What the data for this use case looks like
Credit report data comes from the central bank credit reference center, partner commercial banks, consumer finance institutions, and some public service agencies. Update cycles vary by data provider, ranging from T+1 to T+7. Most documents are fixed-format PDFs or structured reports, with four main sections: personal basic information, credit transaction records, inquiry records, and public information. Fields include name, ID number, credit balance, repayment status, inquiry institution, and others. Credit balance is measured in yuan, inquiry records are counted by number of times. Semantic relevance between fields is relatively high.
What constraints these characteristics impose on vector models and indexing
Diverse data sources and varying levels of structuredness require support for mixed input from PDF parsing and structured fields. Differing update frequencies require flexible configuration of index update triggers. Sections such as credit transaction records and inquiry records contain lengthy content with tight semantic connections; context association must be preserved during chunking. The semantics of some fields depend on fixed formats, so vector models must adapt to the encoding logic of this structured data to avoid semantic loss.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
max_chunk_length | 800–1200 characters | Matches the characteristic that single-segment credit record lengths in credit reports are concentrated in hundreds of characters, avoids splitting chunks that break transaction context |
embedding_dim | 1024 | Matches the output dimensions of mainstream multimodal embedding models, meets the semantic encoding needs of multiple fields in credit reports |
refresh_interval | 3600 seconds | Adapts to the update cycles of most credit reports (T+1 to T+7), prevents index expiration |
chunk_overlap | 50–100 characters | Preserves front and back context of credit transaction records, avoids losing context when chunking |
recall_count | Top 10 entries | Covers scattered critical information across multiple categories such as credit and inquiries in credit reports |
similarity_threshold | 0.75–0.85 | Filters low-match irrelevant credit report fields, improves the accuracy of risk control review |
The parameter values provided on this page are general recommendations for establishing configuration baselines. Actual values are affected by document format, data volume, and business rules. Each scenario requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Interface prompt “No available index model detected.” This occurs when the vector index for credit reports is not bound to the knowledge base configuration of the corresponding risk control agent, or when index generation fails without triggering retries.
- Errors starting with
Invalidreturned when accessing a multimodal embedding model. This happens when the model's input format is not properly configured, or when the multimodal vector index generation process is not enabled, resulting in mismatched model input formats. - Old vector database data cannot be recalled normally after upgrading to a new version. This occurs when the new version adjusts vector encoding dimensions or index structures, and data re-encoding or structure migration is not performed, leading to incompatibility between old and new indexes.
How to confirm configuration is complete
- Upload a single complete credit report, check the chunked content parsed by the knowledge base, confirm that chunk lengths match the
max_chunk_lengthconfiguration. - Initiate a risk control-related query, check the vector recall results returned by the retrieval, confirm that the number of recalled entries matches the
recall_countsetting. - Wait for the duration specified in the
refresh_intervalconfiguration, upload an updated credit report, confirm that the index has completed automatic updates. - Switch to a multimodal embedding model, upload a credit report with structured tables, confirm that vector generation proceeds without errors.
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.