mirror of
https://github.com/infiniflow/ragflow.git
synced 2026-07-23 08:56:42 +08:00
Fix: display timeout error when checking model accessibility (#17080)
This commit is contained in:
Lynn
@@ -594,6 +594,26 @@ def list_provider_instances(tenant_id: str, provider_id_or_name: str):
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return True, instances
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async def _run_verification(label: str, coro, timeout_seconds: int):
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"""
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Run a verification coroutine with timeout and uniform error handling.
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Returns (True, result) on success, or (False, error_message) on failure.
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"""
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try:
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result = await asyncio.wait_for(coro, timeout=timeout_seconds)
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return True, result
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except asyncio.TimeoutError:
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logging.exception("Timeout accessing %s", label)
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return False, f"\nTimeout accessing {label}."
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except asyncio.CancelledError:
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logging.exception("Verification cancelled for %s", label)
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return False, f"\n{label} verification aborted."
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except Exception as e:
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logging.exception("Fail to access %s", label)
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return False, f"\nFail to access {label}.{str(e)}"
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async def verify_api_key(provider_id_or_name: str, api_key: str | dict, base_url: str = None, region: str = None, model_info: list[dict] = None):
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"""
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Verify API key for a provider.
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@@ -652,7 +672,6 @@ async def verify_api_key(provider_id_or_name: str, api_key: str | dict, base_url
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model_verify_result = {}
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# test if api key works
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chat_passed, embd_passed, rerank_passed, ocr_passed, tts_passed, asr_passed, vlm_passed = False, False, False, False, False, False, False
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timeout_seconds = int(os.environ.get("LLM_TIMEOUT_SECONDS", 10))
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extra = {"provider": provider_name}
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msg = ""
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@@ -663,32 +682,40 @@ async def verify_api_key(provider_id_or_name: str, api_key: str | dict, base_url
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except (json.JSONDecodeError, TypeError):
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api_key = {"yiyan_ak": api_key, "yiyan_sk": ""}
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api_key_str = api_key if isinstance(api_key, str) else json.dumps(api_key)
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# check passed types
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passed_types = set()
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for llm in factory_llms:
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model_types = _factory_model_types(llm)
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if not embd_passed and LLMType.EMBEDDING.value in model_types:
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assert provider_name in EmbeddingModel, f"Embedding model from {provider_name} is not supported yet."
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mdl = EmbeddingModel[provider_name](api_key_str, llm["llm_name"], base_url=base_url)
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try:
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arr, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.encode, ["Test if the api key is available"]),
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timeout=timeout_seconds,
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)
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if len(arr[0]) == 0:
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raise Exception("Fail")
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embd_passed = True
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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except Exception as e:
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logging.exception(
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"Fail to access embedding model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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msg += f"\nFail to access embedding model({llm['llm_name']}) using this api key." + str(e)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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elif not chat_passed and LLMType.CHAT.value in model_types:
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assert provider_name in ChatModel, f"Chat model from {provider_name} is not supported yet."
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mdl = ChatModel[provider_name](api_key_str, llm["llm_name"], base_url=base_url, **extra)
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try:
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any_passed = False
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for mt_value in model_types:
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if mt_value in passed_types:
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continue
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passed = False
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if mt_value == LLMType.EMBEDDING.value:
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if provider_name not in EmbeddingModel:
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msg += f"\nEmbedding model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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mdl = EmbeddingModel[provider_name](api_key_str, llm["llm_name"], base_url=base_url)
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label = f"embedding model({llm['llm_name']})"
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ok, result = await _run_verification(label, asyncio.to_thread(mdl.encode, ["Test if the api key is available"]), timeout_seconds)
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if not ok:
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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if len(result[0]) == 0:
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msg += f"\nFail to access {label}."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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elif mt_value == LLMType.CHAT.value:
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if provider_name not in ChatModel:
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msg += f"\nChat model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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mdl = ChatModel[provider_name](api_key_str, llm["llm_name"], base_url=base_url, **extra)
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async def check_streamly():
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async for chunk in mdl.async_chat_streamly(
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@@ -700,145 +727,127 @@ async def verify_api_key(provider_id_or_name: str, api_key: str | dict, base_url
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return True
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return False
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result = await asyncio.wait_for(check_streamly(), timeout=timeout_seconds)
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if result:
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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chat_passed = True
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else:
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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raise Exception("No valid response received")
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except Exception as e:
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logging.exception(
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"Fail to access chat model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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msg += f"\nFail to access model({provider_name}/{llm['llm_name']}) using this api key." + str(e)
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elif not rerank_passed and LLMType.RERANK.value in model_types:
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if provider_name not in RerankModel:
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unsupported_msg = f"Rerank model from {provider_name} is not supported yet."
