memory_BUG_long_term

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -22,7 +22,7 @@ from app.utils.config_utils import resolve_config_id
 logger = get_agent_logger(__name__)
 template_root = os.path.join(PROJECT_ROOT_, 'memory', 'agent', 'utils', 'prompt')
 
-async def write_rag(end_user_id, user_message, ai_message, user_rag_memory_id):
+async def write_rag_agent(end_user_id, user_message, ai_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)
@@ -94,27 +94,31 @@ async def write(storage_type, end_user_id, user_message, ai_message, user_rag_me
         db.close()
 
 async def term_memory_save(long_term_messages,actual_config_id,end_user_id,type,scope):
-    db = next(get_db())
+    with get_db_context() as db_session:
-    try:
+        try:
-        repo = LongTermMemoryRepository(db)
+            repo = LongTermMemoryRepository(db_session)
-        await long_term_storage(long_term_type=AgentMemory_Long_Term.STRATEGY_CHUNK, langchain_messages=long_term_messages,
+            await long_term_storage(long_term_type=AgentMemory_Long_Term.STRATEGY_CHUNK, langchain_messages=long_term_messages,
-                                memory_config=actual_config_id, end_user_id=end_user_id, scope=scope)
+                                    memory_config=actual_config_id, end_user_id=end_user_id, scope=scope)
+
+            from app.core.memory.agent.utils.redis_tool import write_store
+            result = write_store.get_session_by_userid(end_user_id)
+            if type==AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE:
+                data = await format_parsing(result, "dict")
+                chunk_data = data[:scope]
+                if len(chunk_data)==scope:
+                    repo.upsert(end_user_id, chunk_data)
+                    logger.info(f'---------写入短长期-----------')
+            else:
+                long_time_data = write_store.find_user_recent_sessions(end_user_id, 5)
+                long_messages = await messages_parse(long_time_data)
+                repo.upsert(end_user_id, long_messages)
+                logger.info(f'写入短长期：')
+            # yield db_session
+        finally:
+            if db_session.in_transaction():
+                db_session.rollback()
+            db_session.close()
 
-        from app.core.memory.agent.utils.redis_tool import write_store
-        result = write_store.get_session_by_userid(end_user_id)
-        if type==AgentMemory_Long_Term.STRATEGY_CHUNK or AgentMemory_Long_Term.STRATEGY_AGGREGATE:
-            data = await format_parsing(result, "dict")
-            chunk_data = data[:scope]
-            if len(chunk_data)==scope:
-                repo.upsert(end_user_id, chunk_data)
-                logger.info(f'---------写入短长期-----------')
-        else:
-            long_time_data = write_store.find_user_recent_sessions(end_user_id, 5)
-            long_messages = await messages_parse(long_time_data)
-            repo.upsert(end_user_id, long_messages)
-            logger.info(f'写入短长期：')
-    finally:
-        db.close()
 
 '''根据窗口'''
 async def window_dialogue(end_user_id,langchain_messages,memory_config,scope):

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

@@ -64,11 +64,11 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[
 
 
 async def write_long_term(storage_type,end_user_id,message_chat,aimessages,user_rag_memory_id,actual_config_id):
-    from app.services.memory_konwledges_server import write_rag
+    from app.core.memory.agent.langgraph_graph.routing.write_router import write_rag_agent
     from app.core.memory.agent.langgraph_graph.routing.write_router import term_memory_save
     from app.core.memory.agent.langgraph_graph.tools.write_tool import  agent_chat_messages
     if storage_type == AgentMemory_Long_Term.STORAGE_RAG:
-        await write_rag(end_user_id, message_chat, aimessages, user_rag_memory_id)
+        await write_rag_agent(end_user_id, message_chat, aimessages, user_rag_memory_id)
     else:
         # AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
         CHUNK = AgentMemory_Long_Term.STRATEGY_CHUNK