milvus-io /
milvus
| Total Complexity | 136 |
| Total Lines | 1363 |
| Duplicated Lines | 28.03 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like test_search often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | import pdb |
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| 2 | import struct |
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| 3 | from random import sample |
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| 4 | import threading |
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| 5 | import datetime |
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| 6 | import logging |
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| 7 | from time import sleep |
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| 8 | import concurrent.futures |
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| 9 | from multiprocessing import Process |
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| 10 | import pytest |
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| 11 | import numpy |
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| 12 | import sklearn.preprocessing |
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| 13 | from milvus import IndexType, MetricType |
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| 14 | from utils import * |
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| 15 | |||
| 16 | dim = 128 |
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| 17 | collection_id = "test_search" |
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| 18 | add_interval_time = 2 |
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| 19 | vectors = gen_vectors(6000, dim) |
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| 20 | vectors = sklearn.preprocessing.normalize(vectors, axis=1, norm='l2') |
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| 21 | vectors = vectors.tolist() |
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| 22 | top_k = 1 |
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| 23 | nprobe = 1 |
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| 24 | epsilon = 0.001 |
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| 25 | tag = "1970-01-01" |
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| 26 | raw_vectors, binary_vectors = gen_binary_vectors(6000, dim) |
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| 27 | |||
| 28 | |||
| 29 | class TestSearchBase: |
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| 30 | def init_data(self, connect, collection, nb=6000, dim=dim, partition_tags=None): |
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| 31 | ''' |
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| 32 | Generate vectors and add it in collection, before search vectors |
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| 33 | ''' |
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| 34 | global vectors |
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| 35 | if nb == 6000: |
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| 36 | add_vectors = vectors |
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| 37 | else: |
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| 38 | add_vectors = gen_vectors(nb, dim) |
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| 39 | add_vectors = sklearn.preprocessing.normalize(add_vectors, axis=1, norm='l2') |
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| 40 | add_vectors = add_vectors.tolist() |
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| 41 | if partition_tags is None: |
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| 42 | status, ids = connect.insert(collection, add_vectors) |
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| 43 | assert status.OK() |
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| 44 | else: |
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| 45 | status, ids = connect.insert(collection, add_vectors, partition_tag=partition_tags) |
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| 46 | assert status.OK() |
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| 47 | connect.flush([collection]) |
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| 48 | return add_vectors, ids |
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| 49 | |||
| 50 | def init_binary_data(self, connect, collection, nb=6000, dim=dim, insert=True, partition_tags=None): |
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| 51 | ''' |
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| 52 | Generate vectors and add it in collection, before search vectors |
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| 53 | ''' |
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| 54 | ids = [] |
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| 55 | global binary_vectors |
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| 56 | global raw_vectors |
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| 57 | if nb == 6000: |
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| 58 | add_vectors = binary_vectors |
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| 59 | add_raw_vectors = raw_vectors |
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| 60 | else: |
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| 61 | add_raw_vectors, add_vectors = gen_binary_vectors(nb, dim) |
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| 62 | if insert is True: |
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| 63 | if partition_tags is None: |
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| 64 | status, ids = connect.insert(collection, add_vectors) |
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| 65 | assert status.OK() |
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| 66 | else: |
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| 67 | status, ids = connect.insert(collection, add_vectors, partition_tag=partition_tags) |
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| 68 | assert status.OK() |
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| 69 | connect.flush([collection]) |
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| 70 | return add_raw_vectors, add_vectors, ids |
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| 71 | |||
| 72 | """ |
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| 73 | generate valid create_index params |
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| 74 | """ |
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| 75 | |||
| 76 | @pytest.fixture( |
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| 77 | scope="function", |
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| 78 | params=gen_index() |
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| 79 | ) |
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| 80 | def get_index(self, request, connect): |
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| 81 | if str(connect._cmd("mode")[1]) == "CPU": |
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| 82 | if request.param["index_type"] == IndexType.IVF_SQ8H: |
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| 83 | pytest.skip("sq8h not support in CPU mode") |
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| 84 | return request.param |
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| 85 | |||
| 86 | @pytest.fixture( |
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| 87 | scope="function", |
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| 88 | params=gen_simple_index() |
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| 89 | ) |
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| 90 | def get_simple_index(self, request, connect): |
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| 91 | if str(connect._cmd("mode")[1]) == "CPU": |
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| 92 | if request.param["index_type"] == IndexType.IVF_SQ8H: |
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| 93 | pytest.skip("sq8h not support in CPU mode") |
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| 94 | return request.param |
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| 95 | |||
| 96 | View Code Duplication | @pytest.fixture( |
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Duplication
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| 97 | scope="function", |
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| 98 | params=gen_simple_index() |
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| 99 | ) |
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| 100 | def get_jaccard_index(self, request, connect): |
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| 101 | logging.getLogger().info(request.param) |
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| 102 | if request.param["index_type"] == IndexType.IVFLAT or request.param["index_type"] == IndexType.FLAT: |
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| 103 | return request.param |
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| 104 | else: |
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| 105 | pytest.skip("Skip index Temporary") |
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| 106 | |||
| 107 | View Code Duplication | @pytest.fixture( |
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| 108 | scope="function", |
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| 109 | params=gen_simple_index() |
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| 110 | ) |
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| 111 | def get_hamming_index(self, request, connect): |
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| 112 | logging.getLogger().info(request.param) |
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| 113 | if request.param["index_type"] == IndexType.IVFLAT or request.param["index_type"] == IndexType.FLAT: |
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| 114 | return request.param |
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| 115 | else: |
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| 116 | pytest.skip("Skip index Temporary") |
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| 117 | |||
| 118 | @pytest.fixture( |
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| 119 | scope="function", |
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| 120 | params=gen_simple_index() |
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| 121 | ) |
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| 122 | def get_structure_index(self, request, connect): |
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| 123 | logging.getLogger().info(request.param) |
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| 124 | if request.param["index_type"] == IndexType.FLAT: |
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| 125 | return request.param |
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| 126 | else: |
