What the Data for This Category Looks Like
This category covers daily business documents from the iron smelting, steelmaking, continuous casting, quality control, and scheduling departments of integrated iron and steel enterprises. Included documents are heat plans, batching lists, converter smelting records, composition reports, continuous casting process records, and slab judgment sheets. Data sources include hot metal and scrap steel data, L2 process control systems, laboratory test results, MES systems, energy medium systems, and equipment condition monitoring data. Three update frequencies are supported: per heat, real-time, and per shift. Data fields include industrial-specific fields such as heat number, composition content, temperature, and cast sequence number.
Constraints Imposed on Model Access and Configuration
The high real-time performance and multi-source heterogeneous data characteristics of this category create multiple constraints for model access configuration. Multi-source data has format differences across systems. Clear configuration parameters are required to unify data parsing logic across all systems. Per-heat and real-time update rhythms require the model context length to align with the information scope of a single business process. This prevents context overflow that causes response failures. Data fields include industrial-specific terminology. The model must accurately identify and associate corresponding process specification content. Process interruptions or defect transfers can result in significant losses. A stable model access link and error handling mechanism must be configured.
Configuration Settings
This document is based on FastGPT v4.17.0. The following are model access configuration recommendations for iron and steel smelting and continuous casting scenarios:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the complete business document length of a single heat or single cast sequence in iron and steel smelting and continuous casting, to avoid context overflow |
maxTokens | 2000–3000 token | Match the output length requirement of standard Q&A in industrial scenarios, to avoid overly long responses affecting real-time performance |
quoteMaxToken | 1500 token | Limit the length of referenced business document fragments, to ensure matching accuracy between retrieval results and Q&A |
temperature | 0.1–0.3 | Reduce random output, ensure the accuracy of process specification and technical standard Q&A, and meet the rigor requirements of industrial scenarios |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapt to access to third-party model service links, to ensure normal data transmission |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call tools, adapting to the requirements of multi-source data retrieval |
Three Common Configuration Errors
- Issue: The model returns a 401 unauthorized error after access. Cause: The
AIPROXY_API_TOKENparameter is not configured correctly, or the token does not have corresponding service permissions. - Issue: A visual model cannot process surface defect images of continuous casting billets after access. Cause: The
visionparameter is not enabled, or the image input format supported by the model is not matched. - Issue: The token consumption quantity of a single application cannot be counted. Cause: The token statistics module for the corresponding application is not enabled in the FastGPT backend, or the access link of the corresponding model is not associated.
How to Verify Correct Configuration
- Submit a standard heat plan document and corresponding process questions for testing, check whether the model's returned results include correct process parameters and standard requirements.
- View the model access logs, confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are error-free, and there are no connection timeout or authorization failure records. - Enable the token statistics function, verify that token consumption data is normally generated for each Q&A.
- Test the visual model access, upload a surface image of a continuous casting billet, check whether the model can correctly identify and associate the corresponding process standards.
The values provided are common starting points and should be measured against the reader's own samples.