Add/develop memory (#264)
* 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 * 新增长期记忆功能 * 新增长期记忆功能 * 新增长期记忆功能 * 知识库检索多余字段 * 长期
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lixinyue11
@@ -11,7 +11,8 @@ import os
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import time
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from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence
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from app.core.memory.agent.langgraph_graph.tools.write_tool import agent_chat_messages, format_parsing, messages_parse
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from app.core.memory.agent.langgraph_graph.write_graph import long_term_storage
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from app.db import get_db
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from app.core.logging_config import get_business_logger
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from app.core.memory.agent.utils.redis_tool import store
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@@ -145,38 +146,33 @@ class LangChainAgent:
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user_content = f"参考信息：\n{context}\n\n用户问题：\n{user_content}"
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messages.append(HumanMessage(content=user_content))
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return messages
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# TODO 乐力齐 - 累积多组对话批量写入功能已禁用
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# async def term_memory_save(self,messages,end_user_end,aimessages):
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# '''短长期存储redis，为不影响正常使用6句一段话，存储用户名加一个前缀，当数据存够6条返回给neo4j'''
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# end_user_end=f"Term_{end_user_end}"
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# print(messages)
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# print(aimessages)
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# session_id = store.save_session(
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# userid=end_user_end,
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# messages=messages,
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# apply_id=end_user_end,
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# end_user_id=end_user_end,
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# aimessages=aimessages
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# )
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# store.delete_duplicate_sessions()
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# # logger.info(f'Redis_Agent:{end_user_end};{session_id}')
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# return session_id
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# TODO 乐力齐 - 累积多组对话批量写入功能已禁用
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# async def term_memory_redis_read(self,end_user_end):
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# end_user_end = f"Term_{end_user_end}"
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# history = store.find_user_apply_group(end_user_end, end_user_end, end_user_end)
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# # logger.info(f'Redis_Agent:{end_user_end};{history}')
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# messagss_list=[]
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# retrieved_content=[]
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# for messages in history:
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# query = messages.get("Query")
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# aimessages = messages.get("Answer")
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# messagss_list.append(f'用户:{query}。AI回复:{aimessages}')
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# retrieved_content.append({query: aimessages})
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# return messagss_list,retrieved_content
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async def term_memory_save(self,long_term_messages,actual_config_id,end_user_id,type):
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db = next(get_db())
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scope=6
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try:
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repo = LongTermMemoryRepository(db)
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await long_term_storage(long_term_type="chunk", langchain_messages=long_term_messages,
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memory_config=actual_config_id, end_user_id=end_user_id, scope=scope)
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from app.core.memory.agent.utils.redis_tool import write_store
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result = write_store.get_session_by_userid(end_user_id)
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if type=="chunk" or type=="aggregate":
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data = await format_parsing(result, "dict")
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chunk_data = data[:scope]
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if len(chunk_data)==scope:
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repo.upsert(end_user_id, chunk_data)
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logger.info(f'写入短长期：')
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else:
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long_time_data = write_store.find_user_recent_sessions(end_user_id, 5)
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long_messages = await messages_parse(long_time_data)
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repo.upsert(end_user_id, long_messages)
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logger.info(f'写入短长期：')
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finally:
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db.close()
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async def write(self, storage_type, end_user_id, user_message, ai_message, user_rag_memory_id, actual_end_user_id, actual_config_id):
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"""
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写入记忆（支持结构化消息）
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@@ -224,14 +220,6 @@ class LangChainAgent:
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logger.warning(f"No messages to write for user {actual_end_user_id}")
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return
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# 调用 Celery 任务，传递结构化消息列表
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# 数据流：
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# 1. structured_messages 传递给 write_message_task
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# 2. write_message_task 调用 memory_agent_service.write_memory
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# 3. write_memory 调用 write_tools.write，传递 messages 参数
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# 4. write_tools.write 调用 get_chunked_dialogs，传递 messages 参数
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# 5. get_chunked_dialogs 为每条消息创建独立的 Chunk，设置 speaker 字段
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# 6. 每个 Chunk 保存到 Neo4j，包含 speaker 字段
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logger.info(f"[WRITE] Submitting Celery task - user={actual_end_user_id}, messages={len(structured_messages)}, config={actual_config_id}")
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write_id = write_message_task.delay(
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actual_end_user_id, # end_user_id: 用户ID
