What the data for this category looks like
The core data for this category comes from incident timelines, system logs, monitoring metrics, and code commit records, maintained by SREs, technical leads, or quality teams. Daily produced documents include RCA reports, incident tickets, post-mortem meeting minutes, and rectification tracking sheets. Each document contains fixed fields such as incident occurrence time, impact scope, troubleshooting process, root cause conclusion, and rectification items. The data update frequency is once per major incident or once per month. The volume of documents updated in a single batch varies based on the incident's impact scope.
What constraints these characteristics impose on the model integration and configuration link
Multi-source heterogeneous data sources require the model to support parsing input in different formats, including structured monitoring metrics, unstructured log text, and code commit records. The fixed-field document structure requires the model to accurately extract information from specific fields, avoiding irrelevant generated content. The low-frequency update characteristic means the model context does not need frequent refreshing, but single-input context length may be large. Maximum context size must be limited to ensure call efficiency. At the same time, the rigor of root cause analysis requires model output to have high credibility, requiring a balance between generation flexibility and accuracy.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the average context length of documents such as RCA reports and post-mortem meeting minutes, to avoid call failures caused by context overflow |
maxTokens | 2000–3000 token | Meets the output length requirements for root cause analysis and rectification suggestions, balancing generation quality and response speed |
temperature | 0.2–0.4 | Ensures the rigor of root cause deduction, reducing the probability of random assumptions |
toolChoice | auto | Supports automatic tool calls to extract external evidence such as logs and monitoring metrics, assisting root cause verification |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. Do not configure this as a single-model service address. This is an environment variable for the App/Admin server environment | Ensures the correctness and stability of the AI Proxy call chain |
AIPROXY_API_TOKEN | Administrator token consistent with the AI Proxy server configuration. Cannot be empty. This is an environment variable for the App/Admin server environment | Ensures permission security for AI Proxy calls, preventing unauthorized access |
Three common configuration mistakes
- Symptom: Workflow fails to trigger after selecting a simplified model. Cause: The
toolChoiceparameter is not set toauto, preventing the model from calling necessary tools to generate the supporting workflow. - Symptom: Model call times out. Cause: The
maxContextsetting exceeds the model's carrying limit, resulting in excessive context loading time and triggering call timeout. - Symptom: Root cause analysis results have significant deviations. Cause: The
temperaturevalue is set too high, causing the model to generate excessive unsubstantiated random assumptions that deviate from the actual root cause.
How to confirm successful configuration
- Test calling the specified model, confirm the interface returns normally with no error messages.
- Upload a typical RCA report, check that the root cause analysis results generated by the model match the document content as expected.
- Simulate an incident scenario, verify whether the model can correctly call tools to extract external data such as relevant logs and monitoring metrics.
- Check the
maxContextandmaxTokensparameters, confirm that the generated output length meets business requirements.
The values provided are common starting points and should be assessed against the specific samples relevant to the deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.