Merge branch 'refs/heads/develop' into fix/memory_bug_fix

README.md

@@ -334,7 +334,13 @@ step6: Log In to the Frontend Interface.
 ## License
 This project is licensed under the Apache License 2.0. For details, see the LICENSE file.
 
-## Acknowledgements & Community
+## Community & Support
-- Feedback & Issues: Please submit an Issue in the repository for bug reports or discussions.
+
-- Contributions Welcome: When submitting a Pull Request, please create a feature branch and follow conventional commit message guidelines.
+Join our community to ask questions, share your work, and connect with fellow developers.
-- Contact: If you are interested in contributing or collaborating, feel free to reach out at tianyou_hubm@redbearai.com
+
+- **GitHub Issues**: Report bugs, request features, or track known issues via [GitHub Issues](https://github.com/SuanmoSuanyangTechnology/MemoryBear/issues).
+- **GitHub Pull Requests**: Contribute code improvements or fixes through [Pull Requests](https://github.com/SuanmoSuanyangTechnology/MemoryBear/pulls).
+- **GitHub Discussions**: Ask questions, share ideas, and engage with the community in [GitHub Discussions](https://github.com/SuanmoSuanyangTechnology/MemoryBear/discussions).
+- **WeChat**: Scan the QR code below to join our WeChat community group.
+- ![wecom-temp-114020-47fe87a75da439f09f5dc93a01593046](https://github.com/user-attachments/assets/8c81885c-4134-40d5-96e2-7f78cc082dc6)
+- **Contact**: If you are interested in contributing or collaborating, feel free to reach out at tianyou_hubm@redbearai.com

api/app/celery_app.py

@@ -1,4 +1,5 @@
 import os
+import platform
 from datetime import timedelta
 from urllib.parse import quote
 
@@ -14,28 +15,13 @@ celery_app = Celery(
     backend=f"redis://:{quote(settings.REDIS_PASSWORD)}@{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.CELERY_BACKEND}",
 )
 
-# 配置使用本地队列，避免与远程 worker 冲突
+# Default queue for unrouted tasks
-celery_app.conf.task_default_queue = 'localhost_test_wyl'
+celery_app.conf.task_default_queue = 'memory_tasks'
-celery_app.conf.task_default_exchange = 'localhost_test_wyl'
-celery_app.conf.task_default_routing_key = 'localhost_test_wyl'
 
 # macOS 兼容性配置
-import platform
+if platform.system() == 'Darwin':
-
-if platform.system() == 'Darwin':  # macOS
-    # 设置环境变量解决 fork 问题
     os.environ.setdefault('OBJC_DISABLE_INITIALIZE_FORK_SAFETY', 'YES')
 
-    # 使用 solo 池避免多进程问题
-    celery_app.conf.worker_pool = 'solo'
-    
-    # 设置唯一的节点名称
-    import socket
-    import time
-    hostname = socket.gethostname()
-    timestamp = int(time.time())
-    celery_app.conf.worker_name = f"celery@{hostname}-{timestamp}"
-
 # Celery 配置
 celery_app.conf.update(
     # 序列化
@@ -52,36 +38,47 @@ celery_app.conf.update(
     task_ignore_result=False,
     
     # 超时设置
-    task_time_limit=30 * 60,  # 30 分钟硬超时
+    task_time_limit=1800,  # 30分钟硬超时
-    task_soft_time_limit=25 * 60,  # 25 分钟软超时
+    task_soft_time_limit=1500,  # 25分钟软超时
     
-    # Worker 设置 - 针对 macOS 优化
+    # Worker 设置 (per-worker settings are in docker-compose command line)
-    worker_prefetch_multiplier=1,  # 减少预取任务数，避免内存堆积
+    worker_prefetch_multiplier=1,  # Don't hoard tasks, fairer distribution
-    worker_max_tasks_per_child=10,  # 大幅减少每个 worker 执行的任务数，频繁重启防止内存泄漏
-    worker_max_memory_per_child=200000,  # 200MB 内存限制，超过后重启 worker
     
     # 结果过期时间
-    result_expires=3600,  # 结果保存 1 小时
+    result_expires=3600,  # 结果保存1小时
     
     # 任务确认设置
-    task_acks_late=True,  # 任务完成后才确认，避免任务丢失
+    task_acks_late=True,
-    worker_disable_rate_limits=True,  # 禁用速率限制
+    task_reject_on_worker_lost=True,
+    worker_disable_rate_limits=True,
     
-    # 任务路由（可选，用于不同队列）
+    # FLower setting 
-    # task_routes={
+    worker_send_task_events=True,
-    #     'app.core.rag.tasks.parse_document': {'queue': 'document_processing'},
+    task_send_sent_event=True,
-    #     'app.core.memory.agent.read_message': {'queue': 'memory_processing'},
+    
-    #     'app.core.memory.agent.write_message': {'queue': 'memory_processing'},
+    # task routing
-    #     'tasks.process_item': {'queue': 'default'},
+    task_routes={
-    # },
+        # Memory tasks → memory_tasks queue (threads worker)
+        'app.core.memory.agent.read_message_priority': {'queue': 'memory_tasks'},
+        'app.core.memory.agent.read_message': {'queue': 'memory_tasks'},
+        'app.core.memory.agent.write_message': {'queue': 'memory_tasks'},
+        
+        # Document tasks → document_tasks queue (prefork worker)
+        'app.core.rag.tasks.parse_document': {'queue': 'document_tasks'},
+        'app.core.rag.tasks.build_graphrag_for_kb': {'queue': 'document_tasks'},
+        
+        # Beat/periodic tasks → document_tasks queue (prefork worker)
+        'app.tasks.workspace_reflection_task': {'queue': 'document_tasks'},
+        'app.tasks.regenerate_memory_cache': {'queue': 'document_tasks'},
+        'app.tasks.run_forgetting_cycle_task': {'queue': 'document_tasks'},
+        'app.controllers.memory_storage_controller.search_all': {'queue': 'document_tasks'},
+    },
 )
 
 # 自动发现任务模块
 celery_app.autodiscover_tasks(['app'])
 
 # Celery Beat schedule for periodic tasks
-reflection_schedule = timedelta(seconds=settings.REFLECTION_INTERVAL_SECONDS)
-health_schedule = timedelta(seconds=settings.HEALTH_CHECK_SECONDS)
 memory_increment_schedule = timedelta(hours=settings.MEMORY_INCREMENT_INTERVAL_HOURS)
 memory_cache_regeneration_schedule = timedelta(hours=settings.MEMORY_CACHE_REGENERATION_HOURS)
 workspace_reflection_schedule = timedelta(seconds=30)  # 每30秒运行一次settings.REFLECTION_INTERVAL_TIME
@@ -89,12 +86,6 @@ forgetting_cycle_schedule = timedelta(hours=24)  # 每24小时运行一次遗忘
 
 # 构建定时任务配置
 beat_schedule_config = {
-
-    # "check-read-service": {
-    #     "task": "app.core.memory.agent.health.check_read_service",
-    #     "schedule": health_schedule,
-    #     "args": (),
-    # },
     "run-workspace-reflection": {
         "task": "app.tasks.workspace_reflection_task",
         "schedule": workspace_reflection_schedule,

api/app/controllers/memory_agent_controller.py

@@ -9,6 +9,8 @@ from app.db import get_db
 from app.dependencies import cur_workspace_access_guard, get_current_user
 from app.models import ModelApiKey
 from app.models.user_model import User
+from app.core.memory.agent.utils.session_tools import SessionService
+from app.core.memory.agent.utils.redis_tool import store
 from app.repositories import knowledge_repository, WorkspaceRepository
 from app.schemas.memory_agent_schema import UserInput, Write_UserInput
 from app.schemas.response_schema import ApiResponse
@@ -123,7 +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
@@ -158,11 +160,11 @@ async def write_server(
             api_logger.warning("workspace_id 为空，无法使用 rag 存储，将使用 neo4j 存储")
             storage_type = 'neo4j'
     
-    api_logger.info(f"Write service requested for group {user_input.group_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}")
+    api_logger.info(f"Write service requested for group {user_input.end_user_id}, storage_type: {storage_type}, user_rag_memory_id: {user_rag_memory_id}")
     try:
         result = await memory_agent_service.write_memory(
-            user_input.group_id, 
+            user_input.end_user_id,
-            user_input.message, 
+            user_input.messages,
             config_id,
             db,
             storage_type, 
@@ -191,7 +193,7 @@ async def write_server_async(
     Async write service endpoint - enqueues write processing to Celery
     
     Args:
-        user_input: Write request containing message and group_id
+        user_input: Write request containing message and end_user_id
     
     Returns:
         Task ID for tracking async operation
@@ -221,7 +223,7 @@ async def write_server_async(
     try:
         task = celery_app.send_task(
             "app.core.memory.agent.write_message",
-            args=[user_input.group_id, user_input.message, config_id, storage_type, user_rag_memory_id]
+            args=[user_input.end_user_id, user_input.message, config_id, storage_type, user_rag_memory_id]
         )
         api_logger.info(f"Write task queued: {task.id}")
         
@@ -247,7 +249,7 @@ async def read_server(
     - "2": Direct answer based on context
     
     Args:
-        user_input: Read request with message, history, search_switch, and group_id
+        user_input: Read request with message, history, search_switch, and end_user_id
     
     Returns:
         Response with query answer
@@ -271,12 +273,13 @@ async def read_server(
             name="USER_RAG_MERORY",
             workspace_id=workspace_id
         )
-        if knowledge: user_rag_memory_id = str(knowledge.id)
+        if knowledge:
+            user_rag_memory_id = str(knowledge.id)
     
-    api_logger.info(f"Read service: group={user_input.group_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
+    api_logger.info(f"Read service: group={user_input.end_user_id}, storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}, workspace_id={workspace_id}")
     try:
         result = await memory_agent_service.read_memory(
-            user_input.group_id,
+            user_input.end_user_id,
             user_input.message,
             user_input.history,
             user_input.search_switch,
@@ -285,6 +288,19 @@ async def read_server(
             storage_type,
             user_rag_memory_id
         )
+        if str(user_input.search_switch) == "2":
+            retrieve_info = result['answer']
+            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(
+                retrieve_info=retrieve_info,
+                history=history,
+                query=query,
+                config_id=config_id,
+                db=db
+            )
         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
@@ -382,7 +398,7 @@ async def read_server_async(
     try:
         task = celery_app.send_task(
             "app.core.memory.agent.read_message",
-            args=[user_input.group_id, user_input.message, user_input.history, user_input.search_switch,
+            args=[user_input.end_user_id, user_input.message, user_input.history, user_input.search_switch,
                   config_id, storage_type, user_rag_memory_id]
         )
         api_logger.info(f"Read task queued: {task.id}")
@@ -426,7 +442,7 @@ async def get_read_task_result(
                 return success(
                     data={
                         "result": task_result.get("result"),
-                        "group_id": task_result.get("group_id"),
+                        "end_user_id": task_result.get("end_user_id"),
                         "elapsed_time": task_result.get("elapsed_time"),
                         "task_id": task_id
                     },
@@ -503,7 +519,7 @@ async def get_write_task_result(
                 return success(
                     data={
                         "result": task_result.get("result"),
-                        "group_id": task_result.get("group_id"),
+                        "end_user_id": task_result.get("end_user_id"),
                         "elapsed_time": task_result.get("elapsed_time"),
                         "task_id": task_id
                     },
@@ -557,15 +573,30 @@ 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 = ""
+        for msg in reversed(messages_list):
+            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(
-            user_input.message,
+            user_input.messages,
             user_input.config_id,
             db
         )
@@ -588,7 +619,7 @@ async def get_knowledge_type_stats_api(
     会对缺失类型补 0，返回字典形式。
     可选按状态过滤。
     - 知识库类型根据当前用户的 current_workspace_id 过滤
-    - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (group_id) 过滤
+    - memory 是 Neo4j 中 Chunk 的数量，根据 end_user_id (end_user_id) 过滤
     - 如果用户没有当前工作空间或未提供 end_user_id，对应的统计返回 0
     """
     api_logger.info(f"Knowledge type stats requested for workspace_id: {current_user.current_workspace_id}, end_user_id: {end_user_id}")

api/app/controllers/memory_dashboard_controller.py

@@ -5,7 +5,6 @@ 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.schemas.memory_agent_schema import End_User_Information
 from app.schemas.response_schema import ApiResponse
 
 from app.services import memory_dashboard_service, memory_storage_service, workspace_service
@@ -40,54 +39,7 @@ def get_workspace_total_end_users(
     api_logger.info(f"成功获取最新用户总数: total_num={total_end_users.get('total_num', 0)}")
     return success(data=total_end_users, msg="用户数量获取成功")
 
-@router.post("/update/end_users", response_model=ApiResponse)
-async def update_workspace_end_users(
-    user_input: End_User_Information,
-    db: Session = Depends(get_db),
-    current_user: User = Depends(get_current_user),
-):
-    """
-    更新工作空间的宿主信息
-    """
-    username = user_input.end_user_name  # 要更新的用户名
-    end_user_input_id = user_input.id  # 宿主ID
-    workspace_id = current_user.current_workspace_id
 
-    api_logger.info(f"用户 {current_user.username} 请求更新工作空间 {workspace_id} 的宿主信息")
-    api_logger.info(f"更新参数: username={username}, end_user_id={end_user_input_id}")
-
-    try:
-        # 导入更新函数
-        from app.repositories.end_user_repository import update_end_user_other_name
-        import uuid
-        
-        # 转换 end_user_id 为 UUID 类型
-        end_user_uuid = uuid.UUID(end_user_input_id)
-        
-        # 直接更新数据库中的 other_name 字段
-        updated_count = update_end_user_other_name(
-            db=db,
-            end_user_id=end_user_uuid,
-            other_name=username
-        )
-        
-        api_logger.info(f"成功更新宿主 {end_user_input_id} 的 other_name 为: {username}")
-
-        return success(
-            data={
-                "updated_count": updated_count,
-                "end_user_id": end_user_input_id,
-                "updated_other_name": username
-            },
-            msg=f"成功更新 {updated_count} 个宿主的信息"
-        )
-        
-    except Exception as e:
-        api_logger.error(f"更新宿主信息失败: {str(e)}")
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"更新宿主信息失败: {str(e)}"
-        )
 
 
 

api/app/controllers/memory_storage_controller.py

@@ -28,7 +28,6 @@ from app.services.memory_storage_service import (
     search_dialogue,
     search_edges,
     search_entity,
-    search_entity_graph,
     search_statement,
 )
 from fastapi import APIRouter, Depends
@@ -412,21 +411,7 @@ async def search_entity_edges(
         api_logger.error(f"Search edges failed: {str(e)}")
         return fail(BizCode.INTERNAL_ERROR, "边查询失败", str(e))
 
-@router.get("/search/entity_graph", response_model=ApiResponse)
+
-async def search_for_entity_graph(
-    end_user_id: Optional[str] = None,
-    current_user: User = Depends(get_current_user),
-    ) -> dict:
-    """
-    搜索所有实体之间的关系网络
-    """
-    api_logger.info(f"Search entity graph requested for end_user_id: {end_user_id}")
-    try:
-        result = await search_entity_graph(end_user_id)
-        return success(data=result, msg="查询成功")
-    except Exception as e:
-        api_logger.error(f"Search entity graph failed: {str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "实体图查询失败", str(e))
 
 
 @router.get("/analytics/hot_memory_tags", response_model=ApiResponse)

api/app/controllers/service/memory_api_controller.py

@@ -39,7 +39,7 @@ async def write_memory_api_service(
     
     Stores memory content for the specified end user using the Memory API Service.
     """
-    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}")
+    logger.info(f"Memory write request - end_user_id: {payload.end_user_id}, tenant_id: {api_key_auth.tenant_id}")
     
     memory_api_service = MemoryAPIService(db)
     
@@ -50,6 +50,7 @@ async def write_memory_api_service(
         config_id=payload.config_id,
         storage_type=payload.storage_type,
         user_rag_memory_id=payload.user_rag_memory_id,
+        tenant_id=api_key_auth.tenant_id,
     )
     
     logger.info(f"Memory write successful for end_user: {payload.end_user_id}")

api/app/controllers/user_memory_controllers.py

@@ -351,12 +351,11 @@ async def update_end_user_profile(
 
     该接口可以更新用户的姓名、职位、部门、联系方式、电话和入职日期等信息。
     所有字段都是可选的，只更新提供的字段。
-
     """
     workspace_id = current_user.current_workspace_id
     end_user_id = profile_update.end_user_id
 
-    # 检查用户是否已选择工作空间
+    # 验证工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试更新用户信息但未选择工作空间")
         return fail(BizCode.INVALID_PARAMETER, "请先切换到一个工作空间", "current_workspace_id is None")
@@ -366,57 +365,24 @@ async def update_end_user_profile(
         f"workspace={workspace_id}"
     )
 
-    try:
+    # 调用 Service 层处理业务逻辑
-        # 查询终端用户
+    result = user_memory_service.update_end_user_profile(db, end_user_id, profile_update)
-        end_user = db.query(EndUser).filter(EndUser.id == end_user_id).first()
 
-        if not end_user:
+    if result["success"]:
-            api_logger.warning(f"终端用户不存在: end_user_id={end_user_id}")
+        api_logger.info(f"成功更新用户信息: end_user_id={end_user_id}")
-            return fail(BizCode.INVALID_PARAMETER, "终端用户不存在", f"end_user_id={end_user_id}")
+        return success(data=result["data"], msg="更新成功")
+    else:
+        error_msg = result["error"]
+        api_logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={error_msg}")
         
-        # 更新字段（只更新提供的字段，排除 end_user_id）
+        # 根据错误类型映射到合适的业务错误码
-        # 允许 None 值来重置字段（如 hire_date）
+        if error_msg == "终端用户不存在":
-        update_data = profile_update.model_dump(exclude_unset=True, exclude={'end_user_id'})
+            return fail(BizCode.USER_NOT_FOUND, "终端用户不存在", error_msg)
-
+        elif error_msg == "无效的用户ID格式":
-        # 特殊处理 hire_date：如果提供了时间戳，转换为 DateTime
+            return fail(BizCode.INVALID_USER_ID, "无效的用户ID格式", error_msg)
-        if 'hire_date' in update_data:
+        else:
-            hire_date_timestamp = update_data['hire_date']
+            # 只有未预期的错误才使用 INTERNAL_ERROR
-            if hire_date_timestamp is not None:
+            return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", error_msg)
-                update_data['hire_date'] = timestamp_to_datetime(hire_date_timestamp)
-            # 如果是 None，保持 None（允许清空）
-
-        for field, value in update_data.items():
-            setattr(end_user, field, value)
-
-        # 更新 updated_at 时间戳
-        end_user.updated_at = datetime.datetime.now()
-
-        # 更新 updatetime_profile 为当前时间
-        end_user.updatetime_profile = datetime.datetime.now()
-
-        # 提交更改
-        db.commit()
-        db.refresh(end_user)
-
-        # 构建响应数据
-        profile_data = EndUserProfileResponse(
-            id=end_user.id,
-            other_name=end_user.other_name,
-            position=end_user.position,
-            department=end_user.department,
-            contact=end_user.contact,
-            phone=end_user.phone,
-            hire_date=end_user.hire_date,
-            updatetime_profile=end_user.updatetime_profile
-        )
-
-        api_logger.info(f"成功更新用户信息: end_user_id={end_user_id}, updated_fields={list(update_data.keys())}")
-        return success(data=UserMemoryService.convert_profile_to_dict_with_timestamp(profile_data), msg="更新成功")
-
-    except Exception as e:
-        db.rollback()
-        api_logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={str(e)}")
-        return fail(BizCode.INTERNAL_ERROR, "用户信息更新失败", str(e))
 
 @router.get("/memory_space/timeline_memories", response_model=ApiResponse)
 async def memory_space_timeline_of_shared_memories(id: str, label: str,language_type: str="zh",

api/app/core/agent/langchain_agent.py

@@ -154,7 +154,7 @@ class LangChainAgent:
                         userid=end_user_end,
                         messages=messages,
                         apply_id=end_user_end,
-                        group_id=end_user_end,
+                        end_user_id=end_user_end,
                         aimessages=aimessages
                     )
         store.delete_duplicate_sessions()
@@ -173,16 +173,67 @@ class LangChainAgent:
             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）
 
-    async def write(self,storage_type,end_user_id,message,user_rag_memory_id,actual_end_user_id,content,actual_config_id):
+        Args:
+            storage_type: 存储类型 (neo4j/rag)
+            end_user_id: 终端用户ID
+            user_message: 用户消息内容
+            ai_message: AI 回复内容
+            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
+          1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant]
+          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":
-            await write_rag(end_user_id, message, user_rag_memory_id)
+            # 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:
-            write_id = write_message_task.delay(actual_end_user_id, content, actual_config_id, storage_type,
+            # Neo4j 模式：使用结构化消息列表
-                                                user_rag_memory_id)
+            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'Agent:{actual_end_user_id};{write_status}')
+            logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
 
     async def chat(
             self,
@@ -227,29 +278,30 @@ class LangChainAgent:
         actual_end_user_id = end_user_id if end_user_id is not None else "unknown"
         logger.info(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
         print(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
+# # TODO 乐力齐，在长短期记忆存储的时候再使用此代码
+#         history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+#         history_term_memory = history_term_memory_result[0]
+#         db_for_memory = next(get_db())
+#         if memory_flag:
+#             if len(history_term_memory)>=4 and storage_type != "rag":
+#                 history_term_memory = ';'.join(history_term_memory)
+#                 retrieved_content = history_term_memory_result[1]
+#                 print(retrieved_content)
+#                 # 为长期记忆操作获取新的数据库连接
+#                 try:
+#                     repo = LongTermMemoryRepository(db_for_memory)
+#                     repo.upsert(end_user_id, retrieved_content)
+#                     logger.info(
+#                         f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+#                 except Exception as e:
+#                     logger.error(f"Failed to write to LongTermMemory: {e}")
+#                     raise
+#                 finally:
+#                     db_for_memory.close()
 
