What the data for this category looks like
Data sources include enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency follows an annual cycle. Daily documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Each document has corresponding business fields: for example, the rectification ledger includes fields such as issue number, rectification measures, completion status, and supporting material links. There is no unified fixed unit, and adjustments are made based on specific business scenarios.
What constraints do these characteristics impose on the "workflow orchestration" link
Differences in document formats across multiple system sources require workflows to be configured with nodes adapted to different data sources to handle format conversion. Annually updated batch data requires workflows to set reasonable timeout and breakpoint resume mechanisms to avoid timeout interruptions during a single run. Field differences across different documents require workflows to set independent content extraction rules for various documents such as internal control self-assessment reports and rectification ledgers, to ensure the accuracy of data extraction.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind core fields of rectification ledgers and issue lists | Unify data call standards within the workflow, adapt to field differences across multiple documents |
nodeId | Assign unique identifiers according to the business sequence of workflow nodes | Facilitate workflow debugging and cross-node data transfer |
| Data source node configuration | Configure separately according to connected system types, single data pull volume is 500–1000 entries | Adapt to document format differences from multiple system sources, avoid overloading during single data pulls |
| Timeout settings | 600 seconds | Adapt to time consumption requirements of annual batch data extraction, avoid workflow interruptions |
| Text content extraction node | Set extraction rules according to document type, single extracted character count is 800–1200 characters | Adapt to content extraction logic for different documents such as internal control self-assessment reports and rectification ledgers |
| Specified response node | Bind preset rectification prompt messages | Uniformly output prompt content for scenarios where extraction fails |
Three common misconfigurations
- When debugging a workflow, an environment variable configuration error is displayed, with a prompt indicating that the OPENAIBASEURL format is abnormal. This occurs because environment variable parameters within
variableswere not configured correctly, preventing the workflow from calling external tools. - A tool call node fails to execute with no return result. This occurs because nodes corresponding to
nodeIdwere not arranged according to business sequence, leading to interrupted cross-node data transfer. - A text content extraction node returns a null value and does not trigger the specified response. This occurs because matching extraction rules were not set for different document types, making it impossible to identify target fields in rectification ledgers and issue lists.
How to confirm the configuration is successful
- View the workflow's data source node logs to confirm that document data from the corresponding systems can be pulled.
- Trigger the text content extraction node to verify that preset fields can be extracted from different types of documents.
- Simulate an extraction failure scenario to confirm that the specified response node can normally output the preset prompt messages.
- Run the full workflow to confirm that nodes execute in business sequence, with no timeouts or interruptions.
The values provided here 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.