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403 lines
18 KiB
Python
403 lines
18 KiB
Python
import pytest
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from base.client_v2_base import TestMilvusClientV2Base
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from common import common_func as cf
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from common import common_type as ct
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from common.common_type import CaseLabel, CheckTasks
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from idx_faiss import FAISS
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from pymilvus import DataType
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index_type = "FAISS"
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success = "success"
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pk_field_name = "id"
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vector_field_name = "vector"
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dim = ct.default_dim
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default_nb = ct.default_nb
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default_search_params = {"nprobe": 8}
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def _default_search_params_for_faiss_factory(faiss_index_name):
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if faiss_index_name.startswith("IVF"):
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return {"nprobe": 8}
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if faiss_index_name.startswith("HNSW"):
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return {"efSearch": 64}
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return {}
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class TestFaissBase(TestMilvusClientV2Base):
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def _create_collection(self, client, collection_name, vector_data_type=DataType.FLOAT_VECTOR):
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schema, _ = self.create_schema(client)
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schema.add_field(pk_field_name, datatype=DataType.INT64, is_primary=True, auto_id=False)
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if vector_data_type == DataType.SPARSE_FLOAT_VECTOR:
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schema.add_field(vector_field_name, datatype=vector_data_type)
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else:
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schema.add_field(vector_field_name, datatype=vector_data_type, dim=dim)
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self.create_collection(client, collection_name, schema=schema)
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return schema
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def _insert_rows(self, client, collection_name, vector_data_type=DataType.FLOAT_VECTOR):
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vectors = cf.gen_vectors(default_nb, dim=dim, vector_data_type=vector_data_type)
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rows = [{pk_field_name: i, vector_field_name: vectors[i]} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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def _create_faiss_index(
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self, client, collection_name, metric_type="L2", params=None, check_task=None, check_items=None
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):
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index_params = self.prepare_index_params(client)[0]
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index_params.add_index(
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field_name=vector_field_name, metric_type=metric_type, index_type=index_type, params=params
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)
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return self.create_index(client, collection_name, index_params, check_task=check_task, check_items=check_items)
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def _search_and_check(self, client, collection_name, vector_data_type=DataType.FLOAT_VECTOR, search_params=None):
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nq = ct.default_nq
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search_vectors = cf.gen_vectors(nq, dim=dim, vector_data_type=vector_data_type)
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self.search(
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client,
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collection_name,
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search_vectors,
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search_params=search_params,
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limit=ct.default_limit,
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check_task=CheckTasks.check_search_results,
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check_items={
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"enable_milvus_client_api": True,
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"nq": nq,
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"limit": ct.default_limit,
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"pk_name": pk_field_name,
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},
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)
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def _assert_index_params(self, client, collection_name, params, metric_type):
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idx_info = client.describe_index(collection_name, vector_field_name)
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assert idx_info["index_type"] == index_type
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assert idx_info["metric_type"] == metric_type
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for key, value in params.items():
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assert key in idx_info.keys()
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assert str(value) in [str(v) for v in idx_info.values()]
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class TestFaissBuildParams(TestFaissBase):
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("params", FAISS.build_params)
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def test_faiss_build_params(self, params):
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"""
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Test vanilla Faiss factory build parameters.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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vector_data_type = params.get("vector_data_type", DataType.FLOAT_VECTOR)
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metric_type = params.get("metric_type", "L2")
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build_params = params.get("params", None)
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self._create_collection(client, collection_name, vector_data_type)
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self._insert_rows(client, collection_name, vector_data_type)
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if params.get("expected", None) != success:
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self._create_faiss_index(
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client,
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collection_name,
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metric_type=metric_type,
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params=build_params,
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check_task=CheckTasks.err_res,
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check_items=params.get("expected"),
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)
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else:
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self._create_faiss_index(client, collection_name, metric_type=metric_type, params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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if vector_data_type == DataType.FLOAT_VECTOR and params.get("searchable", True):
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search_params = _default_search_params_for_faiss_factory(build_params["faiss_index_name"])
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self._search_and_check(client, collection_name, vector_data_type, search_params=search_params)
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elif vector_data_type != DataType.FLOAT_VECTOR:
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search_params = {}
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self._search_and_check(client, collection_name, vector_data_type, search_params=search_params)
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self._assert_index_params(client, collection_name, build_params, metric_type)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("metric", FAISS.supported_metrics)
