What this category of data looks like
Data for photovoltaic module encapsulation scenarios originates from wafer grading systems, glass adhesive film and backplane batch ledgers, factory MES systems, string welding and lamination equipment, EL/IV testers, and insulation withstand voltage equipment. Daily documentation includes layout diagrams, material batch records, lamination process records, EL defect images, IV test reports, insulation withstand voltage records, and packing lists. Data is updated per individual module or material batch. Fields include wafer current grade, lamination temperature and pressure, module power, crack coordinates, and other details, corresponding to record formats for different equipment and business processes.
Constraints imposed by these characteristics on the HTTP Interfaces and External Systems link
Data is updated frequently per single module or single batch, requiring interfaces to support small-batch, low-latency request processing. Reasonable timeout thresholds must be set to accommodate transmission time. Multi-source heterogeneous data sources (equipment, MES, test reports) require interfaces to support parsing of requests and responses in various formats. Clear field mapping rules must be established to ensure accurate data matching. Equipment inspection and maintenance Q&A requires associating real-time equipment status with historical process records, so interfaces must support precise queries by module ID or batch number. Unstructured data across different document types requires separate handling, so interfaces must distinguish between structured parameters and file upload paths.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {OneAPI assigned secret key} | Matches the authentication requirements of external systems, avoids authentication failure errors |
variables | Bind module ID and material batch number as request variables | Accurately matches business data for single modules or single batches |
responseData | Extract equipment operating parameters and test result fields | Quickly obtain core information required for inspection and maintenance |
| Request timeout threshold | 600 seconds | Adapts to the transmission and parsing time consumption of unstructured data such as EL images and IV reports |
| Tool call configuration | Enable functionCall and bind the equipment maintenance knowledge base | Supports automatic matching of knowledge base content for maintenance Q&A |
| Model access path | Fill in the OneAPI interface address of the locally deployed ChatGLM2 | Adapts to the calling requirements of local models |
Three Common Configuration Errors
- Symptom: Interface calls return a 401 status code, and the same secret key can be accessed normally using Postman. Cause: The
Authorizationparameter configured in FastGPT has an incorrect format, the Bearer prefix is missing, or the secret key was entered incorrectly. - Symptom: The locally deployed ChatGLM2 model cannot be selected when creating a knowledge base. Cause: The model access path and permissions are not correctly configured in config.json, and the service was not restarted to load the updated configuration.
- Symptom: The content extraction node cannot call the self-built model interface. Cause: The functionCall parameter was not correctly enabled in the tool configuration, causing the node to not support custom model interface calls.
How to Verify Successful Configuration
- Perform an interface authentication test, confirm the returned status code is 200, and verify that the
Authorizationconfiguration takes effect. - Enter a test module ID and batch number, check whether the interface returned data includes preset business fields.
- Trigger the tool call process, confirm that the node can automatically associate the bound equipment maintenance knowledge base content and generate corresponding Q&A results.
- Check the system configuration file, confirm that the model access parameters are consistent with the parameters of the locally deployed service, and verify that the model loads normally.
The values provided here are common starting points and should be evaluated against the reader's own samples.
This page configuration applies to FastGPT v4.17.0.