def vector_query(
        self, query: VectorStoreQuery, **kwargs: Any
    ) -> Tuple[List[LabelledNode], List[float]]:
        """Query the graph store with a vector store query."""
        conditions = None
        if query.filters:
            conditions = [
                f"e.{filter.key} {filter.operator.value} {filter.value}"
                for filter in query.filters.filters
            ]
        filters = (
            f" {query.filters.condition.value} ".join(conditions).replace("==", "=")
            if conditions is not None
            else "1 = 1"
        )

        data = self.structured_query(
            f"""MATCH (e:`__Entity__`)
            WHERE e.embedding IS NOT NULL AND ({filters})
            WITH e, vec.euclideanDistance(e.embedding, vecf32($embedding)) AS score
            ORDER BY score LIMIT $limit
            RETURN e.id AS name,
               [l in labels(e) WHERE l <> '__Entity__' | l][0] AS type,
               e{{.* , embedding: Null, name: Null, id: Null}} AS properties,
               score""",
            param_map={
                "embedding": query.query_embedding,
                "dimension": len(query.query_embedding),
                "limit": query.similarity_top_k,
            },
        )
        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["score"])

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