def query(
        self,
        query: VectorStoreQuery,
        **kwargs: Any,
    ) -> VectorStoreQueryResult:
        """
        Query index for top k most similar nodes.

        Args:
            query_embedding (List[float]): query embedding
            similarity_top_k (int): top k most similar nodes

        """
        query_embedding = cast(List[float], query.query_embedding)
        top_k = query.similarity_top_k

        query_results: List[dict[str, Any]] = self._storage.search(
            query_embedding,
            k=top_k,
            select_columns=["document", "vector"],
            metadata_filters=query.filters,
        )

        # if empty, then return an empty response
        if len(query_results) == 0:
            return VectorStoreQueryResult(similarities=[], ids=[])

        nodes = []
        similarities = []
        ids = []

        for result in query_results:
            node = TextNode(
                text=result["document"],
                id_=result["id"],
                embedding=result["vector"],
                metadata=result["metadata"],
            )
            nodes.append(node)
            ids.append(result["id"])
            similarities.append(math.exp(-result["distance"]))

        return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)

        Query index for top k most similar nodes.

        Args:
            query_embedding (List[float]): query embedding
            similarity_top_k (int): top k most similar nodes

        
llama-index-integrations/vector_stores/llama-index-vector-stores-hologres/llama_index/vector_stores/hologres/base.py