"finish_reason"
    ]
    response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]:
    return {
        "choices": [
            {
                "text": "",
                "finish_reason": None,
                "logprobs": None,
            }
        ]
    }
def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]:
    import openai
    min_seconds = 4
    max_seconds = 10
    # Wait 2^x * 1 second between each retry starting with
    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
    return retry(
        reraise=True,
        stop=stop_after_attempt(llm.max_retries),
        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
        retry=(
            retry_if_exception_type(openai.error.Timeout)
            | retry_if_exception_type(openai.error.APIError)
            | retry_if_exception_type(openai.error.APIConnectionError)
            | retry_if_exception_type(openai.error.RateLimitError)
            | retry_if_exception_type(openai.error.ServiceUnavailableError)
        ),
        before_sleep=before_sleep_log(logger, logging.WARNING),
    )
def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) -> Any:
    """Use tenacity to retry the completion call."""
    retry_decorator = _create_retry_decorator(llm)
    @retry_decorator
    def _completion_with_retry(**kwargs: Any) -> Any:
        return llm.client.create(**kwargs)
    return _completion_with_retry(**kwargs)
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/llms/openai.html