What Data for This Category Looks Like
Data for this category comes from databases, data dictionaries, masking tools, and access approval systems, and is updated per project batch or single task. Daily produced documents include masking plans, field mapping tables, masking verification reports, and approval records. Core fields include sensitive type identifier, masking rule code, verification pass rate, and approval status. There is no unified fixed unit, and specific field units adjust based on project configuration. The data processing workflow centers on sensitive field identification, rule configuration, and availability verification. The core goal is to reduce the risk of sensitive information exposure.
What Constraints Do These Characteristics Impose on the Model Integration and Configuration Link
Data sources for this category are scattered and updated per project. This requires the model integration link to support dynamic association of different data sources and document sets per project. Daily documents include multi-dimensional fields. This requires the configuration link to support binding mapping between sensitive fields, masking rules, and model parameters. High update frequency requires the configuration process to support rapid iterative adjustments without repeated full initialization. For test cases and defect analysis scenarios, the configuration needs to support testing processes that simulate different masking rules to facilitate compliance verification. Meanwhile, the high compliance risk of sensitive information processing requires model integration configuration to include parameters for result verification and abnormal interception, preventing unmasked sensitive data from entering the model context directly.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of masked documents, avoiding exceeding model context limits |
maxTokens | 2000–3000 token | Meets the length requirements for generating masking verification reports, balancing output efficiency and content completeness |
quoteMaxToken | 1000 token | Limits the length of referenced masked document fragments to prevent context overload |
temperature | 0.2–0.4 | Reduces output randomness, ensuring consistency and compliance of masking rule execution |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Root address for the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. This is not a single model address |
AIPROXY_API_TOKEN | Fill in according to deployment configuration | AI Proxy administrator token, consistent with the AI Proxy server configuration and cannot be empty. This is not an application API Key or model vendor key |
Three Common Configuration Mistakes
- Phenomenon: The configured masking field mapping is empty after model integration, and preset masking rules cannot be triggered. Cause: The field mapping table is not bound to the model context parameters, so the model cannot identify the range of sensitive fields to process.
- Phenomenon: Unmasked original sensitive information appears in the model-generated masking verification report. Cause: The
temperatureparameter is set outside the reasonable interval, causing the model generation results to deviate from the preset masking rules and leading to execution deviations. - Phenomenon: After repeatedly adding model configurations for the same project, the original configuration is overwritten. Cause: Project unique identifiers are not used to distinguish configuration items, and configuration parameters with the same name are used, resulting in resource overwriting. This is a common configuration conflict issue with duplicate names.
How to Confirm the Configuration Is Set Correctly
- Check the AIPROXYAPIENDPOINT and AIPROXYAPITOKEN environment variables deployed on the App/Admin server. Confirm that AIPROXYAPIENDPOINT is the root address of the AI Proxy service, and AIPROXYAPITOKEN matches the AI Proxy server configuration and is not empty.
- Upload a single test data entry containing sensitive information, trigger the masking process, and verify whether sensitive fields in the output results have been processed according to preset rules.
- Verify the isolation of multi-project configurations, confirm that model configurations for different projects do not overwrite each other.
- Trigger a masking verification task, check whether the system-generated verification report contains complete masking verification records.
The values provided are common starting points and should be measured against the reader's own samples.
This document applies to FastGPT v4.17.0.
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.