V
Vector Database
A specialized type of database designed to store and search **high‑dimensional vector embeddings** numerical representations of data such as text, images, audio, or video. These embeddings capture semantic meaning, so items that are conceptually similar are located close together in vector space, enabling powerful similarity searches. Instead of exact matches like traditional databases, vector databases use algorithms such as **approximate nearest neighbor (ANN)** search with metrics like cosine similarity or Euclidean distance to quickly find the most relevant results. They are a core component of modern AI workflows, especially in **retrieval‑augmented generation (RAG)**, recommendation systems, semantic search, and multimodal applications. Popular examples include Pinecone, Weaviate, Milvus, and Chroma, all of which are optimized for speed, scalability, and integration with large language models.