The text discusses the basics of vector search and large language models (LLMs), two key technologies in artificial intelligence (AI). Vector search is a technique used to find similar data points by representing unstructured data such as text, images, and audio as vectors. These vectors are generated using machine learning techniques called embedding models, which learn to capture meaningful relationships between data points. The goal of vector search is to find the most similar data points to a given query, allowing for efficient information retrieval applications like recommendation systems, natural language processing, and computer vision. Large language models (LLMs) take this concept further by using vectors to represent text and enabling machines to understand and generate human-like language. LLMs have revolutionized the field of natural language processing (NLP), making it possible for machines to perform tasks like answering questions, translating languages, and generating text. The popularity of vector search and LLMs can be attributed to the recent breakthroughs in these technologies, particularly the release of ChatGPT by OpenAI, which made these technologies accessible to a wider audience. As a result, many data companies have started incorporating vector search and other AI-related features into their products.