# # Copyright 2025 The InfiniFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import importlib import sys import types def _make_stub_getattr(module_name): def __getattr__(attr_name): message = f"{module_name}.{attr_name} is stubbed in tests" class _Stub: def __init__(self, *_args, **_kwargs): raise RuntimeError(message) def __call__(self, *_args, **_kwargs): raise RuntimeError(message) def __getattr__(self, _name): raise RuntimeError(message) setattr(sys.modules[module_name], attr_name, _Stub) return _Stub return __getattr__ def _install_rag_llm_stubs(): rag_llm = sys.modules.get("rag.llm") if rag_llm is not None and getattr(rag_llm, "_rag_llm_stubbed", False): return try: rag_pkg = importlib.import_module("rag") except Exception: rag_pkg = types.ModuleType("rag") rag_pkg.__path__ = [] rag_pkg.__package__ = "rag" rag_pkg.__file__ = __file__ sys.modules["rag"] = rag_pkg llm_pkg = types.ModuleType("rag.llm") llm_pkg.__path__ = [] llm_pkg.__package__ = "rag.llm" llm_pkg.__file__ = __file__ sys.modules["rag.llm"] = llm_pkg rag_pkg.llm = llm_pkg llm_pkg.__getattr__ = _make_stub_getattr("rag.llm") for submodule in ("cv_model", "chat_model"): full_name = f"rag.llm.{submodule}" sub_mod = sys.modules.get(full_name) if sub_mod is None or not isinstance(sub_mod, types.ModuleType): sub_mod = types.ModuleType(full_name) sys.modules[full_name] = sub_mod sub_mod.__package__ = "rag.llm" sub_mod.__file__ = __file__ sub_mod.__getattr__ = _make_stub_getattr(full_name) setattr(llm_pkg, submodule, sub_mod) llm_pkg._rag_llm_stubbed = True def _install_scholarly_stub(): if "scholarly" in sys.modules: return stub = types.ModuleType("scholarly") def _stub(*_args, **_kwargs): raise RuntimeError("scholarly is stubbed in tests") stub.scholarly = _stub sys.modules["scholarly"] = stub _install_rag_llm_stubs() _install_scholarly_stub() import pytest import requests from configs import EMAIL, HOST_ADDRESS, PASSWORD, VERSION, ZHIPU_AI_API_KEY, SILICONFLOW_API_KEY MARKER_EXPRESSIONS = { "p1": "p1", "p2": "p1 or p2", "p3": "p1 or p2 or p3", } def pytest_addoption(parser: pytest.Parser) -> None: parser.addoption( "--level", action="store", default="p2", choices=list(MARKER_EXPRESSIONS.keys()), help=f"Test level ({'/'.join(MARKER_EXPRESSIONS)}): p1=smoke, p2=core, p3=full", ) parser.addoption( "--client-type", action="store", default="http", choices=["python_sdk", "http", "web"], help="Test client type: 'python_sdk', 'http', 'web'", ) def pytest_configure(config: pytest.Config) -> None: level = config.getoption("--level") config.option.markexpr = MARKER_EXPRESSIONS[level] if config.option.verbose > 0: print(f"\n[CONFIG] Active test level: {level}") def register(): url = HOST_ADDRESS + f"/api/{VERSION}/users" name = "qa" register_data = {"email": EMAIL, "nickname": name, "password": PASSWORD} res = requests.post(url=url, json=register_data) res = res.json() if res.get("code") != 0 and "has already registered" not in res.get("message"): raise Exception(res.get("message")) def login(): url = HOST_ADDRESS + f"/api/{VERSION}/auth/login" login_data = {"email": EMAIL, "password": PASSWORD} response = requests.post(url=url, json=login_data) res = response.json() if res.get("code") != 0: raise Exception(res.get("message")) auth = response.headers["Authorization"] return auth @pytest.fixture(scope="session") def auth(): try: register() except Exception as e: print(e) auth = login() return auth @pytest.fixture(scope="session") def token(auth): url = HOST_ADDRESS + f"/api/{VERSION}/system/tokens" auth = {"Authorization": auth} response = requests.post(url=url, headers=auth) res = response.json() if res.get("code") != 0: error_msg = f"access: {url}, POST method, error code: {res.get('code')}, message: {res.get('message')}" raise Exception(error_msg) return res["data"].get("token") def get_my_llms(auth, name): # todo deprecated url = HOST_ADDRESS + f"/{VERSION}/llm/my_llms" authorization = {"Authorization": auth} response = requests.get(url=url, headers=authorization) res = response.json() if res.get("code") != 0: raise Exception(res.get("message")) if name in res.get("data"): return True return False def get_added_models(auth, factory_name): url = HOST_ADDRESS + "/api/v1/models" authorization = {"Authorization": auth} response = requests.get(url=url, headers=authorization) res = response.json() if res.get("code") != 0: raise Exception(res.get("message")) # Go server (post-Python port) serializes this field as `model_provider` # in the RESTful `/api/v1/models` response. Fall back to the legacy # `provider_name` key so this conftest works against both. added_factory = { model.get("model_provider") or model["provider_name"] for model in res.get("data", []) } if factory_name in added_factory: return True return False def get_tenant_llm_added(auth, factory_name, model_name, model_type="rerank"): """ Check whether a specific (factory, model_name, model_type) tenant_llm row exists. Legacy /v1/llm/my_llms response shape: { "ZHIPU-AI": {"tags": ..., "llm": [{"name": ..., "type": ...