What is a Vector Database?

The Backbone of Semantic Search and AI Context Retrieval

Storing and Querying Numerical Representations of Data

Enhancing LLMs with External Knowledge via RAG

A **Vector Database** is a specialized type of database designed to efficiently **store and query 'vector embeddings'**. Vector embeddings are numerical representations (lists of numbers) of various data types, such as text, images, audio, or video, where the semantic meaning or characteristics of the data are captured in its numerical proximity. This allows for powerful **similarity searches** – finding data that is 'similar in meaning' rather than just exact keyword matches. Vector databases are fundamental to applications like semantic search, recommendation systems, and critically, **Retrieval-Augmented Generation (RAG)** systems for **LLMs**, enabling them to retrieve relevant external knowledge quickly and accurately to inform their responses in 2025. 📊🔍