What this type of data looks like
Loan ledger data primarily comes from core credit business systems, repayment deduction channels, and internal collection ledger systems. Updates are triggered by individual transactions or daily batch synchronization. Most documents are exported structured files, including Excel summaries, PDF reports, or structured JSON files. Some physical ledgers exist as scanned paper documents. Standard document structures include fields such as unique customer identifier, loan contract number, current repayment due date, actual repayment amount, remaining outstanding principal, overdue days, and collection status. Units include yuan, calendar days, and number of periods. No custom non-standard fields are used.
What constraints do these characteristics bring to the document parsing and chunking workflow?
High proportion of structured fields requires accurate extraction of field correspondences to avoid misalignment or omission. Single documents may contain a large number of entries, with some summary reports reaching thousands of entries. Chunking must not split individual complete loan records. Some documents are scanned paper copies, so OCR must be used for text extraction while preserving the layout associations of original fields. For batch update scenarios, parallel parsing of multiple files must be supported to avoid task queue blocking. Repeated organization identifiers and report dates appear in headers and footers. These must be cleaned before parsing to avoid interfering with subsequent semantic chunking.
Configuration Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Loan ledger documents have a large number of entries. Single-file processing time is long during batch parsing, and 300 seconds covers most conventional scenarios |
maxChunkSize | 800–1200 characters | A single loan ledger entry is approximately 200–300 characters. This range can accommodate 3–4 complete entries and avoid semantic fragmentation |
chunkOverlap | 50–100 characters | Contextual association between adjacent entries must be preserved to avoid losing coherence of loan status across chunks |
OCR_ENABLED | Enabled | Some loan ledgers are scanned paper documents, so OCR is required to extract text content |
PARSE_BATCH_MAX_COUNT | 20 per batch | Balances server load and processing efficiency during batch parsing |
REMOVE_HEADER_FOOTER | Enabled | Loan ledger PDFs often contain repeated organization names and report dates. Cleaning these improves parsing 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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Errors
- Phenomenon: Parsed chunk content is truncated, and some loan ledger entries are not fully displayed. Cause: The
maxChunkSizeparameter is not adjusted to the range suitable for single ledger entry length, causing long entries to be forcibly split. - Phenomenon: After importing a Word-format loan ledger document, embedded repayment voucher image links are lost or cannot be loaded. Cause: Image extraction configuration in document parsing is not enabled, or the domain name mapping rules for image storage are not correctly configured.
- Phenomenon:
ERR_INCOMPLETE_REQUESTerror is returned during batch parsing of loan ledger documents. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to match the processing duration of large files, causing tasks to be interrupted due to timeout.
How to Confirm Correct Configuration
- Upload a single typical loan ledger document, review the parsed text content, and confirm all preset fields are correctly extracted with no misalignment or omission.
- Adjust the
maxChunkSizeandchunkOverlapparameters, then check the chunking results to confirm that single ledger entries are not split across chunks. - Batch upload multiple loan ledger documents of different sizes, monitor the execution status of parsing tasks, and confirm no timeout or failure errors occur.
- Import a loan ledger document containing scanned copies, compare the OCR-extracted text with the original scanned content, and confirm recognition accuracy meets requirements.
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.