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import time |
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import pdb |
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import copy |
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import threading |
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import logging |
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from multiprocessing import Pool, Process |
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import pytest |
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import numpy as np |
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from milvus import DataType |
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from utils import * |
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dim = 128 |
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segment_row_count = 5000 |
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top_k_limit = 2048 |
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collection_id = "search" |
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tag = "1970-01-01" |
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insert_interval_time = 1.5 |
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nb = 6000 |
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top_k = 10 |
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nprobe = 1 |
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epsilon = 0.001 |
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field_name = default_float_vec_field_name |
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default_fields = gen_default_fields() |
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search_param = {"nprobe": 1} |
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entity = gen_entities(1, is_normal=True) |
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raw_vector, binary_entity = gen_binary_entities(1) |
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entities = gen_entities(nb, is_normal=True) |
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raw_vectors, binary_entities = gen_binary_entities(nb) |
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default_query, default_query_vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, 1) |
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def init_data(connect, collection, nb=6000, partition_tags=None): |
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''' |
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Generate entities and add it in collection |
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''' |
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global entities |
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if nb == 6000: |
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insert_entities = entities |
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else: |
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insert_entities = gen_entities(nb, is_normal=True) |
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if partition_tags is None: |
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ids = connect.insert(collection, insert_entities) |
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else: |
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ids = connect.insert(collection, insert_entities, partition_tag=partition_tags) |
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connect.flush([collection]) |
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return insert_entities, ids |
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def init_binary_data(connect, collection, nb=6000, insert=True, partition_tags=None): |
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''' |
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Generate entities and add it in collection |
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''' |
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ids = [] |
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global binary_entities |
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global raw_vectors |
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if nb == 6000: |
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insert_entities = binary_entities |
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insert_raw_vectors = raw_vectors |
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else: |
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insert_raw_vectors, insert_entities = gen_binary_entities(nb) |
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if insert is True: |
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if partition_tags is None: |
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ids = connect.insert(collection, insert_entities) |
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else: |
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ids = connect.insert(collection, insert_entities, partition_tag=partition_tags) |
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connect.flush([collection]) |
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return insert_raw_vectors, insert_entities, ids |
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68
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class TestSearchBase: |
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70
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71
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72
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""" |
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generate valid create_index params |
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""" |
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@pytest.fixture( |
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scope="function", |
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params=gen_index() |
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) |
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def get_index(self, request, connect): |
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if str(connect._cmd("mode")) == "CPU": |
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if request.param["index_type"] in index_cpu_not_support(): |
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pytest.skip("sq8h not support in CPU mode") |
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return request.param |
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85
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@pytest.fixture( |
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scope="function", |
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params=gen_simple_index() |
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) |
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def get_simple_index(self, request, connect): |
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if str(connect._cmd("mode")) == "CPU": |
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if request.param["index_type"] in index_cpu_not_support(): |
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pytest.skip("sq8h not support in CPU mode") |
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return request.param |
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95
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@pytest.fixture( |
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scope="function", |
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params=gen_simple_index() |
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) |
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def get_jaccard_index(self, request, connect): |
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logging.getLogger().info(request.param) |
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if request.param["index_type"] in binary_support(): |
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return request.param |
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else: |
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pytest.skip("Skip index Temporary") |
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106
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@pytest.fixture( |
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scope="function", |
