What Data for This Category Looks Like
Daily documents for this category include winding process cards, insulation treatment records, dynamic balance reports, dielectric strength insulation test reports, and type and factory test reports. Data sources cover electromagnetic design data, copper wire and insulating material batch records, output data from winding equipment, balancing machines, and electrical test benches, as well as production and quality inspection data from ERP and MES systems. Data is updated once per motor or production batch, and all associated documents are synchronized for updates when a type change is made. Documents include professional fields such as winding turns, insulation thickness, test voltage, and temperature rise values, with units mostly being industry standard units such as turns, millimeters, kilovolts, and degrees Celsius.
Constraints Imposed on Model Access and Configuration Workflows
Documents for this category mostly come from production equipment and management systems. Data updates are frequent and closely linked. Model access configurations must support fast loading and verification of high-frequency incremental data.
Documents include professional fields such as winding turns and insulation parameters. Models must accurately identify and cross-check these fields. Configurations must limit context and output length to avoid truncating critical information.
This category is subject to constraints from electromagnetic, insulation, and rotating machinery balance requirements. Technical documents require cross-category cross-review. Configurations must support multi-document associated retrieval.
Data errors can lead to serious consequences such as winding burnout and insulation breakdown. Configurations must reduce randomness in model outputs to ensure consistency of verification results.
How to Set the Configurations
The following configurations are based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of electric motor manufacturing documents, covering multiple associated documents such as winding processes and test reports |
maxTokens | 2000–3000 token | Ensures complete output of professional terminology and test data, avoiding truncation of critical verification results |
quoteMaxToken | 1500 token | Limits the fragment length of referenced documents, ensuring accurate matching of fields such as winding turns and insulation parameters during cross-review |
temperature | 0.1–0.3 | Reduces random output, ensuring consistency of professional parameter verification and avoiding tampering with document field formats |
AIPROXY_API_ENDPOINT | Fill in local or cloud endpoints according to the deployment environment | Adapts to the needs of calling locally deployed large models or public cloud models commonly used by motor manufacturers |
AIPROXY_API_TOKEN | Fill in according to the actual authorization key | Ensures the security of model calls and prevents unauthorized access |
Three Common Configuration Mistakes
- Interface displays
Request Error, and local model configuration fails. The cause is thatAIPROXY_API_ENDPOINTpointing to the locally deployed large model endpoint is not filled in correctly, or the correctAIPROXY_API_TOKENis not configured. - Model response delay increases significantly when concurrency rises, accompanied by database query and write timeouts. The cause is that the reasonable range of
maxContextis not limited, causing each request to load too much motor test report data and increasing system load. - Extra spaces appear in model outputs, or letter capitalization is tampered with. The cause is that
temperatureis set too high, or the prompt does not explicitly require retaining the original document's field format and case rules.
How to Confirm the Configuration Is Successful
- Upload a single winding process card and its corresponding dielectric strength test report, initiate a cross-review task, and check whether the returned results accurately match fields such as winding turns and test voltage.
- Initiate multi-batch concurrent test requests, observe interface response times and system logs, and confirm that there are no persistent timeout errors.
- Call the locally deployed large model, check whether the returned document sorting and verification results conform to professional logic, and there are no abnormal format tampering.
- Verify the validity of
AIPROXY_API_TOKEN, and no unauthorized access-related errors occur in high-concurrency scenarios.
The values provided are common starting points and should be measured against the reader's own samples.