What the data for this category looks like
Data for this category comes from product 3D models and GD&T specifications, process route documents, coordinate measuring machine data, on-site trial feedback, and metrology system records. Daily generated documents include fixture requirement specifications, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data updates are carried out on a per-project basis, triggered by product changes or regular calibrations. Documents include structured fields such as positioning datum coordinates, geometric dimensioning and tolerancing parameters, measurement result values, as well as supporting engineering drawings and operation instructions.
What constraints these characteristics impose on the model access and configuration workflow
This category’s data includes multimodal engineering drawings and structured measurement parameters, and update frequency fluctuates with individual project progress and product changes. This requires the model access link to support unified parsing of multi-source data. Professional content such as GD&T specifications and positioning datum parameters in specialized documents requires the model to have accurate professional semantic understanding capabilities. This avoids errors in datum conversion or scheme generation. At the same time, bid documents must be associated with the latest calibration records and change histories. This requires the configuration link to support association with historical version data, and control of context length to avoid parameter confusion across different projects.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled, supports PNG, STEP format files | This category includes engineering drawings such as fixture drawings and gauge operation manuals, requiring the model to parse image content. This configuration applies to FastGPT v4.17.0 |
maxContext | 8000–12000 token | The total document length for a single project typically falls within this range, to avoid content truncation due to exceeding context limits |
temperature | 0.2–0.4 | Bid documents and quotations must maintain professional rigor, to avoid generating content that deviates from actual parameters |
| Workflow/API tool selection | auto | Requires the model to independently choose whether to call tools to parse engineering data, to adapt to different types of input content |
AIPROXY_API_TOKEN | Use application-specific key | Project data for this category has exclusive attributes, and application-specific keys can isolate configurations and data across different projects |
maxTokens | 2000–3000 token | The output length for a single bid document generation must be controlled within a reasonable range, to avoid redundant content |
Three common mistakes
- Issue: When calling the model to generate bid documents, uploaded fixture drawings or gauge operation manuals are not parsed, and the returned content only includes text information. Cause: The
visionparameter is not enabled, or the multimodal input model environment is not configured. - Issue: After creating an indexing model, a prompt indicating missing necessary model resources is displayed when calling the language model. Cause: The configurations of the indexing model and the language model are mistakenly separated, and resources for both types of models are not associated at the same time.
- Issue: The generated quotation content has deviations that do not match actual process parameters. Cause: The
temperatureparameter is set too high, causing the model's generated content to deviate from professional specifications and actual data.
How to confirm the configuration is complete
- Upload a fixture drawing or gauge operation manual from this category, initiate a test call, and check whether the returned content includes a descriptive analysis of the image content.
- Input a text query containing GD&T parameters, and check whether the returned bid document accurately associates the corresponding parameter content.
- Initiate a call using an application-specific key, compare test results across different projects, and confirm that data confusion across projects does not occur.
- Adjust the
temperatureparameter and initiate a test, observe changes in the rigor of the generated content, and confirm that the parameter setting meets business requirements.
The values provided here are common starting points, and should be evaluated against the reader's own samples.