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test: Add tests for hybrid search group by (#36326) 

related issue: #36295

---------

Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
pull/36373/head
yanliang567 2024-09-19 09:41:12 +08:00 committed by GitHub
parent 167e4fb10d
commit e013ef1908
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3 changed files with 167 additions and 103 deletions
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48
tests/python_client/common/common_func.py
View File

@ -806,10 +806,10 @@ def gen_default_dataframe_data(nb=ct.default_nb, dim=ct.default_dim, start=0, wi
return df return df
def gen_general_default_list_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True, def gen_default_list_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True,
random_primary_key=False, multiple_dim_array=[], multiple_vector_field_name=[], random_primary_key=False, multiple_dim_array=[], multiple_vector_field_name=[],
vector_data_type="FLOAT_VECTOR", auto_id=False, vector_data_type="FLOAT_VECTOR", auto_id=False,
primary_field=ct.default_int64_field_name, nullable_fields={}): primary_field=ct.default_int64_field_name, nullable_fields={}):
insert_list = [] insert_list = []
if not random_primary_key: if not random_primary_key:
int_values = pd.Series(data=[i for i in range(start, start + nb)]) int_values = pd.Series(data=[i for i in range(start, start + nb)])
@ -1244,19 +1244,19 @@ def gen_default_binary_dataframe_data(nb=ct.default_nb, dim=ct.default_dim, star
return df, binary_raw_values return df, binary_raw_values
#
def gen_default_list_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True): # def gen_default_list_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=True):
int_values = [i for i in range(start, start + nb)] # int_values = [i for i in range(start, start + nb)]
float_values = [np.float32(i) for i in range(start, start + nb)] # float_values = [np.float32(i) for i in range(start, start + nb)]
string_values = [str(i) for i in range(start, start + nb)] # string_values = [str(i) for i in range(start, start + nb)]
json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(0, i)]} # json_values = [{"number": i, "string": str(i), "bool": bool(i), "list": [j for j in range(0, i)]}
for i in range(start, start + nb)] # for i in range(start, start + nb)]
float_vec_values = gen_vectors(nb, dim) # float_vec_values = gen_vectors(nb, dim)
if with_json is False: # if with_json is False:
data = [int_values, float_values, string_values, float_vec_values] # data = [int_values, float_values, string_values, float_vec_values]
else: # else:
data = [int_values, float_values, string_values, json_values, float_vec_values] # data = [int_values, float_values, string_values, json_values, float_vec_values]
return data # return data
def gen_default_list_sparse_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=False): def gen_default_list_sparse_data(nb=ct.default_nb, dim=ct.default_dim, start=0, with_json=False):
@ -2347,13 +2347,13 @@ def insert_data(collection_w, nb=ct.default_nb, is_binary=False, is_all_data_typ
auto_id=auto_id, primary_field=primary_field, auto_id=auto_id, primary_field=primary_field,
nullable_fields=nullable_fields) nullable_fields=nullable_fields)
elif vector_data_type in ct.append_vector_type: elif vector_data_type in ct.append_vector_type:
default_data = gen_general_default_list_data(nb // num, dim=dim, start=start, with_json=with_json, default_data = gen_default_list_data(nb // num, dim=dim, start=start, with_json=with_json,
random_primary_key=random_primary_key, random_primary_key=random_primary_key,
multiple_dim_array=multiple_dim_array, multiple_dim_array=multiple_dim_array,
multiple_vector_field_name=vector_name_list, multiple_vector_field_name=vector_name_list,
