Vector embeddings are a fundamental aspect of machine learning, transforming complex data such as text and images into numerical vectors that computers can interpret and analyze. These embeddings allow for more effective processing and identification of related data by representing it in a structured vector space. Various types of embeddings exist, including word, sentence, document, image, graph, audio, and video embeddings, each serving different functions like semantic search, sentiment analysis, image recognition, and recommendation systems. Creating vector embeddings involves selecting an appropriate model, preparing the data, and generating embeddings using either pre-trained or custom-trained models. Applications range across multiple domains, including natural language processing, computer vision, healthcare, and finance. Couchbase, a multi-purpose database, supports efficient storage and retrieval of vector data, integrating with traditional JSON documents and enabling complex queries and scalability. This capability makes Couchbase versatile for advanced search and recommendation features, contrasting with specialized vector databases that focus solely on vector search.