mirror of
https://gitee.com/milvus-io/milvus.git
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ee659d50db
related issue: https://github.com/milvus-io/milvus/issues/40698 --------- Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
2409 lines
130 KiB
Python
2409 lines
130 KiB
Python
import pytest
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from base.client_v2_base import TestMilvusClientV2Base
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from utils.util_log import test_log as log
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from common import common_func as cf
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from common import common_type as ct
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from common.common_type import CaseLabel, CheckTasks
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from utils.util_pymilvus import *
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from common.constants import *
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from pymilvus import DataType
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prefix = "client_search"
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partition_prefix = "client_partition"
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epsilon = ct.epsilon
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default_nb = ct.default_nb
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default_nb_medium = ct.default_nb_medium
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default_nq = ct.default_nq
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default_dim = ct.default_dim
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default_limit = ct.default_limit
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default_search_exp = "id >= 0"
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exp_res = "exp_res"
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default_search_string_exp = "varchar >= \"0\""
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default_search_mix_exp = "int64 >= 0 && varchar >= \"0\""
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default_invaild_string_exp = "varchar >= 0"
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default_json_search_exp = "json_field[\"number\"] >= 0"
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perfix_expr = 'varchar like "0%"'
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default_search_field = ct.default_float_vec_field_name
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default_search_params = ct.default_search_params
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default_primary_key_field_name = "id"
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default_vector_field_name = "vector"
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default_float_field_name = ct.default_float_field_name
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default_bool_field_name = ct.default_bool_field_name
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default_string_field_name = ct.default_string_field_name
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default_int32_array_field_name = ct.default_int32_array_field_name
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default_string_array_field_name = ct.default_string_array_field_name
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class TestMilvusClientSearchInvalid(TestMilvusClientV2Base):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=[False, True])
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def auto_id(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["COSINE", "L2"])
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def metric_type(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are invalid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_collection_name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
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def test_milvus_client_search_invalid_collection_name_string(self, invalid_collection_name):
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"""
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target: test search with invalid collection name
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method: create connection, collection, insert and search with invalid collection name
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 100,
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ct.err_msg: f"collection not found[database=default][collection={invalid_collection_name}]"}
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self.search(client, invalid_collection_name, vectors_to_search, limit=default_limit,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.skip(reason="pymilvus issue 2587")
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@pytest.mark.parametrize("invalid_collection_name", [1])
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def test_milvus_client_search_invalid_collection_name_non_string(self, invalid_collection_name):
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"""
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target: test search with invalid collection name
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method: create connection, collection, insert and search with invalid collection name
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 100,
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ct.err_msg: f"collection not found[database=default][collection={invalid_collection_name}]"}
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self.search(client, invalid_collection_name, vectors_to_search, limit=default_limit,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_data", [1, "12-s","中文", "% $#"])
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def test_milvus_client_search_invalid_data(self, invalid_data):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 100,
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ct.err_msg: f"`search_data` value {invalid_data} is illegal"}
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self.search(client, collection_name, invalid_data, limit=default_limit,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_limit", [-1, ct.min_limit-1, "1", "12-s", "中文", "%$#"])
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def test_milvus_client_search_invalid_limit(self, invalid_limit):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1,
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ct.err_msg: f"`limit` value {invalid_limit} is illegal"}
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self.search(client, collection_name, vectors_to_search, limit=invalid_limit,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_limit", [ct.max_limit+1])
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def test_milvus_client_search_limit_out_of_range(self, invalid_limit):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 65535,
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ct.err_msg: "topk [16385] is invalid, it should be in range [1, 16384], but got 16385"}
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self.search(client, collection_name, vectors_to_search, limit=invalid_limit,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_filter", ["12-s"])
