What Data for This Category Looks Like
Data for this category primarily comes from CAD/CAM software, order BOMs, sheet metal inventory systems, laser cutter and bending machine device logs, and MES systems. Daily documents include 3D part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process transfer cards. Fields include sheet thickness, bending coefficient, hole position coordinates, process number, and more. Units are mostly millimeters, kilograms, and hours. Data updates are triggered by each order, daily production scheduling, and each equipment model change, covering all process nodes from order placement to process completion.
What Constraints These Characteristics Impose During Model Access and Configuration
The multi-variety, small-batch nature and drawing-driven characteristics of this category require model access to support multi-format drawing parsing and dynamic data association. Frequently updated order and equipment model change data require configurations to support rapid synchronization of incremental data, to avoid context redundancy. Field differences across multiple document types require configurations to clearly define field mapping rules for different data sources, to prevent data parsing misalignment. The need to combine real-time equipment-collected logs with static process cards requires configurations to distinguish processing logic for structured and unstructured data, to ensure the accuracy of audit results.
How to Configure Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vision | Enabled | This category relies heavily on audits of image-based documents such as drawings and process cards, so visual parsing capability is required |
maxContext | 8000–12000 token | Covers process flow records and process parameters for multiple processes of a single order, avoiding context truncation that loses critical information |
maxTokens | 4000 token | Adapts to the total length of audit content for a single batch of cutting and bending, balancing output accuracy and response speed |
temperature | 0.2–0.4 | Ensures consistency of audit logic, avoiding deviations in bending sequence judgment caused by parameter fluctuations |
AIPROXY_API_ENDPOINT | Fill in the corresponding node according to the deployment environment | Adapts to domestic or cross-border model invocation links to ensure interface connectivity |
AIPROXY_API_TOKEN | Bind a dedicated invocation key | Restrict access permissions to prevent unauthorized invocation from affecting production data security |
Three Common Misconfigurations
- Enabling
visionstill fails to parse drawing images, because the image parsing switch was not checked in the data source configuration. Only enabling the model-side visual parameter does not take effect. - Setting
maxContextto 6 still has no multi-turn conversation memory, because the corresponding session cache rule was not configured at the same time. Only modifying the model context parameter does not associate the session storage logic. - Testing Tongyi Qianwen via the model proxy interface fails, because
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENwere not filled in correctly, or the interface does not have the corresponding model invocation permission enabled.
How to Confirm Proper Configuration
- Upload a single bending process card and cutting program to verify whether the model can correctly extract core fields such as sheet thickness and bending coefficient, and check whether the extraction results match the document content.
- Initiate two linked audit requests to verify whether the model can complete error checking for the current process based on the previous process record, confirming that context association is effective.
- Call the built-in test interface to submit the configuration parameters, verify that the interface returns a normal status, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Simulate data synchronization after equipment model change, verify whether the configuration can automatically load new process parameters, and confirm that the incremental data synchronization logic is effective.
The values provided are common starting points and should be measured against the reader's own samples.