What the data for this category looks like
Rail transit equipment sector policy and regulatory basis documents primarily come from official documents issued by the National Railway Administration, Ministry of Industry and Information Technology, and industry associations, as well as internal corporate compliance documents. Update cycles are irregular, aligned with industry specification revisions and new national standard releases. Internal documents update alongside project acceptance or compliance checks. Most documents use formal official formats, including chapter numbers, clause content, and attached technical parameter tables. Fields include document number, issuing unit, and effective date. Technical parameters often use specific units such as kN and km/h. Individual documents have lengthy content.
Constraints imposed by these characteristics on document parsing and chunking
Official documents have chapter numbers and clause hierarchies. Chunking must retain structural integrity. Splitting across clauses must be avoided, as this prevents precise matching of Q&A results to policy clauses. Structured content in technical parameter tables must be fully extracted. Parameters and their associated units must not be separated. Long documents and multi-page appendices increase parsing duration, so timeout thresholds must be adjusted. Policy Q&A requires tracing back to specific documents and clauses. Chunking must retain document metadata to ensure answers can link back to original sources.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 1000–1500 characters | Rail transit policy documents often contain long clauses and technical tables. This range retains complete single clauses or full tables, avoiding splitting critical information |
chunkSplitter | Split by Markdown headings + paragraphs | Official policy documents have standard chapter hierarchies. Splitting by headings retains clause integrity and supports traceability requirements |
customPdfParse | Enabled | Accurate extraction of document numbers, clause numbers, and technical parameter tables is required. Custom parsing retains metadata and structured content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Long documents include multi-page technical appendices. Sufficient parsing time is reserved to avoid timeout interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual compliance manuals may exceed standard sizes. Raising the upper limit supports uploading full documents |
chunkSettingMode | Manual mode | Adjust chunking rules for specific clauses and tables to meet the precise traceability needs of policy Q&A |
Three Common Misconfigurations
- Phenomenon: After deploying a custom PDF parsing component via Docker, an error is returned when uploading rail transit policy PDFs. Cause: Marker environment variables are not configured correctly, and the parsing logic for multi-page technical appendices in the documents is not adapted.
- Phenomenon: Specific clause sources cannot be traced back after chunking. Cause: Metadata extraction configuration is not enabled, and document numbers and chapter number information are not retained during chunking.
- Phenomenon: Parsing times out when uploading policy documents larger than 200 MB. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis too low, and sufficient time is not reserved for long document parsing.
How to Verify Proper Configuration
- Upload a rail transit equipment policy PDF, check if the parsing result retains metadata such as document number and issuing unit.
- View the chunk list, confirm that long clauses are not split across chapters, and technical tables are stored as complete chunks.
- Test uploading a long document under 500 MB, confirm that no timeout error is triggered during parsing.
- Initiate a policy-related Q&A, check if the returned results include source information for the corresponding document.
Note: The configuration values listed above are common starting points. Adjustments should be made based on respective specific 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.