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test: Add part of test cases for default and null support (#36186)
issue : #36129 Signed-off-by: binbin lv <binbin.lv@zilliz.com>
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binbin
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@ -1340,7 +1340,7 @@ class TestCollectionSearch(TestcaseBase):
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"""
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@pytest.mark.tags(CaseLabel.L0)
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def test_search_normal(self, nq, dim, auto_id, is_flush, enable_dynamic_field, vector_data_type, null_data_percent):
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def test_search_normal(self, nq, dim, auto_id, is_flush, enable_dynamic_field, vector_data_type):
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"""
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target: test search normal case
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method: create connection, collection, insert and search
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@ -1350,8 +1350,7 @@ class TestCollectionSearch(TestcaseBase):
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collection_w, _, _, insert_ids, time_stamp = \
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self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_flush=is_flush,
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enable_dynamic_field=enable_dynamic_field,
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vector_data_type=vector_data_type,
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nullable_fields={ct.default_float_field_name: null_data_percent})[0:5]
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vector_data_type=vector_data_type)[0:5]
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# 2. generate search data
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vectors = cf.gen_vectors_based_on_vector_type(nq, dim, vector_data_type)
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# 3. search after insert
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@ -1467,7 +1466,9 @@ class TestCollectionSearch(TestcaseBase):
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self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_flush=is_flush,
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enable_dynamic_field=enable_dynamic_field,
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multiple_dim_array=multiple_dim_array,
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vector_data_type=vector_data_type)[0:5]
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vector_data_type=vector_data_type,
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nullable_fields={ct.default_string_field_name: 1},
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default_value_fields={ct.default_float_field_name: np.float32(10.0)})[0:5]
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# 2. generate search data
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vectors = cf.gen_vectors_based_on_vector_type(nq, dim, vector_data_type)
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vector_name_list = cf.extract_vector_field_name_list(collection_w)
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@ -1477,10 +1478,13 @@ class TestCollectionSearch(TestcaseBase):
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collection_w.search(vectors[:nq], search_field,
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default_search_params, default_limit,
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default_search_exp,
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output_fields = [ct.default_float_field_name, ct.default_string_field_name],
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check_task=CheckTasks.check_search_results,
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check_items={"nq": nq,
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"ids": insert_ids,
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"limit": default_limit})
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"limit": default_limit,
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"output_fields": [ct.default_float_field_name,
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ct.default_string_field_name]})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_random_primary_key(self, random_primary_key):
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@ -1562,8 +1566,7 @@ class TestCollectionSearch(TestcaseBase):
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expected: search successfully
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"""
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# initialize with data
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collection_w, insert_data, _, insert_ids = self.init_collection_general(prefix, True)[
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0:4]
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collection_w, insert_data, _, insert_ids = self.init_collection_general(prefix, True)[0:4]
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# search
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collection_w.search(vectors[:nq], default_search_field,
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search_params, default_limit,
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@ -1780,8 +1783,7 @@ class TestCollectionSearch(TestcaseBase):
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auto_id = True
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enable_dynamic_field = True
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collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nb,
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1, auto_id=auto_id,
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dim=dim,
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1, auto_id=auto_id, dim=dim,
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enable_dynamic_field=enable_dynamic_field)[0:5]
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# 2. release collection
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log.info("test_search_collection_after_release_load: releasing collection %s" %
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@ -7016,6 +7018,10 @@ class TestCollectionRangeSearch(TestcaseBase):
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def enable_dynamic_field(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[0, 0.5, 1])
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def null_data_percent(self, request):
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yield request.param
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"""
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******************************************************************
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# The followings are valid range search cases
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@ -7024,7 +7030,7 @@ class TestCollectionRangeSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.parametrize("vector_data_type", ct.all_dense_vector_types)
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@pytest.mark.parametrize("with_growing", [False, True])
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def test_range_search_default(self, index_type, metric, vector_data_type, with_growing):
