What the data for this category looks like
Data sources include incoming silicon wafer inspection, production MES system data, real-time operating data from diffusion furnaces, coating equipment, and printing presses. It also includes wafer-level test results from EL and IV testing equipment, and laboratory physicochemical analysis records. Updates follow production batches, individual wafer test results, and daily summary updates. Daily documents include process cards, diffusion and coating parameter tables, printing and sintering process records, EL defect detection images, IV performance grading data, and yield summary reports. Document fields include etching depth, sheet resistance, coating thickness, defect coordinates, and electrical performance parameters. Some documents include associated data that binds visual images and numerical values.
Constraints imposed on model access and configuration by these characteristics
The coexistence of multi-source wafer-level data, batch-level documents, and visual images for this category requires model access to support multimodal input and long context processing. The total length of associated documents for a single batch may exceed general context limits, so context parameters must be configured appropriately. Production data deployed on internal networks cannot be exposed externally, so model APIs must be accessible in internal network environments, and public network model channels cannot be relied on. Associated analysis of professional process parameters and visual defects requires vector models to adapt to professional terminology in the electronic manufacturing field. Recall results must cover multi-dimensional associated documents. Errors in production links directly affect product performance, so model call stability and failure troubleshooting mechanisms must adapt to network restrictions in internal network environments.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total token count of associated documents for a single photovoltaic cell batch typically falls within this range, preventing truncation of critical process-related information |
vision | Enabled | EL detection images and IV test visual data must be processed to enable cross-review of images and numerical parameters |
temperature | 0.1–0.3 | Process review requires rigor, avoiding inferred results that deviate from actual parameters |
AIPROXY_API_ENDPOINT | Local API address of internally deployed models | Production data resides on internal networks, so only internally accessible model APIs should be used to prevent data leaks |
| Workflow/API tool selection | auto | Automatic judgment of whether to call tools to supplement document data is required to adapt to cross-verification of multi-source documents |
quoteMaxToken | 1500 token | Token proportion of a single core process document, ensuring recalled reference content sufficiently supports review logic |
Three Common Configuration Mistakes
- Model channel calls return
error, and call logs show abnormal data acquisition. The cause is failure to configure an internally accessible API endpoint, and attempts to call public network model channels result in the internal network server being unable to connect to the target interface. - Vector model recall results fail to cover professional process parameters. The cause is using a vector model that is not adapted to the electronic manufacturing field, which cannot accurately match professional fields and terms for photovoltaic cell manufacturing.
- API responses do not return chain-of-thought content, only displaying final review conclusions. The cause is failure to enable relevant configurations for chain-of-thought output, or the selected model channel does not support the return format for chain-of-thought content.
How to Verify a Complete Configuration
Based on FastGPT v4.17.0, the following actions can be used to verify that the configuration is complete:
- Initiate a model call test in the internal network environment, and check whether the interface returns normally without connection timeout or permission errors.
- Import associated documents for a single photovoltaic cell batch, and check whether the model can correctly identify the association between images and numerical parameters.
- View call logs to confirm that model call parameters match the configured items, with no missing parameters or incorrect assignments.
- Trigger an abnormal call scenario, and check whether the failure handling process can normally record logs and provide troubleshooting directions.
The values provided here are common starting points, and should be measured against the reader's own samples.