What the data for this category looks like
Data for this category originates from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Daily processed documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Documents focus on audit-identified issues, rectification requirements, and supporting materials, and contain fields such as issue description, responsible party, rectification measures, and closure status. Data updates occur annually, and are only completed during annual internal control self-evaluation or special audit cycles.
What constraints these characteristics impose on workflow orchestration
Data from multiple systems requires workflows to include multi-data source connection nodes. This ensures required documents can be synchronized from different systems. The annual update frequency means workflows do not need high-frequency triggering. Only annual or special audit cycle launches are required. Diverse document types require workflows to support text extraction and structured parsing nodes for different formats. These nodes handle documents such as audit notices and rectification ledgers separately. The most time-consuming steps — sample extraction, issue generation, and rectification tracking — must be split into independent nodes with verification mechanisms. This prevents single-node timeouts from disrupting the full process. The risk of severe consequences from errors requires workflows to add retry and exception alert configurations. This ensures no gaps in issue closure.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Variable Configuration | Bind the preset issue list and rectification ledger fields in variables | Match the structured field requirements of category documents |
| Node Execution Order | Specify nodeId according to the audit process sequence | Ensure the workflow conforms to the execution logic of the audit closure cycle |
| Text Splicing Length | 1000–1500 characters | Adapt to the text length of individual audit working paper documents |
| Node Timeout Threshold | 600 seconds | Cover the typical time required for extracting business samples |
| Retry Mechanism Configuration | Retry 2 times after failure, with a 30-second interval | Address temporary exceptions from multi-system connections |
| Streaming Output Switch | Enabled | Meet the requirement for real-time feedback on the progress of audit issue generation |
Three common mistakes
- Symptom: When the code execution module calls an external model, streaming results cannot be output. Cause: The streaming output configuration of the workflow is not enabled, or the code node does not adapt to the streaming data transmission format.
- Symptom: The AI question answering node cannot receive information passed from the text splicing node, while the text splicing node can output complete content. Cause: The output of the text splicing node is not bound to the
variablesparameter, or the data flow configuration between nodes is not correctly associated. - Symptom: When calling the workflow via API, variable fields return no content, while splicing works normally during local debugging. Cause: The
variablesparameter is not correctly passed in the API request, or the variable binding configuration in the production environment is inconsistent with the debugging environment.
How to confirm the configuration is complete
- Manually trigger the FastGPT v4.17.0 workflow, check if the input and output of each node match the preset document fields.
- Call the API to initiate a workflow request, confirm that the returned results include all configured variable contents.
- Simulate concurrent execution of multiple nodes, check if the workflow can stably complete the entire process.
- Test the streaming output of the code execution node, confirm that results can be received in segments.
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.