"""
        This method constructs a Cypher query and executes it
        to create a new full text index in Neo4j.
        """
        node_props = text_node_properties or [self.text_node_property]
        fts_index_query = (
            f"CREATE FULLTEXT INDEX {self.keyword_index_name} "
            f"FOR (n:`{self.node_label}`) ON EACH "
            f"[{', '.join(['n.`' + el + '`' for el in node_props])}]"
        )
        self.query(fts_index_query)
    @property
    def embeddings(self) -> Embeddings:
        return self.embedding
    @classmethod
    def __from(
        cls,
        texts: List[str],
        embeddings: List[List[float]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        create_id_index: bool = True,
        search_type: SearchType = SearchType.VECTOR,
        **kwargs: Any,
    ) -> Neo4jVector:
        if ids is None:
            ids = [str(uuid.uuid1()) for _ in texts]
        if not metadatas:
            metadatas = [{} for _ in texts]
        store = cls(
            embedding=embedding,
            search_type=search_type,
            **kwargs,
        )
        # Check if the vector index already exists
        embedding_dimension = store.retrieve_existing_index()
        # If the vector index doesn't exist yet
        if not embedding_dimension:
            store.create_new_index()
        # If the index already exists, check if embedding dimensions match
        elif not store.embedding_dimension == embedding_dimension:
            raise ValueError(