About this page
- Client industry: Loan backlog
- Business direction: Risk control and credit material review
- Capability area: Knowledge base retrieval and recall
What data for this category looks like
Loan backlog data is collected from credit core business systems and repayment performance management systems. It is updated daily in batches, with the latest repayment status and backlog details for individual contracts. Each backlog document corresponds to the full-cycle repayment record of a single credit contract. It uses a structured table format, including fields such as customer unique identifier, contract number, loan amount, remaining principal, repayment date, overdue days, and cumulative repayment amount. The currency unit is RMB yuan. Time fields use natural days as the unit. ID fields are unitless strings.
Constraints on Knowledge Base Retrieval and Recall
The strong structured nature of loan backlog data requires retrieval tools to support both semantic matching and precise field matching. This prevents irrelevant contract records from being recalled. The daily batch update rhythm requires configuring incremental sync tasks for the knowledge base, to reduce sync time. The single-document-per-contract feature requires prioritizing pre-filtering by unique identifiers such as customer ID and contract number, to narrow the recall scope. Risk control scenarios have high data accuracy requirements. Recall results must cover the latest repayment status and overdue information. Sync task latency must meet business needs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Recall Count | Top 10-15 | Each loan backlog document corresponds to a single contract. Too many recalled entries increases context processing load. Too few may miss critical repayment records. |
| Similarity Threshold | 0.75-0.85 | Risk control scenarios require precise matching of contract-related information. This prevents low-relevance non-contract documents from being recalled. |
| Incremental Sync Interval | Daily at 2 AM | Loan backlog data updates in daily batches. Sync interval matches the business update rhythm, reducing system usage during non-peak hours. |
| Chunk Length | 800-1200 characters | Loan backlog data mostly consists of structured table content. Too long a chunk causes semantic segmentation failure. Too short a chunk breaks the integrity of contract records. |
| Full-Text Retrieval Switch | Enabled | Loan backlog data includes a large number of structured fields. Full-text retrieval enables precise matching of unique identifiers such as contract numbers and customer IDs, supplementing the precision of semantic retrieval. |
| Reranked Return Count | Top 5 | Risk control decisions only require 3-5 core contract records. Too many results interfere with audit judgments. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Analyze specific issues on a case-by-case basis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Issue: Searching for an uploaded loan backlog document in the workspace returns no matching results. Cause: The full-text retrieval switch is not enabled, or pre-filter rules omit unique identifier fields such as contract number or customer ID, causing precise matching to fail.
- Issue: The semantic retrieval or full-text retrieval tool returns a "Connection error" error. Cause: The vector database connection configuration is incorrect, or the incremental sync task has incorrect database access permissions, leading to failed connection establishment.
- Issue: Recall results include a large number of non-target contract backlog records. Cause: The recall count is set too high, or the similarity threshold is set too low, leading to low-relevance contract records being recalled.
How to Confirm Successful Configuration
- Enter the knowledge base management page, check the execution log of the incremental sync task, and confirm the sync task completes at the preset interval.
- Initiate a retrieval for a known contract number, and verify that the corresponding document is accurately matched.
- Adjust the similarity threshold and recall count, and verify that the number and relevance of recall results meet business requirements.
- Check the vector database connection status, and confirm there are no "Connection error" type 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.