What the Data for This Category Looks Like
Data sources cover lessee subject qualification documents, ownership documents of leased assets, past performance records, financial audit reports, scanned original lease contracts and structured ledgers. Updates run in two modes: batch storage after project due diligence completion, and incremental updates triggered when lessee performance changes or lease projects are extended. Document structures include structured fields and unstructured attachments. Structured fields cover lease principal, lease term, asset evaluation value and similar items. Unstructured parts mostly consist of PDF scanned reports and stamped documents. Field units are mostly currency or time units, with no complex composite units.
What Constraints These Characteristics Impose on Vector Models and Indexing
Financial leasing due diligence data includes both structured fields and unstructured attachments. Vector models must balance semantic encoding of structured numerical features and natural language text. This avoids ignoring field associations when using single-text vectorization. Data updates use two modes: batch storage and incremental triggers. Index systems must support incremental writing and partial updates. This avoids resource consumption from full index rebuilding. Single due diligence documents may include dozens of pages of financial reports and stamped files. Long text blocks account for a large share. This requires adaptive long-context segmentation and indexing strategies. This prevents key information from being truncated. Some fields have strong business associations. Index retrieval must retain semantic associations between fields. This avoids retrieving irrelevant isolated text blocks.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment length | 800–1200 characters | Financial leasing due diligence documents mostly contain long paragraphs of financial data and contract clauses. This range balances semantic completeness of text blocks and retrieval accuracy |
number of retrieved entries | 10–15 | A single due diligence report has high density of associated information. Too many retrieved entries introduce redundancy. Too few fail to cover all relevant content |
similarity threshold | 0.72–0.80 | Semantic matching degrees of structured fields and unstructured text vary widely. This range filters low-relevance retrieval results |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | A single due diligence report may include multiple PDF attachments. Parsing and vectorization take a long time. This duration prevents task timeout interruptions |
incremental index update frequency | Once per hour | Update frequency of lease project changes is relatively stable. This frequency balances real-time performance and system resource usage |
vector model adaptation type | Models that support numerical feature encoding | Financial leasing due diligence data contains a large number of structured numerical fields. General text vector models cannot fully encode numerical association information |
The parameter values provided on this page are common recommendations for starting configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After uploading financial leasing due diligence documents to the knowledge base, the interface status remains "Indexing" beyond the preset timeout period. Cause: The incremental index switch is not enabled, or the
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than the actual time required for parsing and vectorizing a single document, causing repeated task retries. - Phenomenon: When
segment lengthis set to 3000 characters, some long text blocks fail to be indexed correctly or have content lost. Cause: 3000 characters exceeds the maximum context window limit of the currently used vector model, leading to damaged semantic integrity after forced truncation of text blocks, or the segmentation logic fails to correctly handle cross-page contract clauses. - Phenomenon: The knowledge base search results contain a large number of historical lease data unrelated to the current due diligence project. Cause: The
similarity thresholdis set too low, or no associated retrieval rules are configured for structured fields, leading to false retrieval of low-matching text blocks.
How to Confirm Configuration Is Correct
- Upload a single standard due diligence document. Check the matching degree between the number of parsed text blocks and the number of paragraphs in the original document. Adjust the corresponding configuration until the block distribution meets business expectations.
- Submit an incremental update task. Check the operation logs of the index system. Confirm that only newly added or modified documents are included in the index, and no full index rebuild is triggered.
- Enter a query statement containing structured fields. Verify whether the retrieved results include associated content of the corresponding fields. Adjust relevant configurations to match business retrieval needs.
- Call the official retrieval interface. Check the vector encoding field information in the returned results. Confirm that the vector model adaptation configuration has taken effect.
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.