This documentation applies to FastGPT v4.17.0.
What this category of data looks like
Data for this category originates from raw iron and scrap input data, real-time L2 process control collected data, laboratory composition test reports, workshop MES system data, energy medium system operation records, and equipment status monitoring data. Daily documents include heat plans, batching lists, converter smelting records, composition analysis reports, continuous casting process parameter records, and slab quality judgment sheets. Data update cycles cover single records generated per heat, real-time pushed process parameters, and shift-level summarized scheduling and quality reports. Fields include core indicators such as smelting temperature, component proportion, billet pulling speed, and casting duration.
What constraints these characteristics impose on tool calling and plugin workflows
The high-temperature continuous production and furnace-casting coupling characteristics require tool calling responses to match the push rhythm of real-time process parameters, to avoid process interruptions caused by delays. Multiple heterogeneous data sources require plugins to adapt to different system data format conversion and permission verification logic. Based on the update cycles of per-heat, real-time, and shift-level updates, plugins must support three calling modes: single heat data pulling, real-time process parameter subscription, and batch report export. Since quality defects can propagate to downstream rolling processes, tool calling must include a built-in data verification step to prevent incorrect parameters from entering subsequent workflows.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto or specified functionCall | Adapts to scenarios where multiple plugins must be called during heat auditing, supports automatic tool matching or forced execution of preset quality verification plugins |
| Workflow trigger timing | Bound to after heat plan generation, after converter smelting ends | Matches the business rhythm of triggering audits by process nodes in steel smelting, to avoid invalid calls |
| Plugin call timeout | 600–1200 seconds | Adapts to the average duration of real-time data collection and test result return during the smelting process, to avoid timeout interruptions caused by high-temperature process time consumption |
| Context window size | 8000–12000 tokens | Carries complete content of multiple documents including heat plans, smelting records, and composition reports, to avoid loss of key parameters |
| Number of data entries returned per call | Top 20 entries | Streamlines plugin return content, matches the core focus indicators of auditing scenarios, to avoid data overload |
| Result cache duration | 1800 seconds | Adapts to temporary storage requirements after report generation, to avoid resource occupation from repeated generation |
Three Common Misconfigurations
- Phenomenon: After calling the quality audit plugin, the running data displayed in the conversation log is empty. Cause: The data source configuration for the corresponding heat is not bound, so the plugin cannot pull actual smelting process records.
- Phenomenon: After a custom report export plugin runs, the returned download link address changes continuously, and the final result is generated with delay. Cause: No result cache logic is set for the plugin. The real-time generated report file path updates dynamically during the generation process, and the link is returned before writing is complete.
- Phenomenon: When workflow A calls workflow B, workflow B cannot execute completely, only partial nodes run. Cause: Necessary trigger parameters are not configured in workflow B, or the heat ID passed by workflow A does not conform to the verification rules of workflow B.
How to Verify Correct Configuration
- Manually trigger a plugin call, and verify that the returned data fields include preset core business document content.
- Check the tool call records in the conversation log to confirm that configuration items match actual call parameters.
- Simulate passing a heat ID with an incorrect format, and verify that the plugin triggers the preset verification logic.
- Test batch call scenarios to confirm that the plugin's return results conform to the business rhythm during shift-level summarized report generation.
The values provided are common starting points and should be measured against the reader's own samples.