What the data for this category looks like
Business data for steel rolling and finishing originates from slab or billet batches, heating furnaces, mill automatic control systems, online thickness and width measurement equipment, surface inspection systems, and MES systems. Daily generated documents include rolling plans, heating schedules, rolling specifications, plate shape and thickness records, surface quality reports, and quality certificates. These documents contain fields such as batch identifiers, temperature parameters, thickness tolerances, and surface defect levels. Units include degrees Celsius, millimeters, percentages, and other standard units. Data is generated in real time per coil or individual steel billet. Single-batch data volume changes dynamically with production pace.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
This category’s data updates in real time and is tied to specific batches. Multi-turn dialogue must strictly isolate context across different batches to prevent expired data from interfering with current queries. Document types are diverse and fields are highly specialized. Prompts must clearly distinguish usage scenarios for different documents to avoid mixing parameters from heating schedules and surface quality reports. Single-batch data volume changes dynamically. The multi-turn dialogue context window must adapt to document content of varying lengths, while avoiding response delays caused by overly long contexts. Additionally, errors in rolling data have severe consequences. Prompts must add batch consistency check logic to ensure queries only use valid documents for the current batch.
How to set up configurations
This page is written for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the steel coil batch | Ensure dialogue context isolation for each rolling data batch, prevent cross-batch data mixing |
messages | Retain the most recent 15–20 turns of dialogue | Rolling data updates frequently; excessive historical dialogue will introduce incorrect data from expired batches |
variables | Predefine rolling batch number, inspection time, defect level variables | Match core business fields of steel rolling and finishing, quickly locate corresponding documents |
| Query Optimization | Enable | Helps convert engineers’ colloquial queries into precise instructions that match rolling document fields |
| Maximum Context | 8000–12000 token | Balance context completeness and response speed for multi-turn dialogue, adapt to content length of multiple rolling specifications and inspection reports |
Three common configuration mistakes
- Phenomenon: After running the interface call workflow, the running data in the dialogue log is empty. Cause: The batch data field returned by the workflow is not bound in
variables, so the dialogue context cannot capture running parameters. - Phenomenon: Cross-batch rolling data mixing occurs in multi-turn dialogue. Cause: The unique identifier of the steel coil batch is not configured as
chatId, leading to cross-over of dialogue contexts across different batches. - Phenomenon: The system prompts that the code block is undefined during text content extraction. Cause: A variable name not preconfigured via
variablesis used in the prompt, so the system cannot recognize the variable content.
How to confirm the configuration is complete
- Initiate a query targeting a specified steel coil batch, verify that the dialogue context only includes documents such as rolling plans and inspection reports for that batch.
- Modify the
variablespreset fields, verify that the dialogue automatically matches the query intent for the corresponding business fields. - After calling the interface, check whether the dialogue log captures the running data returned by the workflow.
- Test continuous multi-turn queries, confirm that the context does not introduce incorrect data from expired batches.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.