Vector Databases: The Foundation of Modern RAG Systems

Vector databases are essential infrastructure for RAG (Retrieval-Augmented Generation) systems, enabling machines to understand meaning rather than just keywords through semantic search. They store mathematical representations of data and enable fast similarity searches, which is crucial for building AI systems that can retrieve relevant context from private documents. Understanding how embeddings and vector similarity algorithms work is fundamental for organizations integrating their data with LLMs.