What this category’s data looks like
Daily documents for this category include process sheets, CAD patterns, layout plans, cutting orders, first-piece sample seals, process quality sheets, and packaging size matching sheets, among others. Data sources cover orders and size charts, CAD design files, fabric and accessory inspection records, workshop hanging system data, needle detector test results, and customer sample seal materials.
Data updates follow the rhythm of each order, each style, and each batch. There are many styles and strong seasonality. Fabric cut pieces are irreversible, and manual processes are dense. Process splitting and supporting verification links account for a high proportion of total time consumption.
What constraints do these characteristics impose on the "tool calling and plugins" workflow
Data for this category is updated by order, style, and batch. Tool calling must be strictly bound to the data source of the corresponding batch to avoid cross-batch data confusion.
The coexistence of multiple document types requires tools to support parsing and calling of mixed data such as structured process sheets, CAD drawings, and needle inspection records. The irreversible nature of cut pieces requires tool calling to accurately associate each batch of cut pieces with process data, which cannot be covered by general matching.
The dense manual process feature requires tool calling to support precise matching of process fields, such as field verification logic for sizes and appearance defects.
How to configure the setup
These configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API function-call configuration | Force enabled | This category requires precise calling of specific tools such as databases and CAD parsers, to avoid automatically selecting non-matching tools |
| Tool timeout duration | 300–600 seconds | Need to wait for return results from long processes such as CAD data parsing and process sheet queries |
| Batch call limit | First 3 batch data entries | Matches the batch-based data update feature of this category, to avoid loading excessive irrelevant historical data |
| Variable binding rule | Bind order number + batch number | Corresponds to the data update rhythm by order and batch, ensuring that tool-called data accurately matches the current task |
| Workflow/API tool selection | Fixed target tool specification | Avoid deviations in traceability results caused by incorrect tool selection, and adapt to multi-tool collaboration scenarios |
| Model call retry count | 2 times | Adapts to common workshop network fluctuation scenarios |
Three common configuration mistakes
- Phenomenon: An error occurs when using variables to pass parameters for database connection tools, while manually entering fixed SQL can execute normally. Cause: The variable rule bound to order number + batch number is not configured, causing passed parameters to fail to match the current batch’s data source.
- Phenomenon: Calling model tools in a workflow returns a 400 status code, and channel tests show the request URL is abnormal. Cause: The URL binding rule for tool calling is not correctly configured, or variables are not properly passed to request parameters.
- Phenomenon: The number of tool call return results does not match expectations, and current batch process data is not loaded. Cause: The batch call limit setting does not match the batch-based data update feature of this category.
How to confirm the configuration is complete
- Manually enter fixed parameters to execute tool calling, confirm that process data for the corresponding batch is returned normally.
- After configuring the variable binding rule, pass a test order number and batch number, and verify that tool call parameters are correctly matched.
- Check the configuration status of
functionCallandtoolChoice, confirm that the tool calling mode meets current scenario requirements. - Simulate network fluctuation scenarios, confirm that the tool call timeout and retry configuration can properly trigger retry logic.
The values provided are common starting points and should be evaluated against the reader’s own samples.