What data for this category looks like
Technical documents for metal cutting machining include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. Data sources include product drawings and 3D models, CAPP/CAM systems, machine tool collection data, tool management systems, MES, and measuring and inspection tool data. Updates are triggered by work orders, single-piece machining, spot checks, or tool changes. Each document includes fields such as process number, tool model, cutting parameters, inspection dimensions, and calibration cycle. Units include machining-specific units such as millimeters, revolutions per minute, and newton meters.
Constraints for model access and configuration from these characteristics
The multi-source heterogeneous data, high-frequency update characteristics, and specialized field requirements of this category create multiple constraints for model access and configuration. Multi-source data includes structured parameters and unstructured code documents. Configure model call logic adapted to mixed formats. High-frequency update scenarios require interface parameters that support rapid synchronization. Specialized fields and units require the model to have machining domain term recognition capabilities to avoid parameter parsing errors. Long text documents such as CNC programs require adjustments to the model's context capacity parameters to ensure complete content reading. FastGPT v4.17.0’s configuration interface can adjust relevant parameters to adapt to these constraints.
How to Set Configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapt to complete reading of long documents such as CNC programs and process routes, covering the total context length of multi-source machining documents |
maxTokens | 2000–3000 token | Meet the output length of review results for professional cutting parameters and dimensional requirements, avoiding truncation of key machining content |
quoteMaxToken | 1500 token | Limit the length of a single referenced document fragment, adapting to the core field extraction requirements of single documents such as operation cards and patrol inspection forms |
temperature | 0.2–0.4 | Reduce random output deviations, ensuring accurate and consistent review results for machining parameters and dimensional requirements |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the platform | Adapt to cross-domain call requirements for multi-source machining data, ensuring stable interface requests |
AIPROXY_API_TOKEN | Fill in the key generated by the platform | Ensure permission security for interface access, preventing unauthorized calls |
Three Common Mistakes
- Phenomenon: Model calls work correctly in third-party interface testing tools, but errors return when testing after creating a model node in FastGPT. Error messages include "insufficient permissions" or "invalid interface address". Cause: The permission scope of
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly. FastGPT’s interface verification rules differ from those of third-party tools. - Phenomenon: Response time for technical document cross-review exceeds 30 seconds, and a loading timeout prompt appears on the page. Cause: The
maxContextparameter is not set reasonably, and too many redundant document fragments are introduced, leading to excessive context load for model processing. - Phenomenon: Cutting parameter units or referenced tool models in review results are incorrect, such as identifying "φ10mm" as "φ10cm". Cause: The
temperatureparameter is not set to a reasonable range, leading to random deviations in model output, and failing to meet professional field verification requirements for the machining domain.
How to Confirm Configuration Is Complete
- Call the model interface test, input a CNC program text containing cutting parameters, and check whether the returned result includes complete process parameter verification content.
- Upload a tool list document, and verify whether the length of the referenced fragment by the model conforms to the preset
quoteMaxTokenrange. - Adjust the
temperatureparameter, then input the same process review request multiple times, and confirm that the consistency of the output results meets business requirements. - Simulate a high-frequency update scenario, upload a new patrol inspection form document, and check whether the model can correctly read the latest inspection data.
The values provided are common starting points and should be measured against the reader's own samples.