What the Data for This Category Looks Like
Aerospace equipment marketing content data primarily comes from model design documents, ground test reports, production ledgers, and official promotional materials. Data update timelines adjust based on project initiation, test milestones, and delivery schedules, with no fixed cycle. Individual documents mostly use long-text structures, containing core parameters such as model number, thrust, range, and payload. These parameters include dedicated units, such as kilonewtons, kilometers, and kilograms, alongside scenario-based application descriptions and comparative content with similar products.
Constraints for Multi-turn Dialogue and Prompt Engineering
The long-text and specialized parameter features of aerospace equipment data require multi-turn dialogue to retain sufficient historical context, preventing key model parameters from being truncated. The presence of dedicated units requires prompts to mandate specific unit formats for parameter output, preventing the model from mixing generic units. Data sources with no fixed update cycle require dialogue flows to support real-time calls to the latest knowledge base content, avoiding the use of outdated parameters. The mixed multi-source document structure requires multi-turn dialogue to first confirm the specific model and scenario the user cares about via preliminary questions, then match the corresponding parameters.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | Previous 10 turns of dialogue + 8000 characters of context | Adapts to long parameter texts for aerospace equipment, preventing key information from being truncated |
prompt_template | First confirm the equipment model and application scenario, then call corresponding knowledge base parameters, and annotate dedicated units in outputs | Resolves issues with specialized parameter unit confusion and model matching |
RECALL_TOP_N | Top 6 recall results | Filters redundant content, avoiding parameter confusion across different models in the same series |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to long document parsing time, preventing parsing failures |
similarity_threshold | 0.75–0.85 | Accurately distinguishes parameters for different batches of aerospace equipment in the same series |
clear_context_trigger | Triggered by scenario switching commands | Adapts to context isolation requirements for concurrent inquiries about multiple models |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After multiple rounds of dialogue, answers to the same parameter question have large deviations. Restarting a new conversation restores accuracy. Cause: The context retention range is not limited, and redundant historical dialogue interferes with model parameter matching.
- Phenomenon: Equipment parameters obtained via query SQL in the workflow cannot be automatically displayed in the dialogue window. Cause: The dialogue output node is not configured to bind query results, or the result splicing format is not set.
- Phenomenon: Dialogue history is not cleared after triggering the context clearing command. Cause: The trigger condition for
clear_context_triggeris not correctly configured, or the corresponding workflow node is not associated.
How to Confirm Configurations Are Correct
- Initiate a multi-turn dialogue containing parameters for two different aerospace equipment models, and check whether the model can accurately distinguish their respective thrust, range, and other parameters, and annotate the corresponding units.
- Configure a SQL query node to obtain equipment production data, and check whether the query results are automatically spliced into natural language and displayed in the dialogue window after triggering the dialogue.
- Enter the specified scenario switching command, and check whether the dialogue history is completely cleared, so that subsequent questions are no longer interfered with by previous dialogue content.
- Adjust
similarity_thresholdto the boundary values of the range, and test whether the number and relevance of recall results meet expectations.
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.