What data for this category looks like
Data sources for this category include slab or billet batches, heating furnaces, mill automatic control equipment, online thickness and width measurement devices, surface inspection systems, and MES systems. Daily documents cover rolling plans, heating schedules, rolling regulations, plate shape and thickness records, surface quality reports, and quality certificates. These include fields such as reduction parameters, rolling speed, thickness tolerances, and defect types, with units including millimeters, meters per second, square millimeters and other commonly used industrial units. Data updates occur in real time per coil or per steel billet. Single-batch data is dynamically generated as the rolling process proceeds.
What constraints this category’s characteristics impose on model access and configuration
The real-time coil-level data, specialized industrial fields, and high-frequency update characteristics of this category impose multiple constraints on model access and configuration. Single-batch rolling data scale grows dynamically as the process proceeds, so the configured context window must adapt to the data volume of a single coil to avoid truncating key reduction and speed parameters. Specialized fields such as thickness tolerances and defect types require the model to support understanding of industrial terminology, and input content should not be overly compressed. Real-time updated data per steel billet or coil requires a stable call link to avoid configuration failures caused by data synchronization delays. Exception reports must match production rhythms, so model response times must meet workshop scheduling requirements and not exceed reasonable thresholds.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the scale of professional documents for single-coil rolling data, avoids truncating key parameters |
maxTokens | 2000–3000 token | Matches the conventional output length of exception reports and process documents, meets format requirements |
temperature | 0.2–0.4 | Reduces output randomness, ensures accuracy of process parameters and defect judgments |
AIPROXY_API_ENDPOINT | Set according to deployment region | Adapts to the proxy call logic of FastGPT v4.17.0, reduces network latency |
AIPROXY_API_TOKEN | Bind a dedicated call key | Isolates call permissions across different workshops, ensures security of industrial data calls |
Three common mistakes
- Phenomenon: After configuring a model using a third-party model proxy node, local testing passes but calling fails when creating a new application in FastGPT. Cause:
AIPROXY_API_TOKENis not correctly bound in the FastGPT configuration, or the proxy node does not open industrial scenario call permissions for the corresponding model. - Phenomenon: After uploading documents such as rolling regulations and surface quality reports, professional fields recalled by the knowledge base are missing or incomplete. Cause: The value of
maxContextis too small, causing key parameter content to be truncated during document chunking. - Phenomenon: Defect judgment results in generated exception reports deviate significantly from online inspection data. Cause: The value of
temperatureis too high, causing model output randomness to exceed the strict requirements of industrial scenarios.
How to confirm the configuration is complete
- Call the model test interface, pass the complete document of single-coil rolling data, check whether the returned results include all key professional parameters with no obvious truncation.
- After configuring
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, initiate multiple batch calls, check whether link stability and response times meet production rhythm requirements. - After adjusting the
temperatureparameter, compare multiple generation results of the same exception data, confirm that output consistency meets the strict requirements of industrial scenarios. - Upload typical documents of this category, perform a knowledge base recall test, check the field completeness and matching degree of recall results.
The values provided are common starting points and should be measured against the reader's own samples.