What Data for This Category Includes
This category covers full-process production links for carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. 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 sources include water source and raw material testing data, formula system data, online sugar and acidity monitoring data, filling equipment operation data, visual inspection data, and LIMS system data. Updates are generated per batch, per shift, or via continuous collection. Single-batch data includes multi-dimensional fields such as raw material indicators, equipment operation parameters, and inspection results.
Constraints Imposed by Data Characteristics on Multi-turn Dialogue and Prompt Engineering
Multi-turn dialogue and prompt engineering configurations for this category must align with constraints derived from its data characteristics. First, data is updated continuously per batch and shift. Dialogue context must limit valid time ranges and turn counts to avoid introducing expired or irrelevant historical data. Second, data sources are scattered and have diverse dimensions. Prompts must explicitly specify field ranges for corresponding data sources to avoid confusing parameters from different production links. Third, single-batch data volume is large, and errors carry serious consequences. Multi-turn dialogue must gradually guide users to focus on specific equipment and batches, while verifying whether parameters meet preset standards, to reduce recall risks caused by abnormal feeding or equipment failures.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 token | Matches the number of fields and update frequency of single-batch data to avoid context overload |
| Historical Dialogue Turn Limit | Up to 10 conversation turns | Limits invalid historical information and focuses on the current equipment inspection and maintenance scenario |
| Variable Binding Rule | Bind variables to the current batch’s formula ID and equipment number | Accurately associates exclusive data such as formula sheets and water treatment records for the corresponding batch |
| Multi-turn Follow-up Trigger Condition | Automatically trigger when missing parameters are detected | Guides users to supplement key information such as equipment model and operating duration |
| Prompt Template Priority | Match exclusive templates by product type | Adapts to differences in equipment inspection logic for different product types such as carbonated drinks and juice |
Three Common Misconfigurations
- Symptom: Conversation output does not display the large model’s thought process. Cause: The corresponding configuration switch was not enabled in FastGPT v4.17.0.
- Symptom: Empty values are returned when calling workflows concurrently. Cause: Workflow run times and batch input length were not limited, exceeding system carrying thresholds.
- Symptom: Output results overlay content from previous rounds of variables. Cause:
messagesandvariablesparameters were not properly isolated, leading to residual old data in the context.
How to Confirm Configurations Are Correct
- Initiate a single-batch equipment inspection dialogue, verify that the context only includes relevant documents and parameters for the current batch.
- Run 2–3 concurrent invocation tests, confirm that returned results have no empty values or abnormal overlays.
- Trigger a missing parameter scenario, verify that the automatic follow-up logic works correctly and supplements necessary information.
- Check system logs, confirm that
chatId,messages, andvariablesparameters are passed correctly per configuration.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.