Vector Databases Explained: A Beginner's Guide to Embeddings and Similarity Search

Vector databases store data as numerical vectors, enabling efficient similarity search for AI applications like recommendation systems. Embeddings capture semantic meaning of objects such as words or images, allowing databases to find similar items based on vector proximity. This guide covers key concepts and code examples using Faiss and TensorFlow to build and query vector databases.