What Data Looks Like for This Category
Typical entities are liquid milk, yogurt, milk powder, and dairy ingredient enterprises. Data sources include milk source testing equipment, production MES systems, homogenization and heat treatment equipment, online temperature and flow monitoring systems, CIP cleaning systems, and LIMS laboratory information management systems. Update frequency is per milk tank, per batch, real-time. Daily documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, retained samples and microbial test reports. Document fields correspond to raw milk physical and chemical and microbial indicators, equipment operating parameters, cleaning verification data, and batch traceability information, with units compliant with food processing industry metrology specifications.
Constraints These Characteristics Impose on Tool Calling and Plugins
Production data for dairy processing has strong real-time performance, multi-source heterogeneity, and high batch correlation. Tool calling must match the update frequency of per milk tank and per batch to avoid auditing deviations caused by data lag. Multiple types of production documents have mixed formats, including structured tables and semi-structured equipment logs. Plugins are required to support cross-format field extraction and association. At the same time, the consequences of errors such as raw milk rejection and microbial over-standard are severe. Parameter verification via functionCall is needed to ensure indicators returned by tools comply with industry specifications, preventing invalid parameters from triggering abnormal processes.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to automatic tool calling requirements for multi-source production documents, no manual tool type specification needed |
| PDF parsing plugin enablement | Enable enhanced parsing mode | Most raw milk acceptance records and microbial test reports use PDF format, requiring extraction of structured indicator fields |
| Timeout threshold | 600 seconds | Match the response rhythm of on-site production equipment. An overly long threshold will block batch auditing processes |
| API concurrency limit | First 8 requests | Single batch production data volume is limited. Excessive concurrency will exceed on-site server and network load |
| Workflow/API function-call configuration | Enable parameter verification | Ensure indicators returned by tool calls comply with dairy processing industry specifications, avoiding invalid parameters |
Three Common Mistakes
- Symptom: The PDF enhanced parsing option on the plugin management page is grayed out and unselectable. Cause: No industry-specific document parsing template for dairy processing has been imported. The plugin only supports enabling with pre-configured industry templates.
- Symptom: After deploying version v4.14.8 via Docker, startup fails, with logs indicating plugin pull failure. Cause: No image source acceleration was configured during deployment. Access to the official repository timed out, causing pull failure.
- Symptom: External calls to the workflow API return empty results or time out. Cause: The concurrency limit was not adjusted based on production data update frequency. Excessive concurrency exceeds on-site network bandwidth limits.
How to Verify Successful Configuration
- Upload a standard raw milk acceptance record PDF, call the tool for parsing, and check whether structured fields such as antibiotic indicators and total bacterial count can be extracted.
- Run the FastGPT v4.17.0 plugin pull command in a local test environment, confirm there are no timeout or pull failure logs.
- Initiate an API call for single batch production data, check whether the returned field formats comply with dairy processing business specifications.
- View the workflow monitoring panel, confirm that the tool call response time does not exceed the preset 600-second threshold.
The values provided here are common starting points and should be measured against relevant operational samples.