What the data for this category looks like
Financial leasing marketing content data is sourced primarily from internal product solution libraries, customer acquisition material packs, regulatory compliance documents, and partner qualification materials. Updates are triggered irregularly alongside product iterations or regulatory policy adjustments, and most updates are bulk operations. Documents are mostly categorized by product type, and include three core modules: core terms, application thresholds, and case descriptions. Fields cover lease term (unit: month), financing amount (unit: ten thousand yuan), eligibility qualification requirements, and more. Some documents contain long-form compliance notice clauses.
What constraints do these characteristics impose on retrieval and recall?
The data characteristics of this category impose three types of constraints on the retrieval and recall process. First, multi-source and scattered materials require unified indexing rules to prevent missed results during cross-source retrieval. Second, the irregular bulk update feature demands incremental trigger update logic, to reduce resource consumption from repeated full indexing. Third, documents contain long-form compliance clauses and core fields with explicit units. This requires preserving contextual associations during text segmentation, while supporting precise unit-matched retrieval, to avoid returning content that does not align with the units specified in user queries.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Preserves contextual integrity for long-form compliance clauses, avoiding sentence breaks that disrupt clause completeness |
RECALL_TOP_N | Top 8–10 results | Financial leasing marketing content mostly consists of structured terms, and a small number of precise recall results can cover user query needs |
SIMILARITY_THRESHOLD | 0.75–0.85 | Core fields such as lease term and financing amount have high matching precision requirements, to filter low-correlation recall results |
UPLOAD_FILE_MAX_SIZE | 500 MB | Compliance documents are mostly packaged bulk PDFs, to support large-file batch upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Long-form compliance documents take longer to parse, to prevent mid-process interruptions |
RERANK_TOP_N | Top 3–5 results | Performs secondary re-ranking on initial recall results, to focus on the most relevant compliance and product content |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test against your own samples before finalizing values.
Three Common Misconfigurations
- Symptom: An empty result is returned when initiating retrieval via chat, but retrieval testing on the knowledge base management page returns normal results. Cause: Permission configurations for the application and knowledge base are not synchronized, or the knowledge base index called by the application has not completed incremental updates.
- Symptom: A small number of knowledge base fragments are still returned when the user’s question has no connection to the knowledge base content. Cause: The similarity threshold is set too low, failing to filter low-match recall results, or the re-ranking mechanism is not enabled to filter irrelevant content.
- Symptom: Parsed knowledge base fragments display formula errors or are unreadable. Cause: Parsing rules adapted for mathematical formulas are not configured, or segment length is set too short, resulting in truncated formulas.
How to Verify Correct Configuration
- Upload a test document that includes compliance clauses and structured fields, check that parsed segments retain contextual associations and have no obvious sentence break truncations.
- Launch multiple test queries on the knowledge base management page, covering core fields such as product type, term, and amount, and verify that the matching degree of recall results meets the preset threshold requirements.
- Configure incremental update trigger rules, modify a test document, and verify that the index automatically updates, and updated content can be recalled during chat.
- Enable formula parsing configuration, upload a document containing mathematical formulas, and verify that parsed fragments display formula content normally.
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.