**kwargs: Any,
    ) -> ChatResult:
        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {**params, **kwargs}
        if self.streaming:
            inner_completion = ""
            role = "assistant"
            params["stream"] = True
            function_call: Optional[dict] = None
            for stream_resp in self.completion_with_retry(
                messages=message_dicts, **params
            ):
                role = stream_resp["choices"][0]["delta"].get("role", role)
                token = stream_resp["choices"][0]["delta"].get("content") or ""
                inner_completion += token
                _function_call = stream_resp["choices"][0]["delta"].get("function_call")
                if _function_call:
                    if function_call is None:
                        function_call = _function_call
                    else:
                        function_call["arguments"] += _function_call["arguments"]
                if run_manager:
                    run_manager.on_llm_new_token(token)
            message = _convert_dict_to_message(
                {
                    "content": inner_completion,
                    "role": role,
                    "function_call": function_call,
                }
            )
            return ChatResult(generations=[ChatGeneration(message=message)])
        response = self.completion_with_retry(messages=message_dicts, **params)
        return self._create_chat_result(response)
    def _create_message_dicts(
        self, messages: List[BaseMessage], stop: Optional[List[str]]
    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
        params = dict(self._invocation_params)
        if stop is not None:
            if "stop" in params:
                raise ValueError("`stop` found in both the input and default params.")
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chat_models/openai.html