store.query(
                "CREATE CONSTRAINT IF NOT EXISTS "
                f"FOR (n:`{store.node_label}`) REQUIRE n.id IS UNIQUE;"
            )
        store.add_embeddings(
            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
        )
        return store
[docs]    def add_embeddings(
        self,
        texts: Iterable[str],
        embeddings: List[List[float]],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Add embeddings to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            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": [