-        history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+#                 # 长期记忆写入（
-        history_term_memory = history_term_memory_result[0]
+#                 await self.write(storage_type, actual_end_user_id, history_term_memory, "", user_rag_memory_id, actual_end_user_id, actual_config_id)
-        db_for_memory = next(get_db())
+#             # 注意：不在这里写入用户消息，等 AI 回复后一起写入
-        if memory_flag:
-            if len(history_term_memory)>=4 and storage_type != "rag":
-                history_term_memory = ';'.join(history_term_memory)
-                retrieved_content = history_term_memory_result[1]
-                print(retrieved_content)
-                # 为长期记忆操作获取新的数据库连接
-                try:
-                    repo = LongTermMemoryRepository(db_for_memory)
-                    repo.upsert(end_user_id, retrieved_content)
-                    logger.info(
-                        f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
-                except Exception as e:
-                    logger.error(f"Failed to write to LongTermMemory: {e}")
-                    raise
-                finally:
-                    db_for_memory.close()
-
-                await self.write(storage_type,end_user_id,history_term_memory,user_rag_memory_id,actual_end_user_id,history_term_memory,actual_config_id)
-            await self.write(storage_type,end_user_id,message,user_rag_memory_id,actual_end_user_id,message,actual_config_id)
         try:
             # 准备消息列表
             messages = self._prepare_messages(message, history, context)
@@ -277,8 +329,10 @@ class LangChainAgent:
 
             elapsed_time = time.time() - start_time
             if memory_flag:
-                await self.write(storage_type,end_user_id,content,user_rag_memory_id,actual_end_user_id,content,actual_config_id)
+                # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
-                await self.term_memory_save(message_chat,end_user_id,content)
+                await self.write(storage_type, actual_end_user_id, message_chat, content, user_rag_memory_id, actual_end_user_id, actual_config_id)
+                # TODO 乐力齐 - 累积多组对话批量写入功能已禁用
+                # await self.term_memory_save(message_chat, end_user_id, content)
             response = {
                 "content": content,
                 "model": self.model_name,
@@ -346,27 +400,27 @@ class LangChainAgent:
                     db.close()
             except Exception as e:
                 logger.warning(f"Failed to get db session: {e}")
+# # TODO 乐力齐
+#         history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+#         history_term_memory = history_term_memory_result[0]
+#         if memory_flag:
+#             if len(history_term_memory) >= 4 and storage_type != "rag":
+#                 history_term_memory = ';'.join(history_term_memory)
+#                 retrieved_content = history_term_memory_result[1]
+#                 db_for_memory = next(get_db())
+#                 try:
+#                     repo = LongTermMemoryRepository(db_for_memory)
+#                     repo.upsert(end_user_id, retrieved_content)
+#                     logger.info(
+#                         f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
+#                     # 长期记忆写入
+#                     await self.write(storage_type, end_user_id, history_term_memory, "", user_rag_memory_id, end_user_id, actual_config_id)
+#                 except Exception as e:
+#                     logger.error(f"Failed to write to long term memory: {e}")
+#                 finally:
+#                     db_for_memory.close()
 
-        history_term_memory_result = await self.term_memory_redis_read(end_user_id)
+            # 注意：不在这里写入用户消息，等 AI 回复后一起写入
-        history_term_memory = history_term_memory_result[0]
-        if memory_flag:
-            if len(history_term_memory) >= 4 and storage_type != "rag":
-                history_term_memory = ';'.join(history_term_memory)
-                retrieved_content = history_term_memory_result[1]
-                db_for_memory = next(get_db())
-                try:
-                    repo = LongTermMemoryRepository(db_for_memory)
-                    repo.upsert(end_user_id, retrieved_content)
-                    logger.info(
-                        f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
-                    await self.write(storage_type, end_user_id, history_term_memory, user_rag_memory_id, end_user_id,
-                                     history_term_memory, actual_config_id)
-                except Exception as e:
-                    logger.error(f"Failed to write to long term memory: {e}")
-                finally:
-                    db_for_memory.close()
-
-            await self.write(storage_type, end_user_id, message, user_rag_memory_id, end_user_id, message, actual_config_id)
         try:
             # 准备消息列表
             messages = self._prepare_messages(message, history, context)
@@ -418,8 +472,10 @@ class LangChainAgent:
                 
                 logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件")
                 if memory_flag:
-                    await self.write(storage_type, end_user_id,full_content, user_rag_memory_id, end_user_id,full_content, actual_config_id)
+                    # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
-                    await self.term_memory_save(message_chat, end_user_id, full_content)
+                    await self.write(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, end_user_id, actual_config_id)
+                    # TODO 乐力齐 - 累积多组对话批量写入功能已禁用
+                    # await self.term_memory_save(message_chat, end_user_id, full_content)
                 
             except Exception as e:
                 logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)

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

@@ -18,6 +18,7 @@ template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
 db_session = next(get_db())
 logger = get_agent_logger(__name__)
 
+
 class ProblemNodeService(LLMServiceMixin):
     """问题处理节点服务类"""
 
@@ -25,17 +26,19 @@ class ProblemNodeService(LLMServiceMixin):
         super().__init__()
         self.template_service = TemplateService(template_root)
 
+
 # 创建全局服务实例
 problem_service = ProblemNodeService()
 
+
 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()
@@ -77,7 +80,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
         split_result_dict = []
         for index, item in enumerate(json.loads(split_result)):
             split_data = {
-                "id": f"Q{index+1}",
+                "id": f"Q{index + 1}",
                 "question": item['extended_question'],
                 "type": item['type'],
                 "reason": item['reason']
@@ -130,13 +133,14 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
     # 返回更新后的状态，包含spit_context字段
     return {"spit_data": result}
 
+
 async def Problem_Extension(state: ReadState) -> ReadState:
     """问题扩展节点"""
     # 获取原始数据和分解结果
     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)
@@ -152,7 +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()
@@ -243,6 +247,3 @@ async def Problem_Extension(state: ReadState) -> ReadState:
     }
 
     return {"problem_extension": result}
-
-
-

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

@@ -4,12 +4,11 @@ import os
 import time
 
 from app.core.logging_config import get_agent_logger, log_time
-from app.db import get_db
-
 from app.core.memory.agent.models.summary_models import (
     RetrieveSummaryResponse,
     SummaryResponse,
 )
+from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
 from app.core.memory.agent.services.search_service import SearchService
 from app.core.memory.agent.utils.llm_tools import (
     PROJECT_ROOT_,
@@ -18,7 +17,7 @@ from app.core.memory.agent.utils.llm_tools import (
 from app.core.memory.agent.utils.redis_tool import store
 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
+from app.db import get_db
 
 template_root = os.path.join(PROJECT_ROOT_, 'agent', 'utils', 'prompt')
 logger = get_agent_logger(__name__)
@@ -35,8 +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:
@@ -123,12 +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()
@@ -176,13 +175,14 @@ 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
+        "return_raw_results": True,
+        "include": ["summaries"]  # Only search summary nodes for faster performance
     }
 
     try:

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

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

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

@@ -9,26 +9,21 @@ async def write_node(state: WriteState) -> WriteState:
         Write data to the database/file system.
 
         Args:
-            ctx: FastMCP context for dependency injection
             content: Data content to write
-            user_id: User identifier
+            end_user_id: End user identifier
-            apply_id: Application identifier
-            group_id: Group identifier
             memory_config: MemoryConfig object containing all configuration
 
         Returns:
             dict: Contains 'status', 'saved_to', and 'data' fields
         """
     content=state.get('data','')
-    group_id=state.get('group_id','')
+    end_user_id=state.get('end_user_id','')
     memory_config=state.get('memory_config', '')
     try:
         result=await write(
-            content=content,
+            end_user_id=end_user_id,
-            user_id=group_id,
-            apply_id=group_id,
-            group_id=group_id,
             memory_config=memory_config,
+            messages=content,  # 修复：使用正确的参数名 messages
         )
         logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
 

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

@@ -59,7 +59,6 @@ async def make_read_graph():
         workflow.add_conditional_edges("Retrieve", Retrieve_continue)
         workflow.add_edge("Retrieve_Summary", END)
         workflow.add_conditional_edges("Verify", Verify_continue)
-
         workflow.add_edge("Summary_fails", END)
         workflow.add_edge("Summary", END)
 
@@ -80,7 +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
@@ -96,9 +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

@@ -31,7 +31,7 @@ async def make_write_graph():
         user_id: User identifier
         tools: MCP tools loaded from session
         apply_id: Application identifier
-        group_id: Group identifier
+        end_user_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
     """
     workflow = StateGraph(WriteState)
@@ -49,7 +49,7 @@ async def make_write_graph():
 async def main():
     """主函数 - 运行工作流"""
     message = "今天周一"
-    group_id = 'new_2025test1103'  # 组ID
+    end_user_id = 'new_2025test1103'  # 组ID
 
 
     # 获取数据库会话
@@ -61,9 +61,9 @@ async def main():
     )
     try:
         async with make_write_graph() as graph:
-            config = {"configurable": {"thread_id": group_id}}
+            config = {"configurable": {"thread_id": end_user_id}}
             # 初始状态 - 包含所有必要字段
-            initial_state = {"messages": [HumanMessage(content=message)],  "group_id": group_id, "memory_config": memory_config}
+            initial_state = {"messages": [HumanMessage(content=message)],  "end_user_id": end_user_id, "memory_config": memory_config}
 
             # 获取节点更新信息
             async for update_event in graph.astream(

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",
         content: str = "这是用户的输入",
         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: End user identifier
-        user_id: User identifier
-        apply_id: Application identifier
         content: Dialog content
         ref_id: Reference identifier
         config_id: Configuration ID for processing
@@ -37,13 +33,11 @@ async def get_chunked_dialogs(
 
     # Create DialogData
     conversation_context = ConversationContext(msgs=messages)
-    # Create DialogData with group_id based on the entry's id for uniqueness
+    # Create DialogData with end_user_id
     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
     )
     # Create DialogueChunker and process the dialogue

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

@@ -12,13 +12,11 @@ class WriteState(TypedDict):
     Langgrapg Writing TypedDict
     '''
     messages: Annotated[list[AnyMessage], add_messages]
-    user_id:str
+    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 +26,7 @@ class ReadState(TypedDict):
         messages: 消息列表，支持自动追加
         loop_count: 遍历次数
         search_switch: 搜索类型开关
-        group_id: 组标识
+        end_user_id: 组标识
         config_id: 配置ID，用于过滤结果
         data: 从content_input_node传递的内容数据
         spit_data: 从Split_The_Problem传递的分解结果
@@ -39,7 +37,7 @@ class ReadState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]  # 消息追加模式
     loop_count: int
     search_switch: str
-    group_id: str
+    end_user_id: str
     config_id: str
     data: str  # 新增字段用于传递内容
     spit_data: dict  # 新增字段用于传递问题分解结果

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

@@ -29,25 +29,18 @@ logger = get_agent_logger(__name__)
 
 
 async def write(
-    content: str,
+    end_user_id: str,
-    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.
     
-    Only MemoryConfig is needed - LLM and embedding clients are constructed
-    internally from the config.
-
     Args:
-        content: Dialogue content to process
+        end_user_id: End user identifier
-        user_id: User identifier
-        apply_id: Application identifier
-        group_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
+        messages: Structured message list [{"role": "user", "content": "..."}, ...]
         ref_id: Reference ID, defaults to "wyl20251027"
     """
     # Extract config values
@@ -61,7 +54,7 @@ async def write(
     logger.info(f"LLM model: {memory_config.llm_model_name}")
     logger.info(f"Embedding model: {memory_config.embedding_model_name}")
     logger.info(f"Chunker strategy: {chunker_strategy}")
-    logger.info(f"Group ID: {group_id}")
+    logger.info(f"End User ID: {end_user_id}")
 
     # Construct clients from memory_config using factory pattern with db session
     with get_db_context() as db:
@@ -84,12 +77,25 @@ async def write(
 
     # Step 1: Load and chunk data
     step_start = time.time()
+    
+    # Convert messages list to content string
+    # messages format: [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}, ...]
+    if isinstance(messages, list) and len(messages) > 0:
+        # Extract content from the last user message or concatenate all messages
+        if isinstance(messages[-1], dict) and 'content' in messages[-1]:
+            content = messages[-1]['content']
+        else:
+            # Fallback: concatenate all message contents
+            content = " ".join([msg.get('content', '') for msg in messages if isinstance(msg, dict)])
+    elif isinstance(messages, str):
+        content = messages
+    else:
+        content = str(messages)
+    
     chunked_dialogs = await get_chunked_dialogs(
         chunker_strategy=chunker_strategy,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        user_id=user_id,
+        content=content,  # 修复：使用 content 参数而不是 messages
-        apply_id=apply_id,
-        content=content,
         ref_id=ref_id,
         config_id=config_id,
     )

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

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

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

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

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

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

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

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

@@ -555,7 +555,7 @@ async def run_enhanced_evaluation():
                 search_results = await run_hybrid_search(
                     query_text=q,
                     search_type="hybrid",
-                    group_id="locomo_sk",
+                    end_user_id="locomo_sk",
                     limit=20,
                     include=["statements", "chunks", "entities", "summaries"],
                     alpha=0.6,  # BM25权重

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

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

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

@@ -30,7 +30,7 @@ from app.core.memory.storage_services.search import run_hybrid_search
 from app.core.memory.utils.config.definitions import (
     PROJECT_ROOT,
     SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
+    SELECTED_end_user_id,
     SELECTED_LLM_ID,
 )
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -249,7 +249,7 @@ def get_search_params_by_category(category: str):
 
 async def run_locomo_eval(
     sample_size: int = 1,
-    group_id: str | None = None,
+    end_user_id: str | None = None,
     search_limit: int = 8,
     context_char_budget: int = 4000,  # 保持默认值不变
     llm_temperature: float = 0.0,
@@ -262,7 +262,7 @@ async def run_locomo_eval(
 ) -> Dict[str, Any]:
 
     # 函数内部使用三路检索逻辑，但保持参数签名不变
-    group_id = group_id or SELECTED_GROUP_ID
+    end_user_id = end_user_id or SELECTED_end_user_id
     data_path = os.path.join(PROJECT_ROOT, "data", "locomo10.json")
     if not os.path.exists(data_path):
         data_path = os.path.join(os.getcwd(), "data", "locomo10.json")
@@ -340,7 +340,7 @@ async def run_locomo_eval(
 
     # 关键修复：强制重新摄入纯净的对话数据
     print("🔄 强制重新摄入纯净的对话数据...")
-    await ingest_contexts_via_full_pipeline(contents, group_id, save_chunk_output=True)
+    await ingest_contexts_via_full_pipeline(contents, end_user_id, save_chunk_output=True)
 
     # 使用异步LLM客户端
     with get_db_context() as db:
@@ -405,7 +405,7 @@ async def run_locomo_eval(
                         connector=connector,
                         embedder_client=embedder,
                         query_text=q,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=adjusted_limit,
                         include=["chunks", "statements", "entities", "summaries"],  # 修复：使用正确的类型
                     )
@@ -456,7 +456,7 @@ async def run_locomo_eval(
                     search_results = await search_graph(
                         connector=connector,
                         q=q,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=adjusted_limit
                     )
                     dialogs = search_results.get("dialogues", [])
@@ -486,7 +486,7 @@ async def run_locomo_eval(
                         search_results = await run_hybrid_search(
                             query_text=q,
                             search_type=search_type,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=adjusted_limit,
                             include=["chunks", "statements", "entities", "summaries"],
                             output_path=None,
@@ -524,7 +524,7 @@ async def run_locomo_eval(
                             connector=connector,
                             embedder_client=embedder,
                             query_text=q,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=adjusted_limit,
                             include=["chunks", "statements", "entities", "summaries"],
                         )
@@ -597,7 +597,7 @@ async def run_locomo_eval(
                         "dialogues": [
                             {
                                 "uuid": d.get("uuid", ""),
-                                "group_id": d.get("group_id", ""),
+                                "end_user_id": d.get("end_user_id", ""),
                                 "content": d.get("content", "")[:200] + "..." if len(d.get("content", "")) > 200 else d.get("content", ""),
                                 "score": d.get("score", 0.0)
                             }
@@ -795,7 +795,7 @@ async def run_locomo_eval(
             },
             "samples": samples,
             "params": {
-                "group_id": group_id,
+                "end_user_id": end_user_id,
                 "search_limit": search_limit,
                 "context_char_budget": context_char_budget,
                 "search_type": search_type,
@@ -825,7 +825,7 @@ async def run_locomo_eval(
 def main():
     parser = argparse.ArgumentParser(description="Run LoCoMo evaluation with Qwen search")
     parser.add_argument("--sample_size", type=int, default=1, help="Number of samples to evaluate")
-    parser.add_argument("--group_id", type=str, default=None, help="Group ID for retrieval")
+    parser.add_argument("--end_user_id", type=str, default=None, help="Group ID for retrieval")
     parser.add_argument("--search_limit", type=int, default=8, help="Search limit per query")
     parser.add_argument("--context_char_budget", type=int, default=12000, help="Max characters for context")
     parser.add_argument("--llm_temperature", type=float, default=0.0, help="LLM temperature")
@@ -841,7 +841,7 @@ def main():
 
     result = asyncio.run(run_locomo_eval(
         sample_size=args.sample_size,
-        group_id=args.group_id,
+        end_user_id=args.end_user_id,
         search_limit=args.search_limit,
         context_char_budget=args.context_char_budget,
         llm_temperature=args.llm_temperature,

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

@@ -523,11 +523,11 @@ def generate_query_keywords_cn(question: str) -> List[str]:
 
 
 # 通过别名匹配进行实体关键词检索（多token合并）
-async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], group_id: str | None, limit: int) -> List[Dict[str, Any]]:
+async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[str], end_user_id: str | None, limit: int) -> List[Dict[str, Any]]:
     results: List[Dict[str, Any]] = []
     try:
         for tok in tokens:
-            rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, group_id=group_id, limit=limit)
+            rows = await connector.execute_query(SEARCH_ENTITIES_BY_NAME, q=tok, end_user_id=end_user_id, limit=limit)
             if rows:
                 results.extend(rows)
     except Exception:
@@ -547,15 +547,15 @@ async def _search_entities_by_aliases(connector: Neo4jConnector, tokens: List[st
 # 通过对话/陈述中的entity_ids反查实体名称
 _FETCH_ENTITIES_BY_IDS = """
 MATCH (e:ExtractedEntity)
-WHERE e.id IN $ids AND ($group_id IS NULL OR e.group_id = $group_id)
+WHERE e.id IN $ids AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
-RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type
+RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type
 """
 
-async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], group_id: str | None) -> List[Dict[str, Any]]:
+async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], end_user_id: str | None) -> List[Dict[str, Any]]:
     if not ids:
         return []
     try:
-        rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), group_id=group_id)
+        rows = await connector.execute_query(_FETCH_ENTITIES_BY_IDS, ids=list({i for i in ids if i}), end_user_id=end_user_id)
         return rows or []
     except Exception:
         return []
@@ -565,18 +565,18 @@ async def _fetch_entities_by_ids(connector: Neo4jConnector, ids: List[str], grou
 _TIME_ENTITY_SEARCH = """
 MATCH (e:ExtractedEntity)
 WHERE e.entity_type CONTAINS "TIME" OR e.entity_type CONTAINS "DATE" OR e.name =~ $date_pattern
-AND ($group_id IS NULL OR e.group_id = $group_id)
+AND ($end_user_id IS NULL OR e.end_user_id = $end_user_id)
-RETURN e.id AS id, e.name AS name, e.group_id AS group_id, e.entity_type AS entity_type
+RETURN e.id AS id, e.name AS name, e.end_user_id AS end_user_id, e.entity_type AS entity_type
 LIMIT $limit
 """
 
-async def _search_time_entities(connector: Neo4jConnector, group_id: str | None, limit: int = 5) -> List[Dict[str, Any]]:
+async def _search_time_entities(connector: Neo4jConnector, end_user_id: str | None, limit: int = 5) -> List[Dict[str, Any]]:
     """专门搜索时间相关的实体"""
     try:
         date_pattern = r".*\d{4}.*|.*\d{1,2}月\d{1,2}日.*"
         rows = await connector.execute_query(_TIME_ENTITY_SEARCH,
                                            date_pattern=date_pattern,
-                                           group_id=group_id,
+                                           end_user_id=end_user_id,
                                            limit=limit)
         return rows or []
     except Exception:
@@ -623,7 +623,7 @@ def _resolve_relative_times_cn_en(text: str, anchor: datetime) -> str:
 
 async def run_longmemeval_test(
     sample_size: int = 3,
-    group_id: str = "longmemeval_zh_bak_3",
+    end_user_id: str = "longmemeval_zh_bak_3",
     search_limit: int = 8,
     context_char_budget: int = 4000,
     llm_temperature: float = 0.0,
@@ -677,13 +677,13 @@ async def run_longmemeval_test(
             contexts.extend(selected)
 
         print(f"📥 摄入 {len(contexts)} 个上下文到数据库")
-        if reset_group_before_ingest and group_id:
+        if reset_group_before_ingest and end_user_id:
             try:
                 _tmp_conn = Neo4jConnector()
-                await _tmp_conn.delete_group(group_id)
+                await _tmp_conn.delete_group(end_user_id)
-                print(f"🧹 已清空组 {group_id} 的历史图数据")
+                print(f"🧹 已清空组 {end_user_id} 的历史图数据")
             except Exception as _e:
-                print(f"⚠️ 清空组数据失败（忽略继续）: {group_id} - {_e}")
+                print(f"⚠️ 清空组数据失败（忽略继续）: {end_user_id} - {_e}")
             finally:
                 try:
                     await _tmp_conn.close()
@@ -695,7 +695,7 @@ async def run_longmemeval_test(
         else:
             await _ingest_fn(
                 contexts,
-                group_id,
+                end_user_id,
                 save_chunk_output=save_chunk_output,
                 save_chunk_output_path=save_chunk_output_path,
             )
@@ -750,7 +750,7 @@ async def run_longmemeval_test(
                         connector=connector,
                         embedder_client=embedder,
                         query_text=question,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=search_limit,
                         include=["chunks", "statements", "entities", "summaries"],
                     )
@@ -795,7 +795,7 @@ async def run_longmemeval_test(
                     search_results = await search_graph(
                         connector=connector,
                         q=question,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=search_limit,
                     )
                     chunks = search_results.get("chunks", [])
@@ -830,7 +830,7 @@ async def run_longmemeval_test(
                             connector=connector,
                             embedder_client=embedder,
                             query_text=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=search_limit,
                             include=["chunks", "statements", "entities", "summaries"],
                         )
@@ -848,7 +848,7 @@ async def run_longmemeval_test(
                         kw_res = await search_graph(
                             connector=connector,
                             q=question,
-                            group_id=group_id,
+                            end_user_id=end_user_id,
                             limit=search_limit,
                         )
                         if isinstance(kw_res, dict):
@@ -859,7 +859,7 @@ async def run_longmemeval_test(
                             # 时间推理问题的特殊处理
                             if is_temporal:
                                 # 专门搜索时间实体
-                                time_entities = await _search_time_entities(connector, group_id, search_limit//2)
+                                time_entities = await _search_time_entities(connector, end_user_id, search_limit//2)
                                 if time_entities:
                                     kw_entities.extend(time_entities)
                                 # 添加时间相关关键词检索
@@ -869,7 +869,7 @@ async def run_longmemeval_test(
                                         time_res = await search_graph(
                                             connector=connector,
                                             q=tk,
-                                            group_id=group_id,
+                                            end_user_id=end_user_id,
                                             limit=2,
                                         )
                                         if isinstance(time_res, dict):
@@ -880,7 +880,7 @@ async def run_longmemeval_test(
 
                             # 中文关键词拆分后做别名匹配
                             cn_tokens = _extract_cn_tokens(question)
-                            alias_entities = await _search_entities_by_aliases(connector, cn_tokens, group_id, search_limit)
+                            alias_entities = await _search_entities_by_aliases(connector, cn_tokens, end_user_id, search_limit)
                             if alias_entities:
                                 kw_entities.extend(alias_entities)
 