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logging.warning(unsupported_msg)
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msg += f"\n{unsupported_msg}"
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continue
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mdl = RerankModel[provider_name](api_key_str, llm["llm_name"], base_url=base_url)
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try:
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arr, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.similarity, "What's the weather?", ["Is it sunny today?"]),
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timeout=timeout_seconds,
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)
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if len(arr) == 0 or tc == 0:
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raise Exception("Fail")
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rerank_passed = True
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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logging.debug(f"passed model rerank {llm['llm_name']}")
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except Exception as e:
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logging.exception(
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"Fail to access rerank model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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msg += f"\nFail to access model({provider_name}/{llm['llm_name']}) using this api key." + str(e)
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elif not ocr_passed and LLMType.OCR.value in model_types:
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assert provider_name in OcrModel, f"OCR model from {provider_name} is not supported yet."
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mdl = OcrModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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try:
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ok, reason = await asyncio.wait_for(
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asyncio.to_thread(mdl.check_available),
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timeout=timeout_seconds,
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)
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label = f"model({provider_name}/{llm['llm_name']})"
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ok, result = await _run_verification(label, check_streamly(), timeout_seconds)
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if not ok:
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raise RuntimeError(reason or "Model not available")
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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ocr_passed = True
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except Exception as e:
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logging.exception(
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"Fail to access OCR model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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msg += f"\nFail to access model({provider_name}/{llm['llm_name']})." + str(e)
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elif not tts_passed and LLMType.TTS.value in model_types:
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assert provider_name in TTSModel, f"TTS model from {provider_name} is not supported yet."
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mdl = TTSModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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try:
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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if not result:
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msg += f"\nFail to access {label}.No valid response received"
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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elif mt_value == LLMType.RERANK.value:
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if provider_name not in RerankModel:
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msg += f"\nRerank model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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mdl = RerankModel[provider_name](api_key_str, llm["llm_name"], base_url=base_url)
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label = f"model({provider_name}/{llm['llm_name']})"
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ok, result = await _run_verification(label, asyncio.to_thread(mdl.similarity, "What's the weather?", ["Is it sunny today?"]), timeout_seconds)
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if not ok:
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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arr, tc = result
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if len(arr) == 0 or tc == 0:
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msg += f"\nFail to access {label}."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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elif mt_value == LLMType.OCR.value:
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if provider_name not in OcrModel:
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msg += f"\nOCR model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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mdl = OcrModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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label = f"model({provider_name}/{llm['llm_name']})"
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ok, result = await _run_verification(label, asyncio.to_thread(mdl.check_available), timeout_seconds)
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if not ok:
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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ok2, reason = result
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if not ok2:
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msg += f"\nFail to access {label}.{reason or 'Model not available'}"
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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elif mt_value == LLMType.TTS.value:
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if provider_name not in TTSModel:
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msg += f"\nTTS model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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mdl = TTSModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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def drain_tts():
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for _ in mdl.tts("Hello~ RAGFlower!"):
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pass
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await asyncio.wait_for(
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asyncio.to_thread(drain_tts),
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timeout=timeout_seconds,
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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tts_passed = True
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except Exception as e:
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logging.exception(
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"Fail to access TTS model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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msg += f"\nFail to access model({provider_name}/{llm['llm_name']})." + str(e)
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elif not vlm_passed and LLMType.VISION.value in model_types:
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if provider_name not in CvModel:
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unsupported_msg = f"Image to text model from {provider_name} is not supported yet."
