What the Data for This Category Looks Like
This category serves judicial administrative departments, departmental legal affairs offices, and policy and legal positions in government offices. Daily processed documents include normative document drafts, drafting explanatory notes, legality review opinions, solicited opinion summaries, and filing reports. Data sources include regulatory and rule databases, higher-level law databases, departmental policy document databases, and administrative normative document filing systems. Update frequency is irregular. A single review process is associated with multiple types of documents. Data fields include document number, drafting unit, review node, opinion summary content, filing status, and others. Fields vary across different document types.
What Constraints These Characteristics Impose on the HTTP Interfaces and External Systems Workflow
Dispersed data sources have no fixed update cycle. Interfaces must support on-demand pulling of multi-source heterogeneous data. This prevents audit basis failure caused by expired cache. A single review process links multiple document types. Interfaces must support transmission and verification of batch-associated data. This ensures full review materials can be retrieved completely. Review processes have multiple nodes. Interfaces must support status synchronization and ledger updates. This ensures ledger data aligns with filing systems in real time. Fields differ across document types. Interfaces must support flexible parameter passing. This adapts to submission and storage requirements for different review materials.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Interface authentication method | Configure Authorization header as Bearer Token | Aligns with unified API security specifications for government affairs systems |
| Request parameter template | Configure variables field by review document type | Adapts to field passing requirements for different review materials |
| Response data validation | Check if responseData contains the filing status field | Ensures ledger data aligns with filing systems |
| Single request timeout | 600 seconds | Adapts to time requirements for multi-source data pulling |
| Batch pull limit | Top 100 entries | Prevents timeout caused by excessive data volume in a single request |
| Data source configuration | Set based on actual measurements | Adapts to irregularly updated multi-source data sources |
Three Common Misconfigurations
- Symptom: Interface calls return 504 timeout status codes, or overall response delay increases significantly. Cause: No request rate limiting rules are configured. Too many external system requests are initiated simultaneously, leading to resource competition. This is a common performance issue in multi-API deployments.
- Symptom: Audit ledger fields returned in
responseDataare empty. Filing status data is missing. Cause: Thevariablesparameter for passing associated document IDs is not included in the request. Full associated review material data cannot be pulled. - Symptom: Pulled regulatory basis data does not match the currently active version. This leads to review errors. Cause: No on-demand pulling interface logic is configured. Fixed caching is used, resulting in delayed data updates.
How to Confirm Proper Configuration
- On the FastGPT v4.17.0 interface configuration page, initiate an authentication request. Check if the
Authorizationheader is correctly carried. Confirm authentication is successful. - Initiate an interface request for a single review document. Check if fields returned in
responseDatainclude preset audit ledger content. Confirm data integrity. - Simulate a multi-source data pulling scenario. Verify that the interface can pull document content from different data sources on demand. Confirm that the
variablesparameter passing logic works. - Initiate batch requests. Verify that timeout and rate limiting configurations meet business requirements. Confirm that the system does not experience exceptions due to request overload.
The configuration values listed above are common starting points, and should be evaluated 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.