What the data for this category looks like
Data sources for state-owned large bank intelligent due diligence reports include structured archived data from internal credit management systems, interface return data from the People's Bank of China Credit Reference Center, and public regulatory compliance documents. Update frequency follows two modes: trigger-based updates after a single credit line is completed, and quarterly batch synchronization. Document structure includes three core modules: structured field tables, unstructured interview records, and third-party credit reference attachments. Most documents are multi-page PDF format. Fields include the unified social credit code of the credit subject, credit line, number of overdue items, and others. Some fields have clear unit identifiers.
What constraints these characteristics impose on model access and configuration
Mixed sources of internal structured data and third-party unstructured documents require configuring field mapping rules for multi-source data to match the exclusive credit subject field naming used by large banks. Combined trigger-based and batch update rhythms require configuring both event-triggered synchronization and scheduled task scheduling modes. Multi-page PDF documents with fixed headers and footers require configuring text extraction area filtering parameters to exclude irrelevant identifying content. Clear unit fields require configuring validation rules that bind values and units to avoid unit confusion during model parsing. The distribution of core modules in long texts requires configuring sufficient context window parameters to cover complete report content.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 800 MB | Adapts to the typical size of single due diligence report PDFs from state-owned large banks, to avoid file upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the text extraction time required for multi-page due diligence reports, to prevent mid-parsing interruptions |
maxContext | 8192–16384 tokens | Accommodates the complete core module content of due diligence reports, to avoid context overflow |
Segment Length | 800–1200 characters | Matches the paragraph structure of due diligence reports, to avoid splitting that disrupts business logic |
Recall Count | Top 6–10 entries | Covers scattered risk investigation points in due diligence reports to ensure information completeness |
Similarity Threshold | 0.78–0.82 | Filters irrelevant headers, footers and non-core attachment content to improve retrieval accuracy |
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 case-by-case analysis. It is recommended to conduct tests on your own samples before finalizing settings.
Three common configuration mistakes
- When uploading a due diligence PDF in the format used by some state-owned large banks, the returned content is empty, while some files can be recognized normally. Cause: Some PDFs embed exclusive encryption headers or non-standard fonts from large banks, causing the text extraction plugin to fail to parse properly.
- After initiating a due diligence query request, the model does not automatically call the knowledge base to retrieve due diligence data, and directly returns a generic response. Cause: Relevant parameters for automatic knowledge base triggering are not configured, or the trigger threshold is set to not meet the retrieval requirements of the due diligence scenario.
- An error of mismatched field names occurs when parsing structured data. Cause: Exclusive field mapping rules are not configured, and generic database field naming is used directly, without matching the field definitions of the large bank’s internal credit system.
How to confirm the configuration is complete
- Upload a standard-format state-owned large bank due diligence PDF, verify that the parsed text fully extracts the core modules without irrelevant headers or footers.
- Initiate a test due diligence query, confirm that the model automatically triggers knowledge base retrieval and returns targeted responses based on due diligence data.
- Review the structured data mapping results, confirm that the field names fully match the internal naming conventions of the large bank’s internal credit system.
- Execute a scheduled synchronization task, verify that the updated dataset matches the latest archived content from the internal system.
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.