What data for this category looks like
This category’s data originates from daily work outputs of manufacturing enterprise equipment departments and workshop maintenance teams. It covers documents including equipment ledgers, inspection forms, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor data, operator manual inspection records, maintenance history archives, spare parts inventory ledgers, and statutory inspection information. Update cycles include shift-by-shift inspection submissions, daily/weekly/monthly maintenance plan updates, real-time records when faults occur, and some sensor data is streamed in real time. Document fields include equipment number, model, installation date, inspection point parameters, fault symptoms, corrective actions, spare parts models, and more. Some parameters have unit identifiers such as ℃, mm/s.
Constraints imposed by these characteristics on vector models and indexing
These data characteristics impose multiple constraints on the vector models and indexing link. First, multi-source heterogeneous data formats (structured ledgers, free-text fault records, streaming sensor data) require chunking strategies adapted to different content lengths and structures. This avoids disrupting professional semantic connections. Second, varied update frequencies (real-time sensor data, scheduled inspections, occasional faults) need flexible index update rules. This distinguishes full rebuild and incremental sync trigger scenarios. Third, equipment parameter fields with units require retaining semantic details during vector modeling. This avoids retrieval ambiguity caused by missing units. Fourth, fluctuating sample sizes for occasional fault records require balancing recall precision and coverage. This prevents excessive recall of irrelevant inspection records.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Structured documents such as equipment ledgers and work orders are mostly paragraph-based. This length preserves complete fault association logic and equipment parameter information |
| Overlap Length | 100–150 characters | Continuous point descriptions on inspection forms and maintenance plans require contextual continuity. This avoids disrupting operational logic when chunking |
| Similarity Threshold | 0.75–0.85 | Calibrated through actual testing, adapted to similarity differentiation for professional semantics such as equipment fault descriptions and spare part models |
| Single Search Limit | Top 10 entries | The associated information required for maintenance decisions is limited. Excessive results increase context burden |
| maxToken | 4096 | Adapts to the mainstream embedding model context limit of FastGPT v4.17.0, to accommodate total recalled content from equipment-related documents |
| defaultToken | 1024 | Controls token usage for vector recall results in a single query round, to avoid exceeding model input limits |
Three common misconfigurations
- Symptom: Repeated queries for the same equipment fault description trigger full vector retrieval each time, leading to long response latency. Cause: Query caching is not enabled, and duplicate query cache rules are not configured.
- Symptom: After data is added to a collection, it is not possible to generate equipment-related indexes associated with training orders, and the interface displays "Training order not completed". Cause: The functional boundaries between training orders and collection data addition are confused. Training orders are used for index construction of batch structured data, while collection addition only supports single non-batch text imports.
- Symptom: After uploading on-site equipment images and vectorizing them, retrieval cannot return associated inspection text records. Cause: Multimodal vector metadata binding is not configured. Vectors are only generated for images individually, and no association is established between images and equipment documents.
How to confirm correct configuration
- View the vector database index management interface, confirm that index entries for data sources such as equipment ledgers and work orders have been generated.
- Submit a standard equipment fault description query, verify that the number of returned associated documents matches the single search limit configuration.
- Submit the same equipment inspection query twice, compare response times for both queries, confirm that subsequent query latency is significantly reduced.
- Check the embedding model parameter configuration page, confirm that maxToken and defaultToken values comply with the limits of the current FastGPT version.
The values provided are common starting points, and should be validated against local operational samples.