What Data Looks Like for This Category
The primary data subjects for this category are slaughterhouses, cooked meat and frozen prepared food enterprises. Daily 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 batches, production traceability, temperature collection, metal detection, and laboratory test results. Updates occur per production batch and per shift operation; cold chain temperature records are updated via continuous collection. Data fields include batch numbers, quarantine identifiers, temperature values, test indicators, and some fields have fixed-format codes and units.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Data for this category is updated per batch and via continuous collection. Multi-turn dialogue must bind a unique identifier to associate context for the corresponding batch, to avoid mixing data across batches. Documents contain highly compliant quarantine, temperature, and test content. Prompt engineering must clearly limit the call scope, only using production and compliance documents for this category. Cold chain temperature is continuous time-series data. Multi-turn dialogue must support retrieving relevant records by time interval, while limiting the number of documents called in a single request to avoid interference from redundant information. Raw and cooked product zoning and sterilization temperature are core control points. Prompt engineering must prioritize associating records from corresponding links, to ensure responses focus on process compliance requirements.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to production batch number | Ensure multi-turn dialogue context associates with quarantine and production records for the corresponding batch, avoiding mixed use of data across batches |
messages | Split context by production link | Organize conversation history by links such as slaughtering, marinating, and thermal processing, to improve prompt matching accuracy |
| Single search limit | Top 3 entries | Match the number of core documents per batch for this category, reducing interference from redundant information |
| Maximum context length | 800–1200 characters | Adapt to the total length of core documents per batch for this category, avoiding exceeding model processing limits |
variables | Inject current batch number and quarantine status | Allow prompts to automatically associate exclusive data for the current processing batch, improving response accuracy |
| Question optimization | Calibrate based on actual testing | Adapt to the expression habits of process terminology for this category, optimizing recall accuracy for user questions |
Three Common Misconfigurations
- Phenomenon: Calling the dialogue interface returns results not associated with knowledge base content. Cause: The current batch's knowledge base retrieval context was not injected into
messages, causing the prompt to not trigger knowledge base calls. - Phenomenon: Conversation history records are empty. Cause: The
chatIdparameter was not bound correctly, so the system cannot associate and store the conversation with the corresponding batch's session. - Phenomenon: Interface calls return timeouts. Cause: The single search limit was set too high, loading too many cross-batch cold chain temperature records, exceeding the interface response time limit.
How to Confirm Configurations Are Set Correctly
This verification process applies to FastGPT v4.17.0.
- Initiate an HTTP dialogue request with
chatIdset to the current production batch number; verify that returned results associate with exclusive data for the corresponding batch. - Check conversation history storage, confirm that the
messagesparameter fully records context content from multi-turn interactions. - Adjust the prompt to inject the
variablesparameter; verify that responses automatically include quarantine or production information for the current batch. - Trigger continuous multi-turn questions; confirm that irrelevant document content across batches does not appear in the context.
The values provided here are common starting points, and should be evaluated against the reader's own samples.