What Data Looks Like for This Category
The data for this category comes from slab or billet batches, reheating furnaces, mill automation control systems, online thickness and width measuring equipment, surface inspection devices, and manufacturing execution systems (MES). Data is updated per coil or individual steel product, with some parameters synchronized in real time. Daily associated documents include rolling plans, heating schedules, rolling specifications, shape and thickness records, surface quality reports, and quality certificates. Data fields include batch number, thickness (millimeters), surface defect level, furnace temperature (degrees Celsius), and others. The structure changes dynamically based on rolling process nodes.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompting
This category’s data is updated per coil or individual product, with some synchronized in real time. This requires multi-turn dialogue to bind chatId to track the full-process data of the current steel coil, to avoid mixing up across batches. Daily associated documents include multiple types of process parameters, so prompts must accurately match specific fields such as rolling plans and surface inspection reports, and cannot be called generically. The timeliness of real-time data requires that the dialogue context be limited in rolling range, to avoid old data interfering with current audit judgments. The requirement for coil-level traceability requires that batch association information be retained in dialogue, to ensure that audit results can be mapped to specific steel coils.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current steel coil batch number | Track full-process audit data for a single steel coil, avoid mixing up across batches |
variables | Pass in batch number, thickness deviation (millimeters), surface defect level | Inject real-time inspection data into prompts, match business document fields |
| Context window size | 800–1200 characters | Accommodate multiple process documents and real-time inspection data for a single steel coil, avoid context overflow |
| Rerank trigger condition | Trigger when messages contain inspection fields | Prioritize sorting document fragments related to the current steel coil quality |
messages format | Arrange in the order of system prompt, user input, tool return | Align with the context parsing logic of multi-turn dialogue, ensure the model correctly understands the audit workflow |
| Timeout threshold | 300 seconds | Adapt to synchronization delays of real-time inspection data, avoid dialogue interruptions |
Three Common Misconfigurations
- Phenomenon: The rerank model call record is not displayed in the dialogue details page. Cause: No rerank-triggered context identifier is associated in
messages, causing the system to not synchronize relevant data for this process. - Phenomenon: Placeholders in the prompt are not replaced with actual business parameters, and returned content is unrelated to steel rolling quality audits. Cause: Corresponding fields are not passed via the
variablesparameter, or the passed field names do not match the prompt placeholders. - Phenomenon: After triggering a specified response in multi-turn dialogue, the system ends the session without waiting for new user input. Cause: No continuation trigger rule for
messagesis configured, causing the context to not retain the waiting state.
How to Confirm Configuration is Complete
- Enter the dialogue details page of FastGPT v4.17.0, check whether
messagescontain business variables such as the current steel coil batch number. - Initiate a simulated audit dialogue, enter test content including thickness deviation and surface defects, verify whether prompt placeholders are correctly replaced.
- Check the context window configuration, confirm that the rolling range adapts to the number of process documents for a single steel coil, avoid key data being truncated.
- After triggering the rerank process, check the dialogue log for relevant markers, confirm that the rerank logic is working properly.
The values provided are common starting points and should be measured against the reader's own samples.