`deeplake.filter`.
                    Defaults to None.
                exec_option (str): Options for search execution include
                    "python", "compute_engine", or "tensor_db". Defaults to
                    "python".
                    - "python" - Pure-python implementation running on the client.
                        Can be used for data stored anywhere. WARNING: using this
                        option with big datasets is discouraged due to potential
                        memory issues.
                    - "compute_engine" - Performant C++ implementation of the Deep
                        Lake Compute Engine. Runs on the client and can be used for
                        any data stored in or connected to Deep Lake. It cannot be
                        used with in-memory or local datasets.
                    - "tensor_db" - Performant, fully-hosted Managed Tensor Database.
                        Responsible for storage and query execution. Only available
                        for data stored in the Deep Lake Managed Database.
                        To store datasets in this database, specify
                        `runtime = {"db_engine": True}` during dataset creation.
                distance_metric (str): `L2` for Euclidean, `L1` for Nuclear,
                    `max` for L-infinity distance, `cos` for cosine similarity,
                    'dot' for dot product. Defaults to `L2`.
        Returns:
            List[Document]: List of Documents most similar to the query vector.
        """
        return self._search(
            embedding=embedding,
            k=k,
            use_maximal_marginal_relevance=False,
            return_score=False,
            **kwargs,
        )
[docs]    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """