What data for this category includes
Data sources include central state-owned enterprise (SOE) internal control departments, audit departments, finance departments, subordinate enterprises, and external audit institutions. Data covers enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. Daily processed documents include internal control self-evaluation reports, audit notices, audit work papers, issue lists, rectification ledgers, and rectification closure certificates. Most documents mix structured fields and unstructured paragraphs. Some documents include credential scan copies or approval process screenshots. Data is updated annually.
Constraints on document parsing and chunking
Batch documents updated annually use a unified format but have local field differences. Adapt parsing to document structures exported from multiple source systems to avoid losing some fields or paragraphs. Documents such as audit work papers and rectification ledgers contain large numbers of detailed items and associated numbers. Retain contextual associations during chunking to avoid breaking business logic. Documents with attached credential scan copies require processing both text and image content to prevent missing key supporting materials. Document formats exported from multiple source systems are inconsistent. Adjust parsing rules flexibly to adapt to different export formats. Audit documents have high accuracy requirements. Avoid splitting associated audit issues and corresponding rectification measures into different chunks during chunking.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of detailed items and associated paragraphs in audit work papers and rectification ledgers, avoids splitting critical business logic |
chunkSplitter | Split by paragraph + title | Retains contextual associations between audit issues and corresponding rectification requirements, meets contextual needs for business process guidance |
customPdfParse | Enable image text extraction + OCR | Adapts to documents with attached credential scan copies, extracts supporting text from images, avoids missing key evidence |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses annual audit documents uploaded in batches, prevents task failure due to large document size or overly long parsing time |
chunkSettingMode | Manual configuration | Adapts to inconsistent document formats exported from multiple source systems, allows adjusting chunking rules for different document types |
Three common misconfigurations
- Phenomenon: Only text is extracted after uploading a PDF, and image content is not parsed. Cause: The OCR image extraction configuration for
customPdfParseis not enabled. The default parsing rule ignores text content in embedded images. - Phenomenon: No parsing results are returned after uploading HTML interface documents generated by javadoc. Cause: Custom parsing rules for the corresponding format are not configured. The default parsing rule does not adapt to HTML tag nesting structures, so valid text cannot be extracted.
- Phenomenon: Audit issues and corresponding rectification measures are split into different chunks after chunking. Cause:
chunkSplitteris configured to split by fixed character count, and contextual associations of paragraphs and titles are not retained. This leads to broken critical business logic.
How to confirm configurations are correct
A single typical document, such as an audit work paper, may be uploaded. Parsed text chunks may be reviewed to confirm all core paragraphs and associated fields are included. A PDF document with embedded images may be uploaded. The parsing result may be checked to confirm text content from images is included. HTML documents generated by javadoc may be uploaded. Valid text such as interface descriptions and parameter lists may be verified as extractable. Batch upload testing may be performed using FastGPT v4.17.0. Parsing tasks may be checked to confirm no timeout failures occur, and chunking results meet business logic association requirements.
The values provided are common starting points, and should be evaluated against the user's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.