mirror of https://github.com/milvus-io/milvus.git
744 lines
39 KiB
Python
744 lines
39 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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prefix = "client_index"
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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_multiple_vector_field_name = "vector_new"
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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 TestMilvusClientIndexInvalid(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("name", ["12-s", "12 s", "(mn)", "中文", "%$#"])
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def test_milvus_client_index_invalid_collection_name(self, name):
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"""
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target: test index abnormal case
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method: create index on 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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector")
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# 3. create index
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error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {name}. the first character of a collection "
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f"name must be an underscore or letter: invalid parameter"}
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self.create_index(client, name, index_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("name", ["a".join("a" for i in range(256))])
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def test_milvus_client_index_collection_name_over_max_length(self, name):
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"""
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target: test index abnormal case
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method: create index on collection name over max 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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector")
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# 3. create index
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error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {name}. the length of a collection name "
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f"must be less than 255 characters: invalid parameter"}
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self.create_index(client, name, index_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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def test_milvus_client_index_not_exist_collection_name(self):
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"""
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target: test index abnormal case
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method: create index on not exist 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_unique_str(prefix)
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not_existed_collection_name = cf.gen_unique_str("not_existed_collection")
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector")
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# 3. create index
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error = {ct.err_code: 100,
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ct.err_msg: f"can't find collection[database=default][collection={not_existed_collection_name}]"}
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self.create_index(client, not_existed_collection_name, index_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.skip(reason="pymilvus issue 1885")
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@pytest.mark.parametrize("index", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
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def test_milvus_client_index_invalid_index_type(self, index):
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"""
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target: test index abnormal case
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method: create index on invalid index 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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector", index_type=index)
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# 3. create index
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error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not "
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f"found[database=default][collection=not_existed]"}
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self.create_index(client, collection_name, index_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.skip(reason="pymilvus issue 1885")
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@pytest.mark.parametrize("metric", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))])
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def test_milvus_client_index_invalid_metric_type(self, metric):
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"""
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target: test index abnormal case
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method: create index on invalid 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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector", metric_type=metric)
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# 3. create index
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error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not "
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f"found[database=default][collection=not_existed]"}
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self.create_index(client, collection_name, index_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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def test_milvus_client_index_drop_index_before_release(self):
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"""
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target: test index abnormal case
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method: drop index before release
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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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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error = {ct.err_code: 65535, ct.err_msg: f"index cannot be dropped, collection is loaded, "
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f"please release it first"}
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self.drop_index(client, collection_name, "vector",
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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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def test_milvus_client_create_multiple_diff_index_without_release(self):
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"""
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target: test index abnormal case
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method: create different index on one field without release
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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_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector", index_type="IVF_FLAT", metric_type="L2")
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# 3. create another index
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error = {ct.err_code: 65535, ct.err_msg: "CreateIndex failed: at most one distinct index is allowed per field"}
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self.create_index(client, collection_name, index_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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class TestMilvusClientIndexValid(TestMilvusClientV2Base):
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""" Test case of index 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", "IP"])
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def metric_type(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["TRIE", "STL_SORT", "INVERTED", "AUTOINDEX"])
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def scalar_index(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["TRIE", "INVERTED", "AUTOINDEX", ""])
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def varchar_index(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["STL_SORT", "INVERTED", "AUTOINDEX", ""])
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def numeric_index(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are valid 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.skip("https://github.com/milvus-io/pymilvus/issues/1886")
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[:7],
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ct.default_all_indexes_params[:7]))
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def test_milvus_client_index_default(self, index, params, metric_type):
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"""
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target: test index normal case
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method: create connection, collection, create index, insert and search
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expected: index/search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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res = self.list_indexes(client, collection_name)[0]
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assert res == []
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type)
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# 3. create index
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self.create_index(client, collection_name, index_params)
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# 4. create same index twice
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self.create_index(client, collection_name, index_params)
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# 5. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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# 6. load collection
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self.load_collection(client, collection_name)
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# 7. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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self.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"limit": default_limit})
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# 8. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows,
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"with_vec": True,
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"primary_field": default_primary_key_field_name})
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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.parametrize("index, params",
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zip(ct.all_index_types[:7],
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ct.default_all_indexes_params[:7]))
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def test_milvus_client_index_with_params(self, index, params, metric_type):
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"""
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target: test index with user defined params
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method: create connection, collection, index, insert and search
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expected: index/search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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res = self.list_indexes(client, collection_name)[0]
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assert res == []
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# 2. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector", index_type=index, params=params, metric_type=metric_type)
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# 3. create index
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self.create_index(client, collection_name, index_params)
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# 4. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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# 5. load collection
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self.load_collection(client, collection_name)
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# 6. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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self.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"limit": default_limit})
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# 7. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows,
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"with_vec": True,
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"primary_field": default_primary_key_field_name})
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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.parametrize("index, params",
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zip(ct.all_index_types[:7],
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ct.default_all_indexes_params[:7]))
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def test_milvus_client_index_after_insert(self, index, params, metric_type):
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"""
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target: test index after insert
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method: create connection, collection, insert, index and search
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expected: index/search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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# 3. prepare index params
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type, params=params)
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# 4. create index
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self.create_index(client, collection_name, index_params)
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# 5. load collection
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self.load_collection(client, collection_name)
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# 5. search
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vectors_to_search = rng.random((1, default_dim))
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insert_ids = [i for i in range(default_nb)]
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self.search(client, collection_name, vectors_to_search,
