What Data for This Category Looks Like
Aerospace equipment research reports are primarily sourced from securities firm military industry research teams, publicly disclosed national defense science and technology industry documents, and monthly industry association reports. Update frequency shifts with model project approval, test flight, and mass production milestones, with no fixed cycle. Concentrated updates occur around major event nodes. Most documents are 10–50 page PDFs containing complete aircraft performance parameters, supply chain supporting details, and industry policy interpretations. Fields include empty aircraft weight, maximum range, order amount, and similar metrics, with units typically kilograms, kilometers, and hundreds of millions of yuan. Some documents include structured appendices and technical indicator tables.
What Constraints These Characteristics Impose on Deployment and Upgrade
The long documents and structured appendices of aerospace equipment research reports create strain on file parsing and vector storage, requiring adjustments to parsing timeouts and chunking strategies. The lack of fixed update cycles demands adaptation to dynamic data source access during upgrades, to prevent key node content from being missed by fixed crawl tasks. Parameter fields with specific units require unit matching logic to be retained during retrieval, to avoid confusion between parameters across categories. Additionally, some public research reports include sensitive technical details, so data desensitization verification rules must be enabled during deployment to block the upload of non-compliant content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Most aerospace equipment research reports include multi-page appendices and long text. The default parsing duration is insufficient, so extending the timeout ensures complete parsing |
CHUNK_MAX_SIZE | 800–1200 characters | Research reports contain specialized technical parameters and long sentence descriptions. Too small a chunk size breaks parameter associations, while too large a chunk size reduces vector recall accuracy |
RECALL_TOP_N | Top 8–12 results | Aerospace equipment has dense specialized terminology. A sufficient number of relevant fragments must be recalled before reranking to filter valid content |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some research reports include high-definition model drawings and multi-page appendices, resulting in large single-file sizes. The upload limit must be expanded |
VECTOR_SIMILARITY_THRESHOLD | 0.72–0.85 | Vector similarity of aerospace equipment specialized terminology is relatively high. A reasonable threshold must be set to filter irrelevant recall results |
WORKFLOW_TIMEOUT | 900 seconds | When parsing multiple research reports in batches, workflow execution duration exceeds the default threshold. Extending the timeout ensures the workflow completes successfully |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on local samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When uploading multiple aerospace equipment research reports in batch, some documents fail to parse and return a 504 timeout status code. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default timeout duration is insufficient to parse long documents with multi-page appendices. - Phenomenon: The management console cannot be accessed normally after upgrade. Logs indicate the image was not found. Cause: No version tag for the management console image was configured in docker-compose.yml. No corresponding version was specified during upgrade, leading to failed image pull or use of an outdated image.
- Phenomenon: A workflow-triggered knowledge base search returns empty results with no error prompts. Cause: The empty result fallback logic was not enabled in version v4.8.10. When the number of recalled fragments does not meet the similarity threshold, the process terminates directly and returns empty results.
How to Verify Correct Configuration
- Upload a typical aerospace equipment research report PDF, confirm that parsed text fragments are complete and the parsing process completed successfully.
- Initiate a knowledge base retrieval test, input a specialized term, and verify that the number of returned results matches the configured recall count, confirming the recall logic is active.
- Check that the docker-compose.yml file includes the correct version tag for the management console image, confirming the image pull path is correct during upgrade.
- Modify the port number in OPENAIBASEURL to a non-standard value, initiate a test request, and confirm whether the request is blocked, verifying the proxy verification rules are active.
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.