}
                )
            else:
                response = completion_with_retry(
                    self, prompt=_prompts, run_manager=run_manager, **params
                )
                if not isinstance(response, dict):
                    # V1 client returns the response in an PyDantic object instead of
                    # dict. For the transition period, we deep convert it to dict.
                    response = response.dict()
                choices.extend(response["choices"])
                update_token_usage(_keys, response, token_usage)
                if not system_fingerprint:
                    system_fingerprint = response.get("system_fingerprint")
        return self.create_llm_result(
            choices,
            prompts,
            params,
            token_usage,
            system_fingerprint=system_fingerprint,
        )
    async def _agenerate(
        self,
        prompts: List[str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Call out to OpenAI's endpoint async with k unique prompts."""
        params = self._invocation_params
        params = {**params, **kwargs}
        sub_prompts = self.get_sub_prompts(params, prompts, stop)
        choices = []
        token_usage: Dict[str, int] = {}
        # Get the token usage from the response.
        # Includes prompt, completion, and total tokens used.
        _keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
        system_fingerprint: Optional[str] = None
        for _prompts in sub_prompts:
            if self.streaming:
                if len(_prompts) > 1:
                    raise ValueError("Cannot stream results with multiple prompts.")