What the Data for This Category Looks Like
The quality traceability data for electric motor and generator manufacturing mainly comes from electromagnetic design data, copper wire and insulating material batch records, winding equipment logs, balancing machine detection data, electrical test bench reports, and production work orders from ERP/MES systems. Daily associated documents include winding process cards, insulation treatment records, dynamic balance reports, withstand voltage insulation test reports, and type and factory inspection reports. Data is updated per single motor or per product batch, and synchronized after each process type change. Core fields include winding turns, insulation thickness, test voltage, temperature rise value, batch number, etc. There is no unified fixed format, and field differences exist depending on the production link.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Data for this category is associated by batch and has high professional field requirements. Multi-turn dialogue must maintain context for a single batch to avoid mixing data across batches. There are large differences in document fields, so prompts must explicitly specify extracting professional fields corresponding to the relevant production link to prevent retrieving irrelevant documents. Data has a high update frequency, so the length of dialogue context must be limited to avoid diluting key information with overly long historical messages. At the same time, document formats vary across production links, so prompts must guide the model to match the document structure of the corresponding link to ensure accurate retrieval results.
Configuration Settings
This configuration is formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique batch number for each motor or product batch | Maintain multi-turn dialogue context for a single batch to avoid cross-batch data contamination |
messages | Limit single-turn dialogue context to no more than the previous 12 rounds of history | A single batch has a large volume of documents; overly long context will dilute professional field information, calibrated based on actual testing |
variables | Preset two global variables: batch number and test date | Quickly associate ERP/MES data for the corresponding batch and reduce repeated input content |
| Single search limit | Top 6 matching documents | Documents for a single batch of this category usually do not exceed 10 copies; too many results will increase the model's inference load |
| Maximum context | 800–1200 characters | Professional documents have many fields; overly long content will exceed the model's context window, calibrated based on actual testing |
Three Common Misconfigurations
- Phenomenon: Dialogue return results mix winding data from different motor batches. Cause:
chatIdis not bound to maintain single-batch context, leading to cross-batch context contamination during multi-turn dialogue. - Phenomenon: Knowledge base association function fails to accurately match documents for the corresponding batch. Cause:
variablesare not used to preset the batch number as a retrieval filter condition, resulting in retrieval coverage of all production data. - Phenomenon: Dialogue response times out or content is truncated. Cause: The context length of
messagesis not limited, and too many historical messages cause the model's inference time to exceed the threshold.
How to Confirm Proper Configuration
- Enter keywords for the dynamic balance report of a specific motor batch, check if the documents returned by the dialogue only associate records for that batch.
- Launch a multi-turn dialogue, consecutively ask questions about three different batches, check if the context is maintained independently per batch with no cross-batch mixing.
- Review system logs to confirm whether the batch number parameter in
variablesis correctly included in the retrieval request. - Test a single-turn dialogue with more than 12 rounds of historical messages, check if the system automatically truncates the content or prompts that the context exceeds the limit.
The values provided are common starting points and should be measured against the reader's own samples.