pgvector Explained: AI Vector Search in PostgreSQL (No New Database Needed!) скачать в хорошем качестве

pgvector Explained: AI Vector Search in PostgreSQL (No New Database Needed!)

This video provides a complete explanation of pgvector—the open-source PostgreSQL extension that adds powerful AI-powered vector search to your existing database. Already comfortable with PostgreSQL? You no longer need to learn a new platform or manage separate vector database infrastructure (like Pinecone or Qdrant) to implement AI features. pgvector lets you store embeddings alongside your existing data and use familiar SQL for vector operations. What You Will Learn in this 8-10 Minute Overview: • Why pgvector matters: How it lets millions of developers add AI features using tools they already know. • The Problem Solved: Why managing two databases is a "sync nightmare," and how pgvector simplifies your infrastructure. • Key Concepts: Understanding the vector data type and distance operators like L2 distance (-) and Cosine similarity (=). • Practical Use Cases: Implementing Semantic Search, Recommendations, and RAG/Document Search using pgvector. • Comparison: When PostgreSQL beats specialized vector DBs, and alternatives like Supabase Vector and Pinecone. This is the perfect tutorial for PostgreSQL Users, Backend Developers, and Database Administrators looking for a cost-effective, familiar solution for vector storage and retrieval