Vector Search

Vector search is a technique for finding similar items in high-dimensional vector spaces, typically using embeddings to represent data points like text, images, or user preferences. It enables semantic search by comparing vector representations rather than exact keyword matches, allowing systems to understand meaning and context. This approach powers recommendation systems, semantic search engines, and retrieval-augmented generation (RAG) applications.

Companies urgently need vector search capabilities to power AI applications like RAG systems that combine LLMs with proprietary data, enabling accurate and context-aware responses without retraining models. The explosion of multimodal AI (text, images, audio) requires efficient similarity search across diverse data types, while personalized recommendations and semantic search have become competitive differentiators in e-commerce and content platforms.