>>> # Search using an embedding
            >>> data = vector_store.similarity_search(
            ...     query=<your_query>,
            ...     k=<num_items>,
            ...     exec_option=<preferred_exec_option>,
            ... )
            >>> # Run tql search:
            >>> data = vector_store.similarity_search(
            ...     query=None,
            ...     tql="SELECT * WHERE id == <id>",
            ...     exec_option="compute_engine",
            ... )
        Args:
            k (int): Number of Documents to return. Defaults to 4.
            query (str): Text to look up similar documents.
            **kwargs: Additional keyword arguments include:
                embedding (Callable): Embedding function to use. Defaults to None.
                distance_metric (str): 'L2' for Euclidean, 'L1' for Nuclear, 'max'
                    for L-infinity, 'cos' for cosine, 'dot' for dot product.
                    Defaults to 'L2'.
                filter (Union[Dict, Callable], optional): Additional filter
                    before embedding search.
                    - Dict: Key-value search on tensors of htype json,
                        (sample must satisfy all key-value filters)
                        Dict = {"tensor_1": {"key": value}, "tensor_2": {"key": value}}
                    - Function: Compatible with `deeplake.filter`.
                    Defaults to None.
                exec_option (str): Supports 3 ways to perform searching.
                    'python', 'compute_engine', or 'tensor_db'. Defaults to 'python'.
                    - 'python': Pure-python implementation for the client.
                        WARNING: not recommended for big datasets.
                    - 'compute_engine': C++ implementation of the Compute Engine for
                        the client. Not for in-memory or local datasets.