What this category of data looks like
This data comes primarily from daily toll ledgers, road law enforcement files, toll declaration reports, maintenance project acceptance documents, and cross-section collaboration approval forms for expressway operations. Update frequency varies by business scenario: daily declaration materials are updated each day, while special file review materials are updated quarterly or per project cycle. Document structures include fixed header fields such as declaration subject, section number, traffic volume, and violation record number. They also mix structured tables and unstructured scanned PDF, Word approval text. Field units include physical quantities such as trips, kilometers, and cubic meters. Some documents contain non-standard text paragraphs with handwritten annotations.
What constraints do these characteristics impose on the document parsing and chunking step?
Daily declaration materials receive high-frequency daily updates, so the parsing process requires low-latency adaptability. Single documents for special file review can reach dozens of pages, so stable long-document chunking logic is necessary. Documents include structured tables, scanned PDFs, and unstructured approval text. The parsing link must support table extraction, OCR recognition, and plain text parsing all at once. Fields with fixed formats such as section number and violation record number need to retain contextual association during chunking, to avoid splitting-induced field breakage. Non-standard handwritten annotation paragraphs can cause semantic fragmentation in conventional chunking rules. Chunk boundaries must be adjusted to fit natural semantic units. Large-volume special case PDFs can trigger system resource limits during upload and parsing, so parsing parameters need targeted adjustments.
Reference only; verify against the target FastGPT release and deployment documentation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Fits the paragraph semantic length of expressway declaration materials, avoids field or contextual breakage after splitting |
chunkSplitter | Split by paragraph and title boundary | Adapts to fixed headers and structured fields in documents, preserves semantic integrity |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Covers the volume limit of single dozens-of-page special case files, prevents upload interruptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the time requirements for long-document OCR and parsing, avoids timeout failures |
customPdfParse | Enable OCR mode | Adapts to road law enforcement files in scanned form, improves unstructured text extraction accuracy |
Three Common Mistakes
- Phenomenon: When uploading a single large-volume special case PDF, the parsing progress reaches approximately 90% and an "offset out of range" error occurs. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted, exceeding the system's default file volume and parsing time limits. - Phenomenon: Direct chunking fails to extract valid question-answer pairs, and field breakage appears in chunking results. Cause: The structured fields and fixed headers of the document are not adapted. A globally uniform character chunking rule is used, and the chunking logic is not adjusted to split by paragraph and title boundary.
- Phenomenon: Documents uploaded via the create file collection API have inconsistent chunking results with documents uploaded directly via the platform. Cause: The
chunkSettingModeparameter configuration is not unified. The chunking modes of the API and platform frontend are not consistent, leading to differences in chunking boundaries.
How to Confirm Proper Configuration
- Upload a single typical document matching the business scenario, check the parsed chunking results, and confirm that fields and context have no breakage.
- Test uploading the maximum-volume special case PDF, confirm that the upload progress has no abnormal interruptions, and that the parsing process does not trigger timeout errors.
- Upload the same document via the platform frontend and the create file collection API respectively, compare the chunking results of the two documents, and confirm that the chunking logic is consistent.
- For case documents in scanned form, verify the text integrity of the OCR parsing results, with no obvious recognition gaps.
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.