What the Data for This Category Looks Like
Business data for SMT mounting and electronic assembly comes from customer-provided BOMs, Gerber files, component coordinates, as well as MES systems, mounting machines, SPI/AOI testing equipment, reflow oven temperature records, and similar sources. Daily documents include BOM and coordinate files, changeover lists, stencil parameters, furnace temperature curves, first article confirmation reports, SPI/AOI defect reports, maintenance records, and traceability labels. Data updates occur in real time per work order and each changeover. Some batch component records are updated with incoming material batches. Document fields include component part numbers, reference designators, mounting coordinates, furnace temperature parameters, defect types, and similar items. Some engineering files are in binary format and contain encrypted mounting parameters.
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
The high-frequency real-time updates, mixed multi-type documents, and precise field matching requirements of this category impose multiple constraints on the vector model and indexing process. Real-time synced work order and changeover data require indexes to support incremental updates, avoiding excessive resource consumption from full reindexing. Mixed-format engineering files, structured parameter tables, and defect reports require indexes to adapt to text extraction and structured parsing of multi-source data. The need for precise matching of core business fields such as part numbers and reference designators requires vector models to prioritize entity association and semantic consistency, reducing fuzzy matching errors. The need for cross-work order bidding data integration requires indexes to support associative retrieval of business records across different batches.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of SMT work order documents, avoiding overly fragmented or overly long chunks that impact vector extraction accuracy |
| Overlap Length | 100–150 characters | Preserves contextual associations of core business fields such as part numbers and reference designators across chunks, avoiding entity breakage |
defaultToken | 1024 | Matches the token limit of the default embedding model for FastGPT v4.17.0, ensuring complete text embedding |
maxToken | 2048 | Accommodates complete core information of BOM entries or furnace temperature records, avoiding truncation of critical parameters |
| Similarity Threshold | 0.75–0.85 | Distinguishes business-related and unrelated search results, adapting to the precise matching requirements of bid quotation preparation |
| Single Search Limit | Top 10 entries | Filters core historical work order data, avoiding redundant results that interfere with quotation preparation logic |
Three Common Mistakes
- Symptom: After the knowledge base index is completed, searching for BOM data related to bids returns empty results. Cause: The chunking strategy was not optimized for core fields such as part numbers and reference designators, leading to key entities being split across different chunks, with vector embeddings losing association information.
- Symptom: Timeout errors occur during indexing, with a 504 status code displayed. Cause: Incremental indexing configuration was not enabled, and full indexing of hundreds of thousands of SMT work order data entries exceeded the system response threshold.
- Symptom: A large number of unrelated furnace temperature records are included in search results. Cause: The similarity threshold was set too low, including non-business-related data that does not meet semantic similarity standards in the result set.
How to Confirm Proper Configuration
- Upload a single complete BOM file, check that the chunked text retains core part number and reference designator fields, with no obvious truncation or breakage.
- Perform a retrieval test for a known part number, verify the number and business relevance of returned results, and adjust the similarity threshold to the required range.
- Import 10 simulated work order data entries, verify that incremental indexing only updates newly added data without requiring full reindexing.
- Check the vector storage usage, confirm that the chunk length and
maxTokensettings do not cause excessive storage space consumption.
The values provided are common starting points and should be measured against the reader's own samples.