Vector Databases

Vector databases are optimized for handling unstructured data like text, images, and audio by converting them into numerical vectors through embedding models. Unlike traditional relational databases that use exact matches, vector databases use similarity search algorithms, such as K-Nearest Neighbors, to find data points closest to a query vector in a multi-dimensional space. This capability is fundamental for modern AI applications, including Retrieval-Augmented Generation (RAG), recommendation engines, and semantic search, where understanding the context and relationship between data points is more important than keyword matching.