How To Build An AI-Powered Semantic Search In PostgreSQL With Pgvector

Traditional keyword search only finds the exact words you type. It doesn’t understand what you really mean, so a query like “affordable laptops for coding” may be missed.

The author explains that vector embeddings turn words or items into numbers that capture their meaning. This lets semantic search work by placing related ideas close together in a large set of numbers.

PostgreSQL pgvector can save these embeddings in a VECTOR column. It provides distance functions and supports ANN indexes such as HNSW and IVFFlat, which help find the nearest neighbors quickly.

By mixing vector similarity with normal SQL filters and text search, developers can create product search, chatbot retrieval, and content‑recommendation features all inside a single database.

Model selection, batching, bulk loading, and index tuning are needed to keep the system fast and scalable.