{"record":{"id":"13d9b57732aa2a11","repo":"feder-cr/Jobs_Applier_AI_Agent_AIHawk","slug":"failed-to-get-a-response-from-the-model-after-mult","errorCode":null,"errorMessage":"Failed to get a response from the model after multiple attempts.","messagePattern":"Failed to get a response from the model after multiple attempts\\.","errorType":"exception","errorClass":"Exception","httpStatus":null,"severity":"critical","filePath":"src/libs/resume_and_cover_builder/utils.py","lineNumber":107,"sourceCode":"                parsed_reply = self.parse_llmresult(reply)\n                LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)\n                return reply\n            except (openai.RateLimitError, HTTPStatusError) as err:\n                if isinstance(err, HTTPStatusError) and err.response.status_code == 429:\n                    logger.warning(f\"HTTP 429 Too Many Requests: Waiting for {retry_delay} seconds before retrying (Attempt {attempt + 1}/{max_retries})...\")\n                    time.sleep(retry_delay)\n                    retry_delay *= 2\n                else:\n                    wait_time = self.parse_wait_time_from_error_message(str(err))\n                    logger.warning(f\"Rate limit exceeded or API error. Waiting for {wait_time} seconds before retrying (Attempt {attempt + 1}/{max_retries})...\")\n                    time.sleep(wait_time)\n            except Exception as e:\n                logger.error(f\"Unexpected error occurred: {str(e)}, retrying in {retry_delay} seconds... (Attempt {attempt + 1}/{max_retries})\")\n                time.sleep(retry_delay)\n                retry_delay *= 2\n\n        logger.critical(\"Failed to get a response from the model after multiple attempts.\")\n        raise Exception(\"Failed to get a response from the model after multiple attempts.\")\n\n    def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:\n        # Parse the LLM result into a structured format.\n        content = llmresult.content\n        response_metadata = llmresult.response_metadata\n        id_ = llmresult.id\n        usage_metadata = llmresult.usage_metadata\n\n        parsed_result = {\n            \"content\": content,\n            \"response_metadata\": {\n                \"model_name\": response_metadata.get(\"model_name\", \"\"),\n                \"system_fingerprint\": response_metadata.get(\"system_fingerprint\", \"\"),\n                \"finish_reason\": response_metadata.get(\"finish_reason\", \"\"),\n                \"logprobs\": response_metadata.get(\"logprobs\", None),\n            },\n            \"id\": id_,\n            \"usage_metadata\": {","sourceCodeStart":89,"sourceCodeEnd":125,"githubUrl":"https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/blob/79155b52faccfbd19b834680af285eac70dd2df4/src/libs/resume_and_cover_builder/utils.py#L89-L125","documentation":"The retrying LLM-callable in utils.py wraps model invocation with exponential backoff. After exhausting all attempts (every attempt raised an exception), it logs 'critical' and raises a generic Exception: no response could be obtained from the model.","triggerScenarios":"Persistent LLM API failures across all retries: invalid/missing API key, rate limiting that outlasts the backoff window, network outage, or the provider being down.","commonSituations":"Expired or wrong OPENAI_API_KEY / provider credentials, quota exhausted, flaky network in CI, or a model name that the API rejects on every call.","solutions":["Check the underlying logs/errors printed for each attempt: they reveal the root cause (auth, quota, network, bad model name).","Fix credentials/rate limits: verify the API key env var and provider quota, or reduce request frequency.","Increase max_retries/backoff for transient outages, and add a circuit breaker or fallback model so the pipeline can continue."],"exampleFix":"# before\nresult = llm_callable(prompt)  # raises after retries\n# after\ntry:\n    result = llm_callable(prompt)\nexcept Exception:\n    result = fallback_llm_callable(prompt)  # secondary provider/model","handlingStrategy":"retry","validationCode":"import os\nassert os.environ.get('OPENAI_API_KEY'), 'missing OPENAI_API_KEY'  # adjust per provider","typeGuard":null,"tryCatchPattern":"try:\n    result = llm_callable(prompt)\nexcept Exception as e:\n    if 'Failed to get a response' in str(e):\n        logger.error('LLM unavailable, using fallback')\n        result = fallback_llm_callable(prompt)\n    else:\n        raise","preventionTips":["Surface per-attempt errors in logs so the root cause (auth/quota/network) is visible.","Configure a fallback model/provider and a circuit breaker for long automation runs.","Check API key env vars and quota before starting bulk jobs."],"tags":["python","llm","retry-exhausted","api","network"],"backgroundTag":"llm-api-unavailable","analyzedSha":"79155b52faccfbd19b834680af285eac70dd2df4","analyzedAt":"2026-08-28T14:10:26.659Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}