What this category of data looks like
Three sources supply aviation equipment yield rate data: publicly released industry operation data from military industry regulatory authorities, regular operating announcements of listed military enterprises, and official public disclosures of defense equipment procurement projects. The update schedule follows three distinct patterns:
- Daily market data updates within 1 hour after market close
- Quarterly and semi-annual operating data updates within 3 working days after the corresponding financial report disclosure deadline
- Major order-related data updates simultaneously with official announcement releases
Each data entry includes fields such as equipment model, production batch, unit production cost, unit revenue, delivery quantity, current period delivery revenue, and total current period related costs. The unit for unit production cost is ten thousand yuan per set, unit revenue is ten thousand yuan per set, delivery quantity is units/frames, current period delivery revenue is ten thousand yuan, and total current period related costs is ten thousand yuan.
Constraints for multi-turn dialogue and prompt engineering
The multi-source, varied update schedule of aviation equipment yield rate data imposes specific requirements on multi-turn dialogue processes and prompts. Multi-turn dialogue flows must support filtering by time range and data type (real-time market, financial report, or order). Fields include identifiers like model and production batch, so multi-turn dialogue must allow users to add additional filtering conditions. Prompts must clarify exact field naming rules to avoid confusion between revenue data for different equipment.
The timing difference between real-time data and historical financial report updates requires prompts to force assistants to mark data sources and update times when returning results, preventing data time misalignment. Major order-related yield rate data appears suddenly, so multi-turn dialogue must support fast retrieval of announcement-style datasets. Prompts must clarify the query trigger logic for order-related data.
How to configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Aviation equipment yield rate data includes multiple fields. A long context retains complete historical dialogue filtering conditions and data association logic |
promptTemplate | Fixed to include guiding statements for data type, time range, and model filtering | Aviation equipment data has many categories. This template forces assistants to clarify query dimensions and avoid fuzzy matching |
recallTopK | Top 6–8 | The data volume for a single aviation equipment category is moderate. Too many recalled entries will interfere with results, while too few may miss key information |
stream | Enabled | Aviation equipment yield rate data may include summary results for multiple batches and models. Streaming return gradually displays content to avoid overload |
workflowTriggerModel | gpt-4o-mini | Field recognition and classification for aviation equipment requires medium or higher semantic understanding capabilities. This model efficiently completes prompt parsing and question classification |
globalVariablePersist | Enabled | Multi-turn dialogue requires retaining filtering conditions such as equipment model and time range specified by users. Persistent global variables avoid repeated input |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration mistakes
- The conversation interface returns the console error
Cannot read properties of null (reading 'q'). This occurs because global variable persistence is not enabled, causing the historical query fields of the multi-turn session to be lost and triggering a null pointer read. - The question classification model configured in the workflow fails to accurately recognize prompts related to aviation equipment. This occurs because the model's prompt guiding content is not adjusted for aviation equipment model naming and field rules.
- A parameter verification failure is returned when calling the workflow interface. This occurs because the workflow ID is incorrectly assigned to the
appIdfield instead of using the correct ID of the corresponding application.
How to confirm successful configuration
- Initiate two progressive queries. First, specify the aviation equipment model and data type, then add a time range in the second query, and verify that subsequent conversations automatically retain the filtering conditions from the first query.
- Call the dialogue interface with compliant request parameters, and verify that the interface returns no null pointer-related errors.
- Configure the question classification model for the workflow, input prompts related to the aviation equipment field, and verify that the classification result matches the query intent.
- View the session context log to confirm that the global variable filtering conditions are correctly retained during multi-turn dialogue.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.