What the data for this use case looks like
Internal credit management system customer credit ledgers, People's Bank credit inquiry APIs, publicly available market supervision subject qualification information, and operating cash flow and collateral proof materials collected via offline on-site due diligence form the primary data sources for rural commercial bank intelligent due diligence reports. Data update frequency aligns with credit approval cycles. Routine syncs of existing customer data run quarterly. New credit customer data is entered in real time. Documents combine structured fields and attachments. Structured fields include customer unified social credit code, credit limit (unit: ten thousand yuan), number of overdue repayment incidents (unit: count), and other fields. Most attachments are scanned business licenses and monthly operating reports.
What constraints these characteristics impose on model access and configuration
Rural commercial bank due diligence data includes standardized structured fields and a large volume of offline scanned attachments. This requires model access links to support both structured parsing and unstructured OCR processing. Two update rhythms exist for data: real-time new credit entries and quarterly existing data syncs. This requires configuring flexible knowledge base incremental update rules that can switch between modes. Some core data relies on external credit and industrial and commercial APIs. This requires setting reasonable API call timeout and retry policies. Due diligence data contains sensitive customer operating information. Model context length must be limited to avoid sensitive fields being interfered with by extra context.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Rural commercial bank due diligence reports include structured fields and multiple attachments. Sufficient context is needed to accommodate all parsed fields and attachment content, preventing truncation of critical compliance information. |
embeddingModel | Select a multilingual embedding model adapted to the document language | Due diligence data includes Chinese operating information and cross-border business data with partial foreign language annotations. Multilingual models can improve cross-language recall accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Attachments such as scanned financial statements and operating cash flow records take a long time to parse. 300 seconds covers the parsing process for most standard attachments. |
chunkSize | 800–1000 characters | Structured fields and paragraph-style operating descriptions in due diligence reports require reasonable segmentation. This avoids reduced embedding accuracy from overly long single segments, while ensuring context integrity during recall. |
recallTopK | Top 6–8 results | Rural commercial bank due diligence requires covering multiple dimensions of customer information: subject, credit, and collateral. Recalling 6-8 results balances recall coverage and result precision. |
apiRetryCount | 2 retries | External credit APIs occasionally experience fluctuations. 2 retries improves call stability without excessively consuming API quotas. |
The parameter values provided on this page are all 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 perform testing on deployment-specific samples before finalizing settings.
Three Common Configuration Errors
- Phenomenon: Model output includes the full prompt thinking process field. Cause: The
enableThoughtconfiguration item is not disabled, causing the model to sync internal thinking logs to the final output. This violates compliance output requirements for due diligence reports. - Phenomenon: Frequent 429 status code returns occur when calling external model APIs. Cause: No load balancing strategy for multiple model channels is configured. All requests concentrate on a single model API, exceeding the interface call limit.
- Phenomenon: Recall results for old documents do not change after replacing the embedding model. Cause: A full knowledge base re-embedding operation is not performed. Only vector calculation rules for newly uploaded documents are updated.
How to Confirm Configuration Success
- A standard scanned rural commercial bank due diligence attachment is uploaded. Parsed structured fields are verified for full match against preset fields, confirming OCR and structured parsing configurations are active.
- Real-time data sync for new credit customers is simulated. The knowledge base is checked for automatic incremental update of corresponding documents, confirming update rule configuration is correct.
- A due diligence report generation request is initiated via a test interface. The output result is checked for absence of internal thinking process fields, confirming the
enableThoughtconfiguration has been disabled. - Model call logs are reviewed. Requests are confirmed to be assigned to multi-channel model APIs, with no single interface limit exceeded errors, confirming load balancing configuration is active.
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.