be the same as the embedding model name. However, there are some cases 
    where you may want to use this Embedding class with a model name not 
    supported by tiktoken. This can include when using Azure embeddings or 
    when using one of the many model providers that expose an OpenAI-like 
    API but with different models. In those cases, in order to avoid erroring 
    when tiktoken is called, you can specify a model name to use here."""
    class Config:
        """Configuration for this pydantic object."""
        allow_population_by_field_name = True
    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = get_pydantic_field_names(cls)
        extra = values.get("model_kwargs", {})
        for field_name in list(values):
            if field_name in extra:
                raise ValueError(f"Found {field_name} supplied twice.")
            if field_name not in all_required_field_names:
                logger.warning(
                    f"""WARNING! {field_name} is not default parameter.
                    {field_name} was transferred to model_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)
        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
        if invalid_model_kwargs:
            raise ValueError(
                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
                f"Instead they were passed in as part of `model_kwargs` parameter."
            )
        values["model_kwargs"] = extra
        return values
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict: