milvus-io / milvus

        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):

        '''

        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

        '''

        index_type = get_simple_index["index_type"]

        nq = 2

        metirc_type = "IP"

        entities, ids = init_data(connect, collection)

        get_simple_index["metric_type"] = metirc_type

        connect.create_index(collection, field_name, get_simple_index)

        search_param = get_search_param(index_type)

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

                                        search_params=search_param)

        inside_vecs = entities[-1]["values"]

        max_distance = 0

        for i in range(nb):

            tmp_dis = ip(vecs[0], inside_vecs[i])

            if max_distance < tmp_dis:

                max_distance = tmp_dis

        res = connect.search(collection, query)

        assert abs(res[0]._distances[0] - max_distance) <= gen_inaccuracy(res[0]._distances[0])

    # TODO:

        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):

        '''

        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

        '''

        index_type = get_simple_index["index_type"]

        nq = 2

        entities, ids = init_data(connect, collection)

        connect.create_index(collection, field_name, get_simple_index)

        search_param = get_search_param(index_type)

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

        inside_vecs = entities[-1]["values"]

        min_distance = 1.0

        for i in range(nb):

            tmp_dis = l2(vecs[0], inside_vecs[i])

            if min_distance > tmp_dis:

                min_distance = tmp_dis

        res = connect.search(collection, query)

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

    # TODO

    @pytest.mark.level(2)