What this category of data looks like
Daily documents for this category typically include inspection plans, lists of inspection targets, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Data sources include the "Double Random, One Public" regulatory platform, enterprise credit information disclosure system, industry regulatory systems, and law enforcement officer directory. The update frequency is monthly. Document fields usually include inspection target identifiers, law enforcement officer information, inspection item details, inspection result records, and announcement status, recorded by dimensions such as inspection batches and entries.
What constraints do these characteristics impose during model access and configuration
Multi-source data requires configuring multiple data source access nodes to ensure permission and format compatibility for data synchronization across regulatory systems. The monthly update frequency allows knowledge base synchronization tasks to be set to fixed intervals, avoiding unnecessary resource consumption. Document types include structured lists and unstructured inspection records, so different parsing rules must be adapted to ensure accurate data extraction. Fields include identifying content such as law enforcement officer numbers and unified social credit codes of inspection targets, so precise matching configuration must be enabled during retrieval to avoid irrelevant results. The party building learning Q&A scenario needs to associate both regulatory business data and party building learning materials, so retrieval priority rules for dual knowledge bases must be configured.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the dedicated gateway address for connecting regulatory data, in the format https://[regulatory platform domain]/fastgpt-proxy/v1/chat/completions | Ensure model calls source data from compliant internal interfaces of regulatory systems |
AIPROXY_API_TOKEN | Fill in the dedicated authentication token assigned by the regulatory system | Ensure identity legitimacy for data calls, prevent unauthorized access |
maxContext | 8000–12000 token | Cover the total length requirement of a single regulatory inspection record and associated party building learning materials |
temperature | 0.3–0.5 | Ensure the rigor of party building Q&A content, avoid generating irrelevant or divergent statements |
maxTokens | 2000–3000 token | Control the output length of a single Q&A, adapt to the conciseness requirements of regulatory scenarios |
toolChoice | auto | Allow the model to automatically choose whether to call the regulatory data interface based on question type, improve Q&A accuracy |
Three common mistakes
- Symptom: A "message receiving address verification failed" prompt appears when calling the model, and the interface shows public network access exceptions. Cause: The internal network address of the regulatory system has not been mapped to a publicly accessible domain name, or certificate verification has not been disabled in FastGPT configuration.
- Symptom: The party building Q&A content returned by the model does not associate with regulatory business data, and the results do not meet expectations. Cause: A reasonable range for
maxContexthas not been configured, causing the model to fail to load sufficient regulatory document context. - Symptom: The index model selection box is empty, and the corresponding model cannot be selected. Cause: FastGPT has not been updated to version v4.17.0, or dedicated embedding model dependencies for regulatory data have not been configured.
How to confirm the configuration is complete
- Initiate a test Q&A, enter a party building learning question related to regulatory business, and check whether the returned content associates with the field information of the corresponding regulatory document.
- View the knowledge base synchronization logs, confirm that the most recent synchronization task has completed according to the set interval, with no error records.
- Check the model call authentication records, confirm that all requests have completed verification using the configured
AIPROXY_API_TOKEN. - Adjust the
temperatureparameter and test outputs for different questions, confirm that the content rigor meets the requirements of regulatory scenarios.
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.