What this category of data looks like
This category of data primarily comes from the "Double Random, One Public" supervision platform, enterprise credit information public disclosure system, industry supervision systems, and law enforcement officer directory. Daily generated documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. The typical update frequency is monthly. Each document usually contains fields such as inspection target identifier, inspection items, law enforcement officer information, and inspection results. Field formats must align with the native output structure of existing supervision systems, and there is no universal unified standard.
What constraints do these data characteristics impose on HTTP interfaces and external system integration
The data characteristics of this category impose multiple constraints on HTTP interface and external system integration. Multi-source heterogeneous supervision system data requires interfaces to adapt to different native output formats, with no reliance on universal data structures. The monthly update frequency requires interfaces to support scheduled full or incremental data synchronization, balancing data timeliness and system load. Diverse document types require targeted field mapping rules to ensure accurate matching of policy and regulatory basis during Q&A. High-frequency query data such as law enforcement officer directories require both query efficiency and permission verification logic to prevent sensitive information leaks.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {supervision system API key} | Aligns with the identity verification requirements of supervision systems to prevent unauthorized access |
| Single query limit | Top 20 entries | Matches the monthly update data volume to avoid overload from interface returns |
variables | {"check_type": "Double Random Inspection", "update_cycle": "monthly"} | Limits Q&A scope to double random supervision scenarios to filter irrelevant results |
responseData | Only return content related to inspection item lists and inspection result announcements | Focuses on core document types for policy and regulatory basis Q&A to reduce irrelevant information interference |
| Timeout period | 300 seconds | Adapts to data pull latency across supervision systems to avoid interface call failures |
Three common mistakes
- Symptom: Interface calls frequently trigger MongoDB slow queries and page load timeouts. Cause: Failed to limit the number of data entries per query, pulling full monthly update data leading to excessive database load.
- Symptom: API calls to start a conversation return results containing extra historical records. Cause: No conversation context filtering rule configured in
variables, so full historical session data loads by default. - Symptom: Q&A results returned by the interface include non-supervision inspection document content. Cause: No document type restriction configured via
responseData, failing to filter irrelevant business data.
How to confirm correct configuration
- Call the test interface and check the returned identity verification result to confirm the
Authorizationconfiguration key is valid. - Send a monthly data synchronization request and verify that the number of returned data entries matches the preset single query limit.
- Send a policy Q&A request to confirm that returned results only include specified document types such as inspection item lists and inspection result announcements.
- Send a high-frequency query request to confirm that the interface response time meets the latency requirements of the business scenario.
The values provided here 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.