What data does this category include
Security and compliance review data for customer requirement pool management comes from ticket systems, customer interview records, CRM systems, and product roadmap documents. It is updated weekly or during each product version iteration. Daily documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Common fields include requirement descriptions, customer identifiers, compliance-related tags, priority levels, submission times, and links to associated compliance materials. The data format includes both structured ticket fields and unstructured customer interview text.
What constraints do these characteristics impose on model access and configuration
Multiple scattered data sources require the model access link to support cross-system data pulling, to avoid errors from manual imports. Periodic update frequency requires setting scheduled synchronization tasks in the model configuration, to ensure the requirement pool data used for review is up to date. Diversity of documents and fields requires the model to support parsing both structured and unstructured data. It also needs to validate compliance-related fields, to ensure no required items are missed during review. The security and compliance review scenario requires the model call configuration to be auditable, to facilitate subsequent traceability of the review process.
How to configure the parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000-12000 token | Customer requirement pools include multiple documents and feedback entries. Sufficient context covers the full review scope |
maxTokens | 2000 token | Compliance review results need to include merging suggestions and compliance risk points. This length meets detailed output requirements |
quoteMaxToken | 500 token | Ensures the quoted compliance material content does not exceed the model's processing range |
temperature | 0.2-0.4 | Compliance review requires stable, consistent judgments. Lower temperature values reduce random deviations |
toolChoice | auto | Automatically calls compliance verification tools to handle compliance clauses in requirements, no manual specification needed |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy server configuration. Cannot be empty | Used for identity authentication when the App/Admin server connects to the AI Proxy service |
AIPROXY_API_ENDPOINT | Fill in the root address of the AI Proxy service (for example, http://aiproxy:3000. Do not append /v1 or model service addresses) | Used for the App/Admin server to specify the access address of the AI Proxy service |
Three common configuration mistakes
- Symptom: The model call returns a
401 Unauthorizedstatus code, and the compliance review task cannot be completed. Troubleshooting steps: Check if theAIPROXY_API_TOKENon the App/Admin server matches the AI Proxy server configuration and is not empty. Check ifAIPROXY_API_ENDPOINTis the root address of the AI Proxy service and has the correct format. - Symptom: Random deviations appear in requirement merging results, and compliance risk point judgments are inconsistent. Cause: The
temperaturevalue exceeds 0.5. The model output has excessive randomness, which does not meet the stability requirements of compliance reviews. - Symptom: Repeated configuration parameter modifications are required each time a new compliance review model is accessed, leading to cumbersome operations. Cause: Standardized configuration files are not used to manage model parameters, and one-click configuration reuse is not implemented.
How to verify a successful configuration
- Run a single compliance review task for requirement pool data. Check if the returned result covers all compliance-related fields in the requirement pool.
- Check the environment variable configuration on the App/Admin server. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured as required. - Adjust the
temperatureparameter value. Compare the review results of the same requirement pool twice. Confirm that output consistency meets expectations. - Test batch processing of requirement pool data. Confirm that the model does not experience context overflow or request timeouts.
The values provided here are common starting points. They should be evaluated against the respective sample datasets.
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.