Merge branch 'release/v0.2.3' into develop
# Conflicts: # api/app/core/agent/langchain_agent.py # api/app/core/memory/agent/langgraph_graph/write_graph.py # api/app/repositories/neo4j/graph_saver.py # api/app/services/draft_run_service.py
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Mark
@@ -7,30 +7,21 @@ LangChain Agent 封装
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- 支持流式输出
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- 使用 RedBearLLM 支持多提供商
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"""
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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.core.memory.agent.langgraph_graph.write_graph import write_long_term
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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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from app.core.models import RedBearLLM, RedBearModelConfig
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from app.models.models_model import ModelType
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from app.repositories.memory_short_repository import LongTermMemoryRepository
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from app.services.memory_agent_service import (
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get_end_user_connected_config,
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)
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from app.services.memory_konwledges_server import write_rag
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from app.services.task_service import get_task_memory_write_result
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from app.tasks import write_message_task
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from langchain.agents import create_agent
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
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from langchain_core.tools import BaseTool
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from app.utils.config_utils import resolve_config_id
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logger = get_business_logger()
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@@ -289,105 +280,6 @@ class LangChainAgent:
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return content_parts
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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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#TODO: 魔法数字
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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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# Handle case where no session exists in Redis (returns False)
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if not result or result is False:
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logger.debug(f"No existing session in Redis for user {end_user_id}, skipping short-term memory update")
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return
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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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# TODO: This branch handles type="time" strategy, currently unused.
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# Will be activated when time-based long-term storage is implemented.
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# TODO: 魔法数字 - extract 5 to a constant
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long_time_data = write_store.find_user_recent_sessions(end_user_id, 5)
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# Handle case where no session exists in Redis (returns False or empty)
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if not long_time_data or long_time_data is False:
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logger.debug(f"No recent sessions in Redis for user {end_user_id}")
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return
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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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Args:
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storage_type: 存储类型 (neo4j/rag)
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end_user_id: 终端用户ID
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user_message: 用户消息内容
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ai_message: AI 回复内容
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user_rag_memory_id: RAG 记忆ID
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actual_end_user_id: 实际用户ID
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actual_config_id: 配置ID
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逻辑说明：
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- RAG 模式：组合 user_message 和 ai_message 为字符串格式，保持原有逻辑不变
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- Neo4j 模式：使用结构化消息列表
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1. 如果 user_message 和 ai_message 都不为空：创建配对消息 [user, assistant]
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2. 如果只有 user_message：创建单条用户消息 [user]（用于历史记忆场景）
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3. 每条消息会被转换为独立的 Chunk，保留 speaker 字段
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"""
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db = next(get_db())
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try:
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actual_config_id=resolve_config_id(actual_config_id, db)
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if storage_type == "rag":
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# RAG 模式：组合消息为字符串格式（保持原有逻辑）
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combined_message = f"user: {user_message}\nassistant: {ai_message}"
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await write_rag(end_user_id, combined_message, user_rag_memory_id)
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logger.info(f'RAG_Agent:{end_user_id};{user_rag_memory_id}')
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else:
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# Neo4j 模式：使用结构化消息列表
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structured_messages = []
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# 始终添加用户消息（如果不为空）
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if user_message:
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structured_messages.append({"role": "user", "content": user_message})
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# 只有当 AI 回复不为空时才添加 assistant 消息
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if ai_message:
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structured_messages.append({"role": "assistant", "content": ai_message})
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# 如果没有消息，直接返回
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if not structured_messages:
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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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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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structured_messages, # message: 结构化消息列表 [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
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actual_config_id, # config_id: 配置ID
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storage_type, # storage_type: "neo4j"
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user_rag_memory_id # user_rag_memory_id: RAG记忆ID（Neo4j模式下不使用）
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)
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logger.info(f"[WRITE] Celery task submitted - task_id={write_id}")
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write_status = get_task_memory_write_result(str(write_id))
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logger.info(f'[WRITE] Task result - user={actual_end_user_id}, status={write_status}')
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finally:
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db.close()
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async def chat(
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self,
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message: str,
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@@ -520,14 +412,7 @@ 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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# TODO: DUPLICATE WRITE - Remove this immediate write once batched write (term_memory_save) is verified stable.
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# This writes to Neo4j immediately via Celery task, but term_memory_save also writes to Neo4j
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# when the window buffer reaches scope (6 messages). This causes duplicate entities in the graph.
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# Recommended: Keep only term_memory_save for batched efficiency, or only self.write for real-time.
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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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# Batched long-term memory storage (Redis buffer + Neo4j when window full)
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await self.term_memory_save(long_term_messages,actual_config_id,end_user_id,"chunk")
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await write_long_term(storage_type, end_user_id, message_chat, content, user_rag_memory_id, actual_config_id)
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response = {
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"content": content,
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"model": self.model_name,
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@@ -710,15 +595,7 @@ class LangChainAgent:
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yield total_tokens
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break
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if memory_flag:
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# TODO: DUPLICATE WRITE - Remove this immediate write once batched write (term_memory_save) is verified stable.
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# This writes to Neo4j immediately via Celery task, but term_memory_save also writes to Neo4j
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# when the window buffer reaches scope (6 messages). This causes duplicate entities in the graph.
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# Recommended: Keep only term_memory_save for batched efficiency, or only self.write for real-time.
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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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# Batched long-term memory storage (Redis buffer + Neo4j when window full)
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await self.term_memory_save(long_term_messages, actual_config_id, end_user_id, "chunk")
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await write_long_term(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, actual_config_id)
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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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raise
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