83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
import asyncio
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from typing import AsyncIterable, Optional
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from fastapi import Body
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from sse_starlette.sse import EventSourceResponse
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from chatchat.server.utils import get_OpenAI, get_prompt_template, wrap_done, build_logger
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logger = build_logger()
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async def completion(
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query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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stream: bool = Body(False, description="流式输出"),
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echo: bool = Body(False, description="除了输出之外，还回显输入"),
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model_name: str = Body(None, description="LLM 模型名称。"),
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temperature: float = Body(0.01, description="LLM 采样温度", ge=0.0, le=1.0),
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max_tokens: Optional[int] = Body(
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1024, description="限制LLM生成Token数量，默认None代表模型最大值"
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),
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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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prompt_name: str = Body(
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"default", description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
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),
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):
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# TODO: 因ApiModelWorker 默认是按chat处理的，会对params["prompt"] 解析为messages，因此ApiModelWorker 使用时需要有相应处理
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async def completion_iterator(
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query: str,
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model_name: str = None,
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prompt_name: str = prompt_name,
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echo: bool = echo,
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) -> AsyncIterable[str]:
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try:
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nonlocal max_tokens
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callback = AsyncIteratorCallbackHandler()
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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logger.info(f"model_name:{model_name, prompt_name:{prompt_name}, echo:{echo}}")
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model = get_OpenAI(
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model_name=model_name,
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=[callback],
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echo=echo,
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local_wrap=True,
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)
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prompt_template = get_prompt_template("llm_model", prompt_name)
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prompt = PromptTemplate.from_template(prompt_template, template_format="jinja2")
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logger.info(f"prompt_template:{prompt}")
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chain = LLMChain(prompt=prompt, llm=model)
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# Begin a task that runs in the background.
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task = asyncio.create_task(
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wrap_done(chain.acall({"input": query}), 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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except asyncio.exceptions.CancelledError:
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logger.warning("streaming progress has been interrupted by user.")
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return
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return EventSourceResponse(
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completion_iterator(
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query=query, model_name=model_name, prompt_name=prompt_name
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),
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)
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