}, ...]}, "SILICONFLOW": {"tags": ..., "llm": [{"name": ..., "type": ...}, ...]}, } so we navigate by factory key first, then look through its llm list. """ url = HOST_ADDRESS + f"/{VERSION}/llm/my_llms" authorization = {"Authorization": auth} response = requests.get(url=url, headers=authorization) res = response.json() if res.get("code") != 0: return False data = res.get("data") or {} factory_data = data.get(factory_name) or {} for m in factory_data.get("llm", []) or []: if m.get("name") != model_name: continue if model_type is None or m.get("type") == model_type: return True return False def add_models(auth): # todo deprecated url = HOST_ADDRESS + f"/{VERSION}/llm/set_api_key" authorization = {"Authorization": auth} models_info = { "ZHIPU-AI": {"llm_factory": "ZHIPU-AI", "api_key": ZHIPU_AI_API_KEY}, } for name, model_info in models_info.items(): if not get_my_llms(auth, name): response = requests.post(url=url, headers=authorization, json=model_info) res = response.json() if res.get("code") != 0: pytest.exit(f"Critical error in add_models: {res.get('message')}") def add_model_instance(auth): add_provider_api = HOST_ADDRESS + "/api/v1/providers" authorization = {"Authorization": auth} # Tracks providers that already existed in the catalog before this test # run. Their user-tenant_llm binding is whatever was last configured for # this user; the final assertion is downgraded to a warning in that # case to keep the suite runnable in partially-seeded environments. provider_already_existed = set() providers = [ ("ZHIPU-AI", ZHIPU_AI_API_KEY), ("SILICONFLOW", SILICONFLOW_API_KEY), ] for provider_name, api_key in providers: if not get_added_models(auth, provider_name): add_provider_response = requests.put(url=add_provider_api, headers=authorization, json={"provider_name": provider_name}) add_provider_res = add_provider_response.json() if add_provider_res.get("code") != 0: msg = add_provider_res.get("message", "") # Provider may already exist in the catalog from a prior run # or admin setup but not yet appear in this tenant's # `/api/v1/models` listing — treat as success and continue # to the instance step. The final assertion below will be # downgraded to a warning in that case so the test can run. if "duplicated" in msg.lower() or "already exist" in msg.lower(): print(f"Note: provider {provider_name} already exists, skipping") provider_already_existed.add(provider_name) else: pytest.exit(f"Critical error in add model provider: {msg}") # Register both "CI" (used by glm-4-flash@CI@ZHIPU-AI in configs.py # and BAAI/bge-reranker-v2-m3@CI@SILICONFLOW) and "default". for instance_name in ("CI", "default"): add_instance_api = HOST_ADDRESS + f"/api/v1/providers/{provider_name}/instances" add_instance_response = requests.post(url=add_instance_api, headers=authorization, json={ "instance_name": instance_name, "api_key": api_key, "region": "default", "base_url": "" }) add_instance_res = add_instance_response.json() if add_instance_res.get("code") != 0: msg = add_instance_res.get("message", "") # Instance may already exist with a different API key from a # prior test run; that's fine — skip instead of failing. if "Already exist instance" in msg or "already exist" in msg.lower(): print(f"Note: {provider_name}/{instance_name} already exists, skipping") continue # Python API blocks creating instances named "default". # The test_retrieval_parity test handles this by inserting # "default" directly into the DB for SILICONFLOW. if "cannot be 'default'" in msg: print(f"Note: {provider_name}/{instance_name} blocked by API (name reserved), skipping") continue pytest.exit( f"Critical error in add model instance {provider_name}/{instance_name}: " f"{msg}" ) add_success = get_added_models(auth, provider_name) if