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| 127 | pytest.skip("Skip index Temporary") |
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| 128 | |||
| 129 | """ |
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| 130 | generate top-k params |
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| 131 | """ |
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| 132 | |||
| 133 | @pytest.fixture( |
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| 134 | scope="function", |
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| 135 | params=[1, 99, 1024, 2049, 16385] |
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| 136 | ) |
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| 137 | def get_top_k(self, request): |
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| 138 | yield request.param |
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| 139 | |||
| 140 | def test_search_top_k_flat_index(self, connect, collection, get_top_k): |
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| 141 | ''' |
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| 142 | target: test basic search fuction, all the search params is corrent, change top-k value |
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| 143 | method: search with the given vectors, check the result |
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| 144 | expected: search status ok, and the length of the result is top_k |
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| 145 | ''' |
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| 146 | vectors, ids = self.init_data(connect, collection) |
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| 147 | query_vec = [vectors[0]] |
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| 148 | top_k = get_top_k |
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| 149 | status, result = connect.search(collection, top_k, query_vec) |
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| 150 | if top_k <= 16384: |
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| 151 | assert status.OK() |
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| 152 | assert len(result[0]) == min(len(vectors), top_k) |
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| 153 | assert result[0][0].distance <= epsilon |
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| 154 | assert check_result(result[0], ids[0]) |
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| 155 | else: |
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| 156 | assert not status.OK() |
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| 157 | |||
| 158 | def test_search_top_k_flat_index_metric_type(self, connect, collection): |
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| 159 | ''' |
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| 160 | target: test basic search fuction, all the search params is corrent, change top-k value |
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| 161 | method: search with the given vectors, check the result |
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| 162 | expected: search status ok, and the length of the result is top_k |
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| 163 | ''' |
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| 164 | vectors, ids = self.init_data(connect, collection) |
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| 165 | query_vec = [vectors[0]] |
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| 166 | status, result = connect.search(collection, top_k, query_vec, params={"metric_type": MetricType.IP.value}) |
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| 167 | assert status.OK() |
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| 168 | assert len(result[0]) == min(len(vectors), top_k) |
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| 169 | assert result[0][0].distance >= 1 - epsilon |
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| 170 | assert check_result(result[0], ids[0]) |
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| 171 | |||
| 172 | @pytest.mark.level(2) |
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| 173 | def test_search_top_k_flat_index_metric_type_invalid(self, connect, collection): |
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| 174 | ''' |
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| 175 | target: test basic search fuction, all the search params is corrent, change top-k value |
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| 176 | method: search with the given vectors, check the result |
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| 177 | expected: search status ok, and the length of the result is top_k |
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| 178 | ''' |
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| 179 | vectors, ids = self.init_data(connect, collection) |
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| 180 | query_vec = [vectors[0]] |
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| 181 | status, result = connect.search(collection, top_k, query_vec, params={"metric_type": MetricType.JACCARD.value}) |
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| 182 | assert not status.OK() |
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| 183 | |||
| 184 | def test_search_l2_index_params(self, connect, collection, get_simple_index): |
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| 185 | ''' |
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| 186 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 187 | method: search with the given vectors, check the result |
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| 188 | expected: search status ok, and the length of the result is top_k |
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| 189 | ''' |
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| 190 | top_k = 10 |
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| 191 | index_param = get_simple_index["index_param"] |
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| 192 | index_type = get_simple_index["index_type"] |
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| 193 | logging.getLogger().info(get_simple_index) |
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| 194 | vectors, ids = self.init_data(connect, collection) |
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| 195 | status = connect.create_index(collection, index_type, index_param) |
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| 196 | query_vec = [vectors[0], vectors[1]] |
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| 197 | search_param = get_search_param(index_type) |
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| 198 | status, result = connect.search(collection, top_k, query_vec, params=search_param) |
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| 199 | logging.getLogger().info(result) |
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| 200 | if top_k <= 1024: |
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| 201 | assert status.OK() |
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| 202 | assert len(result[0]) == min(len(vectors), top_k) |
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| 203 | assert check_result(result[0], ids[0]) |
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| 204 | assert result[0][0].distance < result[0][1].distance |
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| 205 | assert result[1][0].distance < result[1][1].distance |
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| 206 | else: |
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| 207 | assert not status.OK() |
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| 208 | |||
| 209 | def test_search_l2_large_nq_index_params(self, connect, collection, get_simple_index): |
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| 210 | ''' |
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| 211 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 212 | method: search with the given vectors, check the result |
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| 213 | expected: search status ok, and the length of the result is top_k |
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| 214 | ''' |
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| 215 | top_k = 10 |
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| 216 | index_param = get_simple_index["index_param"] |
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| 217 | index_type = get_simple_index["index_type"] |
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| 218 | logging.getLogger().info(get_simple_index) |
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| 219 | if index_type == IndexType.IVF_PQ: |
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| 220 | pytest.skip("Skip PQ") |
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| 221 | |||
| 222 | vectors, ids = self.init_data(connect, collection) |
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| 223 | status = connect.create_index(collection, index_type, index_param) |
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| 224 | query_vec = vectors[:1000] |
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| 225 | search_param = get_search_param(index_type) |
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| 226 | status, result = connect.search(collection, top_k, query_vec, params=search_param) |
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| 227 | logging.getLogger().info(result) |
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| 228 | assert status.OK() |
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| 229 | assert len(result[0]) == min(len(vectors), top_k) |
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| 230 | assert check_result(result[0], ids[0]) |
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| 231 | assert result[0][0].distance <= epsilon |
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| 232 | |||
| 233 | def test_search_with_multi_partitions(self, connect, collection): |
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| 234 | ''' |
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| 235 | target: test search with multi partition which contains default tag and other tags |
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| 236 | method: insert vectors into e partition and search with partitions [_default, tag] |
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| 237 | expected: search result is correct |
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| 238 | ''' |
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| 239 | connect.create_partition(collection, tag) |
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| 240 | vectors, ids = self.init_data(connect, collection, nb=10, partition_tags=tag) |
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| 241 | query_vec = [vectors[0]] |
