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

        """
        if query.filters is not None:
            if "where" in kwargs:
                raise ValueError(
                    "Cannot specify metadata filters via both query and kwargs. "
                    "Use kwargs only for chroma specific items that are "
                    "not supported via the generic query interface."
                )
            where = _to_chroma_filter(query.filters)
        else:
            where = kwargs.pop("where", None)

        if not query.query_embedding:
            return self._get(limit=query.similarity_top_k, where=where, **kwargs)

        return self._query(
            query_embeddings=query.query_embedding,
            n_results=query.similarity_top_k,
            where=where,
            **kwargs,
        )

        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-chroma/llama_index/vector_stores/chroma/base.py