200 lines
7.1 KiB
Python
200 lines
7.1 KiB
Python
import os
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import json
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import asyncio
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import traceback
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import uuid
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from fastapi import APIRouter, Body, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from langchain_core.messages import AIMessageChunk
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from src import executor, config, retriever
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from src.core import HistoryManager
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from src.agents import agent_manager
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from src.models import select_model
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from src.utils.logging_config import logger
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chat = APIRouter(prefix="/chat")
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@chat.get("/")
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async def chat_get():
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return "Chat Get!"
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@chat.post("/")
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def chat_post(
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query: str = Body(...),
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meta: dict = Body(None),
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history: list | None = Body(None),
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thread_id: str | None = Body(None)):
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model = select_model()
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meta["server_model_name"] = model.model_name
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history_manager = HistoryManager(history)
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logger.debug(f"Received query: {query} with meta: {meta}")
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def make_chunk(content=None, **kwargs):
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return json.dumps({
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"response": content,
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"meta": meta,
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**kwargs
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}, ensure_ascii=False).encode('utf-8') + b"\n"
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def need_retrieve(meta):
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return meta.get("use_web") or meta.get("use_graph") or meta.get("db_id")
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def generate_response():
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modified_query = query
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refs = None
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# 处理知识库检索
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if meta and need_retrieve(meta):
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chunk = make_chunk(status="searching")
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yield chunk
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try:
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modified_query, refs = retriever(modified_query, history_manager.messages, meta)
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except Exception as e:
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logger.error(f"Retriever error: {e}, {traceback.format_exc()}")
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yield make_chunk(message=f"Retriever error: {e}", status="error")
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return
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yield make_chunk(status="generating")
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messages = history_manager.get_history_with_msg(modified_query, max_rounds=meta.get('history_round'))
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history_manager.add_user(query) # 注意这里使用原始查询
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content = ""
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reasoning_content = ""
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try:
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for delta in model.predict(messages, stream=True):
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if not delta.content and hasattr(delta, 'reasoning_content'):
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reasoning_content += delta.reasoning_content or ""
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chunk = make_chunk(reasoning_content=reasoning_content, status="reasoning")
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yield chunk
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continue
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# 文心一言
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if hasattr(delta, 'is_full') and delta.is_full:
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content = delta.content
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else:
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content += delta.content or ""
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chunk = make_chunk(content=delta.content, status="loading")
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yield chunk
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logger.debug(f"Final response: {content}")
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logger.debug(f"Final reasoning response: {reasoning_content}")
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yield make_chunk(status="finished",
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history=history_manager.update_ai(content),
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refs=refs)
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except Exception as e:
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logger.error(f"Model error: {e}, {traceback.format_exc()}")
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yield make_chunk(message=f"Model error: {e}", status="error")
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return
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return StreamingResponse(generate_response(), media_type='application/json')
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@chat.post("/call")
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async def call(query: str = Body(...), meta: dict = Body(None)):
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model = select_model(model_provider=meta.get("model_provider"), model_name=meta.get("model_name"))
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async def predict_async(query):
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(executor, model.predict, query)
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response = await predict_async(query)
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logger.debug({"query": query, "response": response.content})
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return {"response": response.content}
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@chat.post("/call_lite")
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async def call(query: str = Body(...), meta: dict = Body(None)):
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meta = meta or {}
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async def predict_async(query):
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loop = asyncio.get_event_loop()
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model_provider = meta.get("model_provider", config.model_provider_lite)
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model_name = meta.get("model_name", config.model_name_lite)
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model = select_model(model_provider=model_provider, model_name=model_name)
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return await loop.run_in_executor(executor, model.predict, query)
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response = await predict_async(query)
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logger.debug({"query": query, "response": response.content})
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return {"response": response.content}
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@chat.get("/agent")
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async def get_agent():
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agents = [agent.get_info() for agent in agent_manager.agents.values()]
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return {"agents": agents}
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@chat.post("/agent/{agent_name}")
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def chat_agent(agent_name: str,
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query: str = Body(...),
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history: list = Body(...),
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config: dict = Body({}),
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meta: dict = Body({})):
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meta.update({
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"query": query,
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"agent_name": agent_name,
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"server_model_name": config.get("model", agent_name) ,
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"thread_id": config.get("thread_id"),
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})
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# 将meta和thread_id整合到config中
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def make_chunk(content=None, **kwargs):
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return json.dumps({
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"request_id": meta.get("request_id"),
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"response": content,
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**kwargs
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}, ensure_ascii=False).encode('utf-8') + b"\n"
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try:
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agent = agent_manager.get_runnable_agent(agent_name)
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except Exception as e:
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logger.error(f"Error getting agent {agent_name}: {e}")
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return StreamingResponse(make_chunk(message=f"Error getting agent {agent_name}: {e}", status="error"), media_type='application/json')
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# 从config中获取history_round
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history_round = config.get("history_round")
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history_manager = HistoryManager(history)
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messages = history_manager.get_history_with_msg(query, max_rounds=history_round)
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history_manager.add_user(query)
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# 如果没有thread_id则生成一个
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if "thread_id" not in config or not config["thread_id"]:
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config["thread_id"] = str(uuid.uuid4())
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# 构造运行时配置
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runnable_config = {
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"configurable": {
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**config
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}
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}
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def stream_messages():
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content = ""
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yield make_chunk(status="init", meta=meta)
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for msg, metadata in agent.stream_messages(messages, config_schema=runnable_config):
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if isinstance(msg, AIMessageChunk) and msg.content != "<tool_call>":
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content += msg.content
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yield make_chunk(content=msg.content,
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msg=msg.model_dump(),
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metadata=metadata,
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status="loading")
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else:
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yield make_chunk(msg=msg.model_dump(),
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metadata=metadata,
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status="loading")
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yield make_chunk(status="finished",
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history=history_manager.update_ai(content),
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meta=meta)
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return StreamingResponse(stream_messages(), media_type='application/json')
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@chat.get("/models")
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async def get_chat_models(model_provider: str):
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"""获取指定模型提供商的模型列表"""
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model = select_model(model_provider=model_provider)
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return {"models": model.get_models()}
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