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def test_range_search_default(self, index_type, metric, vector_data_type, with_growing, null_data_percent):
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"""
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target: verify the range search returns correct results
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method: 1. create collection, insert 10k vectors,
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@ -7033,7 +7039,8 @@ class TestCollectionRangeSearch(TestcaseBase):
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4. verified the range search results is same as the search results in the range
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"""
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collection_w = self.init_collection_general(prefix, auto_id=True, insert_data=False, is_index=False,
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vector_data_type=vector_data_type, with_json=False)[0]
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vector_data_type=vector_data_type, with_json=False,
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nullable_fields={ct.default_float_field_name: null_data_percent})[0]
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nb = 1000
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rounds = 10
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for i in range(rounds):
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@ -10897,7 +10904,8 @@ class TestCollectionHybridSearchValid(TestcaseBase):
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self.init_collection_general(prefix, True, dim=dim, is_flush=is_flush,
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primary_field=primary_field, enable_dynamic_field=enable_dynamic_field,
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multiple_dim_array=multiple_dim_array,
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vector_data_type=vector_data_type)[0:5]
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vector_data_type=vector_data_type,
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nullable_fields={ct.default_float_field_name: 1})[0:5]
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# 2. extract vector field name
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vector_name_list = cf.extract_vector_field_name_list(collection_w)
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vector_name_list.append(ct.default_float_vec_field_name)
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@ -12925,3 +12933,432 @@ class TestSparseSearch(TestcaseBase):
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ct.default_sparse_search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
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check_items={"batch_size": batch_size})
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class TestCollectionSearchNoneAndDefaultData(TestcaseBase):
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""" Test case of search interface """
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@pytest.fixture(scope="function", params=[default_nb_medium])
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def nb(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[200])
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def nq(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[32, 128])
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def dim(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[False, True])
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def auto_id(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[False, True])
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def _async(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["JACCARD", "HAMMING"])
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def metrics(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[False, True])
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def is_flush(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[True, False])
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def enable_dynamic_field(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["IP", "COSINE", "L2"])
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def metric_type(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[True, False])
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def random_primary_key(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["FLOAT_VECTOR", "FLOAT16_VECTOR", "BFLOAT16_VECTOR"])
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def vector_data_type(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["STL_SORT", "INVERTED"])
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def numeric_scalar_index(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=["TRIE", "INVERTED", "BITMAP"])
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def varchar_scalar_index(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[200, 600])
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def batch_size(self, request):
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yield request.param
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@pytest.fixture(scope="function", params=[0, 0.5, 1])
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def null_data_percent(self, request):
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yield request.param
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"""
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******************************************************************
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# The following are valid base cases
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******************************************************************
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"""
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@pytest.mark.tags(CaseLabel.L0)
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def test_search_normal_none_data(self, nq, dim, auto_id, is_flush, enable_dynamic_field, vector_data_type, null_data_percent):
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"""
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target: test search normal case with none data inserted
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method: create connection, collection with nullable fields, insert data including none, and search
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expected: 1. search successfully with limit(topK)
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"""
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# 1. initialize with data
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collection_w, _, _, insert_ids, time_stamp = \
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self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_flush=is_flush,
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enable_dynamic_field=enable_dynamic_field,
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vector_data_type=vector_data_type,
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nullable_fields={ct.default_float_field_name: null_data_percent})[0:5]
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# 2. generate search data
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vectors = cf.gen_vectors_based_on_vector_type(nq, dim, vector_data_type)
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# 3. search after insert
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collection_w.search(vectors[:nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp,
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output_fields=[default_int64_field_name,
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default_float_field_name],
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guarantee_timestamp=0,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": nq,
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"ids": insert_ids,
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"limit": default_limit,
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"output_fields": [default_int64_field_name,
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default_float_field_name]})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="issue #36184")
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def test_search_after_none_data_all_field_datatype(self, varchar_scalar_index, numeric_scalar_index,
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null_data_percent, _async):
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"""
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target: test search after different index
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method: test search after different index and corresponding search params
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expected: search successfully with limit(topK)
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"""
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# 1. initialize with data
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nullable_fields = {ct.default_int32_field_name: null_data_percent,
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ct.default_int16_field_name: null_data_percent,
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ct.default_int8_field_name: null_data_percent,
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ct.default_bool_field_name: null_data_percent,
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ct.default_float_field_name: null_data_percent,
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ct.default_double_field_name: null_data_percent,
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ct.default_string_field_name: null_data_percent}
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000, partition_num=1,
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is_all_data_type=True, dim=default_dim, is_index=False,
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nullable_fields=nullable_fields)[0:4]
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# 2. create index on vector field and load
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index = "HNSW"
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params = cf.get_index_params_params(index)
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default_index = {"index_type": index, "params": params, "metric_type": "COSINE"}
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vector_name_list = cf.extract_vector_field_name_list(collection_w)
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vector_name_list.append(ct.default_float_vec_field_name)
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for vector_name in vector_name_list:
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collection_w.create_index(vector_name, default_index)
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# 3. create index on scalar field with None data
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scalar_index_params = {"index_type": varchar_scalar_index, "params": {}}
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collection_w.create_index(ct.default_string_field_name, scalar_index_params)
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# 4. create index on scalar field with default data
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scalar_index_params = {"index_type": numeric_scalar_index, "params": {}}
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collection_w.create_index(ct.default_int64_field_name, scalar_index_params)
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collection_w.create_index(ct.default_int32_field_name, scalar_index_params)
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collection_w.create_index(ct.default_int16_field_name, scalar_index_params)
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collection_w.create_index(ct.default_int8_field_name, scalar_index_params)
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collection_w.create_index(ct.default_bool_field_name, scalar_index_params)
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collection_w.create_index(ct.default_float_field_name, scalar_index_params)
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collection_w.load()
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# 5. search
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search_params = cf.gen_search_param(index, "COSINE")
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limit = search_params[0]["params"]["ef"]
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log.info("Searching with search params: {}".format(search_params[0]))
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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search_param, limit, default_search_exp, _async=_async,
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output_fields=[ct.default_string_field_name, ct.default_float_field_name],
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"ids": insert_ids,
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"limit": limit,
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"_async": _async,
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"output_fields": [ct.default_string_field_name,
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ct.default_float_field_name]})
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@pytest.mark.tags(CaseLabel.L0)
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def test_search_default_value_with_insert(self, nq, dim, auto_id, is_flush, enable_dynamic_field, vector_data_type):
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"""
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target: test search normal case with default value set
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method: create connection, collection with default value set, insert and search
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expected: 1. search successfully with limit(topK)
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"""
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# 1. initialize with data
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collection_w, _, _, insert_ids, time_stamp = \
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self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_flush=is_flush,
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enable_dynamic_field=enable_dynamic_field,
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vector_data_type=vector_data_type,
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default_value_fields={ct.default_float_field_name: np.float32(10.0)})[0:5]
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# 2. generate search data
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vectors = cf.gen_vectors_based_on_vector_type(nq, dim, vector_data_type)
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# 3. search after insert
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collection_w.search(vectors[:nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp,
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output_fields=[default_int64_field_name,
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default_float_field_name],
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guarantee_timestamp=0,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": nq,
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"ids": insert_ids,
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"limit": default_limit,
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"output_fields": [default_int64_field_name,
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default_float_field_name]})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_default_value_without_insert(self, enable_dynamic_field):
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"""
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target: test search normal case with default value set
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method: create connection, collection with default value set, no insert and search
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expected: 1. search successfully with limit(topK)
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"""
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# 1. initialize with data
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collection_w = self.init_collection_general(prefix, False, dim=default_dim,
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enable_dynamic_field=enable_dynamic_field,
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nullable_fields={ct.default_float_field_name: 0},
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default_value_fields={ct.default_float_field_name: np.float32(10.0)})[0]
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# 2. generate search data
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vectors = cf.gen_vectors_based_on_vector_type(default_nq, default_dim, "FLOAT_VECTOR")
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# 3. search after insert
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp,
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guarantee_timestamp=0,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"limit": 0})
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@pytest.mark.tags(CaseLabel.L2)
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def test_search_after_default_data_all_field_datatype(self, varchar_scalar_index, numeric_scalar_index, _async):
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"""
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target: test search after different index
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method: test search after different index and corresponding search params
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expected: search successfully with limit(topK)
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"""
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# 1. initialize with data
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default_value_fields = {ct.default_int32_field_name: np.int32(1),
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ct.default_int16_field_name: np.int32(2),
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ct.default_int8_field_name: np.int32(3),
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ct.default_bool_field_name: True,
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ct.default_float_field_name: np.float32(10.0),
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ct.default_double_field_name: 10.0,
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ct.default_string_field_name: "1"}
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000, partition_num=1,
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is_all_data_type=True, dim=default_dim, is_index=False,
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default_value_fields=default_value_fields)[0:4]
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# 2. create index on vector field and load
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index = "HNSW"
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params = cf.get_index_params_params(index)
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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vector_name_list = cf.extract_vector_field_name_list(collection_w)
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vector_name_list.append(ct.default_float_vec_field_name)
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for vector_name in vector_name_list:
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collection_w.create_index(vector_name, default_index)
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# 3. create index on scalar field with None data
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scalar_index_params = {"index_type": varchar_scalar_index, "params": {}}
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collection_w.create_index(ct.default_string_field_name, scalar_index_params)
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# 4. create index on scalar field with default data
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scalar_index_params = {"index_type": numeric_scalar_index, "params": {}}
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collection_w.create_index(ct.default_int64_field_name, scalar_index_params)
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collection_w.create_index(ct.default_int32_field_name, scalar_index_params)
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collection_w.create_index(ct.default_int16_field_name, scalar_index_params)
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collection_w.create_index(ct.default_int8_field_name, scalar_index_params)
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if numeric_scalar_index != "STL_SORT":
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collection_w.create_index(ct.default_bool_field_name, scalar_index_params)
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collection_w.create_index(ct.default_float_field_name, scalar_index_params)
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collection_w.load()
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# 5. search
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search_params = cf.gen_search_param(index, "L2")
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limit = search_params[0]["params"]["ef"]
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log.info("Searching with search params: {}".format(search_params[0]))
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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output_fields = [ct.default_int64_field_name, ct.default_int32_field_name,
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ct.default_int16_field_name, ct.default_int8_field_name,
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ct.default_bool_field_name, ct.default_float_field_name,
|
||||
ct.default_double_field_name, ct.default_string_field_name]
|
||||
