What the data for this category looks like
Data for this category is generated around the full production lifecycle of individual transformers and high-voltage electrical equipment. Data sources include design BOM, silicon steel sheet and insulating material inbound data, vacuum drying equipment logs, oil test reports, and partial discharge and withstand voltage test records from the test station. Daily documents include winding and core assembly records, vacuum drying records, insulating oil reports, partial discharge and withstand voltage test reports, and nameplate parameter tables. Data is updated per product and per batch of raw materials. Core fields include insulation moisture content, partial discharge values, tap position parameters, and others. Full lifecycle ledger traceability for individual products must be supported.
Constraints on tool calling and plugins from these characteristics
The requirement for full lifecycle ledgers for individual products means tool calling must strictly associate corresponding production and test data with the product’s unique ID, to avoid mixing cross-batch data. Multiple data sources mean plugins must adapt to format parsing of different device logs and test reports, and unify field mapping rules. The update rhythm per product or batch requires that the tool calling context window is limited to the current batch’s documents, to avoid introducing irrelevant data. The professional unit requirements for core insulation and high-voltage test fields mean plugins must retain original unit identifiers when extracting data, to prevent parameter misuse. Additionally, due to the serious consequences of faults, an out-of-range value check step must be added after tool calling.
Configuration settings
The following configurations are formulated based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Supports automatic tool selection for multi-source production data, matching the ledger calling needs of the full production process for individual products |
| Stop sequence | Prefixed with product batch number | Distinguishes production ledger data from different batches, prevents confusion in tool calling results |
| Plugin context window | 800–1200 characters | Matches the total length of core documents for individual products, prevents context overflow |
| Workflow/API function-call configuration | Force return specified core fields | Ensures completeness of extracted parameters such as insulation moisture content and partial discharge values, and retains unit identifiers |
| Tool calling timeout threshold | 60 seconds | Adapts to the response duration of test station device interfaces, avoids excessive waiting that affects report generation |
| Deep thinking tool trigger condition | Only when partial discharge field is abnormal | Enables for abnormal scenarios of core test data, optimizes computing resource usage |
Three common errors
- Symptom: Ledger data returned by tool calling spans multiple product batches, and fields are mixed. Cause: The stop sequence configuration is not bound to the product’s unique identifier, resulting in failure to correctly separate calling results from different batches.
- Symptom: AxiosError 404 is returned when running a code plugin. Cause: The data source interface path configured for the plugin does not match the actual API endpoint of the test station device, or interface permissions are not correctly configured.
- Symptom: Deep thinking tools trigger in all requests, occupying a large amount of computing resources. Cause: The trigger condition for the deep thinking tool is not limited, resulting in resource consumption beyond scene requirements.
How to confirm the configuration is correct
- Submit a ledger aggregation request for a single specified product, check that all data returned by tool calling belongs to the product’s batch and test type.
- Trigger plugin calling, confirm that all returned core fields carry original unit identifiers and have no missing values.
- Simulate a scenario where the partial discharge value is abnormal, check that the deep thinking tool only triggers under this condition.
- Submit multiple test requests, confirm that no persistent 404 errors occur during plugin calling, and interface call stability meets expectations.
The values given are common starting points and should be measured against the reader's own samples.