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def test_milvus_client_search_invalid_filter(self, invalid_filter):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1100,
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ct.err_msg: f"failed to create query plan: predicate is not a boolean expression: {invalid_filter}, "
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f"data type: Int64: invalid parameter"}
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self.search(client, collection_name, vectors_to_search, filter=invalid_filter, limit=default_limit,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_output_fields", [1, "1"])
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def test_milvus_client_search_invalid_output_fields(self, invalid_output_fields):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1,
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ct.err_msg: f"`output_fields` value {invalid_output_fields} is illegal"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit, output_fields=invalid_output_fields,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.skip(reason="pymilvus issue 2588")
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@pytest.mark.parametrize("invalid_search_params", [1, "1"])
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def test_milvus_client_search_invalid_search_params(self, invalid_search_params):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1,
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ct.err_msg: f"`search_params` value {invalid_search_params} is illegal"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit, search_params=invalid_search_params,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_partition_names", [1, "1"])
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def test_milvus_client_search_invalid_partition_names(self, invalid_partition_names):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1,
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ct.err_msg: f"`partition_name_array` value {invalid_partition_names} is illegal"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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partition_names=invalid_partition_names,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_anns_field", [1])
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def test_milvus_client_search_invalid_anns_field(self, invalid_anns_field):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1,
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ct.err_msg: f"`anns_field` value {invalid_anns_field} is illegal"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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anns_field=invalid_anns_field,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("invalid_anns_field", ["not_exist_field"])
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def test_milvus_client_search_not_exist_anns_field(self, invalid_anns_field):
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"""
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target: test search with invalid data
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method: create connection, collection, insert and search with invalid data
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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error = {ct.err_code: 1100,
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ct.err_msg: f"failed to create query plan: failed to get field schema by name: "
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f"fieldName({invalid_anns_field}) not found: invalid parameter"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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anns_field=invalid_anns_field,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="pymilvus issue 1554")
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def test_milvus_client_collection_invalid_primary_field(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with invalid primary field
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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error = {ct.err_code: 1, ct.err_msg: f"Param id_type must be int or string"}
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self.create_collection(client, collection_name, default_dim, id_type="invalid",
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_collection_string_auto_id(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with auto id on string primary key without mx length
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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error = {ct.err_code: 65535, ct.err_msg: f"type param(max_length) should be specified for the "
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f"field({default_primary_key_field_name}) of collection {collection_name}"}
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self.create_collection(client, collection_name, default_dim, id_type="string", auto_id=True,
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L1)
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def test_milvus_client_create_same_collection_different_params(self):
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"""
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target: test high level api: client.create_collection
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method: create
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expected: 1. Successfully to create collection with same params
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2. Report errors for creating collection with same name and different params
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. create collection with same params
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self.create_collection(client, collection_name, default_dim)
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# 3. create collection with same name and different params
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error = {ct.err_code: 1, ct.err_msg: f"create duplicate collection with different parameters, "
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f"collection: {collection_name}"}
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self.create_collection(client, collection_name, default_dim + 1,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L2)
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def test_milvus_client_collection_invalid_metric_type(self):
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"""
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target: test high level api: client.create_collection
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method: create collection with auto id on string primary key
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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error = {ct.err_code: 1100,
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ct.err_msg: "float vector index does not support metric type: invalid: "
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"invalid parameter[expected=valid index params][actual=invalid index params]"}
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self.create_collection(client, collection_name, default_dim, metric_type="invalid",