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| 242 | search_param = get_search_param(IndexType.FLAT) |
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| 243 | status, result = connect.search(collection, top_k, query_vec, partition_tags=["_default", tag], |
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| 244 | params=search_param) |
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| 245 | assert status.OK() |
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| 246 | logging.getLogger().info(result) |
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| 247 | assert len(result[0]) == min(len(vectors), top_k) |
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| 248 | assert check_result(result[0], ids[0]) |
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| 249 | assert result[0][0].distance <= epsilon |
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| 250 | |||
| 251 | def test_search_l2_index_params_partition(self, connect, collection, get_simple_index): |
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| 252 | ''' |
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| 253 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 254 | method: add vectors into collection, search with the given vectors, check the result |
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| 255 | expected: search status ok, and the length of the result is top_k, search collection with partition tag return empty |
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| 256 | ''' |
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| 257 | top_k = 10 |
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| 258 | index_param = get_simple_index["index_param"] |
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| 259 | index_type = get_simple_index["index_type"] |
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| 260 | logging.getLogger().info(get_simple_index) |
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| 261 | if index_type == IndexType.IVF_PQ: |
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| 262 | pytest.skip("Skip PQ") |
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| 263 | status = connect.create_partition(collection, tag) |
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| 264 | vectors, ids = self.init_data(connect, collection) |
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| 265 | status = connect.create_index(collection, index_type, index_param) |
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| 266 | query_vec = [vectors[0]] |
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| 267 | search_param = get_search_param(index_type) |
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| 268 | status, result = connect.search(collection, top_k, query_vec, params=search_param) |
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| 269 | logging.getLogger().info(result) |
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| 270 | assert status.OK() |
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| 271 | assert len(result[0]) == min(len(vectors), top_k) |
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| 272 | assert check_result(result[0], ids[0]) |
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| 273 | assert result[0][0].distance <= epsilon |
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| 274 | status, result = connect.search(collection, top_k, query_vec, partition_tags=[tag], params=search_param) |
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| 275 | logging.getLogger().info(result) |
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| 276 | assert status.OK() |
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| 277 | assert len(result) == 0 |
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| 278 | |||
| 279 | def test_search_l2_index_params_partition_A(self, connect, collection, get_simple_index): |
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| 280 | ''' |
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| 281 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 282 | method: search partition with the given vectors, check the result |
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| 283 | expected: search status ok, and the length of the result is 0 |
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| 284 | ''' |
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| 285 | top_k = 10 |
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| 286 | index_param = get_simple_index["index_param"] |
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| 287 | index_type = get_simple_index["index_type"] |
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| 288 | logging.getLogger().info(get_simple_index) |
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| 289 | if index_type == IndexType.IVF_PQ: |
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| 290 | pytest.skip("Skip PQ") |
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| 291 | |||
| 292 | status = connect.create_partition(collection, tag) |
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| 293 | vectors, ids = self.init_data(connect, collection) |
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| 294 | status = connect.create_index(collection, index_type, index_param) |
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| 295 | query_vec = [vectors[0]] |
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| 296 | search_param = get_search_param(index_type) |
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| 297 | status, result = connect.search(collection, top_k, query_vec, partition_tags=[tag], params=search_param) |
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| 298 | logging.getLogger().info(result) |
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| 299 | assert status.OK() |
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| 300 | assert len(result) == 0 |
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| 301 | |||
| 302 | def test_search_l2_index_params_partition_B(self, connect, collection, get_simple_index): |
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| 303 | ''' |
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| 304 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 305 | method: search with the given vectors, check the result |
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| 306 | expected: search status ok, and the length of the result is top_k |
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| 307 | ''' |
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| 308 | top_k = 10 |
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| 309 | index_param = get_simple_index["index_param"] |
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| 310 | index_type = get_simple_index["index_type"] |
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| 311 | logging.getLogger().info(get_simple_index) |
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| 312 | if index_type == IndexType.IVF_PQ: |
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| 313 | pytest.skip("Skip PQ") |
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| 314 | status = connect.create_partition(collection, tag) |
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| 315 | vectors, ids = self.init_data(connect, collection, partition_tags=tag) |
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| 316 | status = connect.create_index(collection, index_type, index_param) |
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| 317 | query_vec = [vectors[0]] |
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| 318 | search_param = get_search_param(index_type) |
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| 319 | status, result = connect.search(collection, top_k, query_vec, params=search_param) |
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| 320 | logging.getLogger().info(result) |
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| 321 | assert status.OK() |
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| 322 | assert len(result[0]) == min(len(vectors), top_k) |
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| 323 | assert check_result(result[0], ids[0]) |
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| 324 | assert result[0][0].distance <= epsilon |
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| 325 | status, result = connect.search(collection, top_k, query_vec, partition_tags=[tag], params=search_param) |
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| 326 | logging.getLogger().info(result) |
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| 327 | assert status.OK() |
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| 328 | assert len(result[0]) == min(len(vectors), top_k) |
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| 329 | assert check_result(result[0], ids[0]) |
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| 330 | assert result[0][0].distance <= epsilon |
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| 331 | |||
| 332 | def test_search_l2_index_params_partition_C(self, connect, collection, get_simple_index): |
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| 333 | ''' |
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| 334 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 335 | method: search with the given vectors and tags (one of the tags not existed in collection), check the result |
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| 336 | expected: search status ok, and the length of the result is top_k |
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| 337 | ''' |
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| 338 | index_param = get_simple_index["index_param"] |
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| 339 | index_type = get_simple_index["index_type"] |
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| 340 | logging.getLogger().info(get_simple_index) |
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| 341 | if index_type == IndexType.IVF_PQ: |
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| 342 | pytest.skip("Skip PQ") |
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| 343 | status = connect.create_partition(collection, tag) |
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| 344 | vectors, ids = self.init_data(connect, collection, partition_tags=tag) |
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| 345 | status = connect.create_index(collection, index_type, index_param) |
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| 346 | query_vec = [vectors[0]] |
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| 347 | top_k = 10 |
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| 348 | search_param = get_search_param(index_type) |
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| 349 | status, result = connect.search(collection, top_k, query_vec, partition_tags=[tag, "new_tag"], |