@@ -894,7 +894,7 @@ async def run_longmemeval_test(
                             except Exception:
                                 pass
                             if ids:
-                                id_entities = await _fetch_entities_by_ids(connector, ids, group_id)
+                                id_entities = await _fetch_entities_by_ids(connector, ids, end_user_id)
                                 if id_entities:
                                     kw_entities.extend(id_entities)
 
@@ -908,7 +908,7 @@ async def run_longmemeval_test(
                                     sub_res = await search_graph(
                                         connector=connector,
                                         q=str(kw),
-                                        group_id=group_id,
+                                        end_user_id=end_user_id,
                                         limit=max(3, search_limit // 2),
                                     )
                                     if isinstance(sub_res, dict):
@@ -927,7 +927,7 @@ async def run_longmemeval_test(
                                     opt_res = await search_graph(
                                         connector=connector,
                                         q=str(opt),
-                                        group_id=group_id,
+                                        end_user_id=end_user_id,
                                         limit=max(3, search_limit // 2),
                                     )
                                     if isinstance(opt_res, dict):

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

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

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

@@ -27,7 +27,7 @@ from app.core.memory.storage_services.search import run_hybrid_search
 from app.core.memory.utils.config.definitions import (
     PROJECT_ROOT,
     SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
+    SELECTED_end_user_id,
     SELECTED_LLM_ID,
 )
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -135,8 +135,8 @@ def _combine_dialogues_for_hybrid(results: Dict[str, Any]) -> List[Dict[str, Any
     return merged
 
 
-async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]:
+async def run_memsciqa_eval(sample_size: int = 1, end_user_id: str | None = None, search_limit: int = 8, context_char_budget: int = 4000, llm_temperature: float = 0.0, llm_max_tokens: int = 64, search_type: str = "hybrid", memory_config: "MemoryConfig" = None) -> Dict[str, Any]:
-    group_id = group_id or SELECTED_GROUP_ID
+    end_user_id = end_user_id or SELECTED_end_user_id
     # Load data
     data_path = os.path.join(PROJECT_ROOT, "data", "msc_self_instruct.jsonl")
     if not os.path.exists(data_path):
@@ -147,7 +147,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s
     # 改为：每条样本仅摄入一个上下文（完整对话转录），避免多上下文摄入
     # 说明：memsciqa 数据集的每个样本天然只有一个对话，保持按样本一上下文的策略
     contexts: List[str] = [build_context_from_dialog(item) for item in items]
-    await ingest_contexts_via_full_pipeline(contexts, group_id)
+    await ingest_contexts_via_full_pipeline(contexts, end_user_id)
 
     # LLM client (使用异步调用)
     with get_db_context() as db:
@@ -173,7 +173,7 @@ async def run_memsciqa_eval(sample_size: int = 1, group_id: str | None = None, s
                 results = await run_hybrid_search(
                     query_text=question,
                     search_type=search_type,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["dialogues", "statements", "entities"],
                     output_path=None,
@@ -298,7 +298,7 @@ def main():
     load_dotenv()
     parser = argparse.ArgumentParser(description="Evaluate DMR (memsciqa) with graph search and Qwen")
     parser.add_argument("--sample-size", type=int, default=1, help="评测样本数量")
-    parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json")
+    parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json")
     parser.add_argument("--search-limit", type=int, default=8, help="每类检索最大返回数")
     parser.add_argument("--context-char-budget", type=int, default=4000, help="上下文字符预算")
     parser.add_argument("--llm-temperature", type=float, default=0.0, help="LLM 温度")
@@ -309,7 +309,7 @@ def main():
     result = asyncio.run(
         run_memsciqa_eval(
             sample_size=args.sample_size,
-            group_id=args.group_id,
+            end_user_id=args.end_user_id,
             search_limit=args.search_limit,
             context_char_budget=args.context_char_budget,
             llm_temperature=args.llm_temperature,

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

@@ -33,7 +33,7 @@ from app.core.memory.llm_tools.openai_embedder import OpenAIEmbedderClient
 from app.core.memory.utils.config.definitions import (
     PROJECT_ROOT,
     SELECTED_EMBEDDING_ID,
-    SELECTED_GROUP_ID,
+    SELECTED_end_user_id,
     SELECTED_LLM_ID,
 )
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -198,7 +198,7 @@ def load_dataset_memsciqa(data_path: str) -> List[Dict[str, Any]]:
 
 async def run_memsciqa_test(
     sample_size: int = 3,
-    group_id: str | None = None,
+    end_user_id: str | None = None,
     search_limit: int = 8,
     context_char_budget: int = 4000,
     llm_temperature: float = 0.0,
@@ -216,7 +216,7 @@ async def run_memsciqa_test(
     """
 
     # 默认使用指定的 memsci 组 ID
-    group_id = group_id or "group_memsci"
+    end_user_id = end_user_id or "group_memsci"
 
     # 数据路径解析（项目根与当前工作目录兜底）
     if not data_path:
@@ -282,7 +282,7 @@ async def run_memsciqa_test(
                     connector=connector,
                     embedder_client=embedder,
                     query_text=question,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["chunks", "statements", "entities", "summaries"],  # 使用 chunks 而不是 dialogues
                 )
@@ -291,7 +291,7 @@ async def run_memsciqa_test(
                 results = await search_graph(
                     connector=connector,
                     q=question,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=search_limit,
                     include=["chunks", "statements", "entities", "summaries"],  # 使用 chunks 而不是 dialogues
                 )
@@ -499,7 +499,7 @@ async def run_memsciqa_test(
         },
         "samples": samples,
         "params": {
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "search_limit": search_limit,
             "context_char_budget": context_char_budget,
             "llm_temperature": llm_temperature,
@@ -542,7 +542,7 @@ def main():
     result = asyncio.run(
         run_memsciqa_test(
             sample_size=sample_size,
-            group_id=args.group_id,
+            end_user_id=args.end_user_id,
             search_limit=args.search_limit,
             context_char_budget=args.context_char_budget,
             llm_temperature=args.llm_temperature,

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

@@ -15,7 +15,7 @@ except Exception:
         return None
 
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
-from app.core.memory.utils.config.definitions import SELECTED_GROUP_ID, PROJECT_ROOT
+from app.core.memory.utils.config.definitions import SELECTED_end_user_id, PROJECT_ROOT
 
 from app.core.memory.evaluation.memsciqa.evaluate_qa import run_memsciqa_eval
 from app.core.memory.evaluation.longmemeval.qwen_search_eval import run_longmemeval_test
@@ -26,7 +26,7 @@ async def run(
     dataset: str,
     sample_size: int,
     reset_group: bool,
-    group_id: str | None,
+    end_user_id: str | None,
     judge_model: str | None = None,
     search_limit: int | None = None,
     context_char_budget: int | None = None,
@@ -37,17 +37,17 @@ async def run(
     max_contexts_per_item: int | None = None,
 ) -> Dict[str, Any]:
     # 恢复原始风格：统一入口做路由，并沿用各数据集既有默认
-    group_id = group_id or SELECTED_GROUP_ID
+    end_user_id = end_user_id or SELECTED_end_user_id
 
     if reset_group:
         connector = Neo4jConnector()
         try:
-            await connector.delete_group(group_id)
+            await connector.delete_group(end_user_id)
         finally:
             await connector.close()
 
     if dataset == "locomo":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
+        kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id}
         if search_limit is not None:
             kwargs["search_limit"] = search_limit
         if context_char_budget is not None:
@@ -61,7 +61,7 @@ async def run(
         return await run_locomo_eval(**kwargs)
 
     if dataset == "memsciqa":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
+        kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id}
         if search_limit is not None:
             kwargs["search_limit"] = search_limit
         if context_char_budget is not None:
@@ -75,7 +75,7 @@ async def run(
         return await run_memsciqa_eval(**kwargs)
 
     if dataset == "longmemeval":
-        kwargs: Dict[str, Any] = {"sample_size": sample_size, "group_id": group_id}
+        kwargs: Dict[str, Any] = {"sample_size": sample_size, "end_user_id": end_user_id}
         if search_limit is not None:
             kwargs["search_limit"] = search_limit
         if context_char_budget is not None:
@@ -99,8 +99,8 @@ def main():
     parser = argparse.ArgumentParser(description="统一评估入口：memsciqa / longmemeval / locomo")
     parser.add_argument("--dataset", choices=["memsciqa", "longmemeval", "locomo"], required=True)
     parser.add_argument("--sample-size", type=int, default=1, help="先用一条数据跑通")
-    parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 group_id 的图数据")
+    parser.add_argument("--reset-group", action="store_true", help="运行前清空当前 end_user_id 的图数据")
-    parser.add_argument("--group-id", type=str, default=None, help="可选 group_id，默认取 runtime.json")
+    parser.add_argument("--group-id", type=str, default=None, help="可选 end_user_id，默认取 runtime.json")
     parser.add_argument("--judge-model", type=str, default=None, help="可选：longmemeval 判别式评测模型名")
     parser.add_argument("--search-limit", type=int, default=None, help="检索返回的对话节点数量上限（不提供则使用各脚本默认）")
     parser.add_argument("--context-char-budget", type=int, default=None, help="上下文字符预算（不提供则使用各脚本默认）")
@@ -117,7 +117,7 @@ def main():
         args.dataset,
         args.sample_size,
         args.reset_group,
-        args.group_id,
+        args.end_user_id,
         args.judge_model,
         args.search_limit,
         args.context_char_budget,

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

@@ -4,6 +4,7 @@ import os
 import asyncio
 import json
 import numpy as np
+import logging
 
 # Fix tokenizer parallelism warning
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -23,28 +24,29 @@ from app.core.memory.models.message_models import DialogData, Chunk
 try:
     from app.core.memory.llm_tools.openai_client import OpenAIClient
 except Exception:
-    # 在测试或无可用依赖（如 langfuse）环境下，允许惰性导入
     OpenAIClient = Any
 
+# Initialize logger
+logger = logging.getLogger(__name__)
+
 
 class LLMChunker:
-    """基于LLM的智能分块策略"""
+    """LLM-based intelligent chunking strategy"""
     def __init__(self, llm_client: OpenAIClient, chunk_size: int = 1000):
         self.llm_client = llm_client
         self.chunk_size = chunk_size
 
     async def __call__(self, text: str) -> List[Any]:
-        # 使用LLM分析文本结构并进行智能分块
         prompt = f"""
-            请将以下文本分割成语义连贯的段落。每个段落应该围绕一个主题，长度大约在{self.chunk_size}字符左右。
+            Split the following text into semantically coherent paragraphs. Each paragraph should focus on one topic, approximately {self.chunk_size} characters long.
-            请以JSON格式返回结果，包含chunks数组，每个chunk有text字段。
+            Return results in JSON format with a chunks array, each chunk having a text field.
 
-            文本内容：
+            Text content:
             {text[:5000]}
             """
 
         messages = [
-            {"role": "system", "content": "你是一个专业的文本分析助手，擅长将长文本分割成语义连贯的段落。"},
+            {"role": "system", "content": "You are a professional text analysis assistant, skilled at splitting long texts into semantically coherent paragraphs."},
             {"role": "user", "content": prompt}
         ]
 
@@ -171,8 +173,6 @@ class ChunkerClient:
                 base_chunk_size=self.chunk_size,
             )
         elif chunker_config.chunker_strategy == "SentenceChunker":
-            # 某些 chonkie 版本的 SentenceChunker 不支持 tokenizer_or_token_counter 参数
-            # 为了兼容不同版本，这里仅传递广泛支持的参数
             self.chunker = SentenceChunker(
                 chunk_size=self.chunk_size,
                 chunk_overlap=self.chunk_overlap,
@@ -186,100 +186,93 @@ class ChunkerClient:
 
     async def generate_chunks(self, dialogue: DialogData):
         """
-        生成分块，支持异步操作
+        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
         """
+        # Validate dialogue has messages
+        if not dialogue.context or not dialogue.context.msgs:
+            raise ValueError(
+                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
+            if not hasattr(msg, 'role') or not hasattr(msg, 'msg'):
+                raise ValueError(
+                    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
                 try:
-            # 预处理文本：确保对话标记格式统一
+                    sub_chunks = self.chunker(msg_content)
-            content = dialogue.content
+                except Exception as e:
-            content = content.replace('AI：', 'AI:').replace('用户：', '用户:')  # 统一冒号
+                    raise ValueError(
-            content = re.sub(r'(\n\s*)+\n', '\n\n', content)  # 合并多个空行
+                        f"Failed to chunk long message {msg_idx} in dialogue {dialogue.ref_id}: {e}"
+                    )
                 
-            if hasattr(self.chunker, '__call__') and not asyncio.iscoroutinefunction(self.chunker.__call__):
+                for idx, sub_chunk in enumerate(sub_chunks):
-                # 同步分块器
+                    sub_chunk_text = sub_chunk.text if hasattr(sub_chunk, 'text') else str(sub_chunk)
-                chunks = self.chunker(content)
+                    sub_chunk_text = sub_chunk_text.strip()
-            else:
-                # 异步分块器（如LLMChunker）
-                chunks = await self.chunker(content)
                     
-            # 过滤空块和过小的块
+                    if len(sub_chunk_text) < (self.min_characters_per_chunk or 50):
-            valid_chunks = []
+                        continue
-            for c in chunks:
-                chunk_text = getattr(c, 'text', str(c)) if not isinstance(c, str) else c
-                if isinstance(chunk_text, str) and len(chunk_text.strip()) >= (self.min_characters_per_chunk or 50):
-                    valid_chunks.append(c)
                     
-            dialogue.chunks = [
+                    chunk = Chunk(
-                Chunk(
+                        content=f"{msg.role}: {sub_chunk_text}",
-                    content=c.text if hasattr(c, 'text') else str(c),
+                        speaker=msg.role,  # 直接继承角色
                         metadata={
-                        "start_index": getattr(c, "start_index", None),
+                            "message_index": msg_idx,
-                        "end_index": getattr(c, "end_index", None),
+                            "message_role": msg.role,
+                            "sub_chunk_index": idx,
+                            "total_sub_chunks": len(sub_chunks),
                             "chunker_strategy": self.chunker_config.chunker_strategy,
                         },
                     )
-                for c in valid_chunks
+                    dialogue.chunks.append(chunk)
-            ]
-            return dialogue
-
-        except Exception as e:
-            print(f"分块失败: {e}")
-
-            # 改进的后备方案：尝试按对话回合分割
-            try:
-                # 简单的按对话分割
-                dialogue_pattern = r'(AI:|用户:)(.*?)(?=AI:|用户:|$)'
-                matches = re.findall(dialogue_pattern, dialogue.content, re.DOTALL)
-
-                class SimpleChunk:
-                    def __init__(self, text, start_index, end_index):
-                        self.text = text
-                        self.start_index = start_index
-                        self.end_index = end_index
-
-                chunks = []
-                current_chunk = ""
-                current_start = 0
-
-                for match in matches:
-                    speaker, ct = match[0], match[1].strip()
-                    turn_text = f"{speaker} {ct}"
-
-                    if len(current_chunk) + len(turn_text) > (self.chunk_size or 500):
-                        if current_chunk:
-                            chunks.append(SimpleChunk(current_chunk, current_start, current_start + len(current_chunk)))
-                        current_chunk = turn_text
-                        current_start = dialogue.content.find(turn_text, current_start)
             else:
-                        current_chunk += ("\n" + turn_text) if current_chunk else turn_text
+                # 消息不长，直接作为一个 chunk
-
+                chunk = Chunk(
-                if current_chunk:
+                    content=f"{msg.role}: {msg_content}",
-                    chunks.append(SimpleChunk(current_chunk, current_start, current_start + len(current_chunk)))
+                    speaker=msg.role,  # 直接继承角色
-
-                dialogue.chunks = [
-                    Chunk(
-                        content=c.text,
                     metadata={
-                            "start_index": c.start_index,
+                        "message_index": msg_idx,
-                            "end_index": c.end_index,
+                        "message_role": msg.role,
-                            "chunker_strategy": "DialogueTurnFallback",
+                        "chunker_strategy": self.chunker_config.chunker_strategy,
                     },
                 )
-                    for c in chunks
+                dialogue.chunks.append(chunk)
-                ]
         
-            except Exception:
+        # Validate we generated at least one chunk
-                # 最后的手段：单一大块
+        if not dialogue.chunks:
-                dialogue.chunks = [Chunk(
+            raise ValueError(
-                    content=dialogue.content,
+                f"No valid chunks generated for dialogue {dialogue.ref_id}. "
-                    metadata={"chunker_strategy": "SingleChunkFallback"},
+                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:
-        """
+        """Evaluate chunking quality."""
-        评估分块质量
-        """
         if not getattr(dialogue, 'chunks', None):
             return {}
 
@@ -304,11 +297,8 @@ class ChunkerClient:
         return metrics
 
     def save_chunking_results(self, dialogue: DialogData, output_path: str):
-        """
+        """Save chunking results to file with strategy name in filename."""
-        保存分块结果到文件，文件名包含策略名称
-        """
         strategy_name = self.chunker_config.chunker_strategy
-        # 在文件名中添加策略名称
         base_name, ext = os.path.splitext(output_path)
         strategy_output_path = f"{base_name}_{strategy_name}{ext}"
 

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

@@ -92,8 +92,6 @@ class OpenAIClient(LLMClient):
                 config["callbacks"] = [self.langfuse_handler]
 
             response = await chain.ainvoke({"messages": messages}, config=config)
-
-            logger.debug(f"LLM 响应成功: {len(str(response))} 字符")
             return response
 
         except Exception as e:
@@ -149,13 +147,10 @@ class OpenAIClient(LLMClient):
                         config=config
                     )
 
-                    logger.debug(f"使用 PydanticOutputParser 解析成功")
                     return parsed
 
                 except Exception as e:
-                    logger.warning(
+                    logger.debug(f"PydanticOutputParser 解析失败，尝试备用方法: {e}")
-                        f"PydanticOutputParser 解析失败，尝试其他方法: {e}"
-                    )
 
             # 方法 2: 使用 LangChain 的 with_structured_output
             template = """{question}"""
@@ -173,13 +168,17 @@ class OpenAIClient(LLMClient):
 
                     # 验证并返回结果
                     try:
-                        return response_model.model_validate(parsed)
+                        result = response_model.model_validate(parsed)
+                        return result
                     except Exception:
                         # 如果已经是 Pydantic 实例，直接返回
                         if hasattr(parsed, "model_dump"):
                             return parsed
                         # 尝试从 JSON 解析
-                        return response_model.model_validate_json(json.dumps(parsed))
+                        result = response_model.model_validate_json(json.dumps(parsed))
+                        return result
+                else:
+                    logger.warning("with_structured_output 方法不可用")
 
             except Exception as e:
                 logger.error(f"结构化输出失败: {e}")

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.")
@@ -224,6 +216,7 @@ class StatementNode(Node):
         chunk_id: ID of the parent chunk this statement belongs to
         stmt_type: Type of the statement (from ontology)
         statement: The actual statement text content
+        speaker: Optional speaker identifier ('用户' for user messages, 'AI' for AI responses)
         emotion_intensity: Optional emotion intensity (0.0-1.0) - displayed on node
         emotion_target: Optional emotion target (person or object name)
         emotion_subject: Optional emotion subject (self/other/object)
@@ -249,6 +242,12 @@ class StatementNode(Node):
     stmt_type: str = Field(..., description="Type of the statement")
     statement: str = Field(..., description="The statement text content")
     
+    # Speaker identification
+    speaker: Optional[str] = Field(
+        None,
+        description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses"
+    )
+    
     # Emotion fields (ordered as requested, emotion_intensity first for display)
     emotion_intensity: Optional[float] = Field(
         None, 

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

@@ -25,10 +25,10 @@ class ConversationMessage(BaseModel):
     """Represents a single message in a conversation.
 
     Attributes:
-        role: Role of the speaker (e.g., '用户' for user, 'AI' for assistant)
+        role: Role of the speaker (e.g., 'user' for user, 'assistant' for AI assistant)
         msg: Text content of the message
     """
-    role: str = Field(..., description="The role of the speaker (e.g., '用户', 'AI').")
+    role: str = Field(..., description="The role of the speaker (e.g., 'user', 'assistant').")
     msg: str = Field(..., description="The text content of the message.")
 
 
@@ -55,8 +55,9 @@ 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
         stmt_type: Type of the statement (from ontology)
         temporal_info: Temporal information extracted from the statement
@@ -72,8 +73,9 @@ 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.")
     stmt_type: StatementType = Field(..., description="The type of the statement.")
     temporal_info: TemporalInfo = Field(..., description="The temporal information of the statement.")
@@ -118,35 +120,35 @@ class Chunk(BaseModel):
 
     Attributes:
         id: Unique identifier for the chunk
-        text: List of messages in the chunk
         content: The content of the chunk as a formatted string
+        speaker: The speaker/role for this chunk (user/assistant)
         statements: List of statements extracted from this chunk
         chunk_embedding: Optional embedding vector for the chunk
         metadata: Additional metadata as key-value pairs
     """
     id: str = Field(default_factory=lambda: uuid4().hex, description="A unique identifier for the chunk.")
-    text: List[ConversationMessage] = Field(default_factory=list, description="A list of messages in the chunk.")
     content: str = Field(..., description="The content of the chunk as a string.")
+    speaker: Optional[str] = Field(None, description="The speaker/role for this chunk (user/assistant).")
     statements: List[Statement] = Field(default_factory=list, description="A list of statements in the chunk.")
     chunk_embedding: Optional[List[float]] = Field(None, description="The embedding vector of the chunk.")
     metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional metadata for the chunk.")
 
     @classmethod
-    def from_messages(cls, messages: List[ConversationMessage], metadata: Optional[Dict[str, Any]] = None):
+    def from_single_message(cls, message: ConversationMessage, metadata: Optional[Dict[str, Any]] = None):
-        """Create a chunk from a list of messages.
+        """Create a chunk from a single message (1 Message = 1 Chunk).
 
         Args:
-            messages: List of conversation messages
+            message: Single conversation message
             metadata: Optional metadata dictionary
 
         Returns:
-            Chunk instance with formatted content
+            Chunk instance with speaker directly from message.role
         """
-        if metadata is None:
+        return cls(
-            metadata = {}
+            content=f"{message.role}: {message.msg}",
-        # Generate content from messages
+            speaker=message.role,
-        content = "\n".join([f"{msg.role}: {msg.msg}" for msg in messages])
+            metadata=metadata or {}
-        return cls(text=messages, content=content, metadata=metadata)
+        )
     
 
 class DialogData(BaseModel):
@@ -157,9 +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
@@ -173,9 +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.")
@@ -254,5 +252,5 @@ class DialogData(BaseModel):
         """
         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,
@@ -692,6 +693,9 @@ async def run_hybrid_search(
     # Start overall timing
     search_start_time = time.time()
     latency_metrics = {}
+    print(100*'-')
+    print(memory_config)
+    print(100 * '-')
     logger.info(f"using embedding_id:{memory_config.embedding_model_id}...")
 
