Vector similarity explained: metrics, algorithms, & best infrastructure

Vector similarity is a crucial mathematical concept that measures the closeness of two data points in a high-dimensional space and is essential for semantic search, recommendation systems, and AI memory functions. It involves converting data into vectors, capturing semantic relationships beyond simple keyword matches, and using metrics like cosine similarity, dot product, and Euclidean distance to measure similarity. The choice of algorithms like HNSW, ScaNN, and IVF is important for balancing speed, accuracy, and memory efficiency when scaling vector similarity for production use. Redis has emerged as a leading infrastructure for scalable vector similarity, offering sub-millisecond latency and high performance compared to other options like Pinecone, FAISS, Weaviate, and Elasticsearch. RedisVL, introduced by Redis, simplifies indexing and querying, integrates with various AI frameworks, and supports both hosted and self-managed deployments, making it a flexible choice for industries with specific data residency and compliance needs. Ultimately, owning the vector search stack with tools like Redis can provide developers with the flexibility and performance needed to build advanced AI applications.