A vector database stores, indexes, and retrieves high-dimensional vectors, allowing for fast similarity searches. Vectors are converted texts, images, audio, and videos that are represented as numerical embeddings by machine learning models.

Vector databases are foundational in a Retrieval-Augmented Generation (RAG) architecture. These systems also include vector searches, embeddings, and retrieval. Vector database not only searches documents and files, it retrieves semantically relevant content because it can discern the meaning of the input and output, ultimately enhancing the performance and contextuality of the large language models (LLMs).

Why are vector databases important?

Vector databases are critical for similarity search across massive datasets, enabling real-time applications like AI chatbots and AI-powered recommendation engines to understand and respond with meaning and context.