else:
        additional_kwargs = {}
    if role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=content)
    elif role == "assistant" or default_class == AIMessageChunk:
        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
    elif role == "system" or default_class == SystemMessageChunk:
        return SystemMessageChunk(content=content)
    elif role == "function" or default_class == FunctionMessageChunk:
        return FunctionMessageChunk(content=content, name=_dict["name"])
    elif role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=content, role=role)
    else:
        return default_class(content=content)
[docs]class ChatOpenAI(BaseChatModel):
    """`OpenAI` Chat large language models API.
    To use, you should have the ``openai`` python package installed, and the
    environment variable ``OPENAI_API_KEY`` set with your API key.
    Any parameters that are valid to be passed to the openai.create call can be passed
    in, even if not explicitly saved on this class.
    Example:
        .. code-block:: python
            from langchain.chat_models import ChatOpenAI
            openai = ChatOpenAI(model_name="gpt-3.5-turbo")
    """
    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"openai_api_key": "OPENAI_API_KEY"}
[docs]    @classmethod
    def is_lc_serializable(cls) -> bool:
        """Return whether this model can be serialized by Langchain."""
        return True
    client: Any = None  #: :meta private: