filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
                    embedding_function (Callable): Embedding function to use. Defaults
                    to None.
                exec_option (str): DeepLakeVectorStore supports 3 ways to perform
                    searching. It could be either "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.
                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[Tuple[Document, float]]: List of documents most similar to the query
                text with distance in float."""
        return self._search(
            query=query,
            k=k,