-    - Remove punctuation
-    - Normalize whitespace
-    
-    Args:
-        text: Input text to normalize
-        
-    Returns:
-        Normalized text string with consistent formatting
-        
-    Examples:
-        >>> normalize_text("The cat, and the dog")
-        'cat dog'
-        >>> normalize_text("Hello, World!")
-        'hello world'
-    """
-    # Ensure input is a string
-    text = str(text) if text is not None else ""
-    
-    # Convert to lowercase
-    text = text.lower()
-    
-    # Remove commas
-    text = re.sub(r"[\,]", " ", text)
-    
-    # Remove stop words
-    text = re.sub(r"\b(a|an|the|and)\b", " ", text)
-    
-    # Remove punctuation (keep only word characters and whitespace)
-    text = re.sub(r"[^\w\s]", " ", text)
-    
-    # Normalize whitespace (collapse multiple spaces to single space)
-    text = " ".join(text.split())
-    
-    return text
-
-
-def locomo_f1_score(prediction: str, ground_truth: str) -> float:
-    """
-    Calculate LoCoMo F1 score for single-answer questions.
-    
-    Uses token-level precision and recall based on normalized text.
-    Treats tokens as sets (no duplicate counting).
-    
-    Args:
-        prediction: Model's predicted answer
-        ground_truth: Correct answer
-        
-    Returns:
-        F1 score between 0.0 and 1.0
-        
-    Examples:
-        >>> locomo_f1_score("Paris", "Paris")
-        1.0
-        >>> locomo_f1_score("The cat", "cat")
-        1.0
-        >>> locomo_f1_score("dog", "cat")
-        0.0
-    """
-    # Ensure inputs are strings
-    pred_str = str(prediction) if prediction is not None else ""
-    truth_str = str(ground_truth) if ground_truth is not None else ""
-    
-    # Normalize and tokenize
-    pred_tokens = normalize_text(pred_str).split()
-    truth_tokens = normalize_text(truth_str).split()
-    
-    # Handle empty cases
-    if not pred_tokens or not truth_tokens:
-        return 0.0
-    
-    # Convert to sets for comparison
-    pred_set = set(pred_tokens)
-    truth_set = set(truth_tokens)
-    
-    # Calculate true positives (intersection)
-    true_positives = len(pred_set & truth_set)
-    
-    # Calculate precision and recall
-    precision = true_positives / len(pred_set) if pred_set else 0.0
-    recall = true_positives / len(truth_set) if truth_set else 0.0
-    
-    # Calculate F1 score
-    if precision + recall == 0:
-        return 0.0
-    
-    f1 = 2 * precision * recall / (precision + recall)
-    return f1
-
-
-def locomo_multi_f1(prediction: str, ground_truth: str) -> float:
-    """
-    Calculate LoCoMo F1 score for multi-answer questions.
-    
-    Handles comma-separated answers by:
-    1. Splitting both prediction and ground truth by commas
-    2. For each ground truth answer, finding the best matching prediction
-    3. Averaging the F1 scores across all ground truth answers
-    
-    Args:
-        prediction: Model's predicted answer (may contain multiple comma-separated answers)
-        ground_truth: Correct answer (may contain multiple comma-separated answers)
-        
-    Returns:
-        Average F1 score across all ground truth answers (0.0 to 1.0)
-        
-    Examples:
-        >>> locomo_multi_f1("Paris, London", "Paris, London")
-        1.0
-        >>> locomo_multi_f1("Paris", "Paris, London")
-        0.5
-        >>> locomo_multi_f1("Paris, Berlin", "Paris, London")
-        0.5
-    """
-    # Ensure inputs are strings
-    pred_str = str(prediction) if prediction is not None else ""
-    truth_str = str(ground_truth) if ground_truth is not None else ""
-    
-    # Split by commas and strip whitespace
-    predictions = [p.strip() for p in pred_str.split(',') if p.strip()]
-    ground_truths = [g.strip() for g in truth_str.split(',') if g.strip()]
-    
-    # Handle empty cases
-    if not predictions or not ground_truths:
-        return 0.0
-    
-    # For each ground truth, find the best matching prediction
-    f1_scores = []
-    for gt in ground_truths:
-        # Calculate F1 with each prediction and take the maximum
-        best_f1 = max(locomo_f1_score(pred, gt) for pred in predictions)
-        f1_scores.append(best_f1)
-    
-    # Return average F1 across all ground truths
-    return sum(f1_scores) / len(f1_scores)
-
-
-def get_category_name(item: Dict[str, Any]) -> str:
-    """
-    Extract and normalize category name from QA item.
-    
-    Handles both numeric categories (1-4) and string categories with various formats.
-    Supports multiple field names: "cat", "category", "type".
-    
-    Category mapping:
-    - 1 or "multi-hop" -> "Multi-Hop"
-    - 2 or "temporal" -> "Temporal"
-    - 3 or "open domain" -> "Open Domain"
-    - 4 or "single-hop" -> "Single-Hop"
-    
-    Args:
-        item: QA item dictionary containing category information
-        
-    Returns:
-        Standardized category name or "unknown" if not found
-        
-    Examples:
-        >>> get_category_name({"category": 1})
-        'Multi-Hop'
-        >>> get_category_name({"cat": "temporal"})
-        'Temporal'
-        >>> get_category_name({"type": "Single-Hop"})
-        'Single-Hop'
-    """
-    # Numeric category mapping
-    CATEGORY_MAP = {
-        1: "Multi-Hop",
-        2: "Temporal",
-        3: "Open Domain",
-        4: "Single-Hop",
-    }
-    
-    # String category aliases (case-insensitive)
-    TYPE_ALIASES = {
-        "single-hop": "Single-Hop",
-        "singlehop": "Single-Hop",
-        "single hop": "Single-Hop",
-        "multi-hop": "Multi-Hop",
-        "multihop": "Multi-Hop",
-        "multi hop": "Multi-Hop",
-        "open domain": "Open Domain",
-        "opendomain": "Open Domain",
-        "temporal": "Temporal",
-    }
-    
-    # Try "cat" field first (string category)
-    cat = item.get("cat")
-    if isinstance(cat, str) and cat.strip():
-        name = cat.strip()
-        lower = name.lower()
-        return TYPE_ALIASES.get(lower, name)
-    
-    # Try "category" field (can be int or string)
-    cat_num = item.get("category")
-    if isinstance(cat_num, int):
-        return CATEGORY_MAP.get(cat_num, "unknown")
-    elif isinstance(cat_num, str) and cat_num.strip():
-        lower = cat_num.strip().lower()
-        return TYPE_ALIASES.get(lower, cat_num.strip())
-    
-    # Try "type" field as fallback
-    cat_type = item.get("type")
-    if isinstance(cat_type, str) and cat_type.strip():
-        lower = cat_type.strip().lower()
-        return TYPE_ALIASES.get(lower, cat_type.strip())
-    
-    return "unknown"

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

@@ -1,810 +0,0 @@
-# file name: check_neo4j_connection_fixed.py
-import asyncio
-import json
-import math
-import os
-import re
-import sys
-import time
-from datetime import datetime, timedelta
-from typing import Any, Dict, List
-
-from dotenv import load_dotenv
-
-# 1
-# 添加项目根目录到路径
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.dirname(current_dir)
-if project_root not in sys.path:
-    sys.path.insert(0, project_root)
-# 关键：将 src 目录置于最前，确保从当前仓库加载模块
-src_dir = os.path.join(project_root, "src")
-if src_dir not in sys.path:
-    sys.path.insert(0, src_dir)
-
-load_dotenv()
-
-# 首先定义 _loc_normalize 函数，因为其他函数依赖它
-def _loc_normalize(text: str) -> str:
-    text = str(text) if text is not None else ""
-    text = text.lower()
-    text = re.sub(r"[\,]", " ", text)
-    text = re.sub(r"\b(a|an|the|and)\b", " ", text)
-    text = re.sub(r"[^\w\s]", " ", text)
-    text = " ".join(text.split())
-    return text
-
-# 尝试从 metrics.py 导入基础指标
-try:
-    from common.metrics import bleu1, f1_score, jaccard
-    print("✅ 从 metrics.py 导入基础指标成功")
-except ImportError as e:
-    print(f"❌ 从 metrics.py 导入失败: {e}")
-    # 回退到本地实现
-    def f1_score(pred: str, ref: str) -> float:
-        pred_str = str(pred) if pred is not None else ""
-        ref_str = str(ref) if ref is not None else ""
-
-        p_tokens = _loc_normalize(pred_str).split()
-        r_tokens = _loc_normalize(ref_str).split()
-        if not p_tokens and not r_tokens:
-            return 1.0
-        if not p_tokens or not r_tokens:
-            return 0.0
-        p_set = set(p_tokens)
-        r_set = set(r_tokens)
-        tp = len(p_set & r_set)
-        precision = tp / len(p_set) if p_set else 0.0
-        recall = tp / len(r_set) if r_set else 0.0
-        if precision + recall == 0:
-            return 0.0
-        return 2 * precision * recall / (precision + recall)
-
-    def bleu1(pred: str, ref: str) -> float:
-        pred_str = str(pred) if pred is not None else ""
-        ref_str = str(ref) if ref is not None else ""
-
-        p_tokens = _loc_normalize(pred_str).split()
-        r_tokens = _loc_normalize(ref_str).split()
-        if not p_tokens:
-            return 0.0
-
-        r_counts = {}
-        for t in r_tokens:
-            r_counts[t] = r_counts.get(t, 0) + 1
-
-        clipped = 0
-        p_counts = {}
-        for t in p_tokens:
-            p_counts[t] = p_counts.get(t, 0) + 1
-
-        for t, c in p_counts.items():
-            clipped += min(c, r_counts.get(t, 0))
-
-        precision = clipped / max(len(p_tokens), 1)
-        ref_len = len(r_tokens)
-        pred_len = len(p_tokens)
-
-        if pred_len > ref_len or pred_len == 0:
-            bp = 1.0
-        else:
-            bp = math.exp(1 - ref_len / max(pred_len, 1))
-
-        return bp * precision
-
-    def jaccard(pred: str, ref: str) -> float:
-        pred_str = str(pred) if pred is not None else ""
-        ref_str = str(ref) if ref is not None else ""
-
-        p = set(_loc_normalize(pred_str).split())
-        r = set(_loc_normalize(ref_str).split())
-        if not p and not r:
-            return 1.0
-        if not p or not r:
-            return 0.0
-        return len(p & r) / len(p | r)
-
-# 尝试从 qwen_search_eval.py 导入 LoCoMo 特定指标
-try:
-    # 添加 evaluation 目录路径
-    evaluation_dir = os.path.join(project_root, "evaluation")
-    if evaluation_dir not in sys.path:
-        sys.path.insert(0, evaluation_dir)
-
-    # 尝试从不同位置导入
-    try:
-        from locomo.qwen_search_eval import (
-            _resolve_relative_times,
-            loc_f1_score,
-            loc_multi_f1,
-        )
-        print("✅ 从 locomo.qwen_search_eval 导入 LoCoMo 特定指标成功")
-    except ImportError:
-        from qwen_search_eval import _resolve_relative_times, loc_f1_score, loc_multi_f1
-        print("✅ 从 qwen_search_eval 导入 LoCoMo 特定指标成功")
-
-except ImportError as e:
-    print(f"❌ 从 qwen_search_eval.py 导入失败: {e}")
-    # 回退到本地实现 LoCoMo 特定函数
-    def _resolve_relative_times(text: str, anchor: datetime) -> str:
-        t = str(text) if text is not None else ""
-        t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE)
-        t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE)
-        t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE)
-
-        def _ago_repl(m: re.Match[str]) -> str:
-            n = int(m.group(1))
-            return (anchor - timedelta(days=n)).date().isoformat()
-        def _in_repl(m: re.Match[str]) -> str:
-            n = int(m.group(1))
-            return (anchor + timedelta(days=n)).date().isoformat()
-
-        t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE)
-        t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE)
-        t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE)
-        t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE)
-        return t
-
-    def loc_f1_score(prediction: str, ground_truth: str) -> float:
-        p_tokens = _loc_normalize(prediction).split()
-        g_tokens = _loc_normalize(ground_truth).split()
-        if not p_tokens or not g_tokens:
-            return 0.0
-        p = set(p_tokens)
-        g = set(g_tokens)
-        tp = len(p & g)
-        precision = tp / len(p) if p else 0.0
-        recall = tp / len(g) if g else 0.0
-        return (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0
-
-    def loc_multi_f1(prediction: str, ground_truth: str) -> float:
-        predictions = [p.strip() for p in str(prediction).split(',') if p.strip()]
-        ground_truths = [g.strip() for g in str(ground_truth).split(',') if g.strip()]
-        if not predictions or not ground_truths:
-            return 0.0
-        def _f1(a: str, b: str) -> float:
-            return loc_f1_score(a, b)
-        vals = []
-        for gt in ground_truths:
-            vals.append(max(_f1(pred, gt) for pred in predictions))
-        return sum(vals) / len(vals)
-
-
-def smart_context_selection(contexts: List[str], question: str, max_chars: int = 8000) -> str:
-    """基于问题关键词智能选择上下文"""
-    if not contexts:
-        return ""
-
-    # 提取问题关键词（只保留有意义的词）
-    question_lower = question.lower()
-    stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'}
-    question_words = set(re.findall(r'\b\w+\b', question_lower))
-    question_words = {word for word in question_words if word not in stop_words and len(word) > 2}
-
-    print(f"🔍 问题关键词: {question_words}")
-
-    # 给每个上下文打分
-    scored_contexts = []
-    for i, context in enumerate(contexts):
-        context_lower = context.lower()
-        score = 0
-
-        # 关键词匹配得分
-        keyword_matches = 0
-        for word in question_words:
-            if word in context_lower:
-                keyword_matches += 1
-                # 关键词出现次数越多，得分越高
-                score += context_lower.count(word) * 2
-
-        # 上下文长度得分（适中的长度更好）
-        context_len = len(context)
-        if 100 < context_len < 2000:  # 理想长度范围
-            score += 5
-        elif context_len >= 2000:  # 太长可能包含无关信息
-            score += 2
-
-        # 如果是前几个上下文，给予额外分数（通常相关性更高）
-        if i < 3:
-            score += 3
-
-        scored_contexts.append((score, context, keyword_matches))
-
-    # 按得分排序
-    scored_contexts.sort(key=lambda x: x[0], reverse=True)
-
-    # 选择高得分的上下文，直到达到字符限制
-    selected = []
-    total_chars = 0
-    selected_count = 0
-
-    print("📊 上下文相关性分析:")
-    for score, context, matches in scored_contexts[:5]:  # 只显示前5个
-        print(f"  - 得分: {score}, 关键词匹配: {matches}, 长度: {len(context)}")
-
-    for score, context, matches in scored_contexts:
-        if total_chars + len(context) <= max_chars:
-            selected.append(context)
-            total_chars += len(context)
-            selected_count += 1
-        else:
-            # 如果这个上下文得分很高但放不下，尝试截取
-            if score > 10 and total_chars < max_chars - 500:
-                remaining = max_chars - total_chars
-                # 找到包含关键词的部分
-                lines = context.split('\n')
-                relevant_lines = []
-                current_chars = 0
-
-                for line in lines:
-                    line_lower = line.lower()
-                    line_relevance = any(word in line_lower for word in question_words)
-
-                    if line_relevance and current_chars < remaining - 100:
-                        relevant_lines.append(line)
-                        current_chars += len(line)
-
-                if relevant_lines:
-                    truncated = '\n'.join(relevant_lines)
-                    if len(truncated) > 100:  # 确保有足够内容
-                        selected.append(truncated + "\n[相关内容截断...]")
-                        total_chars += len(truncated)
-                        selected_count += 1
-            break  # 不再尝试添加更多上下文
-
-    result = "\n\n".join(selected)
-    print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {total_chars}字符")
-    return result
-
-
-def get_dynamic_search_params(question: str, question_index: int, total_questions: int):
-    """根据问题复杂度和进度动态调整检索参数"""
-
-    # 分析问题复杂度
-    word_count = len(question.split())
-    has_temporal = any(word in question.lower() for word in ['when', 'date', 'time', 'ago'])
-    has_multi_hop = any(word in question.lower() for word in ['and', 'both', 'also', 'while'])
-
-    # 根据进度调整 - 后期问题可能需要更精确的检索
-    progress_factor = question_index / total_questions
-
-    base_limit = 12
-    if has_temporal and has_multi_hop:
-        base_limit = 20
-    elif word_count > 8:
-        base_limit = 16
-
-    # 随着测试进行，逐渐收紧检索范围
-    adjusted_limit = max(8, int(base_limit * (1 - progress_factor * 0.3)))
-
-    # 动态调整最大字符数
-    max_chars = 8000 + 4000 * (1 - progress_factor)
-
-    return {
-        "limit": adjusted_limit,
-        "max_chars": int(max_chars)
-    }
-
-
-class EnhancedEvaluationMonitor:
-    def __init__(self, reset_interval=5, performance_threshold=0.6):
-        self.question_count = 0
-        self.reset_interval = reset_interval
-        self.performance_threshold = performance_threshold
-        self.consecutive_low_scores = 0
-        self.performance_history = []
-        self.recent_f1_scores = []
-
-    def should_reset_connections(self, current_f1=None):
-        """基于计数和性能双重判断"""
-        # 定期重置
-        if self.question_count % self.reset_interval == 0:
-            return True
-
-        # 性能驱动的重置
-        if current_f1 is not None and current_f1 < self.performance_threshold:
-            self.consecutive_low_scores += 1
-            if self.consecutive_low_scores >= 2:  # 连续2个低分就重置
-                print("🚨 连续低分，触发紧急重置")
-                self.consecutive_low_scores = 0
-                return True
-        else:
-            self.consecutive_low_scores = 0
-
-        return False
-
-    def record_performance(self, question_index, metrics, context_length, retrieved_docs):
-        """记录性能指标，检测衰减"""
-        self.performance_history.append({
-            'index': question_index,
-            'metrics': metrics,
-            'context_length': context_length,
-            'retrieved_docs': retrieved_docs,
-            'timestamp': time.time()
-        })
-
-        # 记录最近的F1分数
-        self.recent_f1_scores.append(metrics['f1'])
-        if len(self.recent_f1_scores) > 5:
-            self.recent_f1_scores.pop(0)
-
-    def get_recent_performance(self):
-        """获取近期平均性能"""
-        if not self.recent_f1_scores:
-            return 0.5
-        return sum(self.recent_f1_scores) / len(self.recent_f1_scores)
-
-    def get_performance_trend(self):
-        """分析性能趋势"""
-        if len(self.performance_history) < 2:
-            return "stable"
-
-        recent_metrics = [item['metrics']['f1'] for item in self.performance_history[-5:]]
-        earlier_metrics = [item['metrics']['f1'] for item in self.performance_history[-10:-5]]
-
-        if len(recent_metrics) < 2 or len(earlier_metrics) < 2:
-            return "stable"
-
-        recent_avg = sum(recent_metrics) / len(recent_metrics)
-        earlier_avg = sum(earlier_metrics) / len(earlier_metrics)
-
-        if recent_avg < earlier_avg * 0.8:
-            return "degrading"
-        elif recent_avg > earlier_avg * 1.1:
-            return "improving"
-        else:
-            return "stable"
-
-
-def get_enhanced_search_params(question: str, question_index: int, total_questions: int, recent_performance: float):
-    """基于问题复杂度和近期性能动态调整检索参数"""
-
-    # 基础参数
-    base_params = get_dynamic_search_params(question, question_index, total_questions)
-
-    # 性能自适应调整
-    if recent_performance < 0.5:  # 近期表现差
-        # 增加检索范围，尝试获取更多上下文
-        base_params["limit"] = min(base_params["limit"] + 5, 25)
-        base_params["max_chars"] = min(base_params["max_chars"] + 2000, 12000)
-        print(f"📈 性能自适应：增加检索范围 (limit={base_params['limit']}, max_chars={base_params['max_chars']})")
-
-    elif recent_performance > 0.8:  # 近期表现好
-        # 收紧检索，提高精度
-        base_params["limit"] = max(base_params["limit"] - 2, 8)
-        base_params["max_chars"] = max(base_params["max_chars"] - 1000, 6000)
-        print(f"🎯 性能自适应：提高检索精度 (limit={base_params['limit']}, max_chars={base_params['max_chars']})")
-
-    # 中间阶段特殊处理
-    mid_sequence_factor = abs(question_index / total_questions - 0.5)
-    if mid_sequence_factor < 0.2:  # 在中间30%的问题
-        print("🎯 中间阶段：使用更精确的检索策略")
-        base_params["limit"] = max(base_params["limit"] - 2, 10)  # 减少数量，提高质量
-        base_params["max_chars"] = max(base_params["max_chars"] - 1000, 7000)
-
-    return base_params
-
-
-def enhanced_context_selection(contexts: List[str], question: str, question_index: int, total_questions: int, max_chars: int = 8000) -> str:
-    """考虑问题序列位置的智能选择"""
-
-    if not contexts:
-        return ""
-
-    # 在序列中间阶段使用更严格的筛选
-    mid_sequence_factor = abs(question_index / total_questions - 0.5)  # 距离中心的距离
-
-    if mid_sequence_factor < 0.2:  # 在中间30%的问题
-        print("🎯 中间阶段：使用严格上下文筛选")
-
-        # 提取问题关键词
-        question_lower = question.lower()
-        stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'}
-        question_words = set(re.findall(r'\b\w+\b', question_lower))
-        question_words = {word for word in question_words if word not in stop_words and len(word) > 2}
-
-        # 只保留高度相关的上下文
-        filtered_contexts = []
-        for context in contexts:
-            context_lower = context.lower()
-            relevance_score = sum(3 if word in context_lower else 0 for word in question_words)
-
-            # 额外加分给包含数字、日期的上下文（对事实性问题更重要）
-            if any(char.isdigit() for char in context):
-                relevance_score += 2
-
-            # 提高阈值：只有得分>=3的上下文才保留
-            if relevance_score >= 3:
-                filtered_contexts.append(context)
-            else:
-                print(f"  - 过滤低分上下文: 得分={relevance_score}")
-
-        contexts = filtered_contexts
-        print(f"🔍 严格筛选后保留 {len(contexts)} 个上下文")
-
-    # 使用原有的智能选择逻辑
-    return smart_context_selection(contexts, question, max_chars)
-
-
-async def run_enhanced_evaluation():
-    """使用增强方法进行完整评估 - 解决中间性能衰减问题"""
-    try:
-        from dotenv import load_dotenv
-    except Exception:
-        def load_dotenv():
-            return None
-     
-    # 修正导入路径：使用 app.core.memory.src 前缀
-    from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
-    from app.core.memory.utils.config.definitions import (
-        SELECTED_EMBEDDING_ID,
-        SELECTED_LLM_ID,
-    )
-    from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-    from app.core.models.base import RedBearModelConfig
-    from app.db import get_db_context
-    from app.repositories.neo4j.graph_search import search_graph_by_embedding
-    from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-    from app.services.memory_config_service import MemoryConfigService
-
-    # 加载数据
-    # 获取项目根目录
-    current_file = os.path.abspath(__file__)
-    evaluation_dir = os.path.dirname(os.path.dirname(current_file))  # evaluation目录
-    memory_dir = os.path.dirname(evaluation_dir)  # memory目录
-    data_path = os.path.join(memory_dir, "data", "locomo10.json")
-    with open(data_path, "r", encoding="utf-8") as f:
-        raw = json.load(f)
-
-    qa_items = []
-    if isinstance(raw, list):
-        for entry in raw:
-            qa_items.extend(entry.get("qa", []))
-    else:
-        qa_items.extend(raw.get("qa", []))
-    
-    items = qa_items[:20]  # 测试多少个问题
-    
-    # 初始化增强监控器
-    monitor = EnhancedEvaluationMonitor(reset_interval=5, performance_threshold=0.6)
-    
-    with get_db_context() as db:
-        factory = MemoryClientFactory(db)
-        llm = factory.get_llm_client(SELECTED_LLM_ID)
-    
-    # 初始化embedder
-    with get_db_context() as db:
-        config_service = MemoryConfigService(db)
-        cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID)
-    embedder = OpenAIEmbedderClient(
-        model_config=RedBearModelConfig.model_validate(cfg_dict)
-    )
-    
-    # 初始化连接器
-    connector = Neo4jConnector()
-
-    # 初始化结果字典
-    results = {
-        "questions": [],
-        "overall_metrics": {"f1": 0.0, "b1": 0.0, "j": 0.0, "loc_f1": 0.0},
-        "category_metrics": {},
-        "retrieval_stats": {"total_questions": len(items), "avg_context_length": 0, "avg_retrieved_docs": 0},
-        "performance_trend": "stable",
-        "timestamp": datetime.now().isoformat(),
-        "enhanced_strategy": True
-    }
-
-    total_f1 = 0.0
-    total_bleu1 = 0.0
-    total_jaccard = 0.0
-    total_loc_f1 = 0.0
-    total_context_length = 0
-    total_retrieved_docs = 0
-    category_stats = {}
-
-    try:
-        for i, item in enumerate(items):
-            monitor.question_count += 1
-
-            # 获取近期性能用于重置判断
-            recent_performance = monitor.get_recent_performance()
-
-            # 增强的重置判断
-            should_reset = monitor.should_reset_connections(current_f1=recent_performance)
-            if should_reset and i > 0:
-                print(f"🔄 重置Neo4j连接 (问题 {i+1}/{len(items)}, 近期性能: {recent_performance:.3f})...")
-                await connector.close()
-                connector = Neo4jConnector()  # 创建新连接
-                print("✅ 连接重置完成")
-
-            q = item.get("question", "")
-            ref = item.get("answer", "")
-            ref_str = str(ref) if ref is not None else ""
-
-            print(f"\n🔍 [{i+1}/{len(items)}] 问题: {q}")
-            print(f"✅ 真实答案: {ref_str}")
-
-            # 分类别统计
-            category = "Unknown"
-            if item.get("category") == 1:
-                category = "Multi-Hop"
-            elif item.get("category") == 2:
-                category = "Temporal"
-            elif item.get("category") == 3:
-                category = "Open Domain"
-            elif item.get("category") == 4:
-                category = "Single-Hop"
-
-            # 增强的检索参数
-            search_params = get_enhanced_search_params(q, i, len(items), recent_performance)
-            search_limit = search_params["limit"]
-            max_chars = search_params["max_chars"]
-
-            print(f"🏷️ 类别: {category}, 检索参数: limit={search_limit}, max_chars={max_chars}")
-            
-            # 使用项目标准的混合检索方法
-            t0 = time.time()
-            contexts_all = []
-
-            try:
-                # 使用统一的搜索服务
-                from app.core.memory.storage_services.search import run_hybrid_search
-                
-                print("🔀 使用混合搜索服务...")
-                
-                search_results = await run_hybrid_search(
-                    query_text=q,
-                    search_type="hybrid",
-                    group_id="locomo_sk",
-                    limit=20,
-                    include=["statements", "chunks", "entities", "summaries"],
-                    alpha=0.6,  # BM25权重
-                    embedding_id=SELECTED_EMBEDDING_ID
-                )
-                
-                # 处理搜索结果 - 新的搜索服务返回统一的结构
-                chunks = search_results.get("chunks", [])
-                statements = search_results.get("statements", [])
-                entities = search_results.get("entities", [])
-                summaries = search_results.get("summaries", [])
-                
-                print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要")
-
-                # 构建上下文：优先使用 chunks、statements 和 summaries
-                for c in chunks:
-                    content = str(c.get("content", "")).strip()
-                    if content:
-                        contexts_all.append(content)
-
-                for s in statements:
-                    stmt_text = str(s.get("statement", "")).strip()
-                    if stmt_text:
-                        contexts_all.append(stmt_text)
-                
-                for sm in summaries:
-                    summary_text = str(sm.get("summary", "")).strip()
-                    if summary_text:
-                        contexts_all.append(summary_text)
-
-                # 实体摘要：最多加入前3个高分实体，避免噪声
-                scored = [e for e in entities if e.get("score") is not None]
-                top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3]
-                if top_entities:
-                    summary_lines = []
-                    for e in top_entities:
-                        name = str(e.get("name", "")).strip()
-                        etype = str(e.get("entity_type", "")).strip()
-                        score = e.get("score")
-                        if name:
-                            meta = []
-                            if etype:
-                                meta.append(f"type={etype}")
-                            if isinstance(score, (int, float)):
-                                meta.append(f"score={score:.3f}")
-                            summary_lines.append(f"EntitySummary: {name}{(' [' + ' '.join(meta) + ']') if meta else ''}")
-                    if summary_lines:
-                        contexts_all.append("\n".join(summary_lines))
-
-                print(f"📊 有效上下文数量: {len(contexts_all)}")
-            except Exception as e:
-                print(f"❌ 检索失败: {e}")
-                contexts_all = []
-
-            t1 = time.time()
-            search_time = (t1 - t0) * 1000
-
-            # 增强的上下文选择
-            context_text = ""
-            if contexts_all:
-                # 使用增强的上下文选择
-                context_text = enhanced_context_selection(contexts_all, q, i, len(items), max_chars=max_chars)
-
-                # 如果智能选择后仍然过长，进行最终保护性截断
-                if len(context_text) > max_chars:
-                    print(f"⚠️ 智能选择后仍然过长 ({len(context_text)}字符)，进行最终截断")
-                    context_text = context_text[:max_chars] + "\n\n[最终截断...]"
-
-                # 时间解析
-                anchor_date = datetime(2023, 5, 8)  # 使用固定日期确保一致性
-                context_text = _resolve_relative_times(context_text, anchor_date)
-
-                context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n" + context_text
-
-                print(f"📝 最终上下文长度: {len(context_text)} 字符")
-
-                # 显示不同上下文的预览（不只是第一条）
-                print("🔍 上下文预览:")
-                for j, context in enumerate(contexts_all[:3]):  # 显示前3个上下文
-                    preview = context[:150].replace('\n', ' ')
-                    print(f"  上下文{j+1}: {preview}...")
-                
-                # 🔍 调试：检查答案是否在上下文中
-                if ref_str and ref_str.strip():
-                    answer_found = any(ref_str.lower() in ctx.lower() for ctx in contexts_all)
-                    print(f"🔍 调试：答案 '{ref_str}' 是否在检索到的上下文中？ {'✅ 是' if answer_found else '❌ 否'}")
-                
-            else:
-                print("❌ 没有检索到有效上下文")
-                context_text = "No relevant context found."
-
-            # LLM 回答
-            messages = [
-                {"role": "system", "content": (
-                    "You are a precise QA assistant. Answer following these rules:\n"
-                    "1) Extract the EXACT information mentioned in the context\n"
-                    "2) For time questions: calculate actual dates from relative times\n"
-                    "3) Return ONLY the answer text in simplest form\n"
-                    "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n"
-                    "5) If no clear answer found, respond with 'Unknown'"
-                )},
-                {"role": "user", "content": f"Question: {q}\n\nContext:\n{context_text}"},
-            ]
-
-            t2 = time.time()
-            try:
-                # 使用异步调用
-                resp = await llm.chat(messages=messages)
-                # 兼容不同的响应格式
-                pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown")
-            except Exception as e:
-                print(f"❌ LLM 生成失败: {e}")
-                pred = "Unknown"
-            t3 = time.time()
-            llm_time = (t3 - t2) * 1000
-
-            # 计算指标 - 使用导入的指标函数
-            f1_val = f1_score(pred, ref_str)
-            bleu1_val = bleu1(pred, ref_str)
-            jaccard_val = jaccard(pred, ref_str)
-            loc_f1_val = loc_f1_score(pred, ref_str)
-
-            print(f"🤖 LLM 回答: {pred}")
-            print(f"📈 指标 - F1: {f1_val:.3f}, BLEU-1: {bleu1_val:.3f}, Jaccard: {jaccard_val:.3f}, LoCoMo F1: {loc_f1_val:.3f}")
-            print(f"⏱️ 时间 - 检索: {search_time:.1f}ms, LLM: {llm_time:.1f}ms")
-
-            # 更新统计
-            total_f1 += f1_val
-            total_bleu1 += bleu1_val
-            total_jaccard += jaccard_val
-            total_loc_f1 += loc_f1_val
-            total_context_length += len(context_text)
-            total_retrieved_docs += len(contexts_all)
-
-            if category not in category_stats:
-                category_stats[category] = {"count": 0, "f1_sum": 0.0, "b1_sum": 0.0, "j_sum": 0.0, "loc_f1_sum": 0.0}
-
-            category_stats[category]["count"] += 1
-            category_stats[category]["f1_sum"] += f1_val
-            category_stats[category]["b1_sum"] += bleu1_val
-            category_stats[category]["j_sum"] += jaccard_val
-            category_stats[category]["loc_f1_sum"] += loc_f1_val
-
-            # 记录性能指标
-            metrics = {"f1": f1_val, "bleu1": bleu1_val, "jaccard": jaccard_val, "loc_f1": loc_f1_val}
-            monitor.record_performance(i, metrics, len(context_text), len(contexts_all))
-
-            # 保存结果
-            question_result = {
-                "question": q,
-                "ground_truth": ref_str,
-                "prediction": pred,
-                "category": category,
-                "metrics": metrics,
-                "retrieval": {
-                    "retrieved_documents": len(contexts_all),
-                    "context_length": len(context_text),
-                    "search_limit": search_limit,
-                    "max_chars": max_chars,
-                    "recent_performance": recent_performance
-                },
-                "timing": {
-                    "search_ms": search_time,
-                    "llm_ms": llm_time
-                }
-            }
-
-            results["questions"].append(question_result)
-
-            print("="*60)
-
-    except Exception as e:
-        print(f"❌ 评估过程中发生错误: {e}")
-        # 即使出错，也返回已有的结果
-        import traceback
-        traceback.print_exc()
-
-    finally:
-        await connector.close()
-
-    # 计算总体指标
-    n = len(items)
-    if n > 0:
-        results["overall_metrics"] = {
-            "f1": total_f1 / n,
-            "b1": total_bleu1 / n,
-            "j": total_jaccard / n,
-            "loc_f1": total_loc_f1 / n
-        }
-
-        for category, stats in category_stats.items():
-            count = stats["count"]
-            results["category_metrics"][category] = {
-                "count": count,
-                "f1": stats["f1_sum"] / count,
-                "bleu1": stats["b1_sum"] / count,
-                "jaccard": stats["j_sum"] / count,
-                "loc_f1": stats["loc_f1_sum"] / count
-            }
-
-        results["retrieval_stats"]["avg_context_length"] = total_context_length / n
-        results["retrieval_stats"]["avg_retrieved_docs"] = total_retrieved_docs / n
-
-        # 分析性能趋势
-        results["performance_trend"] = monitor.get_performance_trend()
-        results["reset_interval"] = monitor.reset_interval
-        results["total_questions_processed"] = monitor.question_count
-
-    return results
-
-
-if __name__ == "__main__":
-    print("🚀 运行增强版完整评估（解决中间性能衰减问题）...")
-    print("📋 增强特性:")
-    print("  - 双重重置策略：定期重置 + 性能驱动重置")
-    print("  - 动态检索参数：基于近期性能自适应调整")
-    print("  - 中间阶段严格筛选：提高上下文质量要求")
-    print("  - 连续性能监控：实时检测性能衰减")
-
-    result = asyncio.run(run_enhanced_evaluation())
-
-    print("\n📊 最终评估结果:")
-    print("总体指标:")
-    print(f"  F1: {result['overall_metrics']['f1']:.4f}")
-    print(f"  BLEU-1: {result['overall_metrics']['b1']:.4f}")
-    print(f"  Jaccard: {result['overall_metrics']['j']:.4f}")
-    print(f"  LoCoMo F1: {result['overall_metrics']['loc_f1']:.4f}")
-
-    print("\n分类别指标:")
-    for category, metrics in result['category_metrics'].items():
-        print(f"  {category}: F1={metrics['f1']:.4f}, BLEU-1={metrics['bleu1']:.4f}, Jaccard={metrics['jaccard']:.4f}, LoCoMo F1={metrics['loc_f1']:.4f} (样本数: {metrics['count']})")
-
-    print("\n检索统计:")
-    stats = result['retrieval_stats']
-    print(f"  平均上下文长度: {stats['avg_context_length']:.0f} 字符")
-    print(f"  平均检索文档数: {stats['avg_retrieved_docs']:.1f}")
-
-    print(f"\n性能趋势: {result['performance_trend']}")
-    print(f"重置间隔: 每{result['reset_interval']}个问题")
-    print(f"处理问题总数: {result['total_questions_processed']}")
-    print(f"增强策略: {'启用' if result.get('enhanced_strategy', False) else '未启用'}")
-
-
-    # 保存结果到指定目录
-    # 使用代码文件所在目录的绝对路径
-    current_file_dir = os.path.dirname(os.path.abspath(__file__))
-    output_dir = os.path.join(current_file_dir, "results")
-    os.makedirs(output_dir, exist_ok=True)
-    output_file = os.path.join(output_dir, "enhanced_evaluation_results.json")
-    with open(output_file, "w", encoding="utf-8") as f:
-        json.dump(result, f, ensure_ascii=False, indent=2)
-    print(f"\n详细结果已保存到: {output_file}")