vector_data_type=vector_data_type, vector_data_type=vector_data_type,
auto_id=auto_id, primary_field=primary_field, auto_id=auto_id, primary_field=primary_field,
nullable_fields=nullable_fields) nullable_fields=nullable_fields)
else: else:
default_data = gen_default_rows_data(nb // num, dim=dim, start=start, with_json=with_json, default_data = gen_default_rows_data(nb // num, dim=dim, start=start, with_json=with_json,

6
tests/python_client/testcases/test_insert.py
View File

@ -60,7 +60,7 @@ class TestInsertParams(TestcaseBase):
data = cf.gen_default_list_data(ct.default_nb) data = cf.gen_default_list_data(ct.default_nb)
mutation_res, _ = collection_w.insert(data=data) mutation_res, _ = collection_w.insert(data=data)
assert mutation_res.insert_count == ct.default_nb assert mutation_res.insert_count == ct.default_nb
assert mutation_res.primary_keys == data[0] assert mutation_res.primary_keys == data[0].tolist()
assert collection_w.num_entities == ct.default_nb assert collection_w.num_entities == ct.default_nb
@pytest.mark.tags(CaseLabel.L2) @pytest.mark.tags(CaseLabel.L2)
@ -214,7 +214,7 @@ class TestInsertParams(TestcaseBase):
data = cf.gen_default_list_data(nb=1) data = cf.gen_default_list_data(nb=1)
mutation_res, _ = collection_w.insert(data=data) mutation_res, _ = collection_w.insert(data=data)
assert mutation_res.insert_count == 1 assert mutation_res.insert_count == 1
assert mutation_res.primary_keys == data[0] assert mutation_res.primary_keys == data[0].tolist()
assert collection_w.num_entities == 1 assert collection_w.num_entities == 1
@pytest.mark.tags(CaseLabel.L2) @pytest.mark.tags(CaseLabel.L2)
@ -1417,7 +1417,7 @@ class TestInsertString(TestcaseBase):
data = cf.gen_default_list_data(ct.default_nb) data = cf.gen_default_list_data(ct.default_nb)
mutation_res, _ = collection_w.insert(data=data) mutation_res, _ = collection_w.insert(data=data)
assert mutation_res.insert_count == ct.default_nb assert mutation_res.insert_count == ct.default_nb
assert mutation_res.primary_keys == data[2] assert mutation_res.primary_keys == data[2].tolist()
@pytest.mark.tags(CaseLabel.L0) @pytest.mark.tags(CaseLabel.L0)
@pytest.mark.parametrize("string_fields", [[cf.gen_string_field(name="string_field1")], @pytest.mark.parametrize("string_fields", [[cf.gen_string_field(name="string_field1")],

216
tests/python_client/testcases/test_search.py
View File

@ -7052,7 +7052,7 @@ class TestCollectionRangeSearch(TestcaseBase):
nb = 1000 nb = 1000
rounds = 10 rounds = 10
for i in range(rounds): for i in range(rounds):
data = cf.gen_general_default_list_data(nb=nb, auto_id=True, vector_data_type=vector_data_type, data = cf.gen_default_list_data(nb=nb, auto_id=True, vector_data_type=vector_data_type,
with_json=False, start=i*nb) with_json=False, start=i*nb)
collection_w.insert(data) collection_w.insert(data)
@ -7064,7 +7064,7 @@ class TestCollectionRangeSearch(TestcaseBase):
if with_growing is True: if with_growing is True:
# add some growing segments # add some growing segments
for j in range(rounds//2): for j in range(rounds//2):
data = cf.gen_general_default_list_data(nb=nb, auto_id=True, vector_data_type=vector_data_type, data = cf.gen_default_list_data(nb=nb, auto_id=True, vector_data_type=vector_data_type,
with_json=False, start=(rounds+j)*nb) with_json=False, start=(rounds+j)*nb)
collection_w.insert(data) collection_w.insert(data)
@ -10240,7 +10240,7 @@ class TestSearchIterator(TestcaseBase):
class TestSearchGroupBy(TestcaseBase): class TestSearchGroupBy(TestcaseBase):
""" Test case of search group by """ """ Test case of search group by """
@pytest.mark.tags(CaseLabel.L3) @pytest.mark.tags(CaseLabel.L2)
