generated_responses = await super()._agenerate(prompts, stop, run_manager)
        request_end_time = datetime.datetime.now().timestamp()
        for i in range(len(prompts)):
            prompt = prompts[i]
            generation = generated_responses.generations[i][0]
            resp = {
                "text": generation.text,
                "llm_output": generated_responses.llm_output,
            }
            params = {**self._identifying_params, **kwargs}
            pl_request_id = await promptlayer_api_request_async(
                "langchain.PromptLayerOpenAI.async",
                "langchain",
                [prompt],
                params,
                self.pl_tags,
                resp,
                request_start_time,
                request_end_time,
                get_api_key(),
                return_pl_id=self.return_pl_id,
            )
            if self.return_pl_id:
                if generation.generation_info is None or not isinstance(
                    generation.generation_info, dict
                ):
                    generation.generation_info = {}
                generation.generation_info["pl_request_id"] = pl_request_id
        return generated_responses
[docs]class PromptLayerOpenAIChat(OpenAIChat):
    """Wrapper around OpenAI large language models.
    To use, you should have the ``openai`` and ``promptlayer`` python
    package installed, and the environment variable ``OPENAI_API_KEY``
    and ``PROMPTLAYER_API_KEY`` set with your openAI API key and
    promptlayer key respectively.
    All parameters that can be passed to the OpenAIChat LLM can also
    be passed here. The PromptLayerOpenAIChat adds two optional
    parameters:
        ``pl_tags``: List of strings to tag the request with.