What the data for this category looks like
Data sources include animal quarantine information, supplier batches, production traceability records, temperature collection data, metal detection results, and laboratory microbial test reports. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, and cold chain temperature logs. Data updates trigger per batch, per shift, or via continuous temperature control events. Fields include batch number, quarantine ID, temperature value, test results, and timestamp. Units involve degrees Celsius, batch numbers, and qualified inspection marks, among others.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data updates per batch or shift, and includes biological risk-related quarantine and temperature control records. Multi-turn dialogue must fix association with the current production batch’s context to avoid cross-batch data confusion. Prompts must explicitly limit calls to only that batch’s quarantine, temperature, and test documents, to prevent mixing unrelated batch data. High-frequency updates of continuous temperature control data require dialogue contexts to quickly load the latest temperature records. They also require limiting the volume of data loaded per dialogue turn to avoid timeouts. Prompts must clearly distinguish partitioned data for raw and cooked processing, to prevent cross-contamination-related records from being mixed up.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current production batch | Ensure multi-turn dialogue only associates with ledger data for this batch, preventing cross-batch confusion |
messages | Keep only up to 30 dialogue history entries from the current shift or batch | Control context load volume, adapt to high-frequency updated temperature and test data, avoid timeouts |
variables | Pass three core variables: batch number, quarantine ID, processing time window | Allow prompts to accurately locate documents for the corresponding production stage, narrow retrieval scope |
| Maximum context | 800–1200 characters | Match the average data length of single-batch ledgers for this category, avoid loading redundant information |
| Single search limit | Top 3 results | Prioritize matching the most relevant quarantine, temperature, and test records, reduce irrelevant data interference |
| Query optimization | Enabled | Convert colloquial user queries into precise retrieval instructions for batch ledgers, improve matching accuracy |
Three Common Mistakes
- Symptom: Quarantine certificate images returned in dialogue are incomplete. Cause: The prompt does not explicitly specify the complete field name and storage path for images, leading to only partial thumbnail data being loaded.
- Symptom: Multi-turn dialogue fails to respond within 10 minutes. Cause: The number of loaded
messagesentries is not restricted, resulting in excessive ledger data from historical batches being loaded, exceeding system processing limits. - Symptom: Rule display symbols appear at the end of model response outputs. Cause: The prompt does not explicitly require hiding system preset rule descriptions, leading the model to include configured trace rule content in its output.
How to Verify Proper Configuration
- Enter a query for the current production batch, check that returned ledger data only includes quarantine, temperature, and test records for this batch.
- Submit the same query five consecutive times, confirm that response times stay within a reasonable range with no noticeable timeouts.
- Check that no extra rule display symbols appear at the end of returned results, confirm that the prompt configuration is active.
- Upload ledger data for a test batch, confirm that dialogue correctly associates with the batch number variable passed in
variables. - Confirm that FastGPT v4.17.0 is being used, ensure configuration items are compatible with the current system version.
The values provided here are common starting points, and should be evaluated against operational samples.