Vector Databases Explained — How They Actually Work

Description

Vector databases are one of the most important pieces of infrastructure in modern AI — but how do they actually work under the hood? This animated explainer breaks it all down. In this video, you'll learn what vector databases are, how embedding models convert meaning into coordinates, how the HNSW algorithm delivers sub-millisecond search across billions of vectors, and when you should (and shouldn't) use one. We cover real-world examples from Spotify, Perplexity, and Airbnb, compare Pinecone vs Milvus vs Weaviate vs Qdrant vs pgvector, and break down the trade-offs nobody talks about. ⏱ Timestamps 00:00 — Why traditional databases can't search by meaning 01:03 — The problem with keyword matching 02:11 — How embeddings capture meaning 03:49 — HNSW & approximate nearest neighbor search 04:59 — The full vector search pipeline 05:50 — Real-world examples (Spotify, Perplexity, Airbnb, RAG) 07:12 — The ecosystem (Pinecone, Milvus, Weaviate, Qdrant, pgvector, FAISS) 08:31 — Trade-offs and when NOT to use a vector database 09:58 — The big picture 🔗 More Devsplainers ▸ How Data Centers ACTUALLY Work → https://youtu.be/jiNApJ9bCz0 ▸ What is a CDN? → https://youtu.be/bZqdjLt4q34 ▸ Supabase vs Firebase → https://youtu.be/kE09hDFslZc 📚 What is a vector database? A vector database stores high-dimensional numerical representations (embeddings) of unstructured data like text, images, and audio. Instead of matching exact keywords, it finds items by semantic similarity — searching by meaning rather than strings. This makes it essential for RAG (Retrieval Augmented Generation), semantic search, recommendation systems, and any AI application that needs to retrieve relevant information from large datasets. ✅ Topics covered in this video: • What is a vector database and why it matters for AI • How embedding models convert data into vectors • The RGB analogy for understanding high-dimensional space • HNSW (Hierarchical Navigable Small World) algorithm explained • Approximate nearest neighbor (ANN) search • Metadata filtering in vector search pipelines • Spotify, Perplexity, and Airbnb production use cases • RAG — Retrieval Augmented Generation with vector databases • Pinecone vs Milvus vs Weaviate vs Qdrant vs Chroma • pgvector for PostgreSQL — when it's enough • FAISS, Elasticsearch, and Redis vector search • When to use (and not use) a vector database • Hybrid search: combining vector search with BM25 #VectorDatabase #AI #MachineLearning #RAG #SoftwareEngineering