This document applies to FastGPT v4.17.0.
What data for this category looks like
Data for this category primarily comes from structured documents exported from business systems, App permission lists, public privacy policy texts, and user request records. Daily processed documents include personal information lists, data processing rules, personal information protection impact assessment reports, user authorization texts, and all data is generated around information processing links that can identify natural persons. Data update frequency is adjusted with product version changes or quarterly cycles, and is maintained and updated by the privacy compliance team, security consulting institutions, and legal departments.
What constraints these characteristics impose on model access and configuration
The data characteristics of this category impose multiple constraints on model access and configuration. First, compliance documents from multiple sources have large format differences, requiring models to adapt to text parsing of different structures. Second, data update frequency is high, requiring the model access link to support rapid access and processing of dynamic data sources. Third, the rigor of compliance review requires that model output strictly matches the original document content, avoiding unfounded judgments. Fourth, data involves natural person information, so the scope of data flow must be strictly restricted to avoid information leakage caused by improper configuration.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Needs to accommodate the full context of multiple compliance documents, avoiding truncation of key compliance content in personal information lists and impact assessment reports |
maxTokens | 2000–3000 token | Used for analysis and output of a single compliance document, balancing content completeness and response speed, adapting to batch processing needs of daily review |
quoteMaxToken | 1500 token | Accurately quotes compliance clauses in privacy policies and user authorization texts, limits reference length to ensure coherent context and avoid redundant content interfering with judgments |
temperature | 0.1–0.3 | Compliance review requires consistent and rigorous output; lower temperature parameters reduce generation of uncertain compliance judgment content |
toolChoice | auto | Automatically calls compliance document parsing tools, adapts to processing workflows for multiple types of compliance materials, reduces manual configuration costs |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. Do not fill in single-model addresses such as VLLM/Ollama/Xinference | Required environment variable for the App/Admin server, ensuring model requests are forwarded through a compliant AI Proxy link |
Three common mistakes
- Troubleshooting item: Returns error code 400 when calling the model. Verify whether the App/Admin server has a startup verification failure, that is, the two required server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not correctly configured. - Phenomenon: The compliance judgment output by the model deviates greatly from the original document content. Cause: The value of
maxContextis too small, causing key compliance clauses to be truncated, so the model cannot obtain complete context information. - Phenomenon: Data leakage risk occurs when processing image-based compliance attachments. Cause: The front-end direct upload link is not closed, causing image entities to be directly sent to external services, violating personal information protection compliance requirements.
How to confirm the configuration is correct
- Upload a single personal information list document, initiate a model review request, and check whether the identified natural person information fields in the output completely cover the original document content.
- Check the startup logs of the App/Admin server, confirm that there are no startup verification failure prompts for missing
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN, and confirm that these two required server environment variables are correctly configured. - Adjust the
temperatureparameter to 0.1, initiate the same compliance review request multiple times, check whether the output judgment content is consistent, and confirm that the parameter takes effect. - Import multiple updated compliance documents, check whether the model processing speed meets the business update rhythm requirements, and confirm that dynamic data source adaptation is normal.
The values provided are common starting points and should be measured 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.