What data in this category looks like
This category’s data originates from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. Updates occur monthly. Daily output includes public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Fields cover data item names, affiliated departments, open scopes, desensitization methods, interface addresses, and update cycles. Most content uses structured fields, with accompanying unstructured explanatory text. This category processes shareable or open data assets. Unlike government public disclosure documents, it centers on structured data, interface permissions, and open boundaries.
What constraints these characteristics impose on workflow orchestration
The monthly update cycle requires workflows to support reusable batch processing, eliminating repeated manual data item sorting. The mixed structure of structured fields and unstructured descriptions means workflows must support both structured parameter extraction and natural language text generation. Dispersed data sources across departmental business databases require workflows to include cross-source data pull nodes to maintain data consistency. The need to label desensitization rules and open conditions requires workflows to add compliance check steps to prevent data leaks or incorrect open boundaries. The requirement for standardized interface document generation means workflows must support templated output and automatic field filling.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Global variable configuration | Bind the variables parameter, set to "Data Items to Sort List", "Desensitization Rule Template", "Open Condition Library" | Adapt to the multi-field, batch processing requirements of this category, support cross-node reuse of core data |
| AI chat node | Call a general large model, enable context memory function | Used to generate unstructured text such as data item descriptions and interface documents, maintain content consistency with historical sorting results |
| File upload node | Limit single upload file size to 1000 MB, support CSV and JSON formats | Match the monthly batch data import requirements of this category, adapt to structured data file formats |
| Process timeout setting | Set to 600 seconds | Handle time-consuming operations such as cross-departmental database pulling and batch data cleaning, avoid process interruption mid-run |
| Compliance check node | Configure field integrity check rules | Prevent core fields such as data item names and open scope from being empty |
| Trigger mode setting | Set to scheduled trigger, execute on the 1st of every month | Match the monthly update business rhythm, reduce manual operation costs |
Three common configuration mistakes
- Phenomenon: Custom knowledge base selected variables in the workflow return empty values after submission. Cause: The variable data type was not specified as enumeration in the
variablesconfiguration, causing the system to fail to recognize the optional knowledge base list. - Phenomenon: The workflow does not respond after uploading the monthly data file, and the interface displays a timeout error. Cause: The process timeout setting was not adjusted, and the time taken for cross-departmental database pulling or batch data cleaning exceeded the default threshold, triggering process interruption.
- Phenomenon: Undesensitized sensitive fields appear in the generated public data catalog. Cause: No compliance check node was embedded in the workflow, and documents were generated directly using raw data, violating data open compliance requirements.
How to verify correct configuration
- Trigger a test workflow, upload a test data file in standard format, check whether structured fields are parsed correctly.
- View workflow running logs, confirm that cross-source data pull nodes complete data acquisition within
600 seconds, with no timeout errors. - Generate a test version of the public data resource catalog, verify that core fields are automatically filled and desensitization rules are correctly applied.
- Trigger a scheduled trigger test, confirm that the workflow automatically starts and completes batch processing at the preset time.
Note: The values provided here 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.