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check_task=CheckTasks.err_res, check_items=error)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip("https://github.com/milvus-io/milvus/issues/29880")
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def test_milvus_client_search_not_consistent_metric_type(self, metric_type):
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"""
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target: test search with inconsistent metric type (default is IP) with that of index
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method: create connection, collection, insert and search with not consistent metric type
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expected: Raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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self.create_collection(client, collection_name, default_dim)
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# 2. search
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rng = np.random.default_rng(seed=19530)
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vectors_to_search = rng.random((1, 8))
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search_params = {"metric_type": metric_type}
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error = {ct.err_code: 1100,
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ct.err_msg: f"metric type not match: invalid parameter[expected=IP][actual={metric_type}]"}
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self.search(client, collection_name, vectors_to_search, limit=default_limit,
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search_params=search_params,
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check_task=CheckTasks.err_res, check_items=error)
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
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def test_milvus_client_search_null_expr_vector_field(self, null_expr_op):
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"""
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target: test search with null expression on vector field
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method: create connection, collection, insert and search
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expected: raise exception
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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dim = 5
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# 1. create collection
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schema = self.create_schema(client, enable_dynamic_field=False)[0]
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schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
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auto_id=False)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
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index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
null_expr = default_vector_field_name + " " + null_expr_op
|
|
error = {ct.err_code: 65535,
|
|
ct.err_msg: f"unsupported data type: VECTOR_FLOAT"}
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_not_exist_field(self, null_expr_op):
|
|
"""
|
|
target: test search with null expression on vector field
|
|
method: create connection, collection, insert and search
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
not_exist_field_name = "not_exist_field"
|
|
null_expr = not_exist_field_name + " " + null_expr_op
|
|
error = {ct.err_code: 1100,
|
|
ct.err_msg: f"failed to create query plan: cannot parse expression: "
|
|
f"{null_expr}, error: field {not_exist_field_name} not exist: invalid parameter"}
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_json_key(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on each key of json
|
|
method: create connection, collection, insert and search
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(nullable_field_name, DataType.JSON, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
vectors = cf.gen_vectors(default_nb, dim)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
|
nullable_field_name: {'a': None}} for i in range(default_nb)]
|
|
null_expr = nullable_field_name + "['a']" + " " + null_expr_op
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
|
nullable_field_name: {'a': 1, 'b': None}} for i in range(default_nb)]
|
|
null_expr = nullable_field_name + "['b']" + " " + null_expr_op
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
self.search(client, collection_name, [vectors[0]],
|
|
filter=null_expr)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_array_element(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on each key of json
|
|
method: create connection, collection, insert and search
|
|
expected: raise exception
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(nullable_field_name, DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
|
max_length=64, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
vectors = cf.gen_vectors(default_nb, dim)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
|
nullable_field_name: None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
|
nullable_field_name: [1, 2, 3]} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
null_expr = nullable_field_name + "[0]" + " " + null_expr_op
|
|
error = {ct.err_code: 65535,
|
|
ct.err_msg: f"unsupported data type: ARRAY"}
|
|
self.search(client, collection_name, [vectors[0]],
|
|
filter=null_expr,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
|
|
class TestMilvusClientSearchValid(TestMilvusClientV2Base):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are valid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
def test_milvus_client_search_query_default(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
self.using_database(client, "default")
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.describe_collection(client, collection_name,
|
|
check_task=CheckTasks.check_describe_collection_property,
|
|
check_items={"collection_name": collection_name,
|
|
"dim": default_dim,
|
|
"consistency_level": 0})
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
# assert self.num_entities(client, collection_name)[0] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
# 4. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"pk_name": default_primary_key_field_name})
|
|
self.release_collection(client, collection_name)
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.skip(reason="issue #36484")
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
def test_milvus_client_search_query_self_creation_default(self, nullable):
|
|
"""
|
|
target: test fast create collection normal case
|
|
method: create collection
|
|
expected: create collection with default schema, index, and load successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 128
|
|
# 1. create collection
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64, is_partition_key=True)
|
|
schema.add_field("nullable_field", DataType.INT64, nullable=True, default_value=10)
|
|
schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
|
max_length=64, nullable=True)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [
