What this category's data looks like
Data for this category primarily comes from report systems, involved content detail pages, user historical behavior records, and audit rule libraries. Daily processing documents include report tickets, disposal records, appeal forms, and user notification templates. Fields cover unique report identifiers, involved content IDs, reporter and reported user identifiers, violation type tags, disposal status, and more. Data updates occur mostly in real time or on a daily synchronization schedule. A single processing workflow involves full-link information transfer for single or batch reported content.
What constraints these characteristics impose on model integration and configuration
Because this category processes specific user-submitted report content on a per-case basis, model integration must support low-latency single-item inference to avoid blocking processing flows with batch tasks. The input format that combines structured documents such as report tickets and appeal forms with natural language descriptions requires configuration that supports mixed-format context processing. Real-time updated audit rules and user historical data require configuration that can be dynamically adjusted without interrupting existing services. It must also adapt to field alignment requirements for multi-source data to reduce the risk of processing deviations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the mixed context length of report content, historical records, and audit rules, avoiding truncation of critical violation determination information |
maxTokens | 1500–2000 token | Matches the output length requirement for single report disposal results, balancing response speed and content completeness |
temperature | 0.2–0.4 | Reduces randomness in generated results, ensuring consistency in violation determination and disposal recommendations, and meeting standardized audit rule requirements |
vision | Enabled | Supports recognition of visual evidence such as images and screenshots in reported content, supplementing the basis for violation determination |
AIPROXY_API_ENDPOINT | Fill in the root address of the AI Proxy service according to the deployment environment (for example, http://aiproxy:3000, do not append /v1 or /chat/completions, non-single-model address) | Ensures correct routing between the App/Admin service and the AI Proxy service, adapting to access requirements of different deployment architectures |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy server configuration (cannot be empty) | Completes identity verification between the App/Admin service and the AI Proxy service, ensuring the security of interface calls |
Three common misconfigurations
- Unconfigured
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN: Symptoms include App/Admin service startup verification failure, preventing normal startup. Troubleshooting steps: Check if the two required items are correctly configured in server environment variables. - Multimodal model fails to recognize embedded images in reported content: Symptoms include model output not including recognition descriptions related to images. Causes include not enabling the
visionconfiguration item, or not performing image extraction preprocessing on reported content in formats such as PDF. - Model call times out: Symptoms include request return timeout status codes. Troubleshooting steps: Check the AI Proxy service running status, network connectivity, and upstream model service status.
How to confirm configurations are correct
- Check the App/Admin server environment variable configuration, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly set, and verify that service startup verification passes. - Upload a test sample of reported content containing images, trigger a model call, and confirm that the returned result includes recognition descriptions of image content, verifying that the
visionconfiguration is active. - Adjust the
temperatureconfiguration and initiate a test, compare the differences between the two generated results, and confirm that the impact of the configuration on model output meets expectations. - View the App/Admin server logs, confirm that the request to the AI Proxy service is correctly sent with the
AIPROXY_API_TOKEN, verifying correct routing and identity verification.
The values provided are common starting points and should be evaluated against the reader's own samples.
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.