Fix/memory mcp2 1 (#170)

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* feat(celery): add comprehensive logging to worker and write task

- Initialize logging system in Celery worker entry point with LoggingConfig
- Add logger instance and startup message to celery_worker.py
- Reorganize imports in tasks.py for better readability and consistency
- Add detailed logging to write_message_task for debugging and monitoring
- Log task start with group_id, config_id, and storage_type parameters
- Log service execution and completion status with results
- Add exception handling with error logging and stack trace capture
- Log task completion time and Celery task ID for performance tracking
- Improves observability and troubleshooting of async task execution

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 快速检索，需要在接口部分添加LLM整合

* 快速检索，需要在接口部分添加LLM整合

---------

Co-authored-by: Ke Sun <kesun5@illinois.edu>

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
@@ -291,6 +293,19 @@ 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.group_id, user_input.group_id, user_input.group_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

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"""
 
@@ -25,9 +26,11 @@ 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
@@ -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']
@@ -130,6 +133,7 @@ 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
@@ -243,6 +247,3 @@ async def Problem_Extension(state: ReadState) -> ReadState:
     }
 
     return {"problem_extension": result}
-
-
-

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)
 

api/app/services/memory_agent_service.py

@@ -683,7 +683,67 @@ 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,