What the data for this category looks like
The data for this category comes from slab or billet batches, heating furnaces, mill automatic control systems, online thickness and width measuring equipment, surface inspection devices, and manufacturing execution systems. Data is generated in real time per coil or individual steel material. Daily documents include rolling plans, heating schedules, rolling regulations, shape and thickness records, surface quality reports, and quality certificates. Core fields include rolling reduction, strip thickness, surface defect level, and batch number. Units include millimeters, micrometers, and defect classification items without grade identifiers. Full-process traceability at the coil level is supported.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering workflow
This category of data has strong real-time performance and is generated per coil or individual steel material. Multi-turn dialogue must bind batch numbers as context anchors to avoid confusion between process and inspection data from different batches. Daily document types are diverse and have obvious structural differences. Prompts must clearly distinguish the calling rules for different documents such as rolling plans and surface quality reports. Full-process traceability at the coil level requires that batch association information be retained throughout the dialogue, and context associations must not be lost. Data sources are scattered across multiple systems, so multi-turn dialogue must gradually guide users to clarify the specific source of required data to avoid calling mismatched fields.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind two variables: the current coil’s batch number and rolling batch ID, automatically include in every dialogue turn | This category requires coil-level data traceability to avoid cross-batch confusion |
messages | Retain the most recent 15-20 dialogue turns, calibrated via actual testing | A large volume of real-time generated data means overly long context will introduce redundant information and affect response accuracy |
chatId | Assign a unique chatId to the production session for each coil of steel | Achieve coil-level dialogue isolation, ensuring process and inspection data from different batches do not cross |
| Automatic variable loading | Load the rolling regulations and shape and thickness record data associated with the current batch | Daily documents for this category are generated per coil, and automatic loading can reduce manual input steps |
| Prompt template | Set up separate templates by document type: call rolling plan and heating schedule data for drafting process documents, call surface quality report and online inspection data for drafting exception reports | Business logic and field structures differ significantly across different document types |
| Context length limit | Set to 8000-12000 tokens, calibrated via actual testing | Single-coil data volume for this category is moderate, and overly long context will reduce response speed |
Three Common Misconfigurations
- Phenomenon: Uploaded files such as shape and thickness records and surface quality reports do not participate in dialogue generation, and the dialogue log shows that no corresponding document fields are included in the input parameters. Cause: The uploaded files are not bound to the
variablesvariable, causing the large language model to fail to obtain exclusive document data for this category. - Phenomenon: When calling multimodal functions to generate exception reports, a 400 invalid image error is returned, and the dialogue log shows that the image parameter format is abnormal. Cause: Image identifiers are not passed in accordance with the online inspection image association rules for this category, causing the model to fail to match surface inspection images corresponding to the batch.
- Phenomenon: The configured multi-turn dialogue workflow runs normally within FastGPT, but only responds to the first question after connecting to an external dialogue interface. Cause: The
chatIdparameter is not passed correctly, causing the external interface to fail to maintain coil-level session context and unable to continue the multi-turn dialogue logic.
How to Confirm Proper Configuration
- Launch a test dialogue, input a question corresponding to the business requirements, and check whether the reply includes the currently bound batch number and corresponding business data.
- View the dialogue log to confirm that the
variablesparameter carries relevant fields of the current batch, and themessagesparameter retains necessary context content. - Switch the session identifiers of different batches to confirm that dialogue data from different batches does not cross and confuse.
- Call the multimodal function to upload surface inspection images corresponding to the current batch, confirm that no format errors occur and the reply is associated with the inspection results of the corresponding batch.
- Confirm that the current FastGPT version is v4.17.0, and the configuration items are compatible with this version.
The values given are common starting points and should be measured against the reader's own samples.