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| 350 | params=search_param) |
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| 351 | logging.getLogger().info(result) |
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| 352 | assert status.OK() |
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| 353 | assert len(result[0]) == min(len(vectors), top_k) |
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| 354 | assert check_result(result[0], ids[0]) |
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| 355 | assert result[0][0].distance <= epsilon |
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| 356 | |||
| 357 | @pytest.mark.level(2) |
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| 358 | def test_search_l2_index_params_partition_D(self, connect, collection, get_simple_index): |
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| 359 | ''' |
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| 360 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 361 | method: search with the given vectors and tag (tag name not existed in collection), check the result |
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| 362 | expected: search status ok, and the length of the result is top_k |
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| 363 | ''' |
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| 364 | index_param = get_simple_index["index_param"] |
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| 365 | index_type = get_simple_index["index_type"] |
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| 366 | logging.getLogger().info(get_simple_index) |
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| 367 | status = connect.create_partition(collection, tag) |
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| 368 | vectors, ids = self.init_data(connect, collection, partition_tags=tag) |
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| 369 | status = connect.create_index(collection, index_type, index_param) |
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| 370 | query_vec = [vectors[0]] |
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| 371 | top_k = 10 |
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| 372 | search_param = get_search_param(index_type) |
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| 373 | status, result = connect.search(collection, top_k, query_vec, partition_tags=["new_tag"], params=search_param) |
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| 374 | logging.getLogger().info(result) |
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| 375 | assert not status.OK() |
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| 376 | |||
| 377 | @pytest.mark.level(2) |
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| 378 | def test_search_l2_index_params_partition_E(self, connect, collection, get_simple_index): |
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| 379 | ''' |
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| 380 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
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| 381 | method: search collection with the given vectors and tags, check the result |
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| 382 | expected: search status ok, and the length of the result is top_k |
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| 383 | ''' |
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| 384 | top_k = 10 |
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| 385 | new_tag = "new_tag" |
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| 386 | index_type = get_simple_index["index_type"] |
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| 387 | index_param = get_simple_index["index_param"] |
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| 388 | if index_type == IndexType.IVF_PQ: |
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| 389 | pytest.skip("Skip PQ") |
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| 390 | logging.getLogger().info(get_simple_index) |
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| 391 | status = connect.create_partition(collection, tag) |
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| 392 | status = connect.create_partition(collection, new_tag) |
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| 393 | vectors, ids = self.init_data(connect, collection, partition_tags=tag) |
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| 394 | new_vectors, new_ids = self.init_data(connect, collection, nb=6001, partition_tags=new_tag) |
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| 395 | status = connect.create_index(collection, index_type, index_param) |
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| 396 | query_vec = [vectors[0], new_vectors[0]] |
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| 397 | search_param = get_search_param(index_type) |
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| 398 | status, result = connect.search(collection, top_k, query_vec, partition_tags=[tag, new_tag], |
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| 399 | params=search_param) |
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| 400 | logging.getLogger().info(result) |
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| 401 | assert status.OK() |
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| 402 | assert len(result[0]) == min(len(vectors), top_k) |
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| 403 | assert check_result(result[0], ids[0]) |
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| 404 | assert check_result(result[1], new_ids[0]) |
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| 405 | assert result[0][0].distance <= epsilon |
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| 406 | assert result[1][0].distance <= epsilon |
||
| 407 | status, result = connect.search(collection, top_k, query_vec, partition_tags=[new_tag], params=search_param) |
||
| 408 | logging.getLogger().info(result) |
||
| 409 | assert status.OK() |
||
| 410 | assert len(result[0]) == min(len(vectors), top_k) |
||
| 411 | assert check_result(result[1], new_ids[0]) |
||
| 412 | assert result[1][0].distance <= epsilon |
||
| 413 | |||
| 414 | def test_search_l2_index_params_partition_F(self, connect, collection, get_simple_index): |
||
| 415 | ''' |
||
| 416 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
||
| 417 | method: search collection with the given vectors and tags with "re" expr, check the result |
||
| 418 | expected: search status ok, and the length of the result is top_k |
||
| 419 | ''' |
||
| 420 | tag = "atag" |
||
| 421 | new_tag = "new_tag" |
||
| 422 | index_param = get_simple_index["index_param"] |
||
| 423 | index_type = get_simple_index["index_type"] |
||
| 424 | logging.getLogger().info(get_simple_index) |
||
| 425 | if index_type == IndexType.IVF_PQ: |
||
| 426 | pytest.skip("Skip PQ") |
||
| 427 | status = connect.create_partition(collection, tag) |
||
| 428 | status = connect.create_partition(collection, new_tag) |
||
| 429 | vectors, ids = self.init_data(connect, collection, partition_tags=tag) |
||
| 430 | new_vectors, new_ids = self.init_data(connect, collection, nb=6001, partition_tags=new_tag) |
||
| 431 | status = connect.create_index(collection, index_type, index_param) |
||
| 432 | query_vec = [vectors[0], new_vectors[0]] |
||
| 433 | top_k = 10 |
||
| 434 | search_param = get_search_param(index_type) |
||
| 435 | status, result = connect.search(collection, top_k, query_vec, partition_tags=["new(.*)"], params=search_param) |
||
| 436 | logging.getLogger().info(result) |
||
| 437 | assert status.OK() |
||
| 438 | assert result[0][0].distance > epsilon |
||
| 439 | assert result[1][0].distance <= epsilon |
||
| 440 | status, result = connect.search(collection, top_k, query_vec, partition_tags=["(.*)tag"], params=search_param) |
||
| 441 | logging.getLogger().info(result) |
||
| 442 | assert status.OK() |
||
| 443 | assert result[0][0].distance <= epsilon |
||
| 444 | assert result[1][0].distance <= epsilon |
||
| 445 | |||
| 446 | @pytest.mark.level(2) |
||
| 447 | def test_search_ip_index_params(self, connect, ip_collection, get_simple_index): |
||
| 448 | ''' |
||
| 449 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
||
| 450 | method: search with the given vectors, check the result |
||
| 451 | expected: search status ok, and the length of the result is top_k |
||
| 452 | ''' |
||
| 453 | top_k = 10 |
||
| 454 | index_param = get_simple_index["index_param"] |
||
| 455 | index_type = get_simple_index["index_type"] |
||
| 456 | logging.getLogger().info(get_simple_index) |
||
| 457 | vectors, ids = self.init_data(connect, ip_collection) |
||
| 458 | status = connect.create_index(ip_collection, index_type, index_param) |
||
| 459 | query_vec = [vectors[0]] |
||
| 460 | search_param = get_search_param(index_type) |
||
| 461 | status, result = connect.search(ip_collection, top_k, query_vec, params=search_param) |
||
| 462 | logging.getLogger().info(result) |
||
| 463 | assert status.OK() |
||
| 464 | assert len(result[0]) == min(len(vectors), top_k) |
||
| 465 | assert check_result(result[0], ids[0]) |
||
| 466 | assert result[0][0].distance >= result[0][1].distance |
||
| 467 | |||
| 468 | def test_search_ip_large_nq_index_params(self, connect, ip_collection, get_simple_index): |
||
| 469 | ''' |
||
| 470 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
||
| 471 | method: search with the given vectors, check the result |
||
| 472 | expected: search status ok, and the length of the result is top_k |
||
| 473 | ''' |
||
| 474 | index_param = get_simple_index["index_param"] |
||
| 475 | index_type = get_simple_index["index_type"] |
||
| 476 | logging.getLogger().info(get_simple_index) |
||
| 477 | if index_type in [IndexType.RNSG, IndexType.IVF_PQ]: |
||
| 478 | pytest.skip("rnsg not support in ip, skip pq") |
||
| 479 | vectors, ids = self.init_data(connect, ip_collection) |
||
| 480 | status = connect.create_index(ip_collection, index_type, index_param) |
||
| 481 | query_vec = [] |
||
| 482 | for i in range(1200): |
||
| 483 | query_vec.append(vectors[i]) |
||
| 484 | top_k = 10 |
||
| 485 | search_param = get_search_param(index_type) |
||
| 486 | status, result = connect.search(ip_collection, top_k, query_vec, params=search_param) |
||
| 487 | logging.getLogger().info(result) |
||
| 488 | assert status.OK() |
||
| 489 | assert len(result[0]) == min(len(vectors), top_k) |
||
| 490 | assert check_result(result[0], ids[0]) |
||
| 491 | assert result[0][0].distance >= 1 - gen_inaccuracy(result[0][0].distance) |
||
| 492 | |||
| 493 | @pytest.mark.level(2) |
||
| 494 | def test_search_ip_index_params_partition(self, connect, ip_collection, get_simple_index): |
||
| 495 | ''' |
||
| 496 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
||
