elif not store.embedding_dimension == embedding_dimension:
            raise ValueError(
                f"Index with name {store.index_name} already exists."
                "The provided embedding function and vector index "
                "dimensions do not match.\n"
                f"Embedding function dimension: {store.embedding_dimension}\n"
                f"Vector index dimension: {embedding_dimension}"
            )
        if search_type == SearchType.HYBRID:
            fts_node_label = store.retrieve_existing_fts_index()
            # If the FTS index doesn't exist yet
            if not fts_node_label:
                store.create_new_keyword_index()
            else:  # Validate that FTS and Vector index use the same information
                if not fts_node_label == store.node_label:
                    raise ValueError(
                        "Vector and keyword index don't index the same node label"
                    )
        # Create unique constraint for faster import
        if create_id_index:
            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.