How vector databases work: embeddings, cosine similarity, HNSW, IVF and PQ explained
blaizedsouza · x · 2026-10-04
Amit Shekhar (Outcome School) published a beginner-friendly deep dive into how vector databases power modern AI search, recommendations and document QA:
- Core idea: data stored as numeric vectors, retrieved by semantic similarity rather than exact match.
- Similarity metrics: cosine similarity, dot product, Euclidean distance compared.
- The nearest-neighbour problem: brute force is too slow at scale, motivating ANN indexing.
- Indexing algorithms explained simply: HNSW (hierarchical graph navigation), IVF (inverted file bucketing), PQ (product quantization).
- Includes a code example and real-world applications.
A solid primer for developers building RAG or semantic search systems.
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