What data falls under this category
Data for this category primarily comes from business systems, app permission lists, privacy policies, and user request records. Commonly processed documents include personal information inventories, processing rules, impact assessment reports, and user authorization texts. Fields covered include information type, processing purpose, legal basis, storage period, and more. The update frequency is quarterly or whenever a product version changes, requiring synchronization of the latest permission configurations and user authorization information. This document applies to FastGPT v4.17.0.
What constraints do these characteristics impose on the "model integration and configuration" link
Diverse data sources and inconsistent formats require model integration to support multi-source document parsing and adapt to different formats of compliance documents. High update frequency requires configuring an automatic synchronization mechanism to ensure the model uses the latest permission and authorization data. Fixed document fields must strictly align with compliance requirements; model input and output must match specific fields to avoid generalized analysis. Severe consequences of errors require adding strict validation links in the configuration to ensure generated test cases and defect analysis comply with compliance standards. Differences from other categories require the model to focus on identifiable natural person information processing and avoid interference from irrelevant data.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Compliance-related documents are usually lengthy, so sufficient context must be retained to fully cover all content of privacy policies and personal information inventories |
maxTokens | 2000–3000 token | Matches the conventional output length of compliance test cases and defect analysis reports to avoid output truncation |
vision | Enabled | Supports parsing image content in permission lists and user authorization texts, covering multimodal compliance review scenarios |
temperature | 0.1–0.3 | Generates stable analysis results that meet compliance requirements and reduces subjective bias |
AIPROXY_API_ENDPOINT | Enter the root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions, and do not fill in single-model addresses such as VLLM/Ollama/Xinference | Root access address of the AI Proxy service configured for the App/Admin server, a required environment variable for AI Proxy |
AIPROXY_API_TOKEN | Enter the administrator token consistent with the AI Proxy server configuration, and the value cannot be empty | Administrator identity credential for AI Proxy configured for the App/Admin server, a required environment variable for AI Proxy. Do not use other credentials such as application API keys or model vendor keys |
Three common misconfigurations
- When calling a model deployed locally via VLLM, the model fails to provide image content. The cause is that the
visionconfiguration item is not correctly enabled, or visual model parameters are not associated in the conversation node. - Connection errors occur when configuring the Qwen3-Embedding-8B model. Troubleshooting items include checking if the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigured on the App/Admin server are correct and non-empty, and checking if the upstream model address and credentials in the model channel configuration are correct. - Generated compliance test cases do not cover all document fields. The cause is that
maxContextis set too small, failing to fully load all content of the original document.
How to verify successful configuration
- Check that the
AIPROXY_API_ENDPOINTconfigured on the App/Admin server is the root address of the AI Proxy service and consistent with the AI Proxy server configuration. Initiate a test call to verify connectivity with the AI Proxy service. - After enabling the
visionconfiguration item, an image related to the privacy policy may be uploaded to verify that the model can identify permission clause content in the image. - Import a complete personal information inventory document to verify that the test cases generated by the model cover core fields such as information type and processing purpose in the document.
- Adjust
temperatureto 0.2, call the generation function multiple times to produce defect analysis results, and confirm that the output is stable and meets the basic requirements of compliance review.
The provided values are common starting points and should be evaluated against applicable 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.