def hvicollection(request, connect, dim):
collection_name = fake.collection_name()
collection_param = {
"fields": [
{
"name": Field_Integer,
"type": DataType.INT64
},
{
"name": Field_Vector,
"type": DataType.FLOAT_VECTOR,
"params": {
"dim": dim
}
},
],
"segment_row_limit":
100000,
"auto_id":
False
}
connect.create_collection(collection_name, collection_param)
def teardown():
table_names = connect.list_collections()
for name in table_names:
connect.drop_collection(name)
request.addfinalizer(teardown)
return collection_name
def test_false_insert(self, gcon):
param = {
'collection_name': fake.collection_name(),
'records': records_factory(dim, nq)
}
res, ids = gcon.insert(**param)
assert not res.OK()
def test_insert_scalar_with_id(self, scalar, vec, connect, dim):
collection_name = fake.collection_name()
collection_param = {
"fields": [
{"name": "scalar", "type": scalar},
{"name": "Vec", "type": vec, "params": {"dim": 128}}
],
"auto_id": False
}
try:
connect.create_collection(collection_name, collection_param)
except Exception as e:
pytest.fail(f"Create collection {collection_name} fail: {e}")
if scalar in (DataType.INT32, DataType.INT64):
scalars = [random.randint(0, 10000) for _ in range(10000)]
else:
scalars = [random.random() for _ in range(10000)]
if vec in (DataType.FLOAT_VECTOR,):
vectors = records_factory(dim, 10000)
else:
vectors = binary_records_factory(dim, 10000)
entities = [{"Vec": vector, "scalar": s} for vector, s in zip(vectors, scalars)]
for i, e in enumerate(entities):
e["_id"] = i
try:
connect.insert(collection_name, entities)
except Exception as e:
pytest.fail(f"Unexpected Insert Error: {e}")
finally:
connect.drop_collection(collection_name)
def test_false_vector(self, gcon):
search_param = {"nprobe": 10}
param = {
'collection_name': fake.collection_name(),
'query_records': records_factory(dim, nq),
'top_k': 'string',
'params': search_param
}
with pytest.raises(ParamError):
gcon.search(**param)
param = {
'collection_name': fake.collection_name(),
'query_records': records_factory(dim, nq),
'top_k': 'string',
'params': search_param
}
with pytest.raises(ParamError):
gcon.search(**param)
def test_has_collection(self, gcon, gcollection):
collection_name = fake.collection_name()
status, result = gcon.has_collection(collection_name)
assert status.OK(), status.message
assert not result
result = gcon.has_collection(gcollection)
assert result
with pytest.raises(Exception):
gcon.has_collection(1111)
def gcollection(request, gcon):
table_name = fake.collection_name()
dim = getattr(request.module, "dim", 128)
param = {'collection_name': table_name,
'dimension': dim,
'index_file_size': 1024,
'metric_type': MetricType.L2
}
gcon.create_collection(param)
gcon.flush([table_name])
def teardown():
status, table_names = gcon.list_collections()
for name in table_names:
gcon.drop_collection(name)
request.addfinalizer(teardown)
return table_name
def test_insert_async_with_collection_non_exist(self, connect):
collection_name = fake.collection_name()
with pytest.raises(BaseError):
connect.insert(collection_name, {"A": 1, "V": [1]}, _async=True)
def test_insert_with_collection_non_exist(self, connect):
collection_name = fake.collection_name()
with pytest.raises(CollectionNotExistException):
connect.insert(collection_name, [{"A": 1, "V": [1]}])
def test_collection_schema(self):
res = Prepare.table_schema(fake.collection_name(),
random.randint(0, 999), 1024, MetricType.L2,
{})
assert isinstance(res, milvus_pb2.TableSchema)
def test_false_decribe_collection(self, gcon):
collection_name = fake.collection_name()
res, collection_schema = gcon.describe_collection(collection_name)
assert not res.OK()
assert not collection_schema
