What Data for This Category Looks Like
Aerospace equipment investment research data comes from public development documents, flight test reports, equipment qualification documents, industry standard files, airshow public materials, and publicly disclosed supplier information in the national defense and military industry sector. Updates trigger when new aircraft models are qualified, annual production capacity reports are released, or industry standards are revised. Document structures include fields such as model code, development unit, core performance parameters, supply chain supporting details, and flight test data summaries. Core parameter units include Mach number, kilometer, ton, hour, and others. Some documents have long paragraphs of test records and policy document excerpts.
Constraints on Multi-turn Dialogue and Prompt Engineering
Professional parameters and unit requirements for aerospace equipment investment research data require multi-turn dialogue to retain contextual parameter association information. This prevents unit confusion or incorrect model references. Documents include long paragraphs of flight test data. Prompts must retain full contextual integrity of parameters during retrieval to avoid splitting that breaks parameter associations. Update cycles are not fixed. Prompts must prioritize calling document segments with the latest release dates during dialogue to avoid referencing outdated information. Multi-dimensional supply chain supporting fields require multi-turn dialogue to track specific models or subsystems specified by the user. No repeated questioning of basic information is needed.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recall count | 8-12 results | Aerospace equipment investment research documents have multi-dimensional parameters and supporting details. Too many recall results exceed the context window. Too few results lose critical details |
similarity threshold | 0.75-0.85 | Aerospace equipment has high professional terminology density. Filter low-relevance general documents, retain precisely matched model parameter content |
PARSE_FILE_TIMEOUT_SECONDS | 300-600 seconds | Aerospace equipment flight test reports and qualification documents are typically lengthy. Parsing requires longer wait times |
chunk length | 800-1200 characters | Aerospace equipment documents have long paragraphs of performance test data. Overly long chunks cause contextual fragmentation. Overly short chunks break parameter associations |
UPLOAD_FILE_MAX_SIZE | 200-500 MB | Some supplier annual supporting lists are large Excel or PDF files. Support for large file uploads is required |
maxConversationHistoryTokens | 8000-12000 | Multi-turn investment research dialogue must retain context such as model codes and parameter follow-up questions. Repeated questioning of basic information is avoided |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. Testing against in-house samples is recommended before finalizing.
Three Common Mistakes
- Symptom: An error occurs when parsing the input file address after enabling the workflow file upload node during local deployment. Cause: File storage path and FastGPT mount directory mapping are not configured correctly. The workflow cannot read uploaded aerospace equipment documents.
- Symptom: Unit confusion repeats during multi-turn dialogue, such as incorrectly calculating Mach number as kilometers per hour. Cause: Prompts do not clearly specify unified rules for parameter units. Context is not restricted to retain only the current dialogue's parameter units.
- Symptom: Recalled document segments have outdated model supporting information. Cause: No document update time filtering condition is set. Old documents replaced by new qualification files are recalled.
How to Confirm Correct Configuration
- Upload a flight test report PDF for aerospace equipment. Verify parsed chunks match the set chunk length, with no obvious parameter splitting breaks.
- Initiate a multi-turn dialogue: first request the maximum range of a specific model, then follow up with a question about its supporting engine model. Verify the dialogue context retains the initial model information, without re-initiating questioning.
- Adjust the similarity threshold, then test retrieval of the same keyword. Observe whether recall result count changes as expected.
- View the file storage directory. Confirm uploaded aerospace equipment documents are stored correctly. Confirm the workflow node can read files at the corresponding path.
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.