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

@@ -1,626 +0,0 @@
-"""
-LoCoMo Utilities Module
-
-This module provides helper functions for the LoCoMo benchmark evaluation:
-- Data loading from JSON files
-- Conversation extraction for ingestion
-- Temporal reference resolution
-- Context selection and formatting
-- Retrieval wrapper functions
-- Ingestion wrapper functions
-"""
-
-import os
-import json
-import re
-from datetime import datetime, timedelta
-from typing import List, Dict, Any, Optional
-
-from app.core.memory.utils.definitions import PROJECT_ROOT
-from app.core.memory.evaluation.extraction_utils import ingest_contexts_via_full_pipeline
-
-
-def load_locomo_data(
-    data_path: str, 
-    sample_size: int,
-    conversation_index: int = 0
-) -> List[Dict[str, Any]]:
-    """
-    Load LoCoMo dataset from JSON file.
-    
-    The LoCoMo dataset structure is a list of conversation objects, where each
-    object contains a "qa" list of question-answer pairs.
-    
-    Args:
-        data_path: Path to locomo10.json file
-        sample_size: Number of QA pairs to load (limits total QA items returned)
-        conversation_index: Which conversation to load QA pairs from (default: 0 for first)
-        
-    Returns:
-        List of QA item dictionaries, each containing:
-            - question: str
-            - answer: str
-            - category: int (1-4)
-            - evidence: List[str]
-            
-    Raises:
-        FileNotFoundError: If data_path does not exist
-        json.JSONDecodeError: If file is not valid JSON
-        IndexError: If conversation_index is out of range
-    """
-    if not os.path.exists(data_path):
-        raise FileNotFoundError(f"LoCoMo data file not found: {data_path}")
-    
-    with open(data_path, "r", encoding="utf-8") as f:
-        raw = json.load(f)
-    
-    # LoCoMo data structure: list of objects, each with a "qa" list
-    qa_items: List[Dict[str, Any]] = []
-    
-    if isinstance(raw, list):
-        # Only load QA pairs from the specified conversation
-        if conversation_index < len(raw):
-            entry = raw[conversation_index]
-            if isinstance(entry, dict) and "qa" in entry:
-                qa_items.extend(entry.get("qa", []))
-        else:
-            raise IndexError(
-                f"Conversation index {conversation_index} out of range. "
-                f"Dataset has {len(raw)} conversations."
-            )
-    else:
-        # Fallback: single object with qa list
-        if conversation_index == 0:
-            qa_items.extend(raw.get("qa", []))
-        else:
-            raise IndexError(
-                f"Conversation index {conversation_index} out of range. "
-                f"Dataset has only 1 conversation."
-            )
-    
-    # Return only the requested sample size
-    return qa_items[:sample_size]
-
-
-def extract_conversations(data_path: str, max_dialogues: int = 1) -> List[str]:
-    """
-    Extract conversation texts from LoCoMo data for ingestion.
-    
-    This function extracts the raw conversation dialogues from the LoCoMo dataset
-    so they can be ingested into the memory system. Each conversation is formatted
-    as a multi-line string with "role: message" format.
-    
-    Args:
-        data_path: Path to locomo10.json file
-        max_dialogues: Maximum number of dialogues to extract (default: 1)
-        
-    Returns:
-        List of conversation strings formatted for ingestion.
-        Each string contains multiple lines in format "role: message"
-        
-    Example output:
-        [
-            "User: I went to the store yesterday.\\nAI: What did you buy?\\n...",
-            "User: I love hiking.\\nAI: Where do you like to hike?\\n..."
-        ]
-    """
-    if not os.path.exists(data_path):
-        raise FileNotFoundError(f"LoCoMo data file not found: {data_path}")
-    
-    with open(data_path, "r", encoding="utf-8") as f:
-        raw = json.load(f)
-    
-    # Ensure we have a list of entries
-    entries = raw if isinstance(raw, list) else [raw]
-    
-    contents: List[str] = []
-    
-    for i, entry in enumerate(entries[:max_dialogues]):
-        if not isinstance(entry, dict):
-            continue
-        
-        conv = entry.get("conversation", {})
-        
-        if not isinstance(conv, dict):
-            continue
-        
-        lines: List[str] = []
-        
-        # Collect all session_* messages
-        for key, val in sorted(conv.items()):
-            if isinstance(val, list) and key.startswith("session_"):
-                for msg in val:
-                    if not isinstance(msg, dict):
-                        continue
-                    
-                    role = msg.get("speaker") or "User"
-                    text = msg.get("text") or ""
-                    text = str(text).strip()
-                    
-                    if not text:
-                        continue
-                    
-                    lines.append(f"{role}: {text}")
-        
-        if lines:
-            contents.append("\n".join(lines))
-    
-    return contents
-
-
-def resolve_temporal_references(text: str, anchor_date: datetime) -> str:
-    """
-    Resolve relative temporal references to absolute dates.
-    
-    This function converts relative time expressions (like "today", "yesterday",
-    "3 days ago") into absolute ISO date strings based on an anchor date.
-    
-    Supported patterns:
-    - today, yesterday, tomorrow
-    - X days ago, in X days
-    - last week, next week
-    
-    Args:
-        text: Text containing temporal references
-        anchor_date: Reference date for resolution (datetime object)
-        
-    Returns:
-        Text with temporal references replaced by ISO dates (YYYY-MM-DD format)
-        
-    Example:
-        >>> anchor = datetime(2023, 5, 8)
-        >>> resolve_temporal_references("I saw him yesterday", anchor)
-        "I saw him 2023-05-07"
-    """
-    # Ensure input is a string
-    t = str(text) if text is not None else ""
-    
-    # today / yesterday / tomorrow
-    t = re.sub(
-        r"\btoday\b",
-        anchor_date.date().isoformat(),
-        t,
-        flags=re.IGNORECASE
-    )
-    t = re.sub(
-        r"\byesterday\b",
-        (anchor_date - timedelta(days=1)).date().isoformat(),
-        t,
-        flags=re.IGNORECASE
-    )
-    t = re.sub(
-        r"\btomorrow\b",
-        (anchor_date + timedelta(days=1)).date().isoformat(),
-        t,
-        flags=re.IGNORECASE
-    )
-    
-    # X days ago
-    def _ago_repl(m: re.Match[str]) -> str:
-        n = int(m.group(1))
-        return (anchor_date - timedelta(days=n)).date().isoformat()
-    
-    # in X days
-    def _in_repl(m: re.Match[str]) -> str:
-        n = int(m.group(1))
-        return (anchor_date + timedelta(days=n)).date().isoformat()
-    
-    t = re.sub(
-        r"\b(\d+)\s+days?\s+ago\b",
-        _ago_repl,
-        t,
-        flags=re.IGNORECASE
-    )
-    t = re.sub(
-        r"\bin\s+(\d+)\s+days?\b",
-        _in_repl,
-        t,
-        flags=re.IGNORECASE
-    )
-    
-    # last week / next week (approximate as 7 days)
-    t = re.sub(
-        r"\blast\s+week\b",
-        (anchor_date - timedelta(days=7)).date().isoformat(),
-        t,
-        flags=re.IGNORECASE
-    )
-    t = re.sub(
-        r"\bnext\s+week\b",
-        (anchor_date + timedelta(days=7)).date().isoformat(),
-        t,
-        flags=re.IGNORECASE
-    )
-    
-    return t
-
-
-def select_and_format_information(
-    retrieved_info: List[str],
-    question: str,
-    max_chars: int = 8000
-) -> str:
-    """
-    Intelligently select and format most relevant retrieved information for LLM prompt.
-    
-    This function scores each piece of retrieved information based on keyword matching
-    with the question, then selects the highest-scoring pieces up to the character limit.
-    
-    Scoring criteria:
-    - Keyword matches (higher weight for multiple occurrences)
-    - Context length (moderate length preferred)
-    - Position (earlier contexts get bonus points)
-    
-    Args:
-        retrieved_info: List of retrieved information strings (chunks, statements, entities)
-        question: Question being answered
-        max_chars: Maximum total characters to include in final prompt
-        
-    Returns:
-        Formatted string combining the most relevant information for LLM prompt.
-        Contexts are separated by double newlines.
-        
-    Example:
-        >>> contexts = ["Alice went to Paris", "Bob likes pizza", "Alice visited the Eiffel Tower"]
-        >>> question = "Where did Alice go?"
-        >>> select_and_format_information(contexts, question, max_chars=100)
-        "Alice went to Paris\\n\\nAlice visited the Eiffel Tower"
-    """
-    if not retrieved_info:
-        return ""
-    
-    # Extract question keywords (filter out stop words and short words)
-    question_lower = question.lower()
-    stop_words = {
-        'what', 'when', 'where', 'who', 'why', 'how',
-        'did', 'do', 'does', 'is', 'are', 'was', 'were',
-        'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at'
-    }
-    question_words = set(re.findall(r'\b\w+\b', question_lower))
-    question_words = {
-        word for word in question_words
-        if word not in stop_words and len(word) > 2
-    }
-    
-    # Score each context
-    scored_contexts = []
-    for i, context in enumerate(retrieved_info):
-        context_lower = context.lower()
-        score = 0
-        
-        # Keyword matching score
-        keyword_matches = 0
-        for word in question_words:
-            if word in context_lower:
-                keyword_matches += 1
-                # Multiple occurrences increase score
-                score += context_lower.count(word) * 2
-        
-        # Length score (prefer moderate length)
-        context_len = len(context)
-        if 100 < context_len < 2000:
-            score += 5
-        elif context_len >= 2000:
-            score += 2
-        
-        # Position bonus (earlier contexts often more relevant)
-        if i < 3:
-            score += 3
-        
-        scored_contexts.append((score, context, keyword_matches))
-    
-    # Sort by score (descending)
-    scored_contexts.sort(key=lambda x: x[0], reverse=True)
-    
-    # Select contexts up to character limit
-    selected = []
-    total_chars = 0
-    
-    for score, context, matches in scored_contexts:
-        if total_chars + len(context) <= max_chars:
-            selected.append(context)
-            total_chars += len(context)
-        else:
-            # Try to include high-scoring context by truncating
-            if score > 10 and total_chars < max_chars - 500:
-                remaining = max_chars - total_chars
-                # Find lines with keywords
-                lines = context.split('\n')
-                relevant_lines = []
-                current_chars = 0
-                
-                for line in lines:
-                    line_lower = line.lower()
-                    line_relevance = any(word in line_lower for word in question_words)
-                    
-                    if line_relevance and current_chars < remaining - 100:
-                        relevant_lines.append(line)
-                        current_chars += len(line)
-                
-                if relevant_lines and len('\n'.join(relevant_lines)) > 100:
-                    truncated = '\n'.join(relevant_lines)
-                    selected.append(truncated + "\n[Content truncated...]")
-                    total_chars += len(truncated)
-            break
-    
-    return "\n\n".join(selected)
-
-
-async def retrieve_relevant_information(
-    question: str,
-    group_id: str,
-    search_type: str,
-    search_limit: int,
-    connector: Any,
-    embedder: Any
-) -> List[str]:
-    """
-    Retrieve relevant information from memory graph for a question.
-    
-    This function searches the Neo4j memory graph (populated during ingestion) and
-    returns relevant chunks, statements, and entity information that might help
-    answer the question.
-    
-    The function supports three search types:
-    - "keyword": Full-text search using Cypher queries
-    - "embedding": Vector similarity search using embeddings
-    - "hybrid": Combination of keyword and embedding search with reranking
-    
-    Args:
-        question: Question to search for
-        group_id: Database group ID (identifies which conversation memory to search)
-        search_type: "keyword", "embedding", or "hybrid"
-        search_limit: Max memory pieces to retrieve
-        connector: Neo4j connector instance
-        embedder: Embedder client instance
-        
-    Returns:
-        List of text strings (chunks, statements, entity summaries) from memory graph.
-        Each string represents a piece of retrieved information.
-        
-    Raises:
-        Exception: If search fails (caught and returns empty list)
-    """
-    from app.repositories.neo4j.graph_search import (
-        search_graph,
-        search_graph_by_embedding
-    )
-    from app.core.memory.storage_services.search import run_hybrid_search
-    
-    contexts_all: List[str] = []
-    
-    try:
-        if search_type == "embedding":
-            # Embedding-based search
-            search_results = await search_graph_by_embedding(
-                connector=connector,
-                embedder_client=embedder,
-                query_text=question,
-                group_id=group_id,
-                limit=search_limit,
-                include=["chunks", "statements", "entities", "summaries"],
-            )
-            
-            chunks = search_results.get("chunks", [])
-            statements = search_results.get("statements", [])
-            entities = search_results.get("entities", [])
-            summaries = search_results.get("summaries", [])
-            
-            # Build context from chunks
-            for c in chunks:
-                content = str(c.get("content", "")).strip()
-                if content:
-                    contexts_all.append(content)
-            
-            # Add statements
-            for s in statements:
-                stmt_text = str(s.get("statement", "")).strip()
-                if stmt_text:
-                    contexts_all.append(stmt_text)
-            
-            # Add summaries
-            for sm in summaries:
-                summary_text = str(sm.get("summary", "")).strip()
-                if summary_text:
-                    contexts_all.append(summary_text)
-            
-            # Add top entities (limit to 3 to avoid noise)
-            if entities:
-                scored = [e for e in entities if e.get("score") is not None]
-                top_entities = (
-                    sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3]
-                    if scored else entities[:3]
-                )
-                if top_entities:
-                    summary_lines = []
-                    for e in top_entities:
-                        name = str(e.get("name", "")).strip()
-                        etype = str(e.get("entity_type", "")).strip()
-                        score = e.get("score")
-                        if name:
-                            meta = []
-                            if etype:
-                                meta.append(f"type={etype}")
-                            if isinstance(score, (int, float)):
-                                meta.append(f"score={score:.3f}")
-                            summary_lines.append(
-                                f"EntitySummary: {name}"
-                                f"{(' [' + '; '.join(meta) + ']') if meta else ''}"
-                            )
-                    if summary_lines:
-                        contexts_all.append("\n".join(summary_lines))
-        
-        elif search_type == "keyword":
-            # Keyword-based search
-            search_results = await search_graph(
-                connector=connector,
-                q=question,
-                group_id=group_id,
-                limit=search_limit
-            )
-            
-            dialogs = search_results.get("dialogues", [])
-            statements = search_results.get("statements", [])
-            entities = search_results.get("entities", [])
-            
-            # Build context from dialogues
-            for d in dialogs:
-                content = str(d.get("content", "")).strip()
-                if content:
-                    contexts_all.append(content)
-            
-            # Add statements
-            for s in statements:
-                stmt_text = str(s.get("statement", "")).strip()
-                if stmt_text:
-                    contexts_all.append(stmt_text)
-            
-            # Add entity names
-            if entities:
-                entity_names = [
-                    str(e.get("name", "")).strip()
-                    for e in entities[:5]
-                    if e.get("name")
-                ]
-                if entity_names:
-                    contexts_all.append(f"EntitySummary: {', '.join(entity_names)}")
-        
-        else:  # hybrid
-            # Hybrid search with fallback to embedding
-            try:
-                search_results = await run_hybrid_search(
-                    query_text=question,
-                    search_type=search_type,
-                    group_id=group_id,
-                    limit=search_limit,
-                    include=["chunks", "statements", "entities", "summaries"],
-                    output_path=None,
-                )
-                
-                # Handle flat structure (new API format)
-                if search_results and isinstance(search_results, dict):
-                    chunks = search_results.get("chunks", [])
-                    statements = search_results.get("statements", [])
-                    entities = search_results.get("entities", [])
-                    summaries = search_results.get("summaries", [])
-                    
-                    # Check if we got results
-                    if not (chunks or statements or entities or summaries):
-                        # Try nested structure (backward compatibility)
-                        reranked = search_results.get("reranked_results", {})
-                        if reranked and isinstance(reranked, dict):
-                            chunks = reranked.get("chunks", [])
-                            statements = reranked.get("statements", [])
-                            entities = reranked.get("entities", [])
-                            summaries = reranked.get("summaries", [])
-                        else:
-                            raise ValueError("Hybrid search returned empty results")
-                else:
-                    raise ValueError("Hybrid search returned empty results")
-                
-            except Exception as e:
-                # Fallback to embedding search
-                search_results = await search_graph_by_embedding(
-                    connector=connector,
-                    embedder_client=embedder,
-                    query_text=question,
-                    group_id=group_id,
-                    limit=search_limit,
-                    include=["chunks", "statements", "entities", "summaries"],
-                )
-                chunks = search_results.get("chunks", [])
-                statements = search_results.get("statements", [])
-                entities = search_results.get("entities", [])
-                summaries = search_results.get("summaries", [])
-            
-            # Build context (same for both hybrid and fallback)
-            for c in chunks:
-                content = str(c.get("content", "")).strip()
-                if content:
-                    contexts_all.append(content)
-            
-            for s in statements:
-                stmt_text = str(s.get("statement", "")).strip()
-                if stmt_text:
-                    contexts_all.append(stmt_text)
-            
-            for sm in summaries:
-                summary_text = str(sm.get("summary", "")).strip()
-                if summary_text:
-                    contexts_all.append(summary_text)
-            
-            # Add top entities
-            if entities:
-                scored = [e for e in entities if e.get("score") is not None]
-                top_entities = (
-                    sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3]
-                    if scored else entities[:3]
-                )
-                if top_entities:
-                    summary_lines = []
-                    for e in top_entities:
-                        name = str(e.get("name", "")).strip()
-                        etype = str(e.get("entity_type", "")).strip()
-                        score = e.get("score")
-                        if name:
-                            meta = []
-                            if etype:
-                                meta.append(f"type={etype}")
-                            if isinstance(score, (int, float)):
-                                meta.append(f"score={score:.3f}")
-                            summary_lines.append(
-                                f"EntitySummary: {name}"
-                                f"{(' [' + '; '.join(meta) + ']') if meta else ''}"
-                            )
-                    if summary_lines:
-                        contexts_all.append("\n".join(summary_lines))
-    
-    except Exception as e:
-        # Return empty list on error
-        contexts_all = []
-    
-    return contexts_all
-
-
-async def ingest_conversations_if_needed(
-    conversations: List[str],
-    group_id: str,
-    reset: bool = False
-) -> bool:
-    """
-    Wrapper for conversation ingestion using external extraction pipeline.
-    
-    This function populates the Neo4j database with processed conversation data
-    (chunks, statements, entities) so that the retrieval system has memory to search.
-    
-    The ingestion process:
-    1. Parses conversation text into dialogue messages
-    2. Chunks the dialogues into semantic units
-    3. Extracts statements and entities using LLM
-    4. Generates embeddings for all content
-    5. Stores everything in Neo4j graph database
-    
-    Args:
-        conversations: List of raw conversation texts from LoCoMo dataset
-                      Example: ["User: I went to Paris. AI: When was that?", ...]
-        group_id: Target group ID for database storage
-        reset: Whether to clear existing data first (not implemented in wrapper)
-        
-    Returns:
-        True if successful, False otherwise
-        
-    Note:
-        The external function uses "contexts" to mean "conversation texts".
-        This runs the full extraction pipeline: chunking → entity extraction → 
-        statement extraction → embedding → Neo4j storage.
-    """
-    try:
-        success = await ingest_contexts_via_full_pipeline(
-            contexts=conversations,
-            group_id=group_id,
-            save_chunk_output=True
-        )
-        return success
-    except Exception as e:
-        print(f"[Ingestion] Failed to ingest conversations: {e}")
-        return False

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

@@ -1,878 +0,0 @@
-import argparse
-import asyncio
-import json
-import os
-import statistics
-import time
-from datetime import datetime, timedelta
-from typing import Any, Dict, List
-
-try:
-    from dotenv import load_dotenv
-except Exception:
-    def load_dotenv():
-        return None
-
-import re
-
-from app.core.memory.evaluation.common.metrics import (
-    avg_context_tokens,
-    bleu1,
-    jaccard,
-    latency_stats,
-)
-from app.core.memory.evaluation.common.metrics import f1_score as common_f1
-from app.core.memory.evaluation.extraction_utils import (
-    ingest_contexts_via_full_pipeline,
-)
-from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
-from app.core.memory.storage_services.search import run_hybrid_search
-from app.core.memory.utils.config.definitions import (
-    PROJECT_ROOT,
-    SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
-    SELECTED_LLM_ID,
-)
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.core.models.base import RedBearModelConfig
-from app.db import get_db_context
-from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-from app.services.memory_config_service import MemoryConfigService
-
-
-# 参考 evaluation/locomo/evaluation.py 的 F1 计算逻辑（移除外部依赖，内联实现）
-def _loc_normalize(text: str) -> str:
-    import re
-    # 确保输入是字符串
-    text = str(text) if text is not None else ""
-    text = text.lower()
-    text = re.sub(r"[\,]", " ", text)  # 去掉逗号
-    text = re.sub(r"\b(a|an|the|and)\b", " ", text)
-    text = re.sub(r"[^\w\s]", " ", text)
-    text = " ".join(text.split())
-    return text
-
-# 追加：相对时间归一化为绝对日期（有限支持：today/yesterday/tomorrow/X days ago/in X days/last week/next week）
-def _resolve_relative_times(text: str, anchor: datetime) -> str:
-    import re
-    # 确保输入是字符串
-    t = str(text) if text is not None else ""
-    # today / yesterday / tomorrow
-    t = re.sub(r"\btoday\b", anchor.date().isoformat(), t, flags=re.IGNORECASE)
-    t = re.sub(r"\byesterday\b", (anchor - timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE)
-    t = re.sub(r"\btomorrow\b", (anchor + timedelta(days=1)).date().isoformat(), t, flags=re.IGNORECASE)
-    # X days ago / in X days
-    def _ago_repl(m: re.Match[str]) -> str:
-        n = int(m.group(1))
-        return (anchor - timedelta(days=n)).date().isoformat()
-    def _in_repl(m: re.Match[str]) -> str:
-        n = int(m.group(1))
-        return (anchor + timedelta(days=n)).date().isoformat()
-    t = re.sub(r"\b(\d+)\s+days\s+ago\b", _ago_repl, t, flags=re.IGNORECASE)
-    t = re.sub(r"\bin\s+(\d+)\s+days\b", _in_repl, t, flags=re.IGNORECASE)
-    # last week / next week（以7天近似）
-    t = re.sub(r"\blast\s+week\b", (anchor - timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE)
-    t = re.sub(r"\bnext\s+week\b", (anchor + timedelta(days=7)).date().isoformat(), t, flags=re.IGNORECASE)
-    return t
-
-def loc_f1_score(prediction: str, ground_truth: str) -> float:
-    # 单答案 F1：按词集合计算（近似原始实现，去除词干依赖）
-    # 确保输入是字符串
-    pred_str = str(prediction) if prediction is not None else ""
-    truth_str = str(ground_truth) if ground_truth is not None else ""
-
-    p_tokens = _loc_normalize(pred_str).split()
-    g_tokens = _loc_normalize(truth_str).split()
-    if not p_tokens or not g_tokens:
-        return 0.0
-    p = set(p_tokens)
-    g = set(g_tokens)
-    tp = len(p & g)
-    precision = tp / len(p) if p else 0.0
-    recall = tp / len(g) if g else 0.0
-    return (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0
-
-def loc_multi_f1(prediction: str, ground_truth: str) -> float:
-    # 多答案 F1：prediction 与 ground_truth 以逗号分隔，逐一匹配取最大，再对多个 GT 取平均
-    # 确保输入是字符串
-    pred_str = str(prediction) if prediction is not None else ""
-    truth_str = str(ground_truth) if ground_truth is not None else ""
-
-    predictions = [p.strip() for p in str(pred_str).split(',') if p.strip()]
-    ground_truths = [g.strip() for g in str(truth_str).split(',') if g.strip()]
-    if not predictions or not ground_truths:
-        return 0.0
-    def _f1(a: str, b: str) -> float:
-        return loc_f1_score(a, b)
-    vals = []
-    for gt in ground_truths:
-        vals.append(max(_f1(pred, gt) for pred in predictions))
-    return sum(vals) / len(vals)
-
-# 标准化 LoCoMo 类别名：支持数字 category 与字符串 cat/type
-CATEGORY_MAP_NUM_TO_NAME = {
-    4: "Single-Hop",
-    1: "Multi-Hop",
-    3: "Open Domain",
-    2: "Temporal",
-}
-
-_TYPE_ALIASES = {
-    "single-hop": "Single-Hop",
-    "singlehop": "Single-Hop",
-    "single hop": "Single-Hop",
-    "multi-hop": "Multi-Hop",
-    "multihop": "Multi-Hop",
-    "multi hop": "Multi-Hop",
-    "open domain": "Open Domain",
-    "opendomain": "Open Domain",
-    "temporal": "Temporal",
-}
-
-def get_category_label(item: Dict[str, Any]) -> str:
-    # 1) 直接用字符串 cat
-    cat = item.get("cat")
-    if isinstance(cat, str) and cat.strip():
-        name = cat.strip()
-        lower = name.lower()
-        return _TYPE_ALIASES.get(lower, name)
-    # 2) 数字 category 转名称
-    cat_num = item.get("category")
-    if isinstance(cat_num, int):
-        return CATEGORY_MAP_NUM_TO_NAME.get(cat_num, "unknown")
-    # 3) 备用 type 字段
-    t = item.get("type")
-    if isinstance(t, str) and t.strip():
-        lower = t.strip().lower()
-        return _TYPE_ALIASES.get(lower, t.strip())
-    return "unknown"
-
-
-def smart_context_selection(contexts: List[str], question: str, max_chars: int = 12000) -> str:
-    """基于问题关键词智能选择上下文"""
-    if not contexts:
-        return ""
-
-    # 提取问题关键词（只保留有意义的词）
-    question_lower = question.lower()
-    stop_words = {'what', 'when', 'where', 'who', 'why', 'how', 'did', 'do', 'does', 'is', 'are', 'was', 'were', 'the', 'a', 'an', 'and', 'or', 'but'}
-    question_words = set(re.findall(r'\b\w+\b', question_lower))
-    question_words = {word for word in question_words if word not in stop_words and len(word) > 2}
-
-    print(f"🔍 问题关键词: {question_words}")
-
-    # 给每个上下文打分
-    scored_contexts = []
-    for i, context in enumerate(contexts):
-        context_lower = context.lower()
-        score = 0
-
-        # 关键词匹配得分
-        keyword_matches = 0
-        for word in question_words:
-            if word in context_lower:
-                keyword_matches += 1
-                # 关键词出现次数越多，得分越高
-                score += context_lower.count(word) * 2
-
-        # 上下文长度得分（适中的长度更好）
-        context_len = len(context)
-        if 100 < context_len < 2000:  # 理想长度范围
-            score += 5
-        elif context_len >= 2000:  # 太长可能包含无关信息
-            score += 2
-
-        # 如果是前几个上下文，给予额外分数（通常相关性更高）
-        if i < 3:
-            score += 3
-
-        scored_contexts.append((score, context, keyword_matches))
-
-    # 按得分排序
-    scored_contexts.sort(key=lambda x: x[0], reverse=True)
-
-    # 选择高得分的上下文，直到达到字符限制
-    selected = []
-    total_chars = 0
-    selected_count = 0
-
-    print("📊 上下文相关性分析:")
-    for score, context, matches in scored_contexts[:5]:  # 只显示前5个
-        print(f"  - 得分: {score}, 关键词匹配: {matches}, 长度: {len(context)}")
-
-    for score, context, matches in scored_contexts:
-        if total_chars + len(context) <= max_chars:
-            selected.append(context)
-            total_chars += len(context)
-            selected_count += 1
-        else:
-            # 如果这个上下文得分很高但放不下，尝试截取
-            if score > 10 and total_chars < max_chars - 500:
-                remaining = max_chars - total_chars
-                # 找到包含关键词的部分
-                lines = context.split('\n')
-                relevant_lines = []
-                current_chars = 0
-
-                for line in lines:
-                    line_lower = line.lower()
-                    line_relevance = any(word in line_lower for word in question_words)
-
-                    if line_relevance and current_chars < remaining - 100:
-                        relevant_lines.append(line)
-                        current_chars += len(line)
-
-                if relevant_lines:
-                    truncated = '\n'.join(relevant_lines)
-                    if len(truncated) > 100:  # 确保有足够内容
-                        selected.append(truncated + "\n[相关内容截断...]")
-                        total_chars += len(truncated)
-                        selected_count += 1
-            break  # 不再尝试添加更多上下文
-
-    result = "\n\n".join(selected)
-    print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {total_chars}字符")
-    return result
-
-
-def get_search_params_by_category(category: str):
-    """根据问题类别调整检索参数"""
-    params_map = {
-        "Multi-Hop": {"limit": 20, "max_chars": 15000},
-        "Temporal": {"limit": 16, "max_chars": 10000},
-        "Open Domain": {"limit": 24, "max_chars": 18000},
-        "Single-Hop": {"limit": 12, "max_chars": 8000},
-    }
-    return params_map.get(category, {"limit": 16, "max_chars": 12000})
-
-
-async def run_locomo_eval(
-    sample_size: int = 1,
-    group_id: str | None = None,
-    search_limit: int = 8,
-    context_char_budget: int = 4000,  # 保持默认值不变
-    llm_temperature: float = 0.0,
-    llm_max_tokens: int = 32,
-    search_type: str = "hybrid",  # 保持默认值不变
-    output_path: str | None = None,
-    skip_ingest_if_exists: bool = True,
-    llm_timeout: float = 10.0,
-    llm_max_retries: int = 1
-) -> Dict[str, Any]:
-
-    # 函数内部使用三路检索逻辑，但保持参数签名不变
-    group_id = group_id or SELECTED_GROUP_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")
-    with open(data_path, "r", encoding="utf-8") as f:
-        raw = json.load(f)
-    # LoCoMo 数据结构：顶层为若干对象，每个对象下有 qa 列表
-    qa_items: List[Dict[str, Any]] = []
-    if isinstance(raw, list):
-        for entry in raw:
-            qa_items.extend(entry.get("qa", []))
-    else:
-        qa_items.extend(raw.get("qa", []))
-    items: List[Dict[str, Any]] = qa_items[:sample_size]
-
-    # === 保持原来的数据摄入逻辑 ===
-    entries = raw if isinstance(raw, list) else [raw]
-
-    # 只摄入前1条对话（保持原样）
-    max_dialogues_to_ingest = 1
-    contents: List[str] = []
-    print(f"📊 找到 {len(entries)} 个对话对象，只摄入前 {max_dialogues_to_ingest} 条")
-
-    for i, entry in enumerate(entries[:max_dialogues_to_ingest]):
-        if not isinstance(entry, dict):
-            continue
-
-        conv = entry.get("conversation", {})
-        sample_id = entry.get("sample_id", f"unknown_{i}")
-
-        print(f"🔍 处理对话 {i+1}: {sample_id}")
-
-        lines: List[str] = []
-        if isinstance(conv, dict):
-            # 收集所有 session_* 的消息
-            session_count = 0
-            for key, val in conv.items():
-                if isinstance(val, list) and key.startswith("session_"):
-                    session_count += 1
-                    for msg in val:
-                        role = msg.get("speaker") or "用户"
-                        text = msg.get("text") or ""
-                        text = str(text).strip()
-                        if not text:
-                            continue
-                        lines.append(f"{role}: {text}")
-
-            print(f"  - 包含 {session_count} 个session, {len(lines)} 条消息")
-
-        if not lines:
-            print(f"⚠️  警告: 对话 {sample_id} 没有对话内容，跳过摄入")
-            continue
-
-        contents.append("\n".join(lines))
-
-    print(f"📥 总共摄入 {len(contents)} 个对话的conversation内容")
-
-    # 选择要评测的QA对（从所有对话中选取）
-    indexed_items: List[tuple[int, Dict[str, Any]]] = []
-    if isinstance(raw, list):
-        for e_idx, entry in enumerate(raw):
-            for qa in entry.get("qa", []):
-                indexed_items.append((e_idx, qa))
-    else:
-        for qa in raw.get("qa", []):
-            indexed_items.append((0, qa))
-
-    # 这里使用sample_size来限制评测的QA数量
-    selected = indexed_items[:sample_size]
-    items: List[Dict[str, Any]] = [qa for _, qa in selected]
-
-    print(f"🎯 将评测 {len(items)} 个QA对，数据库中只包含 {len(contents)} 个对话")
-    # === 修改结束 ===
-
-    connector = Neo4jConnector()
-
-    # 关键修复：强制重新摄入纯净的对话数据
-    print("🔄 强制重新摄入纯净的对话数据...")
-    await ingest_contexts_via_full_pipeline(contents, group_id, save_chunk_output=True)
-
-    # 使用异步LLM客户端
-    with get_db_context() as db:
-        factory = MemoryClientFactory(db)
-        llm_client = factory.get_llm_client(SELECTED_LLM_ID)
-    # 初始化embedder用于直接调用
-    with get_db_context() as db:
-        config_service = MemoryConfigService(db)
-        cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID)
-    embedder = OpenAIEmbedderClient(
-        model_config=RedBearModelConfig.model_validate(cfg_dict)
-    )
-    
-    # connector initialized above
-    latencies_llm: List[float] = []
-    latencies_search: List[float] = []
-    # 上下文诊断收集
-    per_query_context_counts: List[int] = []
-    per_query_context_avg_tokens: List[float] = []
-    per_query_context_chars: List[int] = []
-    per_query_context_tokens_total: List[int] = []
-    # 详细样本调试信息
-    samples: List[Dict[str, Any]] = []
-    # 通用指标
-    f1s: List[float] = []
-    b1s: List[float] = []
-    jss: List[float] = []
-    # 参考 LoCoMo 评测的类别专用 F1（multi-hop 使用多答案 F1）
-    loc_f1s: List[float] = []
-    # Per-category aggregation
-    cat_counts: Dict[str, int] = {}
-    cat_f1s: Dict[str, List[float]] = {}
-    cat_b1s: Dict[str, List[float]] = {}
-    cat_jss: Dict[str, List[float]] = {}
-    cat_loc_f1s: Dict[str, List[float]] = {}
-    try:
-        for item in items:
-            q = item.get("question", "")
-            ref = item.get("answer", "")
-            # 确保答案是字符串
-            ref_str = str(ref) if ref is not None else ""
-            cat = get_category_label(item)
-
-            print(f"\n=== 处理问题: {q} ===")
-
-            # 根据类别调整检索参数
-            search_params = get_search_params_by_category(cat)
-            adjusted_limit = search_params["limit"]
-            max_chars = search_params["max_chars"]
-
-            print(f"🏷️ 类别: {cat}, 检索参数: limit={adjusted_limit}, max_chars={max_chars}")
-
-            # 改进的检索逻辑：使用三路检索（statements, dialogues, entities）
-            t0 = time.time()
-            contexts_all: List[str] = []
-            search_results = None  # 保存完整的检索结果
-
-            try:
-                if search_type == "embedding":
-                    # 直接调用嵌入检索，包含三路数据
-                    search_results = await search_graph_by_embedding(
-                        connector=connector,
-                        embedder_client=embedder,
-                        query_text=q,
-                        group_id=group_id,
-                        limit=adjusted_limit,
-                        include=["chunks", "statements", "entities", "summaries"],  # 修复：使用正确的类型
-                    )
-                    chunks = search_results.get("chunks", [])
-                    statements = search_results.get("statements", [])
-                    entities = search_results.get("entities", [])
-                    summaries = search_results.get("summaries", [])
-                    
-                    print(f"✅ 嵌入检索成功: {len(chunks)} chunks, {len(statements)} 条陈述, {len(entities)} 个实体, {len(summaries)} 个摘要")
-                    
-                    # 构建上下文：优先使用 chunks、statements 和 summaries
-                    for c in chunks:
-                        content = str(c.get("content", "")).strip()
-                        if content:
-                            contexts_all.append(content)
-
-                    for s in statements:
-                        stmt_text = str(s.get("statement", "")).strip()
-                        if stmt_text:
-                            contexts_all.append(stmt_text)
-                    
-                    for sm in summaries:
-                        summary_text = str(sm.get("summary", "")).strip()
-                        if summary_text:
-                            contexts_all.append(summary_text)
-
-                    # 实体摘要：最多加入前3个高分实体，避免噪声
-                    scored = [e for e in entities if e.get("score") is not None]
-                    top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3]
-                    if top_entities:
-                        summary_lines = []
-                        for e in top_entities:
-                            name = str(e.get("name", "")).strip()
-                            etype = str(e.get("entity_type", "")).strip()
-                            score = e.get("score")
-                            if name:
-                                meta = []
-                                if etype:
-                                    meta.append(f"type={etype}")
-                                if isinstance(score, (int, float)):
-                                    meta.append(f"score={score:.3f}")
-                                summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}")
-                        if summary_lines:
-                            contexts_all.append("\n".join(summary_lines))
-
-                elif search_type == "keyword":
-                    # 直接调用关键词检索
-                    search_results = await search_graph(
-                        connector=connector,
-                        q=q,
-                        group_id=group_id,
-                        limit=adjusted_limit
-                    )
-                    dialogs = search_results.get("dialogues", [])
-                    statements = search_results.get("statements", [])
-                    entities = search_results.get("entities", [])
-                    print(f"🔤 关键词检索找到 {len(dialogs)} 条对话, {len(statements)} 条陈述, {len(entities)} 个实体")
-
-                    # 构建上下文
-                    for d in dialogs:
-                        content = str(d.get("content", "")).strip()
-                        if content:
-                            contexts_all.append(content)
-                    for s in statements:
-                        stmt_text = str(s.get("statement", "")).strip()
-                        if stmt_text:
-                            contexts_all.append(stmt_text)
-                    # 实体处理（关键词检索的实体可能没有分数）
-                    if entities:
-                        entity_names = [str(e.get("name", "")).strip() for e in entities[:5] if e.get("name")]
-                        if entity_names:
-                            contexts_all.append(f"EntitySummary: {', '.join(entity_names)}")
-
-                else:  # hybrid
-                    # 🎯 关键修复：混合检索使用更严格的回退机制
-                    print("🔀 使用混合检索（带回退机制）...")
-                    try:
-                        search_results = await run_hybrid_search(
-                            query_text=q,
-                            search_type=search_type,
-                            group_id=group_id,
-                            limit=adjusted_limit,
-                            include=["chunks", "statements", "entities", "summaries"],
-                            output_path=None,
-                        )
-                        
-                        # 🎯 关键修复：正确处理混合检索的扁平结构
-                        # 新的API返回扁平结构，直接从顶层获取结果
-                        if search_results and isinstance(search_results, dict):
-                            # 新API返回扁平结构：直接从顶层获取
-                            chunks = search_results.get("chunks", [])
-                            statements = search_results.get("statements", [])
-                            entities = search_results.get("entities", [])
-                            summaries = search_results.get("summaries", [])
-                            
-                            # 检查是否有有效结果
-                            if chunks or statements or entities or summaries:
-                                print(f"✅ 混合检索成功: {len(chunks)} chunks, {len(statements)} 陈述, {len(entities)} 实体, {len(summaries)} 摘要")
-                            else:
-                                # 如果顶层没有结果，尝试旧的嵌套结构（向后兼容）
-                                reranked = search_results.get("reranked_results", {})
-                                if reranked and isinstance(reranked, dict):
-                                    chunks = reranked.get("chunks", [])
-                                    statements = reranked.get("statements", [])
-                                    entities = reranked.get("entities", [])
-                                    summaries = reranked.get("summaries", [])
-                                    print(f"✅ 混合检索成功（使用旧格式reranked结果）: {len(chunks)} chunks, {len(statements)} 陈述")
-                                else:
-                                    raise ValueError("混合检索返回空结果")
-                        else:
-                            raise ValueError("混合检索返回空结果")
-                            
-                    except Exception as e:
-                        print(f"❌ 混合检索失败: {e}，回退到嵌入检索")
-                        search_results = await search_graph_by_embedding(
-                            connector=connector,
-                            embedder_client=embedder,
-                            query_text=q,
-                            group_id=group_id,
-                            limit=adjusted_limit,
-                            include=["chunks", "statements", "entities", "summaries"],
-                        )
-                        chunks = search_results.get("chunks", [])
-                        statements = search_results.get("statements", [])
-                        entities = search_results.get("entities", [])
-                        summaries = search_results.get("summaries", [])
-                        print(f"✅ 回退嵌入检索成功: {len(chunks)} chunks, {len(statements)} 陈述")
-                    
-                    # 🎯 统一处理：构建上下文（所有检索类型共用）
-                    for c in chunks:
-                        content = str(c.get("content", "")).strip()
-                        if content:
-                            contexts_all.append(content)
-                    
-                    for s in statements:
-                        stmt_text = str(s.get("statement", "")).strip()
-                        if stmt_text:
-                            contexts_all.append(stmt_text)
-                    
-                    for sm in summaries:
-                        summary_text = str(sm.get("summary", "")).strip()
-                        if summary_text:
-                            contexts_all.append(summary_text)
-                    
-                    # 实体摘要：最多加入前3个高分实体
-                    if entities:
-                        scored = [e for e in entities if e.get("score") is not None]
-                        top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3]
-                        if top_entities:
-                            summary_lines = []
-                            for e in top_entities:
-                                name = str(e.get("name", "")).strip()
-                                etype = str(e.get("entity_type", "")).strip()
-                                score = e.get("score")
-                                if name:
-                                    meta = []
-                                    if etype:
-                                        meta.append(f"type={etype}")
-                                    if isinstance(score, (int, float)):
-                                        meta.append(f"score={score:.3f}")
-                                    summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}")
-                            if summary_lines:
-                                contexts_all.append("\n".join(summary_lines))
-                
-                # 关键修复：过滤掉包含当前问题答案的上下文
-                filtered_contexts = []
-                for context in contexts_all:
-                    content = str(context)
-                    # 排除包含当前问题标准答案的上下文
-                    if ref_str and ref_str.strip() and ref_str.strip() in content:
-                        print("🚫 过滤掉包含标准答案的上下文")
-                        continue
-                    filtered_contexts.append(context)
-
-                print(f"📊 过滤后保留 {len(filtered_contexts)} 个上下文 (原 {len(contexts_all)} 个)")
-                contexts_all = filtered_contexts
-
-                # 输出完整的检索结果信息
-                print("🔍 检索结果详情:")
-                if search_results:
-                    output_data = {
-                        "statements": [
-                            {
-                                "statement": s.get("statement", "")[:200] + "..." if len(s.get("statement", "")) > 200 else s.get("statement", ""),
-                                "score": s.get("score", 0.0)
-                            }
-                            for s in (statements[:2] if 'statements' in locals() else [])
-                        ],
-                        "dialogues": [
-                            {
-                                "uuid": d.get("uuid", ""),
-                                "group_id": d.get("group_id", ""),
-                                "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""),
-                                "score": d.get("score", 0.0)
-                            }
-                            for d in (dialogs[:2] if 'dialogs' in locals() else [])
-                        ],
-                        "entities": [
-                            {
-                                "name": e.get("name", ""),
-                                "entity_type": e.get("entity_type", ""),
-                                "score": e.get("score", 0.0)
-                            }
-                            for e in (entities[:2] if 'entities' in locals() else [])
-                        ]
-                    }
-                    print(json.dumps(output_data, ensure_ascii=False, indent=2))
-                else:
-                    print("   无检索结果")
-
-            except Exception as e:
-                print(f"❌ {search_type}检索失败: {e}")
-                contexts_all = []
-                search_results = None
-
-            t1 = time.time()
-            latencies_search.append((t1 - t0) * 1000)
-
-            # 使用智能上下文选择
-            context_text = ""
-            if contexts_all:
-                context_text = smart_context_selection(contexts_all, q, max_chars=max_chars)
-
-                # 如果智能选择后仍然过长，进行最终保护性截断
-                if len(context_text) > max_chars:
-                    print(f"⚠️ 智能选择后仍然过长 ({len(context_text)}字符)，进行最终截断")
-                    context_text = context_text[:max_chars] + "\n\n[最终截断...]"
-
-                # 时间解析
-                anchor_date = datetime(2023, 5, 8)  # 使用固定日期确保一致性
-                context_text = _resolve_relative_times(context_text, anchor_date)
-
-                context_text = f"Reference date: {anchor_date.date().isoformat()}\n\n" + context_text
-
-                print(f"📝 最终上下文长度: {len(context_text)} 字符")
-
-                # 显示不同上下文的预览
-                print("🔍 上下文预览:")
-                for j, context in enumerate(contexts_all[:3]):  # 显示前3个上下文
-                    preview = context[:150].replace('\n', ' ')
-                    print(f"  上下文{j+1}: {preview}...")
-
-            else:
-                print("❌ 没有检索到有效上下文")
-                context_text = "No relevant context found."
-
-            # 记录上下文诊断信息
-            per_query_context_counts.append(len(contexts_all))
-            per_query_context_avg_tokens.append(avg_context_tokens([context_text]))
-            per_query_context_chars.append(len(context_text))
-            per_query_context_tokens_total.append(len(_loc_normalize(context_text).split()))
-
-            # LLM 提示词
-            messages = [
-                {"role": "system", "content": (
-                    "You are a precise QA assistant. Answer following these rules:\n"
-                    "1) Extract the EXACT information mentioned in the context\n"
-                    "2) For time questions: calculate actual dates from relative times\n"
-                    "3) Return ONLY the answer text in simplest form\n"
-                    "4) For dates, use format 'DD Month YYYY' (e.g., '7 May 2023')\n"
-                    "5) If no clear answer found, respond with 'Unknown'"
-                )},
-                {"role": "user", "content": f"Question: {q}\n\nContext:\n{context_text}"},
-            ]
-
-            t2 = time.time()
-            # 使用异步调用
-            resp = await llm_client.chat(messages=messages)
-            t3 = time.time()
-            latencies_llm.append((t3 - t2) * 1000)
-            
-            # 兼容不同的响应格式
-            pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else "Unknown")
-            
-            # 计算指标（确保使用字符串）
-            f1_val = common_f1(str(pred), ref_str)
-            b1_val = bleu1(str(pred), ref_str)
-            j_val = jaccard(str(pred), ref_str)
-
-            f1s.append(f1_val)
-            b1s.append(b1_val)
-            jss.append(j_val)
-
-            # Accumulate by category
-            cat_counts[cat] = cat_counts.get(cat, 0) + 1
-            cat_f1s.setdefault(cat, []).append(f1_val)
-            cat_b1s.setdefault(cat, []).append(b1_val)
-            cat_jss.setdefault(cat, []).append(j_val)
-
-            # LoCoMo 专用 F1：multi-hop(1) 使用多答案 F1，其它(2/3/4)使用单答案 F1
-            if item.get("category") in [2, 3, 4]:
-                loc_val = loc_f1_score(str(pred), ref_str)
-            elif item.get("category") in [1]:
-                loc_val = loc_multi_f1(str(pred), ref_str)
-            else:
-                loc_val = loc_f1_score(str(pred), ref_str)
-            loc_f1s.append(loc_val)
-            cat_loc_f1s.setdefault(cat, []).append(loc_val)
-
-            # 保存完整的检索结果信息
-            samples.append({
-                "question": q,
-                "answer": ref_str,
-                "category": cat,
-                "prediction": pred,
-                "metrics": {
-                    "f1": f1_val,
-                    "b1": b1_val,
-                    "j": j_val,
-                    "loc_f1": loc_val
-                },
-                "retrieval": {
-                    "retrieved_documents": len(contexts_all),
-                    "context_length": len(context_text),
-                    "search_limit": adjusted_limit,
-                    "max_chars": max_chars
-                },
-                "timing": {
-                    "search_ms": (t1 - t0) * 1000,
-                    "llm_ms": (t3 - t2) * 1000
-                }
-            })
-
-            print(f"🤖 LLM 回答: {pred}")
-            print(f"✅ 正确答案: {ref_str}")
-            print(f"📈 当前指标 - F1: {f1_val:.3f}, BLEU-1: {b1_val:.3f}, Jaccard: {j_val:.3f}, LoCoMo F1: {loc_val:.3f}")
-
-        # Compute per-category averages and dispersion (std, iqr)
-        def _percentile(sorted_vals: List[float], p: float) -> float:
-            if not sorted_vals:
-                return 0.0
-            if len(sorted_vals) == 1:
-                return sorted_vals[0]
-            k = (len(sorted_vals) - 1) * p
-            f = int(k)
-            c = f + 1 if f + 1 < len(sorted_vals) else f
-            if f == c:
-                return sorted_vals[f]
-            return sorted_vals[f] + (sorted_vals[c] - sorted_vals[f]) * (k - f)
-
-        by_category: Dict[str, Dict[str, float | int]] = {}
-        for c in cat_counts:
-            f_list = cat_f1s.get(c, [])
-            b_list = cat_b1s.get(c, [])
-            j_list = cat_jss.get(c, [])
-            lf_list = cat_loc_f1s.get(c, [])
-            j_sorted = sorted(j_list)
-            j_std = statistics.stdev(j_list) if len(j_list) > 1 else 0.0
-            j_q75 = _percentile(j_sorted, 0.75)
-            j_q25 = _percentile(j_sorted, 0.25)
-            by_category[c] = {
-                "count": cat_counts[c],
-                "f1": (sum(f_list) / max(len(f_list), 1)) if f_list else 0.0,
-                "b1": (sum(b_list) / max(len(b_list), 1)) if b_list else 0.0,
-                "j": (sum(j_list) / max(len(j_list), 1)) if j_list else 0.0,
-                "j_std": j_std,
-                "j_iqr": (j_q75 - j_q25) if j_list else 0.0,
-                # 参考 LoCoMo 评测的类别专用 F1
-                "loc_f1": (sum(lf_list) / max(len(lf_list), 1)) if lf_list else 0.0,
-            }
-
-        # 累加命中（cum accuracy by category）：与 evaluation_stats.py 输出形式相仿
-        cum_accuracy_by_category = {c: sum(cat_loc_f1s.get(c, [])) for c in cat_counts}
-
-        result = {
-            "dataset": "locomo",
-            "items": len(items),
-            "metrics": {
-                "f1": sum(f1s) / max(len(f1s), 1),
-                "b1": sum(b1s) / max(len(b1s), 1),
-                "j": sum(jss) / max(len(jss), 1),
-                # LoCoMo 类别专用 F1 的总体
-                "loc_f1": sum(loc_f1s) / max(len(loc_f1s), 1),
-            },
-            "by_category": by_category,
-            "category_counts": cat_counts,
-            "cum_accuracy_by_category": cum_accuracy_by_category,
-            "context": {
-                "avg_tokens": (sum(per_query_context_avg_tokens) / max(len(per_query_context_avg_tokens), 1)) if per_query_context_avg_tokens else 0.0,
-                "avg_chars": (sum(per_query_context_chars) / max(len(per_query_context_chars), 1)) if per_query_context_chars else 0.0,
-                "count_avg": (sum(per_query_context_counts) / max(len(per_query_context_counts), 1)) if per_query_context_counts else 0.0,
-                "avg_memory_tokens": (sum(per_query_context_tokens_total) / max(len(per_query_context_tokens_total), 1)) if per_query_context_tokens_total else 0.0,
-            },
-            "latency": {
-                "search": latency_stats(latencies_search),
-                "llm": latency_stats(latencies_llm),
-            },
-            "samples": samples,
-            "params": {
-                "group_id": group_id,
-                "search_limit": search_limit,
-                "context_char_budget": context_char_budget,
-                "search_type": search_type,
-                "llm_id": SELECTED_LLM_ID,
-                "retrieval_embedding_id": SELECTED_EMBEDDING_ID,
-                "skip_ingest_if_exists": skip_ingest_if_exists,
-                "llm_timeout": llm_timeout,
-                "llm_max_retries": llm_max_retries,
-                "llm_temperature": llm_temperature,
-                "llm_max_tokens": llm_max_tokens
-            },
-            "timestamp": datetime.now().isoformat()
-        }
-        if output_path:
-            try:
-                os.makedirs(os.path.dirname(output_path), exist_ok=True)
-                with open(output_path, "w", encoding="utf-8") as f:
-                    json.dump(result, f, ensure_ascii=False, indent=2)
-                print(f"✅ 结果已保存到: {output_path}")
-            except Exception as e:
-                print(f"❌ 保存结果失败: {e}")
-        return result
-    finally:
-        await connector.close()
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search")
-    parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate")
-    parser.add_argument("--group_id", type=str, default=None, help="Group ID for retrieval")
-    parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query")
-    parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context")
-    parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature")
-    parser.add_argument("--llm_max_tokens", type=int, default=32, help="LLM max tokens")
-    parser.add_argument("--search_type", type=str, default="embedding", choices=["keyword", "embedding", "hybrid"], help="Search type")
-    parser.add_argument("--output_path", type=str, default=None, help="Output path for results")
-    parser.add_argument("--skip_ingest_if_exists", action="store_true", help="Skip ingest if group exists")
-    parser.add_argument("--llm_timeout", type=float, default=10.0, help="LLM timeout in seconds")
-    parser.add_argument("--llm_max_retries", type=int, default=1, help="LLM max retries")
-    args = parser.parse_args()
-
-    load_dotenv()
-
-    result = asyncio.run(run_locomo_eval(
-        sample_size=args.sample_size,
-        group_id=args.group_id,
-        search_limit=args.search_limit,
-        context_char_budget=args.context_char_budget,
-        llm_temperature=args.llm_temperature,
-        llm_max_tokens=args.llm_max_tokens,
-        search_type=args.search_type,
-        output_path=args.output_path,
-        skip_ingest_if_exists=args.skip_ingest_if_exists,
-        llm_timeout=args.llm_timeout,
-        llm_max_retries=args.llm_max_retries
-    ))
-
-    print("\n" + "="*50)
-    print("📊 最终评测结果:")
-    print(f"   样本数量: {result['items']}")
-    print(f"   F1: {result['metrics']['f1']:.3f}")
-    print(f"   BLEU-1: {result['metrics']['b1']:.3f}")
-    print(f"   Jaccard: {result['metrics']['j']:.3f}")
-    print(f"   LoCoMo F1: {result['metrics']['loc_f1']:.3f}")
-    print(f"   平均上下文长度: {result['context']['avg_chars']:.0f} 字符")
-    print(f"   平均检索延迟: {result['latency']['search']['mean']:.1f}ms")
-    print(f"   平均LLM延迟: {result['latency']['llm']['mean']:.1f}ms")
-
-    if result['by_category']:
-        print("\n📈 按类别细分:")
-        for cat, metrics in result['by_category'].items():
-            print(f"   {cat}:")
-            print(f"     样本数: {metrics['count']}")
-            print(f"     F1: {metrics['f1']:.3f}")
-            print(f"     LoCoMo F1: {metrics['loc_f1']:.3f}")
-            print(f"     Jaccard: {metrics['j']:.3f} (±{metrics['j_std']:.3f}, IQR={metrics['j_iqr']:.3f})")
-
-
-if __name__ == "__main__":
-    main()

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

@@ -1,324 +0,0 @@
-import argparse
-import asyncio
-import json
-import os
-import time
-from datetime import datetime
-from typing import TYPE_CHECKING, Any, Dict, List
-
-if TYPE_CHECKING:
-    from app.schemas.memory_config_schema import MemoryConfig
-
-try:
-    from dotenv import load_dotenv
-except Exception:
-    def load_dotenv():
-        return None
-
-from app.core.memory.evaluation.common.metrics import (
-    avg_context_tokens,
-    exact_match,
-    latency_stats,
-)
-from app.core.memory.evaluation.extraction_utils import (
-    ingest_contexts_via_full_pipeline,
-)
-from app.core.memory.storage_services.search import run_hybrid_search
-from app.core.memory.utils.config.definitions import (
-    PROJECT_ROOT,
-    SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
-    SELECTED_LLM_ID,
-)
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.db import get_db_context
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-
-
-def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str:
-    """基于问题关键词对上下文进行评分选择，并在预算内拼接文本。"""
-    if not contexts:
-        return ""
-    import re
-    # 提取问题关键词（移除停用词）
-    question_lower = (question or "").lower()
-    stop_words = {
-        'what','when','where','who','why','how','did','do','does','is','are','was','were',
-        'the','a','an','and','or','but'
-    }
-    question_words = set(re.findall(r"\b\w+\b", question_lower))
-    question_words = {w for w in question_words if w not in stop_words and len(w) > 2}
-
-    # 评分
-    scored = []
-    for i, ctx in enumerate(contexts):
-        ctx_lower = (ctx or "").lower()
-        score = 0
-        matches = 0
-        for w in question_words:
-            if w in ctx_lower:
-                matches += 1
-                score += ctx_lower.count(w) * 2
-        length = len(ctx)
-        if 100 < length < 2000:
-            score += 5
-        elif length >= 2000:
-            score += 2
-        if i < 3:
-            score += 3
-        scored.append((score, ctx, matches))
-
-    scored.sort(key=lambda x: x[0], reverse=True)
-
-    # 选择直到达到字符限制，必要时截断包含关键词的段落
-    selected: List[str] = []
-    total = 0
-    for score, ctx, _ in scored:
-        if total + len(ctx) <= max_chars:
-            selected.append(ctx)
-            total += len(ctx)
-        else:
-            if score > 10 and total < max_chars - 200:
-                remaining = max_chars - total
-                lines = ctx.split('\n')
-                rel_lines: List[str] = []
-                cur = 0
-                for line in lines:
-                    l = line.lower()
-                    if any(w in l for w in question_words) and cur < remaining - 50:
-                        rel_lines.append(line)
-                        cur += len(line)
-                if rel_lines:
-                    truncated = '\n'.join(rel_lines)
-                    if len(truncated) > 50:
-                        selected.append(truncated + "\n[相关内容截断...]")
-                        total += len(truncated)
-            break
-    return "\n\n".join(selected)
-
-
-def build_context_from_dialog(dialog_obj: Dict[str, Any]) -> str:
-    """Compose a text context from `dialog` list in msc_self_instruct item."""
-    parts: List[str] = []
-    for turn in dialog_obj.get("dialog", []):
-        speaker = turn.get("speaker", "")
-        text = turn.get("text", "")
-        if text:
-            parts.append(f"{speaker}: {text}")
-    return "\n".join(parts)
-
-
-def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any]]:
-    """Combine dialogues from embedding and keyword searches (embedding first)."""
-    if results is None:
-        return []
-    emb = []
-    kw = []
-    if isinstance(results.get("embedding_search"), dict):
-        emb = results.get("embedding_search", {}).get("dialogues", []) or []
-    elif isinstance(results.get("dialogues"), list):
-        emb = results.get("dialogues", []) or []
-    if isinstance(results.get("keyword_search"), dict):
-        kw = results.get("keyword_search", {}).get("dialogues", []) or []
-    seen = set()
-    merged: List[Dict[str, Any]] = []
-    for d in emb:
-        k = (str(d.get("uuid", "")), str(d.get("content", "")))
-        if k not in seen:
-            merged.append(d)
-            seen.add(k)
-    for d in kw:
-        k = (str(d.get("uuid", "")), str(d.get("content", "")))
-        if k not in seen:
-            merged.append(d)
-            seen.add(k)
-    return merged
-
-
-async def run_memsciqa_eval(sample_size: int = 1, 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
-    # Load data
-    data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl")
-    if not os.path.exists(data_path):
-        data_path = os.path.join(os.getcwd(), "data", "msc_self_instruct.jsonl")
-    with open(data_path, "r", encoding="utf-8") as f:
-        lines = f.readlines()
-    items: List[Dict[str, Any]] = [json.loads(l) for l in lines[:sample_size]]
-    # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入
-    # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略
-    contexts: List[str] = [build_context_from_dialog(item) for item in items]
-    await ingest_contexts_via_full_pipeline(contexts, group_id)
-
-    # LLM client (使用异步调用)
-    with get_db_context() as db:
-        factory = MemoryClientFactory(db)
-        llm_client = factory.get_llm_client(SELECTED_LLM_ID)
-
-    # Evaluate each item
-    connector = Neo4jConnector()
-    latencies_llm: List[float] = []
-    latencies_search: List[float] = []
-    contexts_used: List[str] = []
-    correct_flags: List[float] = []
-    f1s: List[float] = []
-    b1s: List[float] = []
-    jss: List[float] = []
-    try:
-        for item in items:
-            question = item.get("self_instruct", {}).get("B", "") or item.get("question", "")
-            reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "")
-            # 检索：对齐 locomo 的三路检索（dialogues/statements/entities）
-            t0 = time.time()
-            try:
-                results = await run_hybrid_search(
-                    query_text=question,
-                    search_type=search_type,
-                    group_id=group_id,
-                    limit=search_limit,
-                    include=["dialogues", "statements", "entities"],
-                    output_path=None,
-                    memory_config=memory_config,
-                )
-            except Exception:
-                results = None
-            t1 = time.time()
-            latencies_search.append((t1 - t0) * 1000)
-
-            # 构建上下文：包含对话、陈述和实体摘要，并智能选择
-            contexts_all: List[str] = []
-            if results:
-                if search_type == "hybrid":
-                    emb = results.get("embedding_search", {}) if isinstance(results.get("embedding_search"), dict) else {}
-                    kw = results.get("keyword_search", {}) if isinstance(results.get("keyword_search"), dict) else {}
-                    emb_dialogs = emb.get("dialogues", [])
-                    emb_statements = emb.get("statements", [])
-                    emb_entities = emb.get("entities", [])
-                    kw_dialogs = kw.get("dialogues", [])
-                    kw_statements = kw.get("statements", [])
-                    kw_entities = kw.get("entities", [])
-                    all_dialogs = emb_dialogs + kw_dialogs
-                    all_statements = emb_statements + kw_statements
-                    all_entities = emb_entities + kw_entities
-
-                    # 简单去重与限制
-                    seen_texts = set()
-                    for d in all_dialogs:
-                        text = str(d.get("content", "")).strip()
-                        if text and text not in seen_texts:
-                            contexts_all.append(text)
-                            seen_texts.add(text)
-                            if len(contexts_all) >= search_limit:
-                                break
-                    for s in all_statements:
-                        text = str(s.get("statement", "")).strip()
-                        if text and text not in seen_texts:
-                            contexts_all.append(text)
-                            seen_texts.add(text)
-                            if len(contexts_all) >= search_limit:
-                                break
-                    # 实体摘要（最多3个）
-                    names = []
-                    merged_entities = all_entities[:]
-                    for e in merged_entities:
-                        name = str(e.get("name", "")).strip()
-                        if name and name not in names:
-                            names.append(name)
-                        if len(names) >= 3:
-                            break
-                    if names:
-                        contexts_all.append("EntitySummary: " + ", ".join(names))
-                else:
-                    dialogs = results.get("dialogues", [])
-                    statements = results.get("statements", [])
-                    entities = results.get("entities", [])
-                    for d in dialogs:
-                        text = str(d.get("content", "")).strip()
-                        if text:
-                            contexts_all.append(text)
-                    for s in statements:
-                        text = str(s.get("statement", "")).strip()
-                        if text:
-                            contexts_all.append(text)
-                    names = [str(e.get("name", "")).strip() for e in entities[:3] if e.get("name")]
-                    if names:
-                        contexts_all.append("EntitySummary: " + ", ".join(names))
-
-            # 智能选择并截断到预算
-            context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) if contexts_all else ""
-            if not context_text:
-                context_text = "No relevant context found."
-            contexts_used.append(context_text[:200])
-
-            # Call LLM (使用异步调用)
-            messages = [
-                {"role": "system", "content": "You are a QA assistant. Answer in English. Strictly follow: 1) If the context contains the answer, copy the shortest exact span from the context as the answer; 2) If the answer cannot be determined from the context, respond with 'Unknown'; 3) Return ONLY the answer text, no explanations."},
-                {"role": "user", "content": f"Question: {question}\n\nContext:\n{context_text}"},
-            ]
-            t2 = time.time()
-            resp = await llm_client.chat(messages=messages)
-            t3 = time.time()
-            latencies_llm.append((t3 - t2) * 1000)
-            pred = resp.content.strip() if hasattr(resp, 'content') else (resp["choices"][0]["message"]["content"].strip() if isinstance(resp, dict) else str(resp).strip())
-            # Metrics: F1, BLEU-1, Jaccard; keep exact match for reference
-            correct_flags.append(exact_match(pred, reference))
-            from app.core.memory.evaluation.common.metrics import (
-                bleu1,
-                f1_score,
-                jaccard,
-            )
-            f1s.append(f1_score(str(pred), str(reference)))
-            b1s.append(bleu1(str(pred), str(reference)))
-            jss.append(jaccard(str(pred), str(reference)))
-
-        # Aggregate metrics
-        acc = sum(correct_flags) / max(len(correct_flags), 1)
-        ctx_avg_tokens = avg_context_tokens(contexts_used)
-        result = {
-            "dataset": "memsciqa",
-            "items": len(items),
-            "metrics": {
-                "accuracy": acc,
-                # Placeholders for extensibility
-                "f1": (sum(f1s) / max(len(f1s), 1)) if f1s else 0.0,
-                "bleu1": (sum(b1s) / max(len(b1s), 1)) if b1s else 0.0,
-                "jaccard": (sum(jss) / max(len(jss), 1)) if jss else 0.0,
-            },
-            "latency": {
-                "search": latency_stats(latencies_search),
-                "llm": latency_stats(latencies_llm),
-            },
-            "avg_context_tokens": ctx_avg_tokens,
-        }
-        return result
-    finally:
-        await connector.close()
-
-
-def main():
-    load_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("--search-limit", type=int, default=8, help="每类检索最大返回数")
-    parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算")
-    parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度")
-    parser.add_argument("--llm-max-tokens", type=int, default=64, help="LLM 最大生成长度")
-    parser.add_argument("--search-type", type=str, choices=["keyword","embedding","hybrid"], default="hybrid", help="检索类型")
-    args = parser.parse_args()
-
-    result = asyncio.run(
-        run_memsciqa_eval(
-            sample_size=args.sample_size,
-            group_id=args.group_id,
-            search_limit=args.search_limit,
-            context_char_budget=args.context_char_budget,
-            llm_temperature=args.llm_temperature,
-            llm_max_tokens=args.llm_max_tokens,
-            search_type=args.search_type,
-        )
-    )
-    print(json.dumps(result, ensure_ascii=False, indent=2))
-
-
-if __name__ == "__main__":
-    main()

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

@@ -1,576 +0,0 @@
-import argparse
-import asyncio
-import json
-import os
-import re
-import time
-from datetime import datetime
-from typing import Any, Dict, List
-
-try:
-    from dotenv import load_dotenv
-except Exception:
-    def load_dotenv():
-        return None
-
-# 路径与模块导入保持与现有评估脚本一致
-import sys
-
-_THIS_DIR = os.path.dirname(os.path.abspath(__file__))
-_PROJECT_ROOT = os.path.dirname(os.path.dirname(_THIS_DIR))
-_SRC_DIR = os.path.join(_PROJECT_ROOT, "src")
-for _p in (_SRC_DIR, _PROJECT_ROOT):
-    if _p not in sys.path:
-        sys.path.insert(0, _p)
-
-# 对齐 locomo_test 的检索逻辑：直接使用 graph_search 与 Neo4jConnector/Embedder1
-from app.core.memory.evaluation.common.metrics import (
-    avg_context_tokens,
-    exact_match,
-    latency_stats,
-)
-from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
-from app.core.memory.utils.config.definitions import (
-    PROJECT_ROOT,
-    SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
-    SELECTED_LLM_ID,
-)
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.core.models.base import RedBearModelConfig
-from app.db import get_db_context
-from app.repositories.neo4j.graph_search import search_graph, search_graph_by_embedding
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-from app.services.memory_config_service import MemoryConfigService
-
-try:
-    from app.core.memory.evaluation.common.metrics import bleu1, f1_score, jaccard
-except Exception:
-    # 兜底：简单实现（必要时）
-    def f1_score(pred: str, ref: str) -> float:
-        ps = pred.lower().split()
-        rs = ref.lower().split()
-        if not ps or not rs:
-            return 0.0
-        tp = len(set(ps) & set(rs))
-        if tp == 0:
-            return 0.0
-        precision = tp / len(ps)
-        recall = tp / len(rs)
-        if precision + recall == 0:
-            return 0.0
-        return 2 * precision * recall / (precision + recall)
-
-    def bleu1(pred: str, ref: str) -> float:
-        ps = pred.lower().split()
-        rs = ref.lower().split()
-        if not ps or not rs:
-            return 0.0
-        overlap = len([w for w in ps if w in rs])
-        return overlap / max(len(ps), 1)
-
-    def jaccard(pred: str, ref: str) -> float:
-        ps = set(pred.lower().split())
-        rs = set(ref.lower().split())
-        union = len(ps | rs)
-        if union == 0:
-            return 0.0
-        return len(ps & rs) / union
-
-
-def smart_context_selection(contexts: List[str], question: str, max_chars: int = 4000) -> str:
-    """基于问题关键词对上下文进行评分选择，并在预算内拼接文本。
-
-    参考 evaluation/memsciqa/evaluate_qa.py 的实现，避免路径导入带来的不稳定。
-    """
-    if not contexts:
-        return ""
-    question_lower = (question or "").lower()
-    stop_words = {
-        'what','when','where','who','why','how','did','do','does','is','are','was','were',
-        'the','a','an','and','or','but'
-    }
-    question_words = set(re.findall(r"\b\w+\b", question_lower))
-    question_words = {w for w in question_words if w not in stop_words and len(w) > 2}
-
-    scored = []
-    for i, ctx in enumerate(contexts):
-        ctx_lower = (ctx or "").lower()
-        score = 0
-        matches = 0
-        for w in question_words:
-            if w in ctx_lower:
-                matches += 1
-                score += ctx_lower.count(w) * 2
-        length = len(ctx)
-        if 100 < length < 2000:
-            score += 5
-        elif length >= 2000:
-            score += 2
-        if i < 3:
-            score += 3
-        scored.append((score, ctx, matches))
-
-    scored.sort(key=lambda x: x[0], reverse=True)
-
-    selected: List[str] = []
-    total = 0
-    for score, ctx, _ in scored:
-        if total + len(ctx) <= max_chars:
-            selected.append(ctx)
-            total += len(ctx)
-        else:
-            if score > 10 and total < max_chars - 200:
-                remaining = max_chars - total
-                lines = ctx.split('\n')
-                rel_lines: List[str] = []
-                cur = 0
-                for line in lines:
-                    l = line.lower()
-                    if any(w in l for w in question_words) and cur < remaining - 50:
-                        rel_lines.append(line)
-                        cur += len(line)
-                if rel_lines:
-                    truncated = '\n'.join(rel_lines)
-                    if len(truncated) > 50:
-                        selected.append(truncated + "\n[相关内容截断...]")
-                        total += len(truncated)
-            break
-    return "\n\n".join(selected)
-
-
-def extract_question_keywords(question: str, max_keywords: int = 8) -> List[str]:
-    """提取问题中的关键词（简单英文分词，去停用词，长度>=3）。"""
-    ql = (question or "").lower()
-    stop_words = {
-        'what','when','where','who','why','how','did','do','does','is','are','was','were',
-        'the','a','an','and','or','but','of','to','in','on','for','with','from','that','this'
-    }
-    words = re.findall(r"\b[\w-]+\b", ql)
-    kws = [w for w in words if w not in stop_words and len(w) >= 3]
-    # 去重保序
-    seen = set()
-    uniq = []
-    for w in kws:
-        if w not in seen:
-            uniq.append(w)
-            seen.add(w)
-        if len(uniq) >= max_keywords:
-            break
-    return uniq
-
-
-def analyze_contexts_simple(contexts: List[str], keywords: List[str], top_n: int = 5) -> List[Dict[str, int | float]]:
-    """对上下文进行简单相关性打分，仅用于控制台可视化。
-
-    评分: score = match_count*200 + min(len(text), 100000)/100
-    """
-    results = []
-    for ctx in contexts:
-        tl = (ctx or "").lower()
-        match_count = sum(1 for k in keywords if k in tl)
-        length = len(ctx)
-        score = match_count * 200 + min(length, 100000) / 100.0
-        results.append({"score": float(f"{score:.0f}"), "match": match_count, "length": length})
-    results.sort(key=lambda x: (x["score"], x["match"], x["length"]), reverse=True)
-    return results[:max(top_n, 0)]
-
-
-# 纯测试脚本不进行摄入；若需摄入请使用 evaluate_qa.py
-
-
-def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]:
-    if not os.path.exists(data_path):
-        raise FileNotFoundError(f"未找到数据集: {data_path}")
-    items: List[Dict[str, Any]] = []
-    with open(data_path, "r", encoding="utf-8") as f:
-        for line in f:
-            line = line.strip()
-            if not line:
-                continue
-            try:
-                items.append(json.loads(line))
-            except Exception:
-                # 跳过坏行但不中断
-                continue
-    return items
-
-
-async def run_memsciqa_test(
-    sample_size: int = 3,
-    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 = "embedding",
-    data_path: str | None = None,
-    start_index: int = 0,
-    verbose: bool = True,
-) -> Dict[str, Any]:
-    """memsciqa 增强测试脚本：结合 evaluate_qa 的三路检索与智能上下文选择。
-
-    - 支持从指定索引开始与评估全部样本（sample_size<=0）
-    - 支持在摄入前重置组（清空图）与跳过摄入
-    - 支持 keyword / embedding / hybrid 三种检索
-    """
-
-    # 默认使用指定的 memsci 组 ID
-    group_id = group_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:
-        proj_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl")
-        cwd_path = os.path.join(os.getcwd(), "data", "msc_self_instruct.jsonl")
-        if os.path.exists(proj_path):
-            data_path = proj_path
-        elif os.path.exists(cwd_path):
-            data_path = cwd_path
-        else:
-            raise FileNotFoundError("未找到数据集: data/msc_self_instruct.jsonl，请确保其存在于项目根目录或当前工作目录的 data 目录下。")
-
-    # 加载数据
-    all_items = load_dataset_memsciqa(data_path)
-    if sample_size is None or sample_size <= 0:
-        items = all_items[start_index:]
-    else:
-        items = all_items[start_index:start_index + sample_size]
-
-    # 初始化 LLM（纯测试：不进行摄入）
-    with get_db_context() as db:
-        factory = MemoryClientFactory(db)
-        llm = factory.get_llm_client(SELECTED_LLM_ID)
-
-    # 初始化 Neo4j 连接与向量检索 Embedder（对齐 locomo_test）
-    connector = Neo4jConnector()
-    embedder = None
-    if search_type in ("embedding", "hybrid"):
-        with get_db_context() as db:
-            config_service = MemoryConfigService(db)
-            cfg_dict = config_service.get_embedder_config(SELECTED_EMBEDDING_ID)
-        embedder = OpenAIEmbedderClient(
-            model_config=RedBearModelConfig.model_validate(cfg_dict)
-        )
-
-    # 评估循环
-    latencies_llm: List[float] = []
-    latencies_search: List[float] = []
-    # 存储完整上下文文本用于统计
-    contexts_used: List[str] = []
-    per_query_context_chars: List[int] = []
-    per_query_context_counts: List[int] = []
-    correct_flags: List[float] = []
-    f1s: List[float] = []
-    b1s: List[float] = []
-    jss: List[float] = []
-    samples: List[Dict[str, Any]] = []
-
-    total_items = len(items)
-    for idx, item in enumerate(items):
-        if verbose:
-            print(f"\n🧪 评估样本: {idx+1}/{total_items}")
-        question = item.get("self_instruct", {}).get("B", "") or item.get("question", "")
-        reference = item.get("self_instruct", {}).get("A", "") or item.get("answer", "")
-
-        # 三路检索：chunks/statements/entities/summaries（对齐 qwen_search_eval.py）
-        t0 = time.time()
-        results = None
-        try:
-            if search_type in ("embedding", "hybrid"):
-                # 使用嵌入检索（与 qwen_search_eval 对齐）
-                results = await search_graph_by_embedding(
-                    connector=connector,
-                    embedder_client=embedder,
-                    query_text=question,
-                    group_id=group_id,
-                    limit=search_limit,
-                    include=["chunks", "statements", "entities", "summaries"],  # 使用 chunks 而不是 dialogues
-                )
-            elif search_type == "keyword":
-                # 关键词检索（直接调用 graph_search）
-                results = await search_graph(
-                    connector=connector,
-                    q=question,
-                    group_id=group_id,
-                    limit=search_limit,
-                    include=["chunks", "statements", "entities", "summaries"],  # 使用 chunks 而不是 dialogues
-                )
-        except Exception:
-            results = None
-        t1 = time.time()
-        search_ms = (t1 - t0) * 1000
-        latencies_search.append(search_ms)
-
-        # 构建上下文：包含 chunks、陈述、摘要和实体（对齐 qwen_search_eval.py）
-        contexts_all: List[str] = []
-        retrieved_counts: Dict[str, int] = {}
-        if results:
-            chunks = results.get("chunks", [])
-            statements = results.get("statements", [])
-            entities = results.get("entities", [])
-            summaries = results.get("summaries", [])
-            retrieved_counts = {
-                "chunks": len(chunks),
-                "statements": len(statements),
-                "entities": len(entities),
-                "summaries": len(summaries),
-            }
-            # 优先使用 chunks
-            for c in chunks:
-                text = str(c.get("content", "")).strip()
-                if text:
-                    contexts_all.append(text)
-            # 然后是 statements
-            for s in statements:
-                text = str(s.get("statement", "")).strip()
-                if text:
-                    contexts_all.append(text)
-            # 然后是 summaries
-            for sm in summaries:
-                text = str(sm.get("summary", "")).strip()
-                if text:
-                    contexts_all.append(text)
-            # 实体摘要：最多加入前3个高分实体（对齐 qwen_search_eval.py）
-            scored = [e for e in entities if e.get("score") is not None]
-            top_entities = sorted(scored, key=lambda x: x.get("score", 0), reverse=True)[:3] if scored else entities[:3]
-            if top_entities:
-                summary_lines = []
-                for e in top_entities:
-                    name = str(e.get("name", "")).strip()
-                    etype = str(e.get("entity_type", "")).strip()
-                    score = e.get("score")
-                    if name:
-                        meta = []
-                        if etype:
-                            meta.append(f"type={etype}")
-                        if isinstance(score, (int, float)):
-                            meta.append(f"score={score:.3f}")
-                        summary_lines.append(f"EntitySummary: {name}{(' [' + '; '.join(meta) + ']') if meta else ''}")
-                if summary_lines:
-                    contexts_all.append("\n".join(summary_lines))
-
-        if verbose:
-            if retrieved_counts:
-                print(f"✅ 检索成功: {retrieved_counts.get('chunks',0)} chunks, {retrieved_counts.get('statements',0)} 条陈述, {retrieved_counts.get('entities',0)} 个实体, {retrieved_counts.get('summaries',0)} 个摘要")
-            print(f"📊 有效上下文数量: {len(contexts_all)}")
-            q_keywords = extract_question_keywords(question, max_keywords=8)
-            if q_keywords:
-                print(f"🔍 问题关键词: {set(q_keywords)}")
-            if contexts_all:
-                analysis = analyze_contexts_simple(contexts_all, q_keywords, top_n=5)
-                if analysis:
-                    print("📊 上下文相关性分析:")
-                    for a in analysis:
-                        print(f"  - 得分: {int(a['score'])}, 关键词匹配: {a['match']}, 长度: {a['length']}")
-                # 打印检索到的上下文预览，便于定位为何为 Unknown
-                print("🔎 上下文预览（最多前10条，每条截断展示）:")
-                for i, ctx in enumerate(contexts_all[:10]):
-                    preview = str(ctx).replace("\n", " ")
-                    if len(preview) > 300:
-                        preview = preview[:300] + "..."
-                    print(f"  [{i+1}] 长度: {len(ctx)} | 片段: {preview}")
-                # 标注参考答案是否出现在任一上下文中
-                ref_lower = (str(reference) or "").lower()
-                if ref_lower:
-                    hits = []
-                    for i, ctx in enumerate(contexts_all):
-                        if ref_lower in str(ctx).lower():
-                            hits.append(i+1)
-                    print(f"🔗 参考答案命中上下文条数: {len(hits)}" + (f" | 命中索引: {hits}" if hits else ""))
-
-        context_text = smart_context_selection(contexts_all, question, max_chars=context_char_budget) if contexts_all else ""
-        if not context_text:
-            context_text = "No relevant context found."
-        contexts_used.append(context_text)
-        per_query_context_chars.append(len(context_text))
-        per_query_context_counts.append(len(contexts_all))
-
-        if verbose:
-            selected_count = (context_text.count("\n\n") + 1) if context_text else 0
-            print(f"✅ 智能选择: {selected_count}个上下文, 总长度: {len(context_text)}字符")
-            # 展示拼接后的上下文片段，便于核查是否包含答案
-            concat_preview = context_text.replace("\n", " ")
-            if len(concat_preview) > 600:
-                concat_preview = concat_preview[:600] + "..."
-            print(f"🧵 拼接上下文预览: {concat_preview}")
-
-        messages = [
-            {
-                "role": "system",
-                "content": (
-                    "You are a QA assistant. Answer in English. Follow these guidelines:\n"
-                    "1) If the context contains information to answer the question, provide a concise answer based on the context;\n"
-                    "2) If the context does not contain enough information to answer the question, respond with 'Unknown';\n"
-                    "3) Keep your answer brief and to the point;\n"
-                    "4) Do not add explanations or additional text beyond the answer."
-                ),
-            },
-            {"role": "user", "content": f"Question: {question}\n\nContext:\n{context_text}"},
-        ]
-
-        t2 = time.time()
-        try:
-            # 使用异步调用
-            resp = await llm.chat(messages=messages)
-            # 更健壮的响应解析，处理不同的LLM响应格式
-            if hasattr(resp, 'content'):
-                pred = resp.content.strip()
-            elif isinstance(resp, dict) and "choices" in resp and len(resp["choices"]) > 0:
-                pred = resp["choices"][0]["message"]["content"].strip()
-            elif isinstance(resp, dict) and "content" in resp:
-                pred = resp["content"].strip()
-            elif isinstance(resp, str):
-                pred = resp.strip()
-            else:
-                pred = "Unknown"
-                print(f"⚠️  LLM响应格式异常: {type(resp)} - {resp}")
-
-            # 检查预测是否为"Unknown"或空，如果是则检查上下文是否真的没有答案
-            if pred.lower() in ["unknown", ""]:
-                # 如果参考答案在上下文中存在，但LLM返回Unknown，可能是提示词问题
-                ref_lower = (str(reference) or "").lower()
-                if ref_lower and any(ref_lower in ctx.lower() for ctx in contexts_all):
-                    print("⚠️  参考答案在上下文中存在但LLM返回Unknown，检查提示词")
-        except Exception as e:
-            # 更详细的错误处理
-            pred = "Unknown"
-            print(f"⚠️  LLM调用异常: {e}")
-        t3 = time.time()
-        llm_ms = (t3 - t2) * 1000
-        latencies_llm.append(llm_ms)
-
-        exact = exact_match(pred, reference)
-        correct_flags.append(exact)
-        f1_val = f1_score(str(pred), str(reference))
-        b1_val = bleu1(str(pred), str(reference))
-        j_val = jaccard(str(pred), str(reference))
-        f1s.append(f1_val)
-        b1s.append(b1_val)
-        jss.append(j_val)
-
-        if verbose:
-            print(f"🤖 LLM 回答: {pred}")
-            print(f"✅ 正确答案: {reference}")
-            print(f"📈 当前指标 - F1: {f1_val:.3f}, BLEU-1: {b1_val:.3f}, Jaccard: {j_val:.3f}")
-            print(f"⏱️ 延迟 - 检索: {search_ms:.0f}ms, LLM: {llm_ms:.0f}ms")
-
-        # 对齐 locomo/qwen_search_eval.py 的样本输出结构
-        samples.append({
-            "question": str(question),
-            "answer": str(reference),
-            "prediction": str(pred),
-            "metrics": {
-                "f1": f1_val,
-                "b1": b1_val,
-                "j": j_val
-            },
-            "retrieval": {
-                "retrieved_documents": len(contexts_all),
-                "context_length": len(context_text),
-                "search_limit": search_limit,
-                "max_chars": context_char_budget
-            },
-            "timing": {
-                "search_ms": search_ms,
-                "llm_ms": llm_ms
-            }
-        })
-
-    # 计算总体指标与聚合
-    acc = sum(correct_flags) / max(len(correct_flags), 1)
-    ctx_avg_tokens = avg_context_tokens(contexts_used)
-    result = {
-        "dataset": "memsciqa",
-        "items": len(items),
-        "metrics": {
-            "f1": (sum(f1s) / max(len(f1s), 1)) if f1s else 0.0,
-            "b1": (sum(b1s) / max(len(b1s), 1)) if b1s else 0.0,
-            "j": (sum(jss) / max(len(jss), 1)) if jss else 0.0,
-        },
-        "context": {
-            "avg_tokens": ctx_avg_tokens,
-            "avg_chars": (sum(per_query_context_chars) / max(len(per_query_context_chars), 1)) if per_query_context_chars else 0.0,
-            "count_avg": (sum(per_query_context_counts) / max(len(per_query_context_counts), 1)) if per_query_context_counts else 0.0,
-            "avg_memory_tokens": 0.0
-        },
-        "latency": {
-            "search": latency_stats(latencies_search),
-            "llm": latency_stats(latencies_llm),
-        },
-        "samples": samples,
-        "params": {
-            "group_id": group_id,
-            "search_limit": search_limit,
-            "context_char_budget": context_char_budget,
-            "llm_temperature": llm_temperature,
-            "llm_max_tokens": llm_max_tokens,
-            "search_type": search_type,
-            "start_index": start_index,
-            "llm_id": SELECTED_LLM_ID,
-            "retrieval_embedding_id": SELECTED_EMBEDDING_ID
-        },
-        "timestamp": datetime.now().isoformat(),
-    }
-    try:
-        await connector.close()
-    except Exception:
-        pass
-    return result
-
-
-def main():
-    load_dotenv()
-    parser = argparse.ArgumentParser(description="memsciqa 测试脚本（三路检索 + 智能上下文选择）")
-    parser.add_argument("--sample-size", type=int, default=30, help="样本数量（<=0 表示全部）")
-    parser.add_argument("--all", action="store_true", help="评估全部样本（覆盖 --sample-size）")
-    parser.add_argument("--start-index", type=int, default=0, help="起始样本索引")
-    parser.add_argument("--group-id", type=str, default="group_memsci", help="图数据库 Group ID（默认 group_memsci）")
-    parser.add_argument("--search-limit", type=int, default=8, help="检索条数上限")
-    parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算")
-    parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度")
-    parser.add_argument("--llm-max-tokens", type=int, default=64, help="LLM 最大输出 token")
-    parser.add_argument("--search-type", type=str, default="embedding", choices=["embedding","keyword","hybrid"], help="检索类型（hybrid 等同于 embedding）")
-    parser.add_argument("--data-path", type=str, default=None, help="数据集路径（默认 data/msc_self_instruct.jsonl）")
-    parser.add_argument("--output", type=str, default=None, help="将评估结果保存到指定文件路径（JSON）")
-    parser.add_argument("--verbose", action="store_true", default=True, help="打印过程日志（默认开启）")
-    parser.add_argument("--quiet", action="store_true", help="关闭过程日志")
-    args = parser.parse_args()
-
-    sample_size = 0 if args.all else args.sample_size
-
-    verbose_flag = False if args.quiet else args.verbose
-    result = asyncio.run(
-        run_memsciqa_test(
-            sample_size=sample_size,
-            group_id=args.group_id,
-            search_limit=args.search_limit,
-            context_char_budget=args.context_char_budget,
-            llm_temperature=args.llm_temperature,
-            llm_max_tokens=args.llm_max_tokens,
-            search_type=args.search_type,
-            data_path=args.data_path,
-            start_index=args.start_index,
-            verbose=verbose_flag,
-        )
-    )
-
-    print(json.dumps(result, ensure_ascii=False, indent=2))
-
-    # 结果保存
-    out_path = args.output
-    if not out_path:
-        eval_dir = os.path.dirname(os.path.abspath(__file__))
-        dataset_results_dir = os.path.join(eval_dir, "results")
-        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-        out_path = os.path.join(dataset_results_dir, f"memsciqa_{result['params']['search_type']}_{ts}.json")
-    try:
-        os.makedirs(os.path.dirname(out_path), exist_ok=True)
-        with open(out_path, "w", encoding="utf-8") as f:
-            json.dump(result, f, ensure_ascii=False, indent=2)
-        print(f"\n💾 结果已保存: {out_path}")
-    except Exception as e:
-        print(f"⚠️ 结果保存失败: {e}")
-
-
-if __name__ == "__main__":
-    main()

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

@@ -1,150 +0,0 @@
-import argparse
-import asyncio
-import json
-import os
-import sys
-from typing import Any, Dict
-
-# Add src directory to Python path for proper imports when running from evaluation directory
-sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'src'))
-
-try:
-    from dotenv import load_dotenv
-except Exception:
-    def load_dotenv():
-        return None
-
-from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-from app.core.memory.utils.config.definitions import SELECTED_GROUP_ID, PROJECT_ROOT
-
-from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval
-from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test
-from app.core.memory.evaluation.locomo.qwen_search_eval import run_locomo_eval
-
-
-async def run(
-    dataset: str,
-    sample_size: int,
-    reset_group: bool,
-    group_id: str | None,
-    judge_model: str | None = None,
-    search_limit: int | None = None,
-    context_char_budget: int | None = None,
-    llm_temperature: float | None = None,
-    llm_max_tokens: int | None = None,
-    search_type: str | None = None,
-    start_index: int | None = None,
-    max_contexts_per_item: int | None = None,
-) -> Dict[str, Any]:
-    # 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
-
-    if reset_group:
-        connector = Neo4jConnector()
-        try:
-            await connector.delete_group(group_id)
-        finally:
-            await connector.close()
-
-    if dataset == "locomo":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
-        if search_limit is not None:
-            kwargs["search_limit"] = search_limit
-        if context_char_budget is not None:
-            kwargs["context_char_budget"] = context_char_budget
-        if llm_temperature is not None:
-            kwargs["llm_temperature"] = llm_temperature
-        if llm_max_tokens is not None:
-            kwargs["llm_max_tokens"] = llm_max_tokens
-        if search_type is not None:
-            kwargs["search_type"] = search_type
-        return await run_locomo_eval(**kwargs)
-
-    if dataset == "memsciqa":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
-        if search_limit is not None:
-            kwargs["search_limit"] = search_limit
-        if context_char_budget is not None:
-            kwargs["context_char_budget"] = context_char_budget
-        if llm_temperature is not None:
-            kwargs["llm_temperature"] = llm_temperature
-        if llm_max_tokens is not None:
-            kwargs["llm_max_tokens"] = llm_max_tokens
-        if search_type is not None:
-            kwargs["search_type"] = search_type
-        return await run_memsciqa_eval(**kwargs)
-
-    if dataset == "longmemeval":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
-        if search_limit is not None:
-            kwargs["search_limit"] = search_limit
-        if context_char_budget is not None:
-            kwargs["context_char_budget"] = context_char_budget
-        if llm_temperature is not None:
-            kwargs["llm_temperature"] = llm_temperature
-        if llm_max_tokens is not None:
-            kwargs["llm_max_tokens"] = llm_max_tokens
-        if search_type is not None:
-            kwargs["search_type"] = search_type
-        if start_index is not None:
-            kwargs["start_index"] = start_index
-        if max_contexts_per_item is not None:
-            kwargs["max_contexts_per_item"] = max_contexts_per_item
-        return await run_longmemeval_test(**kwargs)
-    raise ValueError(f"未知数据集: {dataset}")
-
-
-def main():
-    load_dotenv()
-    parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo")
-    parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True)
-    parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通")
-    parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 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("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名")
-    parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）")
-    parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）")
-    parser.add_argument("--llm-temperature", type=float, default=None, help="生成温度（不提供则使用各脚本默认）")
-    parser.add_argument("--llm-max-tokens", type=int, default=None, help="最大生成 tokens（不提供则使用各脚本默认）")
-    parser.add_argument("--search-type", type=str, default=None, choices=["keyword", "embedding", "hybrid"], help="检索类型（不提供则使用各脚本默认）")
-    # 仅透传到 longmemeval；其他数据集忽略
-    parser.add_argument("--start-index", type=int, default=None, help="仅 longmemeval：起始样本索引（不提供则用脚本默认）")
-    parser.add_argument("--max-contexts-per-item", type=int, default=None, help="仅 longmemeval：每条样本摄入的上下文数量上限（不提供则用脚本默认）")
-    parser.add_argument("--output", type=str, default=None, help="可选：将评估结果保存到指定文件路径（JSON）；不提供时默认保存到 evaluation/<dataset>/results 目录")
-    args = parser.parse_args()
-
-    result = asyncio.run(run(
-        args.dataset,
-        args.sample_size,
-        args.reset_group,
-        args.group_id,
-        args.judge_model,
-        args.search_limit,
-        args.context_char_budget,
-        args.llm_temperature,
-        args.llm_max_tokens,
-        args.search_type,
-        args.start_index,
-        args.max_contexts_per_item,
-    ))
-    print(json.dumps(result, ensure_ascii=False, indent=2))
-
-    # 结果输出逻辑保持不变
-    if args.output:
-        out_path = args.output
-    else:
-        eval_dir = os.path.dirname(os.path.abspath(__file__))
-        dataset_results_dir = os.path.join(eval_dir, args.dataset, "results")
-        out_filename = f"{args.dataset}_{args.sample_size}.json"
-        out_path = os.path.join(dataset_results_dir, out_filename)
-
-    out_dir = os.path.dirname(out_path)
-    if out_dir and not os.path.exists(out_dir):
-        os.makedirs(out_dir, exist_ok=True)
-    with open(out_path, "w", encoding="utf-8") as f:
-        json.dump(result, f, ensure_ascii=False, indent=2)
-    print(f"\n结果已保存到: {out_path}")
-
-
-if __name__ == "__main__":
-    main()

