fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                        of diversity among the results with 0 corresponding
                        to maximum diversity and 1 to minimum diversity.
                        Defaults to 0.5.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        if self._embedding_function is None:
            raise ValueError(
                "For MMR search, you must specify an embedding function on" "creation."
            )
        embedding = self._embedding_function.embed_query(query)
        docs = self.max_marginal_relevance_search_by_vector(
            embedding,
            k,
            fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            where_document=where_document,
        )
        return docs
[docs]    def delete_collection(self) -> None:
        """Delete the collection."""
        self._client.delete_collection(self._collection.name)
[docs]    def get(
        self,
        ids: Optional[OneOrMany[ID]] = None,
        where: Optional[Where] = None,
        limit: Optional[int] = None,
        offset: Optional[int] = None,
        where_document: Optional[WhereDocument] = None,
        include: Optional[List[str]] = None,
    ) -> Dict[str, Any]:
        """Gets the collection.
        Args:
            ids: The ids of the embeddings to get. Optional.
            where: A Where type dict used to filter results by.
                   E.g. `{"color" : "red", "price": 4.20}`. Optional.