This document is written based on FastGPT v4.17.0.
What the data for this category looks like
Data for this category primarily comes from animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory testing. Daily documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature logs, and microbial test reports. Data updates occur per batch or per shift. Cold chain temperature data is collected continuously. Data fields include batch number, quarantine number, processing temperature, test results, and similar items. Common units include batch, hour, and degree Celsius.
Constraints imposed by these characteristics on multi-turn dialogues and prompt engineering
The requirement that batches be tied to quarantine information means multi-turn dialogues must use unique identifiers to avoid context confusion. Continuous temperature control and real-time updates require that batch-related variable data be maintained across sessions to prevent state loss. The diversity of professional documents requires that prompt logic be optimized for specific documents such as quarantine certificates and temperature records. The single-batch or single-shift data granularity requires that the context window not be overly large, to avoid mixing historical data from unrelated batches.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the current production batch number | Ensure multi-turn dialogues are associated with quarantine and production data for the corresponding batch, avoiding context confusion |
messages | Limit the context window to the first 10 historical dialogues | Match the per-batch or per-shift data update rhythm, preventing context overload |
variables | Store the current batch’s quarantine number and cold chain temperature thresholds | Maintain batch-related associated data across sessions, meeting the continuous temperature control update requirements |
| Maximum context length | 800–1200 characters | Adapt to the average length of single-batch documents, avoiding exceeding processing limits |
| Question optimization | Calibrate based on actual testing | Optimize recognition logic for professional documents such as quarantine certificates and temperature records |
| Single match limit | Top 5 documents | Match the batch granularity of data sources, reducing interference from irrelevant information |
Three Common Misconfigurations
- Phenomenon: Low accuracy of the question classification model when identifying quarantine certificates and temperature records. Cause: Prompt logic was not adjusted for professional documents related to meat product slaughter and processing, and general industry classification logic was used instead.
- Phenomenon: Global variables in multi-turn sessions do not retain the current batch’s quarantine information. Cause:
variableswere not bound to the currentchatId, leading to data confusion across sessions. - Phenomenon: Uploaded file interfaces are not correctly associated with the current batch after being called. Cause: No batch-specific identification parameter was included in the request, resulting in a disconnect between file data and the audit workflow.
How to Verify Correct Configuration
- Initiate a first-round dialogue containing a specific production batch number, and confirm that
chatIdis bound to the basic data of the corresponding batch. - Submit three consecutive questions associated with the same batch, and confirm that the quarantine number and temperature threshold in
variablesremain consistent. - Upload a single batch’s quarantine certificate document, and confirm that the matched documents returned by the interface only include relevant records for that batch.
- Trigger the question classification logic, and confirm that the identified classification matches the business scenario of the current dialogue.
The values provided are common starting points and should be measured against the reader's own samples.