What This Category of Data Looks Like
Data for this category originates from winding, insulation, final assembly, and testing departments of motor factories and power equipment enterprises. It includes winding process cards, insulation treatment records, dynamic balance reports, voltage withstand insulation test reports, and type and factory test reports. Data sources cover electromagnetic design data, copper wire and insulation material batches, winding equipment, balancing machines, electrical test benches, and ERP/MES systems. Updates occur once per product or batch, with synchronous updates for each design change. The data includes fields with defined units, such as winding turns, withstand voltage values, and temperature rise. Core associated information links product batches to process steps.
Constraints Imposed on Vector Models and Indexing
Data in this category links multiple document types by batch, and has a high update frequency. Indexes must support fast associated retrieval by batch. Documents include structured fields with units such as winding turns and withstand voltage values. Vector chunking must avoid splitting these key fields, and must retain their full integrity. Data comes from multiple channels including equipment and systems. Unified index data source association logic is required. Additionally, the data covers three constraint types: electromagnetic, insulation, and rotating machinery balance. Vector models must cover multi-dimensional features to avoid missing cross-domain associated information during retrieval.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-section content length of winding process cards and test reports, avoids splitting key fields such as winding turns and withstand voltage values |
| Overlap Length | 100–150 characters | Retains process step association information across chunks, avoids losing context during traceability |
maxToken | 4096 | Adapts to the vector encoding length of batch test reports, covers complete test item content |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-associated test data, focuses on core motor quality parameters |
| Single Search Limit | Top 10 entries | Controls the number of recalled results, quickly locates winding or insulation treatment records for the same batch |
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. |
Three Common Misconfiguration Issues
- Phenomenon: Files larger than 10 MB are split into over 1000 chunks during upload. Some chunks fail vectorization, and occasional failures persist even after repeated retries. Cause: Chunk length settings do not adapt to the paragraph structure of long documents, causing some chunks containing special units (such as turns, kV) to exceed the model token limit.
- Phenomenon: After a server crashes and restarts due to multi-file uploads, the knowledge base remains in a not ready state, and automated index triggering fails. Cause: Incremental index trigger rules are not configured. The system relies on manual triggering, and no scheduled index synchronization mechanism is set up.
- Phenomenon: After updating FastGPT to versions 4.9–4.10, knowledge bases with existing indexes cannot return vector retrieval results. Cause: The embedding dimension of the vector model changes after the version upgrade. The existing vector library for the index is not regenerated, and does not match the new model.
How to Verify Proper Configuration
- For FastGPT v4.17.0, upload a complete winding process card. Check that the number of split chunks matches expectations, and no key fields are abnormally split.
- Submit a quality anomaly query such as "batch records for insulation breakdown". Verify that recalled results include insulation treatment records and test reports for the same batch.
- View vector index logs. Confirm there are no error reports for token limit exceedance or vectorization failures.
- Restart the server. Check that the knowledge base index status automatically changes to ready, with no manual triggering required.
The values provided are common starting points, and should be evaluated against the reader's own samples.