What the 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 manufacturing execution systems (MES). Data updates occur once per coil or individual steel bar. Some key process parameters are synchronized in real time. Daily documentation includes rolling plans, heating schedules, rolling specifications, flatness and thickness records, surface quality reports, and quality certificates. Fields cover batch number, heating temperature, rolling reduction, thickness and width values, surface defect level, quality certificate number, and more. Common units include millimeters, degrees Celsius, tons, and similar units.
What constraints do these characteristics impose on the model integration and configuration workflow
This category has high data update frequency, with many parameters linked to each batch. It combines structured process data and unstructured image-based quality records. This requires model integration and configuration to support batch data processing and multimodal input. Data sources are scattered, and fields use industry-specific units. Configuration must define clear data parsing rules to prevent the model from misinterpreting parameter meanings. Production ledger aggregation needs to cover full-process, multi-batch historical data. The model's context window must hold sufficient information to avoid truncation of critical process or quality data.
How to set the configuration
In FastGPT v4.17.0, the following configuration items can be configured in the advanced settings module of model integration:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single-batch process and quality data for this category is large in volume. Multi-batch ledger aggregation requires sufficient historical information to prevent critical data from being truncated |
maxTokens | 4000–6000 token | A single production report must fully output full-process parameters and results to avoid forced truncation of generated content |
vision | Enabled | Need to process image-based quality records generated by surface inspection equipment to identify surface defect-related information |
temperature | 0.1–0.3 | Production ledgers and reports require consistent parameter accuracy, reducing randomness in generated content |
AIPROXY_API_ENDPOINT | Model service address deployed via Xinference | Connect to locally deployed model services to comply with enterprise data security requirements |
AIPROXY_API_TOKEN | Access key for the corresponding service | Verify access permissions for the model service to secure service calls |
Three common configuration errors
- The interface displays "No available models", and model integration cannot be completed. This occurs because
AIPROXY_API_TOKENis not filled correctly, or the Xinference-deployed model is not in an available state. FastGPT cannot identify valid models in these cases. - A re-ranker model test returns an error, while other configurations function normally. This occurs because dedicated call rules for the re-ranker model are not specified in the model integration configuration. FastGPT calls a base model that does not support re-ranking functionality.
- Some thickness or temperature data is missing from generated production reports. This occurs because the
maxContextconfiguration is not adjusted to an appropriate window size. Multi-batch historical data is truncated, so the model cannot access complete ledger information.
How to confirm successful configuration
- Access the model test page in FastGPT v4.17.0. Input single-batch process data for this category and corresponding surface inspection images. Verify that the model normally returns structured ledger content containing category-specific fields.
- Check the runtime logs of the Xinference service. Confirm that FastGPT's call requests successfully reach the server, with no connection errors or permission denial messages.
- Generate a simulated production report. Verify that the report includes category-specific documentation content such as rolling plans, heating schedules, and surface quality reports, with no missing or abnormal fields.
- Adjust the
temperatureparameter. Compare two generated reports, confirm that parameter changes match expected behavior, with no overly random process parameter outputs.
The values provided are common starting points and should be measured against the reader's own samples.