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params=gen_simple_index() |
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) |
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def get_hamming_index(self, request, connect): |
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logging.getLogger().info(request.param) |
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if request.param["index_type"] in binary_support(): |
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return request.param |
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else: |
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pytest.skip("Skip index Temporary") |
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@pytest.fixture( |
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scope="function", |
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params=gen_simple_index() |
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) |
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def get_structure_index(self, request, connect): |
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logging.getLogger().info(request.param) |
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if request.param["index_type"] == "FLAT": |
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return request.param |
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else: |
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pytest.skip("Skip index Temporary") |
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""" |
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generate top-k params |
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""" |
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@pytest.fixture( |
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scope="function", |
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params=[1, 10, 2049] |
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) |
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def get_top_k(self, request): |
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yield request.param |
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138
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@pytest.fixture( |
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scope="function", |
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params=[1, 10, 1100] |
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141
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) |
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def get_nq(self, request): |
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yield request.param |
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def test_search_flat(self, connect, collection, get_top_k, get_nq): |
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''' |
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target: test basic search fuction, all the search params is corrent, change top-k value |
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method: search with the given vectors, check the result |
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expected: the length of the result is top_k |
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''' |
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top_k = get_top_k |
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nq = get_nq |
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entities, ids = init_data(connect, collection) |
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq) |
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if top_k <= top_k_limit: |
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res = connect.search(collection, query) |
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assert len(res[0]) == top_k |
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assert res[0]._distances[0] <= epsilon |
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assert check_id_result(res[0], ids[0]) |
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else: |
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with pytest.raises(Exception) as e: |
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res = connect.search(collection, query) |
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def test_search_field(self, connect, collection, get_top_k, get_nq): |
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''' |
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target: test basic search fuction, all the search params is corrent, change top-k value |
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method: search with the given vectors, check the result |
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expected: the length of the result is top_k |
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''' |
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top_k = get_top_k |
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nq = get_nq |
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entities, ids = init_data(connect, collection) |
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq) |
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if top_k <= top_k_limit: |
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res = connect.search(collection, query, fields=["float_vector"]) |
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assert len(res[0]) == top_k |
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assert res[0]._distances[0] <= epsilon |
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assert check_id_result(res[0], ids[0]) |
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# TODO |
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res = connect.search(collection, query, fields=["float"]) |
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# TODO |
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else: |
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with pytest.raises(Exception) as e: |
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res = connect.search(collection, query) |
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186
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@pytest.mark.level(2) |
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def test_search_after_index(self, connect, collection, get_simple_index, get_top_k, get_nq): |
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''' |
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target: test basic search fuction, all the search params is corrent, test all index params, and build |
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method: search with the given vectors, check the result |
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expected: the length of the result is top_k |
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''' |
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top_k = get_top_k |
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nq = get_nq |
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196
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index_type = get_simple_index["index_type"] |
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if index_type == "IVF_PQ": |
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pytest.skip("Skip PQ") |
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entities, ids = init_data(connect, collection) |
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connect.create_index(collection, field_name, get_simple_index) |
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search_param = get_search_param(index_type) |
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
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if top_k > top_k_limit: |
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with pytest.raises(Exception) as e: |
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res = connect.search(collection, query) |
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else: |
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res = connect.search(collection, query) |
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assert len(res) == nq |
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assert len(res[0]) >= top_k |
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assert res[0]._distances[0] < epsilon |
