Data Profile for This Category
This category covers the full equipment management workflow for manufacturing enterprises. Data sources include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance history archives, spare parts inventory lists, and special equipment inspection records. Daily documents include equipment ledgers (including equipment numbers, rated parameters), inspection sheets (including measurement points, measured values and units), maintenance plans, work orders, fault analysis reports, spare parts lists, and more. Data update frequencies include shift-based inspections, daily/weekly/monthly maintenance plan updates, and real-time reporting when faults occur.
Constraints on Vector Models and Indexes
Mixed multi-source data formats and update rhythms require vector indexes to support hybrid indexing of structured numerical values, semi-structured lists, and unstructured text. Real-time sensor data and temporary fault-time reports require low-latency incremental update capabilities to avoid index queue backlogs. Strongly correlated fields such as equipment numbers and measurement point units require indexes to bind metadata, ensuring retrieval results can be linked to specific equipment or documents. Data sources with varying update frequencies need adapted index refresh strategies to prevent high-frequency data from overwriting low-frequency historical records.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length of single records from inspection sheets and work orders, retains association of key fields such as equipment numbers and fault codes |
| Overlap Length | 100–150 characters | Prevents critical metadata from being truncated across chunks, ensures context continuity |
| Similarity Threshold | 0.75–0.85 | Differentiates normal equipment operating parameters from abnormal thresholds, adapts to the precision requirements of inspection data |
| Maximum Search Results per Query | Top 10 | Covers recent inspection and maintenance records for the same equipment, meets information needs for fault localization |
defaultToken | 4096 | Adapts to long-text content such as equipment ledgers and fault analysis reports, falls within the parameter range of FastGPT v4.17.0 |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
Three Common Misconfigurations
- Symptom: The index remains in a not-ready state for extended periods after creation, and retrieval cannot be triggered. Cause: The real-time update frequency of equipment sensor data is not adapted, and incremental index tasks are not configured to run on a shift-based or fault-triggered schedule, leading to index queue backlogs.
- Symptom: Retrieval results do not link to specific document sources, making it impossible to trace inspection records or work orders. Cause: Metadata binding configuration is not enabled, and equipment IDs and document types are not stored as index metadata fields.
- Symptom: Embedding model calls fail with an error indicating the current model is not supported. Cause: The selected embedding model is not confirmed to be compatible with the field types of equipment data, or model interface parameters are not configured as required.
How to Verify Proper Configuration
- Manually upload a typical inspection sheet document, wait for indexing to complete, retrieve abnormal measured values within the document, and confirm retrieval results include the uploaded document’s content.
- Check the index status panel, confirm index progress aligns with the configured update frequency, and no long-pending tasks exist in the queue.
- Call the retrieval interface, check that returned results include document metadata fields such as equipment numbers and document creation time.
- Test embedding model generation of vectors for an equipment ledger, confirm no format errors occur during vector generation, and vectors can participate in retrieval normally.
The values provided are common starting points and should be evaluated against the reader's own samples.