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fix: Add hybridsearch result cases (#30834) 

#30694

Signed-off-by: luzhang <luzhang@zilliz.com>
Co-authored-by: luzhang <luzhang@zilliz.com>
pull/30561/head
zhagnlu 2024-03-01 10:59:01 +08:00 committed by GitHub
parent 8addd75481
commit 21b41e96fc
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69
tests/python_client/testcases/test_search.py
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@ -11709,3 +11709,72 @@ class TestCollectionHybridSearchValid(TestcaseBase):
for i in range(len(score_answer[:limit])):
assert score_answer[i] - hybrid_res[0].distances[i] < hybrid_search_epsilon
@pytest.mark.tags(CaseLabel.L1)
def test_hybrid_search_result_L2_order(self):
"""
target: test hybrid search result having correct order for L2 distance
method: create connection, collection, insert and search
expected: hybrid search successfully and result order is correct
"""
# 1. initialize collection with data
collection_w, _, _, insert_ids, time_stamp = \
self.init_collection_general(prefix, True, is_index=False, multiple_dim_array=[default_dim, default_dim])[0:5]
# 2. create index
vector_name_list = cf.extract_vector_field_name_list(collection_w)
vector_name_list.append(ct.default_float_vec_field_name)
for i in range(len(vector_name_list)) :
default_index = { "index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128},}
collection_w.create_index(vector_name_list[i], default_index)
collection_w.load()
# 3. prepare search params
req_list = []
weights = [0.2, 0.3, 0.5]
for i in range(len(vector_name_list)):
vectors = [[random.random() for _ in range(default_dim)] for _ in range(1)]
search_param = {
"data": vectors,
"anns_field": vector_name_list[i],
"param": {"metric_type": "L2", "offset": 0},
"limit": default_limit,
"expr": "int64 > 0"}
req = AnnSearchRequest(**search_param)
req_list.append(req)
# 4. hybrid search
res = collection_w.hybrid_search(req_list, WeightedRanker(*weights), 10)
is_sorted_decrease = lambda lst: all(lst[i]['distance'] >= lst[i+1]['distance'] for i in range(len(lst)-1))
assert is_sorted_decrease(res[0])
print(res)
@pytest.mark.tags(CaseLabel.L1)
def test_hybrid_search_result_order(self):
"""
target: test hybrid search result having correct order for cosine distance
method: create connection, collection, insert and search
expected: hybrid search successfully and result order is correct
"""
# 1. initialize collection with data
collection_w, _, _, insert_ids, time_stamp = \
self.init_collection_general(prefix, True, multiple_dim_array=[default_dim, default_dim])[0:5]
# 2. extract vector field name
vector_name_list = cf.extract_vector_field_name_list(collection_w)
vector_name_list.append(ct.default_float_vec_field_name)
# 3. prepare search params
req_list = []
weights = [0.2, 0.3, 0.5]
for i in range(len(vector_name_list)):
vectors = [[random.random() for _ in range(default_dim)] for _ in range(1)]
search_param = {
"data": vectors,
"anns_field": vector_name_list[i],
"param": {"metric_type": "COSINE", "offset": 0},
"limit": default_limit,
"expr": "int64 > 0"}
req = AnnSearchRequest(**search_param)
req_list.append(req)
# 4. hybrid search
res = collection_w.hybrid_search(req_list, WeightedRanker(*weights), 10)
is_sorted_ascend = lambda lst: all(lst[i]['distance'] <= lst[i+1]['distance'] for i in range(len(lst)-1))
assert is_sorted_ascend(res[0])
print(res)