def load_local(
        cls,
        folder_path: str,
        embedding: Embeddings,
        index_name: str = "index",
        **kwargs: Any,
    ) -> ScaNN:
        """Load ScaNN index, docstore, and index_to_docstore_id from disk.
        Args:
            folder_path: folder path to load index, docstore,
                and index_to_docstore_id from.
            embeddings: Embeddings to use when generating queries
            index_name: for saving with a specific index file name
        """
        path = Path(folder_path)
        scann_path = path / "{index_name}.scann".format(index_name=index_name)
        scann_path.mkdir(exist_ok=True, parents=True)
        # load index separately since it is not picklable
        scann = dependable_scann_import()
        index = scann.scann_ops_pybind.load_searcher(str(scann_path))
        # load docstore and index_to_docstore_id
        with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
            docstore, index_to_docstore_id = pickle.load(f)
        return cls(embedding, index, docstore, index_to_docstore_id, **kwargs)
    def _select_relevance_score_fn(self) -> Callable[[float], float]:
        """
        The 'correct' relevance function
        may differ depending on a few things, including:
        - the distance / similarity metric used by the VectorStore
        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
        - embedding dimensionality
        - etc.
        """
        if self.override_relevance_score_fn is not None:
            return self.override_relevance_score_fn