What data for this category looks like
Dairy processing data comes from milk source testing, MES systems, homogenization and heat treatment equipment, online thermal flow sensors, CIP cleaning systems, and LIMS laboratory management systems. Daily documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling batch records, and reserved sample microbial test reports. Data updates in real time for each milk tank and each batch. Fields include tank number, batch number, test indicator values, and equipment operating parameters. Thermal flow parameters use units of ℃ and m³/h. Reserved sample microbial test reports include indicators such as total bacterial count.
What constraints these characteristics impose on tool calling and plugins
The real-time high-frequency data and strong batch-binding characteristics of dairy processing require tool calling to support low-latency data pulling. This avoids traceability delays caused by latency. The data structure updated by milk tank and batch requires plugins to associate tank numbers and batch numbers to complete data matching. General field-only retrieval cannot be used. The mixed call demand for multiple types of documents requires tools to automatically identify document types and map corresponding fields. The characteristic that liquid raw materials do not have batch-by-batch isolation buffers requires tool calling to trigger an alarm immediately when data anomalies occur. This prevents problem spread.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to the automatic matching needs of multiple types of tools such as raw milk acceptance, equipment operation, and cleaning records |
| Workflow/API function-call configuration | enabled | Support plugins to return structured batch and test data, facilitating subsequent associated analysis |
| Single tool call timeout | 25–35 seconds | Match the pulling delay requirements of real-time thermal flow and CIP parameters, avoiding blocking batch traceability processes |
| Maximum associated document count | Top 15 entries | Cover multiple types of documents associated with a single batch, avoiding exceeding context length limits |
| Data matching rule | Match by batch number + tank number | Align with the data structure characteristics of dairy processing updated by milk tank and batch |
| Exception trigger threshold | Calibrated based on actual testing | Adapt to the raw milk testing and equipment operation standards of different enterprises |
Three common mistakes
- Phenomenon: Tool calls return empty fields. Cause: Data is matched using general retrieval conditions instead of batch number + tank number. Test records for the corresponding milk tank cannot be located.
- Phenomenon: Tool call times out, returning 504 status code. Cause: The set timeout threshold is too low. It does not match the delay requirements of real-time data pulling in dairy processing, resulting in failure to complete pulling of real-time data such as thermal flow parameters.
- Phenomenon: Plugins cannot be found in the interface after deployment. Cause: The plugin file was not placed in the FastGPT-specified plugin directory, or the service was not restarted to load the plugin.
How to confirm the configuration is complete
- Use FastGPT v4.17.0, initiate a traceability request for raw milk acceptance anomalies, and check whether the tool automatically pulls the raw milk test records and ingredient lists for the corresponding batch.
- Simulate a scenario of equipment parameter anomalies, verify whether the tool associates the CIP cleaning records of the corresponding batch according to the configured matching rules.
- View the tool call log, confirm that the
toolChoiceandfunctionCallparameters have taken effect as configured, and there are no call timeout errors. - Upload a complete document package for a single batch, check whether the plugin can correctly map the batch number and tank number to complete data association.
The values provided are common starting points and should be measured against the reader's own samples.