This article walks through building vector similarity search in PostgreSQL using pgvector, covering everything from storing embeddings to querying and indexing them at scale.

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A practical guide to implementing vector similarity search in PostgreSQL using the pgvector extension. Covers installing pgvector, creating tables with vector columns, inserting embeddings, and querying with SQL distance operators. Explains the difference between L2 and cosine distance metrics, when to use each, and how to create HNSW or IVFFlat indexes for performance at scale. Also shows how to combine vector similarity queries with standard SQL WHERE filters for filtered semantic search.

Building Vector Similarity Search in PostgreSQL with pgvector

Putting It Together: Filtered Similarity Search