63 lines
2.7 KiB
Python
63 lines
2.7 KiB
Python
from fastapi import Body
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from fastapi.responses import StreamingResponse
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from configs.model_config import LLM_MODEL, TEMPERATURE
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from server.chat.utils import wrap_done, get_ChatOpenAI
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from langchain import LLMChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from typing import List
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from server.chat.utils import History
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async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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history: List[History] = Body([],
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description="历史对话",
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examples=[[
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{"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"},
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{"role": "assistant", "content": "虎头虎脑"}]]
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),
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stream: bool = Body(False, description="流式输出"),
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model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
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temperature: float = Body(TEMPERATURE, description="LLM 采样温度", gt=0.0, le=1.0),
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# top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
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):
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history = [History.from_data(h) for h in history]
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async def chat_iterator(query: str,
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history: List[History] = [],
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model_name: str = LLM_MODEL,
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) -> AsyncIterable[str]:
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callback = AsyncIteratorCallbackHandler()
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model = get_ChatOpenAI(
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model_name=model_name,
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temperature=temperature,
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callbacks=[callback],
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)
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input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_template() for i in history] + [input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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# Begin a task that runs in the background.
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task = asyncio.create_task(wrap_done(
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chain.acall({"input": query}),
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callback.done),
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)
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if stream:
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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield token
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else:
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answer = ""
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async for token in callback.aiter():
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answer += token
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yield answer
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await task
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return StreamingResponse(chat_iterator(query, history, model_name),
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media_type="text/event-stream")
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