What the data for this category looks like
Aerospace equipment research reports mainly come from military industry consulting firms, public reports from aerospace research institutes, regular announcements from listed companies, and disclosures from industry exhibitions. They primarily serve investment research analysis scenarios for the military track at financial institutions.
Update frequency follows major model milestones: regular quarterly updates, plus special reports issued during model project initiation, first flight, and delivery phases. Most documents are in PDF format, and include sections such as model performance parameters, development progress, supporting supply chains, and industry policy interpretation. Most fields have clear units, such as carrying capacity (tons), orbital altitude (kilometers), and development cycle (months).
What constraints these characteristics impose on multi-turn dialogue and prompting
Parameters in aerospace equipment research reports have clear units, and the use case is financial investment research. Multi-turn dialogue must identify and verify unit consistency to avoid investment research errors caused by unit confusion.
The update rhythm of research reports follows model milestones. Prompts must guide financial investment research users to clearly specify target models and corresponding time nodes, while adapting to the incremental update logic of the knowledge base.
The presence of a large number of structured parameter tables in documents requires multi-turn dialogue to support accurate extraction of specific fields, avoiding generalized answers that impact investment research decisions.
The diversity of data sources requires prompts to clearly specify prioritized data source types to reduce inconsistent data standards.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single aerospace equipment research report often contains multi-page parameter tables. Sufficient context must be retained for multi-turn parameter verification |
recallTopK | Top 8–12 results | Aerospace equipment research reports have strong parameter relevance. Results must cover report content across different dimensions of the same model |
chunkSize | 1000–1500 characters | Parameter tables in aerospace equipment research reports often span paragraphs. Segmentation must retain the integrity of table structures |
parseTimeout | 120 seconds | Aerospace equipment research reports often contain high-definition vector charts. Parsing time is longer than that of general documents |
similarityThreshold | 0.75–0.85 | Professional terminology in aerospace equipment research reports is highly recognizable. Low-relevance recall results must be filtered out |
streamResponse | Enabled | Aerospace equipment research reports have long content. Streaming output reduces the waiting perception of financial investment research users |
The parameter values provided on this page are conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on local samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Dialogue logs do not retain historical interaction content. Cause: The
dialogueLogRetentionconfiguration item is not enabled, causing session data to be automatically cleared. - Phenomenon: The workflow terminates directly after knowledge base search, with no error prompt. Cause: No minimum recall count threshold is configured for
recallTopK. When search results are empty, the workflow does not trigger subsequent AI dialogue nodes. - Phenomenon: AI dialogue output content does not enter subsequent processing modules. Cause: The
dialogueOutputPassthroughconfiguration item is not enabled, causing only the native AI dialogue node to output directly to the dialogue window.
How to Verify Correct Configuration
- Upload 1 to 2 aerospace equipment research reports, run a parsing task, and check if parsing time matches the configured value range.
- Initiate a multi-turn dialogue with clear parameter requirements, and check if the number of recall results matches the configured value.
- Configure a workflow to connect knowledge base search and AI dialogue nodes, initiate a test request, and confirm that the workflow can complete all steps to the final stage.
- Call the API dialogue interface, verify that streaming data is returned, and confirm that the
streamResponseconfiguration item is correctly enabled.
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.