This document is compatible with FastGPT v4.17.0.
Data Structure for This Category
Data for this category comes primarily from daily production and compliance documents of baked goods, cookies, instant noodles, and frozen pastry factories. These documents include formulas and feeding lists, production records, baking or steaming curves, metal detection logs, cleaning and disinfection logs, and factory inspection reports.
Data sources cover raw material compliance certificates, ERP/MES systems, weighing equipment, ovens or frying lines, laboratory testing systems, and food traceability ledgers. Data updates by production batch, team shift, or daily frequency. Core fields include material name, feeding quantity, process parameters, test results, and shelf life. Some documents require linking allergen labels and cleaning change records.
Constraints on Tool Calling and Plugin Workflows
Data updates by batch, shift, or daily frequency. Tool calling must support precise pulling of production and test data for the target batch using time ranges.
The category requires strict control of special fields such as allergen labels and cleaning change records. Plugins must support custom field format validation and associated queries.
Data sources cover multiple systems including equipment, ERP, and laboratories. Tool calling must support parallel aggregation of multiple data sources and format unification.
Product line changes happen frequently. Tool calling must support quick switching of dataset configurations for different production lines to avoid cross-batch data confusion.
Quality audit errors may trigger recalls. Tool calling must include a result validity check step.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Supports multi-tool calling scenarios, automatically matches the data source tools required for the current audit |
| Workflow/API function-call configuration | enabled | Supports custom tool calling, adapts to the category's multi-field validation requirements |
| Timeout Threshold | 60–120 seconds | Category data comes mostly from real-time equipment and business systems, requires reasonable time to pull batch data |
| Custom Field Mapping | Match allergen, feeding quantity, and process curve fields | The category requires strict control of allergens and production parameters, so tool return data must be mapped to corresponding audit fields |
| Batch Call Limit | Top 10 batch records | A single audit only needs to cover the currently targeted batch, avoids excessive data consuming context |
| Data Source Validation Switch | Enabled | Category quality audits require strict matching of compliance documents, so validation of the source validity of tool return data is necessary |
Three Common Mistakes
- Symptom: After adding a MySQL database tool call, the chat returns a 400 status code (no body). Cause: Authentication fields for the database connection are not configured correctly, or the SQL statement for the tool call does not match the category's batch data query format.
- Symptom: When calling a production data interface via custom code, an AggregateError Code: ETIMEDOUT prompt appears. Cause: A reasonable timeout threshold is not set, or the data source interface response speed exceeds the tool call's wait time.
- Symptom: Allergen data returned by the tool is empty, leading to abnormal audit results. Cause: Custom field mapping rules are not configured, so the tool cannot recognize the category-specific allergen label field.
How to Confirm Proper Configuration
- Run a single batch data query, verify that the tool return fields include core category document fields such as formulas, feeding quantities, and metal test results.
- Simulate an abnormal data scenario to trigger tool call validation, confirm that the custom field format validation logic is triggered.
- Adjust the time range of the data source, verify that the tool can accurately pull production data for the target batch.
- Check the tool call logs, confirm that the returned status code and response content match the expected configuration.
The values given are common starting points and should be measured against the reader's own samples.