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check_task=CheckTasks.check_search_results,
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check_items={"enable_milvus_client_api": True,
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"nq": len(vectors_to_search),
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"ids": insert_ids,
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"limit": default_limit})
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# 4. query
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self.query(client, collection_name, filter=default_search_exp,
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check_task=CheckTasks.check_query_results,
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check_items={exp_res: rows,
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"with_vec": True,
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"primary_field": default_primary_key_field_name})
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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_index_auto_index(self, numeric_index, varchar_index, metric_type):
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"""
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target: test index with autoindex on both scalar and vector field
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method: create connection, collection, insert and search
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expected: index/search/query successfully
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"""
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client = self._client()
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collection_name = cf.gen_unique_str(prefix)
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# 1. create collection
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schema = self.create_schema(client)[0]
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schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True)
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schema.add_field(ct.default_int32_field_name, DataType.INT32)
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schema.add_field(ct.default_int16_field_name, DataType.INT16)
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schema.add_field(ct.default_int8_field_name, DataType.INT8)
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schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
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schema.add_field(default_float_field_name, DataType.FLOAT)
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schema.add_field(ct.default_double_field_name, DataType.DOUBLE)
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schema.add_field(ct.default_bool_field_name, DataType.BOOL)
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schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
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self.create_collection(client, collection_name, schema=schema, consistency_level="Strong")
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self.release_collection(client, collection_name)
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self.drop_index(client, collection_name, "vector")
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res = self.list_indexes(client, collection_name)[0]
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assert res == []
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# 2. prepare index params
|
|
index = "AUTOINDEX"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_int32_field_name, index_type=numeric_index, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_int16_field_name, index_type=numeric_index, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_int8_field_name, index_type=numeric_index, metric_type=metric_type)
|
|
index_params.add_index(field_name=default_float_field_name, index_type=numeric_index, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_double_field_name, index_type=numeric_index, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_bool_field_name, index_type="", metric_type=metric_type)
|
|
index_params.add_index(field_name=default_string_field_name, index_type=varchar_index, metric_type=metric_type)
|
|
index_params.add_index(field_name=default_primary_key_field_name, index_type=numeric_index, metric_type=metric_type)
|
|
# 3. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. drop index
|
|
self.drop_index(client, collection_name, default_vector_field_name)
|
|
self.drop_index(client, collection_name, ct.default_int32_field_name)
|
|
self.drop_index(client, collection_name, ct.default_int16_field_name)
|
|
self.drop_index(client, collection_name, ct.default_int8_field_name)
|
|
self.drop_index(client, collection_name, default_float_field_name)
|
|
self.drop_index(client, collection_name, ct.default_double_field_name)
|
|
self.drop_index(client, collection_name, ct.default_bool_field_name)
|
|
self.drop_index(client, collection_name, default_string_field_name)
|
|
self.drop_index(client, collection_name, default_primary_key_field_name)
|
|
# 5. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 6. 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]),
|
|
ct.default_int32_field_name: np.int32(i), ct.default_int16_field_name: np.int16(i),
|
|
ct.default_int8_field_name: np.int8(i), default_float_field_name: i * 1.0,
|
|
ct.default_double_field_name: np.double(i), ct.default_bool_field_name: np.bool_(i),
|
|
default_string_field_name: str(i)} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 7. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 8. 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,
|
|
"limit": default_limit})
|
|
# 9. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_scalar_hybrid_index_small_distinct_before_insert(self, metric_type):
|
|
"""
|
|
target: test index with autoindex on int/varchar with small distinct value (<=100)
|
|
method: create connection, collection, insert and search
|
|
expected: index/search/query successfully (autoindex is bitmap index indeed)
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
int64_field_name = "int"
|
|
schema = self.create_schema(client)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(int64_field_name, DataType.INT64)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
self.create_collection(client, collection_name, schema=schema, consistency_level="Strong")
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, "vector")
|
|
res = self.list_indexes(client, collection_name)[0]
|
|
assert res == []
|
|
# 2. prepare index params
|
|
index = "AUTOINDEX"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name=int64_field_name, index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name=default_string_field_name, index_type=index, metric_type=metric_type)
|
|
# 3. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. 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]),
|
|
int64_field_name: np.random.randint(0, 99), default_string_field_name: str(np.random.randint(0, 99))}
|
|
for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 5. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 6. 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,
|
|
"limit": default_limit})
