What the data for this category looks like
Typical subjects of this category are injection molding, stamping, and die-casting mold enterprises, as well as mold centers within manufacturing enterprises. Daily generated documents include mold design task sheets, DFM reports, part drawings, processing technology cards, trial mold reports, mold modification notices, and mold records. Data sources cover customer product models, mold flow or forming simulation files, CAD/CAM output files, machining and measurement data, and records related to trial mold samples. Data updates progress with project milestones, with each trial mold run or mold modification triggering a data update. Documents contain structured text and 2D/3D drawings. Fields involve professional content such as cavity parameters, forming tolerances, and material shrinkage rates.
What constraints these characteristics impose on the "model access and configuration" link
Data in mold design and manufacturing scenarios mixes structured text and 2D/3D drawings. This requires model access configuration to support multi-modal content processing. Data updates occur frequently during project iterations. Sufficient context length must be configured to cover all review documents for a single project. Cross-verification of professional fields and geometric parameters requires the model to have accurate industry understanding capabilities. Parameter values must balance rigor and processing efficiency. Collaborative review of multi-source data requires a stable API access link. This ensures documents in different formats can be correctly read and analyzed.
How to set the configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of full-process documents for mold projects, avoids truncation of key review information |
vision | Enabled | Supports cross-review of drawing documents such as part drawings and DFM reports, identifies geometric and tolerance details |
temperature | 0.2–0.4 | Ensures consistency of review results, avoids redundant divergence, meets the rigor requirements of mold design |
AIPROXY_API_ENDPOINT | Fill in a publicly accessible deployment address, such as https://your-fastgpt-instance.com/api/v1 | Adapts to cross-domain access requirements for third-party model access, resolves address verification failure issues |
maxTokens | 2000–3000 token | Controls the output length of a single round of review, ensures results focus on core review points such as demolding and shrinkage |
AIPROXY_API_TOKEN | Fill in the authorization key of the corresponding model platform | Completes identity verification for model access, ensures normal call permissions |
Three common mistakes
- Symptom: Third-party model access prompts a message reception address verification failure. Cause:
AIPROXY_API_ENDPOINTwith public network access is not configured, or invalid SSL certificate verification is not disabled as prompted. - Symptom: Redundant divergence appears in review results after the chain-of-thought function is enabled. Cause: The
temperatureparameter value is too high, exceeding the rigor range required for mold design review. - Symptom: Custom model access cannot trigger review normally. Cause: The
AIPROXY_API_TOKENparameter is not filled in correctly, or an incompatible model type is selected.
How to confirm the configuration is complete
- Test the configured model, check that the interface returns a status code of 200. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Upload a mold part drawing and supporting DFM report to trigger cross-review. Check whether geometric and tolerance analysis content related to the drawing is returned. Confirm that the
visionparameter is enabled and functional. - Set
maxContextto 8000 token, upload multiple long documents. Check that no context truncation prompt appears during the review process. Confirm that the parameter configuration is reasonable. - Adjust
temperatureto 0.3, call the same review task multiple times. Check that result differences are within an acceptable range. Confirm that the parameter value meets scenario requirements.
The values provided are common starting points and should be measured against the reader's own samples.