What this type of data looks like
Photovoltaic module encapsulation quality traceability data comes from multiple sources. These sources include battery cell grading records, glass adhesive film backplane batch ledgers, workshop MES systems, string welding and lamination equipment logs, EL defect detection images, IV power test reports, insulation voltage resistance records, and finished product packing lists. Each module generates an independent associated dataset. Batch materials sync updated batch ledgers per production batch. Each single dataset includes structured process parameters, unstructured images and text reports. Fields cover battery cell current gear, lamination temperature and time, crack coordinates, power values, and more. Cross-source association and matching are required for this data.
What constraints these characteristics impose on model access and configuration
Cross-source heterogeneous multi-format data requires model access to support multimodal input. It must adapt to image document parsing such as EL images. The high update frequency of single-module datasets requires limiting the context window size. This avoids excessive processing delays that harm real-time traceability efficiency. The need for precise matching of structured fields requires model output to have high certainty. The setup must also adapt to enterprise internal network isolation environments. This ensures secure transmission of sensitive production data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | Photovoltaic module encapsulation data includes image documents such as EL defect maps and IV test curves. Model support for image parsing is required to complete defect localization |
maxContext | 8000–12000 token | The total token count of batch records, device logs, and test reports associated with a single module is moderate. This range covers all associated data and avoids context overflow |
maxTokens | 2000–3000 token | A single defect analysis output must cover defect localization, cause traceability, and rectification suggestions. This range ensures output completeness |
temperature | 0.1–0.3 | Quality traceability requires precise matching of process parameters and defect associations. A lower temperature reduces output randomness and improves conclusion credibility |
AIPROXY_API_ENDPOINT | Fill in the enterprise internal proxy interface address deployed by FastGPT | Adapt to the workshop network isolation environment to ensure secure transmission of sensitive production data |
AIPROXY_API_TOKEN | Fill in the enterprise-specific proxy authentication token | Used for interface identity verification, restricting unauthorized access to model services |
Three common configuration mistakes
- Phenomenon: After configuring a third-party proxy platform, no channel option appears in the FastGPT interface, or the test request returns a 401 unauthorized error. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled in correctly, or the proxy interface does not adapt to FastGPT’s call format. - Phenomenon: After uploading an EL image or DOC document, analysis cannot be triggered, or the parsing result shows garbled characters. Cause: The
visionconfiguration is not enabled, so the model cannot process images or non-standard format text documents. - Phenomenon: The thinking process reference fragments in the model output have messy formatting, or redundant irrelevant content appears. Cause:
maxContextandquoteMaxTokenare not set reasonably, leading to an overly large context window that introduces too many non-associated data.
How to confirm the configuration is complete
- In FastGPT v4.17.0, a single EL defect map and corresponding batch process records are uploaded. A defect analysis test request is initiated, and verification is performed to confirm whether the model can correctly identify crack locations and associate corresponding battery cell batch parameters.
- The thinking process fragments in the model output are reviewed. Confirmation is performed that referenced content is relevant to the current analysis task, and that no messy formatting or redundancy is present.
- Interface call logs are reviewed. Confirmation is performed that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations have no format errors, and that no connection timeout or authentication failure errors are present. - The
temperatureparameter is adjusted to 0.2 and 0.3. Defect matching results from the two outputs are compared, and confirmation is performed that the impact of parameter adjustment on output certainty meets expectations.
The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.