What Data Looks Like for This Category
Data sources for this category include animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory test results. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Fields include batch number, quarantine number, temperature value (℃), detection time, production shift, and more. Data updates are performed per production batch and shift. Cold chain temperature records are updated via continuous collection.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data in this category is linked by batch and shift, and includes biosecurity-related quarantine and temperature control requirements. Therefore, multi-turn dialogue must bind context for the corresponding batch to avoid mixing data across batches. Continuous collected temperature control data has a large volume, so the length of dialogue context must be limited to prevent exceeding the model's processing limit. Equipment inspection and maintenance must comply with raw/cooked separation and quarantine compliance requirements. Prompt engineering must preset compliance verification logic to ensure returned maintenance recommendations meet category safety standards.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
chatId | Bound to production batch | Links historical document data such as quarantine and temperature control for the corresponding batch |
messages | Retain the first 10 historical conversations | Controls context length to avoid interfering with judgments in equipment maintenance scenarios |
variables | Preset batch number and workshop number variables | Quickly matches equipment inspection records for the corresponding shift |
| Maximum context | 8000–12000 token | Accommodates complete business data such as temperature control and detection across multiple batches |
| Question optimization | Calibrated based on actual testing | Adapts to colloquial transcription of equipment terminology to improve Q&A accuracy |
This page’s configuration settings are based on FastGPT v4.17.0.
Three Common Mistakes
- Phenomenon: Batch field is empty after exporting dialogue history. Cause: Failed to correctly bind the batch variable in
variables, and failed to link business documents for the corresponding batch. - Phenomenon: 504 timeout error is returned when calling the model. Cause: Did not limit the number of retained
messages, total context length exceeds system limits. - Phenomenon: Parsing fails after uploading cold chain temperature record files. Cause: Did not configure document parsing rules for the corresponding CSV format, unable to extract temperature value and detection time fields.
How to Verify Proper Configuration
- Initiate a test dialogue, enter the equipment inspection question for the corresponding batch, confirm if the returned result links quarantine and temperature control information for the current batch.
- Export dialogue history, check that preset fields including batch number and detection time are present.
- Upload a cold chain temperature record file, confirm that temperature value, collection time and other corresponding content can be extracted after parsing.
- Switch production batches, verify that
chatIdupdates automatically to ensure data from different batches is not mixed.
The values provided are common starting points and should be evaluated against individual deployment samples.