     # Clean and normalize the incoming query before use/logging
@@ -715,7 +719,7 @@ async def run_hybrid_search(
         }
     
     # Log the search query
-    log_search_query(query_text, search_type, group_id, limit, include)
+    log_search_query(query_text, search_type, end_user_id, limit, include)
 
     connector = Neo4jConnector()
     results = {}
@@ -732,7 +736,7 @@ async def run_hybrid_search(
                 search_graph(
                     connector=connector,
                     q=query_text,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=limit,
                     include=include
                 )
@@ -769,7 +773,7 @@ async def run_hybrid_search(
                     connector=connector,
                     embedder_client=embedder,
                     query_text=query_text,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=limit,
                     include=include,
                 )
@@ -916,9 +920,7 @@ async def run_hybrid_search(
 
 
 async def search_by_temporal(
-    group_id: Optional[str] = "test",
+    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 +931,7 @@ async def search_by_temporal(
     Temporal search across Statements.
 
     - Matches statements created between start_date and end_date
-    - Optionally filters by group_id
+    - Optionally filters by end_user_id
     - Returns up to 'limit' statements
     """
     connector = Neo4jConnector()
@@ -939,9 +941,7 @@ async def search_by_temporal(
         end_date = normalize_date_safe(end_date)
 
     params = TemporalSearchParams.model_validate({
-        "group_id": group_id,
+        "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 +950,7 @@ 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 +962,7 @@ 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 +983,7 @@ 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 +993,7 @@ async def search_by_keyword_temporal(
     statements = await search_graph_by_keyword_temporal(
         connector=connector,
         query_text=query_text,
-        group_id=params.group_id,
+        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 +1005,7 @@ async def search_by_keyword_temporal(
 
 async def search_chunk_by_chunk_id(
     chunk_id: str,
-    group_id: Optional[str] = "test",
+    end_user_id: Optional[str] = "test",
     limit: int = 1,
 ):
     """
@@ -1023,8 +1015,68 @@ async def search_chunk_by_chunk_id(
     chunks = await search_graph_by_chunk_id(
         connector=connector,
         chunk_id=chunk_id,
-        group_id=group_id,
+        end_user_id=end_user_id,
         limit=limit
     )
     return {"chunks": chunks}
 
+if __name__ == '__main__':
+    # 测试混合检索功能
+    from app.schemas.memory_config_schema import MemoryConfig
+    from app.db import get_db
+    from app.services.memory_config_service import MemoryConfigService
+    
+    # 从数据库获取真实配置
+    db = next(get_db())
+    try:
+        config_service = MemoryConfigService(db)
+        
+        # 使用 config_id=17 获取配置
+        memory_config = config_service.load_memory_config(config_id=17)
+        
+        if not memory_config:
+            print("错误：找不到 config_id=17 的配置")
+            print("请先在数据库中创建配置，或修改 config_id")
+            exit(1)
+        
+        print(f"✓ 成功加载配置: {memory_config.config_name}")
+        print(f"  - Workspace: {memory_config.workspace_name}")
+        print(f"  - LLM Model: {memory_config.llm_model_name}")
+        print(f"  - Embedding Model: {memory_config.embedding_model_name}")
+        print(f"  - Storage Type: {memory_config.storage_type}")
+        print()
+        
+        # 修改这里的参数进行测试
+        test_end_user_id = "021886bc-fab9-4fd5-b607-497b262e0381"  # 修改为你的 end_user_id
+        test_query = "小明擅长什么？"  # 修改为你的查询
+        
+        print(f"开始测试检索...")
+        print(f"  - Query: {test_query}")
+        print(f"  - End User ID: {test_end_user_id}")
+        print(f"  - Search Type: hybrid")
+        print()
+        
+        results = asyncio.run(run_hybrid_search(
+            query_text=test_query,
+            search_type="hybrid",  # 可选: "keyword", "embedding", "hybrid"
+            end_user_id=test_end_user_id,
+            limit=10,
+            include=["statements", "entities", "chunks", "summaries"],
+            output_path=None,
+            memory_config=memory_config,
+            rerank_alpha=0.6,
+            use_forgetting_rerank=False,
+            use_llm_rerank=False
+        ))
+        
+        print("=" * 80)
+        print("检索结果:")
+        print("=" * 80)
+        print(results)
+        
+    except Exception as e:
+        print(f"错误: {e}")
+        import traceback
+        traceback.print_exc()
+    finally:
+        db.close()

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
@@ -550,7 +550,7 @@ class ExtractionOrchestrator:
         self, dialog_data_list: List[DialogData]
     ) -> List[Dict[str, Any]]:
         """
-        从对话中提取情绪信息（优化版：全局陈述句级并行）
+        从对话中提取情绪信息（仅针对用户消息，全局陈述句级并行）
 
         Args:
             dialog_data_list: 对话数据列表
@@ -558,7 +558,7 @@ class ExtractionOrchestrator:
         Returns:
             情绪信息映射列表，每个对话对应一个字典
         """
-        logger.info("开始情绪信息提取（全局陈述句级并行）")
+        logger.info("开始情绪信息提取（仅处理用户消息）")
 
         # 收集所有陈述句及其配置
         all_statements = []
@@ -598,14 +598,21 @@ class ExtractionOrchestrator:
             logger.info("情绪提取未启用，跳过")
             return [{} for _ in dialog_data_list]
 
-        # 收集所有陈述句
+        # 收集所有陈述句（只收集 speaker 为 "user" 的）
+        total_statements = 0
+        filtered_statements = 0
+        
         for d_idx, dialog in enumerate(dialog_data_list):
             for chunk in dialog.chunks:
                 for statement in chunk.statements:
+                    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))
                         statement_metadata.append((d_idx, statement.id))
+                        filtered_statements += 1
 
-        logger.info(f"收集到 {len(all_statements)} 个陈述句，开始全局并行提取情绪")
+        logger.info(f"总陈述句: {total_statements}, 用户陈述句: {filtered_statements}, 开始全局并行提取情绪")
 
         # 初始化情绪提取服务
         from app.services.emotion_extraction_service import EmotionExtractionService
@@ -985,9 +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,
@@ -1005,9 +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,
@@ -1028,11 +1031,10 @@ 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
                         statement_embedding=statement.statement_embedding,
                         valid_at=statement.temporal_validity.valid_at if hasattr(statement, 'temporal_validity') and statement.temporal_validity else None,
                         invalid_at=statement.temporal_validity.invalid_at if hasattr(statement, 'temporal_validity') and statement.temporal_validity else None,
@@ -1052,9 +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,
                     )
@@ -1087,9 +1087,7 @@ class ExtractionOrchestrator:
                                     aliases=getattr(entity, 'aliases', []) or [],  # 传递从三元组提取阶段获取的aliases
                                     name_embedding=getattr(entity, 'name_embedding', None),
                                     is_explicit_memory=getattr(entity, 'is_explicit_memory', False),  # 新增：传递语义记忆标记
-                                    group_id=dialog_data.group_id,
+                                    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,
@@ -1104,9 +1102,7 @@ class ExtractionOrchestrator:
                                 source=statement.id,
                                 target=entity.id,
                                 connect_strength=entity_connect_strength if entity_connect_strength is not None else 'Strong',
-                                group_id=dialog_data.group_id,
+                                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,
                             )
@@ -1126,9 +1122,7 @@ 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,
@@ -1755,14 +1749,14 @@ class ExtractionOrchestrator:
 
 async def get_chunked_dialogs(
     chunker_strategy: str = "RecursiveChunker",
-    group_id: str = "group_1",
+    end_user_id: str = "group_1",
     indices: Optional[List[int]] = None,
 ) -> List[DialogData]:
     """从测试数据生成分块对话
     
     Args:
         chunker_strategy: 分块策略（默认: RecursiveChunker）
-        group_id: 组ID
+        end_user_id: 组ID
         indices: 要处理的数据索引列表（可选）
         
     Returns:
@@ -1826,7 +1820,7 @@ async def get_chunked_dialogs(
         dialog_data = DialogData(
             context=conversation_context,
             ref_id=data['id'],
-            group_id=group_id,
+            end_user_id=end_user_id,
             metadata=dialog_metadata,
         )
         
@@ -1928,7 +1922,7 @@ async def get_chunked_dialogs_from_preprocessed(
 
 async def get_chunked_dialogs_with_preprocessing(
     chunker_strategy: str = "RecursiveChunker",
-    group_id: str = "default",
+    end_user_id: str = "default",
     user_id: str = "default",
     apply_id: str = "default",
     indices: Optional[List[int]] = None,
@@ -1940,7 +1934,7 @@ async def get_chunked_dialogs_with_preprocessing(
     
     Args:
         chunker_strategy: 分块策略
-        group_id: 组ID
+        end_user_id: 组ID
         user_id: 用户ID
         apply_id: 应用ID
         indices: 要处理的数据索引列表
@@ -1968,11 +1962,9 @@ async def get_chunked_dialogs_with_preprocessing(
         indices=indices,
     )
             
-    # 设置 group_id, user_id, apply_id
+    # 设置 end_user_id
     for dd in preprocessed_data:
-        dd.group_id = group_id
+        dd.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/chunk_extraction.py

@@ -22,12 +22,12 @@ class DialogueChunker:
 
         Args:
             chunker_strategy: The chunking strategy to use (default: RecursiveChunker)
-                             Options include: SemanticChunker, RecursiveChunker, LateChunker, NeuralChunker
+                             Options: SemanticChunker, RecursiveChunker, LateChunker, NeuralChunker
         """
         self.chunker_strategy = chunker_strategy
         chunker_config_dict = get_chunker_config(chunker_strategy)
         self.chunker_config = ChunkerConfig.model_validate(chunker_config_dict)
-        # 对于 LLMChunker，需要传入 llm_client
+        
         if self.chunker_config.chunker_strategy == "LLMChunker":
             self.chunker_client = ChunkerClient(self.chunker_config, llm_client)
         else:
@@ -41,29 +41,19 @@ class DialogueChunker:
 
         Returns:
             A list of Chunk objects
+
+        Raises:
+            ValueError: If chunking fails or returns empty chunks
         """
         result_dialogue = await self.chunker_client.generate_chunks(dialogue)
-        # Defensive fallback: ensure at least one chunk is returned for non-empty content
-        try:
         chunks = result_dialogue.chunks
-        except Exception:
-            chunks = []
 
         if not chunks or len(chunks) == 0:
-            # If the dialogue has content, return a single fallback chunk built from messages
+            raise ValueError(
-            content_str = getattr(result_dialogue, "content", "") or getattr(dialogue, "content", "")
+                f"Chunking failed: No chunks generated for dialogue {dialogue.ref_id}. "
-            if content_str and len(content_str.strip()) > 0:
+                f"Messages: {len(dialogue.context.msgs) if dialogue.context else 0}, "
-                fallback_chunk = Chunk.from_messages(
+                f"Strategy: {self.chunker_config.chunker_strategy}"
-                    dialogue.context.msgs,
-                    metadata={
-                        "fallback": "single_chunk",
-                        "chunker_strategy": self.chunker_config.chunker_strategy,
-                        "source": "DialogueChunkerFallback",
-                    },
             )
-                return [fallback_chunk]
-            # No content: return empty list
-            return []
 
         return chunks
 
@@ -72,22 +62,25 @@ class DialogueChunker:
 
         Args:
             dialogue: The processed DialogData object with chunks
-            output_path: Optional path to save the output (default: chunker_output_{strategy}.txt)
+            output_path: Optional path to save the output
 
         Returns:
             The path where the output was saved
         """
         if not output_path:
-            output_path = os.path.join(os.path.dirname(__file__), "..", "..",
+            output_path = os.path.join(
-                                      f"chunker_output_{self.chunker_strategy.lower()}.txt")
+                os.path.dirname(__file__), "..", "..",
+                f"chunker_output_{self.chunker_strategy.lower()}.txt"
+            )
 
-        output_lines = []
+        output_lines = [
-        output_lines.append(f"=== Chunking Results ({self.chunker_strategy}) ===")
+            f"=== Chunking Results ({self.chunker_strategy}) ===",
-        output_lines.append(f"Dialogue ID: {dialogue.ref_id}")
+            f"Dialogue ID: {dialogue.ref_id}",
-        output_lines.append(f"Original conversation has {len(dialogue.context.msgs)} messages")
+            f"Original conversation has {len(dialogue.context.msgs)} messages",
-        output_lines.append(f"Total characters: {len(dialogue.content)}")
+            f"Total characters: {len(dialogue.content)}",
+            f"Generated {len(dialogue.chunks)} chunks:"
+        ]
         
-        output_lines.append(f"Generated {len(dialogue.chunks)} chunks:")
         for i, chunk in enumerate(dialogue.chunks):
             output_lines.append(f"  Chunk {i+1}: {len(chunk.content)} characters")
             output_lines.append(f"    Content preview: {chunk.content}...")

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

@@ -5,8 +5,6 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from app.core.memory.models.message_models import DialogData, Statement
-
-#避免在测试收集阶段因为 OpenAIClient 间接引入 langfuse 导致 ModuleNotFoundError 。这只是类型注解与导入时机的调整，不改变实现。
 from app.core.memory.models.variate_config import StatementExtractionConfig
 from app.core.memory.utils.data.ontology import (
     LABEL_DEFINITIONS,
@@ -22,11 +20,10 @@ logger = logging.getLogger(__name__)
 class ExtractedStatement(BaseModel):
     """Schema for extracted statement from LLM"""
     statement: str = Field(..., description="The extracted statement text")
-    statement_type: str = Field(..., description="FACT, OPINION,SUGGESTION or PREDICTION")
+    statement_type: str = Field(..., description="FACT, OPINION, SUGGESTION or PREDICTION")
     temporal_type: str = Field(..., description="STATIC, DYNAMIC, ATEMPORAL")
     relevence: str = Field(..., description="RELEVANT or IRRELEVANT")
 
-# 统一使用 StatementExtractionResponse 作为 LLM 的结构化返回（仅语句）
 class StatementExtractionResponse(BaseModel):
     statements: List[ExtractedStatement] = Field(default_factory=list, description="List of extracted statements")
     
@@ -58,10 +55,9 @@ class StatementExtractionResponse(BaseModel):
         return v
 
 class StatementExtractor:
-    """Class for extracting statements from dialog chunks using LLM (relations separated)"""
+    """Class for extracting statements from dialog chunks using LLM"""
 
     def __init__(self, llm_client: Any, config: StatementExtractionConfig = None):
-        # 避免在测试收集阶段因为 OpenAIClient 间接引入 langfuse 导致 ModuleNotFoundError 。这只是类型注解与导入时机的调整，不改变实现。
         """Initialize the StatementExtractor with an LLM client and configuration
 
         Args:
@@ -71,21 +67,38 @@ class StatementExtractor:
         self.llm_client = llm_client
         self.config = config or StatementExtractionConfig()
 
-    async def _extract_statements(self, chunk, group_id: Optional[str] = None, dialogue_content: str = None) -> List[Statement]:
+    def _get_speaker_from_chunk(self, chunk) -> Optional[str]:
+        """Get speaker directly from Chunk
+        
+        Args:
+            chunk: Chunk object containing speaker field
+            
+        Returns:
+            Speaker role ("user"/"assistant") or None if cannot be determined
+        """
+        if hasattr(chunk, 'speaker') and chunk.speaker:
+            return chunk.speaker
+        
+        logger.warning(f"Chunk {getattr(chunk, 'id', 'unknown')} has no speaker field or is empty")
+        return None
+
+    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:
             List of ExtractedStatement objects extracted from the chunk
         """
-        # Prepare the chunk content for processing
         chunk_content = chunk.content
         
-        # Render the prompt using helper function
+        if not chunk_content or len(chunk_content.strip()) < 5:
+            logger.warning(f"Chunk {chunk.id} content too short or empty, skipping")
+            return []
+
         prompt_content = await render_statement_extraction_prompt(
             chunk_content=chunk_content,
             definitions=LABEL_DEFINITIONS,
@@ -137,14 +150,18 @@ class StatementExtractor:
                 except (KeyError, ValueError):
                     relevence_info = RelevenceInfo.RELEVANT
                
+                chunk_speaker = self._get_speaker_from_chunk(chunk)
+            
                 chunk_statement = Statement(
                     statement=extracted_stmt.statement,
                     stmt_type=stmt_type,
                     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,
                 )
+                
                 chunk_statements.append(chunk_statement)
 
             # 分离强弱关系分类：不在句子提取阶段进行，也不写入 chunk.metadata
@@ -167,10 +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
         )
 
@@ -208,7 +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")
@@ -226,12 +243,7 @@ class StatementExtractor:
         return output_path
 
     def save_relations(self, dialogs: List[DialogData], output_path: str = None) -> str:
-        """按对话分组聚合强/弱关系并写入 TXT 文件。
+        """Group and aggregate strong/weak relations by dialogue and write to TXT file."""
-        - 每个对话单独成段：输出该对话的 `Dialog ID`、`Group ID`、`Content`
-        - 在该对话段内再分为 Strong Relations / Weak Relations 两部分
-        - Strong: 逐条输出 `Chunk ID` 与 `Triple`
-        - Weak: 逐条输出 `Chunk ID` 与 `Entity`
-        """
         print("\n=== Relations Classify ===")
 
         # 使用全局配置的输出路径
@@ -286,7 +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,
@@ -300,7 +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/forgetting_scheduler.py

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

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

@@ -90,7 +90,7 @@ class ForgettingStrategy:
     
     async def find_forgettable_nodes(
         self,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         min_days_since_access: int = 30
     ) -> List[Dict[str, Any]]:
         """
@@ -102,7 +102,7 @@ class ForgettingStrategy:
         3. Statement 和 Entity 之间存在关系边
         
         Args:
-            group_id: 组 ID（可选，用于过滤特定组的节点）
+            end_user_id: 组 ID（可选，用于过滤特定组的节点）
             min_days_since_access: 最小未访问天数（默认 30 天）
         
         Returns:
@@ -136,8 +136,8 @@ class ForgettingStrategy:
           AND (e.entity_type IS NULL OR e.entity_type <> 'Person')
         """
         
-        if group_id:
+        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 +159,8 @@ 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)
         
@@ -247,8 +247,8 @@ class ForgettingStrategy:
         entity_activation = entity_node['entity_activation']
         entity_importance = entity_node['entity_importance']
         
-        # 获取 group_id（从 statement 或 entity 节点）
+        # 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 +325,7 @@ class ForgettingStrategy:
                 last_access_time: $current_time,
                 access_count: 1,
                 version: 1,
-                group_id: $group_id,
+                end_user_id: $end_user_id,
                 created_at: datetime($current_time),
                 merged_at: datetime($current_time)
             })
@@ -423,7 +423,7 @@ class ForgettingStrategy:
             'inherited_activation': inherited_activation,
             'inherited_importance': inherited_importance,
             'current_time': current_time_iso,
-            'group_id': group_id
+            'end_user_id': end_user_id
         }
         
         try:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

api/app/core/validators/memory_config_validators.py

@@ -89,14 +89,15 @@ def validate_model_exists_and_active(
     start_time = time.time()
     
     try:
-        # First check if model exists at all (without tenant filtering)
+        # OPTIMIZED: Single query with tenant filter
-        model_without_tenant = ModelConfigRepository.get_by_id(db, model_id, tenant_id=None)
+        # We'll check tenant mismatch in the error handling
-        
-        # Then check with tenant filtering
         model = ModelConfigRepository.get_by_id(db, model_id, tenant_id)
         elapsed_ms = (time.time() - start_time) * 1000
         
         if not model:
+            # Model not found with tenant filter - check if it exists without filter
+            model_without_tenant = ModelConfigRepository.get_by_id(db, model_id, tenant_id=None)
+            
             if model_without_tenant:
                 # Model exists but belongs to different tenant
                 logger.warning(
@@ -208,8 +209,11 @@ def validate_embedding_model(
     db: Session,
     tenant_id: Optional[UUID] = None,
     workspace_id: Optional[UUID] = None
-) -> UUID:
+) -> tuple[UUID, str]:
-    """Validate that embedding model is available and return its UUID.
+    """Validate that embedding model is available and return its UUID and name.
+    
+    Returns:
+        Tuple of (embedding_uuid, embedding_name)
     
     Raises:
         InvalidConfigError: If embedding_id is not provided or invalid
@@ -225,14 +229,19 @@ def validate_embedding_model(
             workspace_id=workspace_id
         )
     
-    embedding_uuid, _ = validate_and_resolve_model_id(
+    embedding_uuid, embedding_name = validate_and_resolve_model_id(
         embedding_id, "embedding", db, tenant_id, required=True,
         config_id=config_id, workspace_id=workspace_id
     )
-    print(100*'-')
+    
-    print(embedding_uuid)
+    logger.debug(
-    print(_)
+        "Embedding model validated",
-    print(100*'-')
+        extra={
+            "embedding_uuid": str(embedding_uuid),
+            "embedding_name": embedding_name,
+            "config_id": config_id
+        }
+    )
     
     if embedding_uuid is None:
         raise InvalidConfigError(
@@ -243,7 +252,7 @@ def validate_embedding_model(
             workspace_id=workspace_id
         )
     
-    return embedding_uuid
+    return embedding_uuid, embedding_name
 
 
 def validate_llm_model(

api/app/repositories/data_config_repository.py

@@ -104,38 +104,6 @@ class DataConfigRepository:
       r.statement AS statement
     """
 
-    # Entity graph within group (source node, edge, target node)
-    SEARCH_FOR_ENTITY_GRAPH = """
-    MATCH (n:ExtractedEntity)-[r]->(m:ExtractedEntity)
-    WHERE n.group_id = $group_id
-    RETURN
-      {
-        entity_idx: n.entity_idx,
-        connect_strength: n.connect_strength,
-        description: n.description,
-        entity_type: n.entity_type,
-        name: n.name,
-        fact_summary: COALESCE(n.fact_summary, ''),
-        id: n.id
-      } AS sourceNode,
-      {
-        rel_id: elementId(r),
-        source_id: startNode(r).id,
-        target_id: endNode(r).id,
-        predicate: r.predicate,
-        statement_id: r.statement_id,
-        statement: r.statement
-      } AS edge,
-      {
-        entity_idx: m.entity_idx,
-        connect_strength: m.connect_strength,
-        description: m.description,
-        entity_type: m.entity_type,
-        name: m.name,
-        fact_summary: COALESCE(m.fact_summary, ''),
-        id: m.id
-      } AS targetNode
-    """
     @staticmethod
     def update_reflection_config(
             db: Session,

api/app/repositories/end_user_repository.py

@@ -276,42 +276,6 @@ def get_end_user_by_id(db: Session, end_user_id: uuid.UUID) -> Optional[EndUser]
     end_user = repo.get_end_user_by_id(end_user_id)
     return end_user
 
-def update_end_user_other_name(
-    db: Session, 
-    end_user_id: uuid.UUID,
-    other_name: str
-) -> int:
-    """
-    通过 end_user_id 更新 end_user 表中的 other_name 字段
-    
-    Args:
-        db: 数据库会话
-        end_user_id: 宿主ID
-        other_name: 要更新的用户名
-        
-    Returns:
-        int: 更新的记录数
-    """
-    try:
-        # 执行更新
-        updated_count = (
-            db.query(EndUser)
-            .filter(EndUser.id == end_user_id)
-            .update(
-                {EndUser.other_name: other_name},
-                synchronize_session=False
-            )
-        )
-        
-        db.commit()
-        db_logger.info(f"成功更新宿主 {end_user_id} 的 other_name 为: {other_name}")
-        return updated_count
-        
-    except Exception as e:
-        db.rollback()
-        db_logger.error(f"更新宿主 {end_user_id} 的 other_name 时出错: {str(e)}")
-        raise
-
 # 新增的缓存操作函数（保持与类方法一致的接口）
 def get_by_id(db: Session, end_user_id: uuid.UUID) -> Optional[EndUser]:
     """根据ID获取终端用户（用于缓存操作）"""

api/app/repositories/neo4j/add_edges.py

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

api/app/repositories/neo4j/add_nodes.py

@@ -6,10 +6,10 @@ from app.core.memory.models.graph_models import DialogueNode, StatementNode, Chu
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 
 
