What this category of data looks like
This category of data originates from internal control systems, ERP platforms, financial sharing systems, audit management systems, and procurement contract project systems of central enterprise internal control departments, audit departments, financial departments, their subordinate enterprises, and external audit institutions. The update frequency is annual. Documents processed daily include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Most documents mix structured fields such as issue number, rectification responsible person, and completion deadline with unstructured text. Some documents have multi-level nested directories, with embedded images and doc-format attachments.
What constraints do these characteristics impose on document parsing and chunking
Annual batch-updated documents impose the constraint of large single-parsing volumes, requiring adaptation to heterogeneous formats exported from multiple systems. Documents with multi-level nested directories and embedded doc files and images require parsers to support cross-level content crawling and non-text element parsing. Documents mixing structured fields and unstructured text require distinguishing chunking rules for fields and main text, to avoid splitting key rectification information. Associated documents such as rectification ledgers and closure certificates have high context association requirements. Chunking must retain associated content to avoid splitting key information related to the rectification process. Audit documents have high requirements for information accuracy. Chunking must avoid losing key evidence content, to prevent consequences such as repeated audit issues and regulatory notifications caused by parsing errors.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field and main text lengths of mixed-text documents such as audit working papers and rectification ledgers, avoiding splitting key rectification information |
chunkSplitter | Split by paragraph + title | Adapts to the hierarchical structure of multi-level audit issues and rectification nodes in documents, retaining context association |
customPdfParse | Enable external parser | Adapts to encrypted PDFs or non-standard format audit documents exported within enterprises, improving parsing completeness |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to the need for batch importing annual audit archives, avoiding 413 errors |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large internal control self-evaluation reports, avoiding mid-parsing timeout interruptions |
chunkSettingMode | Manual configuration | For documents mixing structured fields and unstructured text, customize chunking rules to fit business needs |
Three common mistakes
- After uploading a CSV-format issue list, the parsing result only contains the first two columns of data. The cause is that the column selection rule for CSV parsing is not configured; the platform reads only the first two columns by default.
- When uploading large audit archives in a Docker deployment environment, a 413 error is returned. The cause is that the
UPLOAD_FILE_MAX_SIZEconfiguration is not adjusted, exceeding the platform's default file upload limit. - After uploading a Feishu multi-level directory document and its embedded doc files and images, some content is not parsed or retrieved. The cause is that the full directory crawling switch for Feishu documents is not enabled, and the parsing adaptation rules for doc format files and images are not configured.
How to confirm the configuration is correct
- Upload a single 100 MB audit working paper, check that the number of parsed chunks meets expectations, and no key rectification information is lost.
- Upload a CSV-format rectification ledger, check that the parsing result includes all configured fields with no omissions.
- Check the
PARSE_FILE_TIMEOUT_SECONDSparameter in the system configuration, confirm that it matches the document parsing time, with no timeout interruptions. - Test the parsing of a Feishu multi-level directory document, check that all content under subdirectories is included, and embedded doc files and images can be retrieved normally.
The values provided are common starting points and should be measured against the reader'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.