What the data for this category looks like
Core data for this category typically comes from the "Random Check, Public Disclosure" supervision platform, enterprise credit information public disclosure system, industry supervision systems, and law enforcement officer directory. The update frequency is once per month. Daily business documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result public disclosures. Each document has clear business fields, such as the unified social credit code of the inspection target, law enforcement officer certification number, and specific category of inspection items.
What constraints do these characteristics impose on the "workflow orchestration" link
The monthly data update requirement means workflow configurations must include scheduled tasks to synchronize data sources, ensuring the timeliness of policy basis and supervised object information. Data from multiple sources must be verified and correlated within the workflow to avoid non-compliant extraction of supervised objects. Differences in fields across core business documents require workflow nodes to set dedicated parameter mappings for different document types. The most time-consuming links—maintaining inspection items and object libraries, matching law enforcement officers, and entering inspection result public disclosures—must be automated via workflows to reduce manual operations. Verification links must also be configured to prevent missed public disclosures of inspection results or redundant notifications to enterprises.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind inspection plan ID, inspection target scope, law enforcement officer directory ID | Adapt to core business data of this category, enabling the workflow to trigger based on specific supervision tasks |
nodeId | Set as the unique identifier of the law enforcement officer matching node | Ensure accurate workflow branch jumps to match qualified law enforcement officers |
| Single search limit | Top 10 entries | Return sufficient candidates without redundancy when matching the law enforcement officer directory |
| Maximum context | 8000 characters | Carry policy basis, inspection item lists, and other content to meet the context length required for question answering |
| Question optimization | Enabled | Organize vague expressions in supervision policy Q&A to improve answer accuracy |
| API timeout | 600 seconds | Adapt to the time required for cross-system data retrieval to avoid request interruptions |
Three common mistakes
- Failing to return thought processes after calling an API to start a session. This occurs because the thought process return parameter is not configured in
variables, or log output is not enabled for workflow nodes. - An error
localStorage is not definedoccurs when executing code to obtain user identifiers in the workflow. This happens because the workflow runtime environment has no browser global object. Usevariablesto pass required parameters instead. - No matching inspection items are returned after the workflow triggers. This occurs because the correct data source node is not bound, or the single search limit is set too low, resulting in an insufficient candidate range.
How to confirm the configuration is complete
- Trigger a test workflow to check whether the business parameters bound in
variablesload correctly. - Verify that the
nodeIdof the law enforcement officer matching node matches the reference ID of subsequent jump nodes. - Run test cases to confirm that the number of search results matches the preset single search limit.
- View workflow logs to confirm no timeout errors occur in API requests.
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.