What the data for this category looks like
Data for this category comes from team scheduling systems, trouble ticket repositories, monitoring alert platforms, and organizational contact directories. Update frequency is daily or weekly. Documents processed daily include standardized duty schedules, cross-shift handover records, escalation contact lists, and daily duty reports. Document fields include clear duty time periods, pending handover items, emergency contact paths, and more. Some documents are table formats exported from systems. Others are digitized versions of scanned or handwritten paper documents.
What constraints do these characteristics impose on the document parsing and chunking link
Parsing and chunking for duty scheduling and handover documents must preserve field associations, and avoid splitting duty arrangements for the same time period from their corresponding contact information. Frequently updated daily duty schedules must adapt to fast parsing processes. This prevents real-time compliance reviews from being delayed by parsing delays. Scanned daily duty reports require the chunking link to support custom parsing adaptations. This ensures extracted text can be retrieved. The completeness of pending handover items directly relates to compliance requirements. Chunking must ensure a single chunk covers a complete single handover item. This prevents missing key information.
How to set the configuration
This configuration applies to FastGPT v4.17.0 and above versions:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Single chunks for duty schedules and handover records must cover complete time periods or single handover items, to avoid splitting critical associated information |
chunkSplitter | Split by paragraph + title | Duty documents often have clear time period titles and paragraphized handover items. Splitting by this rule preserves contextual logic |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Bulk importing weekly scheduling document packages requires sufficient time to complete parsing and chunking of multi-page documents |
customPdfParse | Enabled | Adapts to image parsing requirements for scanned daily duty reports, ensuring extracted text can be properly indexed |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports bulk importing of weekly scheduling document packages by teams, adapting to daily batch processing needs |
Three common errors
- Phenomenon: Knowledge base search fails to return contact information corresponding to a duty time period. Cause: The
chunkSizesetting is too small. It splits duty arrangements and contacts for the same time period into different chunks, preventing associative matching during searches. - Phenomenon: Parsing results are empty after uploading a scanned daily duty report. Cause: The
customPdfParseconfiguration is not enabled. The default parsing logic cannot recognize text content in scanned images. - Phenomenon: Parsing timeout errors occur when uploading weekly scheduling document packages in bulk. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting value is lower than the actual time required for document parsing. This causes the parsing process to terminate early.
How to confirm the configuration is properly set
- Upload a single standard duty schedule document. Review the parsed chunk results to confirm each chunk contains complete time periods, duty personnel, and corresponding handover items.
- Upload a scanned daily duty report. Verify that retrievable text content can be extracted after parsing.
- Upload a bulk document package. Confirm no parsing timeout errors occur during the process, and all documents have completed chunk indexing.
- Submit a search request containing duty time period keywords. Confirm complete associated information from the corresponding chunks is returned.
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 2026-10-19.