return_score: bool = False,
        exec_option: Optional[str] = None,
        **kwargs: Any,
    ) -> Any[List[Document], List[Tuple[Document, float]]]:
        """
        Return docs similar to query.
        Args:
            query (str, optional): Text to look up similar docs.
            embedding (Union[List[float], np.ndarray], optional): Query's embedding.
            embedding_function (Callable, optional): Function to convert `query`
                into embedding.
            k (int): Number of Documents to return.
            distance_metric (str): `L2` for Euclidean, `L1` for Nuclear, `max`
                for L-infinity distance, `cos` for cosine similarity, 'dot' for dot
                product.
            filter (Union[Dict, Callable], optional): Additional filter prior
                to the embedding search.
                - ``Dict`` - Key-value search on tensors of htype json, on an
                    AND basis (a sample must satisfy all key-value filters to be True)
                    Dict = {"tensor_name_1": {"key": value},
                            "tensor_name_2": {"key": value}}
                - ``Function`` - Any function compatible with `deeplake.filter`.
            use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
            fetch_k (int): Number of Documents for MMR algorithm.
            return_score (bool): Return the score.
            exec_option (str, optional): Supports 3 ways to perform searching.
                Could be "python", "compute_engine" or "tensor_db".
                - ``python`` - Pure-python implementation for the client.
                    WARNING: not recommended for big datasets.
                - ``compute_engine`` - C++ implementation of Deep Lake Compute