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
+        end_user_id: End user ID for multi-tenancy
-        user_id: User ID for user-specific data
-        apply_id: Application ID for application-specific data
         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.")
+    end_user_id: str = Field(..., description="The end user 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.")
     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
+        end_user_id: End user ID for multi-tenancy
-        user_id: User ID for user-specific data
-        apply_id: Application ID for application-specific data
         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.")
+    end_user_id: str = Field(..., description="The end user 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.")
     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
+        end_user_id: End user ID for multi-tenancy
-        user_id: User ID for user-specific data
-        apply_id: Application ID for application-specific data
         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")
+    end_user_id: str = Field(default=..., description="End user ID of dialogue data")
-    user_id: str = Field(..., description="USER ID of dialogue data")
-    apply_id: str = Field(..., description="APPLY 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:
+                if statement.end_user_id is None:
-                    statement.group_id = self.group_id
+                    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",
+    end_user_id: Optional[str] = "test",
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
     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,
+        "end_user_id": end_user_id,
-        "apply_id": apply_id,
-        "user_id": 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,
+        end_user_id=params.end_user_id,
-        apply_id=params.apply_id,
-        user_id=params.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",
+    end_user_id: Optional[str] = "test",
-    apply_id: Optional[str] = None,
-    user_id: Optional[str] = None,
     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,
+        "end_user_id": end_user_id,
-        "apply_id": apply_id,
-        "user_id": 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,
+        end_user_id=params.end_user_id,
-        apply_id=params.apply_id,
-        user_id=params.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
+                # 获取end_user_id，如果不存在则生成默认值
-                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}')
 
@@ -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}"
+        key = f"{getattr(ent, 'end_user_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
+        # 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）
+            # 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, "group_id", None) != getattr(b, "group_id", None):
+            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
+    # 先探测 end_user_id，决定报告写入策略
-    group_id: Optional[str] = None
+    end_user_id: Optional[str] = None
     for dd in dialog_data_list:
-        group_id = getattr(dd, "group_id", None)
+        end_user_id = getattr(dd, "end_user_id", None)
-        if group_id:
+        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
+        # 加载MemoryConfig
-        data_config = None
+        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,
+                end_user_id=dialog_data.end_user_id,
-                user_id=dialog_data.user_id,
-                apply_id=dialog_data.apply_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,
+                    end_user_id=dialog_data.end_user_id,
-                    user_id=dialog_data.user_id,
-                    apply_id=dialog_data.apply_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,
+                        end_user_id=dialog_data.end_user_id,
-                        user_id=dialog_data.user_id,
-                        apply_id=dialog_data.apply_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,
+                        end_user_id=dialog_data.end_user_id,
-                        user_id=dialog_data.user_id,
-                        apply_id=dialog_data.apply_id,
                         run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                         created_at=dialog_data.created_at,
                     )
@@ -1072,13 +1064,16 @@ class ExtractionOrchestrator:
                     if statement.triplet_extraction_info:
                         triplet_info = statement.triplet_extraction_info
 
-                        # 创建实体索引到ID的映射
+                        # 创建实体索引到ID的映射（支持多种索引方式）
                         entity_idx_to_id = {}
                         
                         # 创建实体节点
                         for entity_idx, entity in enumerate(triplet_info.entities):
-                            # 映射实体索引到实体ID
+                            # 映射实体索引到实体ID（使用多个键以提高容错性）
+                            # 1. 使用实体自己的 entity_idx
                             entity_idx_to_id[entity.entity_idx] = entity.id
+                            # 2. 使用枚举索引（从0开始）
+                            entity_idx_to_id[entity_idx] = entity.id
                             
                             if entity.id not in entity_id_set:
                                 entity_connect_strength = getattr(entity, 'connect_strength', 'Strong')
@@ -1095,9 +1090,7 @@ 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,
+                                    end_user_id=dialog_data.end_user_id,
-                                    user_id=dialog_data.user_id,
-                                    apply_id=dialog_data.apply_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 +1105,7 @@ 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,
+                                end_user_id=dialog_data.end_user_id,
-                                user_id=dialog_data.user_id,
-                                apply_id=dialog_data.apply_id,
                                 run_id=dialog_data.run_id,  # 使用 dialog_data 的 run_id
                                 created_at=dialog_data.created_at,
                             )
@@ -1134,9 +1125,7 @@ class ExtractionOrchestrator:
                                     relation_type=triplet.predicate,
                                     statement=statement.statement,
                                     source_statement_id=statement.id,
-                                    group_id=dialog_data.group_id,
+                                    end_user_id=dialog_data.end_user_id,
-                                    user_id=dialog_data.user_id,
-                                    apply_id=dialog_data.apply_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,
@@ -1163,9 +1152,18 @@ class ExtractionOrchestrator:
                                         relationship_result
                                     )
                             else:
-                                logger.warning(
+                                # 改进的警告信息，包含更多调试信息
-                                    f"跳过三元组 - 无法找到实体ID: subject_id={triplet.subject_id}, "
+                                missing_subject = "subject" if not subject_entity_id else ""
-                                    f"object_id={triplet.object_id}, statement_id={statement.id}"
+                                missing_object = "object" if not object_entity_id else ""
+                                missing_both = " and " if (not subject_entity_id and not object_entity_id) else ""
+                                
+                                logger.debug(
+                                    f"跳过三元组 - 无法找到{missing_subject}{missing_both}{missing_object}实体ID: "
+                                    f"subject_id={triplet.subject_id} ({triplet.subject_name}), "
+                                    f"object_id={triplet.object_id} ({triplet.object_name}), "
+                                    f"predicate={triplet.predicate}, "
+                                    f"statement_id={statement.id}, "
+                                    f"available_indices={sorted(entity_idx_to_id.keys())}"
                                 )
 
         logger.info(
@@ -1763,14 +1761,14 @@ 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 +1832,7 @@ 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 +1934,7 @@ 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 +1946,7 @@ 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 +1974,9 @@ 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.end_user_id = end_user_id
-        dd.user_id = user_id
-        dd.apply_id = apply_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,
+            end_user_id=dialog.end_user_id,
-            user_id=dialog.user_id,
+            user_id=dialog.end_user_id,
-            apply_id=dialog.apply_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:
+        if end_user_id:
-            query += " AND n.group_id = $group_id"
+            query += " AND n.end_user_id = $end_user_id"
         query += """
         RETURN n.id as id
         LIMIT $limit
         """
         
         params = {"limit": limit}
-        if group_id:
+        if end_user_id:
-            params["group_id"] = group_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:
+            if end_user_id:
-                query += " WHERE n.group_id = $group_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:
+            if end_user_id:
-                params['group_id'] = group_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:
+        if end_user_id:
-            query += " WHERE n.group_id = $group_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:
+        if end_user_id:
-            params['group_id'] = group_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:
+            if end_user_id:
-                read_query += " WHERE n.group_id = $group_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:
+            if end_user_id:
-                read_params['group_id'] = group_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:
+            if end_user_id:
-                where_conditions.append("n.group_id = $group_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:
+            if end_user_id:
-                update_params['group_id'] = group_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:
+        if end_user_id:
-            query += " AND n.group_id = $group_id"
+            query += " AND n.end_user_id = $end_user_id"
         
         query += """
         RETURN count(n) as total
         """
         
         params = {}
-        if group_id:
+        if end_user_id:
-            params['group_id'] = group_id
+            params['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:
+        if end_user_id:
-            query += " AND s.group_id = $group_id AND e.group_id = $group_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:
+        if end_user_id:
-            params['group_id'] = group_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）
+        # 获取 end_user_id（从 statement 或 entity 节点）
-        group_id = statement_node.get('group_id') or entity_node.get('group_id')
+        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/models/scripts/__init__.py

@@ -0,0 +1 @@
+"""模型配置脚本模块"""

api/app/core/models/scripts/bedrock_models.yaml

@@ -0,0 +1,174 @@
+provider: bedrock
+enabled: true
+models:
+- name: ai21
+  type: llm
+  provider: bedrock
+  description: AI21 Labs大语言模型，completion生成模式，256000上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  logo: bedrock
+- name: amazon nova
+  type: llm
+  provider: bedrock
+  description: Amazon Nova大语言模型，支持智能体思考、工具调用、流式工具调用、视觉能力，300000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+  logo: bedrock
+- name: anthropic claude
+  type: llm
+  provider: bedrock
+  description: Anthropic Claude大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用、文档处理，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  logo: bedrock
+- name: cohere
+  type: llm
+  provider: bedrock
+  description: Cohere大语言模型，支持智能体思考、工具调用、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: deepseek
+  type: llm
+  provider: bedrock
+  description: DeepSeek大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用，32768上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: meta
+  type: llm
+  provider: bedrock
+  description: Meta Llama大语言模型，支持智能体思考、工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  logo: bedrock
+- name: mistral
+  type: llm
+  provider: bedrock
+  description: Mistral AI大语言模型，支持智能体思考、工具调用，32000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  logo: bedrock
+- name: openai
+  type: llm
+  provider: bedrock
+  description: OpenAI大语言模型，支持智能体思考、工具调用、流式工具调用，32768上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: qwen
+  type: llm
+  provider: bedrock
+  description: Qwen大语言模型，支持智能体思考、工具调用、流式工具调用，32768上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  logo: bedrock
+- name: amazon.rerank-v1:0
+  type: rerank
+  provider: bedrock
+  description: amazon.rerank-v1:0重排序模型，5120上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: bedrock
+- name: cohere.rerank-v3-5:0
+  type: rerank
+  provider: bedrock
+  description: cohere.rerank-v3-5:0重排序模型，5120上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: bedrock
+- name: amazon.nova-2-multimodal-embeddings-v1:0
+  type: embedding
+  provider: bedrock
+  description: amazon.nova-2-multimodal-embeddings-v1:0文本嵌入模型，支持视觉能力，8192上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  - vision
+  logo: bedrock
+- name: amazon.titan-embed-text-v1
+  type: embedding
+  provider: bedrock
+  description: amazon.titan-embed-text-v1文本嵌入模型，8192上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock
+- name: amazon.titan-embed-text-v2:0
+  type: embedding
+  provider: bedrock
+  description: amazon.titan-embed-text-v2:0文本嵌入模型，8192上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock
+- name: cohere.embed-english-v3
+  type: embedding
+  provider: bedrock
+  description: Cohere Embed 3 English文本嵌入模型，512上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock
+- name: cohere.embed-multilingual-v3
+  type: embedding
+  provider: bedrock
+  description: Cohere Embed 3 Multilingual文本嵌入模型，512上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本嵌入模型
+  logo: bedrock

api/app/core/models/scripts/dashscope_models.yaml

@@ -0,0 +1,820 @@
+provider: dashscope
+enabled: true
+models:
+- name: deepseek-r1-distill-qwen-14b
+  type: llm
+  provider: dashscope
+  description: DeepSeek-R1-Distill-Qwen-14B大语言模型，支持智能体思考，32000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-r1-distill-qwen-32b
+  type: llm
+  provider: dashscope
+  description: DeepSeek-R1-Distill-Qwen-32B大语言模型，支持智能体思考，32000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-r1
+  type: llm
+  provider: dashscope
+  description: DeepSeek-R1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3.1
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3.1大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3.2-exp
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3.2-exp实验版大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3.2
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3.2大语言模型，支持智能体思考，131072超大上下文窗口，对话模式，支持丰富生成参数调节
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: deepseek-v3
+  type: llm
+  provider: dashscope
+  description: DeepSeek-V3大语言模型，支持智能体思考，64000上下文窗口，对话模式，支持文本与JSON格式输出
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: dashscope
+- name: farui-plus
+  type: llm
+  provider: dashscope
+  description: farui-plus大语言模型，支持多工具调用、智能体思考、流式工具调用，12288上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: glm-4.7
+  type: llm
+  provider: dashscope
+  description: GLM-4.7大语言模型，支持多工具调用、智能体思考、流式工具调用，202752超大上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qvq-max-latest
+  type: llm
+  provider: dashscope
+  description: qvq-max-latest大语言模型，支持视觉、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - vision
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qvq-max
+  type: llm
+  provider: dashscope
+  description: qvq-max大语言模型，支持视觉、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - vision
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-coder-turbo-0919
+  type: llm
+  provider: dashscope
+  description: qwen-coder-turbo-0919代码专用大语言模型，支持智能体思考，131072上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen-max-latest
+  type: llm
+  provider: dashscope
+  description: qwen-max-latest大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-max-longcontext
+  type: llm
+  provider: dashscope
+  description: qwen-max-longcontext长上下文大语言模型，支持多工具调用、智能体思考、流式工具调用，32000上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-max
+  type: llm
+  provider: dashscope
+  description: qwen-max大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-mt-plus
+  type: llm
+  provider: dashscope
+  description: qwen-mt-plus多语言翻译大语言模型，支持智能体思考，16384上下文窗口，对话模式，支持多语种互译与领域翻译适配
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 翻译模型
+  - agent-thought
+  logo: dashscope
+- name: qwen-mt-turbo
+  type: llm
+  provider: dashscope
+  description: qwen-mt-turbo轻量化多语言翻译大语言模型，支持智能体思考，16384上下文窗口，对话模式，支持多语种互译与领域翻译适配
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 翻译模型
+  - agent-thought
+  logo: dashscope
+- name: qwen-plus-0112
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0112大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0125
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0125大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0723
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0723大语言模型，支持多工具调用、智能体思考、流式工具调用，32000上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0806
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0806大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-0919
+  type: llm
+  provider: dashscope
+  description: qwen-plus-0919大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-1125
+  type: llm
+  provider: dashscope
+  description: qwen-plus-1125大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-1127
+  type: llm
+  provider: dashscope
+  description: qwen-plus-1127大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，支持联网搜索，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-plus-1220
+  type: llm
+  provider: dashscope
+  description: qwen-plus-1220大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen-vl-max
+  type: llm
+  provider: dashscope
+  description: qwen-vl-max多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-0809
+  type: llm
+  provider: dashscope
+  description: qwen-vl-plus-0809多模态大模型，支持视觉理解、智能体思考、视频理解，32768上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-2025-01-02
+  type: llm
+  provider: dashscope
+  description: qwen-vl-plus-2025-01-02多模态大模型，支持视觉理解、智能体思考、视频理解，32768上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-2025-01-25
+  type: llm
+  provider: dashscope
+  description: qwen-vl-plus-2025-01-25多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus-latest
+  type: llm
+  provider: dashscope
+  description: qwen-vl-plus-latest多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen-vl-plus
+  type: llm
+  provider: dashscope
+  description: qwen-vl-plus多模态大模型，支持视觉理解、智能体思考、视频理解，131072上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen2.5-0.5b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen2.5-0.5b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，32768上下文窗口，对话模式，未废弃
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-14b
+  type: llm
+  provider: dashscope
+  description: qwen3-14b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-235b-a22b-instruct-2507
+  type: llm
+  provider: dashscope
+  description: qwen3-235b-a22b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-235b-a22b-thinking-2507
+  type: llm
+  provider: dashscope
+  description: qwen3-235b-a22b-thinking-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-235b-a22b
+  type: llm
+  provider: dashscope
+  description: qwen3-235b-a22b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-30b-a3b-instruct-2507
+  type: llm
+  provider: dashscope
+  description: qwen3-30b-a3b-instruct-2507大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-30b-a3b
+  type: llm
+  provider: dashscope
+  description: qwen3-30b-a3b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-32b
+  type: llm
+  provider: dashscope
+  description: qwen3-32b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-4b
+  type: llm
+  provider: dashscope
+  description: qwen3-4b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-8b
+  type: llm
+  provider: dashscope
+  description: qwen3-8b大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-coder-30b-a3b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-30b-a3b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-coder-480b-a35b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-480b-a35b-instruct大语言模型，支持智能体思考，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-coder-plus-2025-09-23
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-plus-2025-09-23大语言模型，支持智能体思考，1000000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-coder-plus
+  type: llm
+  provider: dashscope
+  description: qwen3-coder-plus大语言模型，支持智能体思考，1000000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 代码模型
+  - agent-thought
+  logo: dashscope
+- name: qwen3-max-2025-09-23
+  type: llm
+  provider: dashscope
+  description: qwen3-max-2025-09-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - 联网搜索
+  logo: dashscope
+- name: qwen3-max-2026-01-23
+  type: llm
+  provider: dashscope
+  description: qwen3-max-2026-01-23大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - 联网搜索
+  logo: dashscope
+- name: qwen3-max-preview
+  type: llm
+  provider: dashscope
+  description: qwen3-max-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-max
+  type: llm
+  provider: dashscope
+  description: qwen3-max大语言模型，支持多工具调用、智能体思考、流式工具调用，262144上下文窗口，对话模式，支持联网搜索
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - 联网搜索
+  logo: dashscope
+- name: qwen3-next-80b-a3b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-next-80b-a3b-instruct大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-next-80b-a3b-thinking
+  type: llm
+  provider: dashscope
+  description: qwen3-next-80b-a3b-thinking大语言模型，支持多工具调用、智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwen3-omni-flash-2025-12-01
+  type: llm
+  provider: dashscope
+  description: qwen3-omni-flash-2025-12-01多模态大语言模型，支持视觉、智能体思考、视频、音频能力，65536上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  - audio
+  logo: dashscope
+- name: qwen3-vl-235b-a22b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-235b-a22b-instruct多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-235b-a22b-thinking
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-235b-a22b-thinking多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-30b-a3b-instruct
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-30b-a3b-instruct多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-30b-a3b-thinking
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-30b-a3b-thinking多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-flash
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-flash多模态大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉、视频能力，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - video
+  logo: dashscope
+- name: qwen3-vl-plus-2025-09-23
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-plus-2025-09-23多模态大语言模型，支持视觉、智能体思考、视频能力，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwen3-vl-plus
+  type: llm
+  provider: dashscope
+  description: qwen3-vl-plus多模态大语言模型，支持视觉、智能体思考、视频能力，262144上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - 多模态模型
+  - vision
+  - agent-thought
+  - video
+  logo: dashscope
+- name: qwq-32b
+  type: llm
+  provider: dashscope
+  description: qwq-32b大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwq-plus-0305
+  type: llm
+  provider: dashscope
+  description: qwq-plus-0305大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: qwq-plus
+  type: llm
+  provider: dashscope
+  description: qwq-plus大语言模型，支持智能体思考、流式工具调用，131072上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - stream-tool-call
+  logo: dashscope
+- name: gte-rerank-v2
+  type: rerank
+  provider: dashscope
+  description: gte-rerank-v2重排序模型，4000上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: dashscope
+- name: gte-rerank
+  type: rerank
+  provider: dashscope
+  description: gte-rerank重排序模型，4000上下文窗口
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 重排序模型
+  logo: dashscope
+- name: multimodal-embedding-v1
+  type: embedding
+  provider: dashscope
+  description: multimodal-embedding-v1多模态嵌入模型，支持视觉能力，8192上下文窗口，最大分块数10
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 多模态模型
+  - vision
+  logo: dashscope
+- name: text-embedding-v1
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v1文本嵌入模型，2048上下文窗口，最大分块数25
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope
+- name: text-embedding-v2
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v2文本嵌入模型，2048上下文窗口，最大分块数25
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope
+- name: text-embedding-v3
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v3文本嵌入模型，8192上下文窗口，最大分块数10
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope
+- name: text-embedding-v4
+  type: embedding
+  provider: dashscope
+  description: text-embedding-v4文本嵌入模型，8192上下文窗口，最大分块数10
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 嵌入模型
+  - 文本嵌入
+  logo: dashscope

api/app/core/models/scripts/loader.py

@@ -0,0 +1,143 @@
+"""模型配置加载器 - 用于将预定义模型批量导入到数据库"""
+
+import os
+from pathlib import Path
+from typing import Callable
+
+import yaml
+from sqlalchemy.orm import Session
+from app.models.models_model import ModelBase, ModelProvider
+
+
+def _load_yaml_config(provider: ModelProvider) -> list[dict]:
+    """从YAML文件加载指定供应商的模型配置"""
+    config_dir = Path(__file__).parent
+    config_file = config_dir / f"{provider.value}_models.yaml"
+    
+    if not config_file.exists():
+        return []
+    
+    with open(config_file, 'r', encoding='utf-8') as f:
+        data = yaml.safe_load(f)
+        
+        # 检查是否需要加载（默认为 true）
+        if not data.get('enabled', True):
+            return []
+        
+        return data.get('models', [])
+
+
+def _disable_yaml_config(provider: ModelProvider) -> None:
+    """将YAML文件的enabled标志设置为false"""
+    config_dir = Path(__file__).parent
+    config_file = config_dir / f"{provider.value}_models.yaml"
+    
+    if not config_file.exists():
+        return
+    
+    with open(config_file, 'r', encoding='utf-8') as f:
+        data = yaml.safe_load(f)
+    
+    data['enabled'] = False
+    
+    with open(config_file, 'w', encoding='utf-8') as f:
+        yaml.dump(data, f, allow_unicode=True, sort_keys=False)
+
+
+def load_models(db: Session, providers: list[str] = None, silent: bool = False) -> dict:
+    """
+    加载模型配置到数据库
+    
+    Args:
+        db: 数据库会话
+        providers: 要加载的供应商列表，None表示加载所有
+        silent: 是否静默模式（不输出详细日志）
+        
+    Returns:
+        dict: 加载结果统计 {"success": int, "skipped": int, "failed": int}
+    """
+    result = {"success": 0, "skipped": 0, "failed": 0}
+    
+    # 确定要加载的供应商
+    if providers:
+        target_providers = [ModelProvider(p) if isinstance(p, str) else p for p in providers]
+    else:
+        target_providers = [p for p in ModelProvider if p != ModelProvider.COMPOSITE]
+    
+    for provider in target_providers:
+        # 从YAML文件加载模型配置
+        models = _load_yaml_config(provider)
+        
+        if not models:
+            if not silent:
+                print(f"警告: 供应商 '{provider.value}' 暂无预定义模型")
+            continue
+            
+        if not silent:
+            print(f"\n正在加载 {provider.value} 的 {len(models)} 个模型...")
+        
+        # provider_success = 0
+        for model_data in models:
+            try:
+                # 检查模型是否已存在
+                existing = db.query(ModelBase).filter(
+                    ModelBase.name == model_data["name"],
+                    ModelBase.provider == model_data["provider"]
+                ).first()
+                
+                if existing:
+                    # 更新现有模型配置
+                    for key, value in model_data.items():
+                        setattr(existing, key, value)
+                    db.commit()
+                    if not silent:
+                        print(f"更新成功: {model_data['name']}")
+                    result["success"] += 1
+                    # provider_success += 1
+                else:
+                    # 创建新模型
+                    model = ModelBase(**model_data)
+                    db.add(model)
+                    db.commit()
+                    if not silent:
+                        print(f"添加成功: {model_data['name']}")
+                    result["success"] += 1
+                    # provider_success += 1
+                
+            except Exception as e:
+                db.rollback()
+                if not silent:
+                    print(f"添加失败: {model_data['name']} - {str(e)}")
+                result["failed"] += 1
+        
+        # 如果该供应商的模型全部加载成功，将enabled设置为false
+        # if provider_success == len(models):
+        _disable_yaml_config(provider)
+    
+    return result
+
+
+def load_models_by_provider(db: Session, provider: str) -> dict:
+    """
+    加载指定供应商的模型配置
+    
+    Args:
+        db: 数据库会话
+        provider: 供应商名称（字符串或ModelProvider枚举）
+        
+    Returns:
+        dict: 加载结果统计
+    """
+    provider_enum = ModelProvider(provider) if isinstance(provider, str) else provider
+    return load_models(db, providers=[provider_enum])
+
+
+def get_available_providers() -> list[Callable[[], str]]:
+    """获取所有可用的供应商列表（从ModelProvider枚举获取，排除COMPOSITE）"""
+    return [p.value for p in ModelProvider if p != ModelProvider.COMPOSITE]
+
+
+def get_models_by_provider(provider: str) -> list[dict]:
+    """获取指定供应商的模型配置列表"""
+    provider_enum = ModelProvider(provider) if isinstance(provider, str) else provider
+    return _load_yaml_config(provider_enum)

api/app/core/models/scripts/openai_models.yaml

@@ -0,0 +1,294 @@
+provider: openai
+enabled: true
+models:
+- name: chatgpt-4o-latest
+  type: llm
+  provider: openai
+  description: chatgpt-4o-latest大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  logo: openai
+- name: gpt-3.5-turbo-0125
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-0125大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-3.5-turbo-1106
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-1106大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-3.5-turbo-16k
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-16k大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-3.5-turbo-instruct
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo-instruct大语言模型，4096上下文窗口，文本补全模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  logo: openai
+- name: gpt-3.5-turbo
+  type: llm
+  provider: openai
+  description: gpt-3.5-turbo大语言模型，支持多工具调用、智能体思考、流式工具调用，16385上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-0125-preview
+  type: llm
+  provider: openai
+  description: gpt-4-0125-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-1106-preview
+  type: llm
+  provider: openai
+  description: gpt-4-1106-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-turbo-2024-04-09
+  type: llm
+  provider: openai
+  description: gpt-4-turbo-2024-04-09大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  logo: openai
+- name: gpt-4-turbo-preview
+  type: llm
+  provider: openai
+  description: gpt-4-turbo-preview大语言模型，支持多工具调用、智能体思考、流式工具调用，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  logo: openai
+- name: gpt-4-turbo
+  type: llm
+  provider: openai
+  description: gpt-4-turbo大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力，128000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  logo: openai
+- name: o1-preview
+  type: llm
+  provider: openai
+  description: o1-preview大语言模型，支持智能体思考，128000上下文窗口，对话模式，已废弃
+  is_deprecated: true
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  logo: openai
+- name: o1
+  type: llm
+  provider: openai
+  description: o1大语言模型，支持多工具调用、智能体思考、流式工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+  - structured-output
+  logo: openai
+- name: o3-2025-04-16
+  type: llm
+  provider: openai
+  description: o3-2025-04-16大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o3-mini-2025-01-31
+  type: llm
+  provider: openai
+  description: o3-mini-2025-01-31大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o3-mini
+  type: llm
+  provider: openai
+  description: o3-mini大语言模型，支持智能体思考、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o3-pro-2025-06-10
+  type: llm
+  provider: openai
+  description: o3-pro-2025-06-10大语言模型，支持智能体思考、工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - structured-output
+  logo: openai
+- name: o3-pro
+  type: llm
+  provider: openai
+  description: o3-pro大语言模型，支持智能体思考、工具调用、视觉能力、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - structured-output
+  logo: openai
+- name: o3
+  type: llm
+  provider: openai
+  description: o3大语言模型，支持智能体思考、视觉能力、工具调用、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o4-mini-2025-04-16
+  type: llm
+  provider: openai
+  description: o4-mini-2025-04-16大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: o4-mini
+  type: llm
+  provider: openai
+  description: o4-mini大语言模型，支持智能体思考、工具调用、视觉能力、流式工具调用、结构化输出，200000上下文窗口，对话模式
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 大语言模型
+  - agent-thought
+  - tool-call
+  - vision
+  - stream-tool-call
+  - structured-output
+  logo: openai
+- name: text-embedding-3-large
+  type: embedding
+  provider: openai
+  description: text-embedding-3-large文本向量模型，8191上下文窗口，最大分块数32
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本向量模型
+  logo: openai
+- name: text-embedding-3-small
+  type: embedding
+  provider: openai
+  description: text-embedding-3-small文本向量模型，8191上下文窗口，最大分块数32
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本向量模型
+  logo: openai
+- name: text-embedding-ada-002
+  type: embedding
+  provider: openai
+  description: text-embedding-ada-002文本向量模型，8097上下文窗口，最大分块数32
+  is_deprecated: false
+  is_official: true
+  tags:
+  - 文本向量模型
+  logo: openai

