What Data for This Category Looks Like
Data for this category typically originates from daily business systems of garment OEM/ODM, brand supply chains, and cutting and sewing workshops. Daily documents include process sheets, patterns, layout plans, cutting orders, first sample seals, process quality sheets, and packaging and size matching sheets. Core data sources include orders and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector records, and customer sealed samples. Update frequency follows per order, per style, per batch. Data fields include version parameters, size specifications, fabric and accessory batches, process man-hours, and packaging ratios. Common units are centimeters, pieces, and batch numbers.
Constraints on Tool Calling and Plugins
Data update frequency for this category varies by order, style, and batch. Tool calling must support pulling the latest data for individual business scenarios, to avoid quotation deviations caused by reusing old batch information. There are many types of daily documents, so plugins must adapt to field mapping rules for different documents such as process sheets, layout plans, and packaging and size matching sheets. For example, extract the number of cut pieces from a layout plan, or decompose process man-hours from a process sheet. Fabric cut pieces are irreversible and manual processes are labor-intensive, so tool calling output must include multi-dimensional verification logic. This logic checks sizes, color batches, and packaging ratios to prevent wrong sizes, wrong boxes, or batch defects. It is also necessary to integrate data from multiple systems such as hanging systems and needle detectors, to ensure bid document and quotation cost accounting aligns with actual production processes.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to dynamic tool calling needs for multiple garment production processes, no manual tool specification required |
| Timeout Duration | 1200 seconds | Address time-consuming scenarios including multi-document parsing and cross-system data retrieval |
| Context Window Size | 8000–12000 token | Accommodate full content of multiple documents such as process sheets and layout plans |
| Maximum Plugin Call Count | First 8 entries | Match the number of core data fields for a single order batch |
| Workflow/API function-call configuration | Enabled | Support structured output for field verification of quotations and bid documents |
| Result Verification Switch | Enabled | Automatically verify core business fields such as sizes and packaging ratios |
Three Common Mistakes
- Symptom: Tool calling has no response when using a local model, returning empty results or status code 500. Cause: The tool calling switch is not enabled in the model configuration, or the local model did not load the function call plugin correctly.
- Symptom: Historical records are lost during repeated calls, making it impossible to reuse generated quotation drafts. Cause: Session history saving configuration is not enabled, or the configured save path does not point to the business data directory.
- Symptom: Plugin calls fail when using a general account KEY as the BASEURI, returning permission or connection errors. Cause: The general account KEY does not have plugin call permissions for this category, or the BASEURI does not point to the privately deployed FastGPT instance.
How to Verify Proper Configuration
- In FastGPT v4.17.0, initiate a test call, submit the process sheet and size chart for a single order batch, and check if the tool automatically calls the corresponding plugin to parse the data.
- View the session log to confirm that the
toolChoiceparameter takes effect, and that the tool calling process adheres to the configured maximum call count. - Cross-check the output results to confirm that category-specific fields such as the number of cut pieces and fabric and accessory batches are included.
- Simulate a repeated call, check if the historical session retains the generated bid document draft, and confirm that the configuration is active.
The provided values are common starting points and should be measured against respective samples.