What this category of data looks like
Data for this category mainly comes from enterprise internal control systems, ERP systems, financial shared services systems, audit management systems, and procurement contract project systems. The update frequency is annual. Daily generated documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, rectification closing certificates, and more. Most document structures include issue numbers, rectification requirements, supporting material association fields, and closing status identifiers. Fields are tightly bound to business process nodes.
What constraints do these characteristics impose on the "workflow orchestration" link
Dispersed data sources require configuring multi-system connection nodes in the workflow to ensure accurate cross-system data synchronization. The annual update frequency allows the workflow to be orchestrated according to fixed audit cycles, eliminating the need for high-frequency triggers. Document fields are tightly bound to business nodes, so standardized field variables must be defined via variables to adapt to format differences across documents. The most time-consuming sample extraction and issue sorting process requires setting parallel processing nodes to reduce overall duration. Error consequences involve regulatory notifications and liability investigations, so failure alerts and rollback nodes must be configured to avoid missing audit issues.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Map to audit document standard fields as rectification requirements, issue list number, closing status | Adapt to the tightly bound field structure of documents in this category, ensuring accurate data extraction |
| Single sample extraction limit | Top 200 entries | Match the conventional scope of audit sampling, avoid data overload affecting execution efficiency |
| Node timeout period | 600 seconds | Single request duration when connecting multiple systems usually falls within a reasonable range; 600 seconds covers most synchronization scenarios |
| Workflow trigger rule | Trigger according to annual audit cycle | Match the annual update rhythm of data in this category, avoid unnecessary execution |
nodeId | Assign unique identifiers according to the order of system connection nodes | Distinguish multi-system connection links, avoid node confusion during workflow execution |
| Context history retention limit | Top 30 conversation records | Adapt to the multi-node approval conversation scenario of the audit process, retain appropriate context to ensure logical consistency |
Three Common Mistakes
- Phenomenon: The workflow returns empty fields after executing a system connection node. Cause: Document fields are not matched via
variablesconfiguration, causing extracted data to be unrecognizable by subsequent nodes. - Phenomenon: The workflow editing interface reports an error
Application error: a client-side excin a private deployment environment. Cause: Correct internal network access port mapping is not configured during local deployment, causing the frontend to fail to connect to the backend service. - Phenomenon: After setting a conditional judgment node in the workflow, context history conversation records cannot be cleared according to rules. Cause: The trigger condition of the context clearing node is not configured correctly, or the corresponding
nodeIdis not bound to trigger the clearing action.
How to Confirm Proper Configuration
- Run a test workflow, check whether the extracted document fields match the preset
variablesconfiguration. - Simulate an annual trigger scenario to verify whether the workflow starts and executes according to preset rules.
- Trigger a node exception to check whether preset failure handling actions are correctly triggered.
- Test the conditional judgment node to verify whether context history can be cleared according to rules.
The values provided 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.