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assert check_id_result(res[0], ids[0]) |
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213
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def test_search_index_partition(self, connect, collection, get_simple_index, get_top_k, get_nq): |
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''' |
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target: test basic search fuction, all the search params is corrent, test all index params, and build |
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216
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method: add vectors into collection, search with the given vectors, check the result |
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expected: the length of the result is top_k, search collection with partition tag return empty |
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''' |
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top_k = get_top_k |
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nq = get_nq |
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222
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index_type = get_simple_index["index_type"] |
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if index_type == "IVF_PQ": |
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pytest.skip("Skip PQ") |
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connect.create_partition(collection, tag) |
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entities, ids = init_data(connect, collection) |
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connect.create_index(collection, field_name, get_simple_index) |
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search_param = get_search_param(index_type) |
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
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if top_k > top_k_limit: |
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231
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with pytest.raises(Exception) as e: |
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res = connect.search(collection, query) |
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else: |
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234
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res = connect.search(collection, query) |
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assert len(res) == nq |
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assert len(res[0]) >= top_k |
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assert res[0]._distances[0] < epsilon |
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assert check_id_result(res[0], ids[0]) |
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res = connect.search(collection, query, partition_tags=[tag]) |
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assert len(res) == nq |
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242
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def test_search_index_partition_B(self, connect, collection, get_simple_index, get_top_k, get_nq): |
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243
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''' |
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244
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target: test basic search fuction, all the search params is corrent, test all index params, and build |
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245
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method: search with the given vectors, check the result |
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246
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expected: the length of the result is top_k |
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247
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''' |
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248
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top_k = get_top_k |
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249
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nq = get_nq |
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250
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251
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index_type = get_simple_index["index_type"] |
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252
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if index_type == "IVF_PQ": |
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253
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pytest.skip("Skip PQ") |
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254
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connect.create_partition(collection, tag) |
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255
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entities, ids = init_data(connect, collection, partition_tags=tag) |
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256
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connect.create_index(collection, field_name, get_simple_index) |
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257
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search_param = get_search_param(index_type) |
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258
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
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259
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for tags in [[tag], [tag, "new_tag"]]: |
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260
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if top_k > top_k_limit: |
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261
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with pytest.raises(Exception) as e: |
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262
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res = connect.search(collection, query, partition_tags=tags) |
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263
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else: |
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264
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res = connect.search(collection, query, partition_tags=tags) |
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265
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assert len(res) == nq |
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266
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assert len(res[0]) >= top_k |
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267
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assert res[0]._distances[0] < epsilon |
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268
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assert check_id_result(res[0], ids[0]) |
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269
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270
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@pytest.mark.level(2) |
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271
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def test_search_index_partition_C(self, connect, collection, get_top_k, get_nq): |
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272
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''' |
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273
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target: test basic search fuction, all the search params is corrent, test all index params, and build |
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274
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method: search with the given vectors and tag (tag name not existed in collection), check the result |
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275
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expected: error raised |
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276
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''' |
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277
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top_k = get_top_k |
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278
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nq = get_nq |
