What Is Semantic Search?

Traditional keyword search matches documents based on the literal words in a query, which often misses relevant results that use different terminology. Semantic search solves this by converting both queries and documents into numerical vector representations (embeddings) that capture their meaning. When a user searches for 'how to fix a flat tire,' semantic search understands the intent and can return results about 'tire puncture repair' even if those exact words were not used. The process typically involves an embedding model that maps text into a high-dimensional vector space, where semantically similar content clusters together. These vectors are stored in a vector database, and at query time the system finds the nearest vectors to the query embedding. Semantic search powers modern search engines, recommendation systems, and is a critical component of RAG (Retrieval Augmented Generation) pipelines. It dramatically improves search relevance for natural language queries and is increasingly used in enterprise knowledge management, e-commerce product discovery, and AI-powered customer support systems.