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
                    Engine for the client. Not for in-memory or local datasets.
                - ``tensor_db`` - Hosted Managed Tensor Database for storage
                    and query execution. Only for data in Deep Lake Managed Database.
                    Use runtime = {"db_engine": True} during dataset creation.
            **kwargs: Additional keyword arguments.
        Returns:
            List of Documents by the specified distance metric,
            if return_score True, return a tuple of (Document, score)
        Raises: