What data for this category looks like
Data sources include product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, MES systems, and measuring tool data. Updates are triggered per work order, per workpiece, per sampling batch, or when cutting tools are replaced. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and other materials. Professional parameters include tool life (unit: minutes), process dimension tolerance (unit: mm), cutting speed (unit: r/min), feed rate (unit: mm/r), and other specialized parameters.
What constraints these characteristics impose on the "model access and configuration" link
Documents for this category include long-text CNC programs, professional parameters with units, and real-time production data from multiple heterogeneous sources. The high-frequency update feature requires model access to adapt to high-concurrency calls. Long documents such as CNC programs easily exceed model context limits, so context parameters must be configured specifically. Format differences in multi-source data require access configuration to support unified parsing of multiple data types. Unit and precision requirements for professional fields require the model to retain original parameter expressions, avoiding errors caused by automatic conversion. Meanwhile, batch processing requirements for workshop work orders require configuration to adapt to resource allocation in high-concurrency scenarios.
How to set the configuration
This configuration is based on FastGPT v4.17.0. Specific parameter settings are as follows:
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Adapts to context requirements for long documents such as CNC programs and process routes, matching the average length of single-work-order documents for metal cutting machining |
maxTokens | 2000–3000 token | Meets output length requirements for exception report drafting, while avoiding exceeding single-request limits for model calls |
quoteMaxToken | 1000–1500 token | Limits the length of referenced process documents and machine tool collection data, preventing call failures caused by context overflow |
| Workflow/API tool selection | auto | Automatically selects tools to process structured tool and process data, adapting to multi-step content generation requirements for process document drafting |
temperature | 0.3–0.5 | Maintains accuracy of process parameters, avoiding excessive deviation in descriptions of exception reports |
AIPROXY_API_ENDPOINT | Fill in according to the actual address of the deployment server | Adapts to access requirements for local model servers, resolving response latency issues under high concurrency |
AIPROXY_API_TOKEN | Fill in according to the actually generated key | Ensures access security, preventing unauthorized API calls |
Three common mistakes
- An error occurs when uploading a product 3D model or process drawing, returning a 400 status code or empty results. The cause is that the
visionconfiguration item for image parsing is not enabled, or reading rules for image formats are not correctly configured. - During high-concurrency tests of shared pages, no response occurs after exceeding 60 concurrent requests, while local server resources have not reached their limits. The cause is that the
AIPROXY_API_ENDPOINThas no concurrency adaptation rules configured, ormaxTokensis set too high, causing excessive resource usage per request. - After uploading a CNC program, the model cannot read its content. Valid text exists within the <FilesContent> tag, but empty fields are returned. The cause is that
quoteMaxTokenis set too small, causing intercepted text to exceed valid ranges, or the pre-logic for automatic content extraction is not enabled.
How to confirm the configuration is complete
- Upload a single CNC program with a length of approximately 5000 tokens, check that the model can correctly extract cutting parameters and process requirements without returning errors.
- Simulate 100 concurrent requests to call the exception report drafting interface, check that there are no timeouts or unresponsive situations, and local server resource usage meets expectations.
- Upload a first-piece inspection record with images, check that the model can correctly recognize dimension markings in the images and generate corresponding descriptions.
- View platform logs, confirm that all API calls carry the correct
AIPROXY_API_TOKEN, and there are no unauthorized access records.
The values provided are common starting points and should be measured against the reader's own samples.