What this type of data looks like
Urban public transit internal office documents mainly originate from business system exports: schedules, vehicle maintenance ledgers, route operation reports, employee training manuals, and complaint archives. Update cadences vary widely: schedules are updated daily, maintenance ledgers are updated with each maintenance session, training manuals are revised quarterly, and monthly reports are generated each month. Documents cover structured tables, long-form text manuals, and scanned file archives. Fields include fixed-format data such as 6-digit employee IDs, route numbers, operation duration (hours), and shift types.
Constraints imposed on document parsing and chunking
The diverse formats and business attributes of public transit internal documents create clear constraints for the parsing and chunking process. Structured schedules and operation reports have fixed column fields. When splitting content, preserve table structure integrity to avoid breaking individual business records. Scanned maintenance ledger archives require OCR recognition, so the parsing workflow must support non-editable PDFs. Long-form training manuals and monthly reports have significant length. When splitting, preserve the coherence of chapters and operating procedures. Frequently updated schedules and daily reports require fast parsing, so avoid process timeouts.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the average length of internal public transit documents, and avoids splitting complete safety operation procedures in driver training manuals |
chunkSplitter | Split by paragraph + heading | Preserves the structural hierarchy of route operation reports and training manuals, and avoids breaking logically linked business content |
customPdfParse | Enabled | Meets the OCR parsing requirements for scanned vehicle maintenance ledgers, and extracts text content from scanned archives |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing time required for long-form monthly operation reports, and avoids parsing process timeouts |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploads of packaged monthly maintenance ledger files and annual training manual collections |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types, and adapts to diverse document structures such as schedules and training manuals |
Three common configuration errors
- The symptom is no response or the error message "parsing timed out" when calling the file parsing tool in a conversation. This occurs when the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and parsing time for long-form monthly route operation reports exceeds the default threshold. - The symptom is empty text returned after uploading a scanned vehicle maintenance ledger. This occurs when
customPdfParseis not enabled, so OCR recognition of printed and handwritten text in scanned documents is not activated. - The symptom is that an on-premises older version cannot parse complex formulas and structured tables in documents. This occurs when the version does not support special format content used in public transit documents. Upgrade to FastGPT v4.17.0 to resolve this.
How to verify correct configuration
- Upload a scanned vehicle maintenance ledger, and check if the parsing result includes complete text extracted via OCR.
- Upload a monthly route operation report with more than 10 pages, wait for parsing to finish, and check that the chunked result preserves the report's chapter structure and data integrity.
- Adjust the
chunkSizeparameter, and verify that chunked text does not split complete operating procedures from driver training manuals. - Upload a single packaged document larger than 100 MB, and confirm the upload and parsing process does not trigger a timeout error.
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.