Add/develop memory (#247)

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

* 遗漏的历史映射

api/app/controllers/memory_forget_controller.py

@@ -84,9 +84,7 @@ async def trigger_forgetting_cycle(
         
         connected_config = get_end_user_connected_config(end_user_id, db)
         config_id = connected_config.get("memory_config_id")
-        config_id = resolve_config_id(int(config_id), db)
-
-
+        config_id = resolve_config_id((config_id), db)
 
         if config_id is None:
             api_logger.warning(f"终端用户 {end_user_id} 未关联记忆配置")
@@ -199,7 +197,7 @@ async def update_forgetting_config(
         ApiResponse: 包含更新结果的响应
     """
     workspace_id = current_user.current_workspace_id
-    payload.config_id=resolve_config_id(int(payload.config_id), db)
+    payload.config_id=resolve_config_id((payload.config_id), db)
 
     
     # 检查用户是否已选择工作空间
@@ -330,7 +328,7 @@ async def get_forgetting_curve(
         ApiResponse: 包含遗忘曲线数据的响应
     """
     workspace_id = current_user.current_workspace_id
-    request.config_id = resolve_config_id(int(request.config_id), db)
+    request.config_id = resolve_config_id((request.config_id), db)
     # 检查用户是否已选择工作空间
     if workspace_id is None:
         api_logger.warning(f"用户 {current_user.username} 尝试获取遗忘曲线但未选择工作空间")

api/app/core/agent/langchain_agent.py

@@ -177,7 +177,6 @@ class LangChainAgent:
     #         messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
     #         retrieved_content.append({query: aimessages})
     #     return messagss_list,retrieved_content
-
     async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id):
         """
         写入记忆（支持结构化消息）
@@ -200,7 +199,9 @@ class LangChainAgent:
         """
 
         db = next(get_db())
+        try:
             actual_config_id=resolve_config_id(actual_config_id, db)
+
             if storage_type == "rag":
                 # RAG 模式：组合消息为字符串格式（保持原有逻辑）
                 combined_message = f"user: {user_message}\nassistant: {ai_message}"
@@ -242,7 +243,8 @@ class LangChainAgent:
                 logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
                 write_status = get_task_memory_write_result(str(write_id))
                 logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
-
+        finally:
+            db.close()
     async def chat(
             self,
             message: str,