What Data for This Category Looks Like
Data for this category originates from engineering, production, quality control, and laboratory departments of printed circuit board factories. It includes documents such as engineering work instructions, Gerber parsing records, drilling and lamination parameters, chemical solution analysis, AOI reports, electrical test reports, and slice inspection reports. Data sources cover customer-provided Gerber files, drill tapes, BOMs, CAM data, equipment parameters, bath solution test results, AOI data, electrical test data, and laboratory data. Update frequencies include per production batch, per shift, and continuous collection. Some test data such as AOI reports is generated in real time. Data includes text parameters, CAD format files, and image-based test results. Fields cover professional manufacturing content such as process parameter values, defect coordinates, and concentration metrics.
What Constraints These Characteristics Impose on Model Access and Configuration
Mixed multi-source data types require model access to support multimodal processing of text and images, to adapt to image parsing needs for AOI reports and slice reports. High-frequency updated production data requires the single-processing context length to match the total data volume of a single production batch, to avoid processing failures caused by context overflow. Professional manufacturing fields and document formats require the model to have industry terminology comprehension capabilities. Additionally, quality audit errors can lead to full batch board scrap or high-value claims, so strict control of model output accuracy is needed. This requires reducing the model's randomness parameters to avoid incorrect judgments.
How to Set the Configuration
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
vision | Enabled (for multimodal models) | PCB manufacturing scenarios include image-based test data such as AOI reports and slice reports. Visual understanding capability must be enabled to parse image defect content |
temperature | 0.1–0.3 | Process quality audit requires strict matching of test data and process parameters. Low temperature reduces the probability of model hallucinations, avoiding incorrect defect type judgments |
maxTokens | 8000–12000 token | The total length of process parameters and test reports for a single production batch is large. Long context processing capability must be supported. Adjust based on actual data volume via on-site testing |
quoteMaxToken | 1500–2000 token | Limit the length of a single referenced document, to avoid excessive context resource usage from single files such as AOI reports or Gerber parsing records |
AIPROXY_API_ENDPOINT | Fill in the internal enterprise model proxy address | Adapt to manufacturing data confidentiality requirements, connect to proprietary or compliant third-party large model services |
AIPROXY_API_TOKEN | Fill in the access key assigned by the enterprise | Ensure the security of model calls, prevent unauthorized access to enterprise production and test data |
Three Common Mistakes
- Attempting to call a front-end embedded page results in failed loading. FastGPT v4.17.0 only supports API call mode. Front-end interfaces must be developed via API integration with proprietary systems.
- Model configuration tests pass, but the application cannot function normally. This occurs because the application is not linked to the configured model parameters, or the
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTconfigurations are incorrect, leading to model call failures. - Image-based test data cannot be parsed correctly. This occurs because the
visionparameter is not enabled, or a model with multimodal capabilities is not connected, so image content from AOI reports or slice reports cannot be read.
How to Confirm Configuration Is Complete
- Pass process parameters and AOI report images for a single production batch to the model test interface, verify that the model can correctly identify defect types and locations.
- Check the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations, send a simulated call request, confirm that network connectivity and access permissions are normal. - Adjust the
temperatureparameter to 0.1 and 0.3, compare the defect judgment results from two model outputs, confirm that result consistency meets quality audit requirements. - Pass both Gerber parsing records and electrical test reports simultaneously, verify that the model can integrate the two types of data to complete the full quality audit process.
All provided configuration values are common starting points, and should be validated against deployment-specific samples.