|
|
{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None, "array_field": None} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
if self.has_collection(client, collection_name)[0]:
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_rename_search_query_default(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Bounded")
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
self.describe_collection(client, collection_name,
|
|
check_task=CheckTasks.check_describe_collection_property,
|
|
check_items={"collection_name": collection_name,
|
|
"dim": default_dim,
|
|
"consistency_level": 0})
|
|
old_name = collection_name
|
|
new_name = collection_name + "new"
|
|
self.rename_collection(client, old_name, new_name)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, new_name, rows)
|
|
self.flush(client, new_name)
|
|
# assert self.num_entities(client, collection_name)[0] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, new_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
# 4. query
|
|
self.query(client, new_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"pk_name": default_primary_key_field_name})
|
|
self.release_collection(client, new_name)
|
|
self.drop_collection(client, new_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_array_insert_search(self):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
collections = self.list_collections(client)[0]
|
|
assert collection_name in collections
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{
|
|
default_primary_key_field_name: i,
|
|
default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0,
|
|
default_int32_array_field_name: [i, i + 1, i + 2],
|
|
default_string_array_field_name: [str(i), str(i + 1), str(i + 2)]
|
|
} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.skip(reason="issue 25110")
|
|
def test_milvus_client_search_query_string(self):
|
|
"""
|
|
target: test search (high level api) for string primary key
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, id_type="string", max_length=ct.default_length)
|
|
self.describe_collection(client, collection_name,
|
|
check_task=CheckTasks.check_describe_collection_property,
|
|
check_items={"collection_name": collection_name,
|
|
"dim": default_dim})
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [
|
|
{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
assert self.num_entities(client, collection_name)[0] == default_nb
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
self.search(client, collection_name, vectors_to_search,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
# 4. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"pk_name": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_search_different_metric_types_not_specifying_in_search_params(self, metric_type, auto_id):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, metric_type=metric_type, auto_id=auto_id,
|
|
consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
if auto_id:
|
|
for row in rows:
|
|
row.pop(default_primary_key_field_name)
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
# search_params = {"metric_type": metric_type}
|
|
self.search(client, collection_name, vectors_to_search, limit=default_limit,
|
|
output_fields=[default_primary_key_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.skip("pymilvus issue #1866")
|
|
def test_milvus_client_search_different_metric_types_specifying_in_search_params(self, metric_type, auto_id):
|
|
"""
|
|
target: test search (high level api) normal case
|
|
method: create connection, collection, insert and search
|
|
expected: search successfully with limit(topK)
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, metric_type=metric_type, auto_id=auto_id,
|
|
consistency_level="Strong")
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
if auto_id:
|
|
for row in rows:
|
|
row.pop(default_primary_key_field_name)
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
search_params = {"metric_type": metric_type}
|
|
self.search(client, collection_name, vectors_to_search, limit=default_limit,
|
|
search_params=search_params,
|
|
output_fields=[default_primary_key_field_name],
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_delete_with_ids(self):
|
|
"""
|
|
target: test delete (high level api)
|
|
method: create connection, collection, insert delete, and search
|
|
expected: search/query successfully without deleted data
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
default_nb = 1000
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
pks = self.insert(client, collection_name, rows)[0]
|
|
# 3. delete
|
|
delete_num = 3
|
|
self.delete(client, collection_name, ids=[i for i in range(delete_num)])
|
|
# 4. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
for insert_id in range(delete_num):
|
|
if insert_id in insert_ids:
|
|
insert_ids.remove(insert_id)
|
|
limit = default_nb - delete_num
|
|
self.search(client, collection_name, vectors_to_search, limit=default_nb,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
# 5. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows[delete_num:],
|
|
"with_vec": True,
|
|
"pk_name": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_delete_with_filters(self):
|
|
"""
|
|
target: test delete (high level api)
|
|
method: create connection, collection, insert delete, and search
|
|
expected: search/query successfully without deleted data
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
# 2. insert
|
|
default_nb = 1000
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
|
|
default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
|
|
pks = self.insert(client, collection_name, rows)[0]
|
|
# 3. delete
|
|
delete_num = 3
|
|
self.delete(client, collection_name, filter=f"id < {delete_num}")
|
|
# 4. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(default_nb)]
|
|
for insert_id in range(delete_num):
|
|
if insert_id in insert_ids:
|
|
insert_ids.remove(insert_id)
|
|
limit = default_nb - delete_num
|
|
self.search(client, collection_name, vectors_to_search, limit=default_nb,
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
# 5. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows[delete_num:],
|
|
"with_vec": True,
|
|
"pk_name": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_client_search_with_iterative_filter(self):
|
|
"""
|
|
target: test search with iterative filter
|
|
method: create connection, collection, insert, search with iterative filter
|
|
expected: search successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
dim = 32
|
|
pk_field_name = 'id'
|
|
vector_field_name = 'embeddings'
|
|
str_field_name = 'title'
|
|
json_field_name = 'json_field'
|
|
max_length = 16
|
|
schema.add_field(pk_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(str_field_name, DataType.VARCHAR, max_length=max_length)
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=vector_field_name, metric_type="COSINE",
|
|
index_type="IVF_FLAT", params={"nlist": 128})
|
|
index_params.add_index(field_name=str_field_name)
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{
|
|
pk_field_name: i,
|
|
vector_field_name: list(rng.random((1, dim))[0]),
|
|
str_field_name: cf.gen_str_by_length(max_length),
|
|
json_field_name: {"number": i}
|
|
} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
self.load_collection(client, collection_name)
|
|
|
|
# 3. search
|
|
search_vector = list(rng.random((1, dim))[0])
|
|
search_params = {'hints': "iterative_filter",
|
|
'params': cf.get_search_params_params('IVF_FLAT')}
|
|
self.search(client, collection_name, data=[search_vector], filter='id >= 10',
|
|
search_params=search_params, limit=default_limit)
|
|
not_supported_hints = "not_supported_hints"
|
|
error = {ct.err_code: 0,
|
|
ct.err_msg: f"Create Plan by expr failed: => hints: {not_supported_hints} not supported"}
|
|
search_params = {'hints': not_supported_hints,
|
|
'params': cf.get_search_params_params('IVF_FLAT')}
|
|
self.search(client, collection_name, data=[search_vector], filter='id >= 10',
|
|
search_params=search_params, check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_client_search_with_expr_float_vector(self):
|
|
"""
|
|
target: test search using float vector field as filter
|
|
method: create connection, collection, insert, search with float vector field as filter
|
|
expected: raise error
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
dim = 5
|
|
pk_field_name = 'id'
|
|
vector_field_name = 'embeddings'
|
|
str_field_name = 'title'
|
|
json_field_name = 'json_field'
|
|
max_length = 16
|
|
schema.add_field(pk_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(str_field_name, DataType.VARCHAR, max_length=max_length)
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=vector_field_name, metric_type="COSINE",
|
|
index_type="IVF_FLAT", params={"nlist": 128})
|
|
index_params.add_index(field_name=str_field_name)
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
|
|
rng = np.random.default_rng(seed=19530)
|
|
rows = [{
|
|
pk_field_name: i,
|
|
vector_field_name: list(rng.random((1, dim))[0]),
|
|
str_field_name: cf.gen_str_by_length(max_length),
|
|
json_field_name: {"number": i}
|
|
} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
self.load_collection(client, collection_name)
|
|
|
|
# 3. search
|
|
search_vector = list(rng.random((1, dim))[0])
|
|
raw_vector = [random.random() for _ in range(dim)]
|
|
vectors = np.array(raw_vector, dtype=np.float32)
|
|
error = {ct.err_code: 1100,
|
|
ct.err_msg: f"failed to create query plan: cannot parse expression"}
|
|
self.search(client, collection_name, data=[search_vector], filter=f"{vector_field_name} == {raw_vector}",
|
|
search_params=default_search_params, limit=default_limit,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
self.search(client, collection_name, data=[search_vector], filter=f"{vector_field_name} == {vectors}",
|
|
search_params=default_search_params, limit=default_limit,
|
|
check_task=CheckTasks.err_res, check_items=error)
|
|
|
|
|
|
class TestMilvusClientSearchNullExpr(TestMilvusClientV2Base):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=[False, True])
|
|
def auto_id(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["COSINE", "L2"])
|
|
def metric_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are valid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on int64 fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.INT64, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": i} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_int8(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on int8 fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.INT8, nullable=nullable)
|
|
# schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
|
# max_length=64, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": np.int8(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_int16(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on int16 fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.INT16, nullable=nullable)
|
|
# schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
|
# max_length=64, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": np.int16(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_int32(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on int32 fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.INT32, nullable=nullable)
|
|
# schema.add_field("array_field", DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
|
# max_length=64, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": np.int32(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_float(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on float fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.FLOAT, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": i*1.0} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_double(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on double fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.DOUBLE, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": np.double(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_bool(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on bool fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.BOOL, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": np.bool_(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_varchar(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on varchar fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.VARCHAR, nullable=nullable, max_length=128)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_json(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on json fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.JSON, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
index_params.add_index(field_name=nullable_field_name, index_name="json_index", index_type="INVERTED",
|
|
params={"json_cast_type": "double",
|
|
"json_path": f"{nullable_field_name}['a']['b']"})
|
|
index_params.add_index(field_name=nullable_field_name, index_name="json_index_1", index_type="INVERTED",
|
|
params={"json_cast_type": "varchar",
|
|
"json_path": f"{nullable_field_name}['a']['c']"})
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), nullable_field_name: None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), nullable_field_name: {'a': {'b': i, 'c': None}}} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
output_fields = [nullable_field_name],
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_json_after_flush(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on json fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.JSON, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, default_vector_field_name)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), nullable_field_name: None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), nullable_field_name: {'a': {'b': i, 'c': None}}} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. flush
|
|
self.flush(client, collection_name)
|
|
# 4. create vector and json index
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
index_params.add_index(field_name=nullable_field_name, index_name="json_index", index_type="INVERTED",
|
|
params={"json_cast_type": "DOUBLE",
|
|
"json_path": f"{nullable_field_name}['a']['b']"})
|
|
index_params.add_index(field_name=nullable_field_name, index_name="json_index_1", index_type="INVERTED",
|
|
params={"json_cast_type": "double",
|
|
"json_path": f"{nullable_field_name}['a']['c']"})
|
|
self.create_index(client, collection_name, index_params)
|
|
self.load_collection(client, collection_name)
|
|
# 5. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
output_fields = [nullable_field_name],
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("nullable", [True, False])
|
|
@pytest.mark.parametrize("null_expr_op", ["is null", "IS NULL", "is not null", "IS NOT NULL"])
|
|
def test_milvus_client_search_null_expr_array(self, nullable, null_expr_op):
|
|
"""
|
|
target: test search with null expression on array fields
|
|
method: create connection, collection, insert and search
|
|
expected: search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
dim = 5
|
|
# 1. create collection
|
|
nullable_field_name = "nullable_field"
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, max_length=64, is_primary=True,
|
|
auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(nullable_field_name, DataType.ARRAY, element_type=DataType.INT64, max_capacity=12,
|
|
max_length=64, nullable=nullable)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
rng = np.random.default_rng(seed=19530)
|
|
if nullable:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": None} for i in range(default_nb)]
|
|
else:
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, dim))[0]),
|
|
default_string_field_name: str(i), "nullable_field": [1, 2]} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. search
|
|
vectors_to_search = rng.random((1, dim))
|
|
insert_ids = [str(i) for i in range(default_nb)]
|
|
null_expr = nullable_field_name + " " + null_expr_op
|
|
if nullable:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
insert_ids = []
|
|
limit = 0
|
|
|
|
else:
|
|
limit = default_limit
|
|
else:
|
|
if "not" in null_expr or "NOT" in null_expr:
|
|
limit = default_limit
|
|
else:
|
|
insert_ids = []
|
|
limit = 0
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=null_expr,
|
|
output_fields=[nullable_field_name],
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": limit})
|
|
|
|
|
|
class TestMilvusClientSearchJsonPathIndex(TestMilvusClientV2Base):
|
|
""" Test case of search interface """
|
|
|
|
@pytest.fixture(scope="function", params=["INVERTED"])
|
|
def supported_varchar_scalar_index(self, request):
|
|
yield request.param
|
|
|
|
@pytest.fixture(scope="function", params=["DOUBLE", "VARCHAR", "BOOL", "double", "varchar", "bool"])
|
|
def supported_json_cast_type(self, request):
|
|
yield request.param
|
|
|
|
"""
|
|
******************************************************************
|
|
# The following are valid base cases
|
|
******************************************************************
|
|
"""
|
|
|
|
@pytest.mark.tags(CaseLabel.L0)
|
|
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
|
@pytest.mark.parametrize("is_flush", [True, False])
|
|
def test_milvus_client_search_json_path_index_default(self, enable_dynamic_field, supported_json_cast_type,
|
|
supported_varchar_scalar_index, is_flush):
|
|
"""
|
|
target: test search after the json path index created
|
|
method: Search after creating json path index
|
|
Step: 1. create schema
|
|
2. prepare index_params with the required vector index params
|
|
3. create collection with the above schema and index params
|
|
4. insert
|
|
5. flush if specified
|
|
6. prepare json path index params
|
|
7. create json path index using the above index params created in step 6
|
|
8. create the same json path index again
|
|
9. search with expressions related with the json paths
|
|
expected: Search successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
json_field_name = "my_json"
|
|
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
if not enable_dynamic_field:
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
# 2. insert with different data distribution
|
|
vectors = cf.gen_vectors(default_nb+60, default_dim)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': {"b": i, "b": i}}} for i in
|
|
range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: i} for i in
|
|
range(default_nb, default_nb+10)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {}} for i in
|
|
range(default_nb+10, default_nb+20)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [1, 2, 3]}} for i in
|
|
range(default_nb + 20, default_nb + 30)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [{'b': 1}, 2, 3]}} for i in
|
|
range(default_nb + 30, default_nb + 40)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [{'b': None}, 2, 3]}} for i in
|
|
range(default_nb + 40, default_nb + 50)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': 1}} for i in
|
|
range(default_nb + 50, default_nb + 60)]
|
|
self.insert(client, collection_name, rows)
|
|
if is_flush:
|
|
self.flush(client, collection_name)
|
|
# 2. prepare index params
|
|
index_name = "json_index"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '1',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '2',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '3',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a'][0]['b']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '4',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a'][0]"})
|
|
# 3. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. create same json index twice
|
|
self.create_index(client, collection_name, index_params)
|
|
# 5. search without filter
|
|
vectors_to_search = [vectors[0]]
|
|
insert_ids = [i for i in range(default_nb+60)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
output_fields = [json_field_name],
|
|
consistency_level = "Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
# 6. search with filter on json without output_fields
|
|
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
|
insert_ids = [default_nb/2]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})[0]
|
|
expr = f"{json_field_name} == {default_nb + 5}"
|
|
insert_ids = [default_nb+5]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})
|
|
expr = f"{json_field_name}['a'][0] == 1"
|
|
insert_ids = [i for i in range(default_nb + 20, default_nb + 30)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
expr = f"{json_field_name}['a'][0]['b'] == 1"
|
|
insert_ids = [i for i in range(default_nb + 30, default_nb + 40)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
expr = f"{json_field_name}['a'] == 1"
|
|
insert_ids = [i for i in range(default_nb + 50, default_nb + 60)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
|
def test_milvus_client_search_json_path_index_default_index_name(self, enable_dynamic_field, supported_json_cast_type,
|
|
supported_varchar_scalar_index):
|
|
"""
|
|
target: test json path index without specifying the index_name parameter
|
|
method: create json path index without specifying the index_name parameter
|
|
expected: successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
json_field_name = "my_json"
|
|
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.VARCHAR, is_primary=True, auto_id=False, max_length=128)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
if not enable_dynamic_field:
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
vectors = cf.gen_vectors(default_nb, default_dim)
|
|
rows = [{default_primary_key_field_name: str(i), default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
# 3. prepare index params
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
