What the data for this category looks like
Data sources for metal cutting machining include product drawings and 3D models, CAPP/CAM systems, machine tool collection data, tool management systems, MES systems, and measuring tool calibration records. Data updates trigger per work order, per workpiece, per sampling batch, or when tools are replaced. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. These documents contain fields such as operation number, tool model, inspection dimensions, deviation values, and measuring tool number. Common units are machining-specific ones such as millimeters, revolutions per minute, and feed rate in millimeters per revolution.
What constraints these characteristics impose on model integration and configuration
Data sources for this category are scattered and updated frequently. New documents generate for each work order, tool replacement, or sampling batch. Model integration and configuration must adapt to the context length of multi-source heterogeneous data. Professional documents such as CNC programs and measuring tool calibration records include fields with specialized units. Model output format must match original documents exactly, with no arbitrary changes to symbols, capitalization, or spaces. Frequently updated data creates significant context load. The total number of documents processed per round must be limited. Configuration must also adapt to the input length of different document types, to avoid processing failures caused by context overflow.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multi-operation documents and CNC programs for this category, avoiding context overflow during single-round processing |
maxTokens | 2000–3000 token | Meets the output length requirements for inspection record audit and deviation analysis, avoiding truncation of key conclusions |
quoteMaxToken | 1500 token | Limits the length of a single referenced document, adapting to the character count of single documents such as operation cards and tool lists |
temperature | 0.1–0.3 | Ensures strict output formatting, avoiding arbitrary modifications to symbols, capitalization, or spaces from original documents |
| Workflow/API tool selection | none | This scenario does not require calling external tools. It only completes audit and record organization based on knowledge base documents |
vision | Enabled | Adapts to the recognition and audit of visual documents such as product 3D models and first-piece inspection photos |
Three common configuration mistakes
- Extraneous spaces or capitalized letters appear in model outputs. Cause: The
temperatureparameter is not set to a low range, leading the model to arbitrarily modify the format and symbols of original documents. - A 500 error returns after connecting a locally deployed model. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the local model port is not opened for access permissions. - Context processing timeout or document truncation occurs. Cause:
maxContextis set too large, exceeding the single-round processing limit of FastGPT v4.17.0, orquoteMaxTokenis not limited, leading to excessive length of a single referenced document.
How to confirm proper configuration
- Upload a single operation card and CNC program document, trigger an audit task, and check whether the output result retains the symbols, capitalization, and spaces of the original document.
- Call the interface of the locally deployed model, verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, and no 500 error returns. - Import multiple related documents from the same work order, check that the context loads without truncation, and the output result covers all key information from the related documents.
- Adjust the
temperatureparameter, compare the stability of output formatting across different values, and confirm that the value range meets strict formatting requirements.
The values provided are common starting points and should be evaluated against the reader's own samples.