What data for this category looks like
Typical entities in this category include electroplating plants, electronic connector workshops, and hardware surface treatment workshops. Daily generated documents include bath solution analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data sources cover customer surface treatment specifications, bath solution test results, rectifier operating data, online water quality monitoring data, and hazardous waste management ledgers. Data update frequency falls into two categories: per bath cycle, per shift, and as required by fixed monitoring cycles. Fields and units vary across documents. For example, film thickness uses microns as the unit, and bath solution components are labeled with concentration values.
Constraints imposed on model access and configuration
The multi-source and multi-update rhythm characteristics of category data require the model access link to adapt to synchronization rules for different data sources. It also requires distinguishing processing logic for real-time monitoring data and periodic ledgers. The diversity of document structures and fields requires clear identification priorities and field extraction rules for different documents during configuration. This prevents the model from confusing different types of detection data. Strict error consequence requirements limit the divergence and context length of model-generated content. This ensures audit results comply with process specifications and environmental requirements. The proportion of image-based inspection reports requires configuration items to support visual recognition capabilities. This adapts to unstructured document formats.
Configuration settings
This guide is written for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | The category contains a large number of image-based documents such as film thickness reports and bath solution records, requiring the model to recognize text and numerical information in images |
maxTokens | 8000–12000 token | The total length of associated documents for a single plating batch (bath solution analysis, fixture records, wastewater data) is long, requiring sufficient context capacity |
maxContext | 10–15 associated documents | Must cover full-process data for a single production batch, while avoiding context overload that affects model response speed |
temperature | 0.1–0.3 | Inspection and audit require strict alignment with process specifications, so divergent content should be avoided to ensure result accuracy and consistency |
AIPROXY_API_TOKEN | Exclusive token generated by the platform | Must bind the usage permissions of the corresponding model to prevent permission denied errors |
| Workflow/API tool selection | auto | Must automatically select between calling the knowledge base or directly generating audit conclusions based on user queries, adapting to different inspection scenarios |
Three common configuration errors
- A
403 Token does not have permission to use the modelerror is returned during calls. This occurs when current model permissions are not added in the token configuration of the model platform, or the model version bound to the token does not match the FastGPT configuration. - Image-based inspection reports cannot be recognized correctly. This occurs when the
visionconfiguration item is not enabled, or image-format documents are not uploaded correctly to the knowledge base. - Generated audit content does not comply with preset audit specifications. This occurs when the
temperatureparameter is set too high, causing the model to generate divergent content that fails to strictly match the specific requirements of the electroplating process.
How to verify a successful configuration
- Submit the text or image of a single bath solution analysis record. Check whether the model can correctly extract core fields such as bath solution composition and detection time.
- Initiate a model call test. Check whether the length of the returned result meets audit requirements, and confirm that parameters such as
maxTokenstake effect. - Verify the token configuration. Initiate a model call request, and confirm that no 403 permission error occurs.
- Upload multiple associated documents, such as fixture records and wastewater monitoring data. Check whether the model can integrate associated information to generate a complete audit conclusion.
The configuration values listed are common starting points. They should be evaluated against the specific samples and operational needs of the deployment scenario.