What data for this category looks like
Data for this category originates from slab or billet batches, heating furnaces, mill automation control systems, online thickness and width measurement equipment, surface inspection devices, and MES systems. Daily documentation includes rolling plans, heating schedules, rolling specifications, shape and thickness records, surface quality reports, and quality certificates. Fields include unique batch numbers, temperature values, thickness parameters, defect levels, production speed, and additional relevant metrics. Updates occur in real time per coil or per steel billet. Document structure is stored hierarchically by production batch and process node.
What constraints do these characteristics impose on source citation and traceability
The real-time coil-level updates, multi-source heterogeneous data structure, and strict batch binding requirements of this category create three constraints for citation and traceability.
- Use a unique production batch number as the traceability anchor to avoid cross-batch data confusion.
- Unify association rules for multi-source data, binding automation system values, inspection report text, and MES work orders to corresponding process nodes.
- Ensure the data source version during citation matches the current production batch, to prevent referencing expired process parameters.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Matches the text length of single-coil production records, avoids truncating critical batch information and parameters |
responseData | Enabled | Enables knowledge base to return citation source metadata for binding production batch identifiers |
detail | Enabled | Enables detailed traceability information, displays specific inspection equipment and process node content |
| Single search limit | Top 6 entries | Covers multiple link documents including rolling, inspection, and quality assurance for single-coil production, avoids missing critical records |
| Batch association switch | Enabled | Binds unique production batch number as traceability anchor, prevents cross-batch citation confusion |
| Citation matching threshold | Calibrated via actual measurement | Adapts to numerical precision of rolling parameters and differences in text descriptions |
Three Common Configuration Errors
- Enabling
responseDatayields no citation effect: The interface displays citation markers but does not return metadata or links for specific source documents. The root cause is failure to add production batch-related metadata fields to the knowledge base's metadata mapping rules, so the traceability anchor cannot be bound. - The knowledge base only returns plain text datasets: Citation results only include unformatted text, and do not display inspection equipment, process node, or other detailed information. The root cause is failure to enable the
detailconfiguration, so detailed traceability display for multi-source data is not activated. - Generated content does not match knowledge base content: The produced process document or exception report does not include rolling parameters from the knowledge base. The root cause is that
quoteMaxTokenis set too small, truncating critical batch information and parameter content.
How to Verify Configurations Are Correct
- Initiate a query for a single production batch, and check if returned results include metadata or associated links for documents such as shape and thickness records and surface quality reports for that batch.
- View knowledge base citation logs in FastGPT v4.17.0, and confirm that the unique production batch number is bound as the traceability identifier.
- Adjust the
quoteMaxTokenvalue, and verify that returned citation content fully includes rolling parameters and process node information with no truncation. - Test passing production batch parameters via API, and confirm that the workflow can correctly call knowledge base content for the corresponding batch and generate matching process documents.
The values provided here are common starting points, and should be evaluated against the user's own samples.