What the Data for This Category Looks Like
The core data for this category comes from customer-provided BOMs, Gerber coordinate files, MES systems, mounters, SPI/AOI testing equipment, reflow oven temperature records, and similar sources. Daily maintained documents include changeover sheets, stencil usage records, first article inspection reports, solder joint defect analysis reports, component traceability labels, and more. Data is updated in real time each time a work order is generated or a production line changeover occurs. Individual documents contain both structured and semi-structured fields such as part numbers, reference designators, component batches, furnace temperature curve parameters, mounting coordinates, and others.
What Constraints These Characteristics Impose on the Vector Models and Indexing Workflow
Real-time updated work order data requires indexes to support incremental construction and rapid synchronization, to avoid performance losses caused by full indexing. Scenarios with mixed document types require vector models to adapt to semantic encoding of both text and numeric fields, ensuring accurate vector representations of key information such as part numbers and coordinates. Frequent changeovers and work order updates generate large volumes of incremental data, requiring reasonable planning of index sharding to balance retrieval speed and storage usage. Additionally, the business requires precise matching of strongly associated fields such as part numbers and reference designators, which places high demands on vector recall accuracy.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical length of single documents such as BOMs and furnace temperature records, preventing key parameters from being split across chunks |
| Overlap Length | 100–200 characters | Covers cross-chunk associated information in changeover sheets and first article reports, preventing separation of part numbers and their corresponding mounting requirements |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets the precise matching requirements for part numbers and reference designators in SMT scenarios, reducing false recalls of unrelated data |
defaultToken | 4096 | Carries the context of a complete single process specification, meeting the vector encoding requirements for long documents |
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. |
| Single Search Limit | Top 10 results | Covers the retrieval needs for multiple associated documents under a single work order, avoiding redundant data occupying context space |
Three Common Errors
- Symptom: Vector index construction times out, returning a 504 status code. Cause: Incremental indexing mode is not configured, and full indexing processes the large volume of accumulated historical work order data, exceeding system processing thresholds.
- Symptom: Non-associated component batch information from other work orders appears in retrieval results. Cause: The similarity threshold is set too low, recalling historical data outside the business scope, which does not meet the precision requirements for changeover error prevention.
- Symptom: No valid return results after connecting an external index model. Cause: Model call parameters are not configured in accordance with the FastGPT v4.17.0 interface specifications, resulting in request timeouts or field format mismatches.
How to Confirm Proper Configuration
- Upload a complete work order BOM and first article inspection report, check whether the vector index generation status shows as completed.
- Enter a query statement containing specific part numbers and mounting requirements, verify whether the retrieval results include the key parameters of the corresponding documents.
- Simulate the upload of real-time changeover data, check whether the index completes updates within the preset time.
- View the storage usage of the vector database, confirm that it matches the update scale of the current production line data.
The values provided are common starting points and should be measured against the reader's own samples.