What Data for This Category Looks Like
Data sources for this category include EAM/CMMS systems, equipment sensor collection data, operator point check records, maintenance work order history, spare parts inventory lists, statutory inspection records for special equipment, and more. Daily documents cover equipment ledgers, point check forms, maintenance plans, work orders, fault analysis reports, spare parts lists, and more. Fields include equipment number, point check time, abnormality description, spare parts model, inspection validity period, and more. Update frequencies cover shifts, daily/weekly/monthly cycles, and sudden fault scenarios. Data structures vary significantly by document type, with a mix of short entries and long texts.
What Constraints These Characteristics Impose on Vector Models and Indexes
Multi-source heterogeneous data structures require vector chunking strategies to adapt to different document types. Short point check forms need compact chunk units. Long maintenance work orders need sufficient semantic carrying space. Frequently updated point check and sensor data require incremental indexing to avoid resource waste from full reprocessing. Fixed formats for statutory inspection records require indexes to retain complete field information, ensuring accurate matching of equipment numbers and inspection validity periods during retrieval. Business requirements for fault location and spare part verification require high recall accuracy from vector retrieval, to avoid irrelevant results interfering with maintenance decisions.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to mixed scenarios of short point check form entries and long maintenance work order texts, balancing semantic completeness and retrieval efficiency |
| Overlap Length | 100–150 characters | Prevents cross-chunk semantic breaks, adapting to the continuity of point check records and contextual relevance of work orders |
| Similarity Threshold | 0.72–0.85 | Differentiates normal point check and abnormal fault records, reduces false recall probability, and meets the precision needs of maintenance decisions |
| Single Search Limit | Top 10 results | Matches the number of historical records referenced for maintenance decisions, reducing redundant context burden on the model |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
maxToken | 32768 | Supports batch processing of long-text fault analysis reports, avoiding truncation issues caused by insufficient token count |
Three Common Misconfigurations
- Phenomenon: No matching model appears in the text understanding model dropdown when creating a knowledge base. Cause: The embedding model call address and access permissions are not properly configured in the model channel, or the model type does not match the embedding requirements of the vector index.
- Phenomenon: Batch vectorization only supports single-file parameter adjustment, and cannot retrain the entire dataset in batch. Cause: The batch task configuration switch for the knowledge base is not enabled, or incremental indexing mode is not activated.
- Phenomenon: The number of retrieved results does not match expectations. Cause: The similarity threshold setting is outside the business adaptation range, or the single search limit configuration is not adjusted based on the update frequency of point check records.
How to Verify Proper Configuration
- Upload one typical point check form and one maintenance work order, check that vector generation progress completes normally with no truncation or error prompts.
- Enter a fault description search term for the corresponding device, verify that returned results include historical fault work orders, point check records, and maintenance plans for that device.
- View the model channel page in FastGPT v4.17.0, confirm that the embedding model is activated and has no connection errors.
- Test batch upload of five or more documents of different types, check that vectorization processing can be completed uniformly with no abnormal interruptions or parameter loss.
The values provided are common starting points and should be measured against the reader's own samples.