-async def delete_all_nodes(group_id: str, connector: Neo4jConnector):
+async def delete_all_nodes(end_user_id: str, connector: Neo4jConnector):
     """Delete all nodes in the database."""
-    result = await connector.execute_query(f"MATCH (n {{group_id: '{group_id}'}}) DETACH DELETE n")
+    result = await connector.execute_query(f"MATCH (n {{end_user_id: '{end_user_id}'}}) DETACH DELETE n")
-    print(f"All group_id: {group_id} node and edge deleted successfully")
+    print(f"All end_user_id: {end_user_id} node and edge deleted successfully")
     return result
 
 async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConnector) -> Optional[List[str]]:
@@ -32,9 +32,7 @@ async def add_dialogue_nodes(dialogues: List[DialogueNode], connector: Neo4jConn
         for dialogue in dialogues:
             flattened_dialogues.append({
                 "id": dialogue.id,
-                "group_id": dialogue.group_id,
+                "end_user_id": dialogue.end_user_id,
-                "user_id": dialogue.user_id,
-                "apply_id": dialogue.apply_id,
                 "run_id": dialogue.run_id,
                 "ref_id": dialogue.ref_id,
                 "name": dialogue.name,
@@ -79,9 +77,7 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC
             flattened_statement = {
                 "id": statement.id,
                 "name": statement.name,
-                "group_id": statement.group_id,
+                "end_user_id": statement.end_user_id,
-                "user_id": statement.user_id,
-                "apply_id": statement.apply_id,
                 "run_id": statement.run_id,
                 "chunk_id": statement.chunk_id,
                 # "created_at": statement.created_at.isoformat(),
@@ -101,6 +97,8 @@ async def add_statement_nodes(statements: List[StatementNode], connector: Neo4jC
                 #     "entities": [entity.model_dump() for entity in statement.triplet_extraction_info.entities] if statement.triplet_extraction_info else []
                 # }) if statement.triplet_extraction_info else json.dumps({"triplets": [], "entities": []}),
                 "statement_embedding": statement.statement_embedding if statement.statement_embedding else None,
+                # 添加 speaker 字段（用于基于角色的情绪提取）
+                "speaker": statement.speaker if hasattr(statement, 'speaker') else None,
                 # 添加情绪字段处理
                 "emotion_type": statement.emotion_type,
                 "emotion_intensity": statement.emotion_intensity,
@@ -152,9 +150,7 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) ->
             flattened_chunk = {
                 "id": chunk.id,
                 "name": chunk.name,
-                "group_id": chunk.group_id,
+                "end_user_id": chunk.end_user_id,
-                "user_id": chunk.user_id,
-                "apply_id": chunk.apply_id,
                 "run_id": chunk.run_id,
                 "created_at": chunk.created_at.isoformat() if chunk.created_at else None,
                 "expired_at": chunk.expired_at.isoformat() if chunk.expired_at else None,
@@ -163,7 +159,9 @@ async def add_chunk_nodes(chunks: List[ChunkNode], connector: Neo4jConnector) ->
                 "chunk_embedding": chunk.chunk_embedding if chunk.chunk_embedding else None,
                 "sequence_number": chunk.sequence_number,
                 "start_index": metadata.get("start_index"),
-                "end_index": metadata.get("end_index")
+                "end_index": metadata.get("end_index"),
+                # 添加 speaker 字段（用于基于角色的情绪提取）
+                "speaker": chunk.speaker if hasattr(chunk, 'speaker') else None
             }
             flattened_chunks.append(flattened_chunk)
 
@@ -202,9 +200,7 @@ async def add_memory_summary_nodes(summaries: List[MemorySummaryNode], connector
             flattened.append({
                 "id": s.id,
                 "name": s.name,
-                "group_id": s.group_id,
+                "end_user_id": s.end_user_id,
-                "user_id": s.user_id,
-                "apply_id": s.apply_id,
                 "run_id": s.run_id,
                 "created_at": s.created_at.isoformat() if s.created_at else None,
                 "expired_at": s.expired_at.isoformat() if s.expired_at else None,

api/app/repositories/neo4j/base_neo4j_repository.py

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

api/app/repositories/neo4j/cypher_queries.py

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

api/app/repositories/neo4j/dialog_repository.py

@@ -19,7 +19,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
     """对话仓储
     
     管理对话节点的创建、查询、更新和删除操作。
-    提供按group_id、user_id、ref_id等条件查询对话的方法。
+    提供按end_user_id、user_id、ref_id等条件查询对话的方法。
     
     Attributes:
         connector: Neo4j连接器实例
@@ -54,17 +54,17 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
         
         return DialogueNode(**n)
     
-    async def find_by_group_id(self, group_id: str, limit: int = 100) -> List[DialogueNode]:
+    async def find_by_end_user_id(self, end_user_id: str, limit: int = 100) -> List[DialogueNode]:
-        """根据group_id查询对话
+        """根据end_user_id查询对话
         
         Args:
-            group_id: 组ID
+            end_user_id: 组ID
             limit: 返回结果的最大数量
             
         Returns:
             List[DialogueNode]: 对话列表
         """
-        return await self.find({"group_id": group_id}, limit=limit)
+        return await self.find({"end_user_id": end_user_id}, limit=limit)
     
     async def find_by_user_id(self, user_id: str, limit: int = 100) -> List[DialogueNode]:
         """根据user_id查询对话
@@ -94,14 +94,14 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
     
     async def find_by_group_and_user(
         self,
-        group_id: str,
+        end_user_id: str,
         user_id: str,
         limit: int = 100
     ) -> List[DialogueNode]:
-        """根据group_id和user_id查询对话
+        """根据end_user_id和user_id查询对话
         
         Args:
-            group_id: 组ID
+            end_user_id: 组ID
             user_id: 用户ID
             limit: 返回结果的最大数量
             
@@ -109,20 +109,20 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
             List[DialogueNode]: 对话列表
         """
         return await self.find(
-            {"group_id": group_id, "user_id": user_id},
+            {"end_user_id": end_user_id, "user_id": user_id},
             limit=limit
         )
     
     async def find_recent_dialogs(
         self,
-        group_id: str,
+        end_user_id: str,
         days: int = 7,
         limit: int = 100
     ) -> List[DialogueNode]:
         """查询最近的对话
         
         Args:
-            group_id: 组ID
+            end_user_id: 组ID
             days: 查询最近多少天的对话
             limit: 返回结果的最大数量
             
@@ -131,7 +131,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         AND n.created_at >= datetime() - duration({{days: $days}})
         RETURN n
         ORDER BY n.created_at DESC
@@ -139,7 +139,7 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
         """
         results = await self.connector.execute_query(
             query,
-            group_id=group_id,
+            end_user_id=end_user_id,
             days=days,
             limit=limit
         )
@@ -164,16 +164,16 @@ class DialogRepository(BaseNeo4jRepository[DialogueNode]):
     async def find_by_config_and_group(
         self,
         config_id: str,
-        group_id: str,
+        end_user_id: str,
         limit: int = 100
     ) -> List[DialogueNode]:
-        """根据config_id和group_id查询对话
+        """根据config_id和end_user_id查询对话
         
         支持按配置ID和组ID同时过滤,确保只返回使用特定配置处理的对话。
         
         Args:
             config_id: 配置ID
-            group_id: 组ID
+            end_user_id: 组ID
             limit: 返回结果的最大数量
             
         Returns:

api/app/repositories/neo4j/emotion_repository.py

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

api/app/repositories/neo4j/graph_saver.py

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

api/app/repositories/neo4j/graph_search.py

@@ -33,7 +33,7 @@ async def _update_activation_values_batch(
     connector: Neo4jConnector,
     nodes: List[Dict[str, Any]],
     node_label: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     max_retries: int = 3
 ) -> List[Dict[str, Any]]:
     """
@@ -46,7 +46,7 @@ async def _update_activation_values_batch(
         connector: Neo4j连接器
         nodes: 节点列表，每个节点必须包含 'id' 字段
         node_label: 节点标签（Statement, ExtractedEntity, MemorySummary）
-        group_id: 组ID（可选）
+        end_user_id: 组ID（可选）
         max_retries: 最大重试次数
     
     Returns:
@@ -97,7 +97,7 @@ async def _update_activation_values_batch(
         updated_nodes = await access_manager.record_batch_access(
             node_ids=unique_node_ids,
             node_label=node_label,
-            group_id=group_id
+            end_user_id=end_user_id
         )
         
         logger.info(
@@ -118,7 +118,7 @@ async def _update_activation_values_batch(
 async def _update_search_results_activation(
     connector: Neo4jConnector,
     results: Dict[str, List[Dict[str, Any]]],
-    group_id: Optional[str] = None
+    end_user_id: Optional[str] = None
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     更新搜索结果中所有知识节点的激活值
@@ -129,7 +129,7 @@ async def _update_search_results_activation(
     Args:
         connector: Neo4j连接器
         results: 搜索结果字典，包含不同类型节点的列表
-        group_id: 组ID（可选）
+        end_user_id: 组ID（可选）
     
     Returns:
         Dict[str, List[Dict[str, Any]]]: 更新后的搜索结果
@@ -152,7 +152,7 @@ async def _update_search_results_activation(
                     connector=connector,
                     nodes=results[key],
                     node_label=label,
-                    group_id=group_id
+                    end_user_id=end_user_id
                 )
             )
             update_keys.append(key)
@@ -218,7 +218,7 @@ async def _update_search_results_activation(
 async def search_graph(
     connector: Neo4jConnector,
     q: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 50,
     include: List[str] = None,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -236,7 +236,7 @@ async def search_graph(
     Args:
         connector: Neo4j connector
         q: Query text
-        group_id: Optional group filter
+        end_user_id: Optional group filter
         limit: Max results per category
         include: List of categories to search (default: all)
 
@@ -254,7 +254,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_STATEMENTS_BY_KEYWORD,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("statements")
@@ -263,7 +263,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_ENTITIES_BY_NAME,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("entities")
@@ -272,7 +272,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_CHUNKS_BY_CONTENT,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("chunks")
@@ -281,7 +281,7 @@ async def search_graph(
         tasks.append(connector.execute_query(
             SEARCH_MEMORY_SUMMARIES_BY_KEYWORD,
             q=q,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("summaries")
@@ -308,7 +308,7 @@ async def search_graph(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
@@ -318,7 +318,7 @@ async def search_graph_by_embedding(
     connector: Neo4jConnector,
     embedder_client,
     query_text: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 50,
     include: List[str] = ["statements", "chunks", "entities","summaries"],
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -330,7 +330,7 @@ async def search_graph_by_embedding(
 
     - Computes query embedding with the provided embedder_client
     - Ranks by cosine similarity in Cypher
-    - Filters by group_id if provided
+    - Filters by end_user_id if provided
     - Returns up to 'limit' per included type
     """
     import time
@@ -354,7 +354,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             STATEMENT_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("statements")
@@ -364,7 +364,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             CHUNK_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("chunks")
@@ -374,7 +374,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             ENTITY_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("entities")
@@ -384,7 +384,7 @@ async def search_graph_by_embedding(
         tasks.append(connector.execute_query(
             MEMORY_SUMMARY_EMBEDDING_SEARCH,
             embedding=embedding,
-            group_id=group_id,
+            end_user_id=end_user_id,
             limit=limit,
         ))
         task_keys.append("summaries")
@@ -421,7 +421,7 @@ async def search_graph_by_embedding(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     update_time = time.time() - update_start
     print(f"[PERF] Activation value updates took: {update_time:.4f}s")
@@ -429,7 +429,7 @@ async def search_graph_by_embedding(
     return results
 async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全文索引按名称检索候选实体
     connector: Neo4jConnector,
-    group_id: str,
+    end_user_id: str,
     entities: List[Dict[str, Any]],
     use_contains_fallback: bool = True,
     batch_size: int = 500,
@@ -437,7 +437,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     为第二层去重消歧批量检索候选实体（适配新版 cypher_queries）：
-    - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (group_id, name) 检索候选；
+    - 使用全文索引查询 `SEARCH_ENTITIES_BY_NAME` 按 (end_user_id, name) 检索候选；
     - 保留并发控制与返回结构（incoming_id -> [db_entity_props...]）；
     - 若提供 `entity_type`，在本地对返回结果做类型过滤；
     - `use_contains_fallback` 保留形参以兼容，必要时可扩展二次查询策略。
@@ -461,7 +461,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
                 rows = await connector.execute_query(
                     SEARCH_ENTITIES_BY_NAME,
                     q=name,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     limit=100,
                 )
             except Exception:
@@ -485,7 +485,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
                     rows = await connector.execute_query(
                         SEARCH_ENTITIES_BY_NAME,
                         q=name.lower(),
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         limit=100,
                     )
                     for r in rows:
@@ -516,9 +516,7 @@ async def get_dedup_candidates_for_entities(  # 适配新版查询：使用全
 async def search_graph_by_keyword_temporal(
     connector: Neo4jConnector,
     query_text: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    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,
@@ -531,7 +529,7 @@ async def search_graph_by_keyword_temporal(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements containing query_text created between start_date and end_date
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     if not query_text:
@@ -540,9 +538,7 @@ async def search_graph_by_keyword_temporal(
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_KEYWORD_TEMPORAL,
         q=query_text,
-        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,
@@ -556,7 +552,7 @@ async def search_graph_by_keyword_temporal(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
 
     return results
@@ -564,9 +560,7 @@ async def search_graph_by_keyword_temporal(
 
 async def search_graph_by_temporal(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    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,
@@ -579,14 +573,12 @@ async def search_graph_by_temporal(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created between start_date and end_date
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_TEMPORAL,
-        group_id=group_id,
+        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,
@@ -595,7 +587,7 @@ async def search_graph_by_temporal(
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_TEMPORAL}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, start_date: {start_date}, end_date: {end_date}, valid_date: {valid_date}, invalid_date: {invalid_date}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -603,7 +595,7 @@ async def search_graph_by_temporal(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
@@ -612,14 +604,14 @@ async def search_graph_by_temporal(
 async def search_graph_by_dialog_id(
     connector: Neo4jConnector,
     dialog_id: str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
     """
     Temporal search across Dialogues.
 
     - Matches dialogues with dialog_id
-    - Optionally filters by group_id
+    - Optionally filters by end_user_id
     - Returns up to 'limit' dialogues
     """
     if not dialog_id:
@@ -628,7 +620,7 @@ async def search_graph_by_dialog_id(
 
     dialogues = await connector.execute_query(
         SEARCH_DIALOGUE_BY_DIALOG_ID,
-        group_id=group_id,
+        end_user_id=end_user_id,
         dialog_id=dialog_id,
         limit=limit,
     )
@@ -638,7 +630,7 @@ async def search_graph_by_dialog_id(
 async def search_graph_by_chunk_id(
     connector: Neo4jConnector,
     chunk_id : str,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
     if not chunk_id:
@@ -646,7 +638,7 @@ async def search_graph_by_chunk_id(
         return {"chunks": []}
     chunks = await connector.execute_query(
         SEARCH_CHUNK_BY_CHUNK_ID,
-        group_id=group_id,
+        end_user_id=end_user_id,
         chunk_id=chunk_id,
         limit=limit,
     )
@@ -655,9 +647,9 @@ async def search_graph_by_chunk_id(
 
 async def search_graph_by_created_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
+    
-    user_id: Optional[str] = None,
+    
     created_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -667,20 +659,20 @@ async def search_graph_by_created_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_CREATED_AT,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        apply_id=apply_id,
+        
-        user_id=user_id,
+        
         created_at=created_at,
         limit=limit,
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_CREATED_AT}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, created_at: {created_at}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id} created_at: {created_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -688,16 +680,16 @@ async def search_graph_by_created_at(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_by_valid_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
+    
-    user_id: Optional[str] = None,
+    
     valid_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -707,20 +699,20 @@ async def search_graph_by_valid_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_BY_VALID_AT,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        apply_id=apply_id,
+        
-        user_id=user_id,
+        
         valid_at=valid_at,
         limit=limit,
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_BY_VALID_AT}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, valid_at: {valid_at}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}， valid_at: {valid_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -728,16 +720,16 @@ async def search_graph_by_valid_at(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_g_created_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
+    
-    user_id: Optional[str] = None,
+    
     created_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -747,20 +739,20 @@ async def search_graph_g_created_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_G_CREATED_AT,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        apply_id=apply_id,
+        
-        user_id=user_id,
+        
         created_at=created_at,
         limit=limit,
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_G_CREATED_AT}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, created_at: {created_at}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -768,16 +760,16 @@ async def search_graph_g_created_at(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_g_valid_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
+    
-    user_id: Optional[str] = None,
+    
     valid_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -787,20 +779,20 @@ async def search_graph_g_valid_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_G_VALID_AT,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        apply_id=apply_id,
+        
-        user_id=user_id,
+        
         valid_at=valid_at,
         limit=limit,
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_G_VALID_AT}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, valid_at: {valid_at}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, valid_at: {valid_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -808,16 +800,16 @@ async def search_graph_g_valid_at(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_l_created_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
+    
-    user_id: Optional[str] = None,
+    
     created_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -827,20 +819,20 @@ async def search_graph_l_created_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements created at created_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_L_CREATED_AT,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        apply_id=apply_id,
+        
-        user_id=user_id,
+        
         created_at=created_at,
         limit=limit,
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_L_CREATED_AT}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, created_at: {created_at}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id}, created_at: {created_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -848,16 +840,16 @@ async def search_graph_l_created_at(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results
 
 async def search_graph_l_valid_at(
     connector: Neo4jConnector,
-    group_id: Optional[str] = None,
+    end_user_id: Optional[str] = None,
-    apply_id: Optional[str] = None,
+    
-    user_id: Optional[str] = None,
+    
     valid_at: Optional[str] = None,
     limit: int = 1,
 ) -> Dict[str, List[Dict[str, Any]]]:
@@ -867,20 +859,20 @@ async def search_graph_l_valid_at(
     INTEGRATED: Updates activation values for Statement nodes before returning results
 
     - Matches statements valid at valid_at
-    - Optionally filters by group_id, apply_id, user_id
+    - Optionally filters by end_user_id, apply_id, user_id
     - Returns up to 'limit' statements
     """
     statements = await connector.execute_query(
         SEARCH_STATEMENTS_L_VALID_AT,
-        group_id=group_id,
+        end_user_id=end_user_id,
-        apply_id=apply_id,
+        
-        user_id=user_id,
+        
         valid_at=valid_at,
         limit=limit,
     )
 
     print(f"查询语句为：\n{SEARCH_STATEMENTS_L_VALID_AT}")
-    print(f"查询参数为：\n{{group_id: {group_id}, apply_id: {apply_id}, user_id: {user_id}, valid_at: {valid_at}, limit: {limit}}}")
+    print(f"查询参数为：\n{{end_user_id: {end_user_id},  valid_at: {valid_at}, limit: {limit}}}")
     print(f"查询结果为：\n{statements}")
     
     # 更新 Statement 节点的激活值
@@ -888,7 +880,7 @@ async def search_graph_l_valid_at(
     results = await _update_search_results_activation(
         connector=connector,
         results=results,
-        group_id=group_id
+        end_user_id=end_user_id
     )
     
     return results

api/app/repositories/neo4j/memory_summary_repository.py

@@ -18,7 +18,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     """Memory Summary Repository
     
     Manages CRUD operations for MemorySummary nodes.
-    Provides methods to query summaries by group_id, user_id, and time ranges.
+    Provides methods to query summaries by end_user_id, user_id, and time ranges.
     
