209 lines
7.4 KiB
Python
209 lines
7.4 KiB
Python
import json
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import time
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from typing import Union
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from fastapi import FastAPI
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from langchain_core.messages import AIMessage, ToolMessage, AIMessageChunk
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from langgraph.graph.state import CompiledStateGraph
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from langgraph.prebuilt import create_react_agent
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from langgraph.types import Command
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from pydantic import BaseModel, Field
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from starlette.requests import Request
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from starlette.responses import StreamingResponse
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from app.model.ConversationModel import ConversationService
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from app.tools.tool_list import tool_list
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from app.tools.tool_project_report import tool_project_report
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from app.utils.db_utils import AsyncSessionDep
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from app.utils.llm_utils import create_llm
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from app.utils.postgres_checkpointer import PostgresCheckpointerManager, AsyncPostgresSaverDep
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class ChatMessage(BaseModel):
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id: str = Field(..., description="消息id")
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type: str = Field(..., description="消息类型")
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content: str = Field(..., description="消息内容")
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# 对话接口参数类型
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class ChatParam(BaseModel):
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thread_id: str = Field(..., description="线程id")
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human_message: ChatMessage = Field(..., description="用户消息")
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class ChatAgent:
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agent: Union[CompiledStateGraph, None] = None
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@staticmethod
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async def get_agent() -> CompiledStateGraph:
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if not ChatAgent.agent:
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ChatAgent.agent = create_react_agent(
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model=create_llm("huoshan-think-pro"),
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tools=tool_list,
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checkpointer=await PostgresCheckpointerManager.get_instance(),
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prompt="""
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- 你是一名擅长使用工具的智能助手，你需要根据用户问题来进行回答，请使用中文进行回答。
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- 当用户问题需要调用工具时再调用工具，否则按照你的知识来回答问题。
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- 你每次只能调用一个工具
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- 特别注意，特别注意，特别注意，如果工具的返回结果是一个数组，你只能回复“工具已经执行完毕”
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"""
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)
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return ChatAgent.agent
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@staticmethod
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async def get_chat_state(thread_id: str):
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graph = await ChatAgent.get_agent()
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graph_state = await graph.aget_state(config={"configurable": {"thread_id": thread_id}})
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if graph_state.values.get('messages', None) is None:
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graph_state.values['messages'] = []
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return {
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**graph_state.values,
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"__interrupt__": graph_state.interrupts,
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}
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def add_langgraph_chat_route(app: FastAPI):
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@app.post("/project/analysis")
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async def project_analysis(body: dict):
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return await tool_project_report.ainvoke({
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"project_name": body.get('project_name'),
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"start_time": body.get("start_time", None),
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"end_time": body.get("end_time", None),
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})
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# 流式对话接口
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@app.post("/langgraph/stream")
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async def langgraph_stream(
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body: dict,
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request: Request,
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):
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print(":::::::::::::::::::::::::langgraph_stream:::::::::::::::::::::")
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print(body)
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print(request.state.user)
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print(request.state.token)
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stream_input = body.get('input')
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stream_config = body.get('config')
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stream_config['configurable']['token'] = request.state.token
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stream_config['configurable']['user_id'] = request.state.user.id
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print("stream_input", stream_input)
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print("stream_config", stream_config)
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# 暂时每次对话的时候清理掉对话历史
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# await checkpointer.adelete_thread(stream_config.get('configurable').get('thread_id'))
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async def generator_function():
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graph = await ChatAgent.get_agent()
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# chat_state = await ChatAgent.get_chat_state(thread_id)
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# chat_history_list = chat_state.get('messages')
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# has_emit_message_id 用来优化流式传输，避免将content为空的为AIMessageChunk发送给前端（实际上此时正在输出思考内容）
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# 第一次的时候输出，后续再输出content为空的AIMessageChunk不发送给前端
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has_emit_message_id = {}
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async for chunk in graph.astream(
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stream_input,
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config=stream_config,
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stream_mode=['messages', 'updates']
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):
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print("chunk-------->>>>>>>>>>", chunk)
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result_template = {
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"choices": [{"delta": {}, "index": 0}],
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"created": time.time(),
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"id": "",
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"usage": None
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}
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emit_chunk = {"stream_type": chunk[0], }
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if emit_chunk['stream_type'] == "messages":
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if isinstance(chunk[1], tuple):
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if isinstance(chunk[1][1], dict):
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langgraph_node = chunk[1][1].get("langgraph_node")
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if langgraph_node == "tools" and isinstance(chunk[1][0], AIMessage):
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print("忽略工具中的message消息")
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continue
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# messages模式流式输出，此时 chunk[1][0] 为AIMessageChunk
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chunk_message = chunk[1][0]
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if not chunk_message.content:
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if has_emit_message_id.get(chunk_message.id):
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continue
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else:
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# updates模式流式输出
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for k, v in chunk[1].items():
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if k == 'agent' or k == 'tool':
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chunk_message = v.get('messages')[0]
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elif k == '__interrupt__':
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chunk_message = AIMessage(content='')
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emit_chunk['stream_type'] = 'interrupt'
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emit_chunk['interrupt'] = v[0].value
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emit_chunk = {
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**emit_chunk,
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"msg_id": chunk_message.id,
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"msg_type": chunk_message.type,
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"msg_content": chunk_message.content,
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}
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has_emit_message_id[chunk_message.id] = True
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if isinstance(chunk_message, AIMessage):
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if chunk_message.tool_calls:
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emit_chunk["tool_calls"] = chunk_message.tool_calls
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elif isinstance(chunk_message, ToolMessage):
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emit_chunk["tool_name"] = chunk_message.name
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emit_chunk["additional_kwargs"] = chunk_message.additional_kwargs
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result_template["choices"][0]["delta"]['content'] = emit_chunk
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result_template["choices"][0]["delta"]['role'] = 'assistant'
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result_template['id'] = chunk_message.id
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result_template['created'] = int(time.time())
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yield f"data: {json.dumps(result_template, ensure_ascii=False)}\n\n"
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# yield "data: [DONE]\n\n"
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return StreamingResponse(generator_function(), media_type="text/event-stream")
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# 恢复中断接口
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@app.post("/langgraph/chat_resume/{thread_id}")
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async def langgraph_chat(
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body: dict,
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thread_id: str,
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request: Request,
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):
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graph = await ChatAgent.get_agent()
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chat_state = await ChatAgent.get_chat_state(thread_id)
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chat_history_list = chat_state.get('messages')
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graph_state = await graph.ainvoke(
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Command(resume=body.get('resume_data')),
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config={"configurable": {
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"thread_id": thread_id,
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"token": request.state.token,
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"user_id": request.state.user.id,
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}}
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)
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return {
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**graph_state,
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# 这里不需要加1，因为我们并没有往messages中增加消息
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"messages": graph_state.get('messages')[len(chat_history_list):],
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}
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# 查询聊天记录
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@app.get("/langgraph/chat_state/{thread_id}")
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async def langgraph_chat(thread_id: str):
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return await ChatAgent.get_chat_state(thread_id) # 查询聊天记录
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# 删除聊天记录
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@app.post("/langgraph/chat_remove/{thread_id}")
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async def langgraph_chat(thread_id: str, checkpointer: AsyncPostgresSaverDep, session: AsyncSessionDep):
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await ConversationService.item_delete(session=session, row_dict={"id": thread_id})
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await checkpointer.adelete_thread(thread_id)
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return {"result": "success"}
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