@pytest.mark.parametrize("index_type, metric", zip(["FLAT", "IVF_FLAT", "HNSW"], ct.float_metrics)) @pytest.mark.parametrize("index_type, metric", zip(["FLAT", "IVF_FLAT", "HNSW"], ct.float_metrics))
@pytest.mark.parametrize("vector_data_type", ["FLOAT16_VECTOR", "FLOAT_VECTOR", "BFLOAT16_VECTOR"]) @pytest.mark.parametrize("vector_data_type", ["FLOAT16_VECTOR", "FLOAT_VECTOR", "BFLOAT16_VECTOR"])
def test_search_group_by_default(self, index_type, metric, vector_data_type): def test_search_group_by_default(self, index_type, metric, vector_data_type):
@ -10273,19 +10273,19 @@ class TestSearchGroupBy(TestcaseBase):
nq = 2 nq = 2
limit = 15 limit = 15
search_vectors = cf.gen_vectors(nq, dim=ct.default_dim) search_vectors = cf.gen_vectors(nq, dim=ct.default_dim)
# verify the results are same if gourp by pk # # verify the results are same if gourp by pk
res1 = collection_w.search(data=search_vectors, anns_field=ct.default_float_vec_field_name, # res1 = collection_w.search(data=search_vectors, anns_field=ct.default_float_vec_field_name,
param=search_params, limit=limit, consistency_level=CONSISTENCY_STRONG, # param=search_params, limit=limit, consistency_level=CONSISTENCY_STRONG,
group_by_field=ct.default_int64_field_name)[0] # group_by_field=ct.default_int64_field_name)[0]
res2 = collection_w.search(data=search_vectors, anns_field=ct.default_float_vec_field_name, # res2 = collection_w.search(data=search_vectors, anns_field=ct.default_float_vec_field_name,
param=search_params, limit=limit, consistency_level=CONSISTENCY_STRONG)[0] # param=search_params, limit=limit, consistency_level=CONSISTENCY_STRONG)[0]
hits_num = 0 # hits_num = 0
for i in range(nq): # for i in range(nq):
# assert res1[i].ids == res2[i].ids # assert res1[i].ids == res2[i].ids
hits_num += len(set(res1[i].ids).intersection(set(res2[i].ids))) # hits_num += len(set(res1[i].ids).intersection(set(res2[i].ids)))
hit_rate = hits_num / (nq * limit) # hit_rate = hits_num / (nq * limit)
log.info(f"groupy primary key hits_num: {hits_num}, nq: {nq}, limit: {limit}, hit_rate: {hit_rate}") # log.info(f"groupy primary key hits_num: {hits_num}, nq: {nq}, limit: {limit}, hit_rate: {hit_rate}")
assert hit_rate >= 0.60 # assert hit_rate >= 0.60
# verify that every record in groupby results is the top1 for that value of the group_by_field # verify that every record in groupby results is the top1 for that value of the group_by_field
supported_grpby_fields = [ct.default_int8_field_name, ct.default_int16_field_name, supported_grpby_fields = [ct.default_int8_field_name, ct.default_int16_field_name,
@ -10323,61 +10323,119 @@ class TestSearchGroupBy(TestcaseBase):
assert len(grpby_values) == len(set(grpby_values)) assert len(grpby_values) == len(set(grpby_values))
@pytest.mark.tags(CaseLabel.L0) @pytest.mark.tags(CaseLabel.L0)
@pytest.mark.parametrize("index_type, metric", zip(["FLAT", "IVF_FLAT", "HNSW"], ct.float_metrics)) def test_search_group_size_default(self):
@pytest.mark.parametrize("vector_data_type", ["FLOAT_VECTOR", "FLOAT16_VECTOR", "BFLOAT16_VECTOR"])
@pytest.mark.parametrize("group_strict_size", [True, False])
def test_search_group_size_default(self, index_type, metric, vector_data_type, group_strict_size):
""" """
target: test search group by target: test search group by
method: 1. create a collection with data method: 1. create a collection with 3 different float vectors
2. search with group by int32 with group size 2. build index with 3 different index types and metrics
2. search on 3 different float vector fields with group by varchar field with group size
verify results entity = limit * group_size and group size is full if group_strict_size is True verify results entity = limit * group_size and group size is full if group_strict_size is True
verfiy results group counts = limit if group_strict_size is False verify results group counts = limit if group_strict_size is False
