Cross-Review of Technical Documentation for Iron and Steel Smelting and Continuous Casting: Model Access and Configuration

What the data for this category looks like

Data for this category comes from hot metal and scrap data, L2 process control systems, laboratory testing systems, manufacturing execution systems, energy medium systems, and equipment condition monitoring systems. A heat plan, batching list, converter smelting record, and composition report are generated per heat. L2 process data is synced in real time. Energy medium and equipment condition data are summarized per shift. Most documents are structured forms. They include numeric parameters and text-based judgment results. Fields cover hot metal composition, smelting end point temperature, continuous casting pulling speed, slab defect type, and more. Data formats are unified and highly correlated.

What constraints these characteristics impose on the model access and configuration link

Multiple data sources from different systems and real-time update rhythms require model access to adapt to unified calls of multi-source data. A proxy service must be used to uniformly manage model keys and addresses. Multiple documents associated with a single heat and casting sequence have a large total length. Context configuration must cover sufficient content, while controlling the output length of single-round inference to avoid exceeding model limits. Data includes structured parameters and text judgment results. Some records include equipment status images. Configuration must support multimodal data processing. Technical review results are directly linked to production safety. Model output must maintain high consistency to reduce inference randomness.

How to set the configuration

This page is written based on the FastGPT v4.17.0 configuration logic.

Three common configuration errors

• The phenomenon is that model calls return 404 body not found. The cause is incorrect AIPROXY_API_ENDPOINT configuration, which does not point to the correct proxy service address.
• The phenomenon is that the multimodal model cannot recognize equipment images in process records. The cause is that the vision configuration item is not enabled, so the multimodal data processing function does not take effect.
• The phenomenon is random deviations in review results, with inconsistent review results for the same document across multiple runs. The cause is that the temperature value exceeds the reasonable range of 0.1–0.3, leading to excessive inference randomness.

How to verify successful configuration

• Upload a single converter smelting record, initiate a model call, and check if the returned result includes accurate verification conclusions for core fields such as composition and temperature.
• View the model call log to confirm that the request parameters for AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN match the configured values.
• Upload a continuous casting process record that includes equipment images, verify that the model can normally recognize parameter values in the image without error prompts.
• Adjust maxContext to a value exceeding the configured limit, trigger a content truncation prompt, and confirm that the context configuration takes effect.

The values provided are common starting points and should be measured against the reader's own samples.