pgvector-semantic-search

pgvector for Semantic Search

Semantic search finds content by meaning rather than exact keywords. An embedding model converts text into high-dimensional vectors, where similar meanings map to nearby points. pgvector stores these vectors in PostgreSQL and uses approximate nearest neighbor (ANN) indexes to find the closest matches quickly—scaling to millions of rows without leaving the database. Store your text alongside its embedding, then query by converting your search text to a vector and returning the rows with the smallest distance.

This guide covers pgvector setup and tuning—not embedding model selection or text chunking, which significantly affect search quality. Requires pgvector 0.8.0+ for all features (halfvec, binary_quantize, iterative scan).

Golden Path (Default Setup)

Use this configuration unless you have a specific reason not to.

• Embedding column data type: halfvec(N) where N is your embedding dimension (must match everywhere). Examples use 1536; replace with your dimension N.
• Distance: cosine (<=>)
• Index: HNSW (m = 16, ef_construction = 64). Use halfvec_cosine_ops and query with <=>.
• Query-time recall: SET hnsw.ef_search = 100 (good starting point from published benchmarks, increase for higher recall at higher latency)
• Query pattern: ORDER BY embedding <=> $1::halfvec(N) LIMIT k

This setup provides a strong speed–recall tradeoff for most text-embedding workloads.

Core Rules

• Enable the extension in each database: CREATE EXTENSION IF NOT EXISTS vector;
• Use HNSW indexes by default—superior speed-recall tradeoff, can be created on empty tables, no training step required. Only consider IVFFlat for write-heavy or memory-bound workloads.
• Use halfvec by default—store and index as halfvec for 50% smaller storage and indexes with minimal recall loss.
• Index after bulk loading initial data for best build performance.
• Create indexes concurrently in production: CREATE INDEX CONCURRENTLY ...
• Use cosine distance by default (<=>): For non-normalized embeddings, use cosine. For unit-normalized embeddings, cosine and inner product yield identical rankings; default to cosine.
• Match query operator to index ops: Index with halfvec_cosine_ops requires <=> in queries; halfvec_l2_ops requires <->; mismatched operators won't use the index.
• Always cast query vectors explicitly ($1::halfvec(N)) to avoid implicit-cast failures in prepared statements.
• Always use the same embedding model for data and queries. Similarity search only works when the model generating the vectors is the same.