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

        Args:
            query : vector store query

        Returns:
            VectorStoreQueryResult: Query results

        """
        meta_filters = {}
        if query.filters is not None:
            for filter in query.filters.legacy_filters():
                meta_filters[filter.key] = filter.value

        not_include_fields: Set[str] = {"text_embedding"}
        results = self._awadb_client.Search(
            query=query.query_embedding,
            topn=query.similarity_top_k,
            meta_filter=meta_filters,
            not_include_fields=not_include_fields,
        )

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

        for item_detail in results[0]["ResultItems"]:
            content = ""
            meta_data = {}
            node_id = ""
            for item_key in item_detail:
                if item_key == "embedding_text":
                    content = item_detail[item_key]
                    continue
                elif item_key == "_id":
                    node_id = item_detail[item_key]
                    ids.append(node_id)
                    continue
                elif item_key == "score":
                    similarities.append(item_detail[item_key])
                    continue
                meta_data[item_key] = item_detail[item_key]

            try:
                node = metadata_dict_to_node(meta_data)
                node.set_content(content)
            except Exception:
                # NOTE: deprecated legacy logic for backward compatibility
                metadata, node_info, relationships = legacy_metadata_dict_to_node(
                    meta_data
                )

                node = TextNode(
                    text=content,
                    id_=node_id,
                    metadata=metadata,
                    start_char_idx=node_info.get("start", None),
                    end_char_idx=node_info.get("end", None),
                    relationships=relationships,
                )

            nodes.append(node)

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

        Query index for top k most similar nodes.

        Args:
            query : vector store query

        Returns:
            VectorStoreQueryResult: Query results

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