Vector Database
A vector database is a specialized database designed to store, index, and query high-dimensional vector embeddings efficiently. It enables similarity search and nearest neighbor operations on vector data, which is essential for AI applications like semantic search, recommendation systems, and large language model (LLM) augmentation. Unlike traditional databases that query exact matches, vector databases use approximate nearest neighbor (ANN) algorithms to find similar vectors based on distance metrics like cosine similarity or Euclidean distance.
Developers should learn and use vector databases when building AI-powered applications that require semantic understanding, such as chatbots with memory, image or video similarity search, or retrieval-augmented generation (RAG) for LLMs. They are crucial for handling unstructured data like text, images, and audio by converting it into embeddings and enabling fast, scalable similarity queries, which traditional SQL or NoSQL databases struggle with due to high-dimensional data complexity.
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