What data for this category looks like
Data for this category typically comes from CAD/CAM software, order BOM systems, sheet metal inventory ledgers, laser cutter and bending machine device logs, and workshop MES systems. Daily generated documents include part unfolding drawings, CNC cutting programs, bending process cards, nesting planning diagrams, first article inspection records, and process transfer cards. Data update rhythm aligns with order delivery, daily equipment operations, and each production changeover. Core content centers on bending coefficient compensation for drawing unfolding, nesting scrap management, process sequence verification, and similar tasks. The overall profile is multi-variety, small-batch, and drawing-driven.
Constraints on model access and configuration from these characteristics
Multi-variety, small-batch production requires model access and configuration to flexibly adapt to different sheet metal drawing specifications. Fixed templates must not limit scenario coverage. Multi-source heterogeneous data sources require configuration to support parsing and vector generation rules for multiple document formats. This ensures unified processing logic for different data types such as CAD unfolding drawings and process cards. High-frequency data updates require configuration to support on-demand vector synchronization. This prevents redundant storage or delayed responses. Accuracy requirements for bending compensation and nesting mean model inference parameters must balance detailed accuracy for professional scenarios and response efficiency. Improper parameter settings will lead to audit result deviations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long-text context requirements for sheet metal drawing parsing and splicing multiple process cards, avoiding truncation of critical bending coefficient and process information |
maxTokens | 4000 token | Limits single-round inference output length, ensuring complete and non-redundant output for nesting planning and bending sequence recommendations |
quoteMaxToken | 2000 characters | Limits total length of recalled documents, prioritizing retention of core audit-related data such as unfolding drawing dimensions and first article record deviations |
temperature | 0.3–0.5 | Reduces random output probability, ensuring consistency of bending compensation calculation and process verification results, meeting the rigor requirements of production audits |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the model service provider | Adapts to multi-source model access requirements, supporting integration with professional industrial large models from different vendors |
AIPROXY_API_TOKEN | Fill in the key generated by the service provider | Ensures identity legitimacy for model calls, preventing audit data leakage caused by unauthorized access |
Three common configuration errors
- Issue: Model call failures occur across different FastGPT versions, such as 4.8.9 and FastGPT v4.17.0. Cause: Default configuration values for
AIPROXY_API_ENDPOINTchange after version updates, and corresponding parameters are not synchronized. - Issue: Vector models cannot be properly configured for channels after being enabled. Cause: The key from the corresponding model service provider is not bound according to the generation rules for
AIPROXY_API_TOKEN, leading to identity verification failure. - Issue: Excessive deviation occurs in bending compensation results output by the model. Cause: The
temperatureparameter is set too high, causing non-standard calculation logic during inference that does not comply with sheet metal processing process specifications.
How to confirm successful configuration
- Call the test interface, input a sheet metal unfolding drawing and corresponding process card, check if the model can normally return bending coefficient calculation results and process verification recommendations.
- View model call logs, confirm that configuration parameters for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the information provided by the service provider, with no empty fields or format errors. - Compare multiple model outputs for the same input, confirm that result consistency under the set
temperatureparameter meets the rigor requirements of production audits. - Test vector recall speed in high-frequency data update scenarios, confirm that configurations for
maxContextandmaxTokensdo not cause timeouts or truncation of critical information.
The values provided are common starting points and should be measured against the reader's own samples.