Data Characteristics of This Category
Data sources for credit application risk control include structured application forms submitted by applicants, bank statement PDFs, business license scans, corporate financial statements, and structured business data returned via connected credit reporting interfaces. Data is submitted when a single credit application is initiated. It is submitted in one-time batches, with no scheduled updates. Most documents are structured, with fixed headers and corresponding business fields. Fields include applicant identity information, application amount, repayment term, annual income, and more. Common units are Chinese Yuan, months, or years. Some unstructured files have fixed paragraph divisions.
Constraints for Vector Models and Indexing Workflows
Structured data with multiple linked fields requires indexes to support joint matching of fields and text. This avoids splitting business-related content across chunks. Single documents may contain thousands of words of unstructured business content. The workflow must adapt to the maximum token limit of vector models to prevent truncation of critical information. Data is submitted only when an application is initiated. Indexes must support fast incremental construction without full reconstruction. Some fields contain sensitive information. Sensitive fields must be excluded during vector generation, leaving only non-sensitive business text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Credit application single documents include structured headers and multiple sections of business text. This range preserves field associations while adapting to the 1024 token limit of most vector models. |
chunk_overlap | 100–200 characters | Preserves field context across chunks, preventing matching failures caused by splitting a single business field across multiple chunks. |
vector_db_index_type | HNSW | Credit application recall requires low latency and high precision. The HNSW index balances recall speed and accuracy for datasets at the million-scale level. |
similarity_threshold | 0.75–0.85 | Precise matching is required between application materials and audit rule field associations. A threshold that is too high will miss valid matches, while a threshold that is too low will introduce irrelevant data. |
recall_top_k | Top 10 results | Credit audits require a balance between comprehensiveness and efficiency. Too many recall results increase subsequent processing load, while too few will miss critical matching items. |
parse_structured_field | Enabled | Credit applications include structured fields. Enabling this setting binds fields to text for indexing, improving field-level matching accuracy. |
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. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After uploading Excel-format credit application data, recall results have low matching accuracy and long processing times. Cause: Chunking parameters were not adjusted. The default chunk size is too large, splitting the association between headers and corresponding business rows, preventing vector matching from accurately linking fields.
- Symptom: Input length limit errors occur during vector generation, or key business content is missing from vector results. Cause: Chunk size was not adjusted to match the maximum token limit of the vector model. Thousands of words of financial statements were used as a single chunk, exceeding the model's input limit and causing truncation.
- Symptom: Index files fail to load in a local deployment environment, but work normally on the public cloud version. Cause: Vector model address and hardware parameters for local deployment were not adapted. Parameters pointing to local services such as
ollama_model_endpointwere not configured correctly, leading to failed index construction.
How to Confirm Proper Configuration
- Upload a single typical credit application file, check the chunk preview interface, and confirm that chunk lengths fall within the preset range, with no excessive truncation or overly fragmented chunks.
- Run a simulated recall test, input audit rule keywords, and check whether recall results include relevant business fields and text content, confirming that the matching logic aligns with business requirements.
- Review index construction logs, confirm that no sensitive fields were used for vector generation, and that incremental index construction speed matches the business submission rhythm.
- Compare recall results across different deployment environments, confirm consistency of configuration parameters, and troubleshoot matching deviations caused by environment differences.
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.