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@pytest.mark.parametrize("build_params", [{"faiss_index_name": "Flat"}] + FAISS.metric_factories)
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def test_faiss_on_all_float_metrics(self, metric, build_params):
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"""
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Test vanilla Faiss float index factories on all supported float metrics.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type=metric, params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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search_params = _default_search_params_for_faiss_factory(build_params["faiss_index_name"])
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self._search_and_check(client, collection_name, DataType.FLOAT_VECTOR, search_params=search_params)
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self._assert_index_params(client, collection_name, build_params, metric)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("vector_data_type", ct.all_vector_types)
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def test_faiss_on_all_vector_types(self, vector_data_type):
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"""
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Test vanilla Faiss vector type support.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, vector_data_type)
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self._insert_rows(client, collection_name, vector_data_type)
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if vector_data_type == DataType.BINARY_VECTOR:
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metric_type = "HAMMING"
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build_params = {"faiss_index_name": "BFlat"}
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else:
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metric_type = cf.get_default_metric_for_vector_type(vector_data_type)
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build_params = {"faiss_index_name": "Flat"}
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if vector_data_type not in FAISS.supported_vector_types:
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self._create_faiss_index(
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client,
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collection_name,
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metric_type=metric_type,
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params=build_params,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999, "err_msg": "invalid parameter"},
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)
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else:
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self._create_faiss_index(client, collection_name, metric_type=metric_type, params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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self._search_and_check(client, collection_name, vector_data_type, search_params={})
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self._assert_index_params(client, collection_name, build_params, metric_type)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize(
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"params", [p for p in FAISS.build_params if p.get("expected") == success and p.get("searchable", True)]
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)
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def test_faiss_build_release_load_search(self, params):
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"""
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Test vanilla Faiss index survives the full Milvus build -> release -> load -> search flow.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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vector_data_type = params.get("vector_data_type", DataType.FLOAT_VECTOR)
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metric_type = params.get("metric_type", "L2")
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build_params = params["params"]
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self._create_collection(client, collection_name, vector_data_type)
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self._insert_rows(client, collection_name, vector_data_type)
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self._create_faiss_index(client, collection_name, metric_type=metric_type, params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.release_collection(client, collection_name)
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self.load_collection(client, collection_name)
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search_params = {}
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if vector_data_type == DataType.FLOAT_VECTOR:
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search_params = _default_search_params_for_faiss_factory(build_params["faiss_index_name"])
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self._search_and_check(client, collection_name, vector_data_type, search_params=search_params)
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self._assert_index_params(client, collection_name, build_params, metric_type)
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class TestFaissSearchParams(TestFaissBase):
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("params", FAISS.search_params)
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def test_faiss_search_params(self, params):
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"""
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Test vanilla Faiss search parameter forwarding.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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build_params = params["build_params"]
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="L2", params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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if params.get("expected", None) != success:
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nq = ct.default_nq
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search_vectors = cf.gen_vectors(nq, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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self.search(
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client,
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collection_name,
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search_vectors,
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search_params=params["search_params"],
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limit=ct.default_limit,
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check_task=CheckTasks.err_res,
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check_items=params.get("expected"),
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)
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else:
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self._search_and_check(
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client, collection_name, DataType.FLOAT_VECTOR, search_params=params["search_params"]
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)
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@pytest.mark.tags(CaseLabel.L2)
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def test_faiss_incompatible_search_params(self):
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"""
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Test vanilla Faiss rejects search parameters incompatible with the factory index.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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build_params = {"faiss_index_name": "Flat"}
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="L2", params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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nq = ct.default_nq
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search_vectors = cf.gen_vectors(nq, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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self.search(
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client,
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collection_name,
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search_vectors,
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search_params={"efSearch": 64},
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limit=ct.default_limit,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999, "err_msg": "not supported"},
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)
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize(
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"build_params",
