This document is written based on the configuration logic of FastGPT v4.17.0.
What the data for this category looks like
Core data for the sheet metal cutting and bending category typically comes from CAD/CAM software, order BOMs, sheet metal inventory systems, laser cutters, bending machines, and shopfloor MES. Daily documents usually include unfolded drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece records, and process flow cards. Common fields cover sheet metal specifications, bending coefficients, cutting dimensions, process numbers, transfer times, and more. Units are mostly millimeters, kilograms, and hours.
Data update rhythm aligns with production workflows: BOMs and unfolded drawings are updated when orders are generated, sheet metal inventory and device operation data are synced daily, and bending process parameters and nesting plans are updated when changing production runs.
What constraints these characteristics impose on the "model access and configuration" link
The multi-variety, small-batch production model of this category requires models to flexibly adapt to different sheet metal drawings and process data. The frequent update rhythm requires models to quickly load the latest production records into their context. The diverse data source types require access configurations to be compatible with structured data exported from CAD and time-series data collected from devices.
Bending compensation and nesting accuracy requirements in core links demand models to accurately associate drawings with process ledgers to avoid generation deviations. Errors directly lead to sheet metal waste and assembly mismatches, so configurations must ensure retrieval and generation accuracy, and cannot over-rely on fuzzy matching.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
maxContext | 8000–12000 token | Sheet metal ledgers need to integrate multiple types of data such as unfolded drawings and process flow cards. This value range covers the complete production context for a single batch |
quoteMaxToken | 2000–3000 token | Text and visual description content of nesting diagrams and bending process cards is lengthy. Sufficient reference details must be retained to support summary work |
temperature | 0.2–0.4 | Ledger summary and report generation require consistent content accuracy. A lower temperature reduces generation deviations |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address provided by the manufacturer | Compatibility with dedicated data formats of sheet metal equipment and systems is required to ensure normal data pulling and synchronization |
vision | Enabled | Unfolded drawings, nesting diagrams, and other visual drawing data need to be processed. Enabling vision capabilities allows recognition of dimensions and bending marks in drawings |
maxTokens | 4000–6000 token | Report generation needs to integrate multi-process data. This value range prevents key information from being truncated |
Three common errors
- Phenomenon: Semantic retrieval matches meet standards, but the model returns no relevant production data. Cause: The
maxContextparameter value is too low, failing to include valid data such as retrieved unfolded drawings and process flow cards in the model context. - Phenomenon: Single ledger generation task takes too long. Cause:
quoteMaxTokenis set too large, causing the model to load redundant data from nesting diagrams and bending process cards, slowing down processing speed. - Phenomenon: Cannot call the API interface of external sheet metal equipment normally. Cause:
AIPROXY_API_TOKENis not configured correctly, or interface access is not completed following the Basic Auth authentication rules.
How to confirm the configuration is set correctly
- Upload a single bending process card and nesting diagram, trigger the ledger summary task, and check if the generated report includes core fields such as sheet metal specifications, bending coefficients, and cutting dimensions.
- Simulate a high-traffic query scenario, verify that the interface response time has no obvious abnormalities, and meets business processing rhythm requirements.
- Verify the external API access status, confirm that cutting data from laser cutters and process flow records from MES can be pulled normally.
- Adjust the
temperatureparameter to 0.1 and 0.5, compare the two generated ledger contents, and confirm that result stability meets business needs.
The values provided are common starting points and should be measured against the reader's own samples.