This whitepaper explores embeddings, which are numerical representations of various data types like text and images, and vector stores, which are specialized databases for efficiently managing and searching these embeddings. Embeddings capture the semantic meaning of data, allowing for similarity searches and powering applications that go beyond exact keyword matching. By using vector search algorithms and databases, modern machine learning applications, particularly those involving large language models, can perform tasks such as retrieval-augmented generation, recommendations, and semantic search more effectively.