This page is written for FastGPT v4.17.0.
What Data for This Category Looks Like
Data sources for this category include product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, tool management systems, MES systems, and measurement and inspection tool data. Daily generated documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records.
Data updates trigger per work order, per workpiece, or during sampling inspection and tool replacement actions. Fields include tool life, process dimension deviation, machine tool runtime, and others, with corresponding units of minutes, millimeters, and hours.
Constraints for Model Integration and Configuration
Multi-source heterogeneous data formats include static drawings, CNC program text, and dynamic collection tables. This requires model integration to support multimodal input, and corresponding visual processing parameters must be configured.
High-frequency updated work order-level data requires the context window to accommodate all associated documents for a single task. This avoids truncating key process and inspection information.
Industry-specific fields such as tool wear amount and process dimension deviation require the model to accurately match industry definitions during content generation. Adjust generation parameters to balance accuracy and flexibility.
For offline deployment scenarios, configure local API endpoints. Do not rely on external transit services.
How to Set Configuration Parameters
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled (true) | Required to process visual documents such as product drawings and CNC program screenshots, supports multimodal input |
maxContext | 8000–12000 token | Adapts to the context length of multi-source documents for a single work order, avoids truncating key process and inspection data |
maxTokens | 2000–3000 token | Controls the generation length of a single ledger report, matches the summary requirements of operation cards and patrol inspection records |
quoteMaxToken | 1000–1500 token | Limits the single segment length of referenced documents, avoids redundant content interfering with ledger summary |
temperature | 0.2–0.4 | Reduces generation randomness, ensures the output accuracy of industry-specific fields such as tool life and process dimensions |
| Workflow/API tool selection | auto | Automatically determines whether to call tools to process real-time machine tool data or tool life calculation, adapts to multi-link ledger summary |
AIPROXY_API_ENDPOINT | Locally deployed FastGPT API address | Adapts to offline deployment scenarios, does not rely on external transit services |
AIPROXY_API_TOKEN | Access key corresponding to the deployment environment | Used for identity verification, ensures legitimate API call permissions |
Three Common Misconfigurations
- The model test returns "Request Error", and the model service cannot be connected in offline deployment scenarios. Cause:
AIPROXY_API_ENDPOINTis not correctly filled with the local deployment address, or a validAIPROXY_API_TOKENis not configured, resulting in identity verification failure. - After importing process drawings and CNC programs, the model does not recognize the relevant content and has no valid output. Cause: The
visionparameter is not enabled, and multimodal input support is not configured, so non-text industry documents cannot be processed. - The generated ledger is missing key parameters such as tool life and process dimensions, or the field units are incorrect. Cause: The
temperaturevalue is too high, causing the generation result to deviate from industry standard field definitions and unit specifications.
How to Verify Proper Configuration
- Run the model test function. Check if returned results include metal cutting industry-specific fields such as process routes and tool parameters.
- Import multi-source documents for a single work order, such as operation cards, patrol inspection forms, and tool lists. Verify that the generated ledger completely covers all associated data.
- Adjust the
temperatureparameter and compare generation results. Confirm that field accuracy matches expected behavior as the parameter is adjusted. - View the API call log. Confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations do not trigger permission or connectivity errors.
The values provided here are common starting points, and should be evaluated against the reader's own samples.