collection_w.search(vectors[:default_nq], default_search_field,
|
||||
search_param, 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": limit,
|
||||
"_async": _async,
|
||||
"output_fields": output_fields})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.skip(reason="issue #36003")
|
||||
def test_search_both_default_value_non_data(self, nq, dim, auto_id, is_flush, enable_dynamic_field, vector_data_type):
|
||||
"""
|
||||
target: test search normal case with default value set
|
||||
method: create connection, collection with default value set, insert and search
|
||||
expected: 1. search successfully with limit(topK)
|
||||
"""
|
||||
# 1. initialize with data
|
||||
collection_w, _, _, insert_ids, time_stamp = \
|
||||
self.init_collection_general(prefix, True, auto_id=auto_id, dim=dim, is_flush=is_flush,
|
||||
enable_dynamic_field=enable_dynamic_field,
|
||||
vector_data_type=vector_data_type,
|
||||
nullable_fields={ct.default_float_field_name: 1},
|
||||
default_value_fields={ct.default_float_field_name: np.float32(10.0)})[0:5]
|
||||
# 2. generate search data
|
||||
vectors = cf.gen_vectors_based_on_vector_type(nq, dim, vector_data_type)
|
||||
# 3. search after insert
|
||||
collection_w.search(vectors[:nq], default_search_field,
|
||||
default_search_params, default_limit,
|
||||
default_search_exp,
|
||||
output_fields=[default_int64_field_name,
|
||||
default_float_field_name],
|
||||
guarantee_timestamp=0,
|
||||
check_task=CheckTasks.check_search_results,
|
||||
check_items={"nq": nq,
|
||||
"ids": insert_ids,
|
||||
"limit": default_limit,
|
||||
"output_fields": [default_int64_field_name,
|
||||
default_float_field_name]})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
def test_search_collection_with_non_default_data_after_release_load(self, nq, _async, null_data_percent):
|
||||
"""
|
||||
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
|
||||
nb= 2000
|
||||
dim = 64
|
||||
auto_id = True
|
||||
collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True, nb,
|
||||
1, auto_id=auto_id, dim=dim,
|
||||
nullable_fields={ct.default_string_field_name: null_data_percent},
|
||||
default_value_fields={ct.default_float_field_name: np.float32(10.0)})[0:5]
|
||||
# 2. release collection
|
||||
collection_w.release()
|
||||
# 3. Search the pre-released collection after load
|
||||
collection_w.load()
|
||||
log.info("test_search_collection_awith_non_default_data_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,
|
||||
output_fields = [ct.default_float_field_name, ct.default_string_field_name],
|
||||
check_task=CheckTasks.check_search_results,
|
||||
check_items={"nq": nq,
|
||||
"ids": insert_ids,
|
||||
"limit": default_limit,
|
||||
"_async": _async,
|
||||
"output_fields": [ct.default_float_field_name,
|
||||
ct.default_string_field_name]})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.tags(CaseLabel.GPU)
|
||||
@pytest.mark.skip(reason="issue #36184")
|
||||
def test_search_after_different_index_with_params_none_default_data(self, varchar_scalar_index, numeric_scalar_index,
|
||||
null_data_percent, _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,
|
||||
is_all_data_type=True, dim=default_dim, is_index=False,
|
||||
nullable_fields={ct.default_string_field_name: null_data_percent},
|
||||
default_value_fields={ct.default_float_field_name: np.float32(10.0)})[0:4]
|
||||
# 2. create index on vector field and load
|
||||
index = "HNSW"
|
||||
params = cf.get_index_params_params(index)
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "COSINE"}
|
||||
vector_name_list = cf.extract_vector_field_name_list(collection_w)
|
||||
vector_name_list.append(ct.default_float_vec_field_name)
|
||||
for vector_name in vector_name_list:
|
||||
collection_w.create_index(vector_name, default_index)
|
||||
# 3. create index on scalar field with None data
|
||||
scalar_index_params = {"index_type": varchar_scalar_index, "params": {}}
|
||||
collection_w.create_index(ct.default_string_field_name, scalar_index_params)
|
||||
# 4. create index on scalar field with default data
|
||||
scalar_index_params = {"index_type": numeric_scalar_index, "params": {}}
|
||||
collection_w.create_index(ct.default_float_field_name, scalar_index_params)
|
||||
collection_w.load()
|
||||
# 5. search
|
||||
search_params = cf.gen_search_param(index, "COSINE")
|
||||
limit = search_params[0]["params"]["ef"]
|
||||
log.info("Searching with search params: {}".format(search_params[0]))
|
||||
vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
|
||||
collection_w.search(vectors[:default_nq], default_search_field,
|
||||
search_param, limit, default_search_exp, _async=_async,
|
||||
output_fields=[ct.default_string_field_name, ct.default_float_field_name],
|
||||
check_task=CheckTasks.check_search_results,
|
||||
check_items={"nq": default_nq,
|
||||
"ids": insert_ids,
|
||||
"limit": limit,
|
||||
"_async": _async,
|
||||
"output_fields": [ct.default_string_field_name,
|
||||
ct.default_float_field_name]})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
def test_search_iterator_with_none_data(self, batch_size, null_data_percent):
|
||||
"""
|
||||
target: test search iterator normal
|
||||
method: 1. search iterator
|
||||
2. check the result, expect pk
|
||||
expected: search successfully
|
||||
"""
|
||||
# 1. initialize with data
|
||||
dim = 64
|
||||
collection_w = self.init_collection_general(prefix, True, dim=dim, is_index=False,
|
||||
nullable_fields={ct.default_string_field_name: null_data_percent})[0]
|
||||
collection_w.create_index(field_name, {"metric_type": "L2"})
|
||||
collection_w.load()
|
||||
# 2. search iterator
|
||||
search_params = {"metric_type": "L2"}
|
||||
vectors = cf.gen_vectors_based_on_vector_type(1, dim, "FLOAT_VECTOR")
|
||||
collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
|
||||
check_task=CheckTasks.check_search_iterator,
|
||||
check_items={"batch_size": batch_size})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
def test_search_normal_none_data_partition_key(self, is_flush, enable_dynamic_field, vector_data_type, null_data_percent):
|
||||
"""
|
||||
target: test search normal case with none data inserted
|
||||
method: create connection, collection with nullable fields, insert data including none, and search
|
||||
expected: 1. search successfully with limit(topK)
|
||||
"""
|
||||
# 1. initialize with data
|
||||
collection_w, _, _, insert_ids, time_stamp = \
|
||||
self.init_collection_general(prefix, True, dim=default_dim, is_flush=is_flush,
|
||||
enable_dynamic_field=enable_dynamic_field,
|
||||
vector_data_type=vector_data_type,
|
||||
nullable_fields={ct.default_float_field_name: null_data_percent},
|
||||
is_partition_key=ct.default_float_field_name)[0:5]
|
||||
# 2. generate search data
|
||||
vectors = cf.gen_vectors_based_on_vector_type(default_nq, default_dim, vector_data_type)
|
||||
# 3. search after insert
|
||||
collection_w.search(vectors[:default_nq], default_search_field,
|
||||
default_search_params, default_limit,
|
||||
default_search_exp,
|
||||
output_fields=[default_int64_field_name,
|
||||
default_float_field_name],
|
||||
guarantee_timestamp=0,
|
||||
check_task=CheckTasks.check_search_results,
|
||||
check_items={"nq": default_nq,
|
||||
"ids": insert_ids,
|
||||
"limit": default_limit,
|
||||
"output_fields": [default_int64_field_name,
|
||||
default_float_field_name]})
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user