| 497 | method: search with the given vectors, check the result |
||
| 498 | expected: search status ok, and the length of the result is top_k |
||
| 499 | ''' |
||
| 500 | top_k = 10 |
||
| 501 | index_param = get_simple_index["index_param"] |
||
| 502 | index_type = get_simple_index["index_type"] |
||
| 503 | logging.getLogger().info(index_param) |
||
| 504 | if index_type in [IndexType.RNSG, IndexType.IVF_PQ]: |
||
| 505 | pytest.skip("rnsg not support in ip, skip pq") |
||
| 506 | |||
| 507 | status = connect.create_partition(ip_collection, tag) |
||
| 508 | vectors, ids = self.init_data(connect, ip_collection) |
||
| 509 | status = connect.create_index(ip_collection, index_type, index_param) |
||
| 510 | query_vec = [vectors[0]] |
||
| 511 | search_param = get_search_param(index_type) |
||
| 512 | status, result = connect.search(ip_collection, top_k, query_vec, params=search_param) |
||
| 513 | logging.getLogger().info(result) |
||
| 514 | assert status.OK() |
||
| 515 | assert len(result[0]) == min(len(vectors), top_k) |
||
| 516 | assert check_result(result[0], ids[0]) |
||
| 517 | assert result[0][0].distance >= 1 - gen_inaccuracy(result[0][0].distance) |
||
| 518 | status, result = connect.search(ip_collection, top_k, query_vec, partition_tags=[tag], params=search_param) |
||
| 519 | logging.getLogger().info(result) |
||
| 520 | assert status.OK() |
||
| 521 | assert len(result) == 0 |
||
| 522 | |||
| 523 | @pytest.mark.level(2) |
||
| 524 | def test_search_ip_index_params_partition_A(self, connect, ip_collection, get_simple_index): |
||
| 525 | ''' |
||
| 526 | target: test basic search fuction, all the search params is corrent, test all index params, and build |
||
| 527 | method: search with the given vectors and tag, check the result |
||
| 528 | expected: search status ok, and the length of the result is top_k |
||
| 529 | ''' |
||
| 530 | top_k = 10 |
||
| 531 | index_param = get_simple_index["index_param"] |
||
| 532 | index_type = get_simple_index["index_type"] |
||
| 533 | logging.getLogger().info(index_param) |
||
| 534 | if index_type in [IndexType.RNSG, IndexType.IVF_PQ]: |
||
| 535 | pytest.skip("rnsg not support in ip, skip pq") |
||
| 536 | |||
| 537 | status = connect.create_partition(ip_collection, tag) |
||
| 538 | vectors, ids = self.init_data(connect, ip_collection, partition_tags=tag) |
||
| 539 | status = connect.create_index(ip_collection, index_type, index_param) |
||
| 540 | query_vec = [vectors[0]] |
||
| 541 | search_param = get_search_param(index_type) |
||
| 542 | status, result = connect.search(ip_collection, top_k, query_vec, partition_tags=[tag], params=search_param) |
||
| 543 | logging.getLogger().info(result) |
||
| 544 | assert status.OK() |
||
| 545 | assert len(result[0]) == min(len(vectors), top_k) |
||
| 546 | assert check_result(result[0], ids[0]) |
||
| 547 | assert result[0][0].distance >= 1 - gen_inaccuracy(result[0][0].distance) |
||
| 548 | |||
| 549 | @pytest.mark.level(2) |
||
| 550 | def test_search_vectors_without_connect(self, dis_connect, collection): |
||
| 551 | ''' |
||
| 552 | target: test search vectors without connection |
||
| 553 | method: use dis connected instance, call search method and check if search successfully |
||
| 554 | expected: raise exception |
||
| 555 | ''' |
||
| 556 | query_vectors = [vectors[0]] |
||
| 557 | nprobe = 1 |
||
| 558 | with pytest.raises(Exception) as e: |
||
| 559 | status, ids = dis_connect.search(collection, top_k, query_vectors) |
||
| 560 | |||
| 561 | def test_search_collection_name_not_existed(self, connect, collection): |
||
| 562 | ''' |
||
| 563 | target: search collection not existed |
||
| 564 | method: search with the random collection_name, which is not in db |
||
| 565 | expected: status not ok |
||
| 566 | ''' |
||
| 567 | collection_name = gen_unique_str("not_existed_collection") |
||
| 568 | nprobe = 1 |
||
| 569 | query_vecs = [vectors[0]] |
||
| 570 | status, result = connect.search(collection_name, top_k, query_vecs) |
||
| 571 | assert not status.OK() |
||
| 572 | |||
| 573 | def test_search_collection_name_None(self, connect, collection): |
||
| 574 | ''' |
||
| 575 | target: search collection that collection name is None |
||
| 576 | method: search with the collection_name: None |
||
| 577 | expected: status not ok |
||
| 578 | ''' |
||
| 579 | collection_name = None |
||
| 580 | nprobe = 1 |
||
| 581 | query_vecs = [vectors[0]] |
||
| 582 | with pytest.raises(Exception) as e: |
||
| 583 | status, result = connect.search(collection_name, top_k, query_vecs) |
||
| 584 | |||
| 585 | def test_search_top_k_query_records(self, connect, collection): |
||
| 586 | ''' |
||
| 587 | target: test search fuction, with search params: query_records |
||
| 588 | method: search with the given query_records, which are subarrays of the inserted vectors |
||
| 589 | expected: status ok and the returned vectors should be query_records |
||
| 590 | ''' |
||
| 591 | top_k = 10 |
||
| 592 | vectors, ids = self.init_data(connect, collection) |
||
| 593 | query_vecs = [vectors[0], vectors[55], vectors[99]] |
||
| 594 | status, result = connect.search(collection, top_k, query_vecs) |
||
| 595 | assert status.OK() |
||
| 596 | assert len(result) == len(query_vecs) |
||
| 597 | for i in range(len(query_vecs)): |
||
| 598 | assert len(result[i]) == top_k |
||
| 599 | assert result[i][0].distance <= epsilon |
||
| 600 | |||
| 601 | def test_search_distance_l2_flat_index(self, connect, collection): |
||
| 602 | ''' |
||
| 603 | target: search collection, and check the result: distance |
||
| 604 | method: compare the return distance value with value computed with Euclidean |
||
| 605 | expected: the return distance equals to the computed value |
||
| 606 | ''' |
||
| 607 | nb = 2 |
||
| 608 | vectors, ids = self.init_data(connect, collection, nb=nb) |
||
| 609 | query_vecs = [[0.50 for i in range(dim)]] |
||
| 610 | distance_0 = numpy.linalg.norm(numpy.array(query_vecs[0]) - numpy.array(vectors[0])) |
||
| 611 | distance_1 = numpy.linalg.norm(numpy.array(query_vecs[0]) - numpy.array(vectors[1])) |
||
| 612 | status, result = connect.search(collection, top_k, query_vecs) |
||
| 613 | assert abs(numpy.sqrt(result[0][0].distance) - min(distance_0, distance_1)) <= gen_inaccuracy( |
||
| 614 | result[0][0].distance) |
||
| 615 | |||
| 616 | def test_search_distance_ip_flat_index(self, connect, ip_collection): |
||
| 617 | ''' |
||
| 618 | target: search ip_collection, and check the result: distance |
||
| 619 | method: compare the return distance value with value computed with Inner product |
||
| 620 | expected: the return distance equals to the computed value |
||
| 621 | ''' |
||
| 622 | nb = 2 |
||
| 623 | nprobe = 1 |
||
| 624 | vectors, ids = self.init_data(connect, ip_collection, nb=nb) |
||
| 625 | index_type = IndexType.FLAT |
||
| 626 | index_param = { |
||
| 627 | "nlist": 16384 |
||
| 628 | } |
||
| 629 | connect.create_index(ip_collection, index_type, index_param) |
||
| 630 | logging.getLogger().info(connect.get_index_info(ip_collection)) |
||
| 631 | query_vecs = [[0.50 for i in range(dim)]] |
||
| 632 | distance_0 = numpy.inner(numpy.array(query_vecs[0]), numpy.array(vectors[0])) |
||
| 633 | distance_1 = numpy.inner(numpy.array(query_vecs[0]), numpy.array(vectors[1])) |
||
| 634 | search_param = get_search_param(index_type) |
||
| 635 | status, result = connect.search(ip_collection, top_k, query_vecs, params=search_param) |
||
| 636 | assert abs(result[0][0].distance - max(distance_0, distance_1)) <= gen_inaccuracy(result[0][0].distance) |
||
| 637 | |||
| 638 | View Code Duplication | def test_search_distance_jaccard_flat_index(self, connect, jac_collection): |
|
| 639 | ''' |
||
| 640 | target: search ip_collection, and check the result: distance |
||
| 641 | method: compare the return distance value with value computed with Inner product |
||
| 642 | expected: the return distance equals to the computed value |
||
| 643 | ''' |
||
| 644 | # from scipy.spatial import distance |
||
| 645 | nprobe = 512 |
||
| 646 | int_vectors, vectors, ids = self.init_binary_data(connect, jac_collection, nb=2) |
||
| 647 | index_type = IndexType.FLAT |
||
| 648 | index_param = { |
||
| 649 | "nlist": 16384 |
||
| 650 | } |
||
| 651 | connect.create_index(jac_collection, index_type, index_param) |
||
| 652 | logging.getLogger().info(connect.get_collection_info(jac_collection)) |
||
| 653 | logging.getLogger().info(connect.get_index_info(jac_collection)) |
||
| 654 | query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, jac_collection, nb=1, insert=False) |
||
| 655 | distance_0 = jaccard(query_int_vectors[0], int_vectors[0]) |
||
| 656 | distance_1 = jaccard(query_int_vectors[0], int_vectors[1]) |
||
| 657 | search_param = get_search_param(index_type) |
||
| 658 | status, result = connect.search(jac_collection, top_k, query_vecs, params=search_param) |
||
| 659 | logging.getLogger().info(status) |
||
| 660 | logging.getLogger().info(result) |
||
| 661 | assert abs(result[0][0].distance - min(distance_0, distance_1)) <= epsilon |
||
| 662 | |||
| 663 | def test_search_distance_jaccard_flat_index_metric_type(self, connect, jac_collection): |
||
| 664 | ''' |
||
| 665 | target: search ip_collection, and check the result: distance |
||
| 666 | method: compare the return distance value with value computed with HAMMING |
||
| 667 | expected: the return distance equals to the computed value |
||
| 668 | ''' |
||
| 669 | # from scipy.spatial import distance |
||
| 670 | nprobe = 512 |
||
| 671 | int_vectors, vectors, ids = self.init_binary_data(connect, jac_collection, nb=2) |
||
| 672 | index_type = IndexType.FLAT |
||
| 673 | index_param = { |
||
| 674 | "nlist": 16384 |
||
| 675 | } |
||
| 676 | connect.create_index(jac_collection, index_type, index_param) |
||
| 677 | logging.getLogger().info(connect.get_collection_info(jac_collection)) |
||
| 678 | logging.getLogger().info(connect.get_index_info(jac_collection)) |
||
| 679 | query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, jac_collection, nb=1, insert=False) |
||
| 680 | distance_0 = hamming(query_int_vectors[0], int_vectors[0]) |
||
| 681 | distance_1 = hamming(query_int_vectors[0], int_vectors[1]) |
||
| 682 | search_param = get_search_param(index_type) |
||
| 683 | search_param.update({"metric_type": MetricType.HAMMING.value}) |
||
| 684 | status, result = connect.search(jac_collection, top_k, query_vecs, params=search_param) |
||
| 685 | assert status.OK() |
||
| 686 | logging.getLogger().info(status) |
||
| 687 | logging.getLogger().info(result) |
||
| 688 | assert abs(result[0][0].distance - min(distance_0, distance_1).astype(float)) <= epsilon |
||
| 689 | |||
| 690 | View Code Duplication | def test_search_distance_hamming_flat_index(self, connect, ham_collection): |
|
| 691 | ''' |
||
| 692 | target: search ip_collection, and check the result: distance |
||
| 693 | method: compare the return distance value with value computed with Inner product |
||
| 694 | expected: the return distance equals to the computed value |
||
| 695 | ''' |
||
| 696 | # from scipy.spatial import distance |
||
| 697 | nprobe = 512 |
||
| 698 | int_vectors, vectors, ids = self.init_binary_data(connect, ham_collection, nb=2) |
||
| 699 | index_type = IndexType.FLAT |
||
| 700 | index_param = { |
||
| 701 | "nlist": 16384 |
||
| 702 | } |
||
| 703 | connect.create_index(ham_collection, index_type, index_param) |
||
| 704 | logging.getLogger().info(connect.get_collection_info(ham_collection)) |
||
| 705 | logging.getLogger().info(connect.get_index_info(ham_collection)) |
||
| 706 | query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, ham_collection, nb=1, insert=False) |
||
| 707 | distance_0 = hamming(query_int_vectors[0], int_vectors[0]) |
||
| 708 | distance_1 = hamming(query_int_vectors[0], int_vectors[1]) |
||
| 709 | search_param = get_search_param(index_type) |
||
| 710 | status, result = connect.search(ham_collection, top_k, query_vecs, params=search_param) |
||
| 711 | logging.getLogger().info(status) |
||
| 712 | logging.getLogger().info(result) |
||
| 713 | assert abs(result[0][0].distance - min(distance_0, distance_1).astype(float)) <= epsilon |
||
| 714 | |||
| 715 | def test_search_distance_substructure_flat_index(self, connect, substructure_collection): |
||
| 716 | ''' |
||
| 717 | target: search ip_collection, and check the result: distance |
||
| 718 | method: compare the return distance value with value computed with Inner product |
||
| 719 | expected: the return distance equals to the computed value |
||
| 720 | ''' |
||
| 721 | # from scipy.spatial import distance |
||
| 722 | nprobe = 512 |
||
| 723 | int_vectors, vectors, ids = self.init_binary_data(connect, substructure_collection, nb=2) |
||
| 724 | index_type = IndexType.FLAT |
||
| 725 | index_param = { |
||
| 726 | "nlist": 16384 |
||
| 727 | } |
||
| 728 | connect.create_index(substructure_collection, index_type, index_param) |
||
| 729 | logging.getLogger().info(connect.get_collection_info(substructure_collection)) |
||
| 730 | logging.getLogger().info(connect.get_index_info(substructure_collection)) |
||
| 731 | query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, substructure_collection, nb=1, |
||
| 732 | insert=False) |
||
| 733 | distance_0 = substructure(query_int_vectors[0], int_vectors[0]) |
||
| 734 | distance_1 = substructure(query_int_vectors[0], int_vectors[1]) |
||
| 735 | search_param = get_search_param(index_type) |
||
| 736 | status, result = connect.search(substructure_collection, top_k, query_vecs, params=search_param) |
||
| 737 | logging.getLogger().info(status) |
||
| 738 | logging.getLogger().info(result) |
||
| 739 | assert len(result[0]) == 0 |
||
| 740 | |||
| 741 | View Code Duplication | def test_search_distance_substructure_flat_index_B(self, connect, substructure_collection): |
|
| 742 | ''' |
||
| 743 | target: search ip_collection, and check the result: distance |
||
| 744 | method: compare the return distance value with value computed with SUB |
||
| 745 | expected: the return distance equals to the computed value |
||
| 746 | ''' |
||
| 747 | # from scipy.spatial import distance |
||
| 748 | top_k = 3 |
||
| 749 | nprobe = 512 |
||
| 750 | int_vectors, vectors, ids = self.init_binary_data(connect, substructure_collection, nb=2) |
||
| 751 | index_type = IndexType.FLAT |
||
| 752 | index_param = { |
||
| 753 | "nlist": 16384 |
||
| 754 | } |
||
| 755 | connect.create_index(substructure_collection, index_type, index_param) |
||
| 756 | logging.getLogger().info(connect.get_collection_info(substructure_collection)) |
||
| 757 | logging.getLogger().info(connect.get_index_info(substructure_collection)) |
||
| 758 | query_int_vectors, query_vecs = gen_binary_sub_vectors(int_vectors, 2) |
||
| 759 | search_param = get_search_param(index_type) |
||
| 760 | status, result = connect.search(substructure_collection, top_k, query_vecs, params=search_param) |
||
| 761 | logging.getLogger().info(status) |
||
| 762 | logging.getLogger().info(result) |
||
| 763 | assert len(result[0]) == 1 |
||
| 764 | assert len(result[1]) == 1 |
||
| 765 | assert result[0][0].distance <= epsilon |
||
| 766 | assert result[0][0].id == ids[0] |
||
| 767 | assert result[1][0].distance <= epsilon |
||
| 768 | assert result[1][0].id == ids[1] |
||
| 769 | |||
| 770 | def test_search_distance_superstructure_flat_index(self, connect, superstructure_collection): |
||
| 771 | ''' |
||
| 772 | target: search ip_collection, and check the result: distance |
||
| 773 | method: compare the return distance value with value computed with Inner product |
||
| 774 | expected: the return distance equals to the computed value |
||
| 775 | ''' |
||
| 776 | # from scipy.spatial import distance |
||
| 777 | nprobe = 512 |
||
| 778 | int_vectors, vectors, ids = self.init_binary_data(connect, superstructure_collection, nb=2) |
||
| 779 | index_type = IndexType.FLAT |
||
| 780 | index_param = { |
||
| 781 | "nlist": 16384 |
||
| 782 | } |
||
| 783 | connect.create_index(superstructure_collection, index_type, index_param) |
||
| 784 | logging.getLogger().info(connect.get_collection_info(superstructure_collection)) |
||
| 785 | logging.getLogger().info(connect.get_index_info(superstructure_collection)) |
||
| 786 | query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, superstructure_collection, nb=1, |
||
| 787 | insert=False) |
||
| 788 | distance_0 = superstructure(query_int_vectors[0], int_vectors[0]) |
||
| 789 | distance_1 = superstructure(query_int_vectors[0], int_vectors[1]) |
||
| 790 | search_param = get_search_param(index_type) |
||
| 791 | status, result = connect.search(superstructure_collection, top_k, query_vecs, params=search_param) |
||
| 792 | logging.getLogger().info(status) |
||
| 793 | logging.getLogger().info(result) |
||
| 794 | assert len(result[0]) == 0 |
||
| 795 | |||
| 796 | View Code Duplication | def test_search_distance_superstructure_flat_index_B(self, connect, superstructure_collection): |
|
| 797 | ''' |
||
| 798 | target: search ip_collection, and check the result: distance |
||
| 799 | method: compare the return distance value with value computed with SUPER |
||
| 800 | expected: the return distance equals to the computed value |
||
| 801 | ''' |
||
| 802 | # from scipy.spatial import distance |
||
| 803 | top_k = 3 |
||
| 804 | nprobe = 512 |
||
| 805 | int_vectors, vectors, ids = self.init_binary_data(connect, superstructure_collection, nb=2) |
||
| 806 | index_type = IndexType.FLAT |
||
| 807 | index_param = { |
||
| 808 | "nlist": 16384 |
||
| 809 | } |
||
| 810 | connect.create_index(superstructure_collection, index_type, index_param) |
||
| 811 | logging.getLogger().info(connect.get_collection_info(superstructure_collection)) |
||
| 812 | logging.getLogger().info(connect.get_index_info(superstructure_collection)) |
||
| 813 | query_int_vectors, query_vecs = gen_binary_super_vectors(int_vectors, 2) |
||
| 814 | search_param = get_search_param(index_type) |
||
| 815 | status, result = connect.search(superstructure_collection, top_k, query_vecs, params=search_param) |
||
| 816 | logging.getLogger().info(status) |
||
| 817 | logging.getLogger().info(result) |
||
| 818 | assert len(result[0]) == 2 |
||
| 819 | assert len(result[1]) == 2 |
||
| 820 | assert result[0][0].id in ids |
||
| 821 | assert result[0][0].distance <= epsilon |
||
| 822 | assert result[1][0].id in ids |
||
| 823 | assert result[1][0].distance <= epsilon |
||
| 824 | |||
| 825 | View Code Duplication | def test_search_distance_tanimoto_flat_index(self, connect, tanimoto_collection): |
|
| 826 | ''' |
||
| 827 | target: search ip_collection, and check the result: distance |
||
| 828 | method: compare the return distance value with value computed with Inner product |
||
| 829 | expected: the return distance equals to the computed value |
||
| 830 | ''' |
||
| 831 | # from scipy.spatial import distance |
||
| 832 | nprobe = 512 |
||
| 833 | int_vectors, vectors, ids = self.init_binary_data(connect, tanimoto_collection, nb=2) |
||
| 834 | index_type = IndexType.FLAT |
||
| 835 | index_param = { |
||
| 836 | "nlist": 16384 |
||
| 837 | } |
||
| 838 | connect.create_index(tanimoto_collection, index_type, index_param) |
||
| 839 | logging.getLogger().info(connect.get_collection_info(tanimoto_collection)) |
||
| 840 | logging.getLogger().info(connect.get_index_info(tanimoto_collection)) |
||
| 841 | query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, tanimoto_collection, nb=1, insert=False) |
||
| 842 | distance_0 = tanimoto(query_int_vectors[0], int_vectors[0]) |
||
| 843 | distance_1 = tanimoto(query_int_vectors[0], int_vectors[1]) |
||
| 844 | search_param = get_search_param(index_type) |
||
| 845 | status, result = connect.search(tanimoto_collection, top_k, query_vecs, params=search_param) |
||
| 846 | logging.getLogger().info(status) |
||
| 847 | logging.getLogger().info(result) |
||
| 848 | assert abs(result[0][0].distance - min(distance_0, distance_1)) <= epsilon |
||
| 849 | |||
| 850 | def test_search_distance_ip_index_params(self, connect, ip_collection, get_index): |
||
| 851 | ''' |
||
| 852 | target: search collection, and check the result: distance |
||
| 853 | method: compare the return distance value with value computed with Inner product |
||
| 854 | expected: the return distance equals to the computed value |
||
| 855 | ''' |
||
| 856 | top_k = 2 |
||
| 857 | nprobe = 1 |
||
| 858 | index_param = get_index["index_param"] |
||
| 859 | index_type = get_index["index_type"] |
||
| 860 | if index_type == IndexType.RNSG: |
||
| 861 | pytest.skip("rnsg not support in ip") |
||
| 862 | vectors, ids = self.init_data(connect, ip_collection, nb=2) |
||
| 863 | connect.create_index(ip_collection, index_type, index_param) |
||
| 864 | logging.getLogger().info(connect.get_index_info(ip_collection)) |
||
| 865 | query_vecs = [[0.50 for i in range(dim)]] |
||
| 866 | search_param = get_search_param(index_type) |
||
| 867 | status, result = connect.search(ip_collection, top_k, query_vecs, params=search_param) |
||
| 868 | logging.getLogger().debug(status) |
||
| 869 | logging.getLogger().debug(result) |
||
| 870 | distance_0 = numpy.inner(numpy.array(query_vecs[0]), numpy.array(vectors[0])) |
||
| 871 | distance_1 = numpy.inner(numpy.array(query_vecs[0]), numpy.array(vectors[1])) |
||
| 872 | assert abs(result[0][0].distance - max(distance_0, distance_1)) <= gen_inaccuracy(result[0][0].distance) |
||
| 873 | |||
| 874 | # def test_search_concurrent(self, connect, collection): |
||
| 875 | # vectors, ids = self.init_data(connect, collection, nb=5000) |
||
| 876 | # thread_num = 50 |
||
| 877 | # nq = 1 |
||
| 878 | # top_k = 2 |
||
| 879 | # threads = [] |
||
| 880 | # query_vecs = vectors[:nq] |
||
| 881 | # def search(thread_number): |
||
| 882 | # for i in range(1000000): |
||
| 883 | # status, result = connect.search(collection, top_k, query_vecs, timeout=2) |
||
| 884 | # assert len(result) == len(query_vecs) |
||
| 885 | # assert status.OK() |
||
| 886 | # if i % 1000 == 0: |
||
| 887 | # logging.getLogger().info("In %d, %d" % (thread_number, i)) |
||
| 888 | # logging.getLogger().info("%d finished" % thread_number) |
||
| 889 | # # with concurrent.futures.ThreadPoolExecutor(max_workers=thread_num) as executor: |
||
| 890 | # # future_results = {executor.submit( |
||
| 891 | # # search): i for i in range(1000000)} |
||
| 892 | # # for future in concurrent.futures.as_completed(future_results): |
||
| 893 | # # future.result() |
||
| 894 | # for i in range(thread_num): |
||
| 895 | # t = threading.Thread(target=search, args=(i, )) |
||
| 896 | # threads.append(t) |
||
| 897 | # t.start() |
||
| 898 | # for t in threads: |
||
| 899 | # t.join() |
||
| 900 | |||
| 901 | View Code Duplication | @pytest.mark.level(2) |
|
| 902 | @pytest.mark.timeout(30) |
||
| 903 | def test_search_concurrent_multithreads(self, args): |
||
| 904 | ''' |
||
| 905 | target: test concurrent search with multiprocessess |
||
| 906 | method: search with 10 processes, each process uses dependent connection |
||
| 907 | expected: status ok and the returned vectors should be query_records |
||
| 908 | ''' |
||
| 909 | nb = 100 |
||
| 910 | top_k = 10 |
||
| 911 | threads_num = 4 |
||
| 912 | threads = [] |
||
| 913 | collection = gen_unique_str("test_search_concurrent_multiprocessing") |
||
| 914 | uri = "tcp://%s:%s" % (args["ip"], args["port"]) |
||
| 915 | param = {'collection_name': collection, |
||
| 916 | 'dimension': dim, |
||
| 917 | 'index_type': IndexType.FLAT, |
||
| 918 | 'store_raw_vector': False} |
||
| 919 | # create collection |
||
| 920 | milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
||
| 921 | milvus.create_collection(param) |
||
| 922 | vectors, ids = self.init_data(milvus, collection, nb=nb) |
||
| 923 | query_vecs = vectors[nb // 2:nb] |
||
| 924 | |||
| 925 | def search(milvus): |
||
| 926 | status, result = milvus.search(collection, top_k, query_vecs) |
||
| 927 | assert len(result) == len(query_vecs) |
||
| 928 | for i in range(len(query_vecs)): |
||
| 929 | assert result[i][0].id in ids |
||
| 930 | assert result[i][0].distance == 0.0 |
||
| 931 | |||
| 932 | for i in range(threads_num): |
||
| 933 | milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
||
| 934 | t = threading.Thread(target=search, args=(milvus,)) |
||
| 935 | threads.append(t) |
||
| 936 | t.start() |
||
| 937 | time.sleep(0.2) |
||
| 938 | for t in threads: |
||
| 939 | t.join() |
||
| 940 | |||
| 941 | # TODO: enable |
||
| 942 | View Code Duplication | @pytest.mark.timeout(30) |
|
| 943 | def _test_search_concurrent_multiprocessing(self, args): |
||
| 944 | ''' |
||
| 945 | target: test concurrent search with multiprocessess |
||
| 946 | method: search with 10 processes, each process uses dependent connection |
||
| 947 | expected: status ok and the returned vectors should be query_records |
||
| 948 | ''' |
||
| 949 | nb = 100 |
||
| 950 | top_k = 10 |
||
| 951 | process_num = 4 |
||
| 952 | processes = [] |
||
| 953 | collection = gen_unique_str("test_search_concurrent_multiprocessing") |
||
| 954 | uri = "tcp://%s:%s" % (args["ip"], args["port"]) |
||
| 955 | param = {'collection_name': collection, |
||
| 956 | 'dimension': dim, |
||
| 957 | 'index_type': IndexType.FLAT, |
||
| 958 | 'store_raw_vector': False} |
||
| 959 | # create collection |
||
| 960 | milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
||
| 961 | milvus.create_collection(param) |
||
| 962 | vectors, ids = self.init_data(milvus, collection, nb=nb) |
||
| 963 | query_vecs = vectors[nb // 2:nb] |
||
| 964 | |||
| 965 | def search(milvus): |
||
| 966 | status, result = milvus.search(collection, top_k, query_vecs) |
||
| 967 | assert len(result) == len(query_vecs) |
||
| 968 | for i in range(len(query_vecs)): |
||
| 969 | assert result[i][0].id in ids |
||
| 970 | assert result[i][0].distance == 0.0 |
||
| 971 | |||
| 972 | for i in range(process_num): |
||
| 973 | milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
||
| 974 | p = Process(target=search, args=(milvus,)) |
||
| 975 | processes.append(p) |
||
| 976 | p.start() |
||
| 977 | time.sleep(0.2) |
||
| 978 | for p in processes: |
||
| 979 | p.join() |
||
| 980 | |||
| 981 | View Code Duplication | def test_search_multi_collection_L2(search, args): |
|
| 982 | ''' |
||
| 983 | target: test search multi collections of L2 |
||
| 984 | method: add vectors into 10 collections, and search |
||
| 985 | expected: search status ok, the length of result |
||
| 986 | ''' |
||
| 987 | num = 10 |
||
| 988 | top_k = 10 |
||
| 989 | collections = [] |
||
| 990 | idx = [] |
||
| 991 | for i in range(num): |
||
| 992 | collection = gen_unique_str("test_add_multicollection_%d" % i) |
||
| 993 | uri = "tcp://%s:%s" % (args["ip"], args["port"]) |
||
| 994 | param = {'collection_name': collection, |
||
| 995 | 'dimension': dim, |
||
| 996 | 'index_file_size': 10, |
||
| 997 | 'metric_type': MetricType.L2} |
||
| 998 | # create collection |
||
| 999 | milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
||
| 1000 | milvus.create_collection(param) |
||
| 1001 | status, ids = milvus.insert(collection, vectors) |
||
| 1002 | assert status.OK() |
||
| 1003 | assert len(ids) == len(vectors) |
||
| 1004 | collections.append(collection) |
||
| 1005 | idx.append(ids[0]) |
||
| 1006 | idx.append(ids[10]) |
||
| 1007 | idx.append(ids[20]) |
||
| 1008 | milvus.flush([collection]) |
||
| 1009 | query_vecs = [vectors[0], vectors[10], vectors[20]] |
||
| 1010 | # start query from random collection |
||
| 1011 | for i in range(num): |
||
| 1012 | collection = collections[i] |
||
| 1013 | status, result = milvus.search(collection, top_k, query_vecs) |
||
| 1014 | assert status.OK() |
||
| 1015 | assert len(result) == len(query_vecs) |
||
| 1016 | for j in range(len(query_vecs)): |
||
| 1017 | assert len(result[j]) == top_k |
||
| 1018 | for j in range(len(query_vecs)): |
||
| 1019 | assert check_result(result[j], idx[3 * i + j]) |
||
| 1020 | |||
| 1021 | View Code Duplication | def test_search_multi_collection_IP(search, args): |
|
| 1022 | ''' |
||
| 1023 | target: test search multi collections of IP |
||
| 1024 | method: add vectors into 10 collections, and search |
||
| 1025 | expected: search status ok, the length of result |
||
| 1026 | ''' |
||
| 1027 | num = 10 |
||
| 1028 | top_k = 10 |
||
| 1029 | collections = [] |
||
| 1030 | idx = [] |
||
| 1031 | for i in range(num): |
||
| 1032 | collection = gen_unique_str("test_add_multicollection_%d" % i) |
||
| 1033 | uri = "tcp://%s:%s" % (args["ip"], args["port"]) |
||
| 1034 | param = {'collection_name': collection, |
||
| 1035 | 'dimension': dim, |
||
| 1036 | 'index_file_size': 10, |
||
| 1037 | 'metric_type': MetricType.L2} |
||
| 1038 | # create collection |
||
| 1039 | milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
||
| 1040 | milvus.create_collection(param) |
||
| 1041 | status, ids = milvus.insert(collection, vectors) |
||
| 1042 | assert status.OK() |
||
| 1043 | assert len(ids) == len(vectors) |
||
| 1044 | collections.append(collection) |
||
| 1045 | idx.append(ids[0]) |
||
| 1046 | idx.append(ids[10]) |
||
| 1047 | idx.append(ids[20]) |
||
| 1048 | milvus.flush([collection]) |
||
| 1049 | query_vecs = [vectors[0], vectors[10], vectors[20]] |
||
| 1050 | # start query from random collection |
||
| 1051 | for i in range(num): |
||
| 1052 | collection = collections[i] |
||
| 1053 | status, result = milvus.search(collection, top_k, query_vecs) |
||
| 1054 | assert status.OK() |
||
| 1055 | assert len(result) == len(query_vecs) |
||
| 1056 | for j in range(len(query_vecs)): |
||
| 1057 | assert len(result[j]) == top_k |
||
| 1058 | for j in range(len(query_vecs)): |
||
| 1059 | assert check_result(result[j], idx[3 * i + j]) |
||
| 1060 | |||
| 1061 | @pytest.fixture(params=MetricType) |
||
| 1062 | def get_binary_metric_types(self, request): |
||
| 1063 | if request.param == MetricType.INVALID: |
||
| 1064 | pytest.skip(("metric type invalid")) |
||
| 1065 | if request.param in [MetricType.L2, MetricType.IP]: |
||
| 1066 | pytest.skip(("L2 and IP not support in binary")) |
||
| 1067 | return request.param |
||
| 1068 | |||
| 1069 | # 4678 and # 4683 |
||
| 1070 | def test_search_binary_dim_not_power_of_2(self, connect, get_binary_metric_types): |
||
| 1071 | metric = get_binary_metric_types |
||
| 1072 | collection = gen_unique_str(collection_id) |
||
| 1073 | dim = 200 |
||
| 1074 | top_k = 1 |
||
| 1075 | param = {'collection_name': collection, |
||
| 1076 | 'dimension': dim, |
||
| 1077 | 'index_file_size': 10, |
||
| 1078 | 'metric_type': metric} |
||
| 1079 | status = connect.create_collection(param) |
||
| 1080 | assert status.OK() |
||
| 1081 | int_vectors, vectors, ids = self.init_binary_data(connect, collection, nb=1000, dim=dim) |
||
| 1082 | search_param = get_search_param(IndexType.FLAT) |
||
| 1083 | status, result = connect.search(collection, top_k, vectors[:1], params=search_param) |
||
| 1084 | assert status.OK() |
||
| 1085 | assert result[0][0].id in ids |
||
| 1086 | assert result[0][0].distance == 0.0 |
||
| 1087 | |||
| 1088 | @pytest.fixture(params=MetricType) |
||
| 1089 | def get_metric_types(self, request): |
||
| 1090 | if request.param == MetricType.INVALID: |
||
| 1091 | pytest.skip(("metric type invalid")) |
||
| 1092 | if request.param not in [MetricType.L2, MetricType.IP]: |
||
| 1093 | pytest.skip(("L2 and IP not support in binary")) |
||
| 1094 | return request.param |
||
| 1095 | |||
| 1096 | def test_search_float_dim_not_power_of_2(self, connect, get_metric_types): |
||
| 1097 | metric = get_metric_types |
||
| 1098 | collection = gen_unique_str(collection_id) |
||
| 1099 | dim = 200 |
||
| 1100 | top_k = 1 |
||
| 1101 | param = {'collection_name': collection, |
||
| 1102 | 'dimension': dim, |
||
| 1103 | 'index_file_size': 10, |
||
| 1104 | 'metric_type': metric} |
||
| 1105 | status = connect.create_collection(param) |
||
| 1106 | assert status.OK() |
||
| 1107 | vectors, ids = self.init_data(connect, collection, nb=1000, dim=dim) |
||
| 1108 | search_param = get_search_param(IndexType.FLAT) |
||
| 1109 | status, result = connect.search(collection, top_k, vectors[:1], params=search_param) |
||
| 1110 | assert status.OK() |
||
| 1111 | assert result[0][0].id in ids |
||
| 1112 | |||
| 1113 | """ |
||
| 1114 | ****************************************************************** |
||
| 1115 | # The following cases are used to test `search_vectors` function |
||
| 1116 | # with invalid collection_name top-k / nprobe / query_range |
||
| 1117 | ****************************************************************** |
||
| 1118 | """ |
||
| 1119 | |||
| 1120 | |||
| 1121 | class TestSearchParamsInvalid(object): |
||
| 1122 | nlist = 16384 |
||
| 1123 | index_type = IndexType.IVF_SQ8 |
||
| 1124 | index_param = {"nlist": nlist} |
||
| 1125 | logging.getLogger().info(index_param) |
||
| 1126 | |||
| 1127 | def init_data(self, connect, collection, nb=6000): |
||
| 1128 | ''' |
||
| 1129 | Generate vectors and add it in collection, before search vectors |
||
| 1130 | ''' |
||
| 1131 | global vectors |
||
| 1132 | if nb == 6000: |
||
| 1133 | insert = vectors |
||
| 1134 | else: |
||
| 1135 | insert = gen_vectors(nb, dim) |
||
| 1136 | status, ids = connect.insert(collection, insert) |
||
| 1137 | connect.flush([collection]) |
||
| 1138 | return insert, ids |
||
| 1139 | |||
| 1140 | """ |
||
| 1141 | Test search collection with invalid collection names |
||
| 1142 | """ |
||
| 1143 | |||
| 1144 | @pytest.fixture( |
||
| 1145 | scope="function", |
||
| 1146 | params=gen_invalid_collection_names() |
||
| 1147 | ) |
||
| 1148 | def get_collection_name(self, request): |
||
| 1149 | yield request.param |
||
| 1150 | |||
| 1151 | @pytest.mark.level(2) |
||
| 1152 | def test_search_with_invalid_collectionname(self, connect, get_collection_name): |
||
| 1153 | collection_name = get_collection_name |
||
| 1154 | logging.getLogger().info(collection_name) |
||
| 1155 | nprobe = 1 |
||
| 1156 | query_vecs = gen_vectors(1, dim) |
||
| 1157 | status, result = connect.search(collection_name, top_k, query_vecs) |
||
| 1158 | assert not status.OK() |
||
| 1159 | |||
| 1160 | @pytest.mark.level(1) |
||
| 1161 | def test_search_with_invalid_tag_format(self, connect, collection): |
||
| 1162 | nprobe = 1 |
||
| 1163 | query_vecs = gen_vectors(1, dim) |
||
| 1164 | with pytest.raises(Exception) as e: |
||
| 1165 | status, result = connect.search(collection, top_k, query_vecs, partition_tags="tag") |
||
| 1166 | logging.getLogger().debug(result) |
||
| 1167 | |||
| 1168 | @pytest.mark.level(1) |
||
| 1169 | def test_search_with_tag_not_existed(self, connect, collection): |
||
| 1170 | nprobe = 1 |
||
| 1171 | query_vecs = gen_vectors(1, dim) |
||
| 1172 | status, result = connect.search(collection, top_k, query_vecs, partition_tags=["tag"]) |
||
| 1173 | logging.getLogger().info(result) |
||
| 1174 | assert not status.OK() |
||
| 1175 | |||
| 1176 | """ |
||
| 1177 | Test search collection with invalid top-k |
||
| 1178 | """ |
||
| 1179 | |||
| 1180 | @pytest.fixture( |
||
| 1181 | scope="function", |
||
| 1182 | params=gen_invalid_top_ks() |
||
| 1183 | ) |
||
| 1184 | def get_top_k(self, request): |
||
| 1185 | yield request.param |
||
| 1186 | |||
| 1187 | View Code Duplication | @pytest.mark.level(1) |
|
| 1188 | def test_search_with_invalid_top_k(self, connect, collection, get_top_k): |
||
| 1189 | ''' |
||
| 1190 | target: test search fuction, with the wrong top_k |
||
| 1191 | method: search with top_k |
||
| 1192 | expected: raise an error, and the connection is normal |
||
| 1193 | ''' |
||
| 1194 | top_k = get_top_k |
||
| 1195 | logging.getLogger().info(top_k) |
||
| 1196 | nprobe = 1 |
||
| 1197 | query_vecs = gen_vectors(1, dim) |
||
| 1198 | if isinstance(top_k, int): |
||
| 1199 | status, result = connect.search(collection, top_k, query_vecs) |
||
| 1200 | assert not status.OK() |
||
| 1201 | else: |
||
| 1202 | with pytest.raises(Exception) as e: |
||
| 1203 | status, result = connect.search(collection, top_k, query_vecs) |
||
| 1204 | |||
| 1205 | View Code Duplication | @pytest.mark.level(2) |
|
| 1206 | def test_search_with_invalid_top_k_ip(self, connect, ip_collection, get_top_k): |
||
| 1207 | ''' |
||
| 1208 | target: test search fuction, with the wrong top_k |
||
| 1209 | method: search with top_k |
||
| 1210 | expected: raise an error, and the connection is normal |
||
| 1211 | ''' |
||
| 1212 | top_k = get_top_k |
||
| 1213 | logging.getLogger().info(top_k) |
||
| 1214 | nprobe = 1 |
||
| 1215 | query_vecs = gen_vectors(1, dim) |
||
| 1216 | if isinstance(top_k, int): |
||
| 1217 | status, result = connect.search(ip_collection, top_k, query_vecs) |
||
| 1218 | assert not status.OK() |
||
| 1219 | else: |
||
| 1220 | with pytest.raises(Exception) as e: |
||
| 1221 | status, result = connect.search(ip_collection, top_k, query_vecs) |
||
| 1222 | |||
| 1223 | """ |
||
| 1224 | Test search collection with invalid nprobe |
||
| 1225 | """ |
||
| 1226 | |||
| 1227 | @pytest.fixture( |
||
| 1228 | scope="function", |
||
| 1229 | params=gen_invalid_nprobes() |
||
| 1230 | ) |
||
| 1231 | def get_nprobes(self, request): |
||
| 1232 | yield request.param |
||
| 1233 | |||
| 1234 | View Code Duplication | @pytest.mark.level(1) |
|
| 1235 | def test_search_with_invalid_nprobe(self, connect, collection, get_nprobes): |
||
| 1236 | ''' |
||
| 1237 | target: test search fuction, with the wrong nprobe |
||
| 1238 | method: search with nprobe |
||
| 1239 | expected: raise an error, and the connection is normal |
||
| 1240 | ''' |
||
| 1241 | index_type = IndexType.IVF_SQ8 |
||
| 1242 | index_param = {"nlist": 16384} |
||
| 1243 | connect.create_index(collection, index_type, index_param) |
||
| 1244 | nprobe = get_nprobes |
||
| 1245 | search_param = {"nprobe": nprobe} |
||
| 1246 | logging.getLogger().info(nprobe) |
||
| 1247 | query_vecs = gen_vectors(1, dim) |
||
| 1248 | # if isinstance(nprobe, int): |
||
| 1249 | status, result = connect.search(collection, top_k, query_vecs, params=search_param) |
||
| 1250 | assert not status.OK() |
||
| 1251 | # else: |
||
| 1252 | # with pytest.raises(Exception) as e: |
||
| 1253 | # status, result = connect.search(collection, top_k, query_vecs, params=search_param) |
||
| 1254 | |||
| 1255 | View Code Duplication | @pytest.mark.level(2) |
|
| 1256 | def test_search_with_invalid_nprobe_ip(self, connect, ip_collection, get_nprobes): |
||
| 1257 | ''' |
||
| 1258 | target: test search fuction, with the wrong top_k |
||
| 1259 | method: search with top_k |
||
| 1260 | expected: raise an error, and the connection is normal |
||
| 1261 | ''' |
||
| 1262 | index_type = IndexType.IVF_SQ8 |
||
| 1263 | index_param = {"nlist": 16384} |
||
| 1264 | connect.create_index(ip_collection, index_type, index_param) |
||
| 1265 | nprobe = get_nprobes |
||
| 1266 | search_param = {"nprobe": nprobe} |
||
| 1267 | logging.getLogger().info(nprobe) |
||
| 1268 | query_vecs = gen_vectors(1, dim) |
||
| 1269 | |||
| 1270 | # if isinstance(nprobe, int): |
||
| 1271 | status, result = connect.search(ip_collection, top_k, query_vecs, params=search_param) |
||
| 1272 | assert not status.OK() |
||
| 1273 | # else: |
||
| 1274 | # with pytest.raises(Exception) as e: |
||
| 1275 | # status, result = connect.search(ip_collection, top_k, query_vecs, params=search_param) |
||
| 1276 | |||
| 1277 | def test_search_with_2049_nprobe(self, connect, collection): |
||
| 1278 | ''' |
||
| 1279 | target: test search function, with 2049 nprobe in GPU mode |
||
| 1280 | method: search with nprobe |
||
| 1281 | expected: status not ok |
||
| 1282 | ''' |
||
| 1283 | if str(connect._cmd("mode")[1]) == "CPU": |
||
| 1284 | pytest.skip("Only support GPU mode") |
||
| 1285 | for index in gen_simple_index(): |
||
| 1286 | if index["index_type"] in [IndexType.IVF_PQ, IndexType.IVFLAT, IndexType.IVF_SQ8, IndexType.IVF_SQ8H]: |
||
| 1287 | index_type = index["index_type"] |
||
| 1288 | index_param = index["index_param"] |
||
| 1289 | self.init_data(connect, collection) |
||
| 1290 | connect.create_index(collection, index_type, index_param) |
||
| 1291 | nprobe = 2049 |
||
| 1292 | search_param = {"nprobe": nprobe} |
||
| 1293 | query_vecs = gen_vectors(nprobe, dim) |
||
| 1294 | status, result = connect.search(collection, top_k, query_vecs, params=search_param) |
||
| 1295 | assert status.OK() |
||
| 1296 | |||
| 1297 | View Code Duplication | @pytest.fixture( |
|
| 1298 | scope="function", |
||
| 1299 | params=gen_simple_index() |
||
| 1300 | ) |
||
| 1301 | def get_simple_index(self, request, connect): |
||
| 1302 | if str(connect._cmd("mode")[1]) == "CPU": |
||
| 1303 | if request.param["index_type"] == IndexType.IVF_SQ8H: |
||
| 1304 | pytest.skip("sq8h not support in CPU mode") |
||
| 1305 | if str(connect._cmd("mode")[1]) == "GPU": |
||
| 1306 | if request.param["index_type"] == IndexType.IVF_PQ: |
||
| 1307 | pytest.skip("ivfpq not support in GPU mode") |
||
| 1308 | return request.param |
||
| 1309 | |||
| 1310 | def test_search_with_empty_params(self, connect, collection, args, get_simple_index): |
||
| 1311 | ''' |
||
| 1312 | target: test search fuction, with empty search params |
||
| 1313 | method: search with params |
||
| 1314 | expected: search status not ok, and the connection is normal |
||
| 1315 | ''' |
||
| 1316 | if args["handler"] == "HTTP": |
||
| 1317 | pytest.skip("skip in http mode") |
||
| 1318 | index_type = get_simple_index["index_type"] |
||
| 1319 | index_param = get_simple_index["index_param"] |
||
| 1320 | connect.create_index(collection, index_type, index_param) |
||
| 1321 | query_vecs = gen_vectors(1, dim) |
||
| 1322 | status, result = connect.search(collection, top_k, query_vecs, params={}) |
||
| 1323 | |||
| 1324 | if index_type == IndexType.FLAT: |
||
| 1325 | assert status.OK() |
||
| 1326 | else: |
||
| 1327 | assert not status.OK() |
||
| 1328 | |||
| 1329 | @pytest.fixture( |
||
| 1330 | scope="function", |
||
| 1331 | params=gen_invaild_search_params() |
||
| 1332 | ) |
||
| 1333 | def get_invalid_search_param(self, request, connect): |
||
| 1334 | if str(connect._cmd("mode")[1]) == "CPU": |
||
| 1335 | if request.param["index_type"] == IndexType.IVF_SQ8H: |
||
| 1336 | pytest.skip("sq8h not support in CPU mode") |
||
| 1337 | if str(connect._cmd("mode")[1]) == "GPU": |
||
| 1338 | if request.param["index_type"] == IndexType.IVF_PQ: |
||
| 1339 | pytest.skip("ivfpq not support in GPU mode") |
||
| 1340 | return request.param |
||
| 1341 | |||
| 1342 | def test_search_with_invalid_params(self, connect, collection, get_invalid_search_param): |
||
| 1343 | ''' |
||
| 1344 | target: test search fuction, with invalid search params |
||
| 1345 | method: search with params |
||
| 1346 | expected: search status not ok, and the connection is normal |
||
| 1347 | ''' |
||
| 1348 | index_type = get_invalid_search_param["index_type"] |
||
| 1349 | search_param = get_invalid_search_param["search_param"] |
||
| 1350 | for index in gen_simple_index(): |
||
| 1351 | if index_type == index["index_type"]: |
||
| 1352 | connect.create_index(collection, index_type, index["index_param"]) |
||
| 1353 | query_vecs = gen_vectors(1, dim) |
||
| 1354 | status, result = connect.search(collection, top_k, query_vecs, params=search_param) |
||
| 1355 | assert not status.OK() |
||
| 1356 | |||
| 1357 | |||
| 1358 | def check_result(result, id): |
||
| 1359 | if len(result) >= 5: |
||
| 1360 | return id in [result[0].id, result[1].id, result[2].id, result[3].id, result[4].id] |
||
| 1361 | else: |
||
| 1362 | return id in (i.id for i in result) |
||
| 1363 |
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