import pytest from time import sleep from base.client_base import TestcaseBase 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.utils import * from common.constants import * prefix = "search_collection" search_num = 10 max_dim = ct.max_dim epsilon = ct.epsilon gracefulTime = ct.gracefulTime 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 = "int64 >= 0" default_search_field = ct.default_float_vec_field_name default_search_params = ct.default_search_params default_int64_field_name = ct.default_int64_field_name default_float_field_name = ct.default_float_field_name default_bool_field_name = ct.default_bool_field_name vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)] uid = "test_search" nq = 1 epsilon = 0.001 field_name = default_float_vec_field_name binary_field_name = default_binary_vec_field_name search_param = {"nprobe": 1} entity = gen_entities(1, is_normal=True) entities = gen_entities(default_nb, is_normal=True) raw_vectors, binary_entities = gen_binary_entities(default_nb) default_query, _ = gen_search_vectors_params(field_name, entities, default_top_k, nq) # default_binary_query, _ = gen_search_vectors_params(binary_field_name, binary_entities, default_top_k, nq) class TestCollectionSearchInvalid(TestcaseBase): """ Test case of search interface """ @pytest.fixture(scope="function", params=ct.get_invalid_vectors) def get_invalid_vectors(self, request): yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_fields_type(self, request): if isinstance(request.param, str): pytest.skip("string is valid type for field") yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_fields_value(self, request): if not isinstance(request.param, str): pytest.skip("field value only support string") if request.param == "": pytest.skip("empty field is valid") yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_metric_type(self, request): yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_ints) def get_invalid_limit(self, request): if isinstance(request.param, int) and request.param >= 0: pytest.skip("positive int is valid type for limit") yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_expr_type(self, request): if isinstance(request.param, str): pytest.skip("string is valid type for expr") if request.param is None: pytest.skip("None is valid for expr") yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_expr_value(self, request): if not isinstance(request.param, str): pytest.skip("expression value only support string") if request.param == "": pytest.skip("empty field is valid") yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_partition(self, request): if request.param == []: pytest.skip("empty is valid for partition") if request.param is None: pytest.skip("None is valid for partition") yield request.param @pytest.fixture(scope="function", params=ct.get_invalid_strs) def get_invalid_output_fields(self, request): if request.param == []: pytest.skip("empty is valid for output_fields") if request.param is None: pytest.skip("None is valid for output_fields") yield request.param """ ****************************************************************** # The followings are invalid cases ****************************************************************** """ @pytest.mark.tags(CaseLabel.L1) def test_search_no_connection(self): """ target: test search without connection method: create and delete connection, then search expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. remove connection log.info("test_search_no_connection: removing connection") self.connection_wrap.remove_connection(alias='default') log.info("test_search_no_connection: removed connection") # 3. search without connection log.info("test_search_no_connection: searching without connection") collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "should create connect first"}) @pytest.mark.tags(CaseLabel.L1) def test_search_no_collection(self): """ target: test the scenario which search the non-exist collection method: 1. create collection 2. drop collection 3. search the dropped collection expected: raise exception and report the error """ # 1. initialize without data collection_w = self.init_collection_general(prefix)[0] # 2. Drop collection collection_w.drop() # 3. Search without collection log.info("test_search_no_collection: Searching without collection ") collection_w.search(vectors, default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "collection %s doesn't exist!" % collection_w.name}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_missing(self): """ target: test search with incomplete parameters method: search with incomplete parameters expected: raise exception and report the error """ # 1. initialize without data collection_w = self.init_collection_general(prefix)[0] # 2. search collection with missing parameters log.info("test_search_param_missing: Searching collection %s " "with missing parameters" % collection_w.name) try: collection_w.search() except TypeError as e: assert "missing 4 required positional arguments: 'data', " \ "'anns_field', 'param', and 'limit'" in str(e) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_vectors(self, get_invalid_vectors): """ target: test search with invalid parameter values method: search with invalid data expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search with invalid field invalid_vectors = get_invalid_vectors log.info("test_search_param_invalid_vectors: searching with " "invalid vectors: {}".format(invalid_vectors)) collection_w.search(invalid_vectors, default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "`search_data` value {} is illegal".format(invalid_vectors)}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_dim(self): """ target: test search with invalid parameter values method: search with invalid dim expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix, True)[0] # 2. search with invalid dim log.info("test_search_param_invalid_dim: searching with invalid dim") wrong_dim = 129 vectors = [[random.random() for _ in range(wrong_dim)] for _ in range(default_nq)] collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "The dimension of query entities " "is different from schema"}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_field_type(self, get_invalid_fields_type): """ target: test search with invalid parameter type method: search with invalid field type expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search with invalid field invalid_search_field = get_invalid_fields_type log.info("test_search_param_invalid_field_type: searching with " "invalid field: %s" % invalid_search_field) collection_w.search(vectors[:default_nq], invalid_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "`anns_field` value {} is illegal".format(invalid_search_field)}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_field_value(self, get_invalid_fields_value): """ target: test search with invalid parameter values method: search with invalid field value expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search with invalid field invalid_search_field = get_invalid_fields_value log.info("test_search_param_invalid_field_value: searching with " "invalid field: %s" % invalid_search_field) collection_w.search(vectors[:default_nq], invalid_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "Field %s doesn't exist in schema" % invalid_search_field}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_metric_type(self, get_invalid_metric_type): """ target: test search with invalid parameter values method: search with invalid metric type expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix, True, 10)[0] # 2. search with invalid metric_type log.info("test_search_param_invalid_metric_type: searching with invalid metric_type") invalid_metric = get_invalid_metric_type search_params = {"metric_type": invalid_metric, "params": {"nprobe": 10}} collection_w.search(vectors[:default_nq], default_search_field, search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "metric type not found"}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.xfail(reason="issue 6727") @pytest.mark.parametrize("index, params", zip(ct.all_index_types[:9], ct.default_index_params[:9])) def test_search_invalid_params_type(self, index, params): """ target: test search with invalid search params method: test search with invalid params type expected: raise exception and report the error """ if index == "FLAT": pytest.skip("skip in FLAT index") # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000, is_index=True) # 2. create index and load default_index = {"index_type": index, "params": params, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) collection_w.load() # 3. search invalid_search_params = cf.gen_invaild_search_params_type() for invalid_search_param in invalid_search_params: if index == invalid_search_param["index_type"]: search_params = {"metric_type": "L2", "params": invalid_search_param["search_params"]} collection_w.search(vectors[:default_nq], default_search_field, search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 0, "err_msg": "metric type not found"}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_limit_type(self, get_invalid_limit): """ target: test search with invalid limit type method: search with invalid limit type expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search with invalid field invalid_limit = get_invalid_limit log.info("test_search_param_invalid_limit_type: searching with " "invalid limit: %s" % invalid_limit) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, invalid_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "`limit` value %s is illegal" % invalid_limit}) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("limit", [0, 16385]) def test_search_param_invalid_limit_value(self, limit): """ target: test search with invalid limit value method: search with invalid limit: 0 and maximum expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search with invalid limit (topK) log.info("test_search_param_invalid_limit: searching with " "invalid limit (topK) = %s" % limit) err_msg = "limit %d is too large!" % limit if limit == 0: err_msg = "`limit` value 0 is illegal" collection_w.search(vectors[:default_nq], default_search_field, default_search_params, limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": err_msg}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_expr_type(self, get_invalid_expr_type): """ target: test search with invalid parameter type method: search with invalid search expressions expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2 search with invalid expr invalid_search_expr = get_invalid_expr_type log.info("test_search_param_invalid_expr_type: searching with " "invalid expr: {}".format(invalid_search_expr)) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, invalid_search_expr, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "The type of expr must be string ," "but {} is given".format(type(invalid_search_expr))}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_expr_value(self, get_invalid_expr_value): """ target: test search with invalid parameter values method: search with invalid search expressions expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2 search with invalid expr invalid_search_expr = get_invalid_expr_value log.info("test_search_param_invalid_expr_value: searching with " "invalid expr: %s" % invalid_search_expr) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, invalid_search_expr, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "invalid expression %s" % invalid_search_expr}) @pytest.mark.tags(CaseLabel.L2) def test_search_partition_invalid_type(self, get_invalid_partition): """ target: test search invalid partition method: search with invalid partition type expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search the invalid partition partition_name = get_invalid_partition err_msg = "`partition_name_array` value {} is illegal".format(partition_name) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, partition_name, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": err_msg}) @pytest.mark.tags(CaseLabel.L2) def test_search_with_output_fields_invalid_type(self, get_invalid_output_fields): """ target: test search with output fields method: search with invalid output_field expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix)[0] # 2. search log.info("test_search_with_output_fields_invalid_type: Searching collection %s" % collection_w.name) output_fields = get_invalid_output_fields err_msg = "`output_fields` value {} is illegal".format(output_fields) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, output_fields=output_fields, check_task=CheckTasks.err_res, check_items={ct.err_code: 1, ct.err_msg: err_msg}) @pytest.mark.tags(CaseLabel.L1) def test_search_release_collection(self): """ target: test the scenario which search the released collection method: 1. create collection 2. release collection 3. search the released collection expected: raise exception and report the error """ # 1. initialize without data collection_w = self.init_collection_general(prefix, True, 10)[0] # 2. release collection collection_w.release() # 3. Search the released collection log.info("test_search_release_collection: Searching without collection ") collection_w.search(vectors, default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "collection %s was not loaded " "into memory" % collection_w.name}) @pytest.mark.tags(CaseLabel.L2) def test_search_release_partition(self): """ target: test the scenario which search the released collection method: 1. create collection 2. release partition 3. search with specifying the released partition expected: raise exception and report the error """ # 1. initialize with data partition_num = 1 collection_w = self.init_collection_general(prefix, True, 10, partition_num)[0] par = collection_w.partitions par_name = par[partition_num].name # 2. release partition conn = self.connection_wrap.get_connection()[0] conn.release_partitions(collection_w.name, [par_name]) # 3. Search the released partition log.info("test_search_release_partition: Searching specifying the released partition") limit = 10 collection_w.search(vectors, default_search_field, default_search_params, limit, default_search_exp, [par_name], check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "partition has been released"}) @pytest.mark.tags(CaseLabel.L1) def test_search_with_empty_collection(self): """ target: test search with empty connection method: 1. search the empty collection before load 2. search the empty collection after load expected: 1. raise exception if not loaded 2. return topk=0 if loaded """ # 1. initialize without data collection_w = self.init_collection_general(prefix)[0] # 2. search collection without data before load log.info("test_search_with_empty_collection: Searching empty collection %s" % collection_w.name) err_msg = "collection" + collection_w.name + "was not loaded into memory" collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, timeout=1, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": err_msg}) # 3. search collection without data after load collection_w.load() collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": [], "limit": 0}) # 4. search with data inserted but not load again data = cf.gen_default_dataframe_data(nb=2000) insert_res, _ = collection_w.insert(data) sleep(0.2) # insert_ids = [] # insert_ids.extend(insert_res.primary_keys) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_res.primary_keys, "limit": default_limit}) @pytest.mark.tags(CaseLabel.L1) def test_search_partition_deleted(self): """ target: test search deleted partition method: 1. search the collection 2. delete a partition 3. search the deleted partition expected: raise exception and report the error """ # 1. initialize with data partition_num = 1 collection_w = self.init_collection_general(prefix, True, 1000, partition_num)[0] # 2. delete partitions log.info("test_search_partition_deleted: deleting a partition") par = collection_w.partitions deleted_par_name = par[partition_num].name collection_w.drop_partition(deleted_par_name) log.info("test_search_partition_deleted: deleted a partition") collection_w.load() # 3. search after delete partitions log.info("test_search_partition_deleted: searching deleted partition") collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, [deleted_par_name], check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "PartitonName: %s not found" % deleted_par_name}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.xfail(reason="issue 6731") @pytest.mark.parametrize("index, params", zip(ct.all_index_types[:9], ct.default_index_params[:9])) def test_search_different_index_invalid_params(self, index, params): """ target: test search with different index method: test search with different index expected: searched successfully """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000, partition_num=1, is_index=True) # 2. create different index if params.get("m"): if (default_dim % params["m"]) != 0: params["m"] = default_dim // 4 log.info("test_search_different_index_invalid_params: Creating index-%s" % index) default_index = {"index_type": index, "params": params, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) log.info("test_search_different_index_invalid_params: Created index-%s" % index) collection_w.load() # 3. search log.info("test_search_different_index_invalid_params: Searching after creating index-%s" % index) collection_w.search(vectors, default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": default_limit}) @pytest.mark.tags(CaseLabel.L1) def test_search_index_partition_not_existed(self): """ target: test search not existed partition method: search with not existed partition expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix, True)[0] # 2. create index default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) # 3. search the non exist partition partition_name = "search_non_exist" collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, [partition_name], check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "PartitonName: %s not found" % partition_name}) @pytest.mark.tags(CaseLabel.L1) def test_search_param_invalid_binary(self): """ target: test search within binary data (invalid parameter) method: search with wrong metric type expected: raise exception and report the error """ # 1. initialize with binary data collection_w = self.init_collection_general(prefix, True, is_binary=True)[0] # 2. create index default_index = {"index_type": "BIN_IVF_FLAT", "params": {"nlist": 128}, "metric_type": "JACCARD"} collection_w.create_index("binary_vector", default_index) # 3. search with exception binary_vectors = cf.gen_binary_vectors(3000, default_dim)[1] wrong_search_params = {"metric_type": "L2", "params": {"nprobe": 10}} collection_w.search(binary_vectors[:default_nq], "binary_vector", wrong_search_params, default_limit, default_search_exp, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "unsupported"}) @pytest.mark.tags(CaseLabel.L2) def test_search_binary_flat_with_L2(self): """ target: search binary collection using FlAT with L2 method: search binary collection using FLAT with L2 expected: raise exception and report error """ # 1. initialize with binary data collection_w = self.init_collection_general(prefix, True, is_binary=True)[0] # 2. search and assert query_raw_vector, binary_vectors = cf.gen_binary_vectors(2, default_dim) search_params = {"metric_type": "L2", "params": {"nprobe": 10}} collection_w.search(binary_vectors[:default_nq], "binary_vector", search_params, default_limit, "int64 >= 0", check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "Search failed"}) @pytest.mark.tags(CaseLabel.L1) def test_search_with_output_fields_not_exist(self): """ target: test search with output fields method: search with non-exist output_field expected: raise exception """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True) # 2. search log.info("test_search_with_output_fields_not_exist: Searching collection %s" % collection_w.name) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, output_fields=["int63"], check_task=CheckTasks.err_res, check_items={ct.err_code: 1, ct.err_msg: 'Field int63 not exist'}) @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("output_fields", [[default_search_field], ["%"]]) def test_search_output_field_vector(self, output_fields): """ target: test search with vector as output field method: search with one vector output_field or wildcard for vector expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix, True)[0] # 2. search log.info("test_search_output_field_vector: Searching collection %s" % collection_w.name) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, output_fields=output_fields, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": "Search