What Data for This Category Looks Like
Data sources for this category include data asset inventory results, database metadata, business system documentation, and compliance regulatory documents. Updates occur quarterly or synchronously each time a business system is modified. Daily output documents include structured data catalogs, classification and grading rule lists, critical data identification tables, and review minutes. Data fields include asset ID, sensitive field identification, classification level, review status, and more. There is no unified fixed unit, and adjustments must be made to match the document format.
Constraints Imposed on Workflow Orchestration
Dispersed data sources and varied formats require workflows to support parallel pulling of multiple data sources and format conversion nodes. Unfixed update rhythms require support for both on-demand and scheduled execution triggering modes. Large variations in daily document structures require configurable, dynamically adaptive input parsing rules. The most time-consuming asset sorting and sensitive field identification links must be split into parallel sub-workflows to shorten overall execution cycles. Compliance-related review links require mandatory verification nodes to prevent missed management or grading errors. This configuration is designed using the workflow functions included in FastGPT v4.17.0.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind data catalog path, classification and grading rule version number | Enable dynamic reference to latest business data and compliance rules, avoid version inconsistencies caused by hardcoding |
nodeId | Assign unique identifiers to sensitive field identification, review verification, and report generation nodes | Facilitate quick location of failed nodes, simplify workflow debugging and maintenance |
| Single execution timeout | 1800 seconds | Cover the complete execution cycle of multi-data source traversal and batch asset identification |
| Batch processing threshold | First 200 data assets | Balance single-node load and execution efficiency, adapt to the batch verification logic of compliance reviews |
| Number of failure retries | 2 times | Respond to temporary data source connection fluctuations, reduce the probability of manual intervention |
| Trigger method | Triggered by system changes + quarterly scheduled triggering | Match the two update scenarios of data assets and rules, ensure the timeliness of operation reports |
Three Common Configuration Errors
- Phenomenon: All AI chat node responses appear in the final session conversation. Cause: No node output filtering rules are configured, causing the context of intermediate nodes to be directly spliced into the final result.
- Phenomenon: Unable to reference the dynamically configured knowledge base path via
variableswhen selecting a knowledge base. Cause: The variable binding permission of the workflow is not enabled, or the variable scope configuration is incorrect. - Phenomenon: No automatic retry occurs after workflow execution timeout, causing compliance report generation to interrupt. Cause: No failure retry count is set, or the retry interval configuration is too long to cover temporary network fluctuations.
How to Verify a Successful Configuration
- Trigger the workflow manually, and verify that input parameters correctly bind the data catalog and classification and grading rule paths.
- Review workflow node logs, and confirm that the sensitive field identification node associated with
nodeIdruns normally with no errors. - Simulate a business system change to trigger the workflow, and confirm that the trigger logic works as expected and the report generation cycle aligns with requirements.
- Check the output configuration of all AI chat nodes, and confirm that only content from the final review result node is retained.
The values provided are common starting points and should be measured against the user’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 2026-10-19.