What the Data for This Category Looks Like
This category’s data covers daily business documents from slaughterhouses, cooked meat and frozen prepared food enterprises. Documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data sources include animal quarantine information, supplier batch ledgers, production traceability systems, real-time temperature collection equipment, metal detection equipment, and laboratory test results. Production records are updated per production batch and per shift. Cold chain temperature records are updated via continuous collection. Documents include fixed fields such as batch number, quarantine number, temperature parameters, and test indicators.
Constraints Imposed on Multi-Turn Dialogue and Prompt Engineering
This category’s data is updated frequently per production batch and shift, and is linked to multiple types of documents such as quarantine, temperature control, and test records. Multi-turn dialogue must bind to the context of a specific production batch to avoid mixing data across batches. Data includes time-sequential cold chain temperature records. Multi-turn dialogue must organize information by time dimension to ensure accurate temperature threshold judgments. Documents include unique business fields. Prompts must explicitly specify extraction of corresponding fields to avoid confusion with data from other categories. Real-time updated production records require dialogue to call the latest data. Context cache expiration time must be limited to prevent use of outdated batch information.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique number of the current production batch | Ensure multi-turn dialogue context only links to documents for the corresponding batch, avoiding cross-batch data confusion |
messages | Retain the most recent 20 dialogues and document citations | Cover review needs for the full production process of a single batch, while controlling context volume |
variables | Inject the current batch’s quarantine number and production shift | Allow prompts to accurately match exclusive document data for this batch |
| Single search limit | Call the top 8 relevant documents | Match the retrieval needs for multiple types of linked documents in this category, avoiding missing key records |
| Maximum context length | 8000–12000 token | Adapt to the field and content length of multiple document types, meeting context needs for full-batch review |
| Platform Version | FastGPT v4.17.0 | Ensure configuration items are compatible with the current platform version |
Three Common Misconfigurations
- Symptom: Long dialogue response latency. Cause: No limit on the number of documents retrieved per search, loading excessive non-associated historical batch data.
- Symptom:
Unexpected end of JSON inputerror. Cause: Incorrect configuration ofvariablesinjection of batch-specific parameters, leading to malformed context data passed during model calls. - Symptom: New session must be created for each dialogue, unable to continue previous review context. Cause: No fixed
chatIdbound to the current production batch, leading to new session IDs generated for each dialogue, with context unable to be reused.
How to Verify Proper Configuration
- Initiate a review dialogue for a specific production batch, confirm that context automatically links to quarantine, temperature control, and other documents for that batch.
- Enter a query that includes a batch number, confirm that returned results include exclusive field data for the corresponding batch.
- Launch multiple consecutive dialogues, confirm that
chatIdremains consistent and context is not reset. - Trigger a model call, confirm that no format error-related errors appear.
The values provided are common starting points and should be measured against the reader's own samples.