`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 of Documents selected by maximal marginal relevance.
        Raises:
            ValueError: when MRR search is on but embedding function is
                not specified.
        """
        embedding_function = kwargs.get("embedding") or self._embedding_function
        if embedding_function is None:
            raise ValueError(
                "For MMR search, you must specify an embedding function on"
                " `creation` or during add call."
            )
        return self._search(
            query=query,
            k=k,
            fetch_k=fetch_k,
            use_maximal_marginal_relevance=True,
            lambda_mult=lambda_mult,
            exec_option=exec_option,
            embedding_function=embedding_function,  # type: ignore
            **kwargs,
        )
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
        **kwargs: Any,
    ) -> DeepLake: