What the data for this category looks like
Data for this category comes from MES systems, mounters, SPI/AOI inspection equipment, reflow ovens at EMS factories, consumer electronics and automotive electronics factories. It also includes BOM, Gerber and coordinate files provided by customers. Daily documents include BOM and coordinate files, line change sheets, stencil and furnace temperature records, first article confirmation reports, SPI/AOI inspection reports, maintenance records and traceability labels. Data updates in real time per work order and each line change. Fields include part numbers, reference designators, component batches, furnace temperature parameters and others. Some fields use units such as millimeters and degrees Celsius.
What constraints these characteristics impose on the vector models and indexes stage
Data for this category updates frequently in real time and splits by work order and line change. Indexes must support incremental writing to avoid performance losses from full reconstruction. Documents include structured material parameters, semi-structured inspection reports and other content types. Adapt vector encoding logic for different fields to ensure semantic alignment of similar fields. Data fields include precise part numbers, reference designators and process parameters. Vector models must retain sufficient detail granularity to prevent critical information loss from excessive compression. Work order data volume fluctuates with production line scale. Indexes must support dynamic scaling to match storage and query loads across different production lines.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the single-block content length of BOM and inspection reports for this category, avoid semantic mixing across work orders |
| Overlap Length | 100–150 characters | Retain process parameter associations across chunks, prevent critical information from being split and truncated |
maxToken | 4096 token | Match the input limit of mainstream embedding models, adapt to vector encoding of long documents |
| Similarity Threshold | Calibrated based on actual measurements | This category requires precise matching of strongly associated fields such as part numbers and reference designators. The threshold must be adjusted based on business scenarios |
| Single Search Limit | Top 10 entries | Cover the number of historical records associated with a single work order, avoid recalling excessive irrelevant data |
| Maximum Context | 6000 characters | Match context association requirements during inspection and audit, ensure retrieval results cover sufficient process reference information |
Three Common Misconfigurations
- When a text data set is imported, the system status remains stuck at "Indexing" for an extended period. Cause: The high-frequency real-time work order data updates for this category are not adapted, and the full index process has no incremental optimization, leading to longer processing time than expected.
- After replacing the embedding model, the semantic similarity value reaches 10000+. Cause: The matching rules for the similarity threshold are not adjusted. The score range of the original model is used, and score ranges vary across different models.
- Retrieval results return a large number of records from irrelevant work orders. Cause: The chunk overlap length is set incorrectly, and the semantic association of the same work order is not retained, leading to retrieval matching of unrelated content across work orders.
How to Verify Proper Configuration
- Use the FastGPT v4.17.0 interface to import a typical data set for a single work order, check whether the index completion time meets the update rhythm requirements of the production line.
- Enter core retrieval terms such as part numbers and reference designators, verify whether the retrieval results only include relevant records from the same work order or same line change batch.
- After adjusting the similarity threshold, verify whether the matching accuracy of the retrieval results meets the business requirements of inspection and audit.
- Deploy a multi-replica test environment, confirm that load balancing for index writing and retrieval requests operates normally.
The values provided are common starting points and should be measured against the reader's own samples.