import pytest from base.client_v2_base import TestMilvusClientV2Base from utils.util_log import test_log as log from common import common_func as cf from common import common_type as ct from common.common_type import CaseLabel, CheckTasks from utils.util_pymilvus import * prefix = "client_index" epsilon = ct.epsilon default_nb = ct.default_nb default_nb_medium = ct.default_nb_medium default_nq = ct.default_nq default_dim = ct.default_dim default_limit = ct.default_limit default_search_exp = "id >= 0" exp_res = "exp_res" default_search_string_exp = "varchar >= \"0\"" default_search_mix_exp = "int64 >= 0 && varchar >= \"0\"" default_invaild_string_exp = "varchar >= 0" default_json_search_exp = "json_field[\"number\"] >= 0" perfix_expr = 'varchar like "0%"' default_search_field = ct.default_float_vec_field_name default_search_params = ct.default_search_params default_primary_key_field_name = "id" default_vector_field_name = "vector" default_multiple_vector_field_name = "vector_new" default_float_field_name = ct.default_float_field_name default_bool_field_name = ct.default_bool_field_name default_string_field_name = ct.default_string_field_name default_int32_array_field_name = ct.default_int32_array_field_name default_string_array_field_name = ct.default_string_array_field_name class TestMilvusClientIndexInvalid(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 invalid base cases ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("name", ["12-s", "12 s", "(mn)", "中文", "%$#"]) def test_milvus_client_index_invalid_collection_name(self, name): """ target: test index abnormal case method: create index on invalid collection name expected: raise exception """ 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") # 2. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector") # 3. create index error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {name}. the first character of a collection " f"name must be an underscore or letter: invalid parameter"} self.create_index(client, name, index_params, check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("name", ["a".join("a" for i in range(256))]) def test_milvus_client_index_collection_name_over_max_length(self, name): """ target: test index abnormal case method: create index on collection name over max length expected: raise exception """ 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") # 2. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector") # 3. create index error = {ct.err_code: 1100, ct.err_msg: f"Invalid collection name: {name}. the length of a collection name " f"must be less than 255 characters: invalid parameter"} self.create_index(client, name, index_params, check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L1) def test_milvus_client_index_not_exist_collection_name(self): """ target: test index abnormal case method: create index on not exist collection name expected: raise exception """ client = self._client() collection_name = cf.gen_unique_str(prefix) not_existed_collection_name = cf.gen_unique_str("not_existed_collection") # 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") # 2. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector") # 3. create index error = {ct.err_code: 100, ct.err_msg: f"can't find collection[database=default][collection={not_existed_collection_name}]"} self.create_index(client, not_existed_collection_name, index_params, check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.skip(reason="pymilvus issue 1885") @pytest.mark.parametrize("index", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))]) def test_milvus_client_index_invalid_index_type(self, index): """ target: test index abnormal case method: create index on invalid index type expected: raise exception """ 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") # 2. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector", index_type=index) # 3. create index error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not " f"found[database=default][collection=not_existed]"} self.create_index(client, collection_name, index_params, check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.skip(reason="pymilvus issue 1885") @pytest.mark.parametrize("metric", ["12-s", "12 s", "(mn)", "中文", "%$#", "a".join("a" for i in range(256))]) def test_milvus_client_index_invalid_metric_type(self, metric): """ target: test index abnormal case method: create index on invalid metric type expected: raise exception """ 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") # 2. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector", metric_type=metric) # 3. create index error = {ct.err_code: 100, ct.err_msg: f"can't find collection collection not " f"found[database=default][collection=not_existed]"} self.create_index(client, collection_name, index_params, check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L1) def test_milvus_client_index_drop_index_before_release(self): """ target: test index abnormal case method: drop index before release expected: raise exception """ client = self._client() collection_name = cf.gen_unique_str(prefix) # 1. create collection self.create_collection(client, collection_name, default_dim, consistency_level="Strong") error = {ct.err_code: 65535, ct.err_msg: f"index cannot be dropped, collection is loaded, " f"please release it first"} self.drop_index(client, collection_name, "vector", check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L1) def test_milvus_client_create_multiple_diff_index_without_release(self): """ target: test index abnormal case method: create different index on one field without release expected: raise exception """ client = self._client() collection_name = cf.gen_unique_str(prefix) # 1. create collection self.create_collection(client, collection_name, default_dim, consistency_level="Strong") # 2. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector", index_type="IVF_FLAT", metric_type="L2") # 3. create another index error = {ct.err_code: 65535, ct.err_msg: "CreateIndex failed: at most one distinct index is allowed per field"} self.create_index(client, collection_name, index_params, check_task=CheckTasks.err_res, check_items=error) self.drop_collection(client, collection_name) class TestMilvusClientIndexValid(TestMilvusClientV2Base): """ Test case of index interface """ @pytest.fixture(scope="function", params=[False, True]) def auto_id(self, request): yield request.param @pytest.fixture(scope="function", params=["COSINE", "L2", "IP"]) def metric_type(self, request): yield request.param @pytest.fixture(scope="function", params=["TRIE", "STL_SORT", "INVERTED", "AUTOINDEX"]) def scalar_index(self, request): yield request.param @pytest.fixture(scope="function", params=["TRIE", "INVERTED", "AUTOINDEX", ""]) def varchar_index(self, request): yield request.param @pytest.fixture(scope="function", params=["STL_SORT", "INVERTED", "AUTOINDEX", ""]) def numeric_index(self, request): yield request.param """ ****************************************************************** # The following are valid base cases ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) @pytest.mark.skip("https://github.com/milvus-io/pymilvus/issues/1886") @pytest.mark.parametrize("index, params", zip(ct.all_index_types[:7], ct.default_all_indexes_params[:7])) def test_milvus_client_index_default(self, index, params, metric_type): """ target: test index normal case method: create connection, collection, create 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_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type) # 3. create index self.create_index(client, collection_name, index_params) # 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_with_params(self, index, params, metric_type): """ target: test index with user defined params 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_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. 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) # 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_after_insert(self, index, params, metric_type): """ target: test index after insert method: create connection, collection, insert, index 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") # 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) # 3. prepare index params index_params = self.prepare_index_params(client)[0] index_params.add_index(field_name="vector", index_type=index, metric_type=metric_type, params=params) # 4. create index self.create_index(client, collection_name, index_params) # 5. load collection self.load_collection(client, collection_name) # 5. 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}) # 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, "primary_field": default_primary_key_field_name}) self.drop_collection(client, collection_name) @pytest.mark.tags(CaseLabel.L2) def test_milvus_client_index_auto_index(self, numeric_index, varchar_index, metric_type): """ target: test index with autoindex on both scalar and vector field 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 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(default_float_field_name, DataType.FLOAT) schema.add_field(ct.default_double_field_name, DataType.DOUBLE) schema.add_field(ct.default_bool_field_name, DataType.BOOL) 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=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)