starter_dict["_type"] = self._llm_type
        return starter_dict
[docs]    def save(self, file_path: Union[Path, str]) -> None:
        """Save the LLM.
        Args:
            file_path: Path to file to save the LLM to.
        Example:
        .. code-block:: python
            llm.save(file_path="path/llm.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path
        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)
        # Fetch dictionary to save
        prompt_dict = self.dict()
        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(prompt_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(prompt_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
[docs]class LLM(BaseLLM):
    """Base LLM abstract class.
    The purpose of this class is to expose a simpler interface for working
    with LLMs, rather than expect the user to implement the full _generate method.
    """
    @abstractmethod
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Run the LLM on the given prompt and input."""