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logging.warning(unsupported_msg)
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msg += f"\n{unsupported_msg}"
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continue
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from rag.utils.base64_image import test_image
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mdl = CvModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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try:
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image_data = test_image
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m, tc = await asyncio.wait_for(
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asyncio.to_thread(mdl.describe, image_data),
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timeout=timeout_seconds,
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)
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if not tc and m.find("**ERROR**:") >= 0:
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raise Exception(m)
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vlm_passed = True
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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except Exception as e:
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logging.exception(
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"Fail to access vision model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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msg += f"\nFail to access model({provider_name}/{llm['llm_name']})." + str(e)
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elif not asr_passed and LLMType.ASR.value in model_types:
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if provider_name not in Seq2txtModel:
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unsupported_msg = f"Speech model from {provider_name} is not supported yet."
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logging.warning(unsupported_msg)
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msg += f"\n{unsupported_msg}"
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continue
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mdl = Seq2txtModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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try:
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ok, reason = await asyncio.wait_for(
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asyncio.to_thread(mdl.check_available),
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timeout=timeout_seconds,
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)
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label = f"model({provider_name}/{llm['llm_name']})"
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ok, result = await _run_verification(label, asyncio.to_thread(drain_tts), timeout_seconds)
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if not ok:
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raise RuntimeError(reason or "Model not available")
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asr_passed = True
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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except Exception as e:
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logging.exception(
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"Fail to access ASR model for provider=%s model=%s",
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provider_name,
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llm["llm_name"],
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)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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msg += f"\nFail to access model({provider_name}/{llm['llm_name']})." + str(e)
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if any([embd_passed, chat_passed, rerank_passed, ocr_passed, tts_passed, vlm_passed, asr_passed]):
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msg = ""
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break
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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success = any([embd_passed, chat_passed, rerank_passed, ocr_passed, tts_passed, vlm_passed, asr_passed])
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elif mt_value == LLMType.VISION.value:
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if provider_name not in CvModel:
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msg += f"\nImage to text model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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from rag.utils.base64_image import test_image
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mdl = CvModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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label = f"model({provider_name}/{llm['llm_name']})"
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ok, result = await _run_verification(label, asyncio.to_thread(mdl.describe, test_image), timeout_seconds)
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if not ok:
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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m, tc = result
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if not tc and m.find("**ERROR**:") >= 0:
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msg += f"\nFail to access {label}.{m}"
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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elif mt_value == LLMType.ASR.value:
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if provider_name not in Seq2txtModel:
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msg += f"\nSpeech model from {provider_name} is not supported yet."
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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mdl = Seq2txtModel[provider_name](key=api_key_str, model_name=llm["llm_name"], base_url=base_url)
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label = f"model({provider_name}/{llm['llm_name']})"
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ok, result = await _run_verification(label, asyncio.to_thread(mdl.check_available), timeout_seconds)
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if not ok:
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msg += result
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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ok2, reason = result
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if not ok2:
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msg += f"\nFail to access {label}.{reason or 'Model not available'}"
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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continue
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passed = True
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if passed:
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logging.debug("passed model %s type=%s", llm["llm_name"], mt_value)
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passed_types.add(mt_value)
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.SUCCESS.value
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any_passed = True
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break
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else:
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model_verify_result[llm["llm_name"]] = ModelVerifyStatusEnum.FAIL.value
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if any_passed:
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msg = ""
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success = bool(passed_types)
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return success, "success" if success else msg, model_verify_result
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