What the data for this category looks like
Business-related data for rail transit equipment comes primarily from equipment operation and maintenance manuals, maintenance work orders, approval process documents, and equipment ledgers. There are two update schedules: real-time updates for work orders and approval records, and periodic updates for monthly maintenance specifications and quarterly equipment calibration documents. Documents are categorized by equipment model as the core, and include content such as operation steps, troubleshooting workflows, and approval node requirements. Fields cover equipment numbers, maintenance durations, fault levels, and processing nodes. Units use industrial standard units including hours, counts, and millimeters.
How These Characteristics Impact Vector Models and Indexes
Rail transit equipment business data includes long-form text and structured fields. The length of individual text entries varies significantly, which creates clear requirements for adapting text chunking granularity. Real-time work orders and periodic documents have uneven update schedules, so trigger timing for incremental indexing and full indexing must be differentiated. Document content with dense technical terms will reduce the recall accuracy of general embedding models, so index recall rules must be adjusted for industrial scenarios. Additionally, the multi-field mixed document structure requires field-level indexes to distinguish different business information, preventing irrelevant content from interfering with retrieval results.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the single-section content length of rail transit equipment maintenance process documents, avoids vector truncation caused by overly long single chunks of text |
| Overlap Length | 150–200 characters | Covers the connecting steps of business processes, prevents chunks from cutting off key processing nodes |
| Similarity Threshold | 0.75–0.85 | Filters mismatches caused by technical terms, ensures recall accuracy for business process guidance |
| Single Search Limit | Top 8 entries | Matches multi-step business processing scenarios, balances recall completeness and response speed |
defaultToken | 8192 | Adapts to context carrying for long documents, aligns with the default parameter range of FastGPT v4.17.0 |
maxToken | 16384 | Supports combined retrieval of multiple documents, covers complex business scenarios including equipment ledgers and work orders |
Three Common Mistakes
- The dataset status shows "indexing" for more than 2 hours in the interface: This occurs because the imported rail transit equipment documents contain extremely long single sections of text, and the chunk length parameter was not adjusted, leading to index task timeout.
- The similarity field in search results shows values over 10000: This occurs because the similarity threshold filter was not configured, causing semantic matches of technical terms to be misjudged as exact matches.
- The m3e model shows "no available channel" in the backend model list: This occurs because the API channel for the custom embedding model was not added in the FastGPT backend, and corresponding parameters were not configured according to the official documentation.
How to Verify Correct Configuration
- Upload a single rail transit equipment maintenance process document, check if the chunked text fragments fully cover the process steps without obvious truncation.
- Initiate a query related to business processes, verify that the similarity values of returned results fall within the preset threshold range, with no abnormally high values.
- View the index task logs, confirm there are no timeout errors, and the task completion time meets expectations.
- On the custom model configuration page, confirm that the API channel for the m3e model has been added correctly, and the status shows available.
The values provided here 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.