""" """
collection_w = self.init_collection_general(prefix, auto_id=True, insert_data=False, is_index=False, self._connect()
vector_data_type=vector_data_type, dense_types = ["FLOAT16_VECTOR", "FLOAT_VECTOR", "BFLOAT16_VECTOR"]
is_all_data_type=True, with_json=False)[0] dims = [16, 128, 64]
_index_params = {"index_type": index_type, "metric_type": metric, "params": {"M": 16, "efConstruction": 128}} index_types = ["FLAT", "IVF_SQ8", "HNSW"]
if index_type in ["IVF_FLAT", "FLAT"]: metrics = ct.float_metrics
_index_params = {"index_type": index_type, "metric_type": metric, "params": {"nlist": 128}} fields = [cf.gen_int64_field(is_primary=True), cf.gen_string_field()]
collection_w.create_index(ct.default_float_vec_field_name, index_params=_index_params) for i in range(len(dense_types)):
fields.append(cf.gen_float_vec_field(name=dense_types[i],
vector_data_type=dense_types[i], dim=dims[i]))
schema = cf.gen_collection_schema(fields, auto_id=True)
collection_w = self.init_collection_wrap(name=prefix, schema=schema)
# insert with the same values for scalar fields # insert with the same values for scalar fields
for _ in range(500): nb = 100
data = cf.gen_dataframe_all_data_type(nb=100, auto_id=True, with_json=False) for _ in range(100):
string_values = pd.Series(data=[str(i) for i in range(nb)], dtype="string")
data = [string_values]
for i in range(len(dense_types)):
data.append(cf.gen_vectors(dim=dims[i], nb=nb, vector_data_type=dense_types[i]))
collection_w.insert(data) collection_w.insert(data)
collection_w.flush() collection_w.flush()
collection_w.create_index(ct.default_float_vec_field_name, index_params=_index_params) for i in range(len(dense_types)):
_index_params = {"index_type": index_types[i], "metric_type": metrics[i],
"params": cf.get_index_params_params(index_types[i])}
collection_w.create_index(dense_types[i], _index_params)
collection_w.load() collection_w.load()
search_params = {"metric_type": metric, "params": {"ef": 128}}
nq = 2 nq = 2
limit = 100 limit = 50
group_size = 10 group_size = 5
search_vectors = cf.gen_vectors(nq, dim=ct.default_dim) for j in range(len(dense_types)):
# verify search_vectors = cf.gen_vectors(nq, dim=dims[j], vector_data_type=dense_types[j])
res1 = collection_w.search(data=search_vectors, anns_field=ct.default_float_vec_field_name, search_params = {"params": cf.get_search_params_params(index_types[j])}
param=search_params, limit=limit, consistency_level=CONSISTENCY_STRONG, # when group_strict_size=true, it shall return results with entities = limit * group_size
group_by_field=ct.default_int32_field_name, group_size=group_size, res1 = collection_w.search(data=search_vectors, anns_field=dense_types[j],
group_strict_size=group_strict_size, param=search_params, limit=limit, # consistency_level=CONSISTENCY_STRONG,
output_fields=[ct.default_int32_field_name] group_by_field=ct.default_string_field_name,
)[0] group_size=group_size, group_strict_size=True,
# print(res1) output_fields=[ct.default_string_field_name])[0]
if group_strict_size is True: # when true, it shall return results with entities = limit * group_size
for i in range(nq): for i in range(nq):
for l in range(limit): for l in range(limit):
group_values = [] group_values = []
for k in range(10): for k in range(10):
group_values.append(res1[i][l].fields.get(ct.default_int32_field_name)) group_values.append(res1[i][l].fields.get(ct.default_string_field_name))
assert len(set(group_values)) == 1 assert len(set(group_values)) == 1
assert len(res1[i]) == limit * group_size assert len(res1[i]) == limit * group_size
else: # when False, it shall return results with group counts = limit
# when group_strict_size=false, it shall return results with group counts = limit
res1 = collection_w.search(data=search_vectors, anns_field=dense_types[j],
param=search_params, limit=limit, # consistency_level=CONSISTENCY_STRONG,
group_by_field=ct.default_string_field_name,
group_size=group_size, group_strict_size=False,
output_fields=[ct.default_string_field_name])[0]
for i in range(nq): for i in range(nq):
group_values = [] group_values = []
for l in range(len(res1[i])): for l in range(len(res1[i])):
group_values.append(res1[i][l].fields.get(ct.default_int32_field_name)) group_values.append(res1[i][l].fields.get(ct.default_string_field_name))
assert len(set(group_values)) == limit assert len(set(group_values)) == limit
# hybrid search group by
req_list = []
for j in range(len(dense_types)):
search_params = {
"data": cf.gen_vectors(nq, dim=dims[j], vector_data_type=dense_types[j]),
"anns_field": dense_types[j],
"param": {"params": cf.get_search_params_params(index_types[j])},
"limit": limit,
"expr": "int64 > 0"}
req = AnnSearchRequest(**search_params)
req_list.append(req)
# 4. hybrid search group by
import numpy as np
rank_scorers = ["max", "avg", "sum"]
for scorer in rank_scorers:
res = collection_w.hybrid_search(req_list, WeightedRanker(0.3, 0.3, 0.3), limit=limit,
group_by_field=ct.default_string_field_name,
group_size=group_size, rank_group_scorer=scorer,
output_fields=[ct.default_string_field_name])[0]
for i in range(nq):
group_values = []
for l in range(len(res[i])):
group_values.append(res[i][l].fields.get(ct.default_string_field_name))
assert len(set(group_values)) == limit
# group_distances = []
tmp_distances = [100 for _ in range(group_size)] # init with a large value
group_distances = [res[i][0].distance] # init with the first value
for l in range(len(res[i])-1):
curr_group_value = res[i][l].fields.get(ct.default_string_field_name)
next_group_value = res[i][l+1].fields.get(ct.default_string_field_name)
if curr_group_value == next_group_value:
group_distances.append(res[i][l+1].distance)
else:
if scorer == 'sum':
assert np.sum(group_distances) < np.sum(tmp_distances)
elif scorer == 'avg':
assert np.mean(group_distances) < np.mean(tmp_distances)
else: # default max
assert np.max(group_distances) < np.max(tmp_distances)
tmp_distances = group_distances
group_distances = [res[i][l+1].distance]
@pytest.mark.tags(CaseLabel.L2) @pytest.mark.tags(CaseLabel.L2)
def test_search_max_group_size_and_max_limit(self): def test_search_max_group_size_and_max_limit(self):
""" """
@ -10776,8 +10834,8 @@ class TestSearchGroupBy(TestcaseBase):
check_task=CheckTasks.err_res, check_task=CheckTasks.err_res,
check_items={"err_code": err_code, "err_msg": err_msg}) check_items={"err_code": err_code, "err_msg": err_msg})
@pytest.mark.tags(CaseLabel.L2) @pytest.mark.tags(CaseLabel.L1)
def test_hybrid_search_not_support_group_by(self): def test_hybrid_search_support_group_by(self):
""" """
target: verify that hybrid search does not support groupby target: verify that hybrid search does not support groupby
method: 1. create a collection with multiple vector fields method: 1. create a collection with multiple vector fields
@ -10786,55 +10844,61 @@ class TestSearchGroupBy(TestcaseBase):
verify: the error code and msg verify: the error code and msg
""" """
# 1. initialize collection with data # 1. initialize collection with data
dim = 33 dim = 128
index_type = "HNSW" supported_index = ["HNSW", "FLAT", "IVF_FLAT", "IVF_SQ8"]
metric_type = "COSINE" metric = ct.default_L0_metric
_index_params = {"index_type": index_type, "metric_type": metric_type, "params": {"M": 16, "efConstruction": 128}}
collection_w, _, _, insert_ids, time_stamp = \ collection_w, _, _, insert_ids, time_stamp = \
self.init_collection_general(prefix, True, dim=dim, is_index=False, self.init_collection_general(prefix, True, dim=dim, is_index=False,
