What Data in This Category Includes
Data for this category mainly comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. The update cycle is monthly. Daily supporting documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data fields typically include data item names, open entities, desensitization processing requirements, call interface addresses, update cycles, authorized usage conditions, and more. Most data is in structured formats, and some documents include interface call permission descriptions.
What Constraints These Characteristics Impose on Workflow Orchestration
Data sources for this category are scattered, and most are in structured or semi-structured formats. This requires workflows to adapt to multi-source data pull nodes and be compatible with interface call formats from different platforms. The monthly update cycle requires workflows to be configured with scheduled trigger scheduling to ensure the latest catalogs and lists are obtained each run. The presence of multiple supporting document types requires workflows to include parsing nodes adapted to different document structures, to accurately extract core fields such as data items and desensitization rules. Data fields also include authorized usage conditions, so workflows must add a permission verification link to avoid exposing open data beyond the authorized scope.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Scheduled trigger scheduling | Once per month, fixed at 00:00 on the 5th of each month | Matches the monthly update frequency of data in this category, ensuring the latest catalogs and lists are obtained |
| Multi-source data pull timeout | 600 seconds | Addresses interface response delays from scattered data sources, preventing workflow interruptions from single pull timeouts |
| Document parsing field limit | 800 characters | Adapts to the core field length of structured documents in this category, avoiding interference from redundant content during extraction |
| Variable binding | Bind the data update time field in variables | Used to verify the update cycle of currently pulled data, keeping it consistent with the update frequency marked in the catalog |
| Permission verification rules | Match the open scope field to filter request subjects | Complies with the authorized boundary requirements of data in this category, preventing data leaks |
| Exception retry strategy | Retry 3 times after failure, with a 120-second retry interval | Addresses temporary interface fluctuations during multi-source pulling, ensuring workflow stability |
Three Common Configuration Mistakes
- Text content extraction components return empty results, but normal extraction works after re-adding a component with the same configuration. The symptom is that component output fields are empty, and extraction results displayed on the interface are empty. The cause is that the target document data source in
variableswas not correctly bound during initial configuration, causing the component to read an empty context. Re-adding the component automatically adapts to the variable context of the current workflow. - For text extraction components in workflows, some large language models can extract content normally while others cannot. The symptom is that some nodes return errors such as "extracted content is empty" or "context exceeds limit", while other nodes return normal results. The cause is that different large language models have different context window lengths. Extraction content that exceeds the maximum token number supported by the large language model will result in extraction failure.
- Workflows do not automatically recover after being interrupted. The symptom is that the workflow status shows failed, and no retry trigger records are found in the system logs. The cause is that a global exception capture node was not configured in the workflow, and retries were only set for individual nodes. When an error occurs in a non-target pull node, the retry mechanism cannot be triggered.
How to Confirm the Configuration Is Complete
Reference only; verify against the target FastGPT release and deployment documentation.
- Manually trigger the workflow once, and check whether the pulled data sources include public data catalogs and open lists from the current cycle.
- View the output results of the text extraction component, confirm that core fields such as data item names and desensitization rules have been accurately extracted, and that field matching meets business requirements.
- Verify the scheduled trigger function, wait for the preset scheduling time, and confirm that the workflow starts automatically and completes the entire process.
- Simulate an abnormal interface call, check whether the exception retry node triggers according to the configuration, and whether the workflow automatically recovers.
Note: The configuration values provided are common starting points. Adjust them based on your own samples and testing.
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.