How to Query Vector Embeddings: Similarity Search Tutorial #ai #aiengineering

Description

Time to see semantic search in action! In this video, I show you how to query vector embeddings and retrieve the most similar results using similarity search. What you'll learn: ✅ How to convert a search query into an embedding ✅ Comparing query vectors against stored embeddings ✅ Using Cosine Similarity (the easy way with Supabase) ✅ Creating the match_documents SQL function ✅ Understanding similarity scores and thresholds ✅ Real examples: space exploration, time-based queries, whale communication ✅ What happens when there's no good match This is Part 8 of my AI Engineering series on Embeddings and Vector Databases. We're querying those 10 fake podcast descriptions we stored earlier and watching semantic search understand meaning, not just keywords! Results include similarity scores showing how confident the match is — lower scores mean weaker matches. Next: Building real applications with document search and smart Q&A! 🔔 Subscribe for hands-on AI tutorials!