What Data Looks Like for This Category
Typical entities in this category are EMS factories, consumer electronics and industrial control automotive electronics factories. Daily data sources include BOMs and Gerber coordinate files provided by customers, factory MES systems, solder paste and component batch ledgers, mounting machines, SPI/AOI testing equipment and reflow oven temperature records. Data is synced in real time per work order and each line change. Documents include BOMs and coordinate files, line change sheets, stencils and oven temperature records, first article confirmation reports, SPI/AOI testing reports, maintenance records and traceability labels. Core fields include part numbers, reference designators, component batches, oven temperature parameters and more.
What Constraints Do These Characteristics Impose on the Vector Model and Indexing Workflow
The data characteristics of this category impose multiple constraints on the vector model and indexing workflow. The real-time update per work order and line change requirement means indexes must support incremental sync. This avoids the computing resource usage caused by full reindexing. Documents include structured coordinates, oven temperature parameters and unstructured testing reports. This requires adaptive mixed-format vector encoding to ensure consistent semantic recall across different field types. There is a high need for exact matching of core fields like part numbers and reference designators. This requires balancing semantic similarity and exact field recall. Single work order data volume fluctuates with production line scale. Index recall limits must adapt to processing needs across different production lines.
How to Configure Settings
The following configurations are designed for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents such as BOMs and testing reports, avoids semantic fragmentation within chunks |
| Overlap Length | 100–200 characters | Preserves semantic connections between chunks, prevents loss of part number and reference designator information across chunks |
maxToken | 4096 token | Adapts to the input limit of mainstream vector models, avoids missing critical information due to truncated long text |
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. |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balances the need for exact part number matching and recall of related defect reports |
| Single Search Limit | Top 10 results | Adapts to the rapid response requirements of production line audits, avoids excessive redundant results interfering with judgment |
Three Common Configuration Errors
Large deviation in part number matching from index recall results occurs when vector encoding weights are not adjusted for exact fields such as part numbers and reference designators. Semantic similarity interferes with exact matching. Incremental index update timeouts occur when incremental sync trigger rules are not configured. Full sync of massive work order data each time exceeds system processing limits. Empty fields appear in retrieval results when chunk length is set too large. Maintenance records containing null values are fully included in chunks, diluting key field information during vector encoding.
How to Confirm Correct Configuration
A single BOM and SPI report is uploaded. Chunks generated by the vector index are verified to cover all core fields without obvious semantic fragmentation. A part number query command is entered. Matching accuracy of recall results is verified. The similarity threshold is adjusted to a range that meets business requirements. An incremental update task after a single line change is simulated. Index sync time is checked to confirm it meets production line response requirements. Mixed data from multiple different work orders is imported. Recall results under the single search limit are confirmed to have no redundancy and cover relevant testing records.
The values given are common starting points and should be measured against the reader's own samples.