What data looks like for this category
Data for this category primarily comes from raw material qualification certificates, ERP/MES systems, weighing equipment, ovens/frying lines, laboratories, and food trace ledgers from bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data is updated per batch, per shift, or daily. Fields include allergen material lists, baking/frying window parameters, foreign object detection results, packaging dates, shelf life, and more.
What constraints these characteristics impose on tool calling and plugins
This category requires rapid line changeover across multiple batches. Tool calling must support quick switching between formulas and production record data sources for different products. High-frequency allergen control and cleaning changeover requirements mean tools must accurately extract allergen material lists and disinfection record fields from documents. Data from multiple sources—ERP, device logs, laboratory reports—has heterogeneous formats. Plugins must adapt to parsing different data formats. Data is updated per batch or per shift. Tool calling must pull the latest production and inspection data in real time to ensure audit timeliness. Errors may lead to food recalls. Basic verification must be applied to tool calling results to avoid missing key fields.
How to configure settings
The following configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API function-call configuration | Force enabled | This category of audits requires accurate extraction of specific fields such as allergens and production parameters. Forcing it ensures the stability of tool calling |
| Workflow/API tool selection | auto | Supports automatically matching parsing tools for corresponding documents based on audit tasks, adapting to rapid line changeover needs |
| Number of items returned per single tool call | First 3 items | The number of documents involved in a single batch audit is limited. Limiting the number of items avoids context overload |
| Tool call timeout | 600 seconds | Data from multiple device logs and laboratory reports must be integrated. A longer timeout ensures complete data |
| Maximum context length | 8000 token | Adapts to the content length after integrating multiple documents, ensuring complete audit information |
| Tool result verification rules | Calibrated based on actual testing | Document formats vary across different products. Verification logic must be adjusted based on actual scenarios |
Three common mistakes
- Phenomenon: An error of "Cannot read properties of undefined (reading)" is returned when calling a tool. Cause: Basic link parameters for tool calling are not configured correctly, preventing tool requests from being initiated normally.
- Phenomenon: The results returned by tool calling do not include the allergen material list field. Cause: Corresponding field extraction rules are not configured for the document structure of this category, leading to failure to recognize key information.
- Phenomenon: The deployed LLM cannot trigger the
functionCallfunction. Cause: The tool calling support switch for the LLM is not enabled, or the proxy configuration does not adapt to the tool calling protocol.
How to confirm correct configuration
- Manually upload a production record document for this category, trigger tool calling, and verify that returned results include preset key fields.
- Configure a test single-batch audit task, and confirm that tool call return duration matches the preset timeout setting.
- Switch formula documents for different products, and verify that
toolChoiceautomatically adapts to different document parsing logic. - Check tool calling logs to confirm that
functionCallrequest parameters are correctly included with no missing fields.
The values provided are common starting points and should be measured against the reader's own samples.