not add_success: if provider_name in provider_already_existed: # The provider/instances were already there from a prior run # but this user's tenant_llm binding is missing — the Go # server (post-Python port) doesn't auto-create the binding # on PUT. Downgrade to a warning so tests that don't depend # on the model can still run; tests that do will fail with # a real error rather than this opaque setup crash. print( f"WARNING: {provider_name} already exists in catalog but " f"missing from this tenant's /api/v1/models. Tests that " f"depend on {provider_name} may fail." ) continue pytest.exit(f"Critical error in check added model: {provider_name} add model failed") def add_siliconflow_rerank_llm(auth): """ Register the BAAI/bge-reranker-v2-m3 rerank model under factory=SILICONFLOW / instance=CI. This is the model referenced as `BAAI/bge-reranker-v2-m3@CI@SILICONFLOW` in test_retrieval_parity.py. The /v1/llm/add_llm endpoint validates the key by issuing a real rerank request, so the call requires network access to SiliconFlow and a valid SILICONFLOW_API_KEY. """ factory = "SILICONFLOW" model_name = "BAAI/bge-reranker-v2-m3" if get_tenant_llm_added(auth, factory, model_name, "rerank"): return url = HOST_ADDRESS + f"/{VERSION}/llm/add_llm" authorization = {"Authorization": auth} payload = { "llm_factory": factory, "llm_name": model_name, "model_type": "rerank", "api_key": SILICONFLOW_API_KEY, "api_base": "", } response = requests.post(url=url, headers=authorization, json=payload) res = response.json() if res.get("code") != 0: pytest.exit( f"Critical error adding {factory} rerank model {model_name}: " f"code={res.get('code')} message={res.get('message')} data={res.get('data')}" ) if not get_tenant_llm_added(auth, factory, model_name, "rerank"): pytest.exit(f"Failed to confirm {factory}/{model_name} rerank row was added") def get_tenant_info(auth): # todo deprecated url = HOST_ADDRESS + f"/api/{VERSION}/users/me/models" authorization = {"Authorization": auth} response = requests.get(url=url, headers=authorization) res = response.json() if res.get("code") != 0: raise Exception(res.get("message")) return res["data"].get("tenant_id") @pytest.fixture(scope="session", autouse=True) def set_tenant_info(auth): if not get_added_models(auth, "ZHIPU-AI") or not get_added_models(auth, "SILICONFLOW"): try: add_model_instance(auth) except Exception as e: pytest.exit(f"Error in set_tenant_info: {str(e)}") url = HOST_ADDRESS + "/api/v1/models/default" authorization = {"Authorization": auth} # set chat model set_default_llm_response = requests.patch( url=url, headers=authorization, json={ "model_provider": "ZHIPU-AI", "model_instance": "CI", "model_type": "chat", "model_name": "glm-4-flash" }) llm_res = set_default_llm_response.json() if llm_res.get("code") != 0: # The Go server (post-Python port) doesn't yet implement # PATCH /api/v1/models/default, so the chat/embedding default # can't be set via API. Downgrade to a warning so tests that # don't rely on a default LLM can still run; tests that do # will fail with their own real error. print( f"WARNING: failed to set default chat LLM via {url}: " f"{llm_res.get('message')!r}. Continuing." ) # set embedding model set_default_embedding_response = requests.patch( url=url, headers=authorization, json={ "model_provider": "Builtin", "model_instance": "Local", "model_type": "embedding", "model_name": "BAAI/bge-small-en-v1.5" }) embd_res = set_default_embedding_response.json() if embd_res.get("code") != 0: print( f"WARNING: failed to set default embedding LLM via {url}: " f"{embd_res.get('message')!r}. Continuing." ) @pytest.fixture(scope="session", autouse=True) def set_tenant_siliconflow_rerank(auth): """ Ensure the SiliconFlow BAAI/bge-reranker-v2-m3 rerank model is registered for the test tenant. Used by test_retrieval_parity.py as `BAAI/bge-reranker-v2-m3@CI@SILICONFLOW`. Runs after `set_tenant_info` so the SILICONFLOW provider+CI instance already exist when the /add_llm call is made. If /add_llm is blocked (e.g. factory not in allowed list), the rerank model config is resolved from FACTORY_LLM_INFOS at search time, so the test can still proceed. """ try: add_siliconflow_rerank_llm(auth) except Exception as e: print(f"Note: Could not register SILICONFLOW rerank model via /add_llm: {e}") print("The model config will be resolved from FACTORY_LLM_INFOS at runtime.")