code: from fastapi import Body
  from sse_starlette.sse import EventSourceResponse
  from configs import LLM_MODELS, TEMPERATURE
  from server.utils import wrap_done, get_OpenAI
  from langchain.chains import LLMChain
  from langchain.callbacks import AsyncIteratorCallbackHandler
  from typing import AsyncIterable, Optional
  import asyncio
  from langchain.prompts import PromptTemplate
  
  from server.utils import get_prompt_template
  
  
  async def completion(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
                       stream: bool = Body(False, description="流式输出"),
                       echo: bool = Body(False, description="除了输出之外，还回显输入"),
                       model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
                       temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
                       max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"),
                       # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
                       prompt_name: str = Body("default",
                                               description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
                       ):
  
      #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
      async def completion_iterator(query: str,
                                    model_name: str = LLM_MODELS[0],
                                    prompt_name: str = prompt_name,
                                    echo: bool = echo,
                                    ) -> AsyncIterable[str]:
          nonlocal max_tokens
          callback = AsyncIteratorCallbackHandler()
          if isinstance(max_tokens, int) and max_tokens <= 0:
              max_tokens = None
  
          model = get_OpenAI(
              model_name=model_name,
              temperature=temperature,
              max_tokens=max_tokens,
              callbacks=[callback],
              echo=echo
          )
  
          prompt_template = get_prompt_template("completion", prompt_name)
          prompt = PromptTemplate.from_template(prompt_template)
          chain = LLMChain(prompt=prompt, llm=model)
  
          # Begin a task that runs in the background.
          task = asyncio.create_task(wrap_done(
              chain.acall({"input": query}),
              callback.done),
          )
  
          if stream:
              async for token in callback.aiter():
                  # Use server-sent-events to stream the response
                  yield token
          else:
              answer = ""
              async for token in callback.aiter():
                  answer += token
              yield answer
  
          await task
  
      return EventSourceResponse(completion_iterator(query=query,
                                                   model_name=model_name,
                                                   prompt_name=prompt_name),
                               )
  
apis: langchain.callbacks.AsyncIteratorCallbackHandler langchain.chains.LLMChain langchain.prompts.PromptTemplate.from_template
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