What this category of data looks like
Data for this category typically comes from silicon wafer incoming material records, workshop MES systems, operation logs of diffusion furnaces, coating equipment, and printing presses, EL and IV test results, and laboratory test data. Daily retained documents include process cards, diffusion and coating parameter configuration tables, printing and sintering process records, EL detection images, IV grading data reports, and yield analysis reports. Data updates synchronize with each production batch and each product inspection, and a full workshop-level statistical document is generated daily. Data fields cover process parameters, equipment operating status, test results, grading information, and more, with units including professional industrial parameters such as temperature, power, and current.
What constraints do these characteristics impose on the "model access and configuration" link
This page provides configuration instructions based on FastGPT v4.17.0. The data for this category includes multi-source heterogeneous formats such as images, structured reports, and equipment logs, with fine update granularity and high frequency. These traits impose multiple constraints on model access and configuration. First, enable the corresponding configuration to support visual reasoning for processing EL detection images. Second, limit the context length to avoid overflow, as single-batch data volume is large. Third, adjust tool call logic flexibly, as multi-source data requires the model to automatically adapt to different input formats. In addition, industrial scenarios require real-time response, so set interface call timeouts to match the conventional duration of equipment data return, to avoid disrupting production link response needs.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Required to process image-based inspection data such as EL detection images, to support visual recognition of defects including micro-cracks and color differences |
maxContext | 8000–12000 token | Matches the total character count of single-batch production data, to prevent abnormal model responses caused by context overflow |
maxTokens | 2000–3000 token | Adapts to the conventional response length of equipment inspection and maintenance Q&A, to meet output needs for process parameters and troubleshooting steps |
| Workflow/API tool selection | auto | Automatically selects whether to call tools, to adapt to parsing and retrieval requirements for multi-source heterogeneous data |
temperature | 0.2–0.4 | Reduces random errors, ensures consistency of troubleshooting conclusions, and matches the rigor requirements of industrial scenarios |
AIPROXY_API_TOKEN | Fill in the key provided by the model provider | Completes identity verification for model calls, to ensure only authorized production scenarios can initiate requests |
Three common configuration errors
- Phenomenon: Tool triggering fails during large model calls for equipment inspection Q&A, but knowledge base search tests return results normally. Cause: The
toolChoiceparameter is not configured correctly, or tool call permission binding does not cover process document data sources related to photovoltaic equipment. - Phenomenon: No call logs appear on the model provider page, making troubleshooting of failed equipment data parsing impossible. Cause: FastGPT's call log collection configuration is not enabled, or the log storage path does not meet industrial scenario permission requirements.
- Phenomenon: Maintenance suggestions generated by the model do not match current photovoltaic cell process parameters, with redundant or incorrect configuration content. Cause: The
temperatureparameter is set too high, causing the model to generate random conclusions that deviate from photovoltaic manufacturing scenarios.
How to confirm the configuration is complete
- Upload a single EL detection image and the corresponding batch's IV grading data, trigger the equipment inspection Q&A, and check whether the model correctly identifies image defects and associates corresponding process parameters.
- View the FastGPT call log panel, confirm that each model request generates a normal response and returns references to the corresponding data source.
- Adjust the
temperatureparameter range, and verify whether the consistency of model responses meets the rigor requirements of industrial scenarios. - Access multi-batch production data, check whether the
maxContextparameter can carry batch data normally without context overflow errors.
The values listed above are common starting points, and should be evaluated against operational samples specific to the deployment context.