def _default_approximate_search_query(
        self,
        query_vector: List[float],
        k: int,
        nprobe: str,
        reorder_factor: str,
        vector_field: str = "vector_field",
    ) -> Dict:
        """
        For Approximate k-NN Search, this is the default query.

        Args:
            query_vector(List[float]): Vector embedding to query.
            k(int): Maximum number of results. default: 4.
            nprobe (str): number of cluster units to query; between 1 and method.parameters.nlist.
                No default value.
            reorder_factor (str): reorder_factor for lindorm search; between 1 and 200; default: 10.

        Optional Args:
            vector_field(str): Document field embeddings are stored in. default: "vector_field".

        Return:
            A dictionary representing the query.

        """
        return {
            "size": k,
            "query": {"knn": {vector_field: {"vector": query_vector, "k": k}}},
            "ext": {"lvector": {"nprobe": nprobe, "reorder_factor": reorder_factor}},
        }

        For Approximate k-NN Search, this is the default query.

        Args:
            query_vector(List[float]): Vector embedding to query.
            k(int): Maximum number of results. default: 4.
            nprobe (str): number of cluster units to query; between 1 and method.parameters.nlist.
                No default value.
            reorder_factor (str): reorder_factor for lindorm search; between 1 and 200; default: 10.

        Optional Args:
            vector_field(str): Document field embeddings are stored in. default: "vector_field".

        Return:
            A dictionary representing the query.

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