What the Data for This Category Looks Like
Generate core data for cybersecurity level protection assessment consulting from standardized documents including classification rating reports, gap analysis reports, and assessment reports. Source data from control item lists exported by assessment tools, on-site interview records, security device logs, and system asset ledgers. Update data on individual assessment project cycles. Archive data after a single project is completed. Refresh full data during annual reviews. Include document fields such as control item number, compliance requirement, gap item description, and rectification evidence links. No unified units apply. Create records one per control item dimension.
Constraints Imposed by These Characteristics on Model Integration and Configuration
Bind model calls to project-specific knowledge base shards. This avoids cross-project data confusion. Documents contain mixed structured control items and unstructured rectification evidence. Configure models to support multimodal input processing. Require assessment reports to strictly match level protection standard clauses. Retain original field mappings in model outputs. Do not modify field names arbitrarily. Set model context length to adapt to total token count after multiple documents are spliced. This avoids truncating key compliance clauses. Ensure assessment results are traceable. Bind model call logs to the corresponding project ID. This facilitates subsequent compliance audits.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to total token count after multiple documents from a single project are spliced, avoiding truncation of key compliance clauses |
maxTokens | 2000–3000 token | Matches standard length of assessment report outputs, ensuring full coverage of control item rectification suggestions |
quoteMaxToken | 1500 token | Limits token proportion of referenced documents, reserving sufficient space to generate compliance analysis content |
temperature | 0.1–0.3 | Ensures output content strictly matches level protection standards, reducing subjective bias |
vision | Enabled | Supports parsing non-text data such as screenshots and log images in rectification evidence |
AIPROXY_API_TOKEN | Non-empty administrator token configured for the AI Proxy server, consistent with server-side configuration | Authentication credential for App/Admin services to call AI Proxy. Must not be used as a project-specific secret or application API Key |
Three Common Misconfigurations
- The interface displays 30 context items, but the actual token count sent to the model corresponds to 310 items, creating a mismatch between displayed and actual counts. This occurs when the token conversion rule for the
maxContextparameter is not configured correctly, and the number of items is directly bound as the context length limit without matching actual document token proportions. - A custom-added vector model is incorrectly called as a large language model. This occurs when access configurations for large language models and vector models are not clearly distinguished, and call link parameters for the two model types are confused.
- Image content in rectification evidence cannot be processed, and model outputs do not mention key information in the images. This occurs when the
visionparameter is not enabled, and multimodal model support is not activated.
How to Confirm Correct Configuration
- Check environment variable configuration on the App/Admin server. Confirm
AIPROXY_API_ENDPOINTis the root address of the AI Proxy service (for example, http://aiproxy:3000, do not append paths such as /v1 or /chat/completions). ConfirmAIPROXY_API_TOKENis the non-empty administrator token configured for the corresponding AI Proxy server. - Upload a single classification rating report document, trigger a model call, and check if the output content retains the original control item number field.
- Upload a rectification evidence screenshot, verify if the model can extract text content from the image and generate compliance analysis results.
- View the model call log, confirm the incoming
maxContextparameter value matches the set configuration item value.
The values provided here are common starting points, and should be evaluated against applicable samples for the deployment environment.
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.