What data this category uses
This category’s data typically comes from CAD/CAM software, order BOMs, sheet metal inventory systems, laser cutter and bending machine device logs, and MES production management systems. Daily documents include part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process flow cards. Data fields include bending compensation coefficients, sheet thickness, hole position tolerances, bending angles, and more. Units are mostly millimeters, degrees, and square meters. Update schedules align with order generation, daily inventory counts, and each production changeover.
What constraints these characteristics impose on model access and configuration
Multi-source, heterogeneous data from equipment, software, and management systems varies widely in format. This requires models to adapt to multiple unstructured engineering documents during access. Frequently updated order and changeover data needs parameters configured to support dynamic context length adjustment. Professional fields such as bending coefficients and hole position tolerances require the model to understand industry terminology to avoid parsing errors. The multi-variety, small-batch business model requires configurations that balance fast single-order data access and batch task resource allocation.
How to set configurations
The following configurations are based on FastGPT v4.17.0, and are tailored to the process Q&A needs of sheet metal cutting and bending scenarios.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of splicing multiple sheet metal process documents, covering unfolding drawings, process cards, nesting diagrams, and related content |
maxTokens | 2000–3000 token | Meets the detailed response length required for process Q&A, including professional content such as bending coefficient calculations and nesting suggestions |
temperature | 0.1–0.3 | Maintains accuracy of standard process responses, avoiding random deviations in professional parameters |
vision | Enabled | Supports parsing visual process documents such as unfolding drawings and bending diagrams embedded in PDFs |
AIPROXY_API_ENDPOINT | Fill in the dedicated API address based on the local deployment environment | Adapts to the call path of local large models, ensuring data transmission complies with deployment requirements |
AIPROXY_API_TOKEN | Use a secure key generated through actual testing | Ensures compliance with identity verification for local model calls |
Three common configuration mistakes
- After uploading a process PDF that includes unfolding drawings, the interface shows parsing timeout or request failure, and restarting the server does not resolve the issue. Cause: The
maxContextparameter was not adjusted to fit long documents, and insufficient local model memory allocation causes request interruptions. - After submitting a Q&A about bending coefficient calculation, the returned result deviates significantly from industry standard values. Cause: The
temperaturevalue is set too high, leading to random fluctuations in the generation of professional process parameters. - After uploading a PDF with embedded images, the parsed results do not include the process content corresponding to the images. Cause: The
visionconfiguration item is not enabled, so the model cannot process visual process documents.
How to confirm configurations are correct
- Upload a single bending process card PDF that includes embedded unfolding drawings and process descriptions, and check if the parsed results cover core process parameters.
- Submit a Q&A that includes bending compensation coefficient calculation, and verify that the returned content aligns with general industry process logic.
- Review local model call logs to confirm there are no errors in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and no abnormal request interruptions. - Test context Q&A that splices multiple documents, and confirm the model can correlate process parameters and requirements across different process steps.
The values provided here are common starting points, and should be evaluated against the reader's own samples.