What the data for this category looks like
Data for this category originates from design BOMs, silicon steel sheet and insulating material ledgers, vacuum drying equipment logs, insulating oil test reports, and high-voltage test station records. Documents are generated per production batch or individual product, including winding and core assembly records, vacuum drying records, insulating oil test reports, partial discharge and withstand voltage test reports, and nameplate parameter tables. Data is updated synchronously upon completion of each individual product or batch of raw materials. Fields include insulation distance, water content, partial discharge peak value, tap position, and others. Units include millimeters, percentage, and picocoulombs.
What constraints these characteristics impose on model access and configuration
The need to trace full-process data for individual products requires that models be bound to exclusive datasets for each individual product to avoid mixing cross-batch data. Multiple types of structured and semi-structured documents coexist, so model invocation rules adapted to different field formats must be configured. High-frequency updates of individual product or batch data require that configured indexing and recall logic can quickly synchronize the latest data. Vector model matching must focus on core indicators of individual products to avoid interference from irrelevant data. The verification accuracy of core indicators such as insulation distance and partial discharge value is directly linked to product safety, so the confidence and consistency of model outputs must be constrained to avoid incorrect judgments.
This configuration guide applies to FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Enterprise-exclusive proxy address | Adapt to the enterprise's internal network environment to avoid public network access restrictions |
AIPROXY_API_TOKEN | Exclusive key assigned by the enterprise | Ensure the security of interface call permissions and prevent unauthorized access |
maxContext | 8000–12000 token | Adapt to the total content length of multiple documents for a single product, avoiding truncation of key verification information |
temperature | 0.1–0.3 | Reduce random output probability to ensure consistency and accuracy of insulation parameter verification |
quoteMaxToken | 2000–3000 token | Limit the reference length of recalled documents, focus on core test and assembly data for a single product, and meet the matching accuracy requirements of the vector model |
| Workflow/API tool selection | auto | Allow the model to automatically choose whether to call tools to complete parameter verification, adapting to the cross-check requirements of multiple types of documents |
Three common configuration errors
- A call returns a 404 status code. The cause is that
AIPROXY_API_ENDPOINTis configured as an invalid address, orAIPROXY_API_TOKENis not configured correctly. - A verification task returns a 500 internal error. The cause is that the value of
maxContextexceeds the context upper limit supported by the model, causing context overflow. - Recalled documents do not match the current product being verified. The cause is that an exclusive dataset for the individual product is not bound, resulting in cross-batch data being mixed in.
How to confirm the configuration is complete
- Call the test interface to check whether the configurations of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the preset values, and confirm that the access channel is normal. - Upload the complete documentation for a single transformer to trigger a cross-review task, and verify that the recalled documents only contain relevant data for that product, with no cross-batch data mixed in.
- View the verification results output by the model, confirm that the parameter matching logic complies with business rules, and there are no obvious random deviations.
- Simulate multiple data update operations to check whether indexing synchronization is timely, with no data delay or omission.
The values provided are common starting points and should be measured against the reader's own samples.