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

@@ -18,16 +18,19 @@ 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"""
-    
+
     def __init__(self):
         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
@@ -36,10 +39,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
     memory_config = state.get('memory_config', None)
 
     history = await SessionService(store).get_history(group_id, group_id, group_id)
-    
+
     # 生成 JSON schema 以指导 LLM 输出正确格式
     json_schema = ProblemExtensionResponse.model_json_schema()
-    
+
     system_prompt = await problem_service.template_service.render_template(
         template_name='problem_breakdown_prompt.jinja2',
         operation_name='split_the_problem',
@@ -47,7 +50,7 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
         sentence=content,
         json_schema=json_schema
     )
-    
+
     try:
         # 使用优化的LLM服务
         structured = await problem_service.call_llm_structured(
@@ -57,10 +60,10 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
             response_model=ProblemExtensionResponse,
             fallback_value=[]
         )
-        
+
         # 添加更详细的日志记录
         logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
-        
+
         # 验证结构化响应
         if not structured or not hasattr(structured, 'root'):
             logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
@@ -73,17 +76,17 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
                 [item.model_dump() for item in structured.root],
                 ensure_ascii=False
             )
-        
+
         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']
             }
             split_result_dict.append(split_data)
-        
+
         logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
 
         result = {
@@ -96,13 +99,13 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
                 "original_query": content
             }
         }
-        
+
     except Exception as e:
         logger.error(
             f"Split_The_Problem failed: {e}",
             exc_info=True
         )
-        
+
         # 提供更详细的错误信息
         error_details = {
             "error_type": type(e).__name__,
@@ -110,9 +113,9 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
             "content_length": len(content),
             "llm_model_id": memory_config.llm_model_id if memory_config else None
         }
-        
+
         logger.error(f"Split_The_Problem error details: {error_details}")
-        
+
         # 创建默认的空结果
         result = {
             "context": json.dumps([], ensure_ascii=False),
@@ -126,10 +129,11 @@ async def Split_The_Problem(state: ReadState) -> ReadState:
                 "error": error_details
             }
         }
-    
+
     # 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
@@ -153,10 +157,10 @@ async def Problem_Extension(state: ReadState) -> ReadState:
         data = []
 
     history = await SessionService(store).get_history(group_id, group_id, group_id)
-    
+
     # 生成 JSON schema 以指导 LLM 输出正确格式
     json_schema = ProblemExtensionResponse.model_json_schema()
-    
+
     system_prompt = await problem_service.template_service.render_template(
         template_name='Problem_Extension_prompt.jinja2',
         operation_name='problem_extension',
@@ -242,7 +246,4 @@ async def Problem_Extension(state: ReadState) -> ReadState:
         }
     }
 
-    return {"problem_extension": result}
+    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/core/memory/agent/services/optimized_llm_service.py

@@ -162,7 +162,7 @@ class OptimizedLLMService:
                     return fallback_value
                 elif isinstance(fallback_value, dict):
                     return response_model(**fallback_value)
-
+            
             # 尝试创建空的响应模型
             if hasattr(response_model, 'root'):
                 # RootModel类型
@@ -170,7 +170,7 @@ class OptimizedLLMService:
             else:
                 # 普通BaseModel类型
                 return response_model()
-
+                
         except Exception as e:
             logger.error(f"创建降级响应失败: {e}")
             # 最后的降级策略

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,