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.
            **kwargs: Additional keyword arguments.
        Returns:
            List[Documents] - A list of documents.
        """
        return self._search(
            embedding=embedding,
            k=k,
            fetch_k=fetch_k,
            use_maximal_marginal_relevance=True,
            lambda_mult=lambda_mult,
            exec_option=exec_option,
            **kwargs,
        )
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        exec_option: Optional[str] = None,