Vector Databases & Production Search

You embed a million support tickets into 1,536-dimensional vectors, store them in a Python list, and write a loop that compares each one to the query. It works for the demo. Then a real query lands and your "search" takes nine seconds. That gap between a toy s

8 lessons, each with runnable code in the browser.

  1. Why a Vector Database?
  2. Indexing & Approximate Nearest Neighbor
  3. Metadata Filtering
  4. Hybrid Search: Keyword + Vector
  5. Re-ranking & Scaling
  6. Choosing a Vector DB
  7. Sharding and Multi-Tenant
  8. Monitoring Search Quality

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