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feat(memory): add long-term storage task routing and batching

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Ke Sun
2026-02-03 15:52:45 +08:00
parent 9f2b6390b0
commit f27de7df35
5 changed files with 353 additions and 27 deletions
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14
api/app/core/agent/langchain_agent.py
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@@ -148,6 +148,7 @@ class LangChainAgent:
messages.append(HumanMessage(content=user_content))
return messages
# TODO: 移到memory module
async def term_memory_save(self,long_term_messages,actual_config_id,end_user_id,type):
db = next(get_db())
scope=6
@@ -307,9 +308,12 @@ class LangChainAgent:
elapsed_time = time.time() - start_time
if memory_flag:
long_term_messages=await agent_chat_messages(message_chat,content)
# AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
# TODO: DUPLICATE WRITE - Remove this immediate write once batched write (term_memory_save) is verified stable.
# This writes to Neo4j immediately via Celery task, but term_memory_save also writes to Neo4j
# when the window buffer reaches scope (6 messages). This causes duplicate entities in the graph.
# Recommended: Keep only term_memory_save for batched efficiency, or only self.write for real-time.
await self.write(storage_type, actual_end_user_id, message_chat, content, user_rag_memory_id, actual_end_user_id, actual_config_id)
'''长期'''
# Batched long-term memory storage (Redis buffer + Neo4j when window full)
await self.term_memory_save(long_term_messages,actual_config_id,end_user_id,"chunk")
response = {
"content": content,
@@ -441,9 +445,13 @@ class LangChainAgent:
yield total_tokens
break
if memory_flag:
# AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
# TODO: DUPLICATE WRITE - Remove this immediate write once batched write (term_memory_save) is verified stable.
# This writes to Neo4j immediately via Celery task, but term_memory_save also writes to Neo4j
# when the window buffer reaches scope (6 messages). This causes duplicate entities in the graph.
# Recommended: Keep only term_memory_save for batched efficiency, or only self.write for real-time.
long_term_messages = await agent_chat_messages(message_chat, full_content)
await self.write(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, end_user_id, actual_config_id)
# Batched long-term memory storage (Redis buffer + Neo4j when window full)
await self.term_memory_save(long_term_messages, actual_config_id, end_user_id, "chunk")
except Exception as e: