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[skip ci]Add a test for one index and multi vectors (#12703) 

Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
pull/12691/head
yanliang567 2021-12-03 15:39:33 +08:00 committed by GitHub
parent 7cde4b2d0b
commit 3f5c8106a7
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29
tests/python_client/testcases/test_search_20.py
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@ -1472,6 +1472,35 @@ class TestCollectionSearch(TestcaseBase):
"limit": default_limit,
"_async": _async})
@pytest.mark.tags(CaseLabel.L1)
@pytest.mark.skip(reason="issue #12680")
# TODO: add one more for binary vectors
# @pytest.mark.parametrize("vec_fields", [[cf.gen_float_vec_field(name="test_vector1")],
# [cf.gen_binary_vec_field(name="test_vector1")],
# [cf.gen_binary_vec_field(), cf.gen_binary_vec_field("test_vector1")]])
def test_search_multiple_vectors_with_one_indexed(self):
"""
target: test indexing on one vector fields when there are multi float vec fields
method: 1. create collection with multiple float vector fields
2. insert data and build index on one of float vector fields
3. load collection and search
expected: load and search successfully
"""
vec_fields = [cf.gen_float_vec_field(name="test_vector1")]
schema = cf.gen_schema_multi_vector_fields(vec_fields)
collection_w = self.init_collection_wrap(name=cf.gen_unique_str(prefix), schema=schema)
df = cf.gen_dataframe_multi_vec_fields(vec_fields=vec_fields)
collection_w.insert(df)
assert collection_w.num_entities == ct.default_nb
_index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"}
res, ch = collection_w.create_index(field_name="test_vector1", index_params=_index)
assert ch is True
collection_w.load()
vectors = [[random.random() for _ in range(default_dim)] for _ in range(2)]
search_params = {"metric_type": "L2", "params": {"nprobe": 16}}
res_1, _ = collection_w.search(data=vectors, anns_field="test_vector1",
param=search_params, limit=1)
@pytest.mark.tags(CaseLabel.L1)
def test_search_index_one_partition(self, nb, auto_id, _async):
"""