pgvector-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 que...

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, binaryquantize, iterative scan).

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

Configurazione di pgvector e migliori pratiche per la ricerca semantica con incorporamenti di testo in PostgreSQL Fonte: timescale/pg-aiguide.