data_properties[key] = metadatas[i][key]
                _id = uuids[i]
                # if an embedding strategy is not provided, we let
                # weaviate create the embedding. Note that this will only
                # work if weaviate has been installed with a vectorizer module
                # like text2vec-contextionary for example
                params = {
                    "uuid": _id,
                    "data_object": data_properties,
                    "class_name": index_name,
                }
                if embeddings is not None:
                    params["vector"] = embeddings[i]
                batch.add_data_object(**params)
            batch.flush()
        return cls(
            client,
            index_name,
            text_key,
            embedding=embedding,
            attributes=attributes,
            relevance_score_fn=relevance_score_fn,
            by_text=by_text,
            **kwargs,
        )
[docs]    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
        """Delete by vector IDs.
        Args:
            ids: List of ids to delete.
        """
        if ids is None:
            raise ValueError("No ids provided to delete.")
        # TODO: Check if this can be done in bulk
        for id in ids:
            self._client.data_object.delete(uuid=id)