Vector search

Text, images and products are turned into embeddings, and similar items are those whose vectors are close. Approximate nearest-neighbour indexes such as HNSW make this fast at scale with a small loss of recall.

Where it shows up in real systems

  • Vector database

    Powers semantic search, recommendations and retrieval for AI assistants. Approximate nearest-neighbour indexes such as HNSW trade a little recall for huge speed; they live mostly in memory, so size nodes by the number of vectors.

    In production: pgvector, Pinecone, Weaviate, Milvus, Qdrant, OpenSearch k-NN

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