api/app/core/rag/app/presentation.py

@@ -1,165 +0,0 @@
-import copy
-import re
-from io import BytesIO
-from PIL import Image
-
-from app.core.rag.nlp import tokenize, is_english
-from app.core.rag.nlp import rag_tokenizer
-from app.core.rag.deepdoc.parser import PdfParser, PlainParser
-from app.core.rag.deepdoc.parser.ppt_parser import RAGPptParser as PptParser
-from PyPDF2 import PdfReader as pdf2_read
-from app.core.rag.app.naive import by_plaintext, PARSERS
-
-class Ppt(PptParser):
-    def __call__(self, fnm, from_page, to_page, callback=None):
-        txts = super().__call__(fnm, from_page, to_page)
-
-        callback(0.5, "Text extraction finished.")
-        import aspose.slides as slides
-        import aspose.pydrawing as drawing
-        imgs = []
-        with slides.Presentation(BytesIO(fnm)) as presentation:
-            for i, slide in enumerate(presentation.slides[from_page: to_page]):
-                try:
-                    with BytesIO() as buffered:
-                        slide.get_thumbnail(
-                            0.1, 0.1).save(
-                            buffered, drawing.imaging.ImageFormat.jpeg)
-                        buffered.seek(0)
-                        imgs.append(Image.open(buffered).copy())
-                except RuntimeError as e:
-                    raise RuntimeError(f'ppt parse error at page {i+1}, original error: {str(e)}') from e
-        assert len(imgs) == len(
-            txts), "Slides text and image do not match: {} vs. {}".format(len(imgs), len(txts))
-        callback(0.9, "Image extraction finished")
-        self.is_english = is_english(txts)
-        return [(txts[i], imgs[i]) for i in range(len(txts))]
-
-class Pdf(PdfParser):
-    def __init__(self):
-        super().__init__()
-
-    def __garbage(self, txt):
-        txt = txt.lower().strip()
-        if re.match(r"[0-9\.,%/-]+$", txt):
-            return True
-        if len(txt) < 3:
-            return True
-        return False
-
-    def __call__(self, filename, binary=None, from_page=0,
-                 to_page=100000, zoomin=3, callback=None):
-        from timeit import default_timer as timer
-        start = timer()
-        callback(msg="OCR started")
-        self.__images__(filename if not binary else binary,
-                        zoomin, from_page, to_page, callback)
-        callback(msg="Page {}~{}: OCR finished ({:.2f}s)".format(from_page, min(to_page, self.total_page), timer() - start))
-        assert len(self.boxes) == len(self.page_images), "{} vs. {}".format(
-            len(self.boxes), len(self.page_images))
-        res = []
-        for i in range(len(self.boxes)):
-            lines = "\n".join([b["text"] for b in self.boxes[i]
-                              if not self.__garbage(b["text"])])
-            res.append((lines, self.page_images[i]))
-        callback(0.9, "Page {}~{}: Parsing finished".format(
-            from_page, min(to_page, self.total_page)))
-        return res, []
-
-
-class PlainPdf(PlainParser):
-    def __call__(self, filename, binary=None, from_page=0,
-                 to_page=100000, callback=None, **kwargs):
-        self.pdf = pdf2_read(filename if not binary else BytesIO(binary))
-        page_txt = []
-        for page in self.pdf.pages[from_page: to_page]:
-            page_txt.append(page.extract_text())
-        callback(0.9, "Parsing finished")
-        return [(txt, None) for txt in page_txt], []
-
-
-def chunk(filename, binary=None, from_page=0, to_page=100000,
-          lang="Chinese", callback=None, vision_model=None, parser_config=None, **kwargs):
-    """
-    The supported file formats are pdf, pptx.
-    Every page will be treated as a chunk. And the thumbnail of every page will be stored.
-    PPT file will be parsed by using this method automatically, setting-up for every PPT file is not necessary.
-    """
-    if parser_config is None:
-        parser_config = {}
-    eng = lang.lower() == "english"
-    doc = {
-        "docnm_kwd": filename,
-        "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
-    }
-    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
-    res = []
-    if re.search(r"\.pptx?$", filename, re.IGNORECASE):
-        if not binary:
-            with open(filename, "rb") as f:
-                binary = f.read()
-        ppt_parser = Ppt()
-        for pn, (txt, img) in enumerate(ppt_parser(
-                filename if not binary else binary, from_page, 1000000, callback)):
-            d = copy.deepcopy(doc)
-            pn += from_page
-            d["image"] = img
-            d["doc_type_kwd"] = "image"
-            d["page_num_int"] = [pn + 1]
-            d["top_int"] = [0]
-            d["position_int"] = [(pn + 1, 0, img.size[0], 0, img.size[1])]
-            tokenize(d, txt, eng)
-            res.append(d)
-        return res
-    elif re.search(r"\.pdf$", filename, re.IGNORECASE):
-        layout_recognizer = parser_config.get("layout_recognize", "DeepDOC")
-
-        if isinstance(layout_recognizer, bool):
-            layout_recognizer = "DeepDOC" if layout_recognizer else "Plain Text"
-
-        name = layout_recognizer.strip().lower()
-        parser = PARSERS.get(name, by_plaintext)
-        callback(0.1, "Start to parse.")
-
-        sections, _, _ = parser(
-            filename=filename,
-            binary=binary,
-            from_page=from_page,
-            to_page=to_page,
-            lang=lang,
-            callback=callback,
-            vision_model=vision_model,
-            pdf_cls=Pdf,
-            **kwargs
-        )
-
-        if not sections:
-            return []
-
-        if name in ["tcadp", "docling", "mineru"]:
-            parser_config["chunk_token_num"] = 0
-        
-        callback(0.8, "Finish parsing.")
-
-        for pn, (txt, img) in enumerate(sections):
-            d = copy.deepcopy(doc)
-            pn += from_page
-            if img:
-                d["image"] = img
-            d["page_num_int"] = [pn + 1]
-            d["top_int"] = [0]
-            d["position_int"] = [(pn + 1, 0, img.size[0] if img else 0, 0, img.size[1] if img else 0)]
-            tokenize(d, txt, eng)
-            res.append(d)
-        return res
-
-    raise NotImplementedError(
-        "file type not supported yet(pptx, pdf supported)")
-
-
-if __name__ == "__main__":
-    import sys
-
-    def dummy(a, b):
-        pass
-    chunk(sys.argv[1], callback=dummy)

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/storage/url_signer.py

@@ -36,7 +36,7 @@ def generate_signed_url(
     """
     if base_url is None:
         # Use SERVER_IP or default to localhost
-        server_url = f"http://{settings.SERVER_IP}:8000/api"
+        server_url = settings.FILE_LOCAL_SERVER_URL
         base_url = server_url
 
     # Calculate expiration timestamp

api/app/core/tools/builtin/baidu_search_tool.py

@@ -16,7 +16,7 @@ class BaiduSearchTool(BuiltinTool):
     
     @property
     def description(self) -> str:
-        return "百度搜索 - 搜索引擎服务：网页搜索、新闻搜索、图片搜索、实时结果"
+        return "百度搜索 - 搜索引擎服务：网页搜索、新闻搜索、图片搜索、视频搜索"
     
     def get_required_config_parameters(self) -> List[str]:
         return ["api_key"]
@@ -33,7 +33,7 @@ class BaiduSearchTool(BuiltinTool):
             ToolParameter(
                 name="search_type",
                 type=ParameterType.STRING,
-                description="搜索类型",
+                description="搜索类型, web: 网页搜索；news：新闻搜索；image：图片搜索；video视频搜索",
                 required=False,
                 default="web",
                 enum=["web", "news", "image", "video"]

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/executor.py

@@ -11,17 +11,12 @@ from typing import Any
 from langchain_core.runnables import RunnableConfig
 from langgraph.graph.state import CompiledStateGraph
 
-from app.core.workflow.graph_builder import GraphBuilder
+from app.core.workflow.expression_evaluator import evaluate_expression
+from app.core.workflow.graph_builder import GraphBuilder, StreamOutputConfig
 from app.core.workflow.nodes import WorkflowState
 from app.core.workflow.nodes.base_config import VariableType
 from app.core.workflow.nodes.enums import NodeType
 
-# from app.core.tools.registry import ToolRegistry
-# from app.core.tools.executor import ToolExecutor
-# from app.core.tools.langchain_adapter import LangchainAdapter
-# TOOL_MANAGEMENT_AVAILABLE = True
-# from app.db import get_db
-
 logger = logging.getLogger(__name__)
 
 
@@ -55,6 +50,8 @@ class WorkflowExecutor:
         self.execution_config = workflow_config.get("execution_config", {})
 
         self.start_node_id = None
+        self.end_outputs: dict[str, StreamOutputConfig] = {}
+        self.activate_end: str | None = None
 
         self.checkpoint_config = RunnableConfig(
             configurable={
@@ -127,7 +124,6 @@ class WorkflowExecutor:
             "user_id": self.user_id,
             "error": None,
             "error_node": None,
-            "streaming_buffer": {},  # 流式缓冲区
             "cycle_nodes": [
                 node.get("id")
                 for node in self.workflow_config.get("nodes")
@@ -139,9 +135,8 @@ class WorkflowExecutor:
             }
         }
 
-    def _build_final_output(self, result, elapsed_time):
+    def _build_final_output(self, result, elapsed_time, final_output):
         node_outputs = result.get("node_outputs", {})
-        final_output = self._extract_final_output(node_outputs)
         token_usage = self._aggregate_token_usage(node_outputs)
         conversation_id = None
         for node_id, node_output in node_outputs.items():
@@ -161,6 +156,146 @@ class WorkflowExecutor:
             "error": result.get("error"),
         }
 
+    def _update_scope_activate(self, scope, status=None):
+        """
+        Update the activation state of all End nodes based on a completed scope (node or variable).
+
+        Iterates over all End nodes in `self.end_outputs` and calls
+        `update_activate` on each, which may:
+          - Activate variable segments that depend on the completed node/scope.
+          - Activate the entire End node output if all control conditions are met.
+
+        If any End node becomes active and `self.activate_end` is not yet set,
+        this node will be marked as the currently active End node.
+
+        Args:
+            scope (str): The node ID or scope that has completed execution.
+            status (str | None): Optional status of the node (used for branch/control nodes).
+        """
+        for node in self.end_outputs.keys():
+            self.end_outputs[node].update_activate(scope, status)
+            if self.end_outputs[node].activate and self.activate_end is None:
+                self.activate_end = node
+
+    def _update_stream_output_status(self, activate, data):
+        """
+        Update the stream output state of End nodes based on workflow state updates.
+
+        This method checks which nodes/scopes are activated and propagates
+        activation to End nodes accordingly.
+
+        Args:
+            activate (dict): Mapping of node_id -> bool indicating which nodes/scopes are activated.
+            data (dict): Mapping of node_id -> node runtime data, including outputs.
+
+        Behavior:
+            For each node in `data`:
+            1. If the node is activated (`activate[node_id]` is True),
+               retrieve its output status from `runtime_vars`.
+            2. Call `_update_scope_activate` to propagate the activation
+               to all relevant End nodes and update `self.activate_end`.
+        """
+        for node_id in data.keys():
+            if activate.get(node_id):
+                node_output_status = (
+                    data[node_id]
+                    .get('runtime_vars', {})
+                    .get(node_id)
+                    .get("output")
+                )
+                self._update_scope_activate(node_id, status=node_output_status)
+
+    async def _emit_active_chunks(
+            self,
+            node_outputs: dict,
+            variables: dict,
+            force=False
+    ):
+        """
+        Process and yield all currently active output segments for the currently active End node.
+
+        This method handles stream-mode output for an End node by iterating through its output segments
+        (`OutputContent`). Only segments marked as active (`activate=True`) are processed, unless
+        `force=True`, which allows all segments to be processed regardless of their activation state.
+
+        Behavior:
+        1. Iterates from the current `cursor` position to the end of the outputs list.
+        2. For each segment:
+           - If the segment is literal text (`is_variable=False`), append it directly.
+           - If the segment is a variable (`is_variable=True`), evaluate it using
+             `evaluate_expression` with the given `node_outputs` and `variables`,
+             then transform the result with `_trans_output_string`.
+        3. Yield a stream event of type "message" containing the processed chunk.
+        4. Move the `cursor` forward after processing each segment.
+        5. When all segments have been processed, remove this End node from `end_outputs`
+           and reset `activate_end` to None.
+
+        Args:
+            node_outputs (dict): Current runtime node outputs, used for variable evaluation.
+            variables (dict): Current runtime variables, used for variable evaluation.
+            force (bool, default=False): If True, process segments even if `activate=False`.
+
+        Yields:
+            dict: A stream event of type "message" containing the processed chunk.
+
+        Notes:
+            - Segments that fail evaluation (ValueError) are skipped with a warning logged.
+            - This method only processes the currently active End node (`self.activate_end`).
+            - Use `force=True` for final emission regardless of activation state.
+        """
+
+        end_info = self.end_outputs[self.activate_end]
+
+        while end_info.cursor < len(end_info.outputs):
+            final_chunk = ''
+            current_segment = end_info.outputs[end_info.cursor]
+
+            if not current_segment.activate and not force:
+                # Stop processing until this segment becomes active
+                break
+
+            # Literal segment
+            if not current_segment.is_variable:
+                final_chunk += current_segment.literal
+            else:
+                # Variable segment: evaluate and transform
+                try:
+                    chunk = evaluate_expression(
+                        current_segment.literal,
+                        variables=variables,
+                        node_outputs=node_outputs
+                    )
+                    chunk = self._trans_output_string(chunk)
+                    final_chunk += chunk
+                except ValueError:
+                    # Log failed evaluation but continue streaming
+                    logger.warning(f"[STREAM] Failed to evaluate segment: {current_segment.literal}")
+
+            if final_chunk:
+                yield {
+                    "event": "message",
+                    "data": {
+                        "chunk": final_chunk
+                    }
+                }
+
+            # Advance cursor after processing
+            end_info.cursor += 1
+
+        # Remove End node from active tracking if all segments have been processed
+        if end_info.cursor >= len(end_info.outputs):
+            self.end_outputs.pop(self.activate_end)
+            self.activate_end = None
+
+    @staticmethod
+    def _trans_output_string(content):
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            return "\n".join(content)
+        else:
+            return str(content)
+
     def build_graph(self, stream=False) -> CompiledStateGraph:
         """构建 LangGraph
 
@@ -173,6 +308,7 @@ class WorkflowExecutor:
             stream=stream,
         )
         self.start_node_id = builder.start_node_id
+        self.end_outputs = builder.end_node_map
         graph = builder.build()
         logger.info(f"工作流图构建完成: execution_id={self.execution_id}")
 
@@ -205,14 +341,28 @@ class WorkflowExecutor:
         try:
 
             result = await graph.ainvoke(initial_state, config=self.checkpoint_config)
-
+            full_content = ''
+            for end_id in self.end_outputs.keys():
+                full_content += result.get('runtime_vars', {}).get(end_id, {}).get('output', '')
+            result["messages"].extend(
+                [
+                    {
+                        "role": "user",
+                        "content": input_data.get("message", '')
+                    },
+                    {
+                        "role": "assistant",
+                        "content": full_content
+                    }
+                ]
+            )
             # 计算耗时
             end_time = datetime.datetime.now()
             elapsed_time = (end_time - start_time).total_seconds()
 
             logger.info(f"工作流执行完成: execution_id={self.execution_id}, elapsed_time={elapsed_time:.2f}s")
 
-            return self._build_final_output(result, elapsed_time)
+            return self._build_final_output(result, elapsed_time, full_content)
 
         except Exception as e:
             # 计算耗时（即使失败也记录）
@@ -261,7 +411,7 @@ class WorkflowExecutor:
             "data": {
                 "execution_id": self.execution_id,
                 "workspace_id": self.workspace_id,
-                "timestamp": start_time.isoformat()
+                "timestamp": int(start_time.timestamp() * 1000)
             }
         }
 
@@ -273,7 +423,8 @@ class WorkflowExecutor:
         # 3. Execute workflow
         try:
             chunk_count = 0
-
+            full_content = ''
+            self._update_scope_activate("sys")
             async for event in graph.astream(
                     initial_state,
                     stream_mode=["updates", "debug", "custom"],  # Use updates + debug + custom mode
@@ -293,20 +444,42 @@ class WorkflowExecutor:
                     # Handle custom streaming events (chunks from nodes via stream writer)
                     chunk_count += 1
                     event_type = data.get("type", "node_chunk")  # "message" or "node_chunk"
-                    logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}"
+                    if event_type == "node_chunk":
-                                f"- execution_id: {self.execution_id}")
+                        node_id = data.get("node_id")
-                    yield {
+                        if self.activate_end:
-                        "event": event_type,  # "message" or "node_chunk"
+                            end_info = self.end_outputs.get(self.activate_end)
-                        "data": {
+                            if not end_info or end_info.cursor >= len(end_info.outputs):
-                            "node_id": data.get("node_id"),
+                                continue
-                            "chunk": data.get("chunk"),
+                            current_output = end_info.outputs[end_info.cursor]
-                            "full_content": data.get("full_content"),
+                            if current_output.is_variable and current_output.depends_on_scope(node_id):
-                            "chunk_index": data.get("chunk_index"),
+                                if data.get("done"):
-                            "is_prefix": data.get("is_prefix"),
+                                    end_info.cursor += 1
-                            "is_suffix": data.get("is_suffix"),
+                                    if end_info.cursor >= len(end_info.outputs):
-                            "conversation_id": input_data.get("conversation_id"),
+                                        self.end_outputs.pop(self.activate_end)
+                                        self.activate_end = None
+                                else:
+                                    full_content += data.get("chunk")
+                                    yield {
+                                        "event": "message",
+                                        "data": {
+                                            "chunk": data.get("chunk")
+                                        }
+                                    }
+                        logger.info(f"[CUSTOM] ✅ 收到 {event_type} #{chunk_count} from {data.get('node_id')}"
+                                    f"- execution_id: {self.execution_id}")
+
+                    elif event_type == "node_error":
+                        yield {
+                            "event": event_type,  # "message" or "node_chunk"
+                            "data": {
+                                "node_id": data.get("node_id"),
+                                "status": "failed",
+                                "input": data.get("input_data"),
+                                "elapsed_time": data.get("elapsed_time"),
+                                "output": None,
+                                "error": data.get("error")
+                            }
                         }
-                    }
 
                 elif mode == "debug":
                     # Handle debug information (node execution status)
@@ -325,14 +498,15 @@ class WorkflowExecutor:
                         conversation_id = input_data.get("conversation_id")
                         logger.info(f"[NODE-START] Node starts execution: {node_name} "
                                     f"- execution_id: {self.execution_id}")
-
                         yield {
                             "event": "node_start",
                             "data": {
                                 "node_id": node_name,
                                 "conversation_id": conversation_id,
                                 "execution_id": self.execution_id,
-                                "timestamp": data.get("timestamp"),
+                                "timestamp": int(datetime.datetime.fromisoformat(
+                                    data.get("timestamp")
+                                ).timestamp() * 1000),
                             }
                         }
                     elif event_type == "task_result":
@@ -351,21 +525,82 @@ class WorkflowExecutor:
                                 "node_id": node_name,
                                 "conversation_id": conversation_id,
                                 "execution_id": self.execution_id,
-                                "timestamp": data.get("timestamp"),
+                                "timestamp": int(datetime.datetime.fromisoformat(
-                                "state": result.get("node_outputs", {}).get(node_name),
+                                    data.get("timestamp")
+                                ).timestamp() * 1000),
+                                "input": result.get("node_outputs", {}).get(node_name, {}).get("input"),
+                                "output": result.get("node_outputs", {}).get(node_name, {}).get("output"),
+                                "elapsed_time": result.get("node_outputs", {}).get(node_name, {}).get("elapsed_time"),
                             }
                         }
 
                 elif mode == "updates":
                     # Handle state updates - store final state
-                    # TODO:流式输出点
+                    state = graph.get_state(config=self.checkpoint_config).values
+                    node_outputs = state.get("runtime_vars", {})
+                    variables = state.get("variables", {})
+                    activate = state.get("activate", {})
+                    for _, node_data in data.items():
+                        node_outputs |= node_data.get("runtime_vars", {})
+                        variables |= node_data.get("variables", {})
+
+                    self._update_stream_output_status(activate, data)
+                    wait = False
+                    while self.activate_end and not wait:
+                        async for msg_event in self._emit_active_chunks(
+                                node_outputs=node_outputs,
+                                variables=variables
+                        ):
+                            full_content += msg_event["data"]['chunk']
+                            yield msg_event
+
+                        if self.activate_end:
+                            wait = True
+                        else:
+                            self._update_stream_output_status(activate, data)
+
                     logger.debug(f"[UPDATES] 收到 state 更新 from {list(data.keys())} "
                                  f"- execution_id: {self.execution_id}")
 
+            result = graph.get_state(self.checkpoint_config).values
+            node_outputs = result.get("runtime_vars", {})
+            variables = result.get("variables", {})
+            self.end_outputs = {
+                node_id: node_info
+                for node_id, node_info in self.end_outputs.items()
+                if node_info.activate
+            }
+
+            if self.end_outputs or self.activate_end:
+                while self.activate_end:
+                    async for msg_event in self._emit_active_chunks(
+                            node_outputs=node_outputs,
+                            variables=variables,
+                            force=True
+                    ):
+                        full_content += msg_event["data"]['chunk']
+                        yield msg_event
+
+                    if not self.activate_end and self.end_outputs:
+                        self.activate_end = list(self.end_outputs.keys())[0]
+
             # 计算耗时
             end_time = datetime.datetime.now()
             elapsed_time = (end_time - start_time).total_seconds()
             result = graph.get_state(self.checkpoint_config).values
+            logger.info(result)
+            result["messages"].extend(
+                [
+                    {
+                        "role": "user",
+                        "content": input_data.get("message", '')
+                    },
+                    {
+                        "role": "assistant",
+                        "content": full_content
+                    }
+                ]
+            )
             logger.info(
                 f"Workflow execution completed (streaming), "
                 f"total chunks: {chunk_count}, elapsed: {elapsed_time:.2f}s, execution_id: {self.execution_id}"
@@ -374,7 +609,7 @@ class WorkflowExecutor:
             # 发送 workflow_end 事件
             yield {
                 "event": "workflow_end",
-                "data": self._build_final_output(result, elapsed_time)
+                "data": self._build_final_output(result, elapsed_time, full_content)
             }
 
         except Exception as e:
@@ -396,31 +631,6 @@ class WorkflowExecutor:
                 }
             }
 