|
|
# 7. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L1)
|
|
def test_milvus_client_scalar_hybrid_index_small_to_large_distinct_after_insert(self, metric_type):
|
|
"""
|
|
target: test index with autoindex on int/varchar with small distinct value (<=100) first and
|
|
insert to large distinct (2000+) later
|
|
method: create connection, collection, insert and search
|
|
expected: index/search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
int64_field_name = "int"
|
|
schema = self.create_schema(client)[0]
|
|
schema.add_field(default_primary_key_field_name, DataType.INT64, is_primary=True)
|
|
schema.add_field(ct.default_int32_field_name, DataType.INT32)
|
|
schema.add_field(ct.default_int16_field_name, DataType.INT16)
|
|
schema.add_field(ct.default_int8_field_name, DataType.INT8)
|
|
schema.add_field(default_string_field_name, DataType.VARCHAR, max_length=64)
|
|
schema.add_field(int64_field_name, DataType.INT64)
|
|
schema.add_field(default_vector_field_name, DataType.FLOAT_VECTOR, dim=default_dim)
|
|
self.create_collection(client, collection_name, schema=schema, consistency_level="Strong")
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, "vector")
|
|
res = self.list_indexes(client, collection_name)[0]
|
|
assert res == []
|
|
# 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]),
|
|
int64_field_name: np.random.randint(0, 99), ct.default_int32_field_name: np.int32(i),
|
|
ct.default_int16_field_name: np.int16(i), ct.default_int8_field_name: np.int8(i),
|
|
default_string_field_name: str(np.random.randint(0, 99))} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 3. prepare index params
|
|
index = "AUTOINDEX"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name=default_vector_field_name, index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name=int64_field_name, index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_int32_field_name, index_type="", metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_int16_field_name, metric_type=metric_type)
|
|
index_params.add_index(field_name=ct.default_int8_field_name, index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name=default_string_field_name, index_type=index, metric_type=metric_type)
|
|
# 4. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 5. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 6. 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,
|
|
"limit": default_limit})
|
|
# 7. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
# 8. insert more distinct value to the scalar field to make the autoindex change
|
|
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]),
|
|
int64_field_name: np.random.randint(0, 99), ct.default_int32_field_name: np.int32(i),
|
|
ct.default_int16_field_name: np.int16(i), ct.default_int8_field_name: np.int8(i),
|
|
default_string_field_name: str(np.random.randint(0, 99))} for i in range(default_nb, 2*default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
self.flush(client, collection_name)
|
|
# 9. search
|
|
vectors_to_search = rng.random((1, default_dim))
|
|
insert_ids = [i for i in range(2*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,
|
|
"limit": default_limit})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
def test_milvus_client_index_multiple_vectors(self, numeric_index, metric_type):
|
|
"""
|
|
target: test index for multiple vectors
|
|
method: create connection, collection, index, insert and search
|
|
expected: index/search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, "vector")
|
|
res = self.list_indexes(client, collection_name)[0]
|
|
assert res == []
|
|
# 2. prepare index params
|
|
index = "AUTOINDEX"
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type)
|
|
index_params.add_index(field_name="id", index_type=numeric_index, metric_type=metric_type)
|
|
# 3. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. 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),
|
|
default_multiple_vector_field_name: list(rng.random((1, default_dim))[0])} for i in range(default_nb)]
|
|
self.insert(client, collection_name, rows)
|
|
# 5. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 6. 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,
|
|
"limit": default_limit})
|
|
# 7. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("index, params",
|
|
zip(ct.all_index_types[:7],
|
|
ct.default_all_indexes_params[:7]))
|
|
def test_milvus_client_index_drop_create_same_index(self, index, params, metric_type):
|
|
"""
|
|
target: test index after drop and create same index twice
|
|
method: create connection, collection, create/drop/create index, insert and search
|
|
expected: index create/drop and search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, "vector")
|
|
res = self.list_indexes(client, collection_name)[0]
|
|
assert res == []
|
|
# 2. prepare index params
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name="vector", index_type=index, params=params, metric_type=metric_type)
|
|
# 3. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. drop index
|
|
self.drop_index(client, collection_name, "vector")
|
|
# 4. create same index twice
|
|
self.create_index(client, collection_name, index_params)
|
|
# 5. 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)
|
|
# 6. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 7. 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,
|
|
"limit": default_limit})
|
|
# 8. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|
|
|
|
@pytest.mark.tags(CaseLabel.L2)
|
|
@pytest.mark.parametrize("index, params",
|
|
zip(ct.all_index_types[:7],
|
|
ct.default_all_indexes_params[:7]))
|
|
def test_milvus_client_index_drop_create_different_index(self, index, params, metric_type):
|
|
"""
|
|
target: test index after drop and create different index twice
|
|
method: create connection, collection, create/drop/create index, insert and search
|
|
expected: index create/drop and search/query successfully
|
|
"""
|
|
client = self._client()
|
|
collection_name = cf.gen_unique_str(prefix)
|
|
# 1. create collection
|
|
self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
|
|
self.release_collection(client, collection_name)
|
|
self.drop_index(client, collection_name, "vector")
|
|
res = self.list_indexes(client, collection_name)[0]
|
|
assert res == []
|
|
# 2. prepare index params
|
|
index_params = self.prepare_index_params(client)[0]
|
|
index_params.add_index(field_name="vector", metric_type=metric_type)
|
|
# 3. create index
|
|
self.create_index(client, collection_name, index_params)
|
|
# 4. drop index
|
|
self.drop_index(client, collection_name, "vector")
|
|
# 4. create different index
|
|
index_params.add_index(field_name="vector", index_type=index, params=params, metric_type=metric_type)
|
|
self.create_index(client, collection_name, index_params)
|
|
# 5. 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)
|
|
# 6. load collection
|
|
self.load_collection(client, collection_name)
|
|
# 7. 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,
|
|
"limit": default_limit})
|
|
# 8. query
|
|
self.query(client, collection_name, filter=default_search_exp,
|
|
check_task=CheckTasks.check_query_results,
|
|
check_items={exp_res: rows,
|
|
"with_vec": True,
|
|
"primary_field": default_primary_key_field_name})
|
|
self.drop_collection(client, collection_name)
|