What this category's data looks like
Data sources include animal quarantine information, supplier batches, production traceability, temperature collection, metal detection, and laboratory test results. Daily documents cover quarantine certificates, slaughter and cutting records, ingredient marinating records, heat processing records, cold chain temperature records, and microbial test reports. Data is updated per production batch and each shift. Cold chain temperature data is collected continuously. Fields include batch number, quarantine number, temperature value (℃), test results, timestamp, operator identifier, and other relevant fields. Some records require association with biosafety level identifiers.
Constraints Imposed on Tool Calling and Plugins
Maintaining the correspondence between raw material batches and quarantine certificates requires tool calling to ensure same-batch data processing uses the same conversation context, preventing information fragmentation. High-frequency cold chain temperature data requires controlling data volume per call and request interval to avoid interface overload. Strict control of raw/cooked zoning and sterilization temperature requires tool calling to validate field legitimacy, preventing incorrect data from entering the summary process. Severe consequences of errors caused by biological risks require configuring failure retry and exception capture mechanisms, preventing unqualified raw materials from entering subsequent links.
Configuration Settings
These configurations are adapted for FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | "function" | Force calling the preset ledger processing tool to avoid free generation deviating from business requirements |
| Workflow/API function-call configuration | Bind to the dedicated meat product ledger parsing function | Ensure calling logic matches the category's quarantine, traceability and temperature control requirements |
| Conversation context duration | 3600 seconds | Cover the full cycle of a single batch production, ensure same-batch ledger calls are processed in the same conversation |
| API request timeout | 15–30 seconds | Adapt to transmission delays of high-frequency collected data such as cold chain temperatures, avoid timeout interruptions |
| Batch call batch size | First 10 entries | Control data volume per call, match the reasonable load of the system's single processing |
| Field validation rules | Associate batch number by quarantine number | Match the category's traceability requirements, ensure correct data association logic |
Three Common Misconfigurations
- Phenomenon: Multiple API calls fail to associate the same batch production ledger, and returned results have empty batch numbers. Cause: No conversation context binding is configured, each call generates an independent conversation, and batch association information cannot be retained.
- Phenomenon: The HTTP request component does not carry the preset temperature value parameter, and a parameter error prompt is returned. Cause: The ledger field is not bound to the request parameter in the plugin configuration, causing collected values to not be passed correctly.
- Phenomenon: Calls for temperature control data lasting more than 10 consecutive minutes return a 504 status code. Cause: A single call loads historical temperature records exceeding the system's processing limit, exceeding the response threshold.
How to Verify Correct Configuration
Initiate three ledger calls for the same batch production, review returned results to confirm association with the same quarantine number and batch number. Simulate submitting a cold chain temperature record, review HTTP request logs to confirm that the preset temperature value and batch parameters are carried. Load 10 consecutive temperature control records to initiate a call, confirm that the request does not trigger a timeout error. Review tool call logs to confirm that each call triggers the preset meat product ledger processing function, and no free-generated content appears.
The values provided are common starting points and should be evaluated against applicable operational samples.