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279
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entities, ids = init_data(connect, collection) |
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280
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq) |
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281
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if top_k > top_k_limit: |
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282
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with pytest.raises(Exception) as e: |
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283
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res = connect.search(collection, query, partition_tags=["new_tag"]) |
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284
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else: |
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285
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res = connect.search(collection, query, partition_tags=["new_tag"]) |
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286
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assert len(res) == nq |
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287
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assert len(res[0]) == 0 |
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288
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289
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@pytest.mark.level(2) |
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290
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def test_search_index_partitions(self, connect, collection, get_simple_index, get_top_k): |
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291
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''' |
|
292
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target: test basic search fuction, all the search params is corrent, test all index params, and build |
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293
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method: search collection with the given vectors and tags, check the result |
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294
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expected: the length of the result is top_k |
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295
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''' |
|
296
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top_k = get_top_k |
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297
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nq = 2 |
|
298
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new_tag = "new_tag" |
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299
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index_type = get_simple_index["index_type"] |
|
300
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if index_type == "IVF_PQ": |
|
301
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pytest.skip("Skip PQ") |
|
302
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connect.create_partition(collection, tag) |
|
303
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connect.create_partition(collection, new_tag) |
|
304
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entities, ids = init_data(connect, collection, partition_tags=tag) |
|
305
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|
new_entities, new_ids = init_data(connect, collection, nb=6001, partition_tags=new_tag) |
|
306
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connect.create_index(collection, field_name, get_simple_index) |
|
307
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search_param = get_search_param(index_type) |
|
308
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query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
309
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if top_k > top_k_limit: |
|
310
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with pytest.raises(Exception) as e: |
|
311
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res = connect.search(collection, query) |
|
312
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else: |
|
313
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res = connect.search(collection, query) |
|
314
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assert check_id_result(res[0], ids[0]) |
|
315
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assert not check_id_result(res[1], new_ids[0]) |
|
316
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assert res[0]._distances[0] < epsilon |
|
317
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assert res[1]._distances[0] < epsilon |
|
318
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res = connect.search(collection, query, partition_tags=["new_tag"]) |
|
319
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assert res[0]._distances[0] > epsilon |
|
320
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assert res[1]._distances[0] > epsilon |
|
321
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|
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322
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# TODO: |
|
323
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|
@pytest.mark.level(2) |
|
324
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def _test_search_index_partitions_B(self, connect, collection, get_simple_index, get_top_k): |
|
325
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|
|
''' |
|
326
|
|
|
target: test basic search fuction, all the search params is corrent, test all index params, and build |
|
327
|
|
|
method: search collection with the given vectors and tags, check the result |
|
328
|
|
|
expected: the length of the result is top_k |
|
329
|
|
|
''' |
|
330
|
|
|
top_k = get_top_k |
|
331
|
|
|
nq = 2 |
|
332
|
|
|
tag = "tag" |
|
333
|
|
|
new_tag = "new_tag" |
|
334
|
|
|
index_type = get_simple_index["index_type"] |
|
335
|
|
|
if index_type == "IVF_PQ": |
|
336
|
|
|
pytest.skip("Skip PQ") |
|
337
|
|
|
connect.create_partition(collection, tag) |
|
338
|
|
|
connect.create_partition(collection, new_tag) |
|
339
|
|
|
entities, ids = init_data(connect, collection, partition_tags=tag) |
|
340
|
|
|
new_entities, new_ids = init_data(connect, collection, nb=6001, partition_tags=new_tag) |
|
341
|
|
|
connect.create_index(collection, field_name, get_simple_index) |
|
342
|
|
|
search_param = get_search_param(index_type) |
|
343
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, new_entities, top_k, nq, search_params=search_param) |
|
344
|
|
|
if top_k > top_k_limit: |
|
345
|
|
|
with pytest.raises(Exception) as e: |
|
346
|
|
|
res = connect.search(collection, query) |
|
347
|
|
|
else: |
|
348
|
|
|
res = connect.search(collection, query, partition_tags=["(.*)tag"]) |
|
349
|
|
|
assert not check_id_result(res[0], ids[0]) |
|
350
|
|
|
assert check_id_result(res[1], new_ids[0]) |
|
351
|
|
|
assert res[0]._distances[0] > epsilon |
|
352
|
|
|
assert res[1]._distances[0] < epsilon |
|
353
|
|
|
res = connect.search(collection, query, partition_tags=["new(.*)"]) |
|
354
|
|
|
assert res[0]._distances[0] > epsilon |
|
355
|
|
|
assert res[1]._distances[0] < epsilon |
|
356
|
|
|
|
|
357
|
|
|
# |
|
358
|
|
|
# test for ip metric |
|
359
|
|
|
# |
|
360
|
|
|
@pytest.mark.level(2) |
|
361
|
|
|
def test_search_ip_flat(self, connect, ip_collection, get_simple_index, get_top_k, get_nq): |
|
362
|
|
|
''' |
|
363
|
|
|
target: test basic search fuction, all the search params is corrent, change top-k value |
|
364
|
|
|
method: search with the given vectors, check the result |
|
365
|
|
|
expected: the length of the result is top_k |
|
366
|
|
|
''' |
|
367
|
|
|
top_k = get_top_k |
|
368
|
|
|
nq = get_nq |
|
369
|
|
|
entities, ids = init_data(connect, ip_collection) |
|
370
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq) |
|
371
|
|
|
if top_k <= top_k_limit: |
|
372
|
|
|
res = connect.search(ip_collection, query) |
|
373
|
|
|
assert len(res[0]) == top_k |
|
374
|
|
|
assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) |
|
375
|
|
|
assert check_id_result(res[0], ids[0]) |
|
376
|
|
|
else: |
|
377
|
|
|
with pytest.raises(Exception) as e: |
|
378
|
|
|
res = connect.search(ip_collection, query) |
|
379
|
|
|
|
|
380
|
|
|
