What this category's data looks like
Data sources for this category include iron melt and scrap steel basic data, L2 process control real-time data, laboratory composition reports, MES system production data, energy medium system parameters, and equipment status monitoring data. Daily documents include heat plans, batching lists, converter smelting records, composition test reports, continuous casting process records, and slab quality judgment sheets. Each heat generates core records. Key parameters synchronize in real time. Teams summarize production ledgers per shift. Fields included are heat number, process temperature, composition test values, equipment operating status parameters, and others.
Constraints on model access and configuration
Data for this category is multi-source, heterogeneous, and has significantly different update rhythms. Core production data synchronizes in real time. Heat-level documents generate in batches. Ledger data summarizes per shift. Model access must adapt to multi-frequency data pulling logic, to avoid missing key real-time parameters or redundantly loading expired data. The strongly coupled production characteristics of heats and casting runs limit model context to the current heat and associated casting runs. This prevents cross-batch data from interfering with audit results. The high-temperature continuous process cannot shut down. Model calls must ensure low latency, and timeout configurations must match production cycles. Structured fields across multiple types of professional documents require accurate identification. Configure parsing rules adapted to iron and steel smelting professional terminology.
Configuration settings
The following configurations are based on the parameter logic of FastGPT v4.17.0, adapted to the inspection and process record audit requirements for iron and steel smelting and continuous casting scenarios.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of full-process documents for a single heat, avoiding context overflow that affects audit logic |
maxTokens | 4000 token | Limits the length of single-round generated content, adapting to the conciseness requirements of inspection and audit tasks |
temperature | 0.1–0.3 | Maintains result rigor for inspection and audit tasks, reducing generation randomness |
quoteMaxToken | 2000–3000 token | Retains key fragments of core process records and composition reports, ensuring sufficient audit basis |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address according to the deployment environment | Ensures data transmission security and access permission isolation in production environments |
AIPROXY_API_TOKEN | Bind a key string with exclusive permissions | Differentiates access permissions for production and test environments, preventing unauthorized calls |
Three common configuration mistakes
- The
<think></think>tags in model return content are empty. This occurs because parsing rules adapted to large model output formats are not configured, or the thinking process generation switch is not enabled during model calls. - No search results appear during testing after configuring an external search tool, and background connection errors occur. This occurs because the dedicated access address is not filled in correctly, or the proxy configuration does not adapt to the professional keyword filtering rules for iron and steel smelting scenarios.
- An upload failure error is triggered when uploading documents such as continuous casting process records and composition reports. This occurs because parsing rules matching document formats are not configured, or the file size exceeds the single upload limit allowed by the system.
How to verify a complete configuration
- Manually input the process parameters and document summary for a single heat, and check whether the audit results returned by the model cover core business fields.
- Simulate multi-frequency data pulling, verify that the model context only associates production data for the current heat and associated casting runs, and does not introduce irrelevant batch content.
- Trigger a tool call test, confirm that search results or file analysis results conform to the professional logic of iron and steel smelting scenarios.
- Check the system operation logs, confirm that the response duration of model calls matches the production cycle, and no frequent abnormal errors occur.
The values given are common starting points and should be measured against the reader's own samples.