Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Railway operation ledger data sources include operation logs exported from on-site dispatching systems, paper records scanned by on-site inspection personnel, and revenue reports batch-generated by passenger and freight systems. Update cadence follows: daily single-trip ledgers are generated after each train run, and monthly summary reports are updated per natural month. Document formats include docx inspection ledgers, Excel revenue reports, and PDF dispatch announcements. Fields cover train number, run duration, inspection points, fault code, and more. Units include kilometers, passenger trips, freight tons, minutes, and others. Some documents contain embedded images and standard table structures.
What constraints these characteristics impose on document parsing and chunking
Generic parsing tools lose field associations for structured tables when processing mixed document formats. Specific fields such as equipment codes and train numbers from railway operation ledgers require precise extraction, and must not be split across different chunks. The high update frequency of single-trip train ledgers and monthly summary reports means parsing tools must quickly process batch-uploaded files. Mixed units across different documents require preserving the binding relationship between original fields and units during parsing, to avoid information misalignment after chunking. Large monthly report files have significant file size, so appropriate upload and parsing time limits must be adapted to prevent mid-process failures.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Railway operation ledgers contain long text descriptions and structured tables. This range preserves complete context for a single inspection record or single-trip train data, and avoids splitting critical fields |
chunkSplitter | Split by paragraph and table boundaries | Adapts to mixed-format documents, preserves the integrity of fields within tables, and avoids splitting data across tables |
customPdfParse | Enable structured parsing mode | PDFs for railway operation ledgers often contain standard tables. Enabling this mode allows precise extraction of table fields and text content, and prevents information loss from image-based content |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to the size of batch-exported monthly summary reports, and prevents upload interception |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Reserves sufficient parsing time when processing large monthly reports, and prevents mid-process timeout failures |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different types of railway ledgers (single-trip inspection, monthly summary) to adapt to differentiated document structures |
Three Common Errors
- Symptom: Embedded images cannot be viewed after uploading a PDF ledger, and text annotations for inspection site photos are missing from parsing results. Cause: The image OCR sub-option of
customPdfParsewas not enabled, and only plain text content was extracted. - Symptom: Parsing timeout errors occur after batch uploading monthly ledgers, and logs return a 504 status code. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default timeout duration is insufficient for processing large summary reports. - Symptom: Knowledge base question answering cannot associate train numbers with corresponding inspection records, leading to low answer accuracy. Cause:
chunkSplitterwas configured to split by fixed characters, and table and paragraph boundaries were not preserved, leading to cross-field data splitting.
How to Confirm Proper Configuration
- Upload the PDF of a single-trip train inspection ledger, and check if the parsed structured tables retain complete fields for train number, inspection time, and point name.
- Upload a monthly revenue report Excel file around 100 MB in size, confirm that the upload progress is not interrupted, and that no file size limit exceeded prompt appears after parsing.
- Adjust
chunkSizeto 600 characters and 1200 characters, compare the chunking results, and confirm that complete single-trip train data is not split. - Check the parsing logs, confirm that the structured parsing switch for
customPdfParseis enabled, and that there are no error records for OCR parsing failures.
The values listed here are common starting points, and should be evaluated 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.