What This Category of Data Entails
Data for this category primarily originates from central enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Commonly processed documents include internal control self-evaluation reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Data updates occur annually. Most documents contain structured tables, nested attachments, and multi-page compliance descriptions. Fields typically cover rectification responsible entities, issue descriptions, rectification deadlines, closure status, and similar items. There are no unified fixed units, as values vary based on the document’s business scenario.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
Annual batch-updated documents have inconsistent formatting. Multi-page long documents require precise chunking to avoid breaking the association between rectification issues and their supporting materials. Document structures mixing structured tables, nested attachments, and compliance descriptions require parsing to support table recognition and context association for non-text content. Documents exported from cross-system sources have large format differences. Multi-format parsing support is needed to uniformly process ledgers from different business systems. Association fields related to rectification must retain context. Chunking must avoid losing business association relationships after splitting.
Configuration Setup
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of multi-field paragraphs in rectification ledgers and audit working papers, avoiding loss of business association after splitting |
chunkSplitter | By paragraph + title hierarchy | Matches the title hierarchy of issue lists and rectification requirements in documents, retaining context association |
customPdfParse | Enabled | Adapts to embedded attachments and tables in audit notices and rectification closure certificates, improving recognition accuracy for structured content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets parsing time requirements for multi-page long audit working papers and batch rectification ledgers |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports upload requirements for large annual audit archive documents |
chunkSettingMode | Manual configuration | Adapts to the non-standardized structure of documents in this category, allowing flexible adjustment of chunking rules |
Three Common Misconfigurations
- The symptom is that after configuring a custom URL parsing node, the task returns no errors but empty data. The cause is that custom source support is not enabled in the document parsing configuration, or the document format corresponding to the target URL is not compatible with the current parsing engine.
- The symptom is that after configuring the document parsing node, imported documents cannot be retrieved or read by the model. The cause is that knowledge base synchronization is triggered before parsing is complete, or uploaded files exceed the
UPLOAD_FILE_MAX_SIZElimit without being blocked by prompt. - The symptom is that after importing a Word document with multiple tables and images, table content recognition is incomplete and images have no associated text in the parsing results. The cause is that the
customPdfParseconfiguration is not enabled, or the chunking rule does not set separate recognition logic for structured tables.
How to Verify Successful Configuration
- Review document parsing task logs to confirm all uploaded compliant documents have completed parsing, with no timeout or unsupported format errors.
- Randomly select one rectification ledger document, and verify that the parsed chunked content retains the context association between issues and rectification measures.
- Test the custom URL parsing function to confirm that core fields of the target document can be properly extracted and used to generate chunked content.
- Check knowledge base synchronization records to confirm that parsed document chunks have been successfully written to the vector database, with no empty chunk records.
The values listed above are common starting points and should be evaluated 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.