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

        Args:
            query: VectorStoreQuery object
            kwargs:  may contain 'where', 'metadata_fields', 'args', 'fetch_k'

        """
        embedding = query.query_embedding
        k = query.similarity_top_k
        (nodes, ids, simscores) = self.similarity_search_with_score(
            embedding, k=k, form="node", **kwargs
        )
        return VectorStoreQueryResult(nodes=nodes, ids=ids, similarities=simscores)

        Query index for top k most similar nodes.

        Args:
            query: VectorStoreQuery object
            kwargs:  may contain 'where', 'metadata_fields', 'args', 'fetch_k'

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