|
index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
|
|
# 4. create index
|
|
index_name = json_field_name + '/a/b'
|
|
self.create_index(client, collection_name, index_params)
|
|
# 5. search with filter on json with output_fields
|
|
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
|
vectors_to_search = [vectors[0]]
|
|
insert_ids = [str(int(default_nb / 2))]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
output_fields = [json_field_name],
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.skip(reason="issue #40636")
|
|
def test_milvus_client_search_json_path_index_on_non_json_field(self, supported_json_cast_type,
|
|
supported_varchar_scalar_index):
|
|
"""
|
|
target: test json path index on non-json field
|
|
method: create json path index on int64 field
|
|
expected: successfully with original inverted index
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
schema = self.create_schema(client, enable_dynamic_field=False)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
vectors = cf.gen_vectors(default_nb, default_dim)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
# 2. prepare index params
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
|
index_params.add_index(field_name=default_primary_key_field_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type, "json_path": f"{default_string_field_name}['a']['b']"})
|
|
# 3. create index
|
|
index_name = default_string_field_name
|
|
self.create_index(client, collection_name, index_params)
|
|
self.describe_index(client, collection_name, index_name,
|
|
check_task=CheckTasks.check_describe_index_property,
|
|
check_items={
|
|
#"json_cast_type": supported_json_cast_type, # issue 40426
|
|
"json_path": f"{default_string_field_name}['a']['b']",
|
|
"index_type": supported_varchar_scalar_index,
|
|
"field_name": default_string_field_name,
|
|
"index_name": index_name})
|
|
self.flush(client, collection_name)
|
|
# 5. search with filter on json with output_fields
|
|
expr = f"{default_primary_key_field_name} >= 0"
|
|
vectors_to_search = [vectors[0]]
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
output_fields=[default_string_field_name],
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
|
def test_milvus_client_search_diff_index_same_field_diff_index_name_diff_index_params(self, enable_dynamic_field,
|
|
supported_json_cast_type,
|
|
supported_varchar_scalar_index):
|
|
"""
|
|
target: test search after different json path index with different default index name at the same time
|
|
method: Search after different json path index with different default index name at the same index_params object
|
|
expected: Search successfully
|
|
"""
|
|
if enable_dynamic_field:
|
|
pytest.skip('need to fix the field name when enabling dynamic field')
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
json_field_name = "my_json"
|
|
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
if not enable_dynamic_field:
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
self.load_collection(client, collection_name)
|
|
# 2. insert
|
|
vectors = cf.gen_vectors(default_nb, default_dim)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. prepare index params
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
|
|
self.create_index(client, collection_name, index_params)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=json_field_name,
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a']"})
|
|
self.create_index(client, collection_name, index_params)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=json_field_name,
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}"})
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. release and load collection to make sure new index is loaded
|
|
self.release_collection(client, collection_name)
|
|
self.load_collection(client, collection_name)
|
|
# 5. search with filter on json with output_fields
|
|
expr = f"{json_field_name}['a']['b'] >= 0"
|
|
vectors_to_search = [vectors[0]]
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
output_fields=[default_string_field_name],
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
|
@pytest.mark.parametrize("is_flush", [True, False])
|
|
@pytest.mark.parametrize("is_release", [True, False])
|
|
def test_milvus_client_json_search_index_same_json_path_diff_field(self, enable_dynamic_field, supported_json_cast_type,
|
|
supported_varchar_scalar_index, is_flush, is_release):
|
|
"""
|
|
target: test search after creating same json path for different field
|
|
method: Search after creating same json path for different field
|
|
expected: Search successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
json_field_name = "my_json"
|
|
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
if not enable_dynamic_field:
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
schema.add_field(json_field_name + "1", DataType.JSON)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
# 2. insert
|
|
vectors = cf.gen_vectors(default_nb, default_dim)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': {'b': i}},
|
|
json_field_name + "1": {'a': {'b': i}}} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. flush if specified
|
|
if is_flush:
|
|
self.flush(client, collection_name)
|
|
# 3. release and drop index if specified
|
|
if is_release:
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, default_vector_field_name)
|
|
# 4. prepare index params
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
index_params.add_index(field_name=json_field_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a']['b']"})
|
|
self.create_index(client, collection_name, index_params)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=json_field_name + "1",
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}1['a']['b']"})
|
|
# 5. create index with json path index
|
|
self.create_index(client, collection_name, index_params)
|
|
if is_release:
|
|
self.load_collection(client, collection_name)
|
|
# 6. search with filter on json with output_fields on each json field
|
|
expr = f"{json_field_name}['a']['b'] >= 0"
|
|
vectors_to_search = [vectors[0]]
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
output_fields=[json_field_name],
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
expr = f"{json_field_name}1['a']['b'] >= 0"
|
|
vectors_to_search = [vectors[0]]
|
|
insert_ids = [i for i in range(default_nb)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
output_fields=[json_field_name+"1"],
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
|
@pytest.mark.parametrize("is_flush", [True, False])
|
|
def test_milvus_client_search_json_path_index_before_load(self, enable_dynamic_field, supported_json_cast_type,
|
|
supported_varchar_scalar_index, is_flush):
|
|
"""
|
|
target: test search after creating json path index before load
|
|
method: Search after creating json path index before load
|
|
Step: 1. create schema
|
|
2. prepare index_params with vector index params
|
|
3. create collection with the above schema and index params
|
|
4. release collection
|
|
5. insert
|
|
6. flush if specified
|
|
7. prepare json path index params
|
|
8. create index
|
|
9. load collection
|
|
10. search
|
|
