def vector_query(
        self, query: VectorStoreQuery, **kwargs: Any
    ) -> Tuple[List[LabelledNode], List[float]]:
        """Query the graph store with a vector store query."""
        if self._supports_vector_index:
            data = self.structured_query(
                f"""CALL vector_search.search('{VECTOR_INDEX_NAME}', $limit, $embedding)
                    YIELD node, similarity
                    WITH node, similarity, labels(node) AS all_labels
                    UNWIND all_labels AS label
                    WITH node, similarity, label
                    WHERE NOT label IN ['{BASE_ENTITY_LABEL}', '{BASE_NODE_LABEL}']
                    WITH node, similarity, label, properties(node) AS originalProperties
                    RETURN
                        node.id AS name,
                        label AS type,
                        node{{.* , embedding: Null, name: Null, id: Null}} AS properties,
                        similarity
                """,
                param_map={
                    "embedding": query.query_embedding,
                    "limit": query.similarity_top_k,
                },
            )
        else:
            data = []
        data = data if data else []

        nodes = []
        scores = []
        for record in data:
            node = EntityNode(
                name=record["name"],
                label=record["type"],
                properties=remove_empty_values(record["properties"]),
            )
            nodes.append(node)
            scores.append(record["similarity"])

        return (nodes, scores)
Query the graph store with a vector store query.
llama-index-integrations/graph_stores/llama-index-graph-stores-memgraph/llama_index/graph_stores/memgraph/property_graph.py