What the data for this category looks like
Data sources include internal enterprise production ledgers, pesticide registration compliance documents, fertilizer formula parameter libraries, and industry standard texts. Update frequency: production ledgers are synced daily, compliance documents are updated alongside approval workflows, and formula libraries are adjusted as needed. The document structure includes structured fields and unstructured explanatory text. Structured fields cover product identifiers, composition parameters, production batches, and some parameters have dedicated units.
What constraints do these characteristics impose on the "workflow orchestration" link
The mixed structured and unstructured data feature requires workflows to be configured with layered processing nodes: first complete extraction and verification of structured fields, then process compliance explanatory text. Differences in update frequencies across data sources require workflows to be set with differentiated trigger rules: sync production ledger data daily, pull compliance documents as needed. Dedicated parameter unit requirements require adding format matching logic to the parameter verification step of the workflow to avoid document errors caused by unit mismatches. Additionally, fertilizers and pesticides documents must bind specific industry compliance fields, so workflows must link preset data source nodes instead of using a general data pool.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Global Variables | Configure variables to bind field mappings for production ledgers and compliance documents | Match the structured field characteristics of fertilizer and pesticide data to ensure accurate association of document fields |
| Data Source Trigger Rules | Set according to data update cycles: trigger daily for production ledgers, trigger as needed for compliance documents | Adapt to the update frequencies of different data sources to avoid unnecessary pulls |
| Parameter Verification Threshold | Set to format verification rules matching dedicated units, such as matching the g/L format | Cover the dedicated unit requirements for fertilizer and pesticide parameters to reduce format errors |
| Workflow Retry Count | Set to 3 times, with a retry interval of 60 seconds | Address temporary data source connection exceptions to ensure stable document generation workflows |
| Node Flow Conditions | Bind text processing nodes and compliance verification nodes by nodeId | Ensure structured data processing automatically proceeds to the compliance verification step |
Three Common Misconfigurations
- Issue: Workflow calls to external APIs return format errors, with interface status code 400. Cause:
variablesfor passing structured field parameters were not configured correctly, causing the backend to fail to recognize the format of fertilizer and pesticide-specific composition parameters. - Issue: After importing another person's workflow JSON, the text processing module cannot be found in the local workflow list. Cause: Permissions for the corresponding module were not enabled in the local workspace.
- Issue: Field empty errors occur during workflow node transitions. Cause: Pre-verification for data source triggers was not set, causing the document generation step to run without pulling production ledger data.
How to Confirm Proper Configuration
- Review the
variablesconfiguration of the workflow to confirm all bound fields come from fertilizer and pesticide-specific data sources. - Trigger a test run and check if the node flow logs include field data from production ledgers and compliance documents.
- Simulate a temporary data source disconnection scenario to confirm the workflow trigger retry mechanism works.
- Verify node flow conditions to confirm each
nodeId-bound node follows the correct document drafting workflow order.
All provided configuration values are common starting points, and should be evaluated 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.