What the data for this category looks like
This category of data primarily comes from elderly care service information platforms, civil affairs statistics systems, fire safety inspection records, and institution operation ledgers. Daily associated documents include institution filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. The data update cycle is monthly. Fields typically include institution unified identifiers, filing numbers, bed counts, inspection dates, rectification statuses, subsidy application progress, and similar items. Data formats vary slightly across different sources, and the size of individual data entries differs.
Constraints imposed by these characteristics on HTTP interfaces and external systems
Multiple data sources require integration with multiple independent HTTP interfaces. Different interfaces have distinct authentication rules and return formats, so targeted configuration of request parameters and result parsing logic is needed. The monthly update business rhythm means high-frequency interface calls are unnecessary, so call frequencies can be appropriately reduced to lower resource consumption. Inconsistent field formats require structured cleaning of returned responseData to align with policy query needs for supervision scenarios. Additionally, data errors directly impact core businesses such as subsidy distribution and safety supervision, so interface call failure handling must cover common exception scenarios including timeouts and missing fields.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Configure according to the actual authentication rules of the connected system, such as the Bearer {token} format | Interface authentication requirements vary across supervision data sources, and must match the access specifications of the corresponding system |
variables | Preset fixed parameters such as institution unified identifier and query cycle, with values set to {"orgId": "supervision institution ID", "queryMonth": "YYYY-MM"} | Simplify repeated input and adapt to monthly updated business data query needs |
responseData | Extract according to the field mapping rules of the supervision scenario, retain core fields such as institution name, filing status, and inspection results | Filter redundant data and improve the accuracy of policy and regulatory basis Q&A |
| Request timeout duration | 60-120 seconds | Some civil affairs system interfaces have slow response speeds, avoid query interruptions due to timeouts |
| Request retry count | 2-3 times | Respond to network fluctuations or temporary interface failures and ensure the stability of data acquisition |
| Result filtering rule | Only return supervision data from the past 12 months | Align with the timeliness requirements of policy Q&A and avoid interference from outdated data |
Three common configuration errors
- Chinese content returned by interfaces displays garbled text. Investigation shows that correct request header character set parameters were not configured. Some civil affairs system interfaces return GBK encoding by default. When
Content-Typeis not specified asapplication/json;charset=UTF-8, FastGPT parses content by default using UTF-8, leading to garbled text. - Interface calls fail but requests work normally in Postman. The cause is that request headers or authentication parameters matching those used in Postman were not included in the FastGPT HTTP module. Some systems verify request source header information, and omitting custom headers will trigger interception.
- Interface calls time out. The cause is that the timeout configuration was not adjusted based on the actual response speed of the connected system. Some civil affairs supervision interfaces have response times that exceed conventional thresholds, leading to query interruptions.
How to verify successful configuration
- Manually trigger an HTTP request and check whether Chinese content in the returned results displays normally with no garbled text.
- Compare FastGPT request logs with Postman request parameters to confirm that authentication headers, request bodies, and request header parameters are fully consistent.
- Adjust the timeout duration, then trigger requests multiple times to confirm there are no frequent timeout interruptions.
- Check the extraction results of
responseDatato confirm that only core fields required for supervision scenarios are retained, with no redundant data included.
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.