What the Data for This Category Looks Like
Data for this category comes from formula systems, raw material batch ledgers, internal mixers, Mooney viscometers, vulcanizing machines, and laboratory testing equipment. Daily generated documents include formula sheets, weighing and feeding records, mixing process curves, rubber compound quick test reports, vulcanization process cards, mold usage records, and finished product performance test reports. Data is generated for each truckload raw material batch, each production batch, and each mold-run product output. Fields include material codes, batch numbers, temperature parameters, time parameters, test values, and operator identifiers. Units include degrees Celsius, minutes, MPa, and other standard units.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data is updated frequently, and a single conversation may link to historical data from multiple batches and mold runs. Dialogue contexts must accurately match document fragments corresponding to the target batch. There are large differences in fields across multiple document categories. Prompts must clearly specify the type range of retrieved documents to avoid confusing parameters from formula sheets and vulcanization process cards. Records for each rubber batch and production batch are strongly correlated. Multi-turn dialogue must retain the chatId to associate with the current conversation context, ensuring historical record lookups do not cross batches. Laboratory test data and production equipment data must be associated separately. Prompts must distinguish query logic for production process parameters and finished product test results.
Configuration Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding rule | Bind to the unique identifier of the current production batch | Ensure multi-turn dialogue only associates documents and production records for the corresponding batch |
messages context truncation threshold | Retain the first 20 conversation messages | Control computing resource consumption per conversation, avoid delays caused by overly long contexts |
variables preset configuration | Preset batch number, mold number, and equipment model variables | Quickly locate document data for the target production scenario |
| Context retention duration | Calibrated based on actual testing, range 7–30 days | Match the production cycle of this category, avoid mixing data across batches |
| Retrieved document range restriction | Restrict to formula sheets, mixing curves, and vulcanization process cards | Focus on document types required for core review in this category |
| Conversation timeout duration | 600 seconds | Adapt to multi-turn query scenarios with complex process parameters, avoid mid-conversation interruptions |
Three Common Misconfigurations
- Symptom: Conversation sessions cannot retrieve historical records older than 30 days, with a prompt stating the records do not exist. Cause: The context retention duration is set shorter than the production cycle of the target batch, causing data associated with the old batch’s
chatIdto be automatically cleaned. - Symptom: Single conversation response time exceeds 10 seconds, with page loading lag. Cause: The retrieved document range is not restricted, so the system searches all irrelevant laboratory reports and mold records, leading to context overload.
- Symptom: Audit suggestions generated by the dialogue mix up weighing records from different batches. Cause: The
chatIdis not bound to the current production batch, so the conversation context associates historical data across batches.
How to Verify Proper Configuration
- Initiate a query for the current production batch, verify that the conversation context only returns formula sheets and mixing curves for the corresponding batch, with no cross-batch data.
- Initiate a query involving multiple sequential parameters, verify that response time aligns with the production scenario’s operating rhythm, with no noticeable lag.
- Check whether the session’s
chatIdmatches the unique identifier of the current production batch, to confirm correct context binding. - Test calls to the preset
variables, verify that the system can automatically associate test data for the corresponding batch.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.