What the data for this category looks like
Data sources for the financial leasing research knowledge base include leasing project contracts, rent payment ledgers, lessee due diligence reports, industry regulatory documents, and third-party credit data. Update rhythms cover real-time, low-frequency, and irregular updates. Real-time data updates follow rent repayment cycles, project contracts are fixed after signing, and regulatory documents update irregularly per their release schedule. Document structures include structured fields and unstructured text. Structured fields contain original value of leased assets, rent rate, lease term, guarantor qualification, and other items with clear units. Unstructured text covers contract body, due diligence details, and industry analysis content.
What constraints these characteristics impose on the vector models and indexing link
Mixed structured and unstructured data structures require vector models to adapt to both field-level semantics and full-text semantics, to avoid feature loss caused by a single vectorization logic. Differentiated update rhythms require the indexing system to support flexible switching between incremental updates and full reindexing, to accommodate high-frequency updated rent ledgers and low-frequency updated project contracts. Specialized financial terms such as lease rate, residual value rate, and others require vector models to have financial domain semantic adaptation capabilities; general models cannot accurately match scenario-specific semantics. The wide range of document lengths, from ledger entries of tens of characters to due diligence reports of tens of thousands of characters, requires a segmentation strategy that balances semantic integrity and indexing granularity.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers most document lengths in the financial leasing scenario, from short ledger entries to long due diligence reports, and avoids semantic fragmentation |
top_k | Top 10–15 results | Balances recall rate and result relevance for research scenarios, filters redundant recall content |
similarity_threshold | Calibrated via actual testing | Adapts to the semantic matching accuracy requirements of the financial leasing scenario, and needs to be adjusted based on business retrieval scenarios |
enable_incremental_index | Enabled | Adapts to the high-frequency update requirements of rent ledgers, reduces resource overhead from full index reconstruction |
multi_vector_per_doc | Configured per field group | Distinguishes semantic features of structured fields and unstructured text, improving matching accuracy |
field_specific_embedding | Enabled | Adapts to semantic expression of specialized financial terms such as leased asset valuation and guarantor qualification |
The parameter values provided on this page are general recommendations to serve 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, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Semantic retrieval scores are abnormally high, returning a large number of irrelevant results. Cause: The vector model is not adapted to specialized financial leasing terms. General models cannot accurately distinguish scenario-specific semantics such as lease rate and guarantor qualification, and matching deviations may still occur even after switching to a specialized model.
- Phenomenon: Auxiliary data is not included in the retrieval index. Cause: The vectorization switch for auxiliary fields is not enabled, and vectorization is only performed on the main document content.
- Phenomenon: Generating multiple sets of vectors for a single document fails. Only one set of vectors is generated after configuration, or retrieval cannot match grouped fields. Cause: Group fields are not specified in the
multi_vector_per_docconfiguration. For version v4.8.7, field mapping rules must be explicitly bound.
How to confirm the configuration is correct
- Upload a single leasing project contract, check that the number of generated vector blocks matches the
chunk_sizeconfiguration. - Submit a retrieval request containing keywords for leased asset valuation, verify that the field relevance of returned results conforms to preset matching rules.
- Upload updated rent ledger data, check that the index only performs incremental updates and does not perform full reconstruction.
- View the vector model call log, confirm that the
field_specific_embeddingconfiguration has taken effect, and that the vector dimension matches the server-side configuration.
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.