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@@ -288,30 +276,6 @@ class LangChainAgent:
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actual_end_user_id = end_user_id if end_user_id is not None else "unknown"
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logger.info(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
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print(f'写入类型{storage_type,str(end_user_id), message, str(user_rag_memory_id)}')
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# # TODO 乐力齐，在长短期记忆存储的时候再使用此代码
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# history_term_memory_result = await self.term_memory_redis_read(end_user_id)
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# history_term_memory = history_term_memory_result[0]
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# db_for_memory = next(get_db())
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# if memory_flag:
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# if len(history_term_memory)>=4 and storage_type != "rag":
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# history_term_memory = ';'.join(history_term_memory)
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# retrieved_content = history_term_memory_result[1]
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# print(retrieved_content)
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# # 为长期记忆操作获取新的数据库连接
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# try:
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# repo = LongTermMemoryRepository(db_for_memory)
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# repo.upsert(end_user_id, retrieved_content)
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# logger.info(
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# f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
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# except Exception as e:
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# logger.error(f"Failed to write to LongTermMemory: {e}")
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# raise
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# finally:
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# db_for_memory.close()
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# # 长期记忆写入（
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# await self.write(storage_type, actual_end_user_id, history_term_memory, "", user_rag_memory_id, actual_end_user_id, actual_config_id)
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# # 注意：不在这里写入用户消息，等 AI 回复后一起写入
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try:
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# 准备消息列表
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messages = self._prepare_messages(message, history, context)
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@@ -339,10 +303,11 @@ class LangChainAgent:
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elapsed_time = time.time() - start_time
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if memory_flag:
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long_term_messages=await agent_chat_messages(message_chat,content)
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# AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
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await self.write(storage_type, actual_end_user_id, message_chat, content, user_rag_memory_id, actual_end_user_id, actual_config_id)
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# TODO 乐力齐 - 累积多组对话批量写入功能已禁用
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# await self.term_memory_save(message_chat, end_user_id, content)
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'''长期'''
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await self.term_memory_save(long_term_messages,actual_config_id,end_user_id,"chunk")
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response = {
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"content": content,
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"model": self.model_name,
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@@ -410,25 +375,7 @@ class LangChainAgent:
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db.close()
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except Exception as e:
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logger.warning(f"Failed to get db session: {e}")
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# # TODO 乐力齐
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# history_term_memory_result = await self.term_memory_redis_read(end_user_id)
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# history_term_memory = history_term_memory_result[0]
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# if memory_flag:
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# if len(history_term_memory) >= 4 and storage_type != "rag":
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# history_term_memory = ';'.join(history_term_memory)
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# retrieved_content = history_term_memory_result[1]
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# db_for_memory = next(get_db())
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# try:
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# repo = LongTermMemoryRepository(db_for_memory)
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# repo.upsert(end_user_id, retrieved_content)
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# logger.info(
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# f'写入短长期：{storage_type, str(end_user_id), history_term_memory, str(user_rag_memory_id)}')
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# # 长期记忆写入
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# await self.write(storage_type, end_user_id, history_term_memory, "", user_rag_memory_id, end_user_id, actual_config_id)
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# except Exception as e:
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# logger.error(f"Failed to write to long term memory: {e}")
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# finally:
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# db_for_memory.close()
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# 注意：不在这里写入用户消息，等 AI 回复后一起写入
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try:
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@@ -483,9 +430,9 @@ class LangChainAgent:
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logger.debug(f"Agent 流式完成，共 {chunk_count} 个事件")
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if memory_flag:
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# AI 回复写入（用户消息和 AI 回复配对，一次性写入完整对话）
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long_term_messages = await agent_chat_messages(message_chat, full_content)
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await self.write(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, end_user_id, actual_config_id)
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# TODO 乐力齐 - 累积多组对话批量写入功能已禁用
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# await self.term_memory_save(message_chat, end_user_id, full_content)
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await self.term_memory_save(long_term_messages, actual_config_id, end_user_id, "chunk")
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except Exception as e:
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logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True)
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