Data Profile
Service inquiry data for rail transit equipment primarily comes from documents including equipment operation and maintenance manuals, maintenance procedures, operation dispatch records, and troubleshooting guides. Update cycles are triggered on demand when new equipment launches, procedures are revised, or operations adjust, with no fixed schedule.
Most documents use a chapter-based structure, and include fields such as equipment model, serial number, maintenance cycle, fault code, and technical parameters. Units include millimeters, hours, kilometers, megapascals, and other professional engineering units. Individual document lengths vary widely, with some operation and maintenance manuals exceeding 10 MB.
Constraints for Vector Models and Indexing
Rail transit equipment documents have complex structures, specialized fields, and wide length variations. The indexing process must retain field-level associations, and avoid splitting critical parameters and their units.
Long documents must be chunked to align with chapter logic, to prevent critical fault codes or maintenance steps from being cut off. The on-demand update feature of the data requires indexing to support incremental updates, to avoid resource waste from full reindexing.
Documents with dense specialized terminology require vector models to accurately capture semantic associations. During indexing, priority must be given to linking core fields like equipment serial numbers and fault codes, to improve retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single chapters in rail transit equipment documents, and avoids cutting off specialized paragraphs and fault codes |
| Overlap Length | 100–150 characters | Retains contextual connections between adjacent chunks, and prevents critical maintenance steps from being split across different chunks |
| maxToken | 8192 token | Adapts to the input limit of mainstream open-source vector models, and matches the total token count of chunked long documents |
| defaultToken | 2048 token | Sets the token quota for basic retrieval context, and meets the requirement for full display of single equipment parameter entries |
| Per-Index File Limit | 50 files | Avoids excessive load on a single indexing batch, and prevents server resource overload |
| Similarity Threshold | Calibrated via actual testing | Adjusts matching precision based on the semantic similarity characteristics of rail transit equipment fault descriptions |
Three Common Mistakes
- Issue: When uploading rail transit operation and maintenance manuals larger than 10 MB, some chunks return empty vector values after vectorization, and normal operation resumes only after repeated retries. Cause: The chunk length does not align with the document's line breaks and chapter structure, leading to chunks containing unclosed professional formulas or code blocks, which triggers format validation failures in the vector model.
- Issue: Batch indexing multiple equipment documents causes server crashes. After restarting, the corresponding knowledge base shows a "Not Ready" status, and indexing cannot recover automatically. Cause: Automatic indexing retry configuration is not enabled. The interrupt flag for the batch indexing task is not automatically reset by the system, and manual reset is required.
- Issue: After upgrading to FastGPT v4.17.0, the existing rail transit equipment knowledge base cannot return retrieval results. Cause: The embedding dimension of the new version's vector model has changed. The existing vector database for the index was not regenerated, and does not match the input requirements of the current model.
How to Verify Proper Configuration
- A single rail transit maintenance procedure document larger than 10 MB may be uploaded, and the chunk list checked for chunks without error markers. The chunking logic is confirmed to align with the document structure.
- No more than 50 equipment parameter documents may be batch uploaded, and resource usage during the indexing task monitored. The absence of server overload alerts is confirmed.
- An inquiry including a fault code or equipment model may be input, and retrieval results checked for core fields such as equipment serial number and maintenance cycle. The correctness of the index field configuration is confirmed.
- The vector model version corresponding to FastGPT v4.17.0 may be upgraded, and the index for the corresponding knowledge base regenerated. The match between retrieval results and the semantic requirements of the updated model is confirmed.
The values listed above 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.