What is the difference between exact and approximate nearest-neighbour search in pgvector?

pgvector supports two search modes with very different performance and accuracy characteristics. Choosing the right one depends on dataset size and whether you need perfect recall.

-- EXACT search (no index): scans every row
-- Accurate but slow at scale
SELECT id, embedding <-> '[3,1,2]' AS dist
FROM items
ORDER BY dist
LIMIT 5;

-- APPROXIMATE search (with HNSW index): fast, high recall
-- Create the index:
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);

-- Same query syntax - PostgreSQL uses the index automatically:
SELECT id, embedding <-> '[3,1,2]' AS dist
FROM items
ORDER BY dist
LIMIT 5;
-- Now uses HNSW index for fast approximate search

-- Check which index was used:
EXPLAIN SELECT id FROM items ORDER BY embedding <-> '[1,2,3]' LIMIT 5;
-- Look for 'Index Scan using items_embedding_idx' in the plan

-- Force exact search even when index exists:
SET enable_indexscan = off;
SELECT id FROM items ORDER BY embedding <-> '[1,2,3]' LIMIT 5;
RESET enable_indexscan;

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