A Beginner’s Guide to Vector Embeddings

Vector embeddings are compact numerical representations of raw data such as images or text, transformed into vectors comprising floating-point numbers. They capture structural or semantic relationships within data and help uncover patterns and relationships that might not have been apparent in the original space. Applications like retrieval-augmented generation (RAG), agents, natural language processing (NLP), semantic search, and image search use vector embeddings. There are many types of vector embeddings, including word, sentence, document, graph, image, product, audio, text, and more. Neural networks create embeddings through a process called representation learning, where the network learns to map high-dimensional data into lower-dimensional spaces while preserving important properties of the data. Vector embeddings work by representing features or objects as points in a multidimensional vector space, with relative positions representing meaningful relationships between the features or objects. Developers can use embeddings for various applications like chatbots, semantic search engines, text classification systems, recommendation systems, and more. Creating vector embeddings involves collecting raw data, preprocessing it, breaking it into chunks, converting each chunk into a vector representation, and using an embedding model to create the vector representations. Vector databases are specialized databases designed to handle vectors efficiently and can store and retrieve vector embeddings.