Data characteristics of this category
Business data for photovoltaic module encapsulation comes from battery sheet grading ledgers, glass adhesive film and backplane batch records, workshop MES systems, string welding and lamination equipment logs, and EL/IV tester output files. Daily generated documents include module layout diagrams, material batch traceability forms, lamination process parameter records, EL crack detection images, IV power test reports, insulation voltage withstand test records, and finished product packing lists. Data updates follow this schedule: corresponding test records are generated for each module, and batch files are synchronized for each batch of raw materials. Fields included are material specifications, process parameters, test values, equipment numbers, and test times.
Constraints on the "model access and configuration" link
This category has high-frequency, small-volume multimodal data. This requires model access to adapt to image format input, and the vision configuration must be enabled. Mixed structured and unstructured data comes from multiple sources. Configuration items must support document parsing for different formats. Context length must be limited to avoid interference from redundant information. Each module has independent data update rhythm. The maxTokens value for a single request must match the information scale of a single test record, to avoid exceeding the model's processing limit. Business errors have severe consequences. A fault-tolerance mechanism must be configured to handle scenarios where model calls fail.
How to set the configuration
The following configuration items are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
vision | Enabled | Need to process image-based test data such as EL images and IV test reports |
temperature | 0.2–0.4 | Ensure consistency in quality judgment, avoid random deviations affecting audit results |
maxTokens | 800–1200 token | Adapt to the total information scale of test records and associated documents for a single module |
maxContext | 4000–6000 token | Limit the total context for a single request, avoid redundant data interfering with model judgment |
| Workflow/API tool selection | auto | Automatically select whether to call tools for batch data matching and gear query |
Three common mistakes
- Format errors occur when calling multimodal models, with the output failing to recognize crack descriptions in EL images. The cause is that the
visionconfiguration is not enabled, so the model cannot parse image input data. - Model calls time out or return empty results, with the workflow node status showing a timeout error. The cause is that
maxContextormaxTokensis set too large, exceeding the model's processing limit. - No failure retry mechanism is configured, with the workflow terminating directly after an unstable model call fails. The cause is that no standby model call logic is configured through the workflow's exception branch, and scenarios where the model is unstable are not covered.
How to confirm the configuration is correct
- An EL test image is uploaded to trigger a model call, and the output is checked for a judgment result of the image content, confirming that the
visionconfiguration is effective. - The complete test record for a single module is imported, and the context length of the model output is verified against the preset range, confirming that the
maxContextandmaxTokensconfigurations are reasonable. - A model call failure scenario is simulated, and verification is performed to confirm whether the workflow automatically switches to the standby model, confirming that the fault-tolerance mechanism is configured correctly.
- API-related configurations are checked, a test call is initiated, and confirmation is made that the interface returns normal audit results.
The values provided are common starting points and should be measured against the reader's own samples.