This page is written based on FastGPT v4.17.0.
What Data for This Category Looks Like
The data for this category comes from electromagnetic design documents, copper wire and insulation material batch ledgers, winding equipment operation logs, dynamic balancing machine test data, electrical test bench test results, and ERP and MES systems. Daily produced documents include winding process cards, insulation treatment records, dynamic balancing reports, withstand voltage insulation test records, and type and factory inspection reports. Data is updated once per finished product or batch of materials. All linked documents are updated synchronously when the product type changes. Document fields include process parameters, test values, batch numbers, equipment numbers, and operator information. Units include turns, ohms, degrees Celsius, revolutions per minute, and other category-specific units.
Constraints Imposed by These Characteristics on Model Integration and Configuration
The process and exception documents for this category contain a large number of specialized parameters and test values. Adapt model integration to support multiple types of structured and semi-structured data. Data is updated once per finished product or batch of materials, and all linked documents are updated synchronously when the product type changes. Ensure the model can accurately match the process and test records of the corresponding batch when called. The documents include specialized units and technical terms such as winding turns, ohm values, degrees Celsius, and revolutions per minute. Ensure knowledge base parsing fully retains field and unit information during configuration. Exception reports involve multi-dimensional indicators such as vibration, temperature rise, and efficiency. Configure model calls to support multi-field associated analysis. Reasonably limit the context length to avoid redundant information interfering with model output.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the linked length of process and test documents for a single batch of motor manufacturing, avoiding exceeding the model context limit |
temperature | 0.1–0.3 | Process and exception reports require precise matching of values and procedures, reducing random output |
quoteMaxToken | 2000–3000 token | Retains sufficient professional details such as winding turns and insulation parameters for model reference |
| Similarity threshold | Calibrated based on actual testing | Matches exclusive documents for the corresponding batch, avoiding cross-batch content interfering with results |
AIPROXY_API_ENDPOINT | Fill in the API access address of the locally deployed model | Adapts to the call requirements of privately deployed models such as local QWQ-32B |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding model | Verifies call permissions for privately deployed models |
Three Common Configuration Mistakes
- Symptom: After uploading attachments such as winding process cards and dynamic balancing reports, the model does not respond. Neither simple applications nor workflow applications work. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the local model has not enabled the API access port. - Symptom: The model only returns the single text block with the highest knowledge base matching score, and cannot comprehensively output multiple blocks of content. Cause: The value range of
quoteMaxTokenis not adjusted, or a reasonable upper limit for recalled content is not set. - Symptom: After configuring the document parsing node, the model cannot read uploaded withstand voltage test records or type test reports. Cause: The document parsing node does not retain professional fields and units, or the uploaded file format exceeds the supported range.
How to Confirm Configuration Is Complete
- Upload a single winding process card, trigger model question and answer, and check whether the returned content includes the core process parameters in the document.
- Upload multiple test records from the same batch, and verify whether the model can associate data from different documents for analysis.
- View the API call log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, and there are no connection errors. - Adjust context-related parameters, compare the content range of the two outputs, and confirm that the parameter configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.