What this category of data looks like
Railway operation policy and regulatory basis data mainly comes from formal regulations released by the national railway group, operating rules from individual railway bureaus, annual transport plans, and safety operation standards. Update schedules align with new policy releases and routine revisions. Most documents are in WORD format, with clear chapter numbering and clause structures. Supporting attachments are mostly tabular data. Fields include regulation numbers, effective dates, applicable scopes, penalty clauses, and units such as kilometers, passenger trips, tons, and hours.
What constraints do these characteristics impose on document parsing and chunking
The combination of long documents and large volumes of tabular data requires the parsing process to adapt to both text and structured table splitting logic. Clear chapter structures require chunking to avoid breaking clause integrity, to prevent loss of context association during Q&A. Uploading and parsing large files easily triggers timeouts, so corresponding duration parameters need adjustment. Content with dense professional terminology requires chunking to retain associated context before and after, otherwise it cannot accurately match users' policy query needs. Large row-level tabular data also requires avoiding loss of the binding relationship between table headers and corresponding data rows during chunking.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to scenarios where single files of railway policy documents often exceed standard sizes |
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Parsing long documents and tabular data takes longer, prevents mid-process timeouts |
chunkSplitter | Chunk by chapter + semantics | Matches the clear chapter structure of railway policy documents, retains clause integrity |
chunkSize | 800–1200 characters | Adapts to the input length of most vector models, while retaining clause context |
chunkSettingMode | Manual configuration | Requires special adjustments for tables and attachments in railway documents |
customPdfParse | Enabled | Adapts to complex typesetting of railway policy documents, retains format and associated information |
Three common mistakes
- Uploading a WORD policy document with 100,000 Chinese characters triggers a parsing timeout. The root cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter, using the default short duration configuration. - Importing an Excel spreadsheet with 15,000 rows of operating data leads to lost association between table headers and corresponding data rows during chunking. The root cause is failure to configure context-associated chunking, only using fixed-length splitting.
- Failing to accurately match policy clauses after generating vectors. The root cause is the
chunkSizeparameter value exceeding the supported range of the vector model, leading to truncated chunk content and loss of key information.
How to confirm configurations are correct
- Upload a single long document, confirm that the parsing task completes normally with no timeout error prompts.
- Search for specific policy clauses, confirm that the returned chunked content covers the complete clause context, with no content truncation.
- Import tabular data, confirm that the parsed content retains the binding relationship between table headers and data rows.
- Test the database query node, confirm that returned structured data can be converted into natural language format suitable for Q&A.
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.