The Secret Engine Behind Semantic Search: Vector Databases

The article explains how modern AI systems have moved beyond keyword matching to semantic search powered by vector databases and embeddings. Text is converted into numerical vectors (embeddings) that capture meaning, enabling mathematical comparison of semantic similarity using techniques like cosine similarity. This approach allows search systems to understand user intent and find related content even when exact words differ.