What the Data for This Category Looks Like
Daily documents for this category include mold design task sheets, DFM reports, part drawings, machining process cards, trial mold reports, mold modification notices, and mold records. Data mainly comes from customer product models, mold flow or forming simulation results, CAD/CAM files, machining and measurement data, and trial mold sample inspection data. Data updates are carried out on a per-project basis, with new records and documents generated after each trial mold or mold modification. Each document contains industry-specific fields such as draft angle, forming defects, and assembly accuracy requirements.
Constraints Imposed on Model Integration and Configuration by These Characteristics
The data for this category is multi-source and has varying structures, including structured parameters and unstructured report content. This requires model integration and configuration to support multi-format data parsing. Data is updated iteratively per project, with new records added after each trial mold or mold modification. The configuration must support dynamic association with the latest project documents. Mold-specific fields such as draft angle, shrinkage rate, and assembly accuracy require the configuration to optimize the model's ability to recognize industry-specific terminology. Process records across multiple links need to be associated and verified. The configuration must ensure that the context covers complete project workflow data.
Configuration Settings
This configuration is based on the parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the context storage requirements of long documents such as multiple trial mold reports and machining process cards, covering complete project workflow data |
maxTokens | 2000–3000 token | Meet the generation requirements of long output content such as mold inspection conclusions and mold modification plans, avoiding output truncation |
quoteMaxToken | 1500 token | Limit the length of a single quoted document, avoiding overly long content interfering with the model's review of mold-specific parameters |
temperature | 0.2–0.4 | Reduce random output probability, ensuring consistency and accuracy of inspection and review results |
| Workflow/API tool selection | auto | Automatically determine whether to call tools to process structured data verification, adapting to automated inspection of mold parameters |
Three Common Configuration Mistakes
- Issue: After local deployment, testing a newly added model returns a 403 status code (no body). Cause: The
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters are not configured correctly, causing the request to fail identity verification. - Issue: The chat model cannot recognize mold parameters separated by spaces in documents, leading to incorrect recognition of fields such as draft angle and shrinkage rate. Cause: The prompt does not explicitly specify spaces as parameter separators, or does not impose constraints on the format of structured data.
- Issue: The mold modification suggestions generated by the model conflict with the content of historical machining process cards. Cause: The
maxContextsetting is too small, and complete project historical records are not loaded, causing the model to fail to associate previous process requirements.
How to Confirm Proper Configuration
- Upload a single mold design task sheet and trial mold report, trigger inspection and review, and check whether the model can correctly recognize industry-specific fields such as draft angle and shrinkage rate.
- Initiate two consecutive model calls, verify whether the context retains the inspection conclusions and mold modification requirements from the previous conversation.
- Adjust the
temperatureparameter and initiate a test, check whether the consistency change of the output results meets expectations. - Initiate a model call after configuring identity verification parameters, confirm that there are no identity verification-related errors.
The values provided are common starting points and should be measured against the reader's own samples.