Attributes:
        model: The name of the model to use. Defaults to "amazon/LightGPT".
        aviary_url: The URL for the Aviary backend. Defaults to None.
        aviary_token: The bearer token for the Aviary backend. Defaults to None.
        use_prompt_format: If True, the prompt template for the model will be ignored.
            Defaults to True.
        version: API version to use for Aviary. Defaults to None.
    Example:
        .. code-block:: python
            from langchain.llms import Aviary
            os.environ["AVIARY_URL"] = "<URL>"
            os.environ["AVIARY_TOKEN"] = "<TOKEN>"
            light = Aviary(model='amazon/LightGPT')
            output = light('How do you make fried rice?')
    """
    model: str = "amazon/LightGPT"
    aviary_url: Optional[str] = None
    aviary_token: Optional[str] = None
    # If True the prompt template for the model will be ignored.
    use_prompt_format: bool = True
    # API version to use for Aviary
    version: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        aviary_url = get_from_dict_or_env(values, "aviary_url", "AVIARY_URL")
        aviary_token = get_from_dict_or_env(values, "aviary_token", "AVIARY_TOKEN")
        # Set env viarables for aviary sdk
        os.environ["AVIARY_URL"] = aviary_url