def test_search_ip_after_index(self, connect, ip_collection, get_simple_index, get_top_k, get_nq): |
|
381
|
|
|
''' |
|
382
|
|
|
target: test basic search fuction, all the search params is corrent, test all index params, and build |
|
383
|
|
|
method: search with the given vectors, check the result |
|
384
|
|
|
expected: the length of the result is top_k |
|
385
|
|
|
''' |
|
386
|
|
|
top_k = get_top_k |
|
387
|
|
|
nq = get_nq |
|
388
|
|
|
|
|
389
|
|
|
index_type = get_simple_index["index_type"] |
|
390
|
|
|
if index_type == "IVF_PQ": |
|
391
|
|
|
pytest.skip("Skip PQ") |
|
392
|
|
|
entities, ids = init_data(connect, ip_collection) |
|
393
|
|
|
get_simple_index["metric_type"] = "IP" |
|
394
|
|
|
connect.create_index(ip_collection, field_name, get_simple_index) |
|
395
|
|
|
search_param = get_search_param(index_type) |
|
396
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
397
|
|
|
if top_k > top_k_limit: |
|
398
|
|
|
with pytest.raises(Exception) as e: |
|
399
|
|
|
res = connect.search(ip_collection, query) |
|
400
|
|
|
else: |
|
401
|
|
|
res = connect.search(ip_collection, query) |
|
402
|
|
|
assert len(res) == nq |
|
403
|
|
|
assert len(res[0]) >= top_k |
|
404
|
|
|
assert check_id_result(res[0], ids[0]) |
|
405
|
|
|
assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) |
|
406
|
|
|
|
|
407
|
|
|
@pytest.mark.level(2) |
|
408
|
|
|
def test_search_ip_index_partition(self, connect, ip_collection, get_simple_index, get_top_k, get_nq): |
|
409
|
|
|
''' |
|
410
|
|
|
target: test basic search fuction, all the search params is corrent, test all index params, and build |
|
411
|
|
|
method: add vectors into collection, search with the given vectors, check the result |
|
412
|
|
|
expected: the length of the result is top_k, search collection with partition tag return empty |
|
413
|
|
|
''' |
|
414
|
|
|
top_k = get_top_k |
|
415
|
|
|
nq = get_nq |
|
416
|
|
|
|
|
417
|
|
|
index_type = get_simple_index["index_type"] |
|
418
|
|
|
if index_type == "IVF_PQ": |
|
419
|
|
|
pytest.skip("Skip PQ") |
|
420
|
|
|
connect.create_partition(ip_collection, tag) |
|
421
|
|
|
entities, ids = init_data(connect, ip_collection) |
|
422
|
|
|
get_simple_index["metric_type"] = "IP" |
|
423
|
|
|
connect.create_index(ip_collection, field_name, get_simple_index) |
|
424
|
|
|
search_param = get_search_param(index_type) |
|
425
|
|
|
search_param["metric_type"] = "IP" |
|
426
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
427
|
|
|
if top_k > top_k_limit: |
|
428
|
|
|
with pytest.raises(Exception) as e: |
|
429
|
|
|
res = connect.search(ip_collection, query) |
|
430
|
|
|
else: |
|
431
|
|
|
res = connect.search(ip_collection, query) |
|
432
|
|
|
assert len(res) == nq |
|
433
|
|
|
assert len(res[0]) >= top_k |
|
434
|
|
|
assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) |
|
435
|
|
|
assert check_id_result(res[0], ids[0]) |
|
436
|
|
|
res = connect.search(ip_collection, query, partition_tags=[tag]) |
|
437
|
|
|
assert len(res) == nq |
|
438
|
|
|
|
|
439
|
|
|
@pytest.mark.level(2) |
|
440
|
|
|
def test_search_ip_index_partitions(self, connect, ip_collection, get_simple_index, get_top_k): |
|
441
|
|
|
''' |
|
442
|
|
|
target: test basic search fuction, all the search params is corrent, test all index params, and build |
|
443
|
|
|
method: search ip_collection with the given vectors and tags, check the result |
|
444
|
|
|
expected: the length of the result is top_k |
|
445
|
|
|
''' |
|
446
|
|
|
top_k = get_top_k |
|
447
|
|
|
nq = 2 |
|
448
|
|
|
new_tag = "new_tag" |
|
449
|
|
|
index_type = get_simple_index["index_type"] |
|
450
|
|
|
if index_type == "IVF_PQ": |
|
451
|
|
|
pytest.skip("Skip PQ") |
|
452
|
|
|
connect.create_partition(ip_collection, tag) |
|
453
|
|
|
connect.create_partition(ip_collection, new_tag) |
|
454
|
|
|
entities, ids = init_data(connect, ip_collection, partition_tags=tag) |
|
455
|
|
|
new_entities, new_ids = init_data(connect, ip_collection, nb=6001, partition_tags=new_tag) |
|
456
|
|
|
get_simple_index["metric_type"] = "IP" |
|
457
|
|
|
connect.create_index(ip_collection, field_name, get_simple_index) |
|
458
|
|
|
search_param = get_search_param(index_type) |
|
459
|
|
|
search_param["metric_type"] = "IP" |
|
460
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
461
|
|
|
if top_k > top_k_limit: |
|
462
|
|
|
with pytest.raises(Exception) as e: |
|
463
|
|
|
res = connect.search(ip_collection, query) |
|
464
|
|
|
else: |
|
465
|
|
|
res = connect.search(ip_collection, query) |
|
466
|
|
|
assert check_id_result(res[0], ids[0]) |
|
467
|
|
|
assert not check_id_result(res[1], new_ids[0]) |
|
468
|
|
|
assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) |
|
469
|
|
|
assert res[1]._distances[0] >= 1 - gen_inaccuracy(res[1]._distances[0]) |
|
470
|
|
|
res = connect.search(ip_collection, query, partition_tags=["new_tag"]) |
|
471
|
|
|
assert res[0]._distances[0] < 1 - gen_inaccuracy(res[0]._distances[0]) |
|
472
|
|
|
# TODO: |
|
473
|
|
|
# assert res[1]._distances[0] >= 1 - gen_inaccuracy(res[1]._distances[0]) |
|
474
|
|
|
|
|
475
|
|
|
@pytest.mark.level(2) |
|
476
|
|
|
def test_search_without_connect(self, dis_connect, collection): |
|
477
|
|
|
''' |
|
478
|
|
|
target: test search vectors without connection |
|
479
|
|
|
method: use dis connected instance, call search method and check if search successfully |
|
480
|
|
|
expected: raise exception |
|
481
|
|
|
''' |
|
482
|
|
|
with pytest.raises(Exception) as e: |
|
483
|
|
|
res = dis_connect.search(collection, default_query) |
|
484
|
|
|
|
|
485
|
|
|
def test_search_collection_name_not_existed(self, connect): |
|
486
|
|
|
''' |
|
487
|
|
|
target: search collection not existed |
|
488
|
|
|
method: search with the random collection_name, which is not in db |
|
489
|
|
|
expected: status not ok |
|
490
|
|
|
''' |
|
491
|
|
|
collection_name = gen_unique_str(collection_id) |
|
492
|
|
|
with pytest.raises(Exception) as e: |
|
493
|
|
|
res = connect.search(collection_name, default_query) |
|
494
|
|
|
|
|
495
|
|
|
def test_search_distance_l2(self, connect, collection): |
|
496
|
|
|
''' |
|
497
|
|
|
target: search collection, and check the result: distance |
|
498
|
|
|
method: compare the return distance value with value computed with Euclidean |
|
499
|
|
|
expected: the return distance equals to the computed value |
|
500
|
|
|
''' |
|
501
|
|
|
nq = 2 |
|
502
|
|
|
search_param = {"nprobe" : 1} |
|
503
|
|
|
entities, ids = init_data(connect, collection, nb=nq) |
|
504
|
|
|
query, vecs = gen_query_vectors_rand_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
505
|
|
|
inside_query, inside_vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
506
|
|
|
distance_0 = l2(vecs[0], inside_vecs[0]) |
|
507
|
|
|
distance_1 = l2(vecs[0], inside_vecs[1]) |
|
508
|
|
|
res = connect.search(collection, query) |
|
509
|
|
|
assert abs(np.sqrt(res[0]._distances[0]) - min(distance_0, distance_1)) <= gen_inaccuracy(res[0]._distances[0]) |
|
510
|
|
|
|
|
511
|
|
|
# TODO: distance problem |
|
512
|
|
|
def _test_search_distance_l2_after_index(self, connect, collection, get_simple_index): |
|
513
|
|
|
''' |
|
514
|
|
|
target: search collection, and check the result: distance |
|
515
|
|
|
method: compare the return distance value with value computed with Inner product |
|
516
|
|
|
expected: the return distance equals to the computed value |
|
517
|
|
|
''' |
|
518
|
|
|
index_type = get_simple_index["index_type"] |
|
519
|
|
|
nq = 2 |
|
520
|
|
|
entities, ids = init_data(connect, collection) |
|
521
|
|
|
connect.create_index(collection, field_name, get_simple_index) |
|
522
|
|
|
search_param = get_search_param(index_type) |
|
523
|
|
|
query, vecs = gen_query_vectors_rand_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
524
|
|
|
inside_vecs = entities[-1]["values"] |
|
525
|
|
|
min_distance = 1.0 |
|
526
|
|
|
for i in range(nb): |
|
527
|
|
|
tmp_dis = l2(vecs[0], inside_vecs[i]) |
|
528
|
|
|
if min_distance > tmp_dis: |
|
529
|
|
|
min_distance = tmp_dis |
|
530
|
|
|
res = connect.search(collection, query) |
|
531
|
|
|
assert abs(np.sqrt(res[0]._distances[0]) - min_distance) <= gen_inaccuracy(res[0]._distances[0]) |
|
532
|
|
|
|
|
533
|
|
|
def test_search_distance_ip(self, connect, ip_collection): |
|
534
|
|
|
''' |
|
535
|
|
|
target: search ip_collection, and check the result: distance |
|
536
|
|
|
method: compare the return distance value with value computed with Inner product |
|
537
|
|
|
expected: the return distance equals to the computed value |
|
538
|
|
|
''' |
|
539
|
|
|
nq = 2 |
|
540
|
|
|
search_param = {"nprobe" : 1} |
|
541
|
|
|
entities, ids = init_data(connect, ip_collection, nb=nq) |
|
542
|
|
|
query, vecs = gen_query_vectors_rand_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
543
|
|
|
inside_query, inside_vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
544
|
|
|
distance_0 = ip(vecs[0], inside_vecs[0]) |
|
545
|
|
|
distance_1 = ip(vecs[0], inside_vecs[1]) |
|
546
|
|
|
res = connect.search(ip_collection, query) |
|
547
|
|
|
assert abs(res[0]._distances[0] - max(distance_0, distance_1)) <= gen_inaccuracy(res[0]._distances[0]) |
|
548
|
|
|
|
|
549
|
|
|
# TODO: distance problem |
|
550
|
|
|
def _test_search_distance_ip_after_index(self, connect, ip_collection, get_simple_index): |
|
551
|
|
|
''' |
|
552
|
|
|
target: search collection, and check the result: distance |
|
553
|
|
|
method: compare the return distance value with value computed with Inner product |
|
554
|
|
|
expected: the return distance equals to the computed value |
|
555
|
|
|
''' |
|
556
|
|
|
index_type = get_simple_index["index_type"] |
|
557
|
|
|
nq = 2 |
|
558
|
|
|
entities, ids = init_data(connect, ip_collection) |
|
559
|
|
|
get_simple_index["metric_type"] = "IP" |
|
560
|
|
|
connect.create_index(ip_collection, field_name, get_simple_index) |
|
561
|
|
|
search_param = get_search_param(index_type) |
|
562
|
|
|
search_param["metric_type"] = "IP" |
|
563
|
|
|
query, vecs = gen_query_vectors_rand_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
564
|
|
|
inside_vecs = entities[-1]["values"] |
|
565
|
|
|
max_distance = 0 |
|
566
|
|
|
for i in range(nb): |
|
567
|
|
|
tmp_dis = ip(vecs[0], inside_vecs[i]) |
|
568
|
|
|
if max_distance < tmp_dis: |
|
569
|
|
|
max_distance = tmp_dis |
|
570
|
|
|
res = connect.search(ip_collection, query) |
|
571
|
|
|
assert abs(res[0]._distances[0] - max_distance) <= gen_inaccuracy(res[0]._distances[0]) |
|
572
|
|
|
|
|
573
|
|
|
# TODO: |
|
574
|
|
|
def _test_search_distance_jaccard_flat_index(self, connect, jac_collection): |
|
575
|
|
|
''' |
|
576
|
|
|
target: search ip_collection, and check the result: distance |
|
577
|
|
|
method: compare the return distance value with value computed with Inner product |
|
578
|
|
|
expected: the return distance equals to the computed value |
|
579
|
|
|
''' |
|
580
|
|
|
# from scipy.spatial import distance |
|
581
|
|
|
nprobe = 512 |
|
582
|
|
|
int_vectors, entities, ids = init_binary_data(connect, jac_collection, nb=2) |
|
583
|
|
|
query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, jac_collection, nb=1, insert=False) |
|
584
|
|
|
distance_0 = jaccard(query_int_vectors[0], int_vectors[0]) |
|
585
|
|
|
distance_1 = jaccard(query_int_vectors[0], int_vectors[1]) |
|
586
|
|
|
res = connect.search(jac_collection, query_entities) |
|
587
|
|
|
assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon |
|
588
|
|
|
|
|
589
|
|
|
def _test_search_distance_hamming_flat_index(self, connect, ham_collection): |
|
590
|
|
|
''' |
|
591
|
|
|
target: search ip_collection, and check the result: distance |
|
592
|
|
|
method: compare the return distance value with value computed with Inner product |
|
593
|
|
|
expected: the return distance equals to the computed value |
|
594
|
|
|
''' |
|
595
|
|
|
# from scipy.spatial import distance |
|
596
|
|
|
nprobe = 512 |
|
597
|
|
|
int_vectors, entities, ids = init_binary_data(connect, ham_collection, nb=2) |
|
598
|
|
|
query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, ham_collection, nb=1, insert=False) |
|
599
|
|
|
distance_0 = hamming(query_int_vectors[0], int_vectors[0]) |
|
600
|
|
|
distance_1 = hamming(query_int_vectors[0], int_vectors[1]) |
|
601
|
|
|
res = connect.search(ham_collection, query_entities) |
|
602
|
|
|
assert abs(res[0][0].distance - min(distance_0, distance_1).astype(float)) <= epsilon |
|
603
|
|
|
|
|
604
|
|
|
def _test_search_distance_substructure_flat_index(self, connect, substructure_collection): |
|
605
|
|
|
''' |
|
606
|
|
|
target: search ip_collection, and check the result: distance |
|
607
|
|
|
method: compare the return distance value with value computed with Inner product |
|
608
|
|
|
expected: the return distance equals to the computed value |
|
609
|
|
|
''' |
|
610
|
|
|
# from scipy.spatial import distance |
|
611
|
|
|
nprobe = 512 |
|
612
|
|
|
int_vectors, vectors, ids = self.init_binary_data(connect, substructure_collection, nb=2) |
|
613
|
|
|
index_type = "FLAT" |
|
614
|
|
|
index_param = { |
|
615
|
|
|
"nlist": 16384, |
|
616
|
|
|
"metric_type": "SUBSTRUCTURE" |
|
617
|
|
|
} |
|
618
|
|
|
connect.create_index(substructure_collection, binary_field_name, index_param) |
|
619
|
|
|
logging.getLogger().info(connect.get_collection_info(substructure_collection)) |
|
620
|
|
|
logging.getLogger().info(connect.get_index_info(substructure_collection)) |
|
621
|
|
|
query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, substructure_collection, nb=1, insert=False) |
|
622
|
|
|
distance_0 = substructure(query_int_vectors[0], int_vectors[0]) |
|
623
|
|
|
distance_1 = substructure(query_int_vectors[0], int_vectors[1]) |
|
624
|
|
|
search_param = get_search_param(index_type) |
|
625
|
|
|
status, result = connect.search(substructure_collection, top_k, query_vecs, params=search_param) |
|
626
|
|
|
logging.getLogger().info(status) |
|
627
|
|
|
logging.getLogger().info(result) |
|
628
|
|
|
assert len(result[0]) == 0 |
|
629
|
|
|
|
|
630
|
|
|
def _test_search_distance_substructure_flat_index_B(self, connect, substructure_collection): |
|
631
|
|
|
''' |
|
632
|
|
|
target: search ip_collection, and check the result: distance |
|
633
|
|
|
method: compare the return distance value with value computed with SUB |
|
634
|
|
|
expected: the return distance equals to the computed value |
|
635
|
|
|
''' |
|
636
|
|
|
# from scipy.spatial import distance |
|
637
|
|
|
top_k = 3 |
|
638
|
|
|
nprobe = 512 |
|
639
|
|
|
int_vectors, vectors, ids = self.init_binary_data(connect, substructure_collection, nb=2) |
|
640
|
|
|
index_type = "FLAT" |
|
641
|
|
|
index_param = { |
|
642
|
|
|
"nlist": 16384, |
|
643
|
|
|
"metric_type": "SUBSTRUCTURE" |
|
644
|
|
|
} |
|
645
|
|
|
connect.create_index(substructure_collection, binary_field_name, index_param) |
|
646
|
|
|
logging.getLogger().info(connect.get_collection_info(substructure_collection)) |
|
647
|
|
|
logging.getLogger().info(connect.get_index_info(substructure_collection)) |
|
648
|
|
|
query_int_vectors, query_vecs = gen_binary_sub_vectors(int_vectors, 2) |
|
649
|
|
|
search_param = get_search_param(index_type) |
|
650
|
|
|
status, result = connect.search(substructure_collection, top_k, query_vecs, params=search_param) |
|
651
|
|
|
logging.getLogger().info(status) |
|
652
|
|
|
logging.getLogger().info(result) |
|
653
|
|
|
assert len(result[0]) == 1 |
|
654
|
|
|
assert len(result[1]) == 1 |
|
655
|
|
|
assert result[0][0].distance <= epsilon |
|
656
|
|
|
assert result[0][0].id == ids[0] |
|
657
|
|
|
assert result[1][0].distance <= epsilon |
|
658
|
|
|
assert result[1][0].id == ids[1] |
|
659
|
|
|
|
|
660
|
|
|
def _test_search_distance_superstructure_flat_index(self, connect, superstructure_collection): |
|
661
|
|
|
''' |
|
662
|
|
|
target: search ip_collection, and check the result: distance |
|
663
|
|
|
method: compare the return distance value with value computed with Inner product |
|
664
|
|
|
expected: the return distance equals to the computed value |
|
665
|
|
|
''' |
|
666
|
|
|
# from scipy.spatial import distance |
|
667
|
|
|
nprobe = 512 |
|
668
|
|
|
int_vectors, vectors, ids = self.init_binary_data(connect, superstructure_collection, nb=2) |
|
669
|
|
|
index_type = "FLAT" |
|
670
|
|
|
index_param = { |
|
671
|
|
|
"nlist": 16384, |
|
672
|
|
|
"metric_type": "SUBSTRUCTURE" |
|
673
|
|
|
} |
|
674
|
|
|
connect.create_index(superstructure_collection, binary_field_name, index_param) |
|
675
|
|
|
logging.getLogger().info(connect.get_collection_info(superstructure_collection)) |
|
676
|
|
|
logging.getLogger().info(connect.get_index_info(superstructure_collection)) |
|