     Attributes:
         connector: Neo4j connector instance
@@ -51,17 +51,17 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         
         return dict(n)
     
-    async def find_by_group_id(
+    async def find_by_end_user_id(
         self, 
-        group_id: str, 
+        end_user_id: str,
         limit: int = 1000,
         start_date: Optional[datetime] = None,
         end_date: Optional[datetime] = None
     ) -> List[Dict[str, Any]]:
-        """Query memory summaries by group_id
+        """Query memory summaries by end_user_id
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             limit: Maximum number of results to return
             start_date: Optional start date filter
             end_date: Optional end date filter
@@ -71,10 +71,10 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         """
         
-        params = {"group_id": group_id, "limit": limit}
+        params = {"end_user_id": end_user_id, "limit": limit}
         
         # Add date range filters if provided
         if start_date:
@@ -139,16 +139,16 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     
     async def find_by_group_and_user(
         self,
-        group_id: str,
+        end_user_id: str,
         user_id: str,
         limit: int = 1000,
         start_date: Optional[datetime] = None,
         end_date: Optional[datetime] = None
     ) -> List[Dict[str, Any]]:
-        """Query memory summaries by both group_id and user_id
+        """Query memory summaries by both end_user_id and user_id
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             user_id: User ID to filter by
             limit: Maximum number of results to return
             start_date: Optional start date filter
@@ -159,10 +159,10 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id AND n.user_id = $user_id
+        WHERE n.end_user_id = $end_user_id AND n.user_id = $user_id
         """
         
-        params = {"group_id": group_id, "user_id": user_id, "limit": limit}
+        params = {"end_user_id": end_user_id, "user_id": user_id, "limit": limit}
         
         # Add date range filters if provided
         if start_date:
@@ -184,14 +184,14 @@ class MemorySummaryRepository(BaseNeo4jRepository):
     
     async def find_recent_summaries(
         self,
-        group_id: str,
+        end_user_id: str,
         days: int = 7,
         limit: int = 1000
     ) -> List[Dict[str, Any]]:
         """Query recent memory summaries
         
         Args:
-            group_id: Group ID to filter by
+            end_user_id: Group ID to filter by
             days: Number of recent days to query
             limit: Maximum number of results to return
             
@@ -200,7 +200,7 @@ class MemorySummaryRepository(BaseNeo4jRepository):
         """
         query = f"""
         MATCH (n:{self.node_label})
-        WHERE n.group_id = $group_id
+        WHERE n.end_user_id = $end_user_id
         AND n.created_at >= datetime() - duration({{days: $days}})
         RETURN n
         ORDER BY n.created_at DESC

api/app/repositories/neo4j/neo4j_connector.py

@@ -141,14 +141,14 @@ class Neo4jConnector:
         async with self.driver.session(database="neo4j") as session:
             return await session.execute_read(transaction_func, **kwargs)
     
-    async def delete_group(self, group_id: str):
+    async def delete_group(self, end_user_id: str):
         """删除指定组的所有数据
         
-        删除所有属于指定group_id的节点和边。
+        删除所有属于指定end_user_id的节点和边。
         这是一个危险操作，会永久删除数据。
         
         Args:
-            group_id: 要删除的组ID
+            end_user_id: 要删除的组ID
             
         Example:
             >>> connector = Neo4jConnector()
@@ -157,14 +157,14 @@ class Neo4jConnector:
         """
         # 删除节点（DETACH DELETE会同时删除相关的边）
         await self.driver.execute_query(
-            "MATCH (n) WHERE n.group_id = $group_id DETACH DELETE n",
+            "MATCH (n) WHERE n.end_user_id = $end_user_id DETACH DELETE n",
             database="neo4j",
-            group_id=group_id
+            end_user_id=end_user_id
         )
         # 删除独立的边（如果有的话）
         await self.driver.execute_query(
-            "MATCH ()-[r]->() WHERE r.group_id = $group_id DELETE r",
+            "MATCH ()-[r]->() WHERE r.end_user_id = $end_user_id DELETE r",
             database="neo4j",
-            group_id=group_id
+            end_user_id=end_user_id
         )
-        print(f"Group {group_id} deleted.")
+        print(f"Group {end_user_id} deleted.")

api/app/repositories/neo4j/statement_repository.py

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

api/app/schemas/memory_agent_schema.py

@@ -7,15 +7,11 @@ class UserInput(BaseModel):
     message: str
     history: list[dict]
     search_switch: str
-    group_id: str
+    end_user_id: str
     config_id: Optional[str] = None
 
 
 class Write_UserInput(BaseModel):
-    message: str
+    messages: list[dict]
-    group_id: str
+    end_user_id: str
     config_id: Optional[str] = None
-
-class End_User_Information(BaseModel):
-    end_user_name: str  # 这是要更新的用户名
-    id: str  # 宿主ID，用于匹配条件

api/app/services/draft_run_service.py

@@ -10,11 +10,6 @@ import time
 import uuid
 from typing import Any, AsyncGenerator, Dict, List, Optional
 
-from langchain.tools import tool
-from pydantic import BaseModel, Field
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
 from app.celery_app import celery_app
 from app.core.error_codes import BizCode
 from app.core.exceptions import BusinessException
@@ -28,6 +23,10 @@ from app.services.langchain_tool_server import Search
 from app.services.memory_agent_service import MemoryAgentService
 from app.services.model_parameter_merger import ModelParameterMerger
 from app.services.tool_service import ToolService
+from langchain.tools import tool
+from pydantic import BaseModel, Field
+from sqlalchemy import select
+from sqlalchemy.orm import Session
 
 logger = get_business_logger()
 class KnowledgeRetrievalInput(BaseModel):
@@ -93,7 +92,7 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
             try:
                 memory_content = asyncio.run(
                     MemoryAgentService().read_memory(
-                        group_id=end_user_id,
+                        end_user_id=end_user_id,
                         message=question,
                         history=[],
                         search_switch="2",
@@ -107,9 +106,9 @@ def create_long_term_memory_tool(memory_config: Dict[str, Any], end_user_id: str
                     "app.core.memory.agent.read_message",
                     args=[end_user_id, question, [], "1", config_id, storage_type, user_rag_memory_id]
                 )
-                result = task_service.get_task_memory_read_result(task.id)
+                # result = task_service.get_task_memory_read_result(task.id)
-                status = result.get("status")
+                # status = result.get("status")
-                logger.info(f"读取任务状态：{status}")
+                # logger.info(f"读取任务状态：{status}")
 
             finally:
                 db.close()

api/app/services/emotion_analytics_service.py

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

api/app/services/memory_agent_service.py

@@ -10,27 +10,34 @@ import re
 import time
 import uuid
 from typing import Any, AsyncGenerator, Dict, List, Optional
-import redis
-from langchain_core.messages import HumanMessage
 
+import redis
 from app.core.config import settings
 from app.core.logging_config import get_config_logger, get_logger
 from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
 from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph
 from app.core.memory.agent.logger_file.log_streamer import LogStreamer
-from app.core.memory.agent.utils.messages_tools import merge_multiple_search_results, reorder_output_results
+from app.core.memory.agent.utils.messages_tools import (
+    merge_multiple_search_results,
+    reorder_output_results,
+)
 from app.core.memory.agent.utils.type_classifier import status_typle
+from app.core.memory.agent.utils.write_tools import write  # 新增：直接导入 write 函数
 from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.models.knowledge_model import Knowledge, KnowledgeType
+from app.repositories.memory_short_repository import ShortTermMemoryRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
+from app.schemas.memory_agent_schema import Write_UserInput
 from app.schemas.memory_config_schema import ConfigurationError
 from app.services.memory_base_service import Translation_English
 from app.services.memory_config_service import MemoryConfigService
 from app.services.memory_konwledges_server import (
     write_rag,
 )
+from langchain_core.messages import AIMessage
+from langchain_core.messages import HumanMessage
 from pydantic import BaseModel, Field
 from sqlalchemy import func
 from sqlalchemy.orm import Session
@@ -49,25 +56,24 @@ _neo4j_connector = Neo4jConnector()
 class MemoryAgentService:
     """Service for memory agent operations"""
 
-    def writer_messages_deal(self, messages, start_time, group_id, config_id, message, context):
+    def writer_messages_deal(self, messages, start_time, end_user_id, config_id, message, context):
         duration = time.time() - start_time
-
         if str(messages) == 'success':
-            logger.info(f"Write operation successful for group {group_id} with config_id {config_id}")
+            logger.info(f"Write operation successful for group {end_user_id} with config_id {config_id}")
             # 记录成功的操作
             if audit_logger:
-                audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=True,
+                audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=True,
                                            duration=duration, details={"message_length": len(message)})
             return context
         else:
-            logger.warning(f"Write operation failed for group {group_id}")
+            logger.warning(f"Write operation failed for group {end_user_id}")
 
             # 记录失败的操作
             if audit_logger:
                 audit_logger.log_operation(
                     operation="WRITE",
                     config_id=config_id,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     success=False,
                     duration=duration,
                     error=f"写入失败: {messages[:100]}"
@@ -260,12 +266,12 @@ class MemoryAgentService:
             logger.info("Log streaming completed, cleaning up resources")
             # LogStreamer uses context manager for file handling, so cleanup is automatic
 
-    async def write_memory(self, group_id: str, message: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str:
+    async def write_memory(self, end_user_id: str, messages: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str:
         """
         Process write operation with config_id
 
         Args:
-            group_id: Group identifier (also used as end_user_id)
+            end_user_id: Group identifier (also used as end_user_id)
             message: Message to write
             config_id: Configuration ID from database
             db: SQLAlchemy database session
@@ -281,15 +287,15 @@ class MemoryAgentService:
         # Resolve config_id if None using end_user's connected config
         if config_id is None:
             try:
-                connected_config = get_end_user_connected_config(group_id, db)
+                connected_config = get_end_user_connected_config(end_user_id, db)
                 config_id = connected_config.get("memory_config_id")
                 if config_id is None:
-                    raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.")
+                    raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.")
             except Exception as e:
                 if "No memory configuration found" in str(e):
                     raise  # Re-raise our specific error
-                logger.error(f"Failed to get connected config for end_user {group_id}: {e}")
+                logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}")
-                raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}")
+                raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}")
 
         import time
         start_time = time.time()
@@ -309,20 +315,26 @@ class MemoryAgentService:
             # Log failed operation
             if audit_logger:
                 duration = time.time() - start_time
-                audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg)
+                audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg)
 
             raise ValueError(error_msg)
 
-        try:
-            if storage_type == "rag":
-                result = await write_rag(group_id, message, user_rag_memory_id)
-                return result
-            else:
         async with make_write_graph() as graph:
-                    config = {"configurable": {"thread_id": group_id}}
+            config = {"configurable": {"thread_id": end_user_id}}
+            # Convert structured messages to LangChain messages
+            langchain_messages = []
+            for msg in messages:
+                if msg['role'] == 'user':
+                    langchain_messages.append(HumanMessage(content=msg['content']))
+                elif msg['role'] == 'assistant':
+                    langchain_messages.append(AIMessage(content=msg['content']))
+
             # 初始状态 - 包含所有必要字段
-                    initial_state = {"messages": [HumanMessage(content=message)], "group_id": group_id,
+            initial_state = {
-                                     "memory_config": memory_config}
+                "messages": langchain_messages,
+                "end_user_id": end_user_id,
+                "memory_config": memory_config
+            }
 
             # 获取节点更新信息
             async for update_event in graph.astream(
@@ -333,32 +345,73 @@ class MemoryAgentService:
                 for node_name, node_data in update_event.items():
                     if 'save_neo4j' == node_name:
                         massages = node_data
+            print(massages)
             massagesstatus = massages.get('write_result')['status']
             contents = massages.get('write_result')
-                    return self.writer_messages_deal(massagesstatus, start_time, group_id, config_id, message, contents)
+            # Convert messages back to string for logging
-        except Exception as e:
+            message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
-            # Ensure proper error handling and logging
+            return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents)
-            error_msg = f"Write operation failed: {str(e)}"
+
-            logger.error(error_msg)
+        # try:
-            if audit_logger:
+        #     if storage_type == "rag":
-                duration = time.time() - start_time
+        #         # For RAG storage, convert messages to single string
-                audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg)
+        #         message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
-            raise ValueError(error_msg)
+        #         result = await write_rag(end_user_id, message_text, user_rag_memory_id)
+        #         return result
+        #     else:
+        #         async with make_write_graph() as graph:
+        #             config = {"configurable": {"thread_id": end_user_id}}
+        #             # Convert structured messages to LangChain messages
+        #             langchain_messages = []
+        #             for msg in messages:
+        #                 if msg['role'] == 'user':
+        #                     langchain_messages.append(HumanMessage(content=msg['content']))
+        #                 elif msg['role'] == 'assistant':
+        #                     langchain_messages.append(AIMessage(content=msg['content']))
+        #
+        #             # 初始状态 - 包含所有必要字段
+        #             initial_state = {
+        #                 "messages": langchain_messages,
+        #                 "end_user_id": end_user_id,
+        #                 "memory_config": memory_config
+        #             }
+        #
+        #             # 获取节点更新信息
+        #             async for update_event in graph.astream(
+        #                     initial_state,
+        #                     stream_mode="updates",
+        #                     config=config
+        #             ):
+        #                 for node_name, node_data in update_event.items():
+        #                     if 'save_neo4j' == node_name:
+        #                         massages = node_data
+        #             massagesstatus = massages.get('write_result')['status']
+        #             contents = massages.get('write_result')
+        #             # Convert messages back to string for logging
+        #             message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
+        #             return self.writer_messages_deal(massagesstatus, start_time, end_user_id, config_id, message_text, contents)
+        # except Exception as e:
+        #     # Ensure proper error handling and logging
+        #     error_msg = f"Write operation failed: {str(e)}"
+        #     logger.error(error_msg)
+        #     if audit_logger:
+        #         duration = time.time() - start_time
+        #         audit_logger.log_operation(operation="WRITE", config_id=config_id, end_user_id=end_user_id, success=False, duration=duration, error=error_msg)
+        #     raise ValueError(error_msg)
 
 
 
 
     async def read_memory(
         self,
-        group_id: str,
+        end_user_id: str,
         message: str,
         history: List[Dict],
         search_switch: str,
         config_id: Optional[str],
         db: Session,
         storage_type: str,
-        user_rag_memory_id: str
+        user_rag_memory_id: str) -> Dict:
-    ) -> Dict:
         """
         Process read operation with config_id
 
@@ -368,7 +421,7 @@ class MemoryAgentService:
         - "2": Direct answer based on context
 
         Args:
-            group_id: Group identifier (also used as end_user_id)
+            end_user_id: Group identifier (also used as end_user_id)
             message: User message
             history: Conversation history
             search_switch: Search mode switch
@@ -386,21 +439,22 @@ class MemoryAgentService:
 
         import time
         start_time = time.time()
+        ori_message= message
 
         # Resolve config_id if None using end_user's connected config
         if config_id is None:
             try:
-                connected_config = get_end_user_connected_config(group_id, db)
+                connected_config = get_end_user_connected_config(end_user_id, db)
                 config_id = connected_config.get("memory_config_id")
                 if config_id is None:
-                    raise ValueError(f"No memory configuration found for end_user {group_id}. Please ensure the user has a connected memory configuration.")
+                    raise ValueError(f"No memory configuration found for end_user {end_user_id}. Please ensure the user has a connected memory configuration.")
             except Exception as e:
                 if "No memory configuration found" in str(e):
                     raise  # Re-raise our specific error
-                logger.error(f"Failed to get connected config for end_user {group_id}: {e}")
+                logger.error(f"Failed to get connected config for end_user {end_user_id}: {e}")
-                raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}")
+                raise ValueError(f"Unable to determine memory configuration for end_user {end_user_id}: {e}")
 
-        logger.info(f"Read operation for group {group_id} with config_id {config_id}")
+        logger.info(f"Read operation for group {end_user_id} with config_id {config_id}")
 
         # 导入审计日志记录器
         try:
@@ -426,7 +480,7 @@ class MemoryAgentService:
                 audit_logger.log_operation(
                     operation="READ",
                     config_id=config_id,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     success=False,
                     duration=duration,
                     error=error_msg
@@ -436,15 +490,16 @@ class MemoryAgentService:
 
         # Step 2: Prepare history
         history.append({"role": "user", "content": message})
-        logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}")
+        logger.debug(f"Group ID:{end_user_id}, Message:{message}, History:{history}, Config ID:{config_id}")
 
         # Step 3: Initialize MCP client and execute read workflow
+        graph_exec_start = time.time()
         try:
             async with make_read_graph() as graph:
-                config = {"configurable": {"thread_id": group_id}}
+                config = {"configurable": {"thread_id": end_user_id}}
                 # 初始状态 - 包含所有必要字段
                 initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch,
-                                 "group_id": group_id
+                                 "end_user_id": end_user_id
                     , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id,
                                  "memory_config": memory_config}
                 # 获取节点更新信息
@@ -495,18 +550,72 @@ class MemoryAgentService:
                         if summary_n and summary_n != [] and summary_n != {}:
                             _intermediate_outputs.append(summary_n)
 
+                graph_exec_time = time.time() - graph_exec_start
+                logger.info(f"[PERF] Graph execution completed in {graph_exec_time:.4f}s")
+
                 _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
 
                 optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
                 result = reorder_output_results(optimized_outputs)
 
+                # 保存短期记忆到数据库
+                # 只有 search_switch 不为 "2"（快速检索）时才保存
+                try:
+                    from app.repositories.memory_short_repository import ShortTermMemoryRepository
+                    
+                    retrieved_content = []
+                    repo = ShortTermMemoryRepository(db)
+                    
+                    if str(search_switch) != "2":
+                        for intermediate in _intermediate_outputs:
+                            logger.debug(f"处理中间结果: {intermediate}")
+                            intermediate_type = intermediate.get('type', '')
+                            
+                            if intermediate_type == "search_result":
+                                query = intermediate.get('query', '')
+                                raw_results = intermediate.get('raw_results', {})
+                                reranked_results = raw_results.get('reranked_results', [])
+                                
+                                try:
+                                    statements = [statement['statement'] for statement in reranked_results.get('statements', [])]
+                                except Exception:
+                                    statements = []
+                                
+                                # 去重
+                                statements = list(set(statements))
+                                
+                                if query and statements:
+                                    retrieved_content.append({query: statements})
+                    
+                    # 如果 retrieved_content 为空，设置为空字符串
+                    if retrieved_content == []:
+                        retrieved_content = ''
+                    
+                    # 只有当回答不是"信息不足"且不是快速检索时才保存
+                    if '信息不足，无法回答。' != str(summary) and str(search_switch).strip() != "2":
+                        # 使用 upsert 方法
+                        repo.upsert(
+                            end_user_id=end_user_id,
+                            messages=message,
+                            aimessages=summary,
+                            retrieved_content=retrieved_content,
+                            search_switch=str(search_switch)
+                        )
+                        logger.info(f"成功保存短期记忆: end_user_id={end_user_id}, search_switch={search_switch}")
+                    else:
+                        logger.debug(f"跳过保存短期记忆: summary={summary[:50] if summary else 'None'}, search_switch={search_switch}")
+                        
+                except Exception as save_error:
+                    # 保存失败不应该影响主流程，只记录错误
+                    logger.error(f"保存短期记忆失败: {str(save_error)}", exc_info=True)
+
                 # Log successful operation
                 if audit_logger:
                     duration = time.time() - start_time
                     audit_logger.log_operation(
                         operation="READ",
                         config_id=config_id,
-                        group_id=group_id,
+                        end_user_id=end_user_id,
                         success=True,
                         duration=duration
                     )
@@ -524,14 +633,56 @@ class MemoryAgentService:
                 audit_logger.log_operation(
                     operation="READ",
                     config_id=config_id,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     success=False,
                     duration=duration,
                     error=error_msg
                 )
             raise ValueError(error_msg)
 
+    def get_messages_list(self, user_input: Write_UserInput) -> list[dict]:
+        """
+        Get standardized message list from user input.
         
+        Args:
+            user_input: Write_UserInput object
+        
+        Returns:
+            list[dict]: Message list, each message contains role and content
+            
+        Raises:
+            ValueError: If messages is empty or format is incorrect
+        """
+        from app.core.logging_config import get_api_logger
+        logger = get_api_logger()
+        
+        if len(user_input.messages) == 0:
+            logger.error("Validation failed: Message list cannot be empty")
+            raise ValueError("Message list cannot be empty")
+        
+        for idx, msg in enumerate(user_input.messages):
+            if not isinstance(msg, dict):
+                logger.error(f"Validation failed: Message {idx} is not a dict: {type(msg)}")
+                raise ValueError(f"Message format error: Message must be a dictionary. Error message index: {idx}, type: {type(msg)}")
+            
+            if 'role' not in msg:
+                logger.error(f"Validation failed: Message {idx} missing 'role' field: {msg}")
+                raise ValueError(f"Message format error: Message must contain 'role' field. Error message index: {idx}")
+            
+            if 'content' not in msg:
+                logger.error(f"Validation failed: Message {idx} missing 'content' field: {msg}")
+                raise ValueError(f"Message format error: Message must contain 'content' field. Error message index: {idx}")
+            
+            if msg['role'] not in ['user', 'assistant']:
+                logger.error(f"Validation failed: Message {idx} invalid role: {msg['role']}")
+                raise ValueError(f"Role must be 'user' or 'assistant', got: {msg['role']}. Message index: {idx}")
+            
+            if not msg['content'] or not msg['content'].strip():
+                logger.error(f"Validation failed: Message {idx} content is empty")
+                raise ValueError(f"Message content cannot be empty. Message index: {idx}, role: {msg['role']}")
+        
+        logger.info(f"Validation successful: Structured message list, count: {len(user_input.messages)}")
+        return user_input.messages
 
     async def classify_message_type(self, message: str, config_id: int, db: Session) -> Dict:
         """
@@ -559,7 +710,67 @@ class MemoryAgentService:
         logger.debug(f"Message type: {status}")
         return status
 
-    # ==================== 新增的三个接口方法 ====================
+    async def generate_summary_from_retrieve(
+        self,
+        retrieve_info: str,
+        history: List[Dict],
+        query: str,
+        config_id: str,
+        db: Session
+    ) -> str:
+        """
+        基于检索信息、历史对话和查询生成最终答案
+        
+        使用 Retrieve_Summary_prompt.jinja2 模板调用大模型生成答案
+        
+        Args:
+            retrieve_info: 检索到的信息
+            history: 历史对话记录
+            query: 用户查询
+            config_id: 配置ID
+            db: 数据库会话
+            
+        Returns:
+            生成的答案文本
+        """
+        logger.info(f"Generating summary from retrieve info for query: {query[:50]}...")
+        
+        try:
+            # 加载配置
+            config_service = MemoryConfigService(db)
+            memory_config = config_service.load_memory_config(
+                config_id=config_id,
+                service_name="MemoryAgentService"
+            )
+            
+            # 导入必要的模块
+            from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import summary_llm
+            from app.core.memory.agent.models.summary_models import RetrieveSummaryResponse
+            
+            # 构建状态对象
+            state = {
+                "data": query,
+                "memory_config": memory_config
+            }
+            
+            # 直接调用 summary_llm 函数
+            answer = await summary_llm(
+                state=state,
+                history=history,
+                retrieve_info=retrieve_info,
+                template_name='Retrieve_Summary_prompt.jinja2',
+                operation_name='retrieve_summary',
+                response_model=RetrieveSummaryResponse,
+                search_mode="1"
+            )
+            
+            logger.info(f"Successfully generated summary: {answer[:100] if answer else 'None'}...")
+            return answer if answer else "信息不足，无法回答。"
+            
+        except Exception as e:
+            logger.error(f"生成摘要失败: {str(e)}", exc_info=True)
+            return "信息不足，无法回答。"
+
 
     async def get_knowledge_type_stats(
         self,
@@ -571,7 +782,7 @@ class MemoryAgentService:
         """
         统计知识库类型分布，包含：
         1. PostgreSQL 中的知识库类型：General, Web, Third-party, Folder（根据 workspace_id 过滤）
-        2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/group_id 过滤）
+        2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/end_user_id 过滤）
         3. total: 所有类型的总和
 
         参数：
@@ -657,11 +868,11 @@ class MemoryAgentService:
                 for end_user in end_users:
                     end_user_id_str = str(end_user.id)
                     memory_query = """
-                    MATCH (n:Chunk) WHERE n.group_id = $group_id RETURN count(n) AS Count
+                    MATCH (n:Chunk) WHERE n.end_user_id = $end_user_id RETURN count(n) AS Count
                     """
                     neo4j_result = await _neo4j_connector.execute_query(
                         memory_query,
-                        group_id=end_user_id_str,
+                        end_user_id=end_user_id_str,
                     )
                     chunk_count = neo4j_result[0]["Count"] if neo4j_result else 0
                     total_chunks += chunk_count
@@ -701,7 +912,7 @@ class MemoryAgentService:
         获取指定用户的热门记忆标签
 
         参数：
-        - end_user_id: 用户ID（可选），对应Neo4j中的group_id字段
+        - end_user_id: 用户ID（可选），对应Neo4j中的end_user_id字段
         - limit: 返回标签数量限制
 
         返回格式：
@@ -711,7 +922,7 @@ class MemoryAgentService:
         ]
         """
         try:
-            # by_user=False 表示按 group_id 查询（在Neo4j中，group_id就是用户维度）
+            # by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度）
             tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False)
             payload=[]
             for tag, freq in tags:
@@ -786,21 +997,21 @@ class MemoryAgentService:
             # 查询该用户的语句
             query = (
                 "MATCH (s:Statement) "
-                "WHERE ($group_id IS NULL OR s.group_id = $group_id) AND s.statement IS NOT NULL "
+                "WHERE ($end_user_id IS NULL OR s.end_user_id = $end_user_id) AND s.statement IS NOT NULL "
                 "RETURN s.statement AS statement "
                 "ORDER BY s.created_at DESC LIMIT 100"
             )
-            rows = await connector.execute_query(query, group_id=end_user_id)
+            rows = await connector.execute_query(query, end_user_id=end_user_id)
             statements = [r.get("statement", "") for r in rows if r.get("statement")]
 