expected: Search successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_collection_name_by_testcase_name()
|
|
# 1. create collection
|
|
json_field_name = "my_json"
|
|
schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
if not enable_dynamic_field:
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
# 2. release collection
|
|
self.release_collection(client, collection_name)
|
|
# 3. insert with different data distribution
|
|
vectors = cf.gen_vectors(default_nb+50, default_dim)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in
|
|
range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: i} for i in
|
|
range(default_nb, default_nb+10)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {}} for i in
|
|
range(default_nb+10, default_nb+20)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [1, 2, 3]}} for i in
|
|
range(default_nb + 20, default_nb + 30)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [{'b': 1}, 2, 3]}} for i in
|
|
range(default_nb + 30, default_nb + 40)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [{'b': None}, 2, 3]}} for i in
|
|
range(default_nb + 40, default_nb + 50)]
|
|
self.insert(client, collection_name, rows)
|
|
# 4. flush if specified
|
|
if is_flush:
|
|
self.flush(client, collection_name)
|
|
# 5. prepare index params
|
|
index_name = "json_index"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '1',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '2',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '3',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a'][0]['b']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '4',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a'][0]"})
|
|
# 5. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 6. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 7. search with filter on json without output_fields
|
|
vectors_to_search = [vectors[0]]
|
|
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
|
insert_ids = [default_nb / 2]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})
|
|
expr = f"{json_field_name} == {default_nb + 5}"
|
|
insert_ids = [default_nb + 5]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})
|
|
expr = f"{json_field_name}['a'][0] == 1"
|
|
insert_ids = [i for i in range(default_nb + 20, default_nb + 30)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
expr = f"{json_field_name}['a'][0]['b'] == 1"
|
|
insert_ids = [i for i in range(default_nb + 30, default_nb + 40)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("enable_dynamic_field", [True, False])
|
|
@pytest.mark.parametrize("is_flush", [True, False])
|
|
def test_milvus_client_search_json_path_index_after_release_load(self, enable_dynamic_field, supported_json_cast_type,
|
|
supported_varchar_scalar_index, is_flush):
|
|
"""
|
|
target: test search after creating json path index after release and load
|
|
method: Search after creating json path index after release and load
|
|
Step: 1. create schema
|
|
2. prepare index_params with vector index params
|
|
3. create collection with the above schema and index params
|
|
4. insert
|
|
5. flush if specified
|
|
6. prepare json path index params
|
|
7. create index
|
|
8. release collection
|
|
9. create index again
|
|
10. load collection
|
|
11. search with expressions related with the json paths
|
|
expected: Search successfully
|
|
"""
|
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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# 1. create collection
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json_field_name = "my_json"
|
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schema = self.create_schema(client, enable_dynamic_field=enable_dynamic_field)[0]
|
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schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True, auto_id=False)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
if not enable_dynamic_field:
|
|
schema.add_field(json_field_name, DataType.JSON)
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(default_vector_field_name, metric_type="COSINE")
|
|
self.create_collection(client, collection_name, schema=schema, index_params=index_params)
|
|
# 2. insert with different data distribution
|
|
vectors = cf.gen_vectors(default_nb+50, default_dim)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': {"b": i}}} for i in
|
|
range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: i} for i in
|
|
range(default_nb, default_nb+10)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {}} for i in
|
|
range(default_nb+10, default_nb+20)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [1, 2, 3]}} for i in
|
|
range(default_nb + 20, default_nb + 30)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [{'b': 1}, 2, 3]}} for i in
|
|
range(default_nb + 30, default_nb + 40)]
|
|
self.insert(client, collection_name, rows)
|
|
rows = [{default_primary_key_field_name: i, default_vector_field_name: vectors[i],
|
|
default_string_field_name: str(i), json_field_name: {'a': [{'b': None}, 2, 3]}} for i in
|
|
range(default_nb + 40, default_nb + 50)]
|
|
self.insert(client, collection_name, rows)
|
|
#3. flush if specified
|
|
if is_flush:
|
|
self.flush(client, collection_name)
|
|
# 4. prepare index params
|
|
index_name = "json_index"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type="AUTOINDEX", metric_type="COSINE")
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name, index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type, "json_path": f"{json_field_name}['a']['b']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '1',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '2',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '3',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a'][0]['b']"})
|
|
index_params.add_index(field_name=json_field_name, index_name=index_name + '4',
|
|
index_type=supported_varchar_scalar_index,
|
|
params={"json_cast_type": supported_json_cast_type,
|
|
"json_path": f"{json_field_name}['a'][0]"})
|
|
# 5. create json index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 6. release collection
|
|
self.release_collection(client, collection_name)
|
|
# 7. create json index again
|
|
self.create_index(client, collection_name, index_params)
|
|
# 8. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 9. search with filter on json without output_fields
|
|
vectors_to_search = [vectors[0]]
|
|
expr = f"{json_field_name}['a']['b'] == {default_nb / 2}"
|
|
insert_ids = [default_nb / 2]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})
|
|
expr = f"{json_field_name} == {default_nb + 5}"
|
|
insert_ids = [default_nb + 5]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": 1})
|
|
expr = f"{json_field_name}['a'][0] == 1"
|
|
insert_ids = [i for i in range(default_nb + 20, default_nb + 30)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit})
|
|
expr = f"{json_field_name}['a'][0]['b'] == 1"
|
|
insert_ids = [i for i in range(default_nb + 30, default_nb + 40)]
|
|
self.search(client, collection_name, vectors_to_search,
|
|
filter=expr,
|
|
consistency_level="Strong",
|
|
check_task=CheckTasks.check_search_results,
|
|
check_items={"enable_milvus_client_api": True,
|
|
"nq": len(vectors_to_search),
|
|
"ids": insert_ids,
|
|
"pk_name": default_primary_key_field_name,
|
|
"limit": default_limit}) |