What this category of data looks like
Daily documents for this category include tooling requirement documents, positioning benchmark schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources include product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Data fields include geometric dimensioning and tolerancing parameters, coordinate measuring machine measurement values, calibration cycles, version numbers, and other relevant items. Units include millimeters, micrometers, hours, and other general engineering units. Data updates follow project milestones, each product change, and regular calibrations. No fixed update cycle exists. A single document’s content spans from a single-page drawing to dozens of pages of multi-link records. This page is written based on FastGPT v4.17.0.
What constraints do these characteristics impose on the model access and configuration link
This category’s data combines structured numerical values, semi-structured engineering documents, and drawings. It has no fixed update cycle. Each audit round requires linking multiple historical calibration and change records. Model access must support mixed-format data parsing. Context length must accommodate multi-document association requirements. Tools must be called to complete engineering tasks such as tolerance comparison and data verification. Engineering data has high requirements for output rigor. Limit the degree of model generation divergence to avoid inspection result deviations caused by improper parameter settings. When deploying models locally, adapt to the token consumption scale of engineering data to avoid losing core information such as key geometric dimensioning and tolerancing and coordinate measuring machine measurements due to context truncation.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Need to process documents with drawings such as fixture drawings and gauge operation manuals, and extract key information such as graphic annotations and tolerance annotations |
maxTokens | 8000–12000 token | Single tooling audit documents contain long text content such as GD&T parameters and coordinate measuring machine data, requiring sufficient tokens to generate complete audit results |
maxContext | 10000–15000 token | Need to link multiple historical documents such as change histories and calibration records to avoid loss of key information due to context truncation |
quoteMaxToken | 1500–2000 token | Extract key calibration records and acceptance data from documents as reference basis to ensure that audit content has clear sources |
| Workflow/API tool selection | auto | Need to call tools to complete engineering-specific tasks such as coordinate measuring machine data comparison and tolerance calculation, to improve audit accuracy |
temperature | 0.2–0.4 | Inspection and audit require rigorous results, avoiding content that is overly divergent or does not comply with engineering specifications |
Three common mistakes
- Symptom: After locally accessing DeepSeek-R1:7B, the test returns
model response empty. Cause:AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, or the model has not finished loading. - Symptom: After uploading a fixture drawing, the large language model output does not include drawing-related analysis content. Cause: The
visionconfiguration item is not enabled, or the uploaded drawing format does not meet the parsing requirements of the visual model. - Symptom: An exception occurs when the final step of uploading to the API file library is reached. Cause: The path format of
AIPROXY_API_ENDPOINTis not configured correctly, or the URL contains extra spaces leading to interface call failure.
How to confirm correct configuration
- Check if the
visionconfiguration item is enabled. Upload a standard-format fixture drawing, and verify if the model output includes analysis of drawing annotations and tolerance parameters. - Call the locally deployed model interface. Verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. Check if the interface returns a normal model response. - Upload a document containing multiple change histories. Check if the model output links key historical change information, with no content missing due to context truncation.
- Adjust
temperatureto around 0.3. Generate an inspection and audit result, and check if the output meets engineering rigor requirements without overly divergent content.
The values provided are common starting points and should be measured against the reader's own samples.