             # 查询该用户的热门实体
             entity_query = (
                 "MATCH (e:ExtractedEntity) "
-                "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL "
+                "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' AND e.name IS NOT NULL "
                 "RETURN e.name AS name, count(e) AS frequency "
                 "ORDER BY frequency DESC LIMIT 20"
             )
-            entity_rows = await connector.execute_query(entity_query, group_id=end_user_id)
+            entity_rows = await connector.execute_query(entity_query, end_user_id=end_user_id)
             entities = [f"{r['name']} ({r['frequency']})" for r in entity_rows]
 
             await connector.close()
@@ -853,14 +1064,14 @@ class MemoryAgentService:
             names_to_exclude = ['AI', 'Caroline', 'Melanie', 'Jon', 'Gina', '用户', 'AI助手', 'John', 'Maria']
             hot_tag_query = (
                 "MATCH (e:ExtractedEntity) "
-                "WHERE ($group_id IS NULL OR e.group_id = $group_id) AND e.entity_type <> '人物' "
+                "WHERE ($end_user_id IS NULL OR e.end_user_id = $end_user_id) AND e.entity_type <> '人物' "
                 "AND e.name IS NOT NULL AND NOT e.name IN $names_to_exclude "
                 "RETURN e.name AS name, count(e) AS frequency "
                 "ORDER BY frequency DESC LIMIT 4"
             )
             hot_tag_rows = await connector.execute_query(
                 hot_tag_query,
-                group_id=end_user_id,
+                end_user_id=end_user_id,
                 names_to_exclude=names_to_exclude
             )
             await connector.close()
@@ -1006,6 +1217,10 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
         "memory_config_id": memory_config_id
     }
 
+    print(188*'*')
+    print(result)
+    print(188 * '*')
+
     logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}")
     return result
 
@@ -1033,7 +1248,7 @@ def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session)
     """
     from app.models.app_release_model import AppRelease
     from app.models.end_user_model import EndUser
-    from app.models.memory_config_model import MemoryConfig
+    from app.models.data_config_model import DataConfig
     from sqlalchemy import select
 
     logger.info(f"Batch getting connected configs for {len(end_user_ids)} end_users")

api/app/services/memory_api_service.py

@@ -25,7 +25,7 @@ class MemoryAPIService:
     
     This service provides a thin layer that:
     1. Validates end_user exists and belongs to the authorized workspace
-    2. Maps end_user_id to group_id for memory operations
+    2. Maps end_user_id to end_user_id for memory operations
     3. Delegates to MemoryAgentService for actual memory read/write operations
     """
     
@@ -115,7 +115,7 @@ class MemoryAPIService:
         
         Args:
             workspace_id: Workspace ID for resource validation
-            end_user_id: End user identifier (used as group_id)
+            end_user_id: End user identifier (used as end_user_id)
             message: Message content to store
             config_id: Optional memory configuration ID
             storage_type: Storage backend (neo4j or rag)
@@ -133,13 +133,12 @@ class MemoryAPIService:
         # Validate end_user exists and belongs to workspace
         self.validate_end_user(end_user_id, workspace_id)
         
-        # Use end_user_id as group_id for memory operations
+        # Use end_user_id as end_user_id for memory operations
-        group_id = end_user_id
         
         try:
             # Delegate to MemoryAgentService
             result = await MemoryAgentService().write_memory(
-                group_id=group_id,
+                end_user_id=end_user_id,
                 message=message,
                 config_id=config_id,
                 db=self.db,
@@ -186,7 +185,7 @@ class MemoryAPIService:
         
         Args:
             workspace_id: Workspace ID for resource validation
-            end_user_id: End user identifier (used as group_id)
+            end_user_id: End user identifier (used as end_user_id)
             message: Query message
             search_switch: Search mode (0=deep search with verification, 1=deep search, 2=fast search)
             config_id: Optional memory configuration ID
@@ -205,13 +204,13 @@ class MemoryAPIService:
         # Validate end_user exists and belongs to workspace
         self.validate_end_user(end_user_id, workspace_id)
         
-        # Use end_user_id as group_id for memory operations
+        # Use end_user_id as end_user_id for memory operations
-        group_id = end_user_id
+
         
         try:
             # Delegate to MemoryAgentService
             result = await MemoryAgentService().read_memory(
-                group_id=group_id,
+                end_user_id=end_user_id,
                 message=message,
                 history=[],
                 search_switch=search_switch,

api/app/services/memory_base_service.py

@@ -326,7 +326,7 @@ class MemoryBaseService:
         
         Args:
             summary_id: Summary节点的ID
-            end_user_id: 终端用户ID (group_id)
+            end_user_id: 终端用户ID (end_user_id)
             
         Returns:
             最大emotion_intensity对应的emotion_type，如果没有则返回None
@@ -334,7 +334,7 @@ class MemoryBaseService:
         try:
             query = """
             MATCH (s:MemorySummary)
-            WHERE elementId(s) = $summary_id AND s.group_id = $group_id
+            WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id
             MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
             WHERE stmt.emotion_type IS NOT NULL 
               AND stmt.emotion_intensity IS NOT NULL
@@ -347,7 +347,7 @@ class MemoryBaseService:
             result = await self.neo4j_connector.execute_query(
                 query,
                 summary_id=summary_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             if result and len(result) > 0:
@@ -381,10 +381,10 @@ class MemoryBaseService:
             if end_user_id:
                 query = """
                 MATCH (n:MemorySummary)
-                WHERE n.group_id = $group_id
+                WHERE n.end_user_id = $end_user_id
                 RETURN count(n) as count
                 """
-                result = await self.neo4j_connector.execute_query(query, group_id=end_user_id)
+                result = await self.neo4j_connector.execute_query(query, end_user_id=end_user_id)
             else:
                 query = """
                 MATCH (n:MemorySummary)
@@ -423,12 +423,12 @@ class MemoryBaseService:
             if end_user_id:
                 semantic_query = """
                 MATCH (e:ExtractedEntity)
-                WHERE e.group_id = $group_id AND e.is_explicit_memory = true
+                WHERE e.end_user_id = $end_user_id AND e.is_explicit_memory = true
                 RETURN count(e) as count
                 """
                 semantic_result = await self.neo4j_connector.execute_query(
                     semantic_query, 
-                    group_id=end_user_id
+                    end_user_id=end_user_id
                 )
             else:
                 semantic_query = """
@@ -519,7 +519,7 @@ class MemoryBaseService:
             """
             
             if end_user_id:
-                query += " AND n.group_id = $group_id"
+                query += " AND n.end_user_id = $end_user_id"
             
             query += """
             RETURN sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
@@ -528,7 +528,7 @@ class MemoryBaseService:
             # 设置查询参数
             params = {'threshold': forgetting_threshold}
             if end_user_id:
-                params['group_id'] = end_user_id
+                params['end_user_id'] = end_user_id
             
             # 执行查询
             result = await self.neo4j_connector.execute_query(query, **params)

api/app/services/memory_config_service.py

@@ -125,7 +125,11 @@ class MemoryConfigService:
         try:
             validated_config_id = _validate_config_id(config_id)
             
+            # Step 1: Get config and workspace
+            db_query_start = time.time()
             result = DataConfigRepository.get_config_with_workspace(self.db, validated_config_id)
+            db_query_time = time.time() - db_query_start
+            logger.info(f"[PERF] Config+Workspace query: {db_query_time:.4f}s")
             if not result:
                 elapsed_ms = (time.time() - start_time) * 1000
                 config_logger.error(
@@ -144,16 +148,20 @@ class MemoryConfigService:
             
             memory_config, workspace = result
             
-            # Validate embedding model
+            # Step 2: Validate embedding model (returns both UUID and name)
-            embedding_uuid = validate_embedding_model(
+            embed_start = time.time()
+            embedding_uuid, embedding_name = validate_embedding_model(
                 validated_config_id,
                 memory_config.embedding_id,
                 self.db,
                 workspace.tenant_id,
                 workspace.id,
             )
+            embed_time = time.time() - embed_start
+            logger.info(f"[PERF] Embedding validation: {embed_time:.4f}s")
             
-            # Resolve LLM model
+            # Step 3: Resolve LLM model
+            llm_start = time.time()
             llm_uuid, llm_name = validate_and_resolve_model_id(
                 memory_config.llm_id,
                 "llm",
@@ -163,8 +171,11 @@ class MemoryConfigService:
                 config_id=validated_config_id,
                 workspace_id=workspace.id,
             )
+            llm_time = time.time() - llm_start
+            logger.info(f"[PERF] LLM validation: {llm_time:.4f}s")
             
-            # Resolve optional rerank model
+            # Step 4: Resolve optional rerank model
+            rerank_start = time.time()
             rerank_uuid = None
             rerank_name = None
             if memory_config.rerank_id:
@@ -177,16 +188,12 @@ class MemoryConfigService:
                     config_id=validated_config_id,
                     workspace_id=workspace.id,
                 )
+            rerank_time = time.time() - rerank_start
+            if memory_config.rerank_id:
+                logger.info(f"[PERF] Rerank validation: {rerank_time:.4f}s")
             
-            # Get embedding model name
+            # Note: embedding_name is now returned from validate_embedding_model above
-            embedding_name, _ = validate_model_exists_and_active(
+            # No need for redundant query!
-                embedding_uuid,
-                "embedding",
-                self.db,
-                workspace.tenant_id,
-                config_id=validated_config_id,
-                workspace_id=workspace.id,
-            )
             
             # Create immutable MemoryConfig object
             config = MemoryConfig(

api/app/services/memory_entity_relationship_service.py

@@ -717,8 +717,8 @@ class MemoryInteraction:
             ori_data= await self.connector.execute_query(Memory_Space_Entity, id=self.id)
             if ori_data!=[]:
                 # name = ori_data[0]['name']
-                group_id = [i['group_id'] for i in ori_data][0]
+                end_user_id = [i['end_user_id'] for i in ori_data][0]
-                Space_User = await self.connector.execute_query(Memory_Space_User, group_id=group_id)
+                Space_User = await self.connector.execute_query(Memory_Space_User, end_user_id=end_user_id)
                 if not Space_User:
                     return []
                 user_id=Space_User[0]['id']

api/app/services/memory_episodic_service.py

@@ -34,7 +34,7 @@ class MemoryEpisodicService(MemoryBaseService):
         
         Args:
             summary_id: Summary节点的ID
-            end_user_id: 终端用户ID (group_id)
+            end_user_id: 终端用户ID (end_user_id)
             
         Returns:
             (标题, 类型)元组，如果不存在则返回默认值
@@ -43,14 +43,14 @@ class MemoryEpisodicService(MemoryBaseService):
             # 查询Summary节点的name(作为title)和memory_type(作为type)
             query = """
             MATCH (s:MemorySummary)
-            WHERE elementId(s) = $summary_id AND s.group_id = $group_id
+            WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id
             RETURN s.name AS title, s.memory_type AS type
             """
             
             result = await self.neo4j_connector.execute_query(
                 query,
                 summary_id=summary_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             if not result or len(result) == 0:
@@ -77,7 +77,7 @@ class MemoryEpisodicService(MemoryBaseService):
         
         Args:
             summary_id: Summary节点的ID
-            end_user_id: 终端用户ID (group_id)
+            end_user_id: 终端用户ID (end_user_id)
             
         Returns:
             前3个实体的name属性列表
@@ -87,7 +87,7 @@ class MemoryEpisodicService(MemoryBaseService):
             # 按activation_value降序排序,返回前3个
             query = """
             MATCH (s:MemorySummary)
-            WHERE elementId(s) = $summary_id AND s.group_id = $group_id
+            WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id
             MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
             MATCH (stmt)-[:REFERENCES_ENTITY]->(entity:ExtractedEntity)
             WHERE entity.activation_value IS NOT NULL
@@ -99,7 +99,7 @@ class MemoryEpisodicService(MemoryBaseService):
             result = await self.neo4j_connector.execute_query(
                 query,
                 summary_id=summary_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             # 提取实体名称
@@ -123,7 +123,7 @@ class MemoryEpisodicService(MemoryBaseService):
         
         Args:
             summary_id: Summary节点的ID
-            end_user_id: 终端用户ID (group_id)
+            end_user_id: 终端用户ID (end_user_id)
             
         Returns:
             所有Statement节点的statement属性内容列表
@@ -132,7 +132,7 @@ class MemoryEpisodicService(MemoryBaseService):
             # 查询Summary节点指向的所有Statement节点
             query = """
             MATCH (s:MemorySummary)
-            WHERE elementId(s) = $summary_id AND s.group_id = $group_id
+            WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id
             MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
             WHERE stmt.statement IS NOT NULL AND stmt.statement <> ''
             RETURN stmt.statement AS statement
@@ -141,7 +141,7 @@ class MemoryEpisodicService(MemoryBaseService):
             result = await self.neo4j_connector.execute_query(
                 query,
                 summary_id=summary_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             # 提取statement内容
@@ -214,12 +214,12 @@ class MemoryEpisodicService(MemoryBaseService):
             # 1. 先查询所有情景记忆的总数（不受筛选条件限制）
             total_all_query = """
             MATCH (s:MemorySummary)
-            WHERE s.group_id = $group_id
+            WHERE s.end_user_id = $end_user_id
             RETURN count(s) AS total_all
             """
             total_all_result = await self.neo4j_connector.execute_query(
                 total_all_query, 
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             total_all = total_all_result[0]["total_all"] if total_all_result else 0
             
@@ -229,7 +229,7 @@ class MemoryEpisodicService(MemoryBaseService):
             # 3. 构建Cypher查询
             query = """
             MATCH (s:MemorySummary)
-            WHERE s.group_id = $group_id
+            WHERE s.end_user_id = $end_user_id
             """
             
             # 添加时间范围过滤
@@ -248,7 +248,7 @@ class MemoryEpisodicService(MemoryBaseService):
             ORDER BY s.created_at DESC
             """
             
-            params = {"group_id": end_user_id}
+            params = {"end_user_id": end_user_id}
             if time_filter:
                 params["time_filter"] = time_filter
             if title_keyword:
@@ -333,14 +333,14 @@ class MemoryEpisodicService(MemoryBaseService):
             # 1. 查询指定的MemorySummary节点
             query = """
             MATCH (s:MemorySummary)
-            WHERE elementId(s) = $summary_id AND s.group_id = $group_id
+            WHERE elementId(s) = $summary_id AND s.end_user_id = $end_user_id
             RETURN elementId(s) AS id, s.created_at AS created_at
             """
             
             result = await self.neo4j_connector.execute_query(
                 query,
                 summary_id=summary_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             # 2. 如果节点不存在，返回错误

api/app/services/memory_explicit_service.py

@@ -60,7 +60,7 @@ class MemoryExplicitService(MemoryBaseService):
             # ========== 1. 查询情景记忆（MemorySummary节点） ==========
             episodic_query = """
             MATCH (s:MemorySummary)
-            WHERE s.group_id = $group_id
+            WHERE s.end_user_id = $end_user_id
             RETURN elementId(s) AS id, 
                    s.name AS title,
                    s.content AS content,
@@ -70,7 +70,7 @@ class MemoryExplicitService(MemoryBaseService):
             
             episodic_result = await self.neo4j_connector.execute_query(
                 episodic_query, 
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             # 处理情景记忆数据
@@ -96,7 +96,7 @@ class MemoryExplicitService(MemoryBaseService):
             # ========== 2. 查询语义记忆（ExtractedEntity节点） ==========
             semantic_query = """
             MATCH (e:ExtractedEntity)
-            WHERE e.group_id = $group_id 
+            WHERE e.end_user_id = $end_user_id 
               AND e.is_explicit_memory = true
             RETURN elementId(e) AS id, 
                    e.name AS name,
@@ -107,7 +107,7 @@ class MemoryExplicitService(MemoryBaseService):
             
             semantic_result = await self.neo4j_connector.execute_query(
                 semantic_query, 
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             # 处理语义记忆数据
@@ -189,7 +189,7 @@ class MemoryExplicitService(MemoryBaseService):
             # ========== 1. 先尝试查询情景记忆 ==========
             episodic_query = """
             MATCH (s:MemorySummary)
-            WHERE elementId(s) = $memory_id AND s.group_id = $group_id
+            WHERE elementId(s) = $memory_id AND s.end_user_id = $end_user_id
             RETURN s.name AS title,
                    s.content AS content,
                    s.created_at AS created_at
@@ -198,7 +198,7 @@ class MemoryExplicitService(MemoryBaseService):
             episodic_result = await self.neo4j_connector.execute_query(
                 episodic_query,
                 memory_id=memory_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             if episodic_result and len(episodic_result) > 0:
@@ -229,7 +229,7 @@ class MemoryExplicitService(MemoryBaseService):
             semantic_query = """
             MATCH (e:ExtractedEntity)
             WHERE elementId(e) = $memory_id 
-              AND e.group_id = $group_id 
+              AND e.end_user_id = $end_user_id 
               AND e.is_explicit_memory = true
             RETURN e.name AS name,
                    e.description AS core_definition,
@@ -240,7 +240,7 @@ class MemoryExplicitService(MemoryBaseService):
             semantic_result = await self.neo4j_connector.execute_query(
                 semantic_query,
                 memory_id=memory_id,
-                group_id=end_user_id
+                end_user_id=end_user_id
             )
             
             if semantic_result and len(semantic_result) > 0:

api/app/services/memory_forget_service.py

@@ -132,7 +132,7 @@ class MemoryForgetService:
     async def _get_knowledge_stats(
         self,
         connector: Neo4jConnector,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         forgetting_threshold: float = 0.3
     ) -> Dict[str, Any]:
         """
@@ -140,7 +140,7 @@ class MemoryForgetService:
         
         Args:
             connector: Neo4j 连接器
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
             forgetting_threshold: 遗忘阈值
         
         Returns:
@@ -152,8 +152,8 @@ class MemoryForgetService:
         WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
         """
         
-        if group_id:
+        if end_user_id:
-            query += " AND n.group_id = $group_id"
+            query += " AND n.end_user_id = $end_user_id"
         
         query += """
         WITH n,
@@ -172,8 +172,8 @@ class MemoryForgetService:
         """
         
         params = {'threshold': forgetting_threshold}
-        if group_id:
+        if end_user_id:
-            params['group_id'] = group_id
+            params['end_user_id'] = end_user_id
         
         results = await connector.execute_query(query, **params)
         
@@ -200,7 +200,7 @@ class MemoryForgetService:
     async def _get_pending_forgetting_nodes(
         self,
         connector: Neo4jConnector,
-        group_id: str,
+        end_user_id: str,
         forgetting_threshold: float,
         min_days_since_access: int,
         limit: int = 20
@@ -212,7 +212,7 @@ class MemoryForgetService:
         
         Args:
             connector: Neo4j 连接器
-            group_id: 组ID
+            end_user_id: 组ID
             forgetting_threshold: 遗忘阈值
             min_days_since_access: 最小未访问天数
             limit: 返回节点数量限制
@@ -229,7 +229,7 @@ class MemoryForgetService:
         query = """
         MATCH (n)
         WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary)
-          AND n.group_id = $group_id
+          AND n.end_user_id = $end_user_id
           AND n.activation_value IS NOT NULL
           AND n.activation_value < $threshold
           AND n.last_access_time IS NOT NULL
@@ -250,7 +250,7 @@ class MemoryForgetService:
         """
         
         params = {
-            'group_id': group_id,
+            'end_user_id': end_user_id,
             'threshold': forgetting_threshold,
             'min_access_time_str': min_access_time_str,
             'limit': limit
@@ -291,7 +291,7 @@ class MemoryForgetService:
     async def trigger_forgetting_cycle(
         self,
         db: Session,
-        group_id: str,
+        end_user_id: str,
         max_merge_batch_size: Optional[int] = None,
         min_days_since_access: Optional[int] = None,
         config_id: Optional[int] = None
@@ -303,10 +303,10 @@ class MemoryForgetService:
         
         Args:
             db: 数据库会话
-            group_id: 组ID（即终端用户ID，必填）
+            end_user_id: 组ID（即终端用户ID，必填）
             max_merge_batch_size: 最大融合批次大小（可选）
             min_days_since_access: 最小未访问天数（可选）
-            config_id: 配置ID（必填，由控制器层通过 group_id 获取）
+            config_id: 配置ID（必填，由控制器层通过 end_user_id 获取）
         
         Returns:
             dict: 遗忘报告
@@ -319,7 +319,7 @@ class MemoryForgetService:
         
         # 运行遗忘周期（LLM 客户端将在需要时由 forgetting_strategy 内部获取）
         report = await forgetting_scheduler.run_forgetting_cycle(
-            group_id=group_id,
+            end_user_id=end_user_id,
             max_merge_batch_size=max_merge_batch_size,
             min_days_since_access=min_days_since_access,
             config_id=config_id,
@@ -338,7 +338,7 @@ class MemoryForgetService:
             stats_query = """
             MATCH (n)
             WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
-              AND n.group_id = $group_id
+              AND n.end_user_id = $end_user_id
             RETURN 
                 count(n) as total_nodes,
                 avg(n.activation_value) as average_activation,
@@ -347,7 +347,7 @@ class MemoryForgetService:
             
             stats_results = await connector.execute_query(
                 stats_query,
-                group_id=group_id,
+                end_user_id=end_user_id,
                 threshold=config['forgetting_threshold']
             )
             
@@ -364,7 +364,7 @@ class MemoryForgetService:
             # 保存历史记录到数据库
             self.history_repository.create(
                 db=db,
-                end_user_id=group_id,
+                end_user_id=end_user_id,
                 execution_time=execution_time,
                 merged_count=report['merged_count'],
                 failed_count=report['failed_count'],
@@ -376,7 +376,7 @@ class MemoryForgetService:
             )
             
             api_logger.info(
-                f"已保存遗忘周期历史记录: end_user_id={group_id}, "
+                f"已保存遗忘周期历史记录: end_user_id={end_user_id}, "
                 f"merged_count={report['merged_count']}"
             )
         
@@ -465,7 +465,7 @@ class MemoryForgetService:
     async def get_forgetting_stats(
         self,
         db: Session,
-        group_id: Optional[str] = None,
+        end_user_id: Optional[str] = None,
         config_id: Optional[int] = None
     ) -> Dict[str, Any]:
         """
@@ -475,7 +475,7 @@ class MemoryForgetService:
         
         Args:
             db: 数据库会话
-            group_id: 组ID（可选）
+            end_user_id: 组ID（可选）
             config_id: 配置ID（可选，用于获取遗忘阈值）
         
         Returns:
@@ -493,8 +493,8 @@ class MemoryForgetService:
         WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
         """
         
-        if group_id:
+        if end_user_id:
-            activation_query += " AND n.group_id = $group_id"
+            activation_query += " AND n.end_user_id = $end_user_id"
         
         activation_query += """
         RETURN 
@@ -506,8 +506,8 @@ class MemoryForgetService:
         """
         
         params = {'threshold': forgetting_threshold}
-        if group_id:
+        if end_user_id:
-            params['group_id'] = group_id
+            params['end_user_id'] = end_user_id
         
         activation_results = await connector.execute_query(activation_query, **params)
         