-    @staticmethod
-    def _extract_final_output(node_outputs: dict[str, Any]) -> str | None:
-        """从节点输出中提取最终输出
-
-        优先级：
-        1. 最后一个执行的非 start/end 节点的 output
-        2. 如果没有节点输出，返回 None
-
-        Args:
-            node_outputs: 所有节点的输出
-
-        Returns:
-            最终输出字符串或 None
-        """
-        if not node_outputs:
-            return None
-
-        # 获取最后一个节点的输出
-        last_node_output = list(node_outputs.values())[-1] if node_outputs else None
-
-        if last_node_output and isinstance(last_node_output, dict):
-            return last_node_output.get("output")
-
-        return None
-
     @staticmethod
     def _aggregate_token_usage(node_outputs: dict[str, Any]) -> dict[str, int] | None:
         """聚合所有节点的 token 使用情况
@@ -511,178 +721,3 @@ async def execute_workflow_stream(
     )
     async for event in executor.execute_stream(input_data):
         yield event
-
-# ==================== 工具管理系统集成 ====================
-
-# def get_workflow_tools(workspace_id: str, user_id: str) -> list:
-#     """获取工作流可用的工具列表
-#
-#     Args:
-#         workspace_id: 工作空间ID
-#         user_id: 用户ID
-#
-#     Returns:
-#         可用工具列表
-#     """
-#     if not TOOL_MANAGEMENT_AVAILABLE:
-#         logger.warning("工具管理系统不可用")
-#         return []
-#
-#     try:
-#         db = next(get_db())
-#
-#         # 创建工具注册表
-#         registry = ToolRegistry(db)
-#
-#         # 注册内置工具类
-#         from app.core.tools.builtin import (
-#             DateTimeTool, JsonTool, BaiduSearchTool, MinerUTool, TextInTool
-#         )
-#         registry.register_tool_class(DateTimeTool)
-#         registry.register_tool_class(JsonTool)
-#         registry.register_tool_class(BaiduSearchTool)
-#         registry.register_tool_class(MinerUTool)
-#         registry.register_tool_class(TextInTool)
-#
-#         # 获取活跃的工具
-#         import uuid
-#         tools = registry.list_tools(workspace_id=uuid.UUID(workspace_id))
-#         active_tools = [tool for tool in tools if tool.status.value == "active"]
-#
-#         # 转换为Langchain工具
-#         langchain_tools = []
-#         for tool_info in active_tools:
-#             try:
-#                 tool_instance = registry.get_tool(tool_info.id)
-#                 if tool_instance:
-#                     langchain_tool = LangchainAdapter.convert_tool(tool_instance)
-#                     langchain_tools.append(langchain_tool)
-#             except Exception as e:
-#                 logger.error(f"转换工具失败: {tool_info.name}, 错误: {e}")
-#
-#         logger.info(f"为工作流获取了 {len(langchain_tools)} 个工具")
-#         return langchain_tools
-#
-#     except Exception as e:
-#         logger.error(f"获取工作流工具失败: {e}")
-#         return []
-#
-#
-# class ToolWorkflowNode:
-#     """工具工作流节点 - 在工作流中执行工具"""
-#
-#     def __init__(self, node_config: dict, workflow_config: dict):
-#         """初始化工具节点
-#
-#         Args:
-#             node_config: 节点配置
-#             workflow_config: 工作流配置
-#         """
-#         self.node_config = node_config
-#         self.workflow_config = workflow_config
-#         self.tool_id = node_config.get("tool_id")
-#         self.tool_parameters = node_config.get("parameters", {})
-#
-#     async def run(self, state: WorkflowState) -> WorkflowState:
-#         """执行工具节点"""
-#         if not TOOL_MANAGEMENT_AVAILABLE:
-#             logger.error("工具管理系统不可用")
-#             state["error"] = "工具管理系统不可用"
-#             return state
-#
-#         try:
-#             from sqlalchemy.orm import Session
-#             db = next(get_db())
-#
-#             # 创建工具执行器
-#             registry = ToolRegistry(db)
-#             executor = ToolExecutor(db, registry)
-#
-#             # 准备参数（支持变量替换）
-#             parameters = self._prepare_parameters(state)
-#
-#             # 执行工具
-#             result = await executor.execute_tool(
-#                 tool_id=self.tool_id,
-#                 parameters=parameters,
-#                 user_id=uuid.UUID(state["user_id"]),
-#                 workspace_id=uuid.UUID(state["workspace_id"])
-#             )
-#
-#             # 更新状态
-#             node_id = self.node_config.get("id")
-#             if result.success:
-#                 state["node_outputs"][node_id] = {
-#                     "type": "tool",
-#                     "tool_id": self.tool_id,
-#                     "output": result.data,
-#                     "execution_time": result.execution_time,
-#                     "token_usage": result.token_usage
-#                 }
-#
-#                 # 更新运行时变量
-#                 if isinstance(result.data, dict):
-#                     for key, value in result.data.items():
-#                         state["runtime_vars"][f"{node_id}.{key}"] = value
-#                 else:
-#                     state["runtime_vars"][f"{node_id}.result"] = result.data
-#             else:
-#                 state["error"] = result.error
-#                 state["error_node"] = node_id
-#                 state["node_outputs"][node_id] = {
-#                     "type": "tool",
-#                     "tool_id": self.tool_id,
-#                     "error": result.error,
-#                     "execution_time": result.execution_time
-#                 }
-#
-#             return state
-#
-#         except Exception as e:
-#             logger.error(f"工具节点执行失败: {e}")
-#             state["error"] = str(e)
-#             state["error_node"] = self.node_config.get("id")
-#             return state
-#
-#     def _prepare_parameters(self, state: WorkflowState) -> dict:
-#         """准备工具参数（支持变量替换）"""
-#         parameters = {}
-#
-#         for key, value in self.tool_parameters.items():
-#             if isinstance(value, str) and value.startswith("${") and value.endswith("}"):
-#                 # 变量替换
-#                 var_path = value[2:-1]
-#
-#                 # 支持多层级变量访问，如 ${sys.message} 或 ${node1.result}
-#                 if "." in var_path:
-#                     parts = var_path.split(".")
-#                     current = state.get("variables", {})
-#
-#                     for part in parts:
-#                         if isinstance(current, dict) and part in current:
-#                             current = current[part]
-#                         else:
-#                             # 尝试从运行时变量获取
-#                             runtime_key = ".".join(parts)
-#                             current = state.get("runtime_vars", {}).get(runtime_key, value)
-#                             break
-#
-#                     parameters[key] = current
-#                 else:
-#                     # 简单变量
-#                     variables = state.get("variables", {})
-#                     parameters[key] = variables.get(var_path, value)
-#             else:
-#                 parameters[key] = value
-#
-#         return parameters
-#
-#
-# # 注册工具节点到NodeFactory（如果存在）
-# try:
-#     from app.core.workflow.nodes import NodeFactory
-#     if hasattr(NodeFactory, 'register_node_type'):
-#         NodeFactory.register_node_type("tool", ToolWorkflowNode)
-#     logger.info("工具节点已注册到工作流系统")
-# except Exception as e:
-#     logger.warning(f"注册工具节点失败: {e}")

api/app/core/workflow/graph_builder.py

@@ -1,12 +1,15 @@
 import logging
+import re
 import uuid
 from collections import defaultdict
+from functools import lru_cache
 from typing import Any
 
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import START, END
 from langgraph.graph.state import CompiledStateGraph, StateGraph
 from langgraph.types import Send
+from pydantic import BaseModel, Field
 
 from app.core.workflow.expression_evaluator import evaluate_condition
 from app.core.workflow.nodes import WorkflowState, NodeFactory
@@ -15,6 +18,149 @@ from app.core.workflow.nodes.enums import NodeType, BRANCH_NODES
 logger = logging.getLogger(__name__)
 
 
+class OutputContent(BaseModel):
+    """
+    Represents a single output segment of an End node.
+
+    An output segment can be either:
+    - literal text (static string)
+    - a variable placeholder (e.g. {{ node.field }})
+
+    Each segment has its own activation state, which is especially
+    important in stream mode.
+    """
+
+    literal: str = Field(
+        ...,
+        description="Raw output content. Can be literal text or a variable placeholder."
+    )
+
+    activate: bool = Field(
+        ...,
+        description=(
+            "Whether this output segment is currently active.\n"
+            "- True: allowed to be emitted/output\n"
+            "- False: blocked until activated by branch control"
+        )
+    )
+
+    is_variable: bool = Field(
+        ...,
+        description=(
+            "Whether this segment represents a variable placeholder.\n"
+            "True  -> variable (e.g. {{ node.field }})\n"
+            "False -> literal text"
+        )
+    )
+
+    def depends_on_scope(self, scope: str) -> bool:
+        """
+        Check if this segment depends on a given scope.
+
+        Args:
+            scope (str): Node ID or special variable prefix (e.g., "sys").
+
+        Returns:
+            bool: True if this segment references the given scope.
+        """
+        pattern = rf"\{{\{{\s*{re.escape(scope)}\.[a-zA-Z0-9_]+\s*\}}\}}"
+        return bool(re.search(pattern, self.literal))
+
+
+class StreamOutputConfig(BaseModel):
+    """
+    Streaming output configuration for an End node.
+
+    This configuration describes how the End node output behaves in streaming mode,
+    including:
+    - whether output emission is globally activated
+    - which upstream branch/control nodes gate the activation
+    - how each parsed output segment is streamed and activated
+    """
+
+    activate: bool = Field(
+        ...,
+        description=(
+            "Global activation flag for the End node output.\n"
+            "When False, output segments should not be emitted even if available.\n"
+            "This flag typically becomes True once required control branch conditions "
+            "are satisfied."
+        )
+    )
+
+    control_nodes: dict[str, str] = Field(
+        ...,
+        description=(
+            "Control branch conditions for this End node output.\n"
+            "Mapping of `branch_node_id -> expected_branch_label`.\n"
+            "The End node output becomes globally active when a controlling branch node "
+            "reports a matching completion status."
+        )
+    )
+
+    outputs: list[OutputContent] = Field(
+        ...,
+        description=(
+            "Ordered list of output segments parsed from the output template.\n"
+            "Each segment represents either a literal text block or a variable placeholder "
+            "that may be activated independently."
+        )
+    )
+
+    cursor: int = Field(
+        ...,
+        description=(
+            "Streaming cursor index.\n"
+            "Indicates the next output segment index to be emitted.\n"
+            "Segments with index < cursor are considered already streamed."
+        )
+    )
+
+    def update_activate(self, scope: str, status=None):
+        """
+        Update streaming activation state based on an upstream node or special variable.
+
+        Args:
+            scope (str):
+                Identifier of the completed upstream entity.
+                - If a control branch node, it should match a key in `control_nodes`.
+                - If a variable placeholder (e.g., "sys.xxx"), it may appear in output segments.
+            status (optional):
+                Completion status of the control branch node.
+                Required when `scope` refers to a control node.
+
+        Behavior:
+        1. Control branch nodes:
+           - If `scope` matches a key in `control_nodes` and `status` matches the expected
+             branch label, the End node output becomes globally active (`activate = True`).
+
+        2. Variable output segments:
+           - For each segment that is a variable (`is_variable=True`):
+               - If the segment literal references `scope`, mark the segment as active.
+               - This applies both to regular node variables (e.g., "node_id.field")
+                 and special system variables (e.g., "sys.xxx").
+
+        Notes:
+        - This method does not emit output or advance the streaming cursor.
+        - It only updates activation flags based on upstream events or special variables.
+        """
+
+        # Case 1: resolve control branch dependency
+        if scope in self.control_nodes.keys():
+            if status is None:
+                raise RuntimeError("[Stream Output] Control node activation status not provided")
+            if status == self.control_nodes[scope]:
+                self.activate = True
+
+        # Case 2: activate variable segments related to this node
+        for i in range(len(self.outputs)):
+            if (
+                    self.outputs[i].is_variable
+                    and self.outputs[i].depends_on_scope(scope)
+            ):
+                self.outputs[i].activate = True
+
+
 class GraphBuilder:
     def __init__(
             self,
@@ -29,10 +175,16 @@ class GraphBuilder:
 
         self.start_node_id = None
         self.end_node_ids = []
+        self.node_map = {node["id"]: node for node in self.nodes}
+        self.end_node_map: dict[str, StreamOutputConfig] = {}
+        self._find_upstream_branch_node = lru_cache(
+            maxsize=len(self.nodes) * 2
+        )(self._find_upstream_branch_node)
 
         self.graph = StateGraph(WorkflowState)
         self.add_nodes()
         self.add_edges()
+        self._analyze_end_node_output()
         # EDGES MUST BE ADDED AFTER NODES ARE ADDED.
 
     @property
@@ -43,79 +195,207 @@ class GraphBuilder:
     def edges(self) -> list[dict[str, Any]]:
         return self.workflow_config.get("edges", [])
 
-    def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]:
+    def get_node_type(self, node_id: str) -> str:
-        """
+        """Retrieve the type of node given its ID.
-        Analyze the prefix configuration for End nodes.
 
-        This function scans each End node's output template, identifies
+        Args:
-        references to its direct upstream nodes, and extracts the prefix
+            node_id (str): The unique identifier of the node.
-        string appearing before the first reference.
 
         Returns:
-            tuple:
+            str: The type of the node.
-                - dict[str, str]: Mapping from upstream node ID to its End node prefix
+
-                - set[str]: Set of node IDs that are directly adjacent to End nodes and referenced
+        Raises:
+            RuntimeError: If no node with the given `node_id` exists.
         """
-        import re
+        try:
+            return self.node_map[node_id]["type"]
+        except KeyError:
+            raise RuntimeError(f"Node not found: Id={node_id}")
 
-        prefixes = {}
+    def _find_upstream_branch_node(self, target_node: str) -> tuple[bool, tuple[tuple[str, str]]]:
-        adjacent_and_referenced = set()  # Record nodes directly adjacent to End and referenced
+        """
+        Recursively find all upstream branch (control) nodes that influence the execution
+        of the given target node.
 
-        # 找到所有 End 节点
+        This method walks upstream along the workflow graph starting from `target_node`.
+        It distinguishes between:
+          - branch nodes (node types listed in `BRANCH_NODES`)
+          - non-branch nodes (ordinary processing nodes)
+
+        Traversal rules:
+        1. For each immediate upstream node:
+           - If it is a branch node, it is recorded as an affecting control node.
+           - If it is a non-branch node, the traversal continues recursively upstream.
+        2. If ANY upstream path reaches a START / CYCLE_START node without encountering
+           a branch node, the traversal is considered invalid:
+           - `has_branch` will be False
+           - no branch nodes are returned.
+        3. Only when ALL upstream non-branch paths eventually lead to at least one
+           branch node will `has_branch` be True.
+
+        Special case:
+        - If `target_node` has no upstream nodes AND its type is START or CYCLE_START,
+          it is considered directly reachable from the workflow entry, and therefore
+          has no controlling branch nodes.
+
+        Args:
+            target_node (str):
+                The identifier of the node whose upstream control branches
+                are to be resolved.
+
+        Returns:
+            tuple[bool, tuple[tuple[str, str]]]:
+                - has_branch (bool):
+                    True if every upstream path from `target_node` encounters
+                    at least one branch node.
+                    False if any path reaches a start node without a branch.
+                - branch_nodes (tuple[tuple[str, str]]):
+                    A deduplicated tuple of `(branch_node_id, branch_label)` pairs
+                    representing all branch nodes that can influence `target_node`.
+                    Returns an empty tuple if `has_branch` is False.
+        """
+        source_nodes = [
+            {
+                "id": edge.get("source"),
+                "branch": edge.get("label")
+            }
+            for edge in self.edges
+            if edge.get("target") == target_node
+        ]
+        if not source_nodes and self.get_node_type(target_node) in [NodeType.START, NodeType.CYCLE_START]:
+            return False, tuple()
+
+        branch_nodes = []
+        non_branch_nodes = []
+
+        for node_info in source_nodes:
+            if self.get_node_type(node_info["id"]) in BRANCH_NODES:
+                branch_nodes.append(
+                    (node_info["id"], node_info["branch"])
+                )
+            else:
+                non_branch_nodes.append(node_info["id"])
+
+        has_branch = True
+        for node_id in non_branch_nodes:
+            node_has_branch, nodes = self._find_upstream_branch_node(node_id)
+            has_branch = has_branch and node_has_branch
+            if not has_branch:
+                break
+            branch_nodes.extend(nodes)
+        if not has_branch:
+            branch_nodes = []
+
+        return has_branch, tuple(set(branch_nodes))
+
+    def _analyze_end_node_output(self):
+        """
+        Analyze output templates of all End nodes and generate StreamOutputConfig.
+
+        This method is responsible for parsing the `output` field of End nodes,
+        splitting literal text and variable placeholders (e.g. {{ node.field }}),
+        and determining whether each output segment should be activated immediately
+        or controlled by upstream branch nodes.
+
+        In stream mode:
+        - If the End node is controlled by any upstream branch node, the output
+          will be initially inactive and controlled by those branch nodes.
+        - Otherwise, the output is activated immediately.
+
+        In non-stream mode:
+        - All outputs are activated by default.
+        """
+
+        # Collect all End nodes in the workflow
         end_nodes = [node for node in self.nodes if node.get("type") == "end"]
         logger.info(f"[Prefix Analysis] Found {len(end_nodes)} End nodes")
 
+        # Iterate through each End node to analyze its output
         for end_node in end_nodes:
             end_node_id = end_node.get("id")
-            output_template = end_node.get("config", {}).get("output")
+            config = end_node.get("config", {})
+            output = config.get("output")
 
-            logger.info(f"[Prefix Analysis] End node {end_node_id} template: {output_template}")
+            # Skip End nodes without output configuration
-
+            if not output:
-            if not output_template:
                 continue
 
-            # Find all node references in the template
+            # Regex to split output into:
-            # Matches {{node_id.xxx}} or {{ node_id.xxx }} format (allowing spaces)
+            #    - variable placeholders: {{ ... }}
-            pattern = r'\{\{\s*([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\s*\}\}'
+            #    - normal literal text
-            matches = list(re.finditer(pattern, output_template))
+            #
+            # Example:
+            #   "Hello {{user.name}}!" ->
+            #   ["Hello ", "{{user.name}}", "!"]
+            pattern = r'\{\{.*?\}\}|[^{}]+'
 
-            logger.info(f"[Prefix Analysis] 模板中找到 {len(matches)} 个节点引用")
+            # Strict variable format: {{ node_id.field_name }}
+            variable_pattern_string = r'\{\{\s*[a-zA-Z0-9_]+\.[a-zA-Z0-9_]+\s*\}\}'
+            variable_pattern = re.compile(variable_pattern_string)
 
-            # Identify all direct upstream nodes connected to the End node
+            # Split output into ordered segments
-            direct_upstream_nodes = []
+            output_template = list(re.findall(pattern, output))
-            for edge in self.edges:
-                if edge.get("target") == end_node_id:
-                    source_node_id = edge.get("source")
-                    direct_upstream_nodes.append(source_node_id)
 
-            logger.info(f"[Prefix Analysis] Direct upstream nodes of End node: {direct_upstream_nodes}")
+            # Determine whether each segment is literal text
+            #    True  -> literal (can be directly output)
+            #    False -> variable placeholder (needs runtime value)
+            output_flag = [
+                not bool(variable_pattern.match(item))
+                for item in output_template
+            ]
 
-            # 找到第一个直接上游节点的引用
+            # Stream mode: output activation depends on upstream branch nodes
-            for match in matches:
+            if self.stream:
-                referenced_node_id = match.group(1)
+                # Find upstream branch nodes that can control this End node
-                logger.info(f"[Prefix Analysis] Checking reference: {referenced_node_id}")
+                has_branch, control_nodes = self._find_upstream_branch_node(end_node_id)
 
-                if referenced_node_id in direct_upstream_nodes:
+                # Build StreamOutputConfig for this End node
-                    # 这是直接上游节点的引用，提取前缀
+                self.end_node_map[end_node_id] = StreamOutputConfig(
-                    prefix = output_template[:match.start()]
+                    # If there is no upstream branch, output is active immediately
+                    activate=not has_branch,
 
-                    logger.info(f"[Prefix Analysis] "
+                    # Branch nodes that control activation of this End node
-                                f"✅ Found reference to direct upstream node {referenced_node_id}, prefix: '{prefix}'")
+                    control_nodes=dict(control_nodes),
 
-                    # 标记这个节点为"相邻且被引用"
+                    # Convert output segments into OutputContent objects
-                    adjacent_and_referenced.add(referenced_node_id)
+                    outputs=list(
+                        [
+                            OutputContent(
+                                literal=output_string,
+                                # Literal text can be activated immediately unless blocked by branch
+                                activate=activate,
+                                # Variable segments are marked explicitly
+                                is_variable=not activate
+                            )
+                            for output_string, activate in zip(output_template, output_flag)
+                        ]
+                    ),
+                    # Cursor for streaming output (initially 0)
+                    cursor=0
+                )
+                logger.info(f"[Stream Analysis] end_id: {end_node_id}, "
+                            f"activate: {not has_branch}, "
+                            f"control_nodes: {control_nodes},"
+                            f"output: {output_template},"
+                            f"output_activate: {output_flag}")
 
-                    if prefix:
+            # Non-stream mode: all outputs are activated by default
-                        prefixes[referenced_node_id] = prefix
+            else:
-                        logger.info(f"[Prefix Analysis] "
+                self.end_node_map[end_node_id] = StreamOutputConfig(
-                                    f"✅ Assign prefix for node {referenced_node_id}: '{prefix[:50]}...'")
+                    activate=True,
-
+                    control_nodes={},
-                    # 只处理第一个直接上游节点的引用
+                    outputs=list(
-                    break
+                        [
-
+                            OutputContent(
-        logger.info(f"[Prefix Analysis] Final prefixes: {prefixes}")
+                                literal=output_string,
-        logger.info(f"[Prefix Analysis] Nodes adjacent to End and referenced: {adjacent_and_referenced}")
+                                activate=True,
-        return prefixes, adjacent_and_referenced
+                                is_variable=not activate
+                            )
+                            for output_string, activate in zip(output_template, output_flag)
+                        ]
+                    ),
+                    cursor=0
+                )
 
     def add_nodes(self):
         """Add all nodes from the workflow configuration to the state graph.
@@ -135,9 +415,6 @@ class GraphBuilder:
         Returns:
             None
         """
-        # Analyze End node prefixes if in stream mode
-        end_prefixes, adjacent_and_referenced = self._analyze_end_node_prefixes() if self.stream else ({}, set())
-
         for node in self.nodes:
             node_type = node.get("type")
             node_id = node.get("id")
@@ -171,17 +448,6 @@ class GraphBuilder:
                     related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'"
 
             if node_instance:
-                # Inject End node prefix configuration if in stream mode
-                if self.stream and node_id in end_prefixes:
-                    node_instance._end_node_prefix = end_prefixes[node_id]
-                    logger.info(f"Injected End prefix for node {node_id}")
-
-                # Mark nodes as adjacent and referenced to End node in stream mode
-                if self.stream:
-                    node_instance._is_adjacent_to_end = node_id in adjacent_and_referenced
-                    if node_id in adjacent_and_referenced:
-                        logger.info(f"Node {node_id} marked as adjacent and referenced to End node")
-
                 # Wrap node's run method to avoid closure issues
                 if self.stream:
                     # Stream mode: create an async generator function
@@ -261,6 +527,7 @@ class GraphBuilder:
         for source_node, branches in conditional_edges.items():
             def make_router(src, branch_list):
                 """reate a router function for each source node that routes to a NOP node for later merging."""
+
                 def make_branch_node(node_name, targets):
                     def node(s):
                         # NOTE: NOP NODE MUST NOT MODIFY STATE

api/app/core/workflow/nodes/base_node.py

@@ -67,10 +67,6 @@ class WorkflowState(TypedDict):
     error: str | None
     error_node: str | None
 
-    # Streaming buffer (stores real-time streaming output of nodes)
-    # Format: {node_id: {"chunks": [...], "full_content": "..."}}
-    streaming_buffer: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
-
     # node activate status
     activate: Annotated[dict[str, bool], merge_activate_state]
 
@@ -300,7 +296,7 @@ class BaseNode(ABC):
         """
         if not self.check_activate(state):
             yield self.trans_activate(state)
-            logger.info(f"跳过节点{self.node_id}")
+            logger.info(f"jump node: {self.node_id}")
             return
 
         import time
@@ -313,19 +309,6 @@ class BaseNode(ABC):
             # Get LangGraph's stream writer for sending custom data
             writer = get_stream_writer()
 
-            # Check if this is an End node
-            # End nodes CAN send chunks (for suffix), but only after LLM content
-            is_end_node = self.node_type == "end"
-
-            # Check if this node is adjacent to End node (for message type)
-            is_adjacent_to_end = getattr(self, '_is_adjacent_to_end', False)
-
-            # Determine chunk type: "message" for End and adjacent nodes, "node_chunk" for others
-            chunk_type = "message" if (is_end_node or is_adjacent_to_end) else "node_chunk"
-
-            logger.debug(
-                f"节点 {self.node_id} chunk 类型: {chunk_type} (is_end={is_end_node}, adjacent={is_adjacent_to_end})")
-
             # Accumulate complete result (for final wrapping)
             chunks = []
             final_result = None
@@ -340,66 +323,25 @@ class BaseNode(ABC):
                     raise TimeoutError()
 
                 # Check if it's a completion marker
-                if isinstance(item, dict) and item.get("__final__"):
+                if item.get("__final__"):
                     final_result = item["result"]
-                elif isinstance(item, str):
+                else:
-                    # String is a chunk
                     chunk_count += 1
-                    chunks.append(item)
+                    content = str(item.get("chunk"))
-                    full_content = "".join(chunks)
+                    done = item.get("done", False)
+                    chunks.append(content)
 
                     # Send chunks for all nodes (including End nodes for suffix)
-                    logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {item[:50]}...")
+                    logger.debug(f"节点 {self.node_id} 发送 chunk #{chunk_count}: {content[:50]}...")
 
                     # 1. Send via stream writer (for real-time client updates)
                     writer({
-                        "type": chunk_type,  # "message" or "node_chunk"
+                        "type": "node_chunk",
                         "node_id": self.node_id,
-                        "chunk": item,
+                        "chunk": content,
-                        "full_content": full_content,
+                        "done": done
-                        "chunk_index": chunk_count
                     })
 
-                    # 2. Update streaming buffer in state (for downstream nodes)
-                    # Only non-End nodes need streaming buffer
-                    if not is_end_node:
-                        yield {
-                            "streaming_buffer": {
-                                self.node_id: {
-                                    "full_content": full_content,
-                                    "chunk_count": chunk_count,
-                                    "is_complete": False
-                                }
-                            }
-                        }
-                else:
-                    # Other types are also treated as chunks
-                    chunk_count += 1
-                    chunk_str = str(item)
-                    chunks.append(chunk_str)
-                    full_content = "".join(chunks)
-
-                    # Send chunks for all nodes
-                    writer({
-                        "type": chunk_type,  # "message" or "node_chunk"
-                        "node_id": self.node_id,
-                        "chunk": chunk_str,
-                        "full_content": full_content,
-                        "chunk_index": chunk_count
-                    })
-
-                    # Only non-End nodes need streaming buffer
-                    if not is_end_node:
-                        yield {
-                            "streaming_buffer": {
-                                self.node_id: {
-                                    "full_content": full_content,
-                                    "chunk_count": chunk_count,
-                                    "is_complete": False
-                                }
-                            }
-                        }
-
             elapsed_time = time.time() - start_time
 
             logger.info(f"节点 {self.node_id} 流式执行完成，耗时: {elapsed_time:.2f}s, chunks: {chunk_count}")
@@ -426,16 +368,6 @@ class BaseNode(ABC):
                 "looping": state["looping"]
             }
 
-            # Add streaming buffer for non-End nodes
-            if not is_end_node:
-                state_update["streaming_buffer"] = {
-                    self.node_id: {
-                        "full_content": "".join(chunks),
-                        "chunk_count": chunk_count,
-                        "is_complete": True  # Mark as complete
-                    }
-                }
-
             # Finally yield state update
             # LangGraph will merge this into state
             yield state_update | self.trans_activate(state)
@@ -544,6 +476,11 @@ class BaseNode(ABC):
                 "error_node": self.node_id
             }
         else:
+            writer = get_stream_writer()
+            writer({
+                "type": "node_error",
+                **node_output
+            })
             # 无错误边：抛出异常停止工作流
             logger.error(f"节点 {self.node_id} 执行失败，停止工作流: {error_message}")
             raise Exception(f"节点 {self.node_id} 执行失败: {error_message}")

api/app/core/workflow/nodes/code/__init__.py

@@ -0,0 +1,3 @@
+from app.core.workflow.nodes.code.node import CodeNode
+
+__all__ = ["CodeNode"]

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

@@ -0,0 +1,50 @@
+from typing import Literal
+from pydantic import Field, BaseModel
+
+from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableType
+
+
+class InputVariable(BaseModel):
+    name: str = Field(
+        ...,
+        description="variable name"
+    )
+
+    variable: str = Field(
+        ...,
+        description="variable selector"
+    )
+
+
+class OutputVariable(BaseModel):
+    name: str = Field(
+        ...,
+        description="variable name"
+    )
+
+    type: VariableType = Field(
+        ...,
+        description="variable selector"
+    )
+
+
+class CodeNodeConfig(BaseNodeConfig):
+    input_variables: list[InputVariable] = Field(
+        default_factory=list,
+        description="input variables"
+    )
+
+    output_variables: list[OutputVariable] = Field(
+        default_factory=list,
+        description="output variables"
+    )
+
+    code: str = Field(
+        default="",
+        description="code content"
+    )
+
+    language: Literal['python3', 'nodejs'] = Field(
+        ...,
+        description="language"
+    )