What the data for this category looks like
Business data for electric motor and generator manufacturing comes from electromagnetic design data, copper wire and insulation material batches, winding equipment, balancing machines, electrical test benches, and ERP/MES systems. Data is updated per individual motor or per motor batch, and synced after each type modification. Daily documents include winding process cards, insulation treatment records, dynamic balancing reports, dielectric strength test records, type and factory test reports. Fields cover winding turns, insulation thickness, test voltage, temperature rise values, and similar metrics, with corresponding units of turns, millimeters, kilovolts, and degrees Celsius.
Constraints on model access and configuration
Data is updated per individual motor or per batch, and includes three categories of professional fields: electromagnetic, insulation, and mechanical balancing. Context scope must be limited during model access to avoid redundant data interfering with professional field parsing. Document sources cover multiple types of equipment and systems. Access formats for multi-source data must be adapted, and the token limit for single calls must be controlled to avoid timeouts caused by excessive data volume. Bid documents must strictly match professional values. Generation randomness must be reduced to ensure data accuracy.
How to set configurations
The following configuration instructions are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total length of documents such as test reports and process cards required for a single motor bid, to avoid exceeding the model's context limit |
maxTokens | 2000–3000 token | Meets the generation length requirements for bid documents and quotations, while controlling single-call costs |
quoteMaxToken | 1500 token | Limits the maximum length of recalled professional documents such as winding process cards, ensuring core data is prioritized for reference |
temperature | 0.2–0.4 | Reduces generation randomness to ensure the accuracy of professional values such as winding turns and test voltage |
AIPROXY_API_ENDPOINT | Fill in the official address provided by the model service provider | Adapts the interface paths of various accessed large models |
AIPROXY_API_TOKEN | Fill in the exclusive key applied for | Completes identity verification for model calls and ensures interface security |
Three common misconfigurations
- Phenomenon: Model calls return
Cannot read properties of null (reading 'q'). Cause:AIPROXY_API_TOKENor the interface address is not configured correctly, causing the model to return empty response fields. - Phenomenon: When accessing the DeepSeek Code model, generated quotation content deviates from motor professional data. Cause: The
maxContextscope is not limited, and irrelevant code training data is mixed in. - Phenomenon: Unable to normally access the specified model. Cause: The model has not been confirmed to support calls via the FastGPT proxy interface, or the adaptation path of
AIPROXY_API_ENDPOINTis not configured correctly.
How to confirm correct configuration
- Verify that the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches the official information provided by the model service provider. - Initiate a test call to check whether the returned content includes accurate references to motor manufacturing professional fields such as winding turns and test voltage.
- Check the number of recalled context documents to ensure that only winding process cards, test reports and other data corresponding to the current bid project are included.
- View the call log to confirm that there are no error messages of the
Cannot read properties of nulltype.
The values provided are common starting points and should be measured against the reader's own samples.