What the Data for This Category Looks Like
Data for the photovoltaic module encapsulation category comes from the battery sheet grading system, glass adhesive film backplane batch ledger, workshop MES system, string welding and lamination production equipment, EL defect detector, and IV power tester. Data is updated in real time per individual component and material batch. Daily documents include layout drawings, material batch records, lamination process records, EL defect images, IV test reports, insulation voltage withstand records, and finished product packing lists. Data fields include battery sheet current gear, lamination temperature and pressure parameters, EL crack location, power value, insulation voltage withstand value, and cover all production process nodes for photovoltaic module encapsulation.
Constraints on Model Access and Configuration
This category has high data update frequency and small single-item size. Model access must adapt to high-frequency, small-batch query requests to avoid context overflow. Documents include image-based EL defect images, so models must support visual recognition to parse defect information. Data fields focus on production processes and test results. Configuration must ensure models can accurately correlate battery sheet current gears, lamination parameters, and quotation coefficients. Bid documents must match material batches and power grades, so context length must be limited to avoid irrelevant data interfering with quotation calculations.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to multi-document splicing requirements for photovoltaic module encapsulation, avoids exceeding model context limits |
maxTokens | 2000–3000 tokens | Matches the output length of bid documents and quotation preparation, ensures coverage of complete quotation details and technical descriptions |
quoteMaxToken | 1500 characters | Limits the length of referenced production documents, focuses on material and process data related to the current bid batch |
vision | Enabled | Used to parse EL defect images and IV test report charts, extract crack and power abnormality information |
temperature | 0.2–0.4 | Reduces output randomness, ensures consistency between quotations and process parameters |
AIPROXY_API_ENDPOINT | Fill in according to deployment rules | Adapts to the proxy call standard of FastGPT v4.17.0 |
AIPROXY_API_TOKEN | Fill in according to the obtained key | Completes identity verification for model calls |
Three Common Mistakes
- Phenomenon: The configured
AIPROXY_API_ENDPOINTaddress returns a connection failure during testing. Cause: Incorrect proxy address entered, or access permissions for the corresponding port not enabled. - Phenomenon: The bid document returned after model call does not include EL defect-related content. Cause: The
visionconfiguration item is not enabled, so the model cannot parse image documents such as EL defect images. - Phenomenon: Previous conversation content interferes with current quotation calculation when calling the same workflow ID multiple times. Cause: Correct context retention rounds are not configured, leading to redundant historical data being included in model input.
How to Confirm Proper Configuration
- Upload test EL defect images and IV test reports, confirm the model can accurately extract crack locations and power values.
- Enter simulated bid batch parameters, verify that the quotation details output by the model are associated with corresponding material batches and battery sheet current gears.
- Call the same workflow ID twice, confirm that the context of the second call only includes the content of the current request.
- Test the configured
AIPROXY_API_ENDPOINTaddress, confirm that model call requests can be initiated normally.
The values provided are common starting points and should be evaluated against the reader's own samples.