Embeddings & Semantic Search

Key idea: An embedding turns text into numbers so that similar meanings land close together. That's how search finds the right ticket even when no words match.

  1. Search "we got billed two times" with Both. Where did keyword search go wrong, and why?
  2. Search "back up everything before we leave". Which word fooled keyword search?
  3. Hover the dots on the map. Which ticket sits away from its own group, and does its spot make sense?
  4. Try a query of your own that shares no words with any ticket. Does meaning search still find the right one?
  5. Search "401 error". When is plain keyword search good enough?
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Querywhat you typedEmbedding modeltext → numberspplx-embed-v1-0.6bQuery vectora point in meaningTickets25 support ticketsTicket vectorsembedded onceNearest by meaningcosine similarityKeyword matchshared wordsResultstop matches
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Map of meaning · close together = similar meaning

25 ticketsStep inside
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Behind the scenes

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