What the Data for This Category Looks Like
The sources of aerospace equipment research reports primarily include public reports from military industry associations, specialized securities firm research reports for the national defense and military industry sector, and official documents released by equipment finalization units. Update cycles follow new equipment finalization, annual industry conferences, or equipment fielding progress updates, with no fixed schedule. Document structures contain modules such as core equipment parameter tables, performance indicator analysis, fielding progress tracking, and supporting industrial chain data. Fields include maximum flight Mach number, range (unit: kilometers), fielding quantity (unit: units/frames), publishing organization, and publishing date. Some research reports include original data tables from flight test activities.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
Aerospace equipment research reports have numerous professional parameters with specific units. Multi-turn dialogue must retain context information about equipment models and units to avoid confusion between parameters of different equipment. Research report updates have no fixed cycle, so real-time recall of newly released specialized documents must be supported. Document structures mix plain text and structured tables, so prompts must guide the AI to extract and format structured data. Some research reports contain content limited to public access, so prompts must explicitly specify that only publicly available information may be used. Users often ask for multi-dimensional details about the same equipment, so the context window must be large enough to store key information from multi-turn interactions, preventing loss of previously mentioned equipment models or parameters.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The main body of a single aerospace equipment research report is relatively long, and key context such as equipment models and parameters from multi-turn dialogue must be retained |
recall_top_k | Top 8–12 results | Relevant information about the same equipment is scattered across multiple research reports, so sufficient recall volume is required to cover core content |
similarity_threshold | 0.75–0.85 | Filter general military industry research reports and accurately match query requests focused on aerospace equipment topics |
rerank_top_n | Top 4–6 results | Reduce redundant recall results and focus on the most relevant research report content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Research reports contain a large number of structured tables and parameter data, resulting in longer parsing time |
prompt_template | Customized to "Please combine the recalled aerospace equipment research report content to accurately extract and answer user questions, retain the units of professional parameters, and associate previously mentioned equipment models in multi-turn dialogue" | Adapt to the professional attributes of aerospace equipment and the context association requirements of multi-turn dialogue |
The parameter values provided on this page are common recommendations for starting point 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 settings.
Three Common Configuration Errors
- Phenomenon: When the concurrent call volume reaches 2–3 calls per second, workflow nodes return empty values, and rate-limiting related status codes appear in logs. Cause: The
WORKFLOW_MAX_RUN_TIMESparameter was not adjusted. The default configuration limits the number of concurrently executed workflows, and rate limiting is triggered when the threshold is exceeded. - Phenomenon: The variable B result output by the AI conversation includes the content of variable A from the previous interaction, resulting in result superposition. Cause: Session-level context isolation was not configured. The multi-turn dialogue context cache is not stored independently per session, causing variable content from different interactions to be reused.
- Phenomenon: After configuring input guidance and a word bank, the preset guidance questions are not displayed on the conversation interface. Cause: The input guidance switch for the conversation interface was not enabled, or the trigger rules of the word bank did not align with the trigger conditions of input guidance.
How to Verify Proper Configuration
- Initiate a multi-turn dialogue targeting a specific aerospace equipment model in the test environment, check whether the context window retains the parameters and models mentioned in the previous round. This can be confirmed by viewing the context extraction logs of the conversation history.
- Adjust the concurrent call volume and monitor the workflow return results. Confirm that no empty returns occur when the call volume reaches the expected threshold. This can be done by adjusting the
WORKFLOW_MAX_RUN_TIMESparameter to match concurrent requirements. - After enabling the input guidance function, trigger the word bank matching rules on the conversation interface and check whether the preset guidance questions are displayed. This can be verified by modifying the trigger keywords of the word bank.
- Initiate a query containing professional parameters, check whether the AI output retains the units and accurate values of the parameters. This can be confirmed by comparing with the original data in the recalled research reports.
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.