What Data for This Category Looks Like
Data sources for this category include molten iron and scrap steel collection data, L2 process control data, laboratory test data, MES system data, energy medium system data, and equipment status data. Update cadence follows per heat, real-time synchronization, and shift-level summary. Daily documents include heat plans, batching lists, converter smelting records, composition test reports, continuous casting process records, and slab quality judgment sheets. Data fields include heat number, cast number, molten iron temperature, smelting endpoint parameters, continuous casting pulling speed, energy medium flow rate, and other relevant items. Corresponding units include degrees Celsius, meters per minute, cubic meters per hour, and other relevant units.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompting Workflows
The strong real-time performance and heat-bound nature of data in this category require multi-turn dialogue to anchor to the context of the current heat or cast, to avoid mixing cross-batch data. Differences in update frequencies among multi-source heterogeneous data require prompts to clearly distinguish calling rules for real-time collected parameters and shift-level summary reports, to prevent data timeliness deviations. The strong coupling relationship between heats and casts requires the dialogue flow to synchronously associate documents from steelmaking and continuous casting links, to ensure audit logic covers the entire process. Professional unit requirements for data fields require prompts to include built-in unit verification rules, to avoid parameter matching errors. The characteristics of high-temperature continuous processes also require dialogue to control loading duration, to prevent process interruptions caused by waiting timeouts.
Configuration Settings
This page is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Fill in the current heat number | Bind business context for a single heat to avoid mixing data from multiple heats |
messages | Store conversation history grouped by heat or cast | Match the heat-based update characteristic of data in this category, to ensure coherent dialogue logic |
variables | Fill in the current heat’s molten iron temperature, smelting endpoint parameters, retain units such as degrees Celsius, tons, etc. | Provide real-time business data support for prompts |
| Maximum context length | 800–1200 characters | Adapt to the average length of documents for a single heat, to avoid loading timeouts |
| Document filtering rules | Match associated documents by heat number | Precisely recall relevant data for the current operation, to improve audit efficiency |
| Timeout threshold | 600 seconds | Adapt to the time consumption requirements of multi-source heterogeneous data retrieval |
Three Common Mistakes
- Symptom: A 503 error is displayed when uploading documents on the dialogue page, while background logs indicate the file upload was successful. Cause: Uploaded documents are not stored grouped by heat, causing the system to fail to match the current operation’s context during recall, triggering a resource overload error.
- Symptom: Professional parameter units cannot be correctly identified during dialogue. Cause: Prompts do not include built-in unit verification rules for data in this category, leading to deviations in parameter matching.
- Symptom: Historical data from a different heat is called during multi-turn dialogue. Cause:
chatIdis not correctly configured to bind the current heat number, causing context isolation to fail and mixing business data from unrelated batches.
How to Verify Proper Configuration
- Verify that
chatIdmatches the current operation’s heat number, to ensure correct context binding. - Check that business parameters filled in
variablesmatch the real-time data of the current heat. - Confirm that document filtering rules only recall relevant documents for the current heat, with no cross-batch data mixed in.
- Test dialogue timeout scenarios, to confirm the timeout threshold adapts to the actual time consumption of current data retrieval.
The values provided are common starting points and should be evaluated against samples from the specific deployment.