What This Category of Data Includes
This category of data covers daily business documents from entities including finance bureau performance offices, budget units, and third-party evaluation institutions. Documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports.
Data sources include the unified budget management system, project declaration materials, fund payment data, business system logs, and satisfaction surveys. The update frequency is annual.
Core fields include project performance indicators, fund usage details, and supporting material association relationships. The data must match verification logic used in business processes. There is no unified fixed single-document format. Field combinations must be adjusted dynamically based on business stages.
Constraints Imposed on HTTP APIs and External Systems
The annual update frequency means the API does not require high-frequency invocation, and must support batch data retrieval scenarios.
Multiple data sources require the API to connect to multiple external systems. It must be compatible with identity verification, data formats, and response structures of different systems.
Diverse business documents and dynamic field combinations require the API request body to carry flexible business parameters, and avoid hardcoding fixed fields.
Verifying the correspondence between fund expenditure and performance targets requires the returned data to include strongly linked project ID, fund transaction records, and performance indicator fields. This ensures the accuracy of data verification.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Bearer-prefixed token with a validity period of no more than 7 days | Adapts to identity verification rules of external fiscal systems, and reduces the scope of risk if a token is leaked |
variables | Key-value pairs containing performance project ID, report type, and affiliated unit code | Matches fixed query and verification parameters of fiscal expenditure performance evaluation business, and prevents missing request parameters |
responseData | Parsed into structured JSON data, retain original field names | Adapts to field verification logic of performance evaluation business, and facilitates subsequent data organization and verification |
| Request timeout | 300 seconds | Adapts to the time required for batch pulling annual performance data, and prevents process interruption from mid-call disconnection |
| Retry count | No more than 3 times | Addresses temporary interface fluctuations in external systems, and reduces the impact of single request failure on business processes |
| Data format verification | Enable format verification for image and text fields | Adapts to business requirements of supporting material upload and data verification |
Three Common Errors
- The API call returns a 400 status code, and the log contains "The request failed because the image format". This occurs when
variablesare not correctly configured to pass project-related parameters, or when image format verification rules for fiscal supporting materials are not adapted. This leads to the external system rejecting the request. - Significant differences exist between results from online chat and API calls. This occurs when
variablesare not configured in the API request to pass fixed business context. This leads to inconsistent prompt information and parameters for model calls, resulting in result deviation. - No log records exist for API calls, making troubleshooting impossible. This occurs when FastGPT's API call logging function is not enabled. This prevents locating specific causes of request parameter errors or external system response anomalies.
How to Confirm Proper Configuration
- Call the API with the
Authorizationparameter, and verify the return status code is 200 to confirm identity verification has passed. - Check that the fields returned in
responseDatainclude performance project ID, fund transaction details, and performance indicators, to confirm the data structure meets business requirements. - Simulate a batch data request, verify that the timeout setting matches actual time consumption, with no mid-call disconnections.
- Compare parameters between online chat and API calls, confirm that
variablesconfiguration is consistent, and verify that there is no significant difference in results. - Enable the API logging function in FastGPT v4.17.0, and confirm that request and response data are fully recorded.
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.