mirror of https://github.com/milvus-io/milvus.git
Add test cases of output vector (#27215)
Signed-off-by: nico <cheng.yuan@zilliz.com>pull/27178/head
nico
GitHub
parent
217ed38bb4
commit
206cc14d0d
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@ -850,6 +850,12 @@ def gen_invalid_search_params_type():
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continue
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annoy_search_param = {"index_type": index_type, "search_params": {"search_k": search_k}}
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search_params.append(annoy_search_param)
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elif index_type == "SCANN":
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for reorder_k in ct.get_invalid_ints:
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if isinstance(reorder_k, int):
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continue
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scann_search_param = {"index_type": index_type, "search_params": {"nprobe": 8, "reorder_k": reorder_k}}
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search_params.append(scann_search_param)
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elif index_type == "DISKANN":
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for search_list in ct.get_invalid_ints:
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diskann_search_param = {"index_type": index_type, "search_params": {"search_list": search_list}}
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@ -883,6 +889,10 @@ def gen_search_param(index_type, metric_type="L2"):
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for search_k in [1000, 5000]:
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annoy_search_param = {"metric_type": metric_type, "params": {"search_k": search_k}}
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search_params.append(annoy_search_param)
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elif index_type == "SCANN":
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for reorder_k in [1200, 3000]:
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scann_search_param = {"metric_type": metric_type, "params": {"nprobe": 64, "reorder_k": reorder_k}}
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search_params.append(scann_search_param)
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elif index_type == "DISKANN":
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for search_list in [20, 300, 1500]:
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diskann_search_param = {"metric_type": metric_type, "params": {"search_list": search_list}}
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@ -226,11 +226,11 @@ get_wrong_format_dict = [
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]
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""" Specially defined list """
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all_index_types = ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_PQ", "HNSW", "DISKANN", "BIN_FLAT", "BIN_IVF_FLAT",
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all_index_types = ["FLAT", "IVF_FLAT", "IVF_SQ8", "IVF_PQ", "HNSW", "SCANN", "DISKANN", "BIN_FLAT", "BIN_IVF_FLAT",
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"GPU_IVF_FLAT", "GPU_IVF_PQ"]
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default_index_params = [{"nlist": 128}, {"nlist": 128}, {"nlist": 128}, {"nlist": 128, "m": 16, "nbits": 8},
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{"M": 48, "efConstruction": 500}, {}, {"nlist": 128}, {"nlist": 128},
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{"M": 48, "efConstruction": 500}, {"nlist": 128}, {}, {"nlist": 128}, {"nlist": 128},
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{"nlist": 64}, {"nlist": 64, "m": 16, "nbits": 8}]
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Handler_type = ["GRPC", "HTTP"]
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@ -47,8 +47,8 @@ default_string_field_name = ct.default_string_field_name
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default_json_field_name = ct.default_json_field_name
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default_index_params = {"index_type": "IVF_SQ8", "metric_type": "COSINE", "params": {"nlist": 64}}
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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range_search_supported_index = ct.all_index_types[:6]
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range_search_supported_index_params = ct.default_index_params[:6]
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range_search_supported_index = ct.all_index_types[:7]
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range_search_supported_index_params = ct.default_index_params[:7]
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uid = "test_search"
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nq = 1
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epsilon = 0.001
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@ -318,8 +318,8 @@ class TestCollectionSearchInvalid(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[:6],
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ct.default_index_params[:6]))
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zip(ct.all_index_types[:7],
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ct.default_index_params[:7]))
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def test_search_invalid_params_type(self, index, params):
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"""
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target: test search with invalid search params
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@ -332,8 +332,7 @@ class TestCollectionSearchInvalid(TestcaseBase):
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True, 5000,
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is_index=False)[0:4]
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# 2. create index and load
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default_index = {"index_type": index,
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"params": params, "metric_type": "L2"}
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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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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@ -761,10 +760,10 @@ class TestCollectionSearchInvalid(TestcaseBase):
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check_items={"err_code": 1,
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"err_msg": "PartitonName: %s not found" % deleted_par_name})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[1:5],
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ct.default_index_params[1:5]))
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zip(ct.all_index_types[1:6],
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ct.default_index_params[1:6]))
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def test_search_different_index_invalid_params(self, index, params):
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"""
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target: test search with different index
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@ -779,17 +778,14 @@ class TestCollectionSearchInvalid(TestcaseBase):
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if params.get("m"):
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if (default_dim % params["m"]) != 0:
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params["m"] = default_dim // 4
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log.info(
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"test_search_different_index_invalid_params: Creating index-%s" % index)
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default_index = {"index_type": index,
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"params": params, "metric_type": "L2"}
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log.info("test_search_different_index_invalid_params: Creating index-%s" % index)
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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collection_w.create_index("float_vector", default_index)
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log.info(
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"test_search_different_index_invalid_params: Created index-%s" % index)
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log.info("test_search_different_index_invalid_params: Created index-%s" % index)
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collection_w.load()
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# 3. search
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log.info(
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"test_search_different_index_invalid_params: Searching after creating index-%s" % index)
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log.info("test_search_different_index_invalid_params: Searching after "
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"creating index-%s" % index)
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search_params = cf.gen_invalid_search_param(index)
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collection_w.search(vectors, default_search_field,
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search_params[0], default_limit,
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@ -934,12 +930,10 @@ class TestCollectionSearchInvalid(TestcaseBase):
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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(
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prefix, True, is_index=False)[0]
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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,
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"params": param, "metric_type": "L2"}
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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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@ -2083,8 +2077,8 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.tags(CaseLabel.GPU)
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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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zip(ct.all_index_types[:7],
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ct.default_index_params[:7]))
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def test_search_after_different_index_with_params(self, dim, index, params, auto_id, _async, enable_dynamic_field):
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"""
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target: test search after different index
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@ -2104,14 +2098,12 @@ class TestCollectionSearch(TestcaseBase):
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if params.get("PQM"):
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if (dim % params["PQM"]) != 0:
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params["PQM"] = dim // 4
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default_index = {"index_type": index,
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"params": params, "metric_type": "COSINE"}
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default_index = {"index_type": index, "params": params, "metric_type": "COSINE"}
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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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search_params = cf.gen_search_param(index, "COSINE")
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vectors = [[random.random() for _ in range(dim)]
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for _ in range(default_nq)]
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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for search_param in search_params:
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log.info("Searching with search params: {}".format(search_param))
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limit = default_limit
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@ -2132,8 +2124,8 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.GPU)
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[8:10],
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ct.default_index_params[8:10]))
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zip(ct.all_index_types[9:11],
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ct.default_index_params[9:11]))
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def test_search_after_different_index_with_params_gpu(self, dim, index, params, auto_id, _async,
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enable_dynamic_field):
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"""
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@ -2154,14 +2146,12 @@ class TestCollectionSearch(TestcaseBase):
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if params.get("PQM"):
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if (dim % params["PQM"]) != 0:
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params["PQM"] = dim // 4
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default_index = {"index_type": index,
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"params": params, "metric_type": "L2"}
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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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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search_params = cf.gen_search_param(index)
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vectors = [[random.random() for _ in range(dim)]
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for _ in range(default_nq)]
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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for search_param in search_params:
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log.info("Searching with search params: {}".format(search_param))
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collection_w.search(vectors[:default_nq], default_search_field,
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@ -2200,9 +2190,10 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.tags(CaseLabel.GPU)
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@pytest.mark.skip("issue #27252")
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[:5],
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ct.default_index_params[:5]))
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zip(ct.all_index_types[:7],
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ct.default_index_params[:7]))
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def test_search_after_different_index_with_min_dim(self, index, params, auto_id, _async):
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"""
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target: test search after different index with min dim
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@ -2219,14 +2210,12 @@ class TestCollectionSearch(TestcaseBase):
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params["m"] = min_dim
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if params.get("PQM"):
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params["PQM"] = min_dim
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default_index = {"index_type": index,
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"params": params, "metric_type": "L2"}
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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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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search_params = cf.gen_search_param(index)
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vectors = [[random.random() for _ in range(min_dim)]
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for _ in range(default_nq)]
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vectors = [[random.random() for _ in range(min_dim)] for _ in range(default_nq)]
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for search_param in search_params:
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log.info("Searching with search params: {}".format(search_param))
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collection_w.search(vectors[:default_nq], default_search_field,
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@ -2240,8 +2229,8 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.GPU)
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[8:10],
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ct.default_index_params[8:10]))
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zip(ct.all_index_types[9:11],
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ct.default_index_params[9:11]))
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def test_search_after_different_index_with_min_dim_gpu(self, index, params, auto_id, _async):
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"""
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target: test search after different index with min dim
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@ -2258,14 +2247,12 @@ class TestCollectionSearch(TestcaseBase):
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params["m"] = min_dim
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if params.get("PQM"):
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params["PQM"] = min_dim
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default_index = {"index_type": index,
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"params": params, "metric_type": "L2"}
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default_index = {"index_type": index, "params": params, "metric_type": "L2"}
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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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search_params = cf.gen_search_param(index)
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vectors = [[random.random() for _ in range(min_dim)]
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for _ in range(default_nq)]
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vectors = [[random.random() for _ in range(min_dim)] for _ in range(default_nq)]
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for search_param in search_params:
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log.info("Searching with search params: {}".format(search_param))
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collection_w.search(vectors[:default_nq], default_search_field,
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@ -2280,8 +2267,8 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.tags(CaseLabel.GPU)
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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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zip(ct.all_index_types[:7],
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ct.default_index_params[:7]))
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def test_search_after_index_different_metric_type(self, dim, index, params, auto_id, _async,
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enable_dynamic_field, metric_type):
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"""
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@ -2314,18 +2301,14 @@ class TestCollectionSearch(TestcaseBase):
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if params.get("PQM"):
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if (dim % params["PQM"]) != 0:
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params["PQM"] = dim // 4
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log.info(
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"test_search_after_index_different_metric_type: Creating index-%s" % index)
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default_index = {"index_type": index,
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"params": params, "metric_type": metric_type}
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log.info("test_search_after_index_different_metric_type: Creating index-%s" % index)
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default_index = {"index_type": index, "params": params, "metric_type": metric_type}
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collection_w.create_index("float_vector", default_index)
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log.info(
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"test_search_after_index_different_metric_type: Created index-%s" % index)
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log.info("test_search_after_index_different_metric_type: Created index-%s" % index)
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collection_w.load()
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# 4. search
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search_params = cf.gen_search_param(index, metric_type)
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vectors = [[random.random() for _ in range(dim)]
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for _ in range(default_nq)]
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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for search_param in search_params:
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log.info("Searching with search params: {}".format(search_param))
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limit = default_limit
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@ -2350,8 +2333,8 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="issue 24957")
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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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zip(ct.all_index_types[:7],
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ct.default_index_params[:7]))
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def test_search_after_release_recreate_index(self, dim, index, params, auto_id, _async,
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enable_dynamic_field, metric_type):
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"""
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@ -2383,17 +2366,14 @@ class TestCollectionSearch(TestcaseBase):
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if params.get("PQM"):
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if (dim % params["PQM"]) != 0:
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params["PQM"] = dim // 4
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log.info(
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"test_search_after_release_recreate_index: Creating index-%s" % index)
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default_index = {"index_type": index,
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"params": params, "metric_type": "COSINE"}
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log.info("test_search_after_release_recreate_index: Creating index-%s" % index)
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default_index = {"index_type": index, "params": params, "metric_type": "COSINE"}
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collection_w.create_index("float_vector", default_index)
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log.info("test_search_after_release_recreate_index: Created index-%s" % index)
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collection_w.load()
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# 4. search
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search_params = cf.gen_search_param(index, "COSINE")
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vectors = [[random.random() for _ in range(dim)]
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for _ in range(default_nq)]
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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for search_param in search_params:
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log.info("Searching with search params: {}".format(search_param))
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collection_w.search(vectors[:default_nq], default_search_field,
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@ -2402,8 +2382,7 @@ class TestCollectionSearch(TestcaseBase):
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# 5. re-create index
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collection_w.release()
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collection_w.drop_index()
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default_index = {"index_type": index,
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"params": params, "metric_type": metric_type}
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default_index = {"index_type": index, "params": params, "metric_type": metric_type}
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collection_w.create_index("float_vector", default_index)
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collection_w.load()
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for search_param in search_params:
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@ -2422,8 +2401,8 @@ class TestCollectionSearch(TestcaseBase):
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@pytest.mark.tags(CaseLabel.GPU)
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[8:10],
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ct.default_index_params[8:10]))
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zip(ct.all_index_types[9:11],
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ct.default_index_params[9:11]))
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def test_search_after_index_different_metric_type_gpu(self, dim, index, params, auto_id, _async, enable_dynamic_field):
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"""
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target: test search with different metric type
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@ -2443,18 +2422,14 @@ class TestCollectionSearch(TestcaseBase):
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if params.get("PQM"):
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if (dim % params["PQM"]) != 0:
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params["PQM"] = dim // 4
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log.info(
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"test_search_after_index_different_metric_type: Creating index-%s" % index)
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default_index = {"index_type": index,
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"params": params, "metric_type": "IP"}
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log.info("test_search_after_index_different_metric_type: Creating index-%s" % index)
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default_index = {"index_type": index, "params": params, "metric_type": "IP"}
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collection_w.create_index("float_vector", default_index)
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log.info(
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"test_search_after_index_different_metric_type: Created index-%s" % index)
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log.info("test_search_after_index_different_metric_type: Created index-%s" % index)
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collection_w.load()
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# 3. search
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search_params = cf.gen_search_param(index, "IP")
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vectors = [[random.random() for _ in range(dim)]
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for _ in range(default_nq)]
|
||||
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,
|
||||
|
|
@ -2828,7 +2803,6 @@ class TestCollectionSearch(TestcaseBase):
|
|||
min(distance_0, distance_1)) <= epsilon
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.skip("substructure obsolete")
|
||||
@pytest.mark.parametrize("index", ["BIN_FLAT"])
|
||||
def test_search_binary_substructure_flat_index(self, auto_id, _async, index, is_flush):
|
||||
"""
|
||||
|
|
@ -2847,15 +2821,13 @@ class TestCollectionSearch(TestcaseBase):
|
|||
= self.init_collection_general(prefix, True, default_nb, is_binary=True, auto_id=auto_id,
|
||||
dim=dim, is_index=False, is_flush=is_flush)[0:5]
|
||||
# 2. create index
|
||||
default_index = {"index_type": index, "params": {
|
||||
"nlist": 128}, "metric_type": "SUBSTRUCTURE"}
|
||||
default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "SUBSTRUCTURE"}
|
||||
collection_w.create_index("binary_vector", default_index)
|
||||
collection_w.load()
|
||||
# 3. generate search vectors
|
||||
_, binary_vectors = cf.gen_binary_vectors(nq, dim)
|
||||
# 4. search and compare the distance
|
||||
search_params = {"metric_type": "SUBSTRUCTURE",
|
||||
"params": {"nprobe": 10}}
|
||||
search_params = {"metric_type": "SUBSTRUCTURE", "params": {"nprobe": 10}}
|
||||
res = collection_w.search(binary_vectors[:nq], "binary_vector",
|
||||
search_params, default_limit, "int64 >= 0",
|
||||
_async=_async)[0]
|
||||
|
|
@ -2866,7 +2838,6 @@ class TestCollectionSearch(TestcaseBase):
|
|||
assert len(res) <= default_limit
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.skip("superstructure obsolete")
|
||||
@pytest.mark.parametrize("index", ["BIN_FLAT"])
|
||||
def test_search_binary_superstructure_flat_index(self, auto_id, _async, index, is_flush):
|
||||
"""
|
||||
|
|
@ -2885,15 +2856,13 @@ class TestCollectionSearch(TestcaseBase):
|
|||
= self.init_collection_general(prefix, True, default_nb, is_binary=True, auto_id=auto_id,
|
||||
dim=dim, is_index=False, is_flush=is_flush)[0:5]
|
||||
# 2. create index
|
||||
default_index = {"index_type": index, "params": {
|
||||
"nlist": 128}, "metric_type": "SUPERSTRUCTURE"}
|
||||
default_index = {"index_type": index, "params": {"nlist": 128}, "metric_type": "SUPERSTRUCTURE"}
|
||||
collection_w.create_index("binary_vector", default_index)
|
||||
collection_w.load()
|
||||
# 3. generate search vectors
|
||||
_, binary_vectors = cf.gen_binary_vectors(nq, dim)
|
||||
# 4. search and compare the distance
|
||||
search_params = {"metric_type": "SUPERSTRUCTURE",
|
||||
"params": {"nprobe": 10}}
|
||||
search_params = {"metric_type": "SUPERSTRUCTURE", "params": {"nprobe": 10}}
|
||||
res = collection_w.search(binary_vectors[:nq], "binary_vector",
|
||||
search_params, default_limit, "int64 >= 0",
|
||||
_async=_async)[0]
|
||||
|
|
@ -3330,10 +3299,10 @@ class TestCollectionSearch(TestcaseBase):
|
|||
"_async": _async,
|
||||
"output_fields": output_fields})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
@pytest.mark.parametrize("metrics", ct.float_metrics)
|
||||
@pytest.mark.parametrize("limit", [20, 1200])
|
||||
def test_search_output_field_vector_after_different_index_metrics(self, index, params, metrics, limit):
|
||||
|
|
@ -3345,15 +3314,7 @@ class TestCollectionSearch(TestcaseBase):
|
|||
4. check the result vectors should be equal to the inserted
|
||||
expected: search success
|
||||
"""
|
||||
if index in ["IVF_SQ8", "IVF_PQ"]:
|
||||
pytest.skip("IVF_SQ8 and IVF_PQ do not support output vector now")
|
||||
if index == "DISKANN" and metrics == "IP":
|
||||
pytest.skip("DISKANN(IP) does not support output vector now")
|
||||
if metrics == "COSINE":
|
||||
pytest.skip("COSINE does not support output vector now")
|
||||
# 1. create a collection and insert data
|
||||
collection_w, _vectors = self.init_collection_general(
|
||||
prefix, True, is_index=False)[:2]
|
||||
collection_w, _vectors = self.init_collection_general(prefix, True, is_index=False)[:2]
|
||||
|
||||
# 2. create index and load
|
||||
default_index = {"index_type": index, "params": params, "metric_type": metrics}
|
||||
|
|
@ -3379,9 +3340,10 @@ class TestCollectionSearch(TestcaseBase):
|
|||
"original_entities": _vectors,
|
||||
"output_fields": [field_name]})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index", ["HNSW", "BIN_FLAT", "BIN_IVF_FLAT"])
|
||||
def test_search_output_field_vector_after_binary_index(self, index):
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
@pytest.mark.parametrize("metrics", ct.binary_metrics[:2])
|
||||
@pytest.mark.parametrize("index", ["BIN_FLAT", "BIN_IVF_FLAT", "HNSW"])
|
||||
def test_search_output_field_vector_after_binary_index(self, metrics, index):
|
||||
"""
|
||||
target: test search with output vector field after binary index
|
||||
method: 1. create a collection and insert data
|
||||
|
|
@ -3396,14 +3358,48 @@ class TestCollectionSearch(TestcaseBase):
|
|||
collection_w.insert(data)
|
||||
|
||||
# 2. create index and load
|
||||
default_index = {"index_type": index, "metric_type": "JACCARD",
|
||||
"params": {"nlist": 128, "efConstruction": 64, "M": 10}}
|
||||
params = {"M": 48, "efConstruction": 500} if index == "HNSW" else {"nlist": 128}
|
||||
default_index = {"index_type": index, "metric_type": metrics, "params": params}
|
||||
collection_w.create_index(binary_field_name, default_index)
|
||||
collection_w.load()
|
||||
|
||||
# 3. search with output field vector
|
||||
search_params = {"metric_type": "JACCARD"}
|
||||
search_params = cf.gen_search_param(index, metrics)
|
||||
binary_vectors = cf.gen_binary_vectors(1, default_dim)[1]
|
||||
for search_param in search_params:
|
||||
res = collection_w.search(binary_vectors, binary_field_name,
|
||||
search_param, 2, default_search_exp,
|
||||
output_fields=[binary_field_name])[0]
|
||||
|
||||
# 4. check the result vectors should be equal to the inserted
|
||||
assert res[0][0].entity.binary_vector == [data[binary_field_name][res[0][0].id]]
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("metrics", ct.structure_metrics)
|
||||
@pytest.mark.parametrize("index", ["BIN_FLAT"])
|
||||
def test_search_output_field_vector_after_structure_metrics(self, metrics, index):
|
||||
"""
|
||||
target: test search with output vector field after binary index
|
||||
method: 1. create a collection and insert data
|
||||
2. create index and load
|
||||
3. search with output field vector
|
||||
4. check the result vectors should be equal to the inserted
|
||||
expected: search success
|
||||
"""
|
||||
dim = 8
|
||||
# 1. create a collection and insert data
|
||||
collection_w = self.init_collection_general(prefix, dim=dim, is_binary=True, is_index=False)[0]
|
||||
data = cf.gen_default_binary_dataframe_data(dim=dim)[0]
|
||||
collection_w.insert(data)
|
||||
|
||||
# 2. create index and load
|
||||
default_index = {"index_type": index, "metric_type": metrics, "params": {"nlist": 128}}
|
||||
collection_w.create_index(binary_field_name, default_index)
|
||||
collection_w.load()
|
||||
|
||||
# 3. search with output field vector
|
||||
search_params = {"metric_type": metrics, "params": {"nprobe": 10}}
|
||||
binary_vectors = cf.gen_binary_vectors(ct.default_nq, dim)[1]
|
||||
res = collection_w.search(binary_vectors, binary_field_name,
|
||||
search_params, 2, default_search_exp,
|
||||
output_fields=[binary_field_name])[0]
|
||||
|
|
@ -3485,7 +3481,7 @@ class TestCollectionSearch(TestcaseBase):
|
|||
enable_dynamic_field=enable_dynamic_field)[0]
|
||||
|
||||
# 2. search with output field vector
|
||||
output_fields = [default_int64_field_name,ndefault_string_field_name, default_search_field]
|
||||
output_fields = [default_int64_field_name, default_string_field_name, default_search_field]
|
||||
collection_w.search(vectors[:1], default_search_field,
|
||||
default_search_params, default_limit, default_search_exp,
|
||||
output_fields=output_fields,
|
||||
|
|
@ -4254,8 +4250,8 @@ class TestSearchBase(TestcaseBase):
|
|||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
def test_search_index_empty_partition(self, index, params):
|
||||
"""
|
||||
target: test basic search function, all the search params are correct, test all index params, and build
|
||||
|
|
@ -4272,8 +4268,7 @@ class TestSearchBase(TestcaseBase):
|
|||
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
||||
# 2. create partition
|
||||
partition_name = "search_partition_empty"
|
||||
collection_w.create_partition(
|
||||
partition_name=partition_name, description="search partition empty")
|
||||
collection_w.create_partition(partition_name=partition_name, description="search partition empty")
|
||||
par = collection_w.partitions
|
||||
# collection_w.load()
|
||||
# 3. create different index
|
||||
|
|
@ -4283,8 +4278,7 @@ class TestSearchBase(TestcaseBase):
|
|||
if params.get("PQM"):
|
||||
if (dim % params["PQM"]) != 0:
|
||||
params["PQM"] = dim // 4
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "COSINE"}
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "COSINE"}
|
||||
collection_w.create_index("float_vector", default_index)
|
||||
collection_w.load()
|
||||
|
||||
|
|
@ -4305,8 +4299,8 @@ class TestSearchBase(TestcaseBase):
|
|||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
def test_search_index_partitions(self, index, params, get_top_k):
|
||||
"""
|
||||
target: test basic search function, all the search params are correct, test all index params, and build
|
||||
|
|
@ -4332,8 +4326,7 @@ class TestSearchBase(TestcaseBase):
|
|||
if params.get("PQM"):
|
||||
if (dim % params["PQM"]) != 0:
|
||||
params["PQM"] = dim // 4
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "COSINE"}
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "COSINE"}
|
||||
collection_w.create_index("float_vector", default_index)
|
||||
collection_w.load()
|
||||
res, _ = collection_w.search(vectors[:nq], default_search_field,
|
||||
|
|
@ -4368,8 +4361,8 @@ class TestSearchBase(TestcaseBase):
|
|||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
def test_search_ip_after_index(self, index, params):
|
||||
"""
|
||||
target: test basic search function, all the search params are correct, test all index params, and build
|
||||
|
|
@ -4385,8 +4378,7 @@ class TestSearchBase(TestcaseBase):
|
|||
dim=dim, is_index=False)[0:5]
|
||||
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
||||
# 2. create ip index
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "IP"}
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
|
||||
collection_w.create_index("float_vector", default_index)
|
||||
collection_w.load()
|
||||
search_params = {"metric_type": "IP", "params": {"nprobe": 10}}
|
||||
|
|
@ -4430,8 +4422,8 @@ class TestSearchBase(TestcaseBase):
|
|||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
def test_search_ip_index_empty_partition(self, index, params):
|
||||
"""
|
||||
target: test basic search function, all the search params are correct, test all index params, and build
|
||||
|
|
@ -4448,13 +4440,11 @@ class TestSearchBase(TestcaseBase):
|
|||
vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
|
||||
# 2. create partition
|
||||
partition_name = "search_partition_empty"
|
||||
collection_w.create_partition(
|
||||
partition_name=partition_name, description="search partition empty")
|
||||
collection_w.create_partition(partition_name=partition_name, description="search partition empty")
|
||||
par = collection_w.partitions
|
||||
# collection_w.load()
|
||||
# 3. create different index
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "IP"}
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
|
||||
collection_w.create_index("float_vector", default_index)
|
||||
collection_w.load()
|
||||
|
||||
|
|
@ -4476,8 +4466,8 @@ class TestSearchBase(TestcaseBase):
|
|||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
def test_search_ip_index_partitions(self, index, params):
|
||||
"""
|
||||
target: test basic search function, all the search params are correct, test all index params, and build
|
||||
|
|
@ -4496,8 +4486,7 @@ class TestSearchBase(TestcaseBase):
|
|||
par_name = collection_w.partitions[0].name
|
||||
# collection_w.load()
|
||||
# 3. create different index
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "IP"}
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "IP"}
|
||||
collection_w.create_index("float_vector", default_index)
|
||||
collection_w.load()
|
||||
|
||||
|
|
@ -5544,8 +5533,8 @@ class TestSearchPagination(TestcaseBase):
|
|||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
def test_search_pagination_after_different_index(self, index, params, auto_id, offset, _async):
|
||||
"""
|
||||
target: test search pagination after different index
|
||||
|
|
@ -5565,14 +5554,12 @@ class TestSearchPagination(TestcaseBase):
|
|||
if params.get("PQM"):
|
||||
if (dim % params["PQM"]) != 0:
|
||||
params["PQM"] = dim // 4
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "L2"}
|
||||
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)]
|
||||
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
||||
for search_param in search_params:
|
||||
res = collection_w.search(vectors[:default_nq], default_search_field, search_param,
|
||||
default_limit + offset, default_search_exp, _async=_async)[0]
|
||||
|
|
@ -6711,14 +6698,12 @@ class TestCollectionRangeSearch(TestcaseBase):
|
|||
if params.get("PQM"):
|
||||
if (dim % params["PQM"]) != 0:
|
||||
params["PQM"] = dim // 4
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "L2"}
|
||||
default_index = {"index_type": index, "params": params, "metric_type": "L2"}
|
||||
collection_w.create_index("float_vector", default_index)
|
||||
collection_w.load()
|
||||
# 3. range search
|
||||
search_params = cf.gen_search_param(index)
|
||||
vectors = [[random.random() for _ in range(dim)]
|
||||
for _ in range(default_nq)]
|
||||
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
||||
for search_param in search_params:
|
||||
search_param["params"]["radius"] = 1000
|
||||
search_param["params"]["range_filter"] = 0
|
||||
|
|
@ -6754,18 +6739,14 @@ class TestCollectionRangeSearch(TestcaseBase):
|
|||
if params.get("PQM"):
|
||||
if (dim % params["PQM"]) != 0:
|
||||
params["PQM"] = dim // 4
|
||||
log.info(
|
||||
"test_range_search_after_index_different_metric_type: Creating index-%s" % index)
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": "IP"}
|
||||
log.info("test_range_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_range_search_after_index_different_metric_type: Created index-%s" % index)
|
||||
log.info("test_range_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)]
|
||||
vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
|
||||
for search_param in search_params:
|
||||
search_param["params"]["radius"] = 0
|
||||
search_param["params"]["range_filter"] = 1000
|
||||
|
|
@ -9011,8 +8992,8 @@ class TestSearchIterator(TestcaseBase):
|
|||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.skip("issue #25145")
|
||||
@pytest.mark.parametrize("index, params",
|
||||
zip(ct.all_index_types[:6],
|
||||
ct.default_index_params[:6]))
|
||||
zip(ct.all_index_types[:7],
|
||||
ct.default_index_params[:7]))
|
||||
@pytest.mark.parametrize("metrics", ct.float_metrics)
|
||||
def test_search_iterator_after_different_index_metrics(self, index, params, metrics):
|
||||
"""
|
||||
|
|
@ -9023,10 +9004,8 @@ class TestSearchIterator(TestcaseBase):
|
|||
"""
|
||||
# 1. initialize with data
|
||||
batch_size = 100
|
||||
collection_w = self.init_collection_general(
|
||||
prefix, True, is_index=False)[0]
|
||||
default_index = {"index_type": index,
|
||||
"params": params, "metric_type": metrics}
|
||||
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
||||
default_index = {"index_type": index, "params": params, "metric_type": metrics}
|
||||
collection_w.create_index(field_name, default_index)
|
||||
collection_w.load()
|
||||
# 2. search iterator
|
||||
|
|
|
|||
Loading…
Reference in New Issue