)
        # Reorganize the results from query to match search order.
        vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors}
        ordered_result_embeddings = [vectors[x] for x in ids]
        # Get the new order of results.
        new_ordering = maximal_marginal_relevance(
            np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult
        )
        # Reorder the values and return.
        ret = []
        for x in new_ordering:
            # Function can return -1 index
            if x == -1:
                break
            else:
                ret.append(documents[x])
        return ret
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        collection_name: str = "LangChainCollection",
        connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION,
        consistency_level: str = "Session",
        index_params: Optional[dict] = None,
        search_params: Optional[dict] = None,
        drop_old: bool = False,
        **kwargs: Any,
    ) -> Milvus:
        """Create a Milvus collection, indexes it with HNSW, and insert data.
        Args:
            texts (List[str]): Text data.
            embedding (Embeddings): Embedding function.
            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
                Defaults to None.
            collection_name (str, optional): Collection name to use. Defaults to
                "LangChainCollection".
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/milvus.html