677
|
|
|
query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, superstructure_collection, nb=1, insert=False) |
|
678
|
|
|
distance_0 = superstructure(query_int_vectors[0], int_vectors[0]) |
|
679
|
|
|
distance_1 = superstructure(query_int_vectors[0], int_vectors[1]) |
|
680
|
|
|
search_param = get_search_param(index_type) |
|
681
|
|
|
status, result = connect.search(superstructure_collection, top_k, query_vecs, params=search_param) |
|
682
|
|
|
logging.getLogger().info(status) |
|
683
|
|
|
logging.getLogger().info(result) |
|
684
|
|
|
assert len(result[0]) == 0 |
|
685
|
|
|
|
|
686
|
|
|
def _test_search_distance_superstructure_flat_index_B(self, connect, superstructure_collection): |
|
687
|
|
|
''' |
|
688
|
|
|
target: search ip_collection, and check the result: distance |
|
689
|
|
|
method: compare the return distance value with value computed with SUPER |
|
690
|
|
|
expected: the return distance equals to the computed value |
|
691
|
|
|
''' |
|
692
|
|
|
# from scipy.spatial import distance |
|
693
|
|
|
top_k = 3 |
|
694
|
|
|
nprobe = 512 |
|
695
|
|
|
int_vectors, vectors, ids = self.init_binary_data(connect, superstructure_collection, nb=2) |
|
696
|
|
|
index_type = "FLAT" |
|
697
|
|
|
index_param = { |
|
698
|
|
|
"nlist": 16384, |
|
699
|
|
|
"metric_type": "SUBSTRUCTURE" |
|
700
|
|
|
} |
|
701
|
|
|
connect.create_index(superstructure_collection, binary_field_name, index_param) |
|
702
|
|
|
logging.getLogger().info(connect.get_collection_info(superstructure_collection)) |
|
703
|
|
|
logging.getLogger().info(connect.get_index_info(superstructure_collection)) |
|
704
|
|
|
query_int_vectors, query_vecs = gen_binary_super_vectors(int_vectors, 2) |
|
705
|
|
|
search_param = get_search_param(index_type) |
|
706
|
|
|
status, result = connect.search(superstructure_collection, top_k, query_vecs, params=search_param) |
|
707
|
|
|
logging.getLogger().info(status) |
|
708
|
|
|
logging.getLogger().info(result) |
|
709
|
|
|
assert len(result[0]) == 2 |
|
710
|
|
|
assert len(result[1]) == 2 |
|
711
|
|
|
assert result[0][0].id in ids |
|
712
|
|
|
assert result[0][0].distance <= epsilon |
|
713
|
|
|
assert result[1][0].id in ids |
|
714
|
|
|
assert result[1][0].distance <= epsilon |
|
715
|
|
|
|
|
716
|
|
|
def _test_search_distance_tanimoto_flat_index(self, connect, tanimoto_collection): |
|
717
|
|
|
''' |
|
718
|
|
|
target: search ip_collection, and check the result: distance |
|
719
|
|
|
method: compare the return distance value with value computed with Inner product |
|
720
|
|
|
expected: the return distance equals to the computed value |
|
721
|
|
|
''' |
|
722
|
|
|
# from scipy.spatial import distance |
|
723
|
|
|
nprobe = 512 |
|
724
|
|
|
int_vectors, vectors, ids = self.init_binary_data(connect, tanimoto_collection, nb=2) |
|
725
|
|
|
index_type = "FLAT" |
|
726
|
|
|
index_param = { |
|
727
|
|
|
"nlist": 16384, |
|
728
|
|
|
"metric_type": "TANIMOTO" |
|
729
|
|
|
} |
|
730
|
|
|
connect.create_index(tanimoto_collection, binary_field_name, index_param) |
|
731
|
|
|
logging.getLogger().info(connect.get_collection_info(tanimoto_collection)) |
|
732
|
|
|
logging.getLogger().info(connect.get_index_info(tanimoto_collection)) |
|
733
|
|
|
query_int_vectors, query_vecs, tmp_ids = self.init_binary_data(connect, tanimoto_collection, nb=1, insert=False) |
|
734
|
|
|
distance_0 = tanimoto(query_int_vectors[0], int_vectors[0]) |
|
735
|
|
|
distance_1 = tanimoto(query_int_vectors[0], int_vectors[1]) |
|
736
|
|
|
search_param = get_search_param(index_type) |
|
737
|
|
|
status, result = connect.search(tanimoto_collection, top_k, query_vecs, params=search_param) |
|
738
|
|
|
logging.getLogger().info(status) |
|
739
|
|
|
logging.getLogger().info(result) |
|
740
|
|
|
assert abs(result[0][0].distance - min(distance_0, distance_1)) <= epsilon |
|
741
|
|
|
|
|
742
|
|
|
@pytest.mark.timeout(30) |
|
743
|
|
|
def test_search_concurrent_multithreads(self, connect, args): |
|
744
|
|
|
''' |
|
745
|
|
|
target: test concurrent search with multiprocessess |
|
746
|
|
|
method: search with 10 processes, each process uses dependent connection |
|
747
|
|
|
expected: status ok and the returned vectors should be query_records |
|
748
|
|
|
''' |
|
749
|
|
|
nb = 100 |
|
750
|
|
|
top_k = 10 |
|
751
|
|
|
threads_num = 4 |
|
752
|
|
|
threads = [] |
|
753
|
|
|
collection = gen_unique_str(collection_id) |
|
754
|
|
|
uri = "tcp://%s:%s" % (args["ip"], args["port"]) |
|
755
|
|
|
# create collection |
|
756
|
|
|
milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
|
757
|
|
|
milvus.create_collection(collection, default_fields) |
|
758
|
|
|
entities, ids = init_data(milvus, collection) |
|
759
|
|
|
def search(milvus): |
|
760
|
|
|
res = connect.search(collection, default_query) |
|
761
|
|
|
assert len(res) == 1 |
|
762
|
|
|
assert res[0]._entities[0].id in ids |
|
763
|
|
|
assert res[0]._distances[0] < epsilon |
|
764
|
|
|
for i in range(threads_num): |
|
765
|
|
|
milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
|
766
|
|
|
t = threading.Thread(target=search, args=(milvus, )) |
|
767
|
|
|
threads.append(t) |
|
768
|
|
|
t.start() |
|
769
|
|
|
time.sleep(0.2) |
|
770
|
|
|
for t in threads: |
|
771
|
|
|
t.join() |
|
772
|
|
|
|
|
773
|
|
|
@pytest.mark.timeout(30) |
|
774
|
|
|
def test_search_concurrent_multithreads_single_connection(self, connect, args): |
|
775
|
|
|
''' |
|
776
|
|
|
target: test concurrent search with multiprocessess |
|
777
|
|
|
method: search with 10 processes, each process uses dependent connection |
|
778
|
|
|
expected: status ok and the returned vectors should be query_records |
|
779
|
|
|
''' |
|
780
|
|
|
nb = 100 |
|
781
|
|
|
top_k = 10 |
|
782
|
|
|
threads_num = 4 |
|
783
|
|
|
threads = [] |
|
784
|
|
|
collection = gen_unique_str(collection_id) |
|
785
|
|
|
uri = "tcp://%s:%s" % (args["ip"], args["port"]) |
|
786
|
|
|
# create collection |
|
787
|
|
|
milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) |
|
788
|
|
|
milvus.create_collection(collection, default_fields) |
|
789
|
|
|
entities, ids = init_data(milvus, collection) |
|
790
|
|
|
def search(milvus): |
|
791
|
|
|
res = connect.search(collection, default_query) |
|
792
|
|
|
assert len(res) == 1 |
|
793
|
|
|
assert res[0]._entities[0].id in ids |
|
794
|
|
|
assert res[0]._distances[0] < epsilon |
|
795
|
|
|
for i in range(threads_num): |
|
796
|
|
|
t = threading.Thread(target=search, args=(milvus, )) |
|
797
|
|
|
threads.append(t) |
|
798
|
|
|
t.start() |
|
799
|
|
|
time.sleep(0.2) |
|
800
|
|
|
for t in threads: |
|
801
|
|
|
t.join() |
|
802
|
|
|
|
|
803
|
|
|
def test_search_multi_collections(self, connect, args): |
|
804
|
|
|
''' |
|
805
|
|
|
target: test search multi collections of L2 |
|
806
|
|
|
method: add vectors into 10 collections, and search |
|
807
|
|
|
expected: search status ok, the length of result |
|
808
|
|
|
''' |
|
809
|
|
|
num = 10 |
|
810
|
|
|
top_k = 10 |
|
811
|
|
|
nq = 20 |
|
812
|
|
|
for i in range(num): |
|
813
|
|
|
collection = gen_unique_str(collection_id+str(i)) |
|
814
|
|
|
connect.create_collection(collection, default_fields) |
|
815
|
|
|
entities, ids = init_data(connect, collection) |
|
816
|
|
|
assert len(ids) == nb |
|
817
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, nq, search_params=search_param) |
|
818
|
|
|
res = connect.search(collection, query) |
|
819
|
|
|
assert len(res) == nq |
|
820
|
|
|
for i in range(nq): |
|
821
|
|
|
assert check_id_result(res[i], ids[i]) |
|
822
|
|
|
assert res[i]._distances[0] < epsilon |
|
823
|
|
|
assert res[i]._distances[1] > epsilon |
|
824
|
|
|
|
|
825
|
|
|
|
|
826
|
|
|
class TestSearchDSL(object): |
|
827
|
|
|
|
|
828
|
|
|
""" |
|
829
|
|
|
****************************************************************** |
|
830
|
|
|
# The following cases are used to build invalid query expr |
|
831
|
|
|
****************************************************************** |
|
832
|
|
|
""" |
|
833
|
|
|
|
|
834
|
|
|
# TODO: assert exception |
|
835
|
|
|
def test_query_no_must(self, connect, collection): |
|
836
|
|
|
''' |
|
837
|
|
|
method: build query without must expr |
|
838
|
|
|
expected: error raised |
|
839
|
|
|
''' |
|
840
|
|
|
# entities, ids = init_data(connect, collection) |
|
841
|
|
|
query = update_query_expr(default_query, keep_old=False): |
