What this category of data looks like
Data for railway operation policy and regulatory basis comes from official transport safety, passenger service, train dispatching and other regulations released by national railway authorities. It also includes regional operation rules issued by railway bureaus, and temporary control notices released alongside route adjustments or emergency response needs. Formal regulations are updated annually or during major policy adjustments. Temporary notices are released immediately as operational requirements change. Document structures typically include document numbers, issuing entities, effective dates, and chapter and clause sections. Some documents attach operation flowcharts or table attachments. Fields include document number, issuing entity, effective time, clause number, and applicable scenario. Clause levels are marked with units such as "article", "paragraph", and "item". Time formats use standard Gregorian dates.
Constraints Imposed on Vector Models and Indexes
Railway policy data mixes long formal regulations and short temporary notices. Chunking must balance chapter integrity and length limits. This avoids splitting independent core clauses. Multiple metadata fields require retrieval support for filtering by effective time and applicable scenario. This ensures only currently valid policy content is retrieved. Non-fixed update frequencies require indexes to support incremental synchronization. This avoids full repeated calculations. Dense professional terminology requires vector models to adapt to specialized railway operation vocabulary. This improves retrieval accuracy.
How to Configure Settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the chapter and paragraph length of railway policy documents, avoids splitting independent clauses |
| Overlap Length | 150–200 characters | Retains cross-chunk clause context, prevents professional terminology from being truncated |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Distinguishes the similarity level of policy clauses, filters irrelevant generic regulations |
maxToken | 8000 token | Adapts to the input upper limit of mainstream embedding models, avoids failure when vectorizing long documents |
| Single Search Limit | Top 8 results | Matches query scenarios where multiple policy clauses are associated, controls retrieval redundancy |
| Reranking | Enabled | Sorts retrieved policy clauses by relevance, prioritizes matches to the core scenario of user questions |
Three Common Mistakes
- Index status remains stuck in "creating" after importing policy documents. Cause: Chunk parameters are not adjusted to fit the long paragraphs of railway policy documents. A single chunk exceeds the input limit of the embedding model, causing the indexing process to block.
- Vector calculation scores are abnormally high, and multiple results have identical scores. Cause: Expired old policy files are not filtered. Duplicate old clauses and new regulations are vectorized simultaneously, leading to deviations in similarity calculation.
- Vectorization processing speed is too slow. Cause: Incremental indexing mode is not enabled. Every update triggers full reprocessing of all railway policy documents, consuming significant computing resources.
How to Confirm Configuration Is Complete
- Upload a single typical railway policy document. Check that chunked fragments retain complete clauses with no truncated professional terminology.
- Simulate queries for high-frequency policy questions. Verify that the effective time of retrieved results meets current operational requirements, with no expired clauses included.
- Test incremental update of a single temporary notice. Confirm that the index only processes the new file, with no full reindexing log output.
- Check the vectorization logs in FastGPT v4.17.0. Confirm that the
maxTokenparameter does not trigger truncation warnings, and embedding model calls function normally.
The values provided here are common starting points, and should be evaluated against the user'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.