What This Category of Data Looks Like
Daily documents for this category include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data sources include enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Data updates occur annually. Documents contain both structured fields (such as issue number, rectification responsible person, closure time) and unstructured supporting materials. No unified fixed standard format exists across documents. Some documents include batch sampled voucher attachments.
Constraints for Document Parsing and Chunking
Annual batch document updates create large overall file volumes. Parsing and chunking workflows must adapt to large files. Documents mix structured fields and unstructured supporting materials. Parsing and chunking must support both table recognition and text extraction. Documents imported from multiple source systems have inconsistent formats. Parsing logic must support different upload formats. Audit working papers contain large numbers of sampled voucher attachments. Parsing must accurately split associated issue items and rectification content to avoid cross-document field confusion. Parsing deviations may cause missed or misaligned audit issue records. This impacts subsequent rectification tracking and closed-loop management.
Configuration Settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents includes structured fields and unstructured supporting materials. A chunk size of 800–1200 characters preserves complete meaning for individual audit issues and rectification ledger entries, avoiding semantic fragmentation after splitting. |
chunkSplitter | Switch based on document type: use paragraph + title splitting for structured documents, use line break + semantic block splitting for unstructured supporting materials | Internal control self-assessment reports and audit notices use fixed title structures. Rectification ledgers are structured lists. Adapting splitting logic to document type improves chunking accuracy. |
customPdfParse | Enabled | Documents in this category often have complex tables, such as multi-column details in rectification closure certificates. Enabling custom PDF parsing improves table recognition and content extraction completeness. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Audit working papers may include packaged files with batch voucher attachments. Supporting large single-file uploads prevents parsing failures from file size limits. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Annual batch document parsing requires longer processing time. A 600-second timeout stops large-volume documents from being interrupted by parsing timeouts. |
chunkSettingMode | Manual configuration | Documents from different sources have large format differences. Manual configuration allows flexible adjustment of chunking rules to match multi-system imported document characteristics. |
Three Common Configuration Mistakes
- Symptom: No parsing result appears after uploading a PDF document, or a file read failure error is returned. Cause: The
customPdfParseparameter is not configured correctly, or the locally deployed parsing tool has not opened the required port. This prevents FastGPT from calling the parsing service. - Symptom: Table content is missing or formatted incorrectly in chunking results. Cause: The
chunkSplitterrule adapted to structured documents is not enabled. The default chunking logic cannot recognize table structures, leading to split or omitted table content. - Symptom: Parsing tasks time out and stop. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not set to a reasonable duration. Parsing large-volume audit working papers takes longer than the default threshold, causing task termination.
How to Verify Correct Configuration
- Upload a single standard internal control self-assessment report. Check if parsed chunked content includes complete issue descriptions and rectification requirements, with no semantic fragmentation.
- Upload a rectification closure certificate with complex tables. Verify that table fields in the parsing result are complete, with no missing or misaligned content.
- Upload a single packaged audit working paper file with a 400 MB volume. Confirm the parsing task completes normally with no timeout errors.
- Use the FastGPT built-in parsing interface test tool. Submit test documents, confirm the returned chunked data format matches expectations.
The values listed above are common starting points. They 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.