Investigation Summary
- You reported an error in the catapa-glchat project (Sentry Event ID: 685690d10ea8498faeb4de5718a3de99).
- I have retrieved the Sentry event details, including the error message, timestamp, and pod/server involved.
- To fully investigate (logs and metrics), I need a bit more information from you.
Findings from Sentry (Traces)
- Timestamp (WIB): 2026-01-19T18:38:36.599+07:00
- Error message: invalid_request_error: prompt is too long: 219921 tokens > 200000 maximum
- Conversation ID: f245ace3-bccb-45aa-ae05-b17cfefe455f
- Server/Pod: glchatprod-glchat-be-5675c5b574-kvl2b
- Route: /message
- The error occurred after 3 retry attempts to the Anthropic API, each failing due to the prompt exceeding the model’s token limit.
Next Steps Needed for Log and Metrics Correlation
To search logs and metrics, I need to know:
- The Kubernetes namespace for pod glchatprod-glchat-be-5675c5b574-kvl2b (examples: internal-tools-prod, gl-connectors-prod, etc.)
- Alternatively, the container name (examples: backend-server, glchat-guide, etc.)
- For metrics: which cluster (options: eks-gl-production, eks-gl-staging, etc.) and the namespace.
Once you provide at least the namespace (and ideally the cluster for metrics), I will:
- Search logs for this error and conversation ID within ±5 seconds of the error.
- Check CPU/memory metrics for the pod around the error time for resource anomalies.
Root Cause Analysis (from Sentry)
- The error is application-level: your backend tried to send a prompt to the Anthropic LLM API that was too large (219,921 tokens, while the maximum allowed is 200,000).
- The retry logic did not resolve the issue, as the input was always too large.
- This is not a resource or infrastructure problem, but a request validation/limiting issue in your code.
Recommendations (Immediate)
- Implement prompt length validation in your backend before sending requests to the LLM API. Reject or truncate prompts that exceed the model’s token limit (200,000 tokens for Anthropic).
- Improve error handling: if a prompt is too long, do not retry with the same input—surface a clear error to the user or calling service.
- (Optional) Add logging/monitoring for prompt sizes to catch similar issues early.
Once you provide the namespace (and cluster), I will complete the log and metrics correlation to confirm there are no related infrastructure issues.
Let me know the namespace and cluster, and I’ll finish the full root cause and cross-source analysis!