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In this lecture, we break down how a vector database works internally — from data ingestion to similarity search. 📌 What you'll learn: How raw data becomes vector embeddings What embedding models do How embeddings are stored with metadata Why vector indexing (ANN) is needed for fast search How query embeddings are generated How similarity ...
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