What this category's data looks like
Core data for this category is generated by manufacturing enterprise IPQC, FQC, quality engineering, and laboratory teams. Daily documents include standardized business documents such as control plans, patrol inspection records, SPC reports, finished product inspection reports, non-conforming product review forms, release forms, and quality monthly reports. Data sources cover MES systems, online inspection equipment, laboratory systems, process parameter ledgers, customer standard libraries, and historical defect libraries. Updates are collected continuously per production batch and shift frequency. Documents include standard fields such as inspection items, defect types, judgment results, and release status. No custom special units are used.
What constraints these characteristics impose on workflow orchestration
Multiple scattered data sources require workflows to include configurations for automatic pulling and format alignment across multiple data sources. The high-frequency update rhythm per batch and shift requires workflow trigger logic to bind to production nodes and shift nodes, to avoid data lag. The standardization level of document fields depends on upstream process and inspection procedures. Flexible field mapping configuration items must be reserved. Dynamic updates of historical defect libraries and customer standards require workflows to support real-time calls to the latest rule libraries. Cross-team non-conforming review and release audit processes require layered approval nodes and permission controls. Incorrect configurations may lead to risks such as widespread batch non-conformities and accidental release of non-conforming products.
How to set configurations
This chapter’s configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Node Settings | Bind to production batch completion event | Matches the data update characteristics per production batch |
| Variable Configuration | Associate the inspection batch ID in variables | Enables precise association and automatic pulling of multi-source data |
| Approval Node Configuration | Set nodeId according to inspection hierarchy | Adapts to layered approval processes for IPQC, FQC, and quality engineering teams |
| Knowledge Base Call Limit | Top 10 historical defect records | Matches query requirements for historical defect libraries, avoids redundant data |
| Timeout Threshold | 600 seconds | Adapts to the collection and processing duration of laboratory inspection data |
| Workflow Archiving Rule | Bind to release form generation event | Matches document retention requirements for finished product release |
Three common configuration mistakes
- Phenomenon: The workflow’s knowledge base search step runs normally, but the AI conversation stage does not return inspection data associated with the knowledge base. Cause: No knowledge base association parameters are configured in
variables, so the conversation node cannot call retrieved business data. - Phenomenon: A field empty error pops up when workflow nodes transition. Cause: No multi-source data field mapping rules are configured, so upstream document fields cannot match the input requirements of workflow nodes.
- Phenomenon: The workflow does not respond for a long time after triggering, and finally triggers a timeout error. Cause: The timeout threshold is set too short, which does not adapt to the collection and processing duration of laboratory inspection data.
How to confirm the configuration is complete
- Manually trigger the workflow bound to a production batch, and check whether patrol inspection records and SPC reports for the corresponding batch are automatically pulled.
- Enter the variable configuration page, and confirm that the inspection batch ID in
variablesis associated with the corresponding data source. - Submit a simulated non-conforming product review application, and check whether approval nodes transfer according to the configured hierarchy.
- After triggering the workflow, check the running log to confirm that the number of knowledge base call records matches the preset range.
The values provided here are common starting points and should be evaluated against the reader's own samples.