the client. Not for in-memory or local datasets.
                    - 'tensor_db': Managed Tensor Database for storage and query.
                        Only for data in Deep Lake Managed Database.
                        Use `runtime = {"db_engine": True}` during dataset creation.
                deep_memory (bool): Whether to use the Deep Memory model for improving
                    search results. Defaults to False if deep_memory is not specified
                    in the Vector Store initialization. If True, the distance metric
                    is set to "deepmemory_distance", which represents the metric with
                    which the model was trained. The search is performed using the Deep
                    Memory model. If False, the distance metric is set to "COS" or
                    whatever distance metric user specifies.
        Returns:
            List[Document]: List of Documents most similar to the query vector.
        """
        return self._search(
            query=query,
            k=k,
            use_maximal_marginal_relevance=False,
            return_score=False,
            **kwargs,
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: Union[List[float], np.ndarray],
        k: int = 4,
        **kwargs: Any,
    ) -> List[Document]:
        """
        Return docs most similar to embedding vector.
        Examples:
            >>> # Search using an embedding
            >>> data = vector_store.similarity_search_by_vector(
            ...    embedding=<your_embedding>,
            ...    k=<num_items_to_return>,
            ...    exec_option=<preferred_exec_option>,
            ... )
        Args:
            embedding (Union[List[float], np.ndarray]):
                Embedding to find similar docs.
            k (int): Number of Documents to return. Defaults to 4.
            **kwargs: Additional keyword arguments including: