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What an embedding actually is
A model turns text into a list of numbers positioned in a space where similar meanings sit close together. 'Leaking pipe joint' and 'compression fitting' land near each other even though they share no words. That is the whole magic, and it is genuinely useful.
Why pure vector search fails in B2B
Search for article number 'M8x40-A2' and a semantic model will helpfully return something conceptually similar. Your buyer does not want similar. They want that exact part. Semantics without exactness is worse than keywords.
Hybrid search is the answer
Run keyword search and vector search together and blend the scores, with exact matches on SKU and EAN always winning. You get typo tolerance and meaning without ever losing the part number.
Start with your zero-result log
Before buying a vector database, read what people searched for and found nothing. If those are mostly typos and synonyms, a synonym list fixes it for free. If they are descriptions of problems, that is when vectors earn their cost.
- Vectors match meaning; they ignore exactness.
- Hybrid search: exact SKU always wins.
- Read the zero-result log before buying anything.
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