What the data for this category looks like
Data for this category primarily originates from risk control systems, audit records, user behavior logs, and report evidence. Commonly retained documents include ban records, appeal materials, review opinions, and user notifications. Each data entry typically contains account identifier, violation judgment basis, appeal content, and audit conclusion fields. Data updates run on a daily or real-time schedule, synchronized with appeal submissions, audit completions, and disposal activation actions.
Constraints imposed by these characteristics on model integration and configuration
Data for this category comes from multiple dispersed systems. Full account behavior and appeal materials must be associated. Therefore, model integration must support splicing and associating multi-source context. The real-time update feature requires configuration to support low-latency calls. This avoids appeal review timeouts. Violation types and appeal reasons vary widely across different accounts. Parameters adapted to diverse text must be configured. Incorrectly banned or reinstated banned accounts may trigger legal complaints. The model’s judgment accuracy must match business verification requirements.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Requires splicing multi-source data including ban records, appeal materials, and user behavior logs to cover full account-level associated information |
maxTokens | 2000–3000 token | Outputs review opinions and appeal conclusions, requires sufficient text length to carry complete judgment logic |
quoteMaxToken | 1500 token | Requires quoting original violation evidence and appeal materials to avoid truncation of key judgment basis |
temperature | 0.3–0.5 | Review judgments must maintain rigor to avoid misjudgments caused by random fluctuations |
toolChoice | auto | Automatically selects whether to call auxiliary tools to verify evidence based on appeal content |
AIPROXY_API_ENDPOINT | Fill in the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions | This variable is an environment variable for the App/Admin server, adapted to internal network environments and compliance requirements |
Three common misconfigurations
- Phenomenon: Model streaming responses are empty. Terminal curl calls work normally, but no return appears within the platform. Troubleshooting steps: Check the
maxContextparameter configuration. Check whether theAIPROXY_API_ENDPOINTenvironment variable for the App/Admin server is correctly set to the root address of the AI Proxy service. - Phenomenon: The model cannot be called normally after an upgrade. A parameter error prompt appears. Troubleshooting steps: Check whether the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTenvironment variables for the App/Admin server are correctly configured. Check whether thetemperaturevalue falls within the range supported by the current model. - Phenomenon: Appeal review results include misjudgments. Banned accounts are reinstated. Troubleshooting steps: Check whether the
temperaturevalue is too high. Check whether thequoteMaxTokenconfiguration is too small, causing key evidence to be truncated.
How to confirm configuration is complete
- Run a terminal curl call to the root address of
AIPROXY_API_ENDPOINTto verify AI Proxy service connectivity. Confirm thatAIPROXY_API_TOKENmatches the AI Proxy server configuration and is non-empty. Confirm that the basic link is active. - Import a single typical appeal data sample. Verify the model’s context splicing range for the output. Confirm that the
maxContextandquoteMaxTokenconfigurations cover all necessary business fields. - Adjust the
temperatureparameter. Run the same appeal sample multiple times. Verify the stability of the output results. Confirm that the value meets business rigor requirements. - Trigger batch test cases. Check whether the model output format matches the fields specified in business documents. Confirm that the configuration adapts to business output specifications.
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.