What data for this category looks like
This category’s data originates from water source and raw material testing, formula systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data updates occur per production batch, per shift inspection, or via continuous collection. Document fields include batch number, sugar content value, acidity value, filling liquid level threshold, sealing pressure parameters, packaging material batch number, and shelf life. Corresponding units include mg/L, %, kPa, ml, and other standard units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering Workflows
Data for this category is sourced from multiple systems and updated by production batch. Multi-turn dialogue must continuously track context for the current production batch to avoid mixing up process parameters from different batches. Data includes abnormal images generated by visual inspection systems. Support for image-based dialogue is required to identify abnormalities such as filling liquid level deviations and sealing defects. Document fields use specialized units and specific parameter thresholds. Prompts must explicitly require generated outputs to match required field formats and units. Exception reports must link to records from specific production links. Multi-turn dialogue must guide step-by-step input of production links, abnormal phenomena, and corresponding detection data.
Configuration Settings
These configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Max Context | Previous 10 turns of dialogue + last 8000 characters | Matches the process data volume of a single production batch for this category, prevents context overflow |
chatId | Bind to the unique identifier of the current production batch | Ensures multi-turn dialogue always links to process and exception data for the corresponding batch |
messages | Concatenate historical dialogue in the order of production links | Maintains logical consistency for process parameter deduction |
variables | Preset variables for current batch water source type and packaging type | Reduces repeated input and improves prompt accuracy |
| Single exception identification limit | Top 5 visual inspection exception records | Focuses on core exceptions for the current batch, avoids information overload |
Three Common Configuration Mistakes
- When calling a multimodal model to process filling visual exception images, return format errors occur. The cause is failing to explicitly configure supported image formats, and not specifying in the prompt that eligible image files must be uploaded.
- Online dialogue experiences prolonged unresponsiveness. The cause is failing to limit the cumulative length of
messages, causing the context to exceed the system's processing limit. - Generated process document fields lack units or do not meet preset formats. The cause is not presetting field rules via
variables, only relying on general prompts for guidance.
How to Verify Successful Configuration
- Initiate a test dialogue, upload an eligible filling exception image, confirm the model can identify the image content and link it to the current batch's process data.
- Check the batch identifier bound to
chatId, confirm that the context information for this batch is always retained in multi-turn dialogue. - Generate an exception report, verify that the fields in the report include preset units and format requirements.
- Test more than 10 consecutive turns of dialogue, confirm the system does not exhibit unresponsive behavior or context loss.
The values provided here are common starting points and should be evaluated against applicable on-site samples.