What Data for This Category Looks Like
Data for this category comes from electromagnetic design documents, copper wire and insulating material batch records, winding equipment operation logs, balancing machine test data, electrical test bench reports, and production work orders from ERP/MES systems. Data is updated per individual motor or batch of generators, and synchronized each time a process type change occurs. Daily documents include winding process cards, insulation treatment records, dynamic balance reports, withstand voltage insulation test records, type and factory test reports. Core fields include winding turns, paint dipping and drying temperature, dynamic balance residual unbalance, and withstand voltage test voltage value. Units include turns, degrees Celsius, gram-centimeters, kilovolts, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The strong binding characteristic of single-unit or single-batch data requires that dialogues associate the ledger context of the corresponding batch via chatId, to avoid mixing cross-batch data. Differences in fields across multiple types of professional documents and requirements for professional units mean prompts must clearly specify extraction rules and unit matching logic for different documents. Frequently updated scattered data sources will cause context length to expand rapidly. The effective range of dialogue history must be limited to prevent the model from calling redundant data. Professional test data has precision requirements, so prompts must clearly define outlier judgment dimensions to avoid incorrect conclusions from fuzzy matching.
How to Configure Settings
The configuration examples on this page are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chatId | Production work order number for a single motor or generator batch | Binds exclusive ledger context for a single batch, avoids cross-batch data mixing |
messages | Only pass dialogue history and corresponding ledger documents for the current batch | Prevents introduction of irrelevant batch context, ensures accuracy of data called by the model |
| Maximum context length | 8000–12000 tokens | Controls total length of dialogue history and documents, adapts to processing load of professional data |
variables | Preset qualified ranges for winding turns, qualified voltage intervals for withstand voltage tests | Standardizes business rules as reusable variables, simplifies rule descriptions in prompts |
| Single search limit | Top 4 matching documents | Filters non-core historical records, focuses on valid production data for the current batch |
| Timeout period | 600 seconds | Prevents dialogue interruption from long wait times, matches search and calculation time for professional data |
Three Common Misconfigurations
- Symptom: No response for an extended period after dialogue initiation, interface continuously displays "Searching knowledge base" with no results returned. Cause: Failed to bind single-batch ledger via
chatId, causing search scope to cover all historical data and exceed system load threshold. - Symptom: Model cannot associate context from the previous dialogue, cannot continue ledger analysis for the current batch. Cause: Incorrectly configured context transfer rules for the
messagesparameter, or maximum context length set too small, truncating valid dialogue history. - Symptom: Generated report fields have chaotic formatting, do not match units and precision requirements of business documents. Cause: Prompts failed to clearly specify units and value ranges for professional fields, and did not standardize business rules using
variables.
How to Verify Correct Configuration
- Initiate a query about winding turns for a single motor, verify that results returned by the model match the exclusive ledger data for that work order.
- Initiate two consecutive queries, verify that the model can associate context from the previous query and continue analysis logic for the current batch.
- Trigger a request to generate a production ledger summary report, verify that output fields include preset professional terms and units, with formatting meeting business requirements.
- Simulate multiple concurrent dialogue requests, observe that system responses remain stable with no abnormal errors.
The values provided here are common starting points, and should be evaluated against the samples utilized in actual operations.