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[
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{"faiss_index_name": "Flat"},
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{"faiss_index_name": "IVF64,Flat"},
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{"faiss_index_name": "HNSW16,Flat"},
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],
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)
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def test_faiss_search_with_scalar_filter(self, build_params):
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"""
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Test vanilla Faiss search honors Milvus scalar filter bitset.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="L2", params=build_params)
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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search_params = _default_search_params_for_faiss_factory(build_params["faiss_index_name"])
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search_vectors = cf.gen_vectors(1, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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results = client.search(
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collection_name,
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search_vectors,
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filter=f"{pk_field_name} >= 100",
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search_params=search_params,
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limit=ct.default_limit,
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)
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assert len(results) == 1
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assert len(results[0]) == ct.default_limit
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assert all(hit["id"] >= 100 for hit in results[0])
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@pytest.mark.tags(CaseLabel.L2)
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def test_faiss_flat_range_search(self):
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"""
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Test vanilla Faiss float Flat range search. The current adapter implements
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RangeSearch for float FAISS indexes.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="L2", params={"faiss_index_name": "Flat"})
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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search_vectors = cf.gen_vectors(1, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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range_params = {"radius": 100000.0, "range_filter": 0.0}
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self.search(
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client,
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collection_name,
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search_vectors,
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search_params=range_params,
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limit=ct.default_limit,
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check_task=CheckTasks.check_search_results,
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check_items={
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"enable_milvus_client_api": True,
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"nq": 1,
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"limit": ct.default_limit,
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"pk_name": pk_field_name,
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},
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)
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@pytest.mark.tags(CaseLabel.L2)
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def test_faiss_binary_range_search_not_supported(self):
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"""
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Test vanilla Faiss binary range search is explicitly not implemented.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, DataType.BINARY_VECTOR)
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self._insert_rows(client, collection_name, DataType.BINARY_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="HAMMING", params={"faiss_index_name": "BFlat"})
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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search_vectors = cf.gen_vectors(1, dim=dim, vector_data_type=DataType.BINARY_VECTOR)
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self.search(
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client,
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collection_name,
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search_vectors,
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search_params={"radius": 1000, "range_filter": 0},
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limit=ct.default_limit,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999, "err_msg": "RangeSearch unsupported for binary faiss indexes"},
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)
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@pytest.mark.tags(CaseLabel.L2)
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def test_faiss_pq_search_selector_not_supported(self):
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"""
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Test vanilla Faiss IndexPQ rejects Milvus search because Milvus passes
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an ID selector/bitset and native FAISS IndexPQ does not support it.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="L2", params={"faiss_index_name": "PQ8x4"})
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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search_vectors = cf.gen_vectors(1, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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self.search(
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client,
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collection_name,
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search_vectors,
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filter=f"{pk_field_name} >= 100",
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search_params={},
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limit=ct.default_limit,
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check_task=CheckTasks.err_res,
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check_items={"err_code": 999, "err_msg": "selector not supported"},
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)
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@pytest.mark.tags(CaseLabel.L2)
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def test_faiss_search_iterator_not_supported(self):
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"""
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Test vanilla Faiss search iterator is rejected because the adapter does
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not expose raw-vector retrieval.
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"""
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client = self._client()
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collection_name = cf.gen_collection_name_by_testcase_name()
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self._create_collection(client, collection_name, DataType.FLOAT_VECTOR)
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self._insert_rows(client, collection_name, DataType.FLOAT_VECTOR)
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self._create_faiss_index(client, collection_name, metric_type="L2", params={"faiss_index_name": "Flat"})
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self.wait_for_index_ready(client, collection_name, index_name=vector_field_name)
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self.load_collection(client, collection_name)
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search_vectors = cf.gen_vectors(1, dim=dim, vector_data_type=DataType.FLOAT_VECTOR)
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self.search_iterator(
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client,
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collection_name,
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data=search_vectors,
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batch_size=100,
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search_params={},
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check_task=CheckTasks.err_res,
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check_items={"err_code": 65535, "err_msg": "Failed to create iterators from index"},
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)
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