What this category of data entails
Data for this category originates from engineering, production, quality control, and laboratory teams at printed circuit board factories. It covers engineering production instructions, Gerber parsing records, drilling and lamination parameters, chemical solution analysis reports, AOI and electrical test reports, and slice inspection reports. Data sources include customer-provided Gerber files, drill tapes, BOM lists, CAM output results, equipment operating parameters, bath solution test data, and online and laboratory test records. Data updates occur per production batch, per shift, or via continuous collection. Each document includes fields such as process parameters, test results, and batch numbers. Some parameters include units, such as copper hole thickness μm and etching rate μm/min.
Constraints imposed on workflow orchestration
Data sources are scattered, and update frequencies vary. Workflows must support scheduled synchronization and incremental pulling of multi-source data to avoid reloading historical data. Field structures differ widely across documents. Unified field mapping rules must be configured to ensure accurate matching of process parameters and technical standards. Production batch-level updates require workflows to support batch-triggered tasks, eliminating resource waste from full data parsing. Strong associations between test reports and process parameters require adding a data correlation verification step to workflows. This ensures complete data for the corresponding batch can be retrieved during Q&A.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Condition | Triggered by currently uploaded attachments | Avoid loading historical documents, only process PCB production batch data associated with the current conversation |
variables | Bind current batch number | Accurately associate documents for the corresponding production batch such as Gerber files and drilling parameters, avoid mixing data across batches |
nodeId | Bind the last AI node | Only aggregate output results from the final node, block redundant content from preceding nodes |
| Context Window | 8000–12000 token | Adapt to the long text characteristics of PCB process documents, retain key parameters and test records |
| Incremental Sync Toggle | Enabled | Pull data per production batch, reduce resource consumption from full parsing |
| Timeout Threshold | 300 seconds | Adapt to the parsing and association verification duration of complex PCB process documents, avoid abnormal task interruptions |
This page is written based on the configuration specifications for FastGPT v4.17.0
Three common configuration errors
- Loading full historical documents after workflow triggers: Failing to configure the rule to trigger by currently uploaded attachments, and defaulting to pulling all associated documents, leading to wasted parsing resources and redundant results.
- Final replies including output from preceding AI nodes: Failing to bind
nodeIdto the last AI node, failing to block output from intermediate steps, which does not meet the requirement of only aggregating final results. - Cross-version workflow import failure: Failing to adapt to configuration format differences between versions, directly importing old version workflow files, leading to incorrect node configuration mapping.
How to verify correct configuration
- Upload a single PCB production batch document, trigger the workflow, and check the parsing log to confirm only the currently uploaded document content is loaded.
- Run the full workflow, then verify the final output content to confirm it only includes aggregated results from the last AI node, with no redundant information from preceding steps.
- After importing cross-version workflow configurations, check that each node’s configuration items match the interface options for the current version, with no missing or incorrectly mapped settings.
- Trigger a task set to run per production batch, check the data sync log to confirm only process and test data for the corresponding batch is pulled.
The configuration values listed are common starting points. Adjust them based on your own sample data.