|
842
|
|
|
with pytest.raises(Exception) as e: |
|
843
|
|
|
res = connect.search(collection, query) |
|
844
|
|
|
|
|
845
|
|
|
# TODO: |
|
846
|
|
|
def test_query_no_vector_term_only(self, connect, collection): |
|
847
|
|
|
''' |
|
848
|
|
|
method: build query without must expr |
|
849
|
|
|
expected: error raised |
|
850
|
|
|
''' |
|
851
|
|
|
# entities, ids = init_data(connect, collection) |
|
852
|
|
|
expr = { |
|
853
|
|
|
"must": [gen_default_term_expr] |
|
854
|
|
|
} |
|
855
|
|
|
query = update_query_expr(default_query, keep_old=False, expr=expr): |
|
856
|
|
|
with pytest.raises(Exception) as e: |
|
857
|
|
|
res = connect.search(collection, query) |
|
858
|
|
|
|
|
859
|
|
|
def test_query_wrong_format(self, connect, collection): |
|
860
|
|
|
''' |
|
861
|
|
|
method: build query without must expr, with wrong expr name |
|
862
|
|
|
expected: error raised |
|
863
|
|
|
''' |
|
864
|
|
|
# entities, ids = init_data(connect, collection) |
|
865
|
|
|
expr = { |
|
866
|
|
|
"must1": [gen_default_term_expr] |
|
867
|
|
|
} |
|
868
|
|
|
query = update_query_expr(default_query, keep_old=False, expr=expr): |
|
869
|
|
|
with pytest.raises(Exception) as e: |
|
870
|
|
|
res = connect.search(collection, query) |
|
871
|
|
|
|
|
872
|
|
|
def test_query_empty(self, connect, collection): |
|
873
|
|
|
''' |
|
874
|
|
|
method: search with empty query |
|
875
|
|
|
expected: error raised |
|
876
|
|
|
''' |
|
877
|
|
|
query = {} |
|
878
|
|
|
with pytest.raises(Exception) as e: |
|
879
|
|
|
res = connect.search(collection, query) |
|
880
|
|
|
|
|
881
|
|
|
def test_query_with_wrong_format_term(self, connect, collection): |
|
882
|
|
|
''' |
|
883
|
|
|
method: build query with wrong term expr |
|
884
|
|
|
expected: error raised |
|
885
|
|
|
''' |
|
886
|
|
|
expr = gen_default_term_expr |
|
887
|
|
|
expr["term"] = 1 |
|
888
|
|
|
query = update_query_expr(default_query, expr=expr) |
|
889
|
|
|
with pytest.raises(Exception) as e: |
|
890
|
|
|
res = connect.search(collection, query) |
|
891
|
|
|
|
|
892
|
|
|
|
|
893
|
|
|
""" |
|
894
|
|
|
****************************************************************** |
|
895
|
|
|
# The following cases are used to test `search` function |
|
896
|
|
|
# with invalid collection_name, or invalid query expr |
|
897
|
|
|
****************************************************************** |
|
898
|
|
|
""" |
|
899
|
|
|
|
|
900
|
|
|
class TestSearchInvalid(object): |
|
901
|
|
|
|
|
902
|
|
|
""" |
|
903
|
|
|
Test search collection with invalid collection names |
|
904
|
|
|
""" |
|
905
|
|
|
@pytest.fixture( |
|
906
|
|
|
scope="function", |
|
907
|
|
|
params=gen_invalid_strs() |
|
908
|
|
|
) |
|
909
|
|
|
def get_collection_name(self, request): |
|
910
|
|
|
yield request.param |
|
911
|
|
|
|
|
912
|
|
|
@pytest.fixture( |
|
913
|
|
|
scope="function", |
|
914
|
|
|
params=gen_invalid_strs() |
|
915
|
|
|
) |
|
916
|
|
|
def get_invalid_tag(self, request): |
|
917
|
|
|
yield request.param |
|
918
|
|
|
|
|
919
|
|
|
@pytest.fixture( |
|
920
|
|
|
scope="function", |
|
921
|
|
|
params=gen_invalid_strs() |
|
922
|
|
|
) |
|
923
|
|
|
def get_invalid_field(self, request): |
|
924
|
|
|
yield request.param |
|
925
|
|
|
|
|
926
|
|
|
@pytest.fixture( |
|
927
|
|
|
scope="function", |
|
928
|
|
|
params=gen_simple_index() |
|
929
|
|
|
) |
|
930
|
|
|
def get_simple_index(self, request, connect): |
|
931
|
|
|
if str(connect._cmd("mode")) == "CPU": |
|
932
|
|
|
if request.param["index_type"] in index_cpu_not_support(): |
|
933
|
|
|
pytest.skip("sq8h not support in CPU mode") |
|
934
|
|
|
return request.param |
|
935
|
|
|
|
|
936
|
|
|
@pytest.mark.level(2) |
|
937
|
|
|
def test_search_with_invalid_collection(self, connect, get_collection_name): |
|
938
|
|
|
collection_name = get_collection_name |
|
939
|
|
|
with pytest.raises(Exception) as e: |
|
940
|
|
|
res = connect.search(collection_name, default_query) |
|
941
|
|
|
|
|
942
|
|
|
@pytest.mark.level(1) |
|
943
|
|
|
def test_search_with_invalid_tag(self, connect, collection): |
|
944
|
|
|
tag = " " |
|
945
|
|
|
with pytest.raises(Exception) as e: |
|
946
|
|
|
res = connect.search(collection, default_query, partition_tags=tag) |
|
947
|
|
|
|
|
948
|
|
|
@pytest.mark.level(2) |
|
949
|
|
|
def test_search_with_invalid_field_name(self, connect, collection, get_invalid_field): |
|
950
|
|
|
fields = [get_invalid_field] |
|
951
|
|
|
with pytest.raises(Exception) as e: |
|
952
|
|
|
res = connect.search(collection, default_query, fields=fields) |
|
953
|
|
|
|
|
954
|
|
|
@pytest.mark.level(1) |
|
955
|
|
|
def test_search_with_not_existed_field_name(self, connect, collection): |
|
956
|
|
|
fields = [gen_unique_str("field_name")] |
|
957
|
|
|
with pytest.raises(Exception) as e: |
|
958
|
|
|
res = connect.search(collection, default_query, fields=fields) |
|
959
|
|
|
|
|
960
|
|
|
""" |
|
961
|
|
|
Test search collection with invalid query |
|
962
|
|
|
""" |
|
963
|
|
|
@pytest.fixture( |
|
964
|
|
|
scope="function", |
|
965
|
|
|
params=gen_invalid_ints() |
|
966
|
|
|
) |
|
967
|
|
|
def get_top_k(self, request): |
|
968
|
|
|
yield request.param |
|
969
|
|
|
|
|
970
|
|
|
@pytest.mark.level(1) |
|
971
|
|
|
def test_search_with_invalid_top_k(self, connect, collection, get_top_k): |
|
972
|
|
|
''' |
|
973
|
|
|
target: test search fuction, with the wrong top_k |
|
974
|
|
|
method: search with top_k |
|
975
|
|
|
expected: raise an error, and the connection is normal |
|
976
|
|
|
''' |
|
977
|
|
|
top_k = get_top_k |
|
978
|
|
|
default_query["bool"]["must"][0]["vector"][field_name]["topk"] = top_k |
|
979
|
|
|
with pytest.raises(Exception) as e: |
|
980
|
|
|
res = connect.search(collection, default_query) |
|
981
|
|
|
|
|
982
|
|
|
""" |
|
983
|
|
|
Test search collection with invalid search params |
|
984
|
|
|
""" |
|
985
|
|
|
@pytest.fixture( |
|
986
|
|
|
scope="function", |
|
987
|
|
|
params=gen_invaild_search_params() |
|
988
|
|
|
) |
|
989
|
|
|
def get_search_params(self, request): |
|
990
|
|
|
yield request.param |
|
991
|
|
|
|
|
992
|
|
|
# TODO: This case can all pass, but it's too slow |
|
993
|
|
|
@pytest.mark.level(2) |
|
994
|
|
|
def _test_search_with_invalid_params(self, connect, collection, get_simple_index, get_search_params): |
|
995
|
|
|
''' |
|
996
|
|
|
target: test search fuction, with the wrong nprobe |
|
997
|
|
|
method: search with nprobe |
|
998
|
|
|
expected: raise an error, and the connection is normal |
|
999
|
|
|
''' |
|
1000
|
|
|
search_params = get_search_params |
|
1001
|
|
|
index_type = get_simple_index["index_type"] |
|
1002
|
|
|
entities, ids = init_data(connect, collection) |
|
1003
|
|
|
connect.create_index(collection, field_name, get_simple_index) |
|
1004
|
|
|
if search_params["index_type"] != index_type: |
|
1005
|
|
|
pytest.skip("Skip case") |
|
1006
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, 1, search_params=search_params["search_params"]) |
|
1007
|
|
|
with pytest.raises(Exception) as e: |
|
1008
|
|
|
res = connect.search(collection, query) |
|
1009
|
|
|
|
|
1010
|
|
|
def test_search_with_empty_params(self, connect, collection, args, get_simple_index): |
|
1011
|
|
|
''' |
|
1012
|
|
|
target: test search fuction, with empty search params |
|
1013
|
|
|
method: search with params |
|
1014
|
|
|
expected: raise an error, and the connection is normal |
|
1015
|
|
|
''' |
|
1016
|
|
|
index_type = get_simple_index["index_type"] |
|
1017
|
|
|
if args["handler"] == "HTTP": |
|
1018
|
|
|
pytest.skip("skip in http mode") |
|
1019
|
|
|
if index_type == "FLAT": |
|
1020
|
|
|
pytest.skip("skip in FLAT index") |
|
1021
|
|
|
entities, ids = init_data(connect, collection) |
|
1022
|
|
|
connect.create_index(collection, field_name, get_simple_index) |
|
1023
|
|
|
query, vecs = gen_query_vectors_inside_entities(field_name, entities, top_k, 1, search_params={}) |
|
1024
|
|
|
with pytest.raises(Exception) as e: |
|
1025
|
|
|
res = connect.search(collection, query) |
|
1026
|
|
|
|
|
1027
|
|
|
|
|
1028
|
|
|
def check_id_result(result, id): |
|
1029
|
|
|
limit_in = 5 |
|
1030
|
|
|
ids = [entity.id for entity in result] |
|
1031
|
|
|
if len(result) >= limit_in: |
|
1032
|
|
|
return id in ids[:limit_in] |
|
1033
|
|
|
else: |
|
1034
|
|
|
return id in ids |
|
1035
|
|
|
|
milvus-io /
milvus
