What data for this category looks like
This category of data comes from electromagnetic design documents, copper wire and insulating material batch ledgers, winding equipment operation logs, balancing machine collection data, electrical test bench test results, and ERP/MES system records. Documents including winding process cards, insulation treatment records, dynamic balance reports, withstand voltage insulation test reports, type and factory test reports are generated daily. Fields include winding turns, paint dipping process parameters, unbalance amount, withstand voltage value, temperature rise data, and more. Data updates occur per product or per batch of materials, and are synchronized each time a product type is changed.
What constraints these characteristics impose on model integration and configuration
The batch association characteristic of this category of data requires context isolation configured by batch dimension during model integration, to prevent confusion of audit results across batches of materials. Most engineering documents use numerical fields with units, so numerical parsing format rules must be specified in the model configuration. The high-frequency update characteristic requires limiting the context length of single-batch data when configuring maxContext, to avoid context overflow. Differences in fields across different document types require clear field mapping relationships during integration, to ensure the model can accurately identify core parameters required for audits.
How to set configurations
This configuration is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total text length of single-batch motor winding processes and test reports, to avoid context overflow |
maxTokens | 2000–3000 token | Meets the detailed output requirements of audit reports, covering full-process analysis such as winding turns verification and insulation parameter verification |
quoteMaxToken | 1500–2000 token | Limits the length of quoted document fragments, ensuring only core data related to the current audit item is extracted |
temperature | 0.1–0.3 | Maintains consistency of audit logic, avoiding random deviations in engineering parameter judgments |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployment address of the platform | Adapts to the proxy configuration for model integration, ensuring correct request routing |
AIPROXY_API_TOKEN | Fill in according to the valid token assigned by the platform | Completes identity verification, ensuring interface call permissions |
Three common configuration mistakes
- Phenomenon: The model reports an error during testing, prompting interface authentication failure or "no available model". Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled correctly in the configuration, or the proxy address and token do not match. - Phenomenon: Knowledge base query results show cross-batch data association, such as using winding data from batch A for audit of batch B. Cause: No context isolation rules by batch dimension are configured, causing the context to cache document data from multiple batches.
- Phenomenon: Audit results show numerical judgment deviations, such as incorrect winding turns verification or abnormal insulation parameter judgment. Cause: The
temperaturevalue is set too high, causing random fluctuations in the model's judgment of engineering numerical values.
How to confirm the configuration is complete
- Call the model test interface, input a segment of the winding process card for a single batch of motors, and check that the interface returns no authentication errors or parameter analysis errors.
- Upload a single-batch test report document, confirm that the knowledge base can correctly parse and associate the metadata of the corresponding batch, with no cross-batch data mixed in.
- Adjust the
temperatureparameter, then input the same audit scenario multiple times, and check that the consistency of the results meets expectations. - View the context log, confirm that only relevant document fragments of the current audit batch are loaded, with no irrelevant data included in the context.
The values provided are common starting points and should be measured against the reader's own samples.