What data for this category looks like
Data for this category comes from customer product models, mold flow or forming simulation files, CAD/CAM exported drawings, machining and measurement data, and sample test part inspection records. Commonly generated business documents include mold design task sheets, DFM reports, part machining process cards, mold trial reports, mold modification notices, and mold history logs. Common fields include mold number, part material, demolding angle, shrinkage rate, mold trial defect type, and more. Data updates are carried out on a per-project basis, and are synchronized after each mold trial or mold modification.
Constraints imposed by these characteristics on the model access and configuration workflow
This category has diverse data sources, including structured CAD drawings, numerical simulation files, and unstructured mold trial reports. Configured model access rules must support multi-format input. Data updates occur frequently with project iterations. Deployments must support dynamic loading of the latest mold trial and modification data, to avoid generating process documents from expired materials. A large number of engineering-specific terms are used. The model context window must be large enough to hold professional vocabulary and long text content, while preventing context overflow that causes parsing failures. Individual mold project documents have long content. Reasonable limits must be set on the model’s input and output lengths to ensure generation efficiency and accuracy.
How to set configurations
This configuration guide is based on FastGPT v4.17.0. Specific configuration items are listed below:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Individual mold-related documents can be thousands of characters long. This range accommodates complete parsed content of mold trial reports and design drawings, and is calibrated based on actual testing |
maxTokens | 2000–3000 token | Drafting process documents and exception reports requires outputting complete process cards or defect rectification plans. The output length must cover full content |
quoteMaxToken | 1500 token | Reference content such as mold trial data and CAD drawing annotations must be associated and cited. This limit prevents total reference snippet length from causing context overflow |
temperature | 0.2–0.4 | Engineering documents require strict consistency. Reducing randomness ensures accuracy of process parameters and defect descriptions |
AIPROXY_API_ENDPOINT | Official interface address matching the selected large model | Adapts to large model interfaces commonly used in mold engineering, ensuring accurate parsing of professional terms |
AIPROXY_API_TOKEN | Exclusive interface key assigned by the platform | Ensures interface call security, preventing unauthorized access |
Three common configuration errors
- Incorrect interface address entered when configuring
AIPROXY_API_ENDPOINT. This causes connection timeouts or "Cannot read p"-type errors during calls. The cause is failure to match the large model’s official standard interface path. - Failing to limit the
maxContextparameter leads to overly long context. The model becomes stuck and unresponsive during processing. The cause is failure to adjust the context window based on actual length of mold documents. - Passing unformatted process data strings to the model triggers a "Question is empty" error. The cause is input content containing leftover empty fields or invalid characters.
How to confirm successful configuration
- Use the FastGPT built-in interface testing tool, input a simplified mold trial defect report text, and verify that structured rectification suggestions are returned normally, with no timeouts or error messages.
- Check the
maxContextparameter configuration value, confirm that it matches the average length of currently imported mold documents, with no obvious context overflow risk. - Input the same mold trial defect description multiple times, verify that output process parameters and defect rectification plans are consistent, meeting the strict requirements of engineering documents.
- Verify the
AIPROXY_API_TOKENconfiguration content, confirm that no extra spaces or incorrect characters are included, to avoid call interruptions caused by authentication failure.
The values provided here are common starting points, and should be evaluated against samples specific to the target deployment.