What the Data for This Category Looks Like
Aerospace equipment investment research data primarily originates from public bulletins of military industry associations, regular disclosure reports from full aircraft and aeroengine enterprises, policy documents in the national defense and military industry sector, and public information from model development and flight test milestones. Update cycles are inconsistent: some data is released quarterly or annually as regular reports, while other information is updated ad-hoc alongside model project initiation and flight test milestones. Document formats include full aircraft parameter tables, R&D progress descriptions, supply chain details, policy interpretations, and other types. Fields covered include model number, thrust, range, unit cost, production capacity, and more. Units follow professional measurement standards such as kilonewtons, kilometers, 100 million yuan, and aircraft unit counts.
Constraints Imposed on Knowledge Base Retrieval and Recall by These Characteristics
Multi-source heterogeneous data sources require the retrieval pipeline to support cross-data-source metadata filtering, to prevent mixing of unrelated military industry content. Uneven update cycles require adaptation to an incremental indexing mechanism, only updating indexes for newly added or modified content to reduce resource consumption from full reindexing. Diverse document structures require chunking strategies that preserve the integrity of professional parameter tables, and avoid forced splitting of coherent parameter entries. Specialized fields and units require metadata association during retrieval, to avoid retrieval errors caused by unit ambiguity, and retain complete semantics of core identifiers such as model numbers.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_length | 800–1200 characters | Aerospace equipment documents often contain coherent parameter tables and R&D paragraphs. Excessively long chunks will destroy parameter relevance, while excessively short chunks will split specialized terminology |
recall_count | Top 8–12 results | Investment research scenarios require coverage of multi-dimensional parameter and progress information. Too few results cannot support complete analysis, while too many will increase model processing load |
similarity_threshold | 0.72–0.85 | Aerospace equipment parameters have high specialization. A low threshold will introduce irrelevant general military industry documents, while a high threshold will miss critical information for specialized sub-models |
re_rank_return_count | Top 3–5 results | Investment research decisions require focusing on core information. Retain the most relevant specialized content after re-ranking, to avoid interference from redundant data |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large full aircraft manuals and supply chain report files for aerospace equipment takes a long time. Timeouts will cause parsing failures |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Large technical documents and supply chain detail files for aerospace equipment have large file sizes. Too small a limit will prevent complete data from being uploaded |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: After configuring a re-ranking model, the order of knowledge base retrieval results does not change. Cause: The complete pipeline of first retrieving then re-ranking retrieval results is not enabled, or the re-ranking model does not adapt to the vector matching characteristics of aerospace equipment specialized terminology.
- Phenomenon: Entered aerospace equipment model numbers and specialized parameter terms are split into scattered tokens stored in the
fullTextTokensfield. Cause: The specialized term retention configuration is not enabled, or the chunk length is set too short, leading to forced term splitting. - Phenomenon: Aerospace equipment design drawings and flight test photos uploaded to the knowledge base cannot be displayed normally in retrieval results. Cause: Keyword extraction for image metadata is not configured, or the retrieval pipeline does not associate image file names and alt text as retrieval basis.
How to Verify Proper Configuration
- Upload an aerospace equipment document containing model parameters and R&D progress, and check that the parsed chunks retain complete parameter entries without forced splitting of specialized terminology.
- Input an aerospace equipment specialized keyword, verify that the number of retrieved and recalled results matches the set
recall_count, and that the results include relevant content from the target document. - After enabling the re-ranking model, input the same keyword again, verify that the result order differs from the unenabled state, and that core relevant content appears at the top.
- Upload a document containing images, and check that thumbnails of the images and associated file name keywords are displayed in the retrieval results.
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.