@@ -539,8 +539,8 @@ class MemoryForgetService:
         WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
         """
         
-        if group_id:
+        if end_user_id:
-            distribution_query += " AND n.group_id = $group_id"
+            distribution_query += " AND n.end_user_id = $end_user_id"
         
         distribution_query += """
         WITH n,
@@ -558,8 +558,8 @@ class MemoryForgetService:
         """
         
         dist_params = {}
-        if group_id:
+        if end_user_id:
-            dist_params['group_id'] = group_id
+            dist_params['end_user_id'] = end_user_id
         
         distribution_results = await connector.execute_query(distribution_query, **dist_params)
         
@@ -582,11 +582,11 @@ class MemoryForgetService:
         # 获取最近7个日期的历史趋势数据（每天取最后一次执行）
         recent_trends = []
         try:
-            if group_id:
+            if end_user_id:
                 # 查询所有历史记录
                 history_records = self.history_repository.get_recent_by_end_user(
                     db=db,
-                    end_user_id=group_id
+                    end_user_id=end_user_id
                 )
                 
                 # 按日期分组（一天可能有多次执行，取最后一次）
@@ -632,7 +632,7 @@ class MemoryForgetService:
         # 获取待遗忘节点列表（前20个满足遗忘条件的节点）
         pending_nodes = []
         try:
-            if group_id:
+            if end_user_id:
                 # 验证 min_days_since_access 配置值
                 min_days = config.get('min_days_since_access')
                 if min_days is None or not isinstance(min_days, (int, float)) or min_days < 0:
@@ -643,7 +643,7 @@ class MemoryForgetService:
                 
                 pending_nodes = await self._get_pending_forgetting_nodes(
                     connector=connector,
-                    group_id=group_id,
+                    end_user_id=end_user_id,
                     forgetting_threshold=forgetting_threshold,
                     min_days_since_access=int(min_days),
                     limit=20

api/app/services/memory_konwledges_server.py

@@ -450,12 +450,12 @@ async def create_document_chunk(
 
     return success(data=chunk, msg="文档块创建成功")
 
-async def write_rag(group_id, message, user_rag_memory_id):
+async def write_rag(end_user_id, message, user_rag_memory_id):
     """
     将消息写入 RAG 知识库
 
     Args:
-        group_id: 组ID，用作文件标题
+        end_user_id: 组ID，用作文件标题
         message: 消息内容
         user_rag_memory_id: 知识库ID（必须是有效的UUID）
 
@@ -487,10 +487,10 @@ async def write_rag(group_id, message, user_rag_memory_id):
     db = next(db_gen)
 
     try:
-        create_data = CustomTextFileCreate(title=group_id, content=message)
+        create_data = CustomTextFileCreate(title=end_user_id, content=message)
         current_user = SimpleUser(user_rag_memory_id)
         # 检查文档是否已存在
-        document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{group_id}.txt")
+        document = find_document_id_by_kb_and_filename(db=db, kb_id=user_rag_memory_id, file_name=f"{end_user_id}.txt")
         print('======',document)
         api_logger.info(f"查找文档结果: document_id={document}")
         if document is not None:
@@ -508,7 +508,7 @@ async def write_rag(group_id, message, user_rag_memory_id):
             return result
         else:
             # 文档不存在，创建新文档
-            api_logger.info(f"文档不存在，创建新文档: group_id={group_id}")
+            api_logger.info(f"文档不存在，创建新文档: end_user_id={end_user_id}")
             result = await memory_konwledges_up(
                 kb_id=user_rag_memory_id,
                 parent_id=user_rag_memory_id,
@@ -520,13 +520,13 @@ async def write_rag(group_id, message, user_rag_memory_id):
             new_document_id = find_document_id_by_kb_and_filename(
                 db=db,
                 kb_id=user_rag_memory_id,
-                file_name=f"{group_id}.txt"
+                file_name=f"{end_user_id}.txt"
             )
 
             if new_document_id:
                 await parse_document_by_id(new_document_id, db=db, current_user=current_user)
             else:
-                api_logger.error(f"创建文档后无法找到文档ID: group_id={group_id}")
+                api_logger.error(f"创建文档后无法找到文档ID: end_user_id={end_user_id}")
             return result
     finally:
         # 确保数据库会话被关闭

api/app/services/memory_storage_service.py

@@ -183,7 +183,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
                 "config_name": config.config_name,
                 "config_desc": config.config_desc,
                 "workspace_id": str(config.workspace_id) if config.workspace_id else None,
-                "group_id": config.group_id,
+                "end_user_id": config.end_user_id,
                 "user_id": config.user_id,
                 "apply_id": config.apply_id,
                 "llm_id": config.llm_id,
@@ -391,7 +391,7 @@ _neo4j_connector = Neo4jConnector()
 async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_DIALOGUE,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     data = {"search_for": "dialogue", "num": result[0]["num"]}
     return data
@@ -400,7 +400,7 @@ async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]:
 async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_CHUNK,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     data = {"search_for": "chunk", "num": result[0]["num"]}
     return data
@@ -409,7 +409,7 @@ async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]:
 async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_STATEMENT,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     data = {"search_for": "statement", "num": result[0]["num"]}
     return data
@@ -418,7 +418,7 @@ async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]:
 async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_ENTITY,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     data = {"search_for": "entity", "num": result[0]["num"]}
     return data
@@ -427,7 +427,7 @@ async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]:
 async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_ALL,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
 
     # 检查结果是否为空或长度不足
@@ -462,7 +462,7 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A
     """
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_ALL,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
 
     # 检查结果是否为空或长度不足
@@ -493,7 +493,7 @@ async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, A
 async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_DETIALS,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     return result
 
@@ -501,7 +501,7 @@ async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, An
 async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]:
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_EDGES,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     return result
 
@@ -510,7 +510,7 @@ async def search_entity_graph(end_user_id: Optional[str] = None) -> Dict[str, An
     """搜索所有实体之间的关系网络（group 维度）。"""
     result = await _neo4j_connector.execute_query(
         DataConfigRepository.SEARCH_FOR_ENTITY_GRAPH,
-        group_id=end_user_id,
+        end_user_id=end_user_id,
     )
     # 对source_node 和 target_node 的 fact_summary进行截取，只截取前三条的内容（需要提取前三条“来源”）
     for item in result:

api/app/services/pilot_run_service.py

@@ -91,7 +91,7 @@ async def run_pilot_extraction(
         dialog = DialogData(
             context=context,
             ref_id="pilot_dialog_1",
-            group_id=str(memory_config.workspace_id),
+            end_user_id=str(memory_config.workspace_id),
             user_id=str(memory_config.tenant_id),
             apply_id=str(memory_config.config_id),
             metadata={"source": "pilot_run", "input_type": "frontend_text"},

api/app/services/user_memory_service.py

@@ -155,10 +155,10 @@ class MemoryInsightHelper:
         """
         query = """
         MATCH (d:Dialogue)
-        WHERE d.group_id = $group_id AND d.created_at IS NOT NULL AND d.created_at <> ''
+        WHERE d.end_user_id = $end_user_id AND d.created_at IS NOT NULL AND d.created_at <> ''
         RETURN d.created_at AS creation_time
         """
-        records = await self.neo4j_connector.execute_query(query, group_id=self.user_id)
+        records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id)
         
         if not records:
             return []
@@ -211,17 +211,17 @@ class MemoryInsightHelper:
     async def get_social_connections(self) -> dict | None:
         """Find the user with whom the most memories are shared."""
         query = """
-        MATCH (c1:Chunk {group_id: $group_id})
+        MATCH (c1:Chunk {end_user_id: $end_user_id})
         OPTIONAL MATCH (c1)-[:CONTAINS]->(s:Statement)
         OPTIONAL MATCH (s)<-[:CONTAINS]-(c2:Chunk)
-        WHERE c1.group_id <> c2.group_id AND s IS NOT NULL AND c2 IS NOT NULL
+        WHERE c1.end_user_id <> c2.end_user_id AND s IS NOT NULL AND c2 IS NOT NULL
-        WITH c2.group_id AS other_user_id, COUNT(DISTINCT s) AS common_statements
+        WITH c2.end_user_id AS other_user_id, COUNT(DISTINCT s) AS common_statements
         WHERE common_statements > 0
         RETURN other_user_id, common_statements
         ORDER BY common_statements DESC
         LIMIT 1
         """
-        records = await self.neo4j_connector.execute_query(query, group_id=self.user_id)
+        records = await self.neo4j_connector.execute_query(query, end_user_id=self.user_id)
         if not records or not records[0].get("other_user_id"):
             return None
         
@@ -230,7 +230,7 @@ class MemoryInsightHelper:
         
         time_range_query = """
         MATCH (c:Chunk)
-        WHERE c.group_id IN [$user_id, $other_user_id]
+        WHERE c.end_user_id IN [$user_id, $other_user_id]
         RETURN min(c.created_at) AS start_time, max(c.created_at) AS end_time
         """
         time_records = await self.neo4j_connector.execute_query(
@@ -294,11 +294,11 @@ class UserSummaryHelper:
         """Fetch recent statements authored by the user/group for context."""
         query = (
             "MATCH (s:Statement) "
-            "WHERE s.group_id = $group_id AND s.statement IS NOT NULL "
+            "WHERE s.end_user_id = $end_user_id AND s.statement IS NOT NULL "
             "RETURN s.statement AS statement, s.created_at AS created_at "
             "ORDER BY created_at DESC LIMIT $limit"
         )
-        rows = await self.connector.execute_query(query, group_id=self.user_id, limit=limit)
+        rows = await self.connector.execute_query(query, end_user_id=self.user_id, limit=limit)
         records = []
         for r in rows:
             try:
@@ -357,6 +357,101 @@ class UserMemoryService:
                     data[key] = UserMemoryService._datetime_to_timestamp(original_value)
         return data
     
+    def update_end_user_profile(
+        self,
+        db: Session,
+        end_user_id: str,
+        profile_update: Any
+    ) -> Dict[str, Any]:
+        """
+        更新终端用户的基本信息
+        
+        Args:
+            db: 数据库会话
+            end_user_id: 终端用户ID (UUID)
+            profile_update: 包含更新字段的 Pydantic 模型
+            
+        Returns:
+            {
+                "success": bool,
+                "data": dict,  # 更新后的用户档案数据
+                "error": Optional[str]
+            }
+        """
+        try:
+            # 转换为UUID并查询用户
+            user_uuid = uuid.UUID(end_user_id)
+            repo = EndUserRepository(db)
+            end_user = repo.get_by_id(user_uuid)
+            
+            if not end_user:
+                logger.warning(f"终端用户不存在: end_user_id={end_user_id}")
+                return {
+                    "success": False,
+                    "data": None,
+                    "error": "终端用户不存在"
+                }
+            
+            # 获取更新数据（排除 end_user_id 字段）
+            update_data = profile_update.model_dump(exclude_unset=True, exclude={'end_user_id'})
+            
+            # 特殊处理 hire_date：如果提供了时间戳，转换为 DateTime
+            if 'hire_date' in update_data:
+                hire_date_timestamp = update_data['hire_date']
+                if hire_date_timestamp is not None:
+                    from app.core.api_key_utils import timestamp_to_datetime
+                    update_data['hire_date'] = timestamp_to_datetime(hire_date_timestamp)
+                # 如果是 None，保持 None（允许清空）
+            
+            # 更新字段
+            for field, value in update_data.items():
+                setattr(end_user, field, value)
+            
+            # 更新时间戳
+            end_user.updated_at = datetime.now()
+            end_user.updatetime_profile = datetime.now()
+            
+            # 提交更改
+            db.commit()
+            db.refresh(end_user)
+            
+            # 构建响应数据
+            from app.schemas.end_user_schema import EndUserProfileResponse
+            profile_data = EndUserProfileResponse(
+                id=end_user.id,
+                other_name=end_user.other_name,
+                position=end_user.position,
+                department=end_user.department,
+                contact=end_user.contact,
+                phone=end_user.phone,
+                hire_date=end_user.hire_date,
+                updatetime_profile=end_user.updatetime_profile
+            )
+            
+            logger.info(f"成功更新用户信息: end_user_id={end_user_id}, updated_fields={list(update_data.keys())}")
+            
+            return {
+                "success": True,
+                "data": self.convert_profile_to_dict_with_timestamp(profile_data),
+                "error": None
+            }
+            
+        except ValueError:
+            logger.error(f"无效的 end_user_id 格式: {end_user_id}")
+            return {
+                "success": False,
+                "data": None,
+                "error": "无效的用户ID格式"
+            }
+        except Exception as e:
+            db.rollback()
+            logger.error(f"用户信息更新失败: end_user_id={end_user_id}, error={str(e)}")
+            return {
+                "success": False,
+                "data": None,
+                "error": str(e)
+            }
+    
     async def get_cached_memory_insight(
         self, 
         db: Session, 
@@ -1057,7 +1152,7 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
     import re
     
     # 创建 UserSummaryHelper 实例
-    user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_GROUP_ID", "group_123"))
+    user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_end_user_id", "group_123"))
     
     try:
         # 1) 收集上下文数据
@@ -1178,10 +1273,10 @@ async def analytics_node_statistics(
         if end_user_id:
             query = f"""
             MATCH (n:{node_type})
-            WHERE n.group_id = $group_id
+            WHERE n.end_user_id = $end_user_id
             RETURN count(n) as count
             """
-            result = await _neo4j_connector.execute_query(query, group_id=end_user_id)
+            result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id)
         else:
             query = f"""
             MATCH (n:{node_type})
@@ -1292,10 +1387,10 @@ async def analytics_memory_types(
             # 查询 Statement 节点数量
             query = """
             MATCH (n:Statement)
-            WHERE n.group_id = $group_id
+            WHERE n.end_user_id = $end_user_id
             RETURN count(n) as count
             """
-            result = await _neo4j_connector.execute_query(query, group_id=end_user_id)
+            result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id)
             statement_count = result[0]["count"] if result and len(result) > 0 else 0
             # 取三分之一作为隐性记忆数量
             implicit_count = round(statement_count / 3)
@@ -1409,7 +1504,7 @@ async def analytics_graph_data(
         包含节点、边和统计信息的字典
     """
     try:
-        # 1. 获取 group_id
+        # 1. 获取 end_user_id
         user_uuid = uuid.UUID(end_user_id)
         repo = EndUserRepository(db)
         end_user = repo.get_by_id(user_uuid)
@@ -1433,7 +1528,7 @@ async def analytics_graph_data(
             # 基于中心节点的扩展查询
             node_query = f"""
             MATCH path = (center)-[*1..{depth}]-(connected)
-            WHERE center.group_id = $group_id
+            WHERE center.end_user_id = $end_user_id
               AND elementId(center) = $center_node_id
             WITH collect(DISTINCT center) + collect(DISTINCT connected) as all_nodes
             UNWIND all_nodes as n
@@ -1444,7 +1539,7 @@ async def analytics_graph_data(
             LIMIT $limit
             """
             node_params = {
-                "group_id": end_user_id,
+                "end_user_id": end_user_id,
                 "center_node_id": center_node_id,
                 "limit": limit
             }
@@ -1452,7 +1547,7 @@ async def analytics_graph_data(
             # 按节点类型过滤查询
             node_query = """
             MATCH (n)
-            WHERE n.group_id = $group_id
+            WHERE n.end_user_id = $end_user_id
               AND labels(n)[0] IN $node_types
             RETURN 
                 elementId(n) as id,
@@ -1461,7 +1556,7 @@ async def analytics_graph_data(
             LIMIT $limit
             """
             node_params = {
-                "group_id": end_user_id,
+                "end_user_id": end_user_id,
                 "node_types": node_types,
                 "limit": limit
             }
@@ -1469,7 +1564,7 @@ async def analytics_graph_data(
             # 查询所有节点
             node_query = """
             MATCH (n)
-            WHERE n.group_id = $group_id
+            WHERE n.end_user_id = $end_user_id
             RETURN 
                 elementId(n) as id,
                 labels(n)[0] as label,
@@ -1477,7 +1572,7 @@ async def analytics_graph_data(
             LIMIT $limit
             """
             node_params = {
-                "group_id": end_user_id,
+                "end_user_id": end_user_id,
                 "limit": limit
             }
 

api/app/tasks.py

@@ -382,12 +382,12 @@ def build_graphrag_for_kb(kb_id: uuid.UUID):
 
 
 @celery_app.task(name="app.core.memory.agent.read_message", bind=True)
-def read_message_task(self, group_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]:
+def read_message_task(self, end_user_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]:
 
     """Celery task to process a read message via MemoryAgentService.
 
     Args:
-        group_id: Group ID for the memory agent (also used as end_user_id)
+        end_user_id: Group ID for the memory agent (also used as end_user_id)
         message: User message to process
         history: Conversation history
         search_switch: Search switch parameter
@@ -408,7 +408,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str,
             from app.services.memory_agent_service import get_end_user_connected_config
             db = next(get_db())
             try:
-                connected_config = get_end_user_connected_config(group_id, db)
+                connected_config = get_end_user_connected_config(end_user_id, db)
                 actual_config_id = connected_config.get("memory_config_id")
             finally:
                 db.close()
@@ -420,7 +420,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str,
         db = next(get_db())
         try:
             service = MemoryAgentService()
-            return await service.read_memory(group_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id)
+            return await service.read_memory(end_user_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id)
         finally:
             db.close()
 
@@ -448,7 +448,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str,
         return {
             "status": "SUCCESS",
             "result": result,
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "config_id": config_id,
             "elapsed_time": elapsed_time,
             "task_id": self.request.id
@@ -464,7 +464,7 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str,
         return {
             "status": "FAILURE",
             "error": detailed_error,
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "config_id": config_id,
             "elapsed_time": elapsed_time,
             "task_id": self.request.id
@@ -472,11 +472,11 @@ def read_message_task(self, group_id: str, message: str, history: List[Dict[str,
 
 
 @celery_app.task(name="app.core.memory.agent.write_message", bind=True)
-def write_message_task(self, group_id: str, message: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]:
+def write_message_task(self, end_user_id: str, message: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]:
     """Celery task to process a write message via MemoryAgentService.
     
     Args:
-        group_id: Group ID for the memory agent (also used as end_user_id)
+        end_user_id: Group ID for the memory agent (also used as end_user_id)
         message: Message to write
         config_id: Optional configuration ID
         
@@ -489,7 +489,7 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
     from app.core.logging_config import get_logger
     logger = get_logger(__name__)
     
-    logger.info(f"[CELERY WRITE] Starting write task - group_id={group_id}, config_id={config_id}, storage_type={storage_type}")
+    logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id}, storage_type={storage_type}")
     start_time = time.time()
     
     # Resolve config_id if None
@@ -499,7 +499,7 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
             from app.services.memory_agent_service import get_end_user_connected_config
             db = next(get_db())
             try:
-                connected_config = get_end_user_connected_config(group_id, db)
+                connected_config = get_end_user_connected_config(end_user_id, db)
                 actual_config_id = connected_config.get("memory_config_id")
             finally:
                 db.close()
@@ -512,7 +512,7 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
         try:
             logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory")
             service = MemoryAgentService()
-            result = await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
+            result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
             logger.info(f"[CELERY WRITE] Write completed successfully: {result}")
             return result
         except Exception as e:
@@ -547,7 +547,7 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
         return {
             "status": "SUCCESS",
             "result": result,
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "config_id": config_id,
             "elapsed_time": elapsed_time,
             "task_id": self.request.id
@@ -566,7 +566,7 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
         return {
             "status": "FAILURE",
             "error": detailed_error,
-            "group_id": group_id,
+            "end_user_id": end_user_id,
             "config_id": config_id,
             "elapsed_time": elapsed_time,
             "task_id": self.request.id
@@ -612,7 +612,7 @@ def check_read_service_task() -> Dict[str, str]:
         payload = {
             "user_id": "健康检查",
             "apply_id": "健康检查",
-            "group_id": "健康检查",
+            "end_user_id": "健康检查",
             "message": "你好",
             "history": [],
             "search_switch": "2",
@@ -1112,7 +1112,7 @@ def run_forgetting_cycle_task(self, config_id: Optional[int] = None) -> Dict[str
                 # 运行遗忘周期
                 report = await forget_service.trigger_forgetting(
                     db=db,
-                    group_id=None,  # 处理所有组
+                    end_user_id=None,  # 处理所有组
                     config_id=config_id
                 )
                 

api/docker-compose.yml

@@ -7,10 +7,6 @@ services:
       - "8002:8000"
     env_file:
       - .env
-    environment:
-      - SERVER_IP=0.0.0.0
-      # 如果代码里必须要 MCP_SERVER_URL，可以先注释或指向占位
-      # - MCP_SERVER_URL=
     volumes:
       - ./files:/files
       - /etc/localtime:/etc/localtime:ro
@@ -19,20 +15,53 @@ services:
     networks:
       - default
       - celery
+    depends_on:
+      - worker-memory
+      - worker-document
 
-  # Celery worker
+  # Memory worker - Memory read/write tasks (threads pool for asyncio)
-  worker:
+  worker-memory:
     image: redbear-mem-open:latest
-    container_name: worker
+    container_name: worker-memory
     env_file:
       - .env
     volumes:
       - ./files:/files
       - /etc/localtime:/etc/localtime:ro
-    command: celery -A app.celery_worker.celery_app worker --loglevel=info
+    command: celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=threads --concurrency=100 --queues=memory_tasks -n memory_worker@%h
     restart: unless-stopped
     networks:
       - celery
 
+  # Document worker - Document parsing tasks (prefork for CPU-bound)
+  worker-document:
+    image: redbear-mem-open:latest
+    container_name: worker-document
+    env_file:
+      - .env
+    volumes:
+      - ./files:/files
+      - /etc/localtime:/etc/localtime:ro
+    command: celery -A app.celery_worker.celery_app worker -E --loglevel=info --pool=prefork --concurrency=4 --queues=document_tasks --max-tasks-per-child=100 -n document_worker@%h
+    restart: unless-stopped
+    networks:
+      - celery
+
+  # Celery Beat - scheduler
+  beat:
+    image: redbear-mem-open:latest
+    container_name: celery-beat
+    env_file:
+      - .env
+    volumes:
+      - ./files:/files
+      - /etc/localtime:/etc/localtime:ro
+    command: celery -A app.celery_worker.celery_app beat --loglevel=info
+    restart: unless-stopped
+    networks:
+      - celery
+    depends_on:
+      - worker-memory
+
 networks:
   celery:

api/pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "bcrypt==5.0.0",
     "billiard==4.2.2",
     "celery==5.5.3",
+    "flower==2.0.1",
     "cffi==2.0.0",
     "click==8.3.0",
     "click-didyoumean==0.3.1",
@@ -138,6 +139,7 @@ dependencies = [
     "python-calamine>=0.4.0",
     "xlrd==2.0.2",
     "deprecated>=1.3.1",
+    "flower>=2.0.1",
 ]
 
 [tool.pytest.ini_options]

api/requirements.txt

@@ -6,6 +6,7 @@ async-timeout==5.0.1
 bcrypt==5.0.0
 billiard==4.2.2
 celery==5.5.3
+flower==2.0.1
 cffi==2.0.0
 click==8.3.0
 click-didyoumean==0.3.1