What this category of data looks like
Data for this category mainly comes from customer product models, mold flow or forming simulation files, CAD/CAM output files, machining and measurement records, mold trial reports, and mold modification notices. Daily documents include mold design task sheets, DFM reports, part drawings, machining process cards, and mold records. Data is updated per project, and synchronized after each mold trial or modification. Fields include mold number, part material properties, defect type, number of mold modifications, and other items, involving engineering units such as dimensions and mechanical properties.
What constraints these characteristics impose on model access and configuration
Project-level data in mold design and manufacturing has diverse formats, including engineering drawings, simulation files, and structured reports. Model access must support multi-format input parsing. Data update frequency changes with mold trial and modification milestones. Single-project data volume grows with iteration times. Context length must be limited to avoid overflow. Defect traceability requires associating historical mold trial records and modification documents. The configuration must retain the ability to link historical data. It must also adapt to scenarios where structured fields and unstructured text of tooling data are mixed for input.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-segment historical data generated from multiple mold trials and modifications in mold projects, avoiding analysis interruptions caused by context overflow |
maxTokens | 2000–3000 token | Meets text output length requirements for defect type analysis and mold modification plan suggestions |
quoteMaxToken | 1500 token | Associates core content of referenced documents such as mold trial reports and DFM reports, ensuring completeness of traceability basis |
temperature | 0.3–0.5 | Maintains rigor in defect analysis, avoiding generation of mold modification suggestions without engineering basis |
AIPROXY_API_ENDPOINT | Fill in according to the supplier's official documentation | Adapts to interface addresses of different model suppliers, ensuring requests are sent normally to the corresponding service |
AIPROXY_API_TOKEN | Fill in according to the key assigned by the supplier | Completes identity verification for model calls, ensuring security of interface access |
Three common mistakes
- The body parameter field in the interface cannot be fully cleared. Original content remains displayed when the page is reopened after submission. This occurs when configuration changes are not saved, or page cache is not synchronized and updated.
- Nodes terminate directly after a model request error, with no error logs or prompts. This occurs when no exception capture node is configured, or no error handling branch is bound.
- When adding configurations of the same model from different suppliers, the system prompts duplicate parameters and prevents saving. This occurs when the same model from different suppliers is not assigned a unique identifier, and conflicts occur when only model ID is used as the unique key.
How to confirm the configuration is complete
In FastGPT v4.17.0, verify configuration through the following executable actions:
- Check the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENto ensure consistency with the official configuration of the selected supplier. - Submit a test request, and confirm the interface return status code is 200, with no timeout or authentication error prompts.
- Adjust the value of the
temperatureparameter, and verify whether the rigor of the output content meets the requirements of defect analysis. - Save the configuration and re-enter the page, confirming that the values of parameters such as
maxContextandmaxTokenshave not undergone abnormal changes.
The values given are common starting points and should be measured against the reader's own samples.