"""Return True if the embeddings are managed by Databricks Vector Search."""
        return (
            self._is_delta_sync_index()
            and self._embedding_source_column_name() is not None
        )
    def _infer_embedding_dimension(self) -> int:
        """Infer the embedding dimension from the embedding function."""
        assert self.embeddings is not None, "embedding model is required."
        return len(self.embeddings.embed_query("test"))
    def _op_require_direct_access_index(self, op_name: str) -> None:
        """
        Raise ValueError if the operation is not supported for direct-access index."""
        if not self._is_direct_access_index():
            raise ValueError(f"`{op_name}` is only supported for direct-access index.")
    @staticmethod
    def _require_arg(arg: Any, arg_name: str) -> None:
        """Raise ValueError if the required arg with name `arg_name` is None."""
        if not arg:
            raise ValueError(f"`{arg_name}` is required for this index.")