Vector search and artificial intelligence (AI) have become increasingly popular in recent years, with many technology companies integrating these technologies into their offerings. Vector search involves representing unstructured data like text, images, and audio as arrays of numbers, or vectors, which are produced by embedding models trained on large datasets. These models capture relationships and similarities between data, enabling efficient analysis for tasks such as recommendation systems and image recognition. Large Language Models (LLMs), which understand and associate concepts similarly to human minds, contribute to the vector search process by using text embeddings to perform natural language tasks. The technology for vector search has been available since 2016, but its popularity surged with the release of OpenAI's ChatGPT in 2022, which allowed the public to interact with LLMs easily. This increased the demand for vector search as LLMs work with embeddings, leading many data companies to adopