milvus-io / milvus

        assert abs(np.sqrt(res[0]._distances[0]) - min_distance) <= gen_inaccuracy(res[0]._distances[0])

    # TODO

    @pytest.mark.level(2)

    def test_search_distance_ip(self, connect, collection):

        '''

        target: search collection, and check the result: distance

        method: compare the return distance value with value computed with Inner product

        expected: the return distance equals to the computed value

        '''

        nq = 2

        metirc_type = "IP"

        search_param = {"nprobe": 1}

        entities, ids = init_data(connect, collection, nb=nq)

        query, vecs = gen_query_vectors(field_name, entities, top_k, nq, rand_vector=True, metric_type=metirc_type,

                                        search_params=search_param)

        inside_query, inside_vecs = gen_query_vectors(field_name, entities, top_k, nq, search_params=search_param)

        distance_0 = ip(vecs[0], inside_vecs[0])

        distance_1 = ip(vecs[0], inside_vecs[1])

        res = connect.search(collection, query)

        assert abs(res[0]._distances[0] - max(distance_0, distance_1)) <= gen_inaccuracy(res[0]._distances[0])

    # TODO: distance problem

    def _test_search_distance_ip_after_index(self, connect, collection, get_simple_index):

        with pytest.raises(Exception) as e:

            res = connect.search(collection_name, default_query)

    def test_search_distance_l2(self, connect, collection):

        '''

        target: search collection, and check the result: distance

        method: compare the return distance value with value computed with Euclidean

        expected: the return distance equals to the computed value

        '''

        nq = 2

        search_param = {"nprobe": 1}

        entities, ids = init_data(connect, collection, nb=nq)

        query, vecs = gen_query_vectors(field_name, entities, top_k, nq, rand_vector=True, search_params=search_param)

        inside_query, inside_vecs = gen_query_vectors(field_name, entities, top_k, nq, search_params=search_param)

        distance_0 = l2(vecs[0], inside_vecs[0])

        distance_1 = l2(vecs[0], inside_vecs[1])

        res = connect.search(collection, query)

        assert abs(np.sqrt(res[0]._distances[0]) - min(distance_0, distance_1)) <= gen_inaccuracy(res[0]._distances[0])

    # TODO: distance problem

    def _test_search_distance_l2_after_index(self, connect, collection, get_simple_index):