What the Data for This Category Looks Like
Data primarily comes from station daily inspection records, pipeline operation ledgers, gas safety inspection reports, internal training materials, and administrative notices. Update frequency varies by document type: inspection records are generated daily, operation ledgers are updated monthly, and training materials are updated quarterly. Documents include structured tables with unit fields such as pressure values and burial depth, long-text analysis reports, and scanned inspection archives. Some documents contain cross-page tables or scanned content with handwritten annotations.
How These Characteristics Create Constraints for Document Parsing and Chunking
Daily batch-generated large inspection records can trigger upload timeouts or offset errors. Structured ledgers with unit fields and cross-page tables require complete structure retention. If structure is lost, subsequent searches cannot link point locations and hidden danger information. Scanned inspection reports first require OCR recognition, plus extraction of handwritten annotation text to avoid losing key hidden danger descriptions. Long-text safety analysis reports need to retain contextual coherence of professional terms, to prevent chunking from breaking term association logic. Batch-uploaded multiple inspection records need to avoid repeated parsing, otherwise server load increases.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the text length of gas inspection reports and operation ledgers, retains contextual coherence of professional terms, and avoids breaking cross-page table association logic |
chunkSplitter | Split by header, footer, and paragraph | Adapts to the multi-chapter structure of internal documents, distinguishes inspection records of different stations, and avoids mixing content from multiple stations in the same chunk |
customPdfParse | Enable structured table extraction + OCR completion | Processes inspection reports with scanned content, retains table fields and OCR results of handwritten annotations, and avoids field loss |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to single large-volume annual operation ledgers, avoids triggering file size limits during upload |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large-volume PDFs, avoids parsing failure due to timeout |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types, adapting to differentiated parsing needs of inspection records, ledgers, and scanned documents |
Three Common Configuration Errors
- Phenomenon: When uploading large-volume PDFs, an "offset out of range" error appears at 90% progress. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted, leading to large file parsing timeout or exceeding preset size limits. - Phenomenon: Repeated inspection point locations and parameter records appear in chunking results. Cause: The
chunkSplitteris not configured to split by paragraph or station, leading to adjacent chunks containing repeated headers and basic information. - Phenomenon: Unit fields (such as kPa, meters) in structured ledgers are split into different chunks. Cause: The
chunkSizeis set too small, breaking the contextual association of unit fields, leading to failure to match point locations and corresponding parameters during subsequent searches.
How to Verify the Configuration Is Correct
- Upload a single large-volume gas operation ledger PDF, check that the parsing progress completes normally, with no offset or timeout errors.
- Extract chunking results from a structured inspection ledger, verify that table fields and units are fully retained and not randomly split.
- Upload a scanned safety inspection report, check that printed text and handwritten annotations extracted via OCR are complete, with no key content lost.
- Compare results before and after adjusting chunking parameters, confirm that contextual coherence of professional terms and cross-page tables meets expectations.
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.