diff --git a/api/app/core/agent/langchain_agent.py b/api/app/core/agent/langchain_agent.py index e4204e83..e519ea53 100644 --- a/api/app/core/agent/langchain_agent.py +++ b/api/app/core/agent/langchain_agent.py @@ -7,33 +7,21 @@ LangChain Agent 封装 - 支持流式输出 - 使用 RedBearLLM 支持多提供商 """ -import os + import time from typing import Any, AsyncGenerator, Dict, List, Optional, Sequence -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, format_parsing, messages_parse -from app.core.memory.agent.langgraph_graph.write_graph import long_term_storage -from app.core.memory.agent.utils.write_tools import write +from app.core.memory.agent.langgraph_graph.write_graph import write_long_term from app.db import get_db from app.core.logging_config import get_business_logger -from app.core.memory.agent.utils.redis_tool import store from app.core.models import RedBearLLM, RedBearModelConfig from app.models.models_model import ModelType -from app.repositories.memory_short_repository import LongTermMemoryRepository -from app.schemas.memory_agent_schema import AgentMemory_Long_Term from app.services.memory_agent_service import ( get_end_user_connected_config, ) -from app.services.memory_konwledges_server import write_rag -from app.services.task_service import get_task_memory_write_result -from app.tasks import write_message_task from langchain.agents import create_agent from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage from langchain_core.tools import BaseTool - -from app.utils.config_utils import resolve_config_id - logger = get_business_logger() @@ -224,14 +212,7 @@ class LangChainAgent: elapsed_time = time.time() - start_time if memory_flag: - if storage_type == "rag": - await write_rag(end_user_id, message_chat, content, user_rag_memory_id) - else: - 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） - await long_term_storage(long_term_type="chunk",langchain_messages=long_term_messages,memory_config=actual_config_id,end_user_id=end_user_id,scope=2) - '''长期''' - await term_memory_save(long_term_messages,actual_config_id,end_user_id,"chunk") + await write_long_term(storage_type, end_user_id, message_chat, content, user_rag_memory_id, actual_config_id) response = { "content": content, "model": self.model_name, @@ -362,16 +343,7 @@ class LangChainAgent: yield total_tokens break if memory_flag: - if storage_type == AgentMemory_Long_Term.STORAGE_RAG: - await write_rag(end_user_id, message_chat, full_content, 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 - SCOPE=AgentMemory_Long_Term.DEFAULT_SCOPE - long_term_messages = await agent_chat_messages(message_chat, full_content) - await long_term_storage(long_term_type=CHUNK,langchain_messages=long_term_messages,memory_config=actual_config_id,end_user_id=end_user_id,scope=SCOPE) - await term_memory_save(long_term_messages, actual_config_id, end_user_id, CHUNK,scope=SCOPE) - + await write_long_term(storage_type, end_user_id, message_chat, full_content, user_rag_memory_id, actual_config_id) except Exception as e: logger.error(f"Agent astream_events 失败: {str(e)}", exc_info=True) raise diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py index 64a1296c..b788d1ec 100644 --- a/api/app/core/memory/agent/langgraph_graph/write_graph.py +++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py @@ -7,13 +7,14 @@ from contextlib import asynccontextmanager from langgraph.constants import END, START from langgraph.graph import StateGraph -from app.core.memory.agent.langgraph_graph.tools.write_tool import format_parsing, messages_parse from app.db import get_db from app.core.logging_config import get_agent_logger from app.core.memory.agent.utils.llm_tools import WriteState from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node +from app.schemas.memory_agent_schema import AgentMemory_Long_Term from app.services.memory_config_service import MemoryConfigService + warnings.filterwarnings("ignore", category=RuntimeWarning) logger = get_agent_logger(__name__) @@ -62,6 +63,21 @@ async def long_term_storage(long_term_type:str="chunk",langchain_messages:list=[ await aggregate_judgment(end_user_id, langchain_messages, memory_config) +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 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) + 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 + SCOPE = AgentMemory_Long_Term.DEFAULT_SCOPE + long_term_messages = await agent_chat_messages(message_chat, aimessages) + await long_term_storage(long_term_type=CHUNK, langchain_messages=long_term_messages, + memory_config=actual_config_id, end_user_id=end_user_id, scope=SCOPE) + await term_memory_save(long_term_messages, actual_config_id, end_user_id, CHUNK, scope=SCOPE) + # async def main(): # """主函数 - 运行工作流""" # langchain_messages = [