What this category of data looks like
Data governance teams, security consultants, and legal and compliance professionals use this category of data. Daily processed data includes data catalogs, classification and grading rules, important data identification forms, and review minutes. Data sources cover data asset inventory results, database metadata, business system descriptions, and regulatory documents. Update frequency follows a quarterly schedule, or when business systems change. Data structure primarily consists of structured tables and semi-structured documents. Fields include asset number, sensitivity level, owning system, and other attributes. There is no unified single-document length limit.
What constraints do these characteristics impose on the model access and configuration link
Diverse data sources require model access to support multi-format input, preventing information loss from format incompatibility. Periodic and sudden update frequency requires configuration to include a regular knowledge base sync mechanism. This mechanism keeps classification and grading rules aligned with the latest data assets. Structured document features and diverse fields require model call context parameters to cover sufficient length, avoiding truncation of core rules and asset information. The high time cost of sensitive field identification and rule review requires optimizing model call parameter settings. This reduces invalid repeated calls and improves review efficiency. Strict compliance review requirements mandate that model output formats meet standardized specifications, preventing compliance rectification failures from format errors.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total length of documents such as data catalogs and classification and grading rules usually falls within this range, which prevents truncation of core review basis content |
maxTokens | 2000–3000 token | This value covers conventional output lengths, as complete grading conclusions must be returned for analysis of a single sensitive field identification or review minute |
quoteMaxToken | 1500 token | This value retains complete reference fragments, as metadata snippets from data asset inventory must be fully cited as review evidence |
toolChoice | auto | Automatic mode adapts to review needs across multiple scenarios, as it allows the model to independently determine whether to call retrieval tools to match classification and grading rules |
temperature | 0.1–0.3 | This value range reduces random deviations in output, as compliance review requires stable conclusions that follow preset rules |
AIPROXY_API_TOKEN | AI Proxy administrator token, consistent with the AI Proxy server configuration and not empty | This variable is an environment variable for the App/Admin server. Do not confuse it with application API keys or model vendor secrets |
Three common configuration errors
- Phenomenon: Model calls return
404 body not found. Cause: The App/Admin server environment variablesAIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or do not match.AIPROXY_API_ENDPOINTis the root address of the AI Proxy service, and must not have/v1or model-related paths appended. - Phenomenon: Knowledge base returned content cannot be recognized as standard JSON format. Cause: No reasonable
maxTokensvalue is set, leading to truncated model output that destroys the integrity of the JSON structure. - Phenomenon: When mixing different index models and text models, the matching accuracy of classification and grading rules decreases. Cause: The
temperatureparameter is not configured uniformly, leading to differences in output rigor and format across different models.
How to confirm the configuration is complete
- Upload a single data catalog document for testing, and check whether the classification and grading results returned by the model match the preset classification and grading rules.
- View the service startup verification logs for FastGPT v4.17.0, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly configured and pass service startup verification; view model call logs to confirm that model call requests return normally. - Batch import quarterly updated business system description documents, and verify that there are no obvious format errors in sensitive field identification results processed by the model.
- Adjust the value range of the
temperatureparameter, compare review minute analysis results across different configurations, and confirm that the output meets the stability requirements of compliance review.
The values provided are common starting points, and should be evaluated against local deployment 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.