What this category of data looks like
Data sources include publicly disclosed financing announcements from listed companies, financing ledgers published by military industry associations, and financing filing information from airlines and supporting enterprises. Updates are made daily. Most documents are in structured Excel or standardized PDF formats. Core fields include equipment model, financing amount, financing subject, investor, financing purpose, and disclosure date. Amount units are mostly ten thousand yuan or hundred million yuan; some cross-border financing projects are marked with USD as the currency.
What constraints these characteristics impose on vector models and indexing
The large number of structured fields and numerical content requires vector retrieval to support binding filtering of structured fields, to avoid indiscriminate recall of irrelevant entries. The daily update frequency requires the index to adapt to incremental synchronization mode, reducing time costs of full reconstruction. The structured and concentrated length of single financing entries requires a chunking strategy that preserves entry integrity, avoiding splitting core information of the same financing. Some entries include multi-currency amounts, requiring vector models to adapt to semantic encoding of numerical values and currency types, improving retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Most single financing entries for aerospace equipment financing daily reports are 200–500 characters long; chunking preserves entry integrity and avoids splitting core information of the same financing |
enable_structured_filter | Enabled | The data includes structured fields such as equipment model and financing amount; binding retrieval is required to achieve accurate recall |
index_update_mode | Incremental update | Data is updated daily; full reconstruction takes too long; incremental synchronization adapts to daily update frequency |
recall_top_k | Top 20 entries | The number of daily report entries is relatively large; enough candidates must be recalled first for subsequent processing |
vector_model | m3e-base | The data includes numerical fields such as financing amount and date; this model’s semantic encoding effect for structured content is suitable for the current scenario |
similarity_threshold | 0.75–0.85 | Financing daily report entries have relatively high similarity; a reasonable threshold must be set to filter irrelevant results |
The parameter values provided on this page are all common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing values.
Three Common Mistakes
- Issue: After upgrading from 4.9.0 to 4.9.3, previously queryable aerospace equipment financing daily report content can no longer be recalled. Cause: The new version adjusted the compatibility logic for index updates by default, and incremental synchronization or full index reconstruction was not re-performed.
- Issue: After uploading Excel-format financing daily reports, retrieval results show field misalignment or single financing information split across multiple results. Cause: Excel structured parsing configuration was not enabled; documents were segmented by irregular rows, destroying the integrity of financing entries.
- Issue: Multiple highly duplicate financing entries appear in retrieval results, and cannot be merged correctly. Cause: Search result merging configuration was not enabled, or a reasonable duplicate determination threshold was not set.
How to Confirm Proper Configuration
- Upload a single test aerospace equipment financing daily report entry, perform retrieval, and verify that the recall result includes the target entry.
- View the update log in the index management interface, confirm that the daily incremental synchronization task executes normally with no error records.
- Switch the vector model configuration to the aiproxy address, initiate a test retrieval, and confirm that the returned results are normal.
- Enable the structured filtering switch, enter an equipment model keyword, and verify that retrieval results only return financing entries for the corresponding model.
- Enable the search result merging configuration, enter a keyword with high repetition, and verify that retrieval results have merged duplicate entries.
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.