What the data for this category looks like
Rail transit equipment document data originates from three main sources: technical manuals, process specifications, and inspection reports during the design phase; work order records and change documents during the operation and maintenance phase; and contract attachments during the commercial phase. Data is updated either via bulk imports at project startup, or single incremental updates during daily operation and maintenance. Most documents follow a fixed hierarchical structure, with dedicated fields such as equipment model, batch number, and inspection date. Parameter units mostly use industrial standard units including mm, kN, and MPa. The length of individual documents varies widely. Some full-vehicle technical documents span dozens of pages.
What constraints these characteristics impose on vector models and indexes
Fixed document hierarchical structure requires chunking logic that aligns with chapter boundaries, to avoid splitting cross-hierarchy technical parameters across different chunks. Dedicated fields and units require indexes to associate metadata, to ensure field accuracy during retrieval. Long documents and bulk import update patterns require incremental index triggering logic that matches project and work order update nodes, to avoid full repeated indexing. High precision for professional terminology requires recall precision higher than general scenarios, placing stricter constraints on similarity thresholds and chunk completeness.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-section content length of rail transit equipment technical documents, ensuring complete information per chunk |
| Overlap Length | 150–200 characters | Prevents cross-chunk related technical parameters from being truncated |
| Maximum Paragraph Depth | 3 | Matches the standard hierarchical structure of technical documents (main chapter - subchapter - subsection) |
maxToken | 4096 token | Adapts to the input length limit of multimodal embedding models |
| Single Search Limit | Top 6–10 results | Balances recall coverage and retrieval response speed |
| Similarity Threshold | 0.72–0.85 | Meets the matching precision requirement for rail transit equipment professional terminology |
Three Common Misconfigurations
- When connecting a multimodal embedding model, the
{"error":{"code":"Invalidformat error is returned. The cause is thatmaxTokenis not set to match the model's input length limit, resulting in ultra-long document chunks failing to complete vectorization. - After rebuilding the knowledge base index, field missing occurs in associated retrieval results for historical documents. The cause is that metadata mapping rules are not updated synchronously, leading to inconsistent field association logic between new and old indexes.
- Chunk preview shows chaotic paragraph recognition, merging content from different chapters into the same chunk. The cause is that maximum paragraph depth is set beyond the document's actual hierarchy, causing the model to misjudge chunk boundaries.
How to Verify Proper Configuration
- Upload a typical rail transit equipment technical manual, check if the chunk preview splits content by chapter, with no mixed cross-hierarchy content.
- Call the retrieval interface, input a professional technical question, and verify that the similarity scores of returned results fall within the set threshold range.
- Run an incremental index task, confirm that newly uploaded operation and maintenance work orders are correctly associated with the corresponding project's document library.
- Export the metadata list of the vector database, confirm that dedicated fields such as equipment model and batch number have been correctly indexed and associated.
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.