What the data for this category looks like
Aerospace equipment-related due diligence data sources include official technical documents from military scientific research institutions and complete aircraft manufacturers, public certification documents from airworthiness certification bodies, and original flight test records. The data update rhythm aligns with model project initiation, formal qualification, and batch improvement. Document structures include technical parameter chapters, test validation records, compliance explanation attachments, structured parameter tables, and unstructured long text descriptions. Fields include physical quantity parameters with clear engineering units, model numbers, supplier qualification numbers, and airworthiness certification marks. Some documents include high-definition drawings and test video attachments.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Multi-source and heterogeneous document sources require the retrieval chain to support multi-format parsing and permission grading, to adapt to differentiated processing of classified and public content. Long professional documents require a segmentation strategy that balances semantic integrity and retrieval efficiency, avoiding damage to the context logic of technical descriptions. Parameter fields with clear engineering units require matching unit consistency during retrieval, avoiding parameter confusion across units. The update rhythm aligned with model iterations requires the knowledge base to support incremental synchronization mechanisms, to ensure the timeliness of recalled content. Strictly constrained technical parameters require recalled results to have high relevance, ensuring that parameters cited in due diligence reports match document records.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Segment Length | 800–1200 characters | Aerospace equipment documents contain long professional descriptions. Excessive length will destroy semantic connections, while insufficient length will lose context logic. This range adapts to mixed content of technical parameters and test records |
Recall Count | Top 6–8 results | Aerospace equipment due diligence requires covering multi-dimensional parameters. Too many results will introduce irrelevant content, while too few will fail to cover complete technical dimensions |
Similarity Threshold | 0.75–0.85 | Aerospace equipment parameters have strict matching requirements. A high matching precision is needed to avoid recalling parameters from non-corresponding models |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Individual aerospace equipment documents have long length. The parsing process requires sufficient time to complete multi-format splitting and vectorization preprocessing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some complete aircraft design documents contain a large number of high-definition drawings and test video attachments. Large-capacity file upload support is required |
Reranked Return Count | Top 3–4 results | Due diligence reports only require core relevant technical parameters. Reranking filters low-correlation recall results to improve content accuracy |
The parameter values provided on this page are common recommended starting points 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 your own samples before finalizing settings.
Three common mistakes
- The symptom is that retrieval results return parameters of non-target models. The cause is that unit matching verification is not configured, causing parameters with different units to be included in the results.
- The symptom is that when there is no public document for the corresponding model in the knowledge base, the system returns vague guess content. The cause is that the empty recall fallback configuration is not turned off, causing the model to generate incorrect answers based on irrelevant content.
- The symptom is that incomplete technical content is output during query. The cause is that the
Segment Lengthis set too short, causing the context of professional technical content to be split and lost, preventing complete semantics from being associated during recall.
How to confirm the configuration is correct
- Upload a design document for aerospace equipment, check the segmented content after system parsing, and confirm that each segment contains complete technical descriptions and parameters, with no obvious context breaks.
- Initiate a parameter retrieval for a specific aerospace equipment model, and verify that the units of the returned results match the units recorded in the document, with no content confused across units.
- Trigger a retrieval scenario where the knowledge base has no corresponding content, and confirm that the system does not return any speculative answers, only returning empty results or a prompt that no matching content exists.
- Monitor retrieval time and disk IO usage, confirm that single retrieval time is within a reasonable range, and IO usage does not show abnormal fluctuations.
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.