What the data for this category looks like
Railway operation service documents mainly come from official operation manuals, rule documents exported from ticketing systems, and route update announcements. Their update frequency adjusts with new route openings and new rule releases. Most document structures include fixed process steps, multi-column timetables and fare tables, and embedded images such as station layouts and equipment diagrams. Document fields include train number, station name, travel duration, ticket price, processing time limit, and other items. Units include professional operation terms such as minutes, yuan, and kilometers.
What constraints do these characteristics impose on the document parsing and chunking link?
The multi-column tables, embedded images, and professional terminology of railway operation documents create multiple constraints for the parsing and chunking process. Multi-column timetables and fare tables are prone to parsing misalignment, requiring special table recognition logic. Embedded station diagrams need simultaneous extraction of image text and descriptions, to avoid losing image-related information when only text descriptions are identified. Long documents such as national railway operation charts have large single-file sizes and high content volumes, requiring adjustments to chunking and timeout configurations. Professional operation terms such as multiple unit train and ticket seat must be accurately identified, to avoid splitting term-related content during chunking.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Railway service documents mostly contain process steps and tables. This range preserves the complete semantics of a single process step or single table |
chunkSplitter | Split by paragraph + title | Railway documents have clear process titles and step paragraphs. This splitting method avoids splitting cross-step content |
customPdfParse | Enable image recognition mode | Railway documents often include station layouts and equipment diagrams. Enabling this mode extracts both image text and descriptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading large railway operation chart PDF files, meeting full document parsing requirements |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large railway documents require long parsing times. This duration prevents mid-parsing timeout interruptions |
chunkSettingMode | Manual configuration | Adapts to the professional structure of railway documents. Manual adjustment improves chunking accuracy |
Three common configuration mistakes
- Multi-column timetable tables show column misalignment after parsing. This happens because the setup does not adapt to the multi-column layout of railway documents. The default parsing logic cannot correctly identify cross-column table structures.
- After uploading a document with station diagrams, the parsing result only includes text descriptions and no image content. This happens because the image recognition configuration of
customPdfParseis not enabled, and only plain text from the document is extracted. - Parsing times out after uploading a large railway operation chart PDF. This happens because
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a sufficient duration. The default timeout duration is not long enough to complete full content parsing.
How to confirm the configuration is correct
- Upload a railway ticketing rule PDF containing multi-column tables. Check if the parsed text retains the original table's column alignment.
- Upload a document with station diagrams. Check if the parsing result includes both image descriptions and embedded text content.
- Upload a single 500 MB railway operation chart document. Confirm that no timeout error occurs during parsing.
- Adjust
chunkSizeto 1000 characters. Check if the chunked content fully includes all steps of a single train travel process.
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.