enable_dynamic_field=False, multiple_dim_array=[dim, dim])[0:5] enable_dynamic_field=False,
multiple_dim_array=[dim, dim, dim])[0:5]
# 2. extract vector field name # 2. extract vector field name
vector_name_list = cf.extract_vector_field_name_list(collection_w) vector_name_list = cf.extract_vector_field_name_list(collection_w)
vector_name_list.append(ct.default_float_vec_field_name) vector_name_list.append(ct.default_float_vec_field_name)
for vector_name in vector_name_list: for i in range(len(vector_name_list)):
collection_w.create_index(vector_name, _index_params) index = supported_index[i]
_index_params = {"index_type": index, "metric_type": metric,
"params": cf.get_index_params_params(index)}
collection_w.create_index(vector_name_list[i], _index_params)
collection_w.load() collection_w.load()
# 3. prepare search params # 3. prepare search params
req_list = [] req_list = []
for vector_name in vector_name_list: for vector_name in vector_name_list:
search_param = { search_param = {
"data": [[random.random() for _ in range(dim)] for _ in range(1)], "data": [[random.random() for _ in range(dim)] for _ in range(ct.default_nq)],
"anns_field": vector_name, "anns_field": vector_name,
"param": {"metric_type": metric_type, "offset": 0}, "param": {"metric_type": metric, "offset": 0},
"limit": default_limit, "limit": default_limit,
# "group_by_field": ct.default_int64_field_name,
"expr": "int64 > 0"} "expr": "int64 > 0"}
req = AnnSearchRequest(**search_param) req = AnnSearchRequest(**search_param)
req_list.append(req) req_list.append(req)
# 4. hybrid search # 4. hybrid search group by
err_code = 9999 res = collection_w.hybrid_search(req_list, WeightedRanker(0.1, 0.9, 1, 0.2), default_limit,
err_msg = f"not support search_group_by operation in the hybrid search" group_by_field=ct.default_string_field_name,
collection_w.hybrid_search(req_list, WeightedRanker(0.1, 0.9, 1), default_limit, output_fields=[ct.default_string_field_name],
group_by_field=ct.default_int64_field_name, check_task=CheckTasks.check_search_results,
check_task=CheckTasks.err_res, check_items={"nq": ct.default_nq, "limit": default_limit})[0]
check_items={"err_code": err_code, "err_msg": err_msg}) print(res)
for i in range(ct.default_nq):
group_values = []
for l in range(ct.default_limit):
group_values.append(res[i][l].fields.get(ct.default_string_field_name))
assert len(group_values) == len(set(group_values))
# 5. hybrid search with group by on one vector field # 5. hybrid search with RRFRanker on one vector field with group by
req_list = [] req_list = []
for vector_name in vector_name_list[:1]: for vector_name in vector_name_list[:1]:
search_param = { search_param = {
"data": [[random.random() for _ in range(dim)] for _ in range(1)], "data": [[random.random() for _ in range(dim)] for _ in range(1)],
"anns_field": vector_name, "anns_field": vector_name,
"param": {"metric_type": metric_type, "offset": 0}, "param": {"metric_type": metric, "offset": 0},
"limit": default_limit, "limit": default_limit,
# "group_by_field": ct.default_int64_field_name,
"expr": "int64 > 0"} "expr": "int64 > 0"}
req = AnnSearchRequest(**search_param) req = AnnSearchRequest(**search_param)
req_list.append(req) req_list.append(req)
collection_w.hybrid_search(req_list, RRFRanker(), default_limit, collection_w.hybrid_search(req_list, RRFRanker(), default_limit,
group_by_field=ct.default_int64_field_name, group_by_field=ct.default_string_field_name,
check_task=CheckTasks.err_res, check_task=CheckTasks.check_search_results,
check_items={"err_code": err_code, "err_msg": err_msg}) check_items={"nq": 1, "limit": default_limit})
@pytest.mark.tags(CaseLabel.L1) @pytest.mark.tags(CaseLabel.L1)
def test_multi_vectors_search_one_vector_group_by(self): def test_multi_vectors_search_one_vector_group_by(self):