mirror of https://github.com/milvus-io/milvus.git
Add test cases of GetVectorById (#23666)
Signed-off-by: nico <cheng.yuan@zilliz.com>pull/23725/head
nico
GitHub
parent
47eeb1fc0b
commit
c5f4a47eed
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@ -456,7 +456,7 @@ def gen_invalid_field_types():
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return field_types
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def gen_invaild_search_params_type():
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def gen_invalid_search_params_type():
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invalid_search_key = 100
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search_params = []
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for index_type in ct.all_index_types:
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@ -327,7 +327,7 @@ class TestCollectionSearchInvalid(TestcaseBase):
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collection_w.create_index("float_vector", default_index)
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collection_w.load()
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# 3. search
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invalid_search_params = cf.gen_invaild_search_params_type()
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invalid_search_params = cf.gen_invalid_search_params_type()
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message = "Search params check failed"
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for invalid_search_param in invalid_search_params:
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if index == invalid_search_param["index_type"]:
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@ -828,6 +828,7 @@ class TestCollectionSearchInvalid(TestcaseBase):
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ct.err_msg: "Field int63 not exist"})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.skip(reason="Now support output vector field")
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@pytest.mark.parametrize("output_fields", [[default_search_field], ["%"]])
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def test_search_output_field_vector(self, output_fields):
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"""
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@ -844,6 +845,32 @@ class TestCollectionSearchInvalid(TestcaseBase):
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default_search_params, default_limit,
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default_search_exp, output_fields=output_fields)
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@pytest.mark.tags(CaseLabel.L3)
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@pytest.mark.parametrize("index, param", zip(ct.all_index_types[-2:], ct.default_index_params[-2:]))
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def test_search_output_field_vector_after_gpu_index(self, index, param):
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"""
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target: test search with vector as output field
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method: 1. create a collection and insert data
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2. create an index which doesn't output vectors
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3. load and search
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expected: raise exception and report the error
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"""
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# 1. create a collection and insert data
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collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
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# 2. create an index which doesn't output vectors
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default_index = {"index_type": index, "params": param, "metric_type": "L2"}
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collection_w.create_index(field_name, default_index)
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# 3. load and search
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collection_w.load()
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search_params = cf.gen_search_param(index)[0]
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collection_w.search(vectors[:default_nq], field_name, search_params,
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default_limit, output_fields=[field_name],
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check_task=CheckTasks.err_res,
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check_items={"err_code": 1,
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"err_msg": "not supported"})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("output_fields", [["*%"], ["**"], ["*", "@"]])
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def test_search_output_field_invalid_wildcard(self, output_fields):
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@ -2772,6 +2799,33 @@ class TestCollectionSearch(TestcaseBase):
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assert len(res[0][0].entity._row_data) != 0
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assert default_int64_field_name in res[0][0].entity._row_data
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_with_output_vector_field(self, auto_id, _async):
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"""
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target: test search with output fields
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method: search with one output_field
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expected: search success
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"""
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# 1. initialize with data
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
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auto_id=auto_id)[0:4]
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# 2. search
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log.info("test_search_with_output_field: Searching collection %s" % collection_w.name)
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res = 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, _async=_async,
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output_fields=[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": default_limit,
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"_async": _async})[0]
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if _async:
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res.done()
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res = res.result()
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assert len(res[0][0].entity._row_data) != 0
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assert field_name in res[0][0].entity._row_data
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@pytest.mark.tags(CaseLabel.L2)
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def test_search_with_output_fields(self, nb, nq, dim, auto_id, _async):
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"""
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@ -2803,6 +2857,156 @@ class TestCollectionSearch(TestcaseBase):
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assert len(res[0][0].entity._row_data) != 0
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assert (default_int64_field_name and default_float_field_name) in res[0][0].entity._row_data
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="issue #23601")
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[:6],
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ct.default_index_params[:6]))
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def test_search_output_field_vector_after_different_index(self, index, params):
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"""
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target: test search with output vector field after different index
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method: 1. create a collection and insert data
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2. create index and load
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3. search with output field vector
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4. check the result vectors should be equal to the inserted
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expected: search success
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"""
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# 1. create a collection and insert data
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collection_w = self.init_collection_general(prefix, is_index=False)[0]
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data = cf.gen_default_dataframe_data()
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collection_w.insert(data)
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# 2. create index and load
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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collection_w.create_index(field_name, default_index)
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collection_w.load()
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# 3. search with output field vector
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search_params = cf.gen_search_param(index)[0]
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res = collection_w.search(vectors[:1], default_search_field,
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search_params, 2, default_search_exp,
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output_fields=[field_name],
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check_task=CheckTasks.check_search_results,
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check_items={"nq": 1,
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"limit": 2})[0]
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# 4. check the result vectors should be equal to the inserted
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log.info(res[0][0].id)
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log.info(res[0][0].entity.float_vector)
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log.info(data['float_vector'][0])
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assert res[0][0].entity.float_vector == data[field_name][res[0][0].id]
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# log.info(data['float_vector'][1])
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="issue #23661")
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@pytest.mark.parametrize("index", ct.all_index_types[6:8])
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def test_search_output_field_vector_after_binary_index(self, index):
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"""
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target: test search with output vector field after binary index
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method: 1. create a collection and insert data
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2. create index and load
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3. search with output field vector
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4. check the result vectors should be equal to the inserted
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expected: search success
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"""
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# 1. create a collection and insert data
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collection_w = self.init_collection_general(prefix, is_binary=True, is_index=False)[0]
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data = cf.gen_default_binary_dataframe_data()[0]
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collection_w.insert(data)
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# 2. create index and load
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default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "JACCARD"}
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collection_w.create_index(binary_field_name, default_index)
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collection_w.load()
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# 3. search with output field vector
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search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}}
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binary_vectors = cf.gen_binary_vectors(1, default_dim)[1]
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res = collection_w.search(binary_vectors, binary_field_name,
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ct.default_search_binary_params, 2, default_search_exp,
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output_fields=[binary_field_name])[0]
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# 4. check the result vectors should be equal to the inserted
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log.info(res[0][0].id)
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log.info(res[0][0].entity.float_vector)
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log.info(data['binary_vector'][0])
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assert res[0][0].entity.binary_vector == data[binary_field_name][res[0][0].id]
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# log.info(data['float_vector'][1])
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("dim", [32, 128, 768])
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def test_search_output_field_vector_with_different_dim(self, dim):
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"""
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target: test search with output vector field after binary index
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method: 1. create a collection and insert data
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2. create index and load
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3. search with output field vector
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4. check the result vectors should be equal to the inserted
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expected: search success
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"""
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# 1. create a collection and insert data
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collection_w = self.init_collection_general(prefix, is_index=False, dim=dim)[0]
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data = cf.gen_default_dataframe_data(dim=dim)
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collection_w.insert(data)
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# 2. create index and load
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index_params = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
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collection_w.create_index("float_vector", index_params)
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collection_w.load()
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# 3. search with output field vector
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vectors = cf.gen_vectors(default_nq, dim=dim)
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res = collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit, default_search_exp,
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output_fields=[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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"limit": default_limit})[0]
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# 4. check the result vectors should be equal to the inserted
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log.info(res[0][0].id)
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log.info(res[0][0].entity.float_vector)
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log.info(data['float_vector'][0])
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for i in range(default_limit):
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assert len(res[0][i].entity.float_vector) == len(data[field_name][res[0][i].id])
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("dim", [32, 128, 768])
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def test_search_output_field_vector_with_different_dim(self, dim):
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"""
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target: test search with output vector field after binary index
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method: 1. create a collection and insert data
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2. create index and load
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3. search with output field vector
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4. check the result vectors should be equal to the inserted
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expected: search success
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"""
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# 1. create a collection and insert data
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collection_w = self.init_collection_general(prefix, is_index=False, dim=dim)[0]
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data = cf.gen_default_dataframe_data(dim=dim)
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collection_w.insert(data)
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# 2. create index and load
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index_params = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
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collection_w.create_index("float_vector", index_params)
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collection_w.load()
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# 3. search with output field vector
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vectors = cf.gen_vectors(default_nq, dim=dim)
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res = collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit, default_search_exp,
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output_fields=[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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"limit": default_limit})[0]
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# 4. check the result vectors should be equal to the inserted
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log.info(res[0][0].id)
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log.info(res[0][0].entity.float_vector)
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log.info(data['float_vector'][0])
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for i in range(default_limit):
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assert len(res[0][i].entity.float_vector) == len(data[field_name][res[0][i].id])
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("output_fields", [["*"], ["*", default_float_field_name]])
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def test_search_with_output_field_wildcard(self, output_fields, auto_id, _async):
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