What this category of data looks like
The core data for this category comes from the dual random inspection and public disclosure regulatory platform, enterprise credit information public disclosure system, industry supervision system, and law enforcement officer directory. Updates occur monthly. Daily documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. These materials contain structured fields such as unified social credit codes, law enforcement officer ID numbers, inspection item entries, rectification requirements, and unstructured explanatory content.
What constraints these characteristics impose on model access and configuration
Monthly updated data sources require the knowledge base synchronization cycle to match the monthly update rhythm, to avoid data lag affecting inspection plan generation. Mixed processing of multiple document types requires the model to support both structured field extraction and unstructured document generation. Connecting data from multiple sources requires configuring access rules adapted to different systems, to prevent data format incompatibility. Format compliance requirements for on-site inspection records and rectification notices require adding format verification associated logic in the model configuration, to prevent the generation of document content that does not meet regulatory requirements.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | This category of documents includes multiple associated materials, requiring full loading of context content such as inspection plans and inspection target lists |
maxTokens | 2000–3000 token | Length requirements for generating single documents such as on-site inspection records and rectification notices |
temperature | 0.2–0.4 | Regulatory documents must remain rigorous and standardized, and excessive divergence should be avoided |
AIPROXY_API_ENDPOINT | Exclusive interface address of the deployment node | Isolate business data call links for different regulatory departments |
AIPROXY_API_TOKEN | Token bound with exclusive permissions | Restrict only authorized regulatory systems to call model interfaces |
toolChoice | auto | Automatically select whether to call the knowledge base to retrieve associated data such as inspection item lists |
Three common errors
- Phenomenon: The generated inspection result announcement omits the inspection target field. Cause:
maxContextis not configured to adapt to context loading for multiple inspection target lists, resulting in partial data being truncated. - Phenomenon: An error indicating reception address verification failure occurs when configuring external access links. Cause: A publicly accessible interface address is not configured, or certificate verification is not disabled in scenarios without a valid SSL certificate.
- Phenomenon: Non-standard expressions appear in generated rectification notices. Cause:
temperatureis not set within a reasonable range, causing document content to deviate from the rigorous style required by regulatory requirements.
How to confirm the configuration is complete
- Run the monthly data source synchronization task, verify that the number of inspection target lists and inspection item lists in the synchronized knowledge base matches the source system.
- Submit a single inspection plan generation request, verify that the model returned content includes all required fields and the format complies with regulatory document specifications.
- Call the model interface for testing, confirm that the token consumption of the returned result falls within the configured
maxTokensrange. - Check the interface permission configuration, confirm that only authorized regulatory system IP addresses can access the model access address.
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 2026-09-28.