This page is written based on the tool calling and plugin configuration logic of FastGPT v4.17.0.
What the data for this category looks like
The data for this category comes from Gerber files, drill files, and BOMs provided by customers, as well as factory internal CAM system data, production equipment parameters, bath chemistry test records, AOI inspection reports, electrical testing reports, and laboratory slice reports. Daily documents include engineering production instructions, Gerber analysis records, drilling and lamination parameter tables, chemical analysis ledgers, and defect inspection reports. Data is collected in real time or continuously alongside production batches and shifts. Document fields include graphic layer numbers, aperture units, drilling rotation speeds, defect coordinates, and some parameters use fixed units of measurement.
Constraints for Tool Calling and Plugin Workflows
Multi-source, heterogeneous data formats require tool calling to support parsing of multiple file types including Gerber, drill files, and CSV, to prevent format parsing failures. Frequently updated production data requires tool calling to support real-time triggering, to match the documentation review rhythm for each batch or shift. Electrical connectivity verification for multi-layer PCBs requires calling dedicated parameter comparison plugins. These plugins must associate drilling, lamination, and chemical parameters with inspection reports for cross-verification. The parameter matching logic of tool calling must cover multi-dimensional fields such as layer numbers, aperture sizes, and bath solution concentrations, to ensure review results align with manufacturing requirements. Use the toolChoice configuration to automatically adapt multi-tool calling logic, covering scenarios including document parsing, parameter comparison, and defect verification.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to parallel multi-tool calling needs, covering scenarios such as Gerber parsing, parameter comparison, and defect verification |
| Tool calling timeout | 120–180 seconds | Match average processing durations for Gerber parsing and slice report analysis, to avoid interrupting the review process due to timeout |
| Concurrent call limit | First 3 entries | Control system resource usage for single-batch reviews, adapting to the rhythm of batch import of production data |
| Workflow/API function-call configuration | enabled | Enable structured tool calling, ensuring parameter comparison results can be directly read by subsequent production processes |
| Thought output switch | Enabled | Support output of intermediate results during tool calling, to facilitate troubleshooting of parameter matching exceptions during cross-review |
Three Common Mistakes
- 400 status code errors occur, and tool calling requests have no response. The cause is failure to implement format verification for multi-source files, with undefined file type parameters passed in.
- Concurrent tool calling execution occurs, with multiple identical review tasks triggered simultaneously. The cause is failure to set trigger conditions for tool calling, and failure to limit the number of tool calls for single-batch reviews.
- Tool calling fails when using a non-designated model. The cause is failure to configure the tool calling format supported by the model; the function calling parameter format of some models does not match preset plugins.
How to Verify Correct Configuration
- Upload a single batch of Gerber files and corresponding engineering production instructions, trigger tool calling, and confirm that files are parsed normally and parameter comparison results are returned.
- Simulate multi-batch data import, check concurrent tool calling execution status, and confirm that unrestricted concurrent triggering does not occur.
- Switch to different models for tool calling tests, confirm that non-designated models can normally call configured plugins.
- View tool calling logs, confirm that structured output parameters match actual document fields, with no missing or incorrect entries.
The values given are common starting points and should be measured against the reader's own samples.