if batch_size == 0:
            return []
        batched = [
            elements[i : i + batch_size] for i in range(0, len(elements), batch_size)
        ]
        ingest().eval(
            batched,
            self.ds,
            num_workers=min(self.num_workers, len(batched) // max(self.num_workers, 1)),
            **kwargs,
        )
        self.ds.commit(allow_empty=True)
        if self.verbose:
            self.ds.summary()
        return ids
    def _search_helper(
        self,
        query: Any[str, None] = None,
        embedding: Any[float, None] = None,
        k: int = 4,
        distance_metric: str = "L2",
        use_maximal_marginal_relevance: Optional[bool] = False,
        fetch_k: Optional[int] = 20,
        filter: Optional[Any[Dict[str, str], Callable, str]] = None,
        return_score: Optional[bool] = False,
        **kwargs: Any,
    ) -> Any[List[Document], List[Tuple[Document, float]]]:
        """Return docs most similar to query.
        Args:
            query: Text to look up documents similar to.
            embedding: Embedding function to use. Defaults to None.
            k: Number of Documents to return. Defaults to 4.
            distance_metric: `L2` for Euclidean, `L1` for Nuclear,
                `max` L-infinity distance, `cos` for cosine similarity,
                'dot' for dot product. Defaults to `L2`.
            filter: Attribute filter by metadata example {'key': 'value'}. It can also
            take [Deep Lake filter]
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/deeplake.html