What this type of data looks like
Data sources for joint-stock bank intelligent due diligence reports include internal credit approval ledgers, national credit reporting systems, enterprise industrial and commercial public information, quarterly financial reports, and regulatory submission reports. Data update rhythm is adjusted based on project progress. Single-project data is updated alongside credit link progress. Batch due diligence data is synchronized weekly. Most document structures contain five core chapters: basic enterprise information, financial indicators, credit history, guarantee status, and industry ratings. Fields include credit balance (ten thousand yuan), overdue days (days), guarantee type, and others. Units follow standard metrics such as ten thousand yuan and days. Guarantee type uses enumerated values.
Constraints imposed on model access and configuration
Multi-source external data access requires configuration of corresponding interface authentication parameters to ensure lawful calls of external data such as credit reports and financial statements. Different update rhythms require configuration of incremental synchronization trigger rules, to distinguish synchronization cycles for single projects and batch due diligence. Long-text multi-chapter structures require adaptation of segment lengths to avoid semantic breaks that impair model extraction of core information. Clear field units and enumerated values require configuration of verification rules to prevent model output that does not comply with internal bank specifications.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Chapter text length of joint-stock bank due diligence reports is mostly around 1000 characters, adapting to long-text segmentation to avoid semantic breaks |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Due diligence reports often contain multi-page DOC/PDF files with long parsing times, avoiding timeout interrupting the parsing process |
Recall count | Top 8–10 entries | Due diligence reports require association of multi-dimensional fields such as finance, credit, and guarantee, sufficient recall volume covers key information |
Similarity threshold | 0.75–0.85 | Most due diligence data fields are structured enumerations or precise numerical values, a higher threshold prevents irrelevant data from mixing into the result set |
UPLOAD_FILE_MAX_SIZE | 500 MB | Single due diligence report attachments such as annual financial report PDFs for joint-stock banks may reach hundreds of megabytes, adapting to large file upload requirements |
modelTemperature | 0.1–0.3 | Due diligence reports require precise output, a lower temperature coefficient reduces model hallucinations |
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 specific analysis. It is recommended to test on own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: Model tests return "field format mismatch" errors. Cause: Field verification rules are not configured, and data units and enumerated values are not limited, resulting in model output that does not comply with bank due diligence field specifications.
- Phenomenon: Uploaded DOC format due diligence reports fail parsing, returning garbled content. Cause: OCR parsing configuration for DOC files is not enabled, or the configuration does not adapt to commonly used encrypted DOC template formats used by banks.
- Phenomenon: Insufficient number of recalled due diligence data entries, key credit information is not extracted. Cause: Recall count is set too low, or similarity threshold is set too high, filtering some historical credit data that meets required relevance levels.
How to confirm successful configuration
- Upload a standard joint-stock bank due diligence report DOC file, check that parsed text segments are complete, with no obvious semantic breaks.
- Initiate a due diligence data query request, verify that returned field units and enumerated values comply with internal bank specifications.
- Adjust recall count and similarity threshold, verify whether recall data coverage range meets due diligence requirements under different configurations.
- Run model test cases, check that output content has no redundant thinking processes, and has no garbled characters or format 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.