embeddings: List of list of embedding vectors.
            metadatas: List of metadatas associated with the texts.
            kwargs: vectorstore specific parameters
        """
        if ids is None:
            ids = [str(uuid.uuid1()) for _ in texts]
        if not metadatas:
            metadatas = [{} for _ in texts]
        import_query = (
            "UNWIND $data AS row "
            "CALL { WITH row "
            f"MERGE (c:`{self.node_label}` {{id: row.id}}) "
            "WITH c, row "
            f"CALL db.create.setVectorProperty(c, "
            f"'{self.embedding_node_property}', row.embedding) "
            "YIELD node "
            f"SET c.`{self.text_node_property}` = row.text "
            "SET c += row.metadata } IN TRANSACTIONS OF 1000 ROWS"
        )
        parameters = {
            "data": [
                {"text": text, "metadata": metadata, "embedding": embedding, "id": id}
                for text, metadata, embedding, id in zip(
                    texts, metadatas, embeddings, ids
                )
            ]
        }
        self.query(import_query, params=parameters)
        return ids
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.