Reference only; verify against the target FastGPT release and deployment documentation.
What data looks like for this category
Typical data for this category comes from central enterprise internal control departments, audit departments, finance departments, subordinate enterprises, and external audit institutions. Daily documents 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. Updates occur annually. Document structures cover multiple types: long-text reports, structured tables, and short proof documents. Some documents are scanned non-editable formats. Core information focuses on business sample extraction, audit issue identification, rectification tracking, and closed-loop rectification cancellation.
Constraints on Document Parsing and Chunking
Multi-source, heterogeneous data sources mean document parsing must adapt to different cross-system export formats, to avoid information loss from format incompatibility. Annual batch document updates require setting reasonable timeout and size limits for the parsing link, to avoid single-task interruptions. Diverse document types require chunking strategies to balance long-text logical integrity and structured table field association. Fixed splitting must not break the binding relationship between audit issues and rectification measures. Scanned documents require the parsing link to support OCR recognition capabilities, to resolve parsing exceptions for non-editable documents. High audit process rigor means parsing failures must trigger clear alert and retry mechanisms, to avoid erroneous data flowing into subsequent audit links.
Configuration Setup
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the text length of internal control self-assessment reports and rectification ledgers, avoids splitting that breaks the association between issues and rectification measures |
chunkSplitter | Split by paragraph + heading hierarchy | Matches the hierarchical structure of audit documents, retains logical associations of issue lists and rectification ledgers |
customPdfParse | Enable OCR enhancement mode | Adapts to non-editable PDFs such as scanned audit notices and closure certificates, resolves OCR recognition exceptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time consumption for annual batch documents, avoids timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large audit working paper packages exported from ERP systems |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types to meet multi-source heterogeneous data requirements |
Three Common Configuration Mistakes
- Symptom: Uploading a PDF or PPT results in an empty parsing result, or logs show an OCR error. Cause: OCR enhanced parsing mode is not enabled, so non-editable documents such as scanned audit notices and closure certificates cannot be recognized.
- Symptom: Feishu knowledge base fails to sync specified PDF or PPT documents, and the sync task shows failure. Cause: Parsing adaptation for the corresponding document type is not configured, or the single file size exceeds the preset upload limit.
- Symptom: JSON parsing errors occur after file parsing and chunking, or associated information is lost. Cause: Chunking rules are set to fixed character splitting, not split by paragraph + heading hierarchy, which breaks the logical structure of the document.
How to Verify Proper Configuration
- Upload a typical internal control self-assessment report, check if the chunking results retain the contextual association between issue descriptions and rectification requirements.
- Upload a scanned audit notice, check if the parsing result contains complete text content with no obvious recognition gaps.
- Upload a large audit working paper package, check if the parsing task completes within the preset timeout period with no timeout errors.
- Test the Feishu knowledge base sync function, confirm that PDF, PPT and other documents can be synced and parsed normally.
The values listed above are common starting points. They should be evaluated against the reader’s own sample documents.
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.