doesn't support " "vector field as output_fields"}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("output_fields", [["*%"], ["**"], ["*", "@"]]) def test_search_output_field_invalid_wildcard(self, output_fields): """ target: test search with invalid output wildcard method: search with invalid output_field wildcard expected: raise exception and report the error """ # 1. initialize with data collection_w = self.init_collection_general(prefix, True)[0] # 2. search log.info("test_search_output_field_vector: Searching collection %s" % collection_w.name) collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, output_fields=output_fields, check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": f"Field {output_fields[-1]} not exist"}) class TestCollectionSearch(TestcaseBase): """ Test case of search interface """ @pytest.fixture(scope="function", params=[default_nb, default_nb_medium]) def nb(self, request): yield request.param @pytest.fixture(scope="function", params=[2, 500]) def nq(self, request): yield request.param @pytest.fixture(scope="function", params=[8, 128]) def dim(self, request): yield request.param @pytest.fixture(scope="function", params=[False, True]) def auto_id(self, request): yield request.param @pytest.fixture(scope="function", params=[False, True]) def _async(self, request): yield request.param """ ****************************************************************** # The following are valid base cases ****************************************************************** """ @pytest.mark.tags(CaseLabel.L0) def test_search_normal(self, nq, dim, auto_id): """ target: test search normal case method: create connection, collection, insert and search expected: search successfully with limit(topK) """ # 1. initialize with data collection_w, _, _, insert_ids = \ self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim) # 2. search log.info("test_search_normal: searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit}) @pytest.mark.tag(CaseLabel.L0) def test_search_with_hit_vectors(self, nq, dim, auto_id): """ target: test search with vectors in collections method: create connections,collection insert and search vectors in collections expected: search successfully with limit(topK) and can be hit at top 1 (min distance is 0) """ collection_w, _vectors, _, insert_ids = \ self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim) # get vectors that inserted into collection vectors = np.array(_vectors[0]).tolist() vectors = [vectors[i][-1] for i in range(nq)] log.info("test_search_with_hit_vectors: searching collection %s" % collection_w.name) search_res, _ = collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit}) log.info("test_search_with_hit_vectors: checking the distance of top 1") for hits in search_res: # verify that top 1 hit is itself,so min distance is 0 assert hits.distances[0] == 0.0 @pytest.mark.tags(CaseLabel.L1) def test_search_with_empty_vectors(self, dim, auto_id, _async): """ target: test search with empty query vector method: search using empty query vector expected: search successfully with 0 results """ # 1. initialize without data collection_w = self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim)[0] # 2. search collection without data log.info("test_search_with_empty_vectors: Searching collection %s " "using empty vector" % collection_w.name) collection_w.search([], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": 0, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("search_params", [{}, {"params": {}}, {"params": {"nprobe": 10}}]) def test_search_normal_default_params(self, dim, auto_id, search_params, _async): """ target: test search normal case method: create connection, collection, insert and search expected: search successfully with limit(topK) """ # 1. initialize with data collection_w, _, _, insert_ids = \ self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim) # 2. search log.info("test_search_normal: searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)] collection_w.search(vectors[:default_nq], default_search_field, search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L1) def test_search_before_after_delete(self, nq, dim, auto_id, _async): """ target: test search function before and after deletion method: 1. search the collection 2. delete a partition 3. search the collection expected: the deleted entities should not be searched """ # 1. initialize with data nb = 1000 limit = 1000 partition_num = 1 collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, partition_num, auto_id=auto_id, dim=dim) # 2. search all the partitions before partition deletion vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] log.info("test_search_before_after_delete: searching before deleting partitions") collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": limit, "_async": _async}) # 3. delete partitions log.info("test_search_before_after_delete: deleting a partition") par = collection_w.partitions deleted_entity_num = par[partition_num].num_entities entity_num = nb - deleted_entity_num collection_w.drop_partition(par[partition_num].name) log.info("test_search_before_after_delete: deleted a partition") collection_w.load() # 4. search non-deleted part after delete partitions log.info("test_search_before_after_delete: searching after deleting partitions") collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids[:entity_num], "limit": limit - deleted_entity_num, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_partition_after_release_one(self, nq, dim, auto_id, _async): """ target: test search function before and after release method: 1. search the collection 2. release a partition 3. search the collection expected: the deleted entities should not be searched """ # 1. initialize with data nb = 1000 limit = 1000 partition_num = 1 collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, partition_num, auto_id=auto_id, dim=dim) # 2. search all the partitions before partition deletion vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] log.info("test_search_partition_after_release_one: searching before deleting partitions") collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": limit, "_async": _async}) # 3. release one partition log.info("test_search_partition_after_release_one: releasing a partition") par = collection_w.partitions deleted_entity_num = par[partition_num].num_entities entity_num = nb - deleted_entity_num conn = self.connection_wrap.get_connection()[0] conn.release_partitions(collection_w.name, [par[partition_num].name]) log.info("test_search_partition_after_release_one: released a partition") # 4. search collection after release one partition log.info("test_search_partition_after_release_one: searching after deleting partitions") collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids[:entity_num], "limit": limit - deleted_entity_num, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_partition_after_release_all(self, nq, dim, auto_id, _async): """ target: test search function before and after release method: 1. search the collection 2. release all partitions 3. search the collection expected: 0 entity should be searched """ # 1. initialize with data nb = 1000 limit = 1000 collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, 1, auto_id=auto_id, dim=dim) # 2. search all the partitions before partition deletion vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] log.info("test_search_partition_after_release_all: searching before deleting partitions") collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": limit, "_async": _async}) # 3. release all partitions log.info("test_search_partition_after_release_all: releasing a partition") par = collection_w.partitions conn = self.connection_wrap.get_connection()[0] conn.release_partitions(collection_w.name, [par[0].name, par[1].name]) log.info("test_search_partition_after_release_all: released a partition") # 4. search collection after release all partitions collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": [], "limit": 0, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_collection_after_release_load(self, nb, nq, dim, auto_id, _async): """ target: search the pre-released collection after load method: 1. create collection 2. release collection 3. load collection 4. search the pre-released collection expected: search successfully """ # 1. initialize without data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, 1, auto_id=auto_id, dim=dim) # 2. release collection log.info("test_search_collection_after_release_load: releasing collection %s" % collection_w.name) collection_w.release() log.info("test_search_collection_after_release_load: released collection %s" % collection_w.name) # 3. Search the pre-released collection after load log.info("test_search_collection_after_release_load: loading collection %s" % collection_w.name) collection_w.load() log.info("test_search_collection_after_release_load: searching after load") vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.xfail(reason="issue 6997") def test_search_partition_after_release_load(self, nb, nq, dim, auto_id, _async): """ target: search the pre-released collection after load method: 1. create collection 2. release a partition 3. load partition 4. search the pre-released partition expected: search successfully """ # 1. initialize without data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, 1, auto_id=auto_id, dim=dim) # 2. release collection log.info("test_search_partition_after_release_load: releasing a partition") par = collection_w.partitions conn = self.connection_wrap.get_connection()[0] conn.release_partitions(collection_w.name, [par[1].name]) log.info("test_search_partition_after_release_load: released a partition") # 3. Search the collection after load limit = 1000 collection_w.load() log.info("test_search_partition_after_release_load: searching after load") vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": limit, "_async": _async}) # 4. Search the pre-released partition after load if limit > par[1].num_entities: limit_check = par[1].num_entities else: limit_check = limit collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, [par[1].name], _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids[par[0].num_entities:], "limit": limit_check, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_load_flush_load(self, nb, nq, dim, auto_id, _async): """ target: test search when load before flush method: 1. search the collection 2. insert data and load 3. flush, and load expected: search success with limit(topK) """ # 1. initialize with data collection_w = self.init_collection_general(prefix, auto_id=auto_id, dim=dim)[0] # 2. insert data insert_ids = cf.insert_data(collection_w, nb, auto_id=auto_id, dim=dim)[3] # 3. load data collection_w.load() # 4. flush and load collection_w.num_entities collection_w.load() # 5. search for new data without load vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_new_data(self, nq, dim, auto_id, _async): """ target: test search new inserted data without load method: 1. search the collection 2. insert new data 3. search the collection without load again expected: new data should be searched """ # 1. initialize with data limit = 1000 nb_old = 500 collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb_old, auto_id=auto_id, dim=dim) # 2. search for original data after load vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] log.info("test_search_new_data: searching for original data after load") collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": nb_old, "_async": _async}) # 3. insert new data nb_new = 300 insert_ids_new = cf.insert_data(collection_w, nb_new, auto_id=auto_id, dim=dim)[3] insert_ids.extend(insert_ids_new) # gracefulTime is default as 1s which allows data # could not be searched instantly in gracefulTime time.sleep(gracefulTime) # 4. search for new data without load collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": nb_old + nb_new, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_max_dim(self, auto_id, _async): """ target: test search with max configuration method: create connection, collection, insert and search with max dim expected: search successfully with limit(topK) """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 100, auto_id=auto_id, dim=max_dim) # 2. search nq = 2 log.info("test_search_max_dim: searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(max_dim)] for _ in range(nq)] collection_w.search(vectors[:nq], default_search_field, default_search_params, nq, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": nq, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("index, params", zip(ct.all_index_types[:9], ct.default_index_params[:9])) def test_search_after_different_index_with_params(self, dim, index, params, auto_id, _async): """ target: test search after different index method: test search after different index and corresponding search params expected: search successfully with limit(topK) """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000, partition_num=1, auto_id=auto_id, dim=dim, is_index=True) # 2. create index and load if params.get("m"): if (dim % params["m"]) != 0: params["m"] = dim // 4 if params.get("PQM"): if (dim % params["PQM"]) != 0: params["PQM"] = dim // 4 default_index = {"index_type": index, "params": params, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) collection_w.load() # 3. search search_params = cf.gen_search_param(index) vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)] for search_param in search_params: log.info("Searching with search params: {}".format(search_param)) collection_w.search(vectors[:default_nq], default_search_field, search_param, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("index, params", zip(ct.all_index_types[:9], ct.default_index_params[:9])) def test_search_after_index_different_metric_type(self, dim, index, params, auto_id, _async): """ target: test search with different metric type method: test search with different metric type expected: searched successfully """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000, partition_num=1, auto_id=auto_id, dim=dim, is_index=True) # 2. create different index if params.get("m"): if (dim % params["m"]) != 0: params["m"] = dim // 4 if params.get("PQM"): if (dim % params["PQM"]) != 0: params["PQM"] = dim // 4 log.info("test_search_after_index_different_metric_type: Creating index-%s" % index) default_index = {"index_type": index, "params": params, "metric_type": "IP"} collection_w.create_index("float_vector", default_index) log.info("test_search_after_index_different_metric_type: Created index-%s" % index) collection_w.load() # 3. search search_params = cf.gen_search_param(index, "IP") vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)] for search_param in search_params: log.info("Searching with search params: {}".format(search_param)) collection_w.search(vectors[:default_nq], default_search_field, search_param, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_collection_multiple_times(self, nb, nq, dim, auto_id, _async): """ target: test search for multiple times method: search for multiple times expected: searched successfully """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, auto_id=auto_id, dim=dim) # 2. search for multiple times vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] for i in range(search_num): log.info("test_search_collection_multiple_times: searching round %d" % (i + 1)) collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_sync_async_multiple_times(self, nb, nq, dim, auto_id): """ target: test async search after sync search case method: create connection, collection, insert, sync search and async search expected: search successfully with limit(topK) """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, auto_id=auto_id, dim=dim) # 2. search log.info("test_search_sync_async_multiple_times: searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] for i in range(search_num): log.info("test_search_sync_async_multiple_times: searching round %d" % (i + 1)) for _async in [False, True]: collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_multiple_vectors(self, nb, nq, dim, auto_id, _async): """ target: test search with multiple vectors method: create connection, collection with multiple vectors, insert and search expected: search successfully with limit(topK) """ # 1. connect self._connect() # 2. create collection with multiple vectors c_name = cf.gen_unique_str(prefix) fields = [cf.gen_int64_field(is_primary=True), cf.gen_float_field(), cf.gen_float_vec_field(dim=dim), cf.gen_float_vec_field(name="tmp", dim=dim)] schema = cf.gen_collection_schema(fields=fields, auto_id=auto_id) collection_w = self.collection_wrap.init_collection(c_name, schema=schema, check_task=CheckTasks.check_collection_property, check_items={"name": c_name, "schema": schema})[0] # 3. insert vectors = [[random.random() for _ in range(dim)] for _ in range(nb)] vectors_tmp = [[random.random() for _ in range(dim)] for _ in range(nb)] data = [[i for i in range(nb)], [np.float32(i) for i in range(nb)], vectors, vectors_tmp] if auto_id: data = [[np.float32(i) for i in range(nb)], vectors, vectors_tmp] res = collection_w.insert(data) insert_ids = res.primary_keys assert collection_w.num_entities == nb # 4. load collection_w.load() # 5. search all the vectors log.info("test_search_multiple_vectors: searching collection %s" % collection_w.name) collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) collection_w.search(vectors[:nq], "tmp", default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L1) def test_search_index_one_partition(self, nb, auto_id, _async): """ target: test search from partition method: search from one partition expected: searched successfully """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, partition_num=1, auto_id=auto_id, is_index=True) # 2. create index default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) collection_w.load() # 3. search in one partition log.info("test_search_index_one_partition: searching (1000 entities) through one partition") limit = 1000 par = collection_w.partitions if limit > par[1].num_entities: limit_check = par[1].num_entities else: limit_check = limit search_params = {"metric_type": "L2", "params": {"nprobe": 128}} collection_w.search(vectors[:default_nq], default_search_field, search_params, limit, default_search_exp, [par[1].name], _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids[par[0].num_entities:], "limit": limit_check, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_index_partitions(self, nb, nq, dim, auto_id, _async): """ target: test search from partitions method: search from partitions expected: searched successfully """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, partition_num=1, auto_id=auto_id, dim=dim, is_index=True) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] # 2. create index default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) collection_w.load() # 3. search through partitions log.info("test_search_index_partitions: searching (1000 entities) through partitions") par = collection_w.partitions log.info("test_search_index_partitions: partitions: %s" % par) limit = 1000 collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, [par[0].name, par[1].name], _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("partition_names", [["(.*)"], ["search(.*)"]]) def test_search_index_partitions_fuzzy(self, nb, nq, dim, partition_names, auto_id, _async): """ target: test search from partitions method: search from partitions with fuzzy partition name expected: searched successfully """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, partition_num=1, auto_id=auto_id, dim=dim) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] # 2. create index default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) # 3. search through partitions log.info("test_search_index_partitions_fuzzy: searching through partitions") limit = 1000 limit_check = limit par = collection_w.partitions if partition_names == ["search(.*)"]: insert_ids = insert_ids[par[0].num_entities:] if limit > par[1].num_entities: limit_check = par[1].num_entities collection_w.search(vectors[:nq], default_search_field, default_search_params, limit, default_search_exp, partition_names, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": limit_check, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_index_partition_empty(self, nq, dim, auto_id, _async): """ target: test search the empty partition method: search from the empty partition expected: searched successfully with 0 results """ # 1. initialize with data collection_w = self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_index=True)[0] vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] # 2. create empty partition partition_name = "search_partition_empty" collection_w.create_partition(partition_name=partition_name, description="search partition empty") par = collection_w.partitions log.info("test_search_index_partition_empty: partitions: %s" % par) collection_w.load() # 3. create index default_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} collection_w.create_index("float_vector", default_index) # 4. search the empty partition log.info("test_search_index_partition_empty: searching %s " "entities through empty partition" % default_limit) collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, [partition_name], _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": [], "limit": 0, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT"]) def test_search_binary_jaccard_flat_index(self, nq, dim, auto_id, _async, index): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with JACCARD expected: the return distance equals to the computed value """ # 1. initialize with binary data collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2, is_binary=True, auto_id=auto_id, dim=dim, is_index=True) # 2. create index default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "JACCARD"} collection_w.create_index("binary_vector", default_index) collection_w.load() # 3. compute the distance query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, dim) distance_0 = cf.jaccard(query_raw_vector[0], binary_raw_vector[0]) distance_1 = cf.jaccard(query_raw_vector[0], binary_raw_vector[1]) # 4. search and compare the distance search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}} res = collection_w.search(binary_vectors[:nq], "binary_vector", search_params, default_limit, "int64 >= 0", _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": 2, "_async": _async})[0] if _async: res.done() res = res.result() assert abs(res[0].distances[0] - min(distance_0, distance_1)) <= epsilon @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT"]) def test_search_binary_hamming_flat_index(self, nq, dim, auto_id, _async, index): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with HAMMING expected: the return distance equals to the computed value """ # 1. initialize with binary data collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2, is_binary=True, auto_id=auto_id, dim=dim, is_index=True) # 2. create index default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "HAMMING"} collection_w.create_index("binary_vector", default_index) # 3. compute the distance collection_w.load() query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, dim) distance_0 = cf.hamming(query_raw_vector[0], binary_raw_vector[0]) distance_1 = cf.hamming(query_raw_vector[0], binary_raw_vector[1]) # 4. search and compare the distance search_params = {"metric_type": "HAMMING", "params": {"nprobe": 10}} res = collection_w.search(binary_vectors[:nq], "binary_vector", search_params, default_limit, "int64 >= 0", _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": 2, "_async": _async})[0] if _async: res.done() res = res.result() assert abs(res[0].distances[0] - min(distance_0, distance_1)) <= epsilon @pytest.mark.tags(CaseLabel.L2) @pytest.mark.xfail(reason="issue 6843") @pytest.mark.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT"]) def test_search_binary_tanimoto_flat_index(self, nq, dim, auto_id, _async, index): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with TANIMOTO expected: the return distance equals to the computed value """ # 1. initialize with binary data collection_w, _, binary_raw_vector, insert_ids = self.init_collection_general(prefix, True, 2, is_binary=True, auto_id=auto_id, dim=dim, is_index=True) log.info("auto_id= %s, _async= %s" % (auto_id, _async)) # 2. create index default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "TANIMOTO"} collection_w.create_index("binary_vector", default_index) collection_w.load() # 3. compute the distance query_raw_vector, binary_vectors = cf.gen_binary_vectors(3000, dim) distance_0 = cf.tanimoto(query_raw_vector[0], binary_raw_vector[0]) distance_1 = cf.tanimoto(query_raw_vector[0], binary_raw_vector[1]) # 4. search and compare the distance search_params = {"metric_type": "TANIMOTO", "params": {"nprobe": 10}} res = collection_w.search(binary_vectors[:nq], "binary_vector", search_params, default_limit, "int64 >= 0", _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": 2, "_async": _async})[0] if _async: res.done() res = res.result() assert abs(res[0].distances[0] - min(distance_0, distance_1)) <= epsilon @pytest.mark.tags(CaseLabel.L1) @pytest.mark.parametrize("expression", cf.gen_normal_expressions()) def test_search_with_expression(self, dim, expression, _async): """ target: test search with different expressions method: test search with different expressions expected: searched successfully with correct limit(topK) """ # 1. initialize with data nb = 1000 collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True, nb, dim=dim, is_index=True) # filter result with expression in collection _vectors = _vectors[0] expression = expression.replace("&&", "and").replace("||", "or") filter_ids = [] for i, _id in enumerate(insert_ids): int64 = _vectors.int64[i] float = _vectors.float[i] if not expression or eval(expression): filter_ids.append(_id) # 2. create index index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}} collection_w.create_index("float_vector", index_param) collection_w.load() # 3. search with expression log.info("test_search_with_expression: searching with expression: %s" % expression) vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)] search_res, _ = collection_w.search(vectors[:default_nq], default_search_field, default_search_params, nb, expression, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": min(nb, len(filter_ids)), "_async": _async}) if _async: search_res.done() search_res = search_res.result() filter_ids_set = set(filter_ids) for hits in search_res: ids = hits.ids assert set(ids).issubset(filter_ids_set) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.xfail(reason="issue 7910") @pytest.mark.parametrize("bool_type", [True, False, "true", "false", 1, 0, 2]) def test_search_with_expression_bool(self, dim, auto_id, _async, bool_type): """ target: test search with different bool expressions method: search with different bool expressions expected: searched successfully with correct limit(topK) """ # 1. initialize with data nb = 1000 collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True, nb, is_all_data_type=True, auto_id=auto_id, dim=dim) # 2. create index index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}} collection_w.create_index("float_vector", index_param) collection_w.load() # 3. filter result with expression in collection filter_ids = [] bool_type_cmp = bool_type if bool_type == "true": bool_type_cmp = True if bool_type == "false": bool_type_cmp = False for i, _id in enumerate(insert_ids): if _vectors[0][f"{default_bool_field_name}"][i] == bool_type_cmp: filter_ids.append(_id) # 4. search with different expressions expression = f"{default_bool_field_name} == {bool_type}" log.info("test_search_with_expression: searching with expression: %s" % expression) vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)] search_res, _ = collection_w.search(vectors[:default_nq], default_search_field, default_search_params, nb, expression, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": min(nb, len(filter_ids)), "_async": _async}) if _async: search_res.done() search_res = search_res.result() filter_ids_set = set(filter_ids) for hits in search_res: ids = hits.ids assert set(ids).issubset(filter_ids_set) @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("expression", cf.gen_normal_expressions_field(default_float_field_name)) def test_search_with_expression_auto_id(self, dim, expression, _async): """ target: test search with different expressions method: test search with different expressions expected: searched successfully with correct limit(topK) """ # 1. initialize with data nb = 1000 collection_w, _vectors, _, insert_ids = self.init_collection_general(prefix, True, nb, auto_id=True, dim=dim, is_index=True) # filter result with expression in collection _vectors = _vectors[0] expression = expression.replace("&&", "and").replace("||", "or") filter_ids = [] for i, _id in enumerate(insert_ids): exec(f"{default_float_field_name} = _vectors.{default_float_field_name}[i]") if not expression or eval(expression): filter_ids.append(_id) # 2. create index index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}} collection_w.create_index("float_vector", index_param) collection_w.load() # 3. search with different expressions log.info("test_search_with_expression: searching with expression: %s" % expression) vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)] search_res, _ = collection_w.search(vectors[:default_nq], default_search_field, default_search_params, nb, expression, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": min(nb, len(filter_ids)), "_async": _async}) if _async: search_res.done() search_res = search_res.result() filter_ids_set = set(filter_ids) for hits in search_res: ids = hits.ids assert set(ids).issubset(filter_ids_set) @pytest.mark.tags(CaseLabel.L2) def test_search_expression_all_data_type(self, nb, nq, dim, auto_id, _async): """ target: test search using different supported data type method: search using different supported data type expected: search success """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, is_all_data_type=True, auto_id=auto_id, dim=dim) # 2. search log.info("test_search_expression_all_data_type: Searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] search_exp = "int64 >= 0 && int32 >= 0 && int16 >= 0 " \ "&& int8 >= 0 && float >= 0 && double >= 0" res = collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, search_exp, _async=_async, output_fields=[default_int64_field_name, default_float_field_name], check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async})[0] if _async: res.done() res = res.result() assert len(res[0][0].entity._row_data) != 0 assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data @pytest.mark.tags(CaseLabel.L2) def test_search_with_output_fields_empty(self, nb, nq, dim, auto_id, _async): """ target: test search with output fields method: search with empty output_field expected: search success """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, auto_id=auto_id, dim=dim) # 2. search log.info("test_search_with_output_fields_empty: Searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] res = collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, output_fields=[], check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async})[0] if _async: res.done() res = res.result() assert len(res[0][0].entity._row_data) == 0 @pytest.mark.tags(CaseLabel.L1) def test_search_with_output_field(self, auto_id, _async): """ target: test search with output fields method: search with one output_field expected: search success """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, auto_id=auto_id) # 2. search log.info("test_search_with_output_field: Searching collection %s" % collection_w.name) res = collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, output_fields=[default_int64_field_name], check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": default_limit, "_async": _async})[0] if _async: res.done() res = res.result() assert len(res[0][0].entity._row_data) != 0 assert default_int64_field_name in res[0][0].entity._row_data @pytest.mark.tags(CaseLabel.L2) def test_search_with_output_fields(self, nb, nq, dim, auto_id, _async): """ target: test search with output fields method: search with multiple output_field expected: search success """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, is_all_data_type=True, auto_id=auto_id, dim=dim) # 2. search log.info("test_search_with_output_fields: Searching collection %s" % collection_w.name) vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] res = collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, output_fields=[default_int64_field_name, default_float_field_name], check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async})[0] if _async: res.done() res = res.result() assert len(res[0][0].entity._row_data) != 0 assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data @pytest.mark.tags(CaseLabel.L2) @pytest.mark.parametrize("output_fields", [["*"], ["*", default_float_field_name]]) def test_search_with_output_field_wildcard(self, output_fields, auto_id, _async): """ target: test search with output fields using wildcard method: search with one output_field (wildcard) expected: search success """ # 1. initialize with data collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, auto_id=auto_id) # 2. search log.info("test_search_with_output_field_wildcard: Searching collection %s" % collection_w.name) res = collection_w.search(vectors[:default_nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, output_fields=output_fields, check_task=CheckTasks.check_search_results, check_items={"nq": default_nq, "ids": insert_ids, "limit": default_limit, "_async": _async})[0] if _async: res.done() res = res.result() assert len(res[0][0].entity._row_data) != 0 assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data @pytest.mark.tags(CaseLabel.L2) def test_search_multi_collections(self, nb, nq, dim, auto_id, _async): """ target: test search multi collections of L2 method: add vectors into 10 collections, and search expected: search status ok, the length of result """ self._connect() collection_num = 10 for i in range(collection_num): # 1. initialize with data log.info("test_search_multi_collections: search round %d" % (i + 1)) collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, auto_id=auto_id, dim=dim) # 2. search vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] log.info("test_search_multi_collections: searching %s entities (nq = %s) from collection %s" % (default_limit, nq, collection_w.name)) collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) @pytest.mark.tags(CaseLabel.L2) def test_search_concurrent_multi_threads(self, nb, nq, dim, auto_id, _async): """ target: test concurrent search with multi-processes method: search with 10 processes, each process uses dependent connection expected: status ok and the returned vectors should be query_records """ # 1. initialize with data threads_num = 10 threads = [] collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, nb, auto_id=auto_id, dim=dim) def search(collection_w): vectors = [[random.random() for _ in range(dim)] for _ in range(nq)] collection_w.search(vectors[:nq], default_search_field, default_search_params, default_limit, default_search_exp, _async=_async, check_task=CheckTasks.check_search_results, check_items={"nq": nq, "ids": insert_ids, "limit": default_limit, "_async": _async}) # 2. search with multi-processes log.info("test_search_concurrent_multi_threads: searching with %s processes" % threads_num) for i in range(threads_num): t = threading.Thread(target=search, args=(collection_w,)) threads.append(t) t.start() time.sleep(0.2) for t in threads: t.join() """ ****************************************************************** # The following cases are copied from test_search.py ****************************************************************** """ def init_data(connect, collection, nb=3000, partition_names=None, auto_id=True): """ Generate entities and add it in collection """ global entities if nb == 3000: insert_entities = entities else: insert_entities = gen_entities(nb, is_normal=True) if partition_names is None: res = connect.insert(collection, insert_entities) else: res = connect.insert(collection, insert_entities, partition_name=partition_names) connect.flush([collection]) ids = res.primary_keys return insert_entities, ids def init_binary_data(connect, collection, nb=3000, insert=True, partition_names=None): """ Generate entities and add it in collection """ ids = [] global binary_entities global raw_vectors if nb == 3000: insert_entities = binary_entities insert_raw_vectors = raw_vectors else: insert_raw_vectors, insert_entities = gen_binary_entities(nb) if insert is True: if partition_names is None: res = connect.insert(collection, insert_entities) else: res = connect.insert(collection, insert_entities, partition_name=partition_names) connect.flush([collection]) ids = res.primary_keys return insert_raw_vectors, insert_entities, ids def check_id_result(result, id): limit_in = 5 ids = [entity.id for entity in result] if len(result) >= limit_in: return id in ids[:limit_in] else: return id in ids class TestSearchBase: """ generate valid create_index params """ @pytest.fixture( scope="function", params=gen_index() ) def get_index(self, request, connect): # if str(connect._cmd("mode")) == "CPU": # if request.param["index_type"] in index_cpu_not_support(): # pytest.skip("sq8h not support in CPU mode") return request.param @pytest.fixture( scope="function", params=gen_simple_index() ) def get_simple_index(self, request, connect): # if str(connect._cmd("mode")) == "CPU": # if request.param["index_type"] in index_cpu_not_support(): # pytest.skip("sq8h not support in CPU mode") return copy.deepcopy(request.param) @pytest.fixture( scope="function", params=gen_binary_index() ) def get_jaccard_index(self, request, connect): logging.getLogger().info(request.param) if request.param["index_type"] in binary_support(): return request.param # else: # pytest.skip("Skip index Temporary") @pytest.fixture( scope="function", params=gen_binary_index() ) def get_hamming_index(self, request, connect): logging.getLogger().info(request.param) if request.param["index_type"] in binary_support(): return request.param # else: # pytest.skip("Skip index Temporary") @pytest.fixture( scope="function", params=gen_binary_index() ) def get_structure_index(self, request, connect): logging.getLogger().info(request.param) if request.param["index_type"] == "FLAT": return request.param # else: # pytest.skip("Skip index Temporary") """ generate top-k params """ @pytest.fixture( scope="function", params=[1, 10] ) def get_top_k(self, request): yield request.param @pytest.fixture( scope="function", params=[1, 10, 1100] ) def get_nq(self, request): yield request.param @pytest.mark.tags(CaseLabel.L2) def test_search_flat_top_k(self, connect, collection, get_nq): """ target: test basic search function, all the search params is correct, change top-k value method: search with the given vectors, check the result expected: the length of the result is top_k """ top_k = 16385 # max top k is 16384 nq = get_nq entities, ids = init_data(connect, collection) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq) if top_k <= max_top_k: connect.load_collection(collection) res = connect.search(collection, **query) assert len(res[0]) == top_k assert res[0]._distances[0] <= epsilon assert check_id_result(res[0], ids[0]) else: with pytest.raises(Exception) as e: res = connect.search(collection, **query) @pytest.mark.skip("r0.3-test") def _test_search_field(self, connect, collection, get_top_k, get_nq): """ target: test basic search function, all the search params is correct, change top-k value method: search with the given vectors, check the result expected: the length of the result is top_k """ top_k = get_top_k nq = get_nq entities, ids = init_data(connect, collection) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq) if top_k <= max_top_k: connect.load_collection(collection) res = connect.search(collection, **query, fields=["float_vector"]) assert len(res[0]) == top_k assert res[0]._distances[0] <= epsilon assert check_id_result(res[0], ids[0]) res = connect.search(collection, **query, fields=["float"]) for i in range(nq): assert entities[1]["values"][:nq][i] in [r.entity.get('float') for r in res[i]] else: with pytest.raises(Exception): connect.search(collection, **query) def _test_search_after_delete(self, connect, collection, get_top_k, get_nq): """ target: test basic search function before and after deletion, all the search params is correct, change top-k value. check issue #4200 method: search with the given vectors, check the result expected: the deleted entities do not exist in the result. """ top_k = get_top_k nq = get_nq entities, ids = init_data(connect, collection, nb=10000) first_int64_value = entities[0]["values"][0] first_vector = entities[2]["values"][0] search_param = get_search_param("FLAT") query, vecs = gen_search_vectors_params(field_name, entities, top_k, nq, search_params=search_param) vecs[:] = [] vecs.append(first_vector) res = None if top_k > max_top_k: with pytest.raises(Exception): connect.search(collection, **query, fields=['int64']) # pytest.skip("top_k value is larger than max_topp_k") pass else: res = connect.search(collection, **query, fields=['int64']) assert len(res) == 1 assert len(res[0]) >= top_k assert res[0][0].id == ids[0] assert res[0][0].entity.get("int64") == first_int64_value assert res[0]._distances[0] < epsilon assert check_id_result(res[0], ids[0]) connect.delete_entity_by_id(collection, ids[:1]) connect.flush([collection]) res2 = connect.search(collection, **query, fields=['int64']) assert len(res2) == 1 assert len(res2[0]) >= top_k assert res2[0][0].id != ids[0] if top_k > 1: assert res2[0][0].id == res[0][1].id assert res2[0][0].entity.get("int64") == res[0][1].entity.get("int64") @pytest.mark.tags(CaseLabel.L2) def test_search_index_empty_partition(self, connect, collection, get_simple_index, get_top_k, get_nq): """ target: test basic search function, all the search params is correct, test all index params, and build method: add vectors into collection, search with the given vectors, check the result expected: the length of the result is top_k, search collection with partition tag return empty """ top_k = get_top_k nq = get_nq index_type = get_simple_index["index_type"] if index_type in skip_pq(): pytest.skip("Skip PQ") connect.create_partition(collection, default_tag) entities, ids = init_data(connect, collection) connect.create_index(collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq, search_params=search_param) if top_k > max_top_k: with pytest.raises(Exception) as e: res = connect.search(collection, **query) else: connect.load_collection(collection) res = connect.search(collection, **query) assert len(res) == nq assert len(res[0]) >= top_k assert res[0]._distances[0] < epsilon assert check_id_result(res[0], ids[0]) connect.release_collection(collection) connect.load_partitions(collection, [default_tag]) res = connect.search(collection, **query, partition_names=[default_tag]) assert len(res[0]) == 0 @pytest.mark.tags(CaseLabel.L2) def test_search_index_partitions(self, connect, collection, get_simple_index, get_top_k): """ target: test basic search function, all the search params is correct, test all index params, and build method: search collection with the given vectors and tags, check the result expected: the length of the result is top_k """ top_k = get_top_k nq = 2 new_tag = "new_tag" index_type = get_simple_index["index_type"] if index_type in skip_pq(): pytest.skip("Skip PQ") connect.create_partition(collection, default_tag) connect.create_partition(collection, new_tag) entities, ids = init_data(connect, collection, partition_names=default_tag) new_entities, new_ids = init_data(connect, collection, nb=6001, partition_names=new_tag) connect.create_index(collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq, search_params=search_param) if top_k > max_top_k: with pytest.raises(Exception) as e: res = connect.search(collection, **query) else: connect.load_collection(collection) res = connect.search(collection, **query) assert check_id_result(res[0], ids[0]) assert not check_id_result(res[1], new_ids[0]) assert res[0]._distances[0] < epsilon assert res[1]._distances[0] < epsilon res = connect.search(collection, **query, partition_names=[new_tag]) assert res[0]._distances[0] > epsilon assert res[1]._distances[0] > epsilon connect.release_collection(collection) @pytest.mark.tags(CaseLabel.L2) def test_search_ip_flat(self, connect, collection, get_simple_index, get_top_k, get_nq): """ target: test basic search function, all the search params is correct, change top-k value method: search with the given vectors, check the result expected: the length of the result is top_k """ top_k = get_top_k nq = get_nq entities, ids = init_data(connect, collection) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq, metric_type="IP") connect.load_collection(collection) res = connect.search(collection, **query) assert len(res[0]) == top_k assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) assert check_id_result(res[0], ids[0]) @pytest.mark.tags(CaseLabel.L2) def test_search_ip_after_index(self, connect, collection, get_simple_index, get_top_k, get_nq): """ target: test basic search function, all the search params is correct, test all index params, and build method: search with the given vectors, check the result expected: the length of the result is top_k """ top_k = get_top_k nq = get_nq index_type = get_simple_index["index_type"] if index_type in skip_pq(): pytest.skip("Skip PQ") entities, ids = init_data(connect, collection) get_simple_index["metric_type"] = "IP" connect.create_index(collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq, metric_type="IP", search_params=search_param) connect.load_collection(collection) res = connect.search(collection, **query) assert len(res) == nq assert len(res[0]) >= top_k assert check_id_result(res[0], ids[0]) assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) @pytest.mark.tags(CaseLabel.L2) def test_search_ip_index_empty_partition(self, connect, collection, get_simple_index, get_top_k, get_nq): """ target: test basic search function, all the search params is correct, test all index params, and build method: add vectors into collection, search with the given vectors, check the result expected: the length of the result is top_k, search collection with partition tag return empty """ top_k = get_top_k nq = get_nq metric_type = "IP" index_type = get_simple_index["index_type"] if index_type in skip_pq(): pytest.skip("Skip PQ") connect.create_partition(collection, default_tag) entities, ids = init_data(connect, collection) get_simple_index["metric_type"] = metric_type connect.create_index(collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq, metric_type=metric_type, search_params=search_param) if top_k > max_top_k: with pytest.raises(Exception) as e: res = connect.search(collection, **query) else: connect.load_collection(collection) res = connect.search(collection, **query) assert len(res) == nq assert len(res[0]) >= top_k assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) assert check_id_result(res[0], ids[0]) res = connect.search(collection, **query, partition_names=[default_tag]) assert len(res[0]) == 0 @pytest.mark.tags(CaseLabel.L2) def test_search_ip_index_partitions(self, connect, collection, get_simple_index, get_top_k): """ target: test basic search function, all the search params is correct, test all index params, and build method: search collection with the given vectors and tags, check the result expected: the length of the result is top_k """ top_k = get_top_k nq = 2 metric_type = "IP" new_tag = "new_tag" index_type = get_simple_index["index_type"] if index_type in skip_pq(): pytest.skip("Skip PQ") connect.create_partition(collection, default_tag) connect.create_partition(collection, new_tag) entities, ids = init_data(connect, collection, partition_names=default_tag) new_entities, new_ids = init_data(connect, collection, nb=6001, partition_names=new_tag) get_simple_index["metric_type"] = metric_type connect.create_index(collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, _ = gen_search_vectors_params(field_name, entities, top_k, nq, metric_type="IP", search_params=search_param) connect.load_collection(collection) res = connect.search(collection, **query) assert check_id_result(res[0], ids[0]) assert not check_id_result(res[1], new_ids[0]) assert res[0]._distances[0] >= 1 - gen_inaccuracy(res[0]._distances[0]) assert res[1]._distances[0] >= 1 - gen_inaccuracy(res[1]._distances[0]) res = connect.search(collection, **query, partition_names=["new_tag"]) assert res[0]._distances[0] < 1 - gen_inaccuracy(res[0]._distances[0]) # TODO: # assert res[1]._distances[0] >= 1 - gen_inaccuracy(res[1]._distances[0]) @pytest.mark.tags(CaseLabel.L2) def test_search_without_connect(self, dis_connect, collection): """ target: test search vectors without connection method: use dis connected instance, call search method and check if search successfully expected: raise exception """ with pytest.raises(Exception) as e: res = dis_connect.search(collection, **default_query) @pytest.mark.tags(CaseLabel.L2) def test_search_collection_not_existed(self, connect): """ target: search collection not existed method: search with the random collection_name, which is not in db expected: status not ok """ collection_name = gen_unique_str(uid) with pytest.raises(Exception) as e: res = connect.search(collection_name, **default_query) @pytest.mark.tags(CaseLabel.L0) def test_search_distance_l2(self, connect, collection): """ target: search collection, and check the result: distance method: compare the return distance value with value computed with Euclidean expected: the return distance equals to the computed value """ nq = 2 search_param = {"nprobe": 1} entities, ids = init_data(connect, collection, nb=nq) query, vecs = gen_search_vectors_params(field_name, entities, default_top_k, nq, rand_vector=True, search_params=search_param) inside_query, inside_vecs = gen_search_vectors_params(field_name, entities, default_top_k, nq, search_params=search_param) distance_0 = l2(vecs[0], inside_vecs[0]) distance_1 = l2(vecs[0], inside_vecs[1]) connect.load_collection(collection) res = connect.search(collection, **query) assert abs(np.sqrt(res[0]._distances[0]) - min(distance_0, distance_1)) <= gen_inaccuracy(res[0]._distances[0]) @pytest.mark.tags(CaseLabel.L2) def test_search_distance_l2_after_index(self, connect, id_collection, get_simple_index): """ target: search collection, and check the result: distance method: compare the return distance value with value computed with Inner product expected: the return distance equals to the computed value """ index_type = get_simple_index["index_type"] nq = 2 entities, ids = init_data(connect, id_collection, auto_id=False) connect.create_index(id_collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, vecs = gen_search_vectors_params(field_name, entities, default_top_k, nq, rand_vector=True, search_params=search_param) inside_vecs = entities[-1]["values"] min_distance = 1.0 min_id = None for i in range(default_nb): tmp_dis = l2(vecs[0], inside_vecs[i]) if min_distance > tmp_dis: min_distance = tmp_dis min_id = ids[i] connect.load_collection(id_collection) res = connect.search(id_collection, **query) tmp_epsilon = epsilon check_id_result(res[0], min_id) # if index_type in ["ANNOY", "IVF_PQ"]: # tmp_epsilon = 0.1 # TODO: # assert abs(np.sqrt(res[0]._distances[0]) - min_distance) <= tmp_epsilon @pytest.mark.tags(CaseLabel.L2) def test_search_distance_ip(self, connect, collection): """ target: search collection, and check the result: distance method: compare the return distance value with value computed with Inner product expected: the return distance equals to the computed value """ nq = 2 metirc_type = "IP" search_param = {"nprobe": 1} entities, ids = init_data(connect, collection, nb=nq) query, vecs = gen_search_vectors_params(field_name, entities, default_top_k, nq, rand_vector=True, metric_type=metirc_type, search_params=search_param) inside_query, inside_vecs = gen_search_vectors_params(field_name, entities, default_top_k, nq, search_params=search_param) distance_0 = ip(vecs[0], inside_vecs[0]) distance_1 = ip(vecs[0], inside_vecs[1]) connect.load_collection(collection) res = connect.search(collection, **query) assert abs(res[0]._distances[0] - max(distance_0, distance_1)) <= epsilon @pytest.mark.tags(CaseLabel.L2) def test_search_distance_ip_after_index(self, connect, id_collection, get_simple_index): """ target: search collection, and check the result: distance method: compare the return distance value with value computed with Inner product expected: the return distance equals to the computed value """ index_type = get_simple_index["index_type"] nq = 2 metirc_type = "IP" entities, ids = init_data(connect, id_collection, auto_id=False) get_simple_index["metric_type"] = metirc_type connect.create_index(id_collection, field_name, get_simple_index) search_param = get_search_param(index_type) query, vecs = gen_search_vectors_params(field_name, entities, default_top_k, nq, rand_vector=True, metric_type=metirc_type, search_params=search_param) inside_vecs = entities[-1]["values"] max_distance = 0 max_id = None for i in range(default_nb): tmp_dis = ip(vecs[0], inside_vecs[i]) if max_distance < tmp_dis: max_distance = tmp_dis max_id = ids[i] connect.load_collection(id_collection) res = connect.search(id_collection, **query) tmp_epsilon = epsilon check_id_result(res[0], max_id) # if index_type in ["ANNOY", "IVF_PQ"]: # tmp_epsilon = 0.1 # TODO: # assert abs(res[0]._distances[0] - max_distance) <= tmp_epsilon @pytest.mark.tags(CaseLabel.L0) def test_search_distance_jaccard_flat_index(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with L2 expected: the return distance equals to the computed value """ nq = 1 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, binary_collection, nb=1, insert=False) distance_0 = jaccard(query_int_vectors[0], int_vectors[0]) distance_1 = jaccard(query_int_vectors[0], int_vectors[1]) query, vecs = gen_search_vectors_params(binary_field_name, query_entities, default_top_k, nq, metric_type="JACCARD") connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert abs(res[0]._distances[0] - min(distance_0, distance_1)) <= epsilon @pytest.mark.tags(CaseLabel.L2) def test_search_binary_flat_with_L2(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with L2 expected: the return distance equals to the computed value """ nq = 1 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, binary_collection, nb=1, insert=False) query, vecs = gen_search_vectors_params(binary_field_name, query_entities, default_top_k, nq, metric_type="L2") with pytest.raises(Exception) as e: connect.search(binary_collection, **query) @pytest.mark.tags(CaseLabel.L2) def test_search_distance_hamming_flat_index(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with Inner product expected: the return distance equals to the computed value """ nq = 1 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, binary_collection, nb=1, insert=False) distance_0 = hamming(query_int_vectors[0], int_vectors[0]) distance_1 = hamming(query_int_vectors[0], int_vectors[1]) query, vecs = gen_search_vectors_params(binary_field_name, query_entities, default_top_k, nq, metric_type="HAMMING") connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert abs(res[0][0].distance - min(distance_0, distance_1).astype(float)) <= epsilon @pytest.mark.tags(CaseLabel.L2) def test_search_distance_substructure_flat_index(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: search with new random binary entities and SUBSTRUCTURE metric type expected: the return distance equals to the computed value """ nq = 1 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, binary_collection, nb=1, insert=False) distance_0 = substructure(query_int_vectors[0], int_vectors[0]) distance_1 = substructure(query_int_vectors[0], int_vectors[1]) query, vecs = gen_search_vectors_params(binary_field_name, query_entities, default_top_k, nq, metric_type="SUBSTRUCTURE") connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert len(res[0]) == 0 @pytest.mark.tags(CaseLabel.L2) def test_search_distance_substructure_flat_index_B(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: search with entities that related to inserted entities expected: the return distance equals to the computed value """ top_k = 3 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_vecs = gen_binary_sub_vectors(int_vectors, 2) query, vecs = gen_search_vectors_params(binary_field_name, entities, top_k, nq, metric_type="SUBSTRUCTURE", replace_vecs=query_vecs) connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert res[0][0].distance <= epsilon assert res[0][0].id == ids[0] assert res[1][0].distance <= epsilon assert res[1][0].id == ids[1] @pytest.mark.tags(CaseLabel.L2) def test_search_distance_superstructure_flat_index(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with Inner product expected: the return distance equals to the computed value """ nq = 1 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, binary_collection, nb=1, insert=False) distance_0 = superstructure(query_int_vectors[0], int_vectors[0]) distance_1 = superstructure(query_int_vectors[0], int_vectors[1]) query, vecs = gen_search_vectors_params(binary_field_name, query_entities, default_top_k, nq, metric_type="SUPERSTRUCTURE") connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert len(res[0]) == 0 @pytest.mark.tags(CaseLabel.L2) def test_search_distance_superstructure_flat_index_B(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with SUPER expected: the return distance equals to the computed value """ top_k = 3 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_vecs = gen_binary_super_vectors(int_vectors, 2) query, vecs = gen_search_vectors_params(binary_field_name, entities, top_k, nq, metric_type="SUPERSTRUCTURE", replace_vecs=query_vecs) connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert len(res[0]) == 2 assert len(res[1]) == 2 assert res[0][0].id in ids assert res[0][0].distance <= epsilon assert res[1][0].id in ids assert res[1][0].distance <= epsilon @pytest.mark.tags(CaseLabel.L2) def test_search_distance_tanimoto_flat_index(self, connect, binary_collection): """ target: search binary_collection, and check the result: distance method: compare the return distance value with value computed with Inner product expected: the return distance equals to the computed value """ nq = 1 int_vectors, entities, ids = init_binary_data(connect, binary_collection, nb=2) query_int_vectors, query_entities, tmp_ids = init_binary_data(connect, binary_collection, nb=1, insert=False) distance_0 = tanimoto(query_int_vectors[0], int_vectors[0]) distance_1 = tanimoto(query_int_vectors[0], int_vectors[1]) query, vecs = gen_search_vectors_params(binary_field_name, query_entities, default_top_k, nq, metric_type="TANIMOTO") connect.load_collection(binary_collection) res = connect.search(binary_collection, **query) assert abs(res[0][0].distance - min(distance_0, distance_1)) <= epsilon @pytest.mark.tags(CaseLabel.L2) @pytest.mark.timeout(300) def test_search_concurrent_multithreads_single_connection(self, connect, args): """ target: test concurrent search with multi processes method: search with 10 processes, each process uses dependent connection expected: status ok and the returned vectors should be query_records """ nb = 100 top_k = 10 threads_num = 4 threads = [] collection = gen_unique_str(uid) uri = "tcp://%s:%s" % (args["ip"], args["port"]) # create collection milvus = get_milvus(args["ip"], args["port"], handler=args["handler"]) milvus.create_collection(collection, default_fields) entities, ids = init_data(milvus, collection) connect.load_collection(collection) def search(milvus): res = milvus.search(collection, **default_query) assert len(res) == 1 assert res[0]._entities[0].id in ids assert res[0]._distances[0] < epsilon for i in range(threads_num): t = MyThread(target=search, args=(milvus,)) threads.append(t) t.start() time.sleep(0.2) for t in threads: t.join() @pytest.mark.tags(CaseLabel.L2) def test_search_multi_collections(self, connect, args): """ target: test search multi collections of L2 method: add vectors into 10 collections, and search expected: search status ok, the length of result """ num = 10 top_k = 10 nq = 20 collection_names = [] for i in range(num): collection = gen_unique_str(uid + str(i)) connect.create_collection(collection, default_fields) collection_names.append(collection) entities, ids = init_data(connect, collection) assert len(ids) == default_nb query, vecs = gen_search_vectors_params(field_name, entities, top_k, nq, search_params=search_param) connect.load_collection(collection) res = connect.search(collection, **query) assert len(res) == nq for i in range(nq): assert check_id_result(res[i], ids[i]) assert res[i]._distances[0] < epsilon assert res[i]._distances[1] > epsilon for i in range(num): connect.drop_collection(collection_names[i]) class TestSearchDSL(object): """ ****************************************************************** # The following cases are used to build invalid query expr ****************************************************************** """ @pytest.mark.tags(CaseLabel.L0) def test_query_vector_only(self, connect, collection): """ target: test search normal scenario method: search vector only expected: search status ok, the length of result """ entities, ids = init_data(connect, collection) connect.load_collection(collection) res = connect.search(collection, **default_query) assert len(res) == nq assert len(res[0]) == default_top_k @pytest.mark.tags(CaseLabel.L0) def test_query_empty(self, connect, collection): """ method: search with empty query expected: error raised """ query = {} with pytest.raises(Exception) as e: res = connect.search(collection, query)