Embeddings & Vector Search in Rails — Semantic Search with pgvector

Streaming AI responses looks cool. But here's the problem: the AI doesn't know anything about your business. Ask it about your users, orders, or documents and it hallucinates. Embeddings fix this. They turn text into vectors — mathematical fingerprints that capture meaning. Similar ideas cluster together in vector space. Search stops being keyword matching and starts being concept matching . This post adds semantic search to your Rails app using pgvector and the neighbor gem. By the end, you'll search your content by meaning, not keywords. What We're Building A document search where "cloud computing" finds articles about AWS, Azure, and deployment — even if they never use the words "cloud" or "computing". Setup First, get pgvector running. If you're on a VPS (which you should be): # Ubuntu/Debian sudo apt install postgresql-16-pgvector # Or use the Docker image # postgres:16 with pgvector extension Enable the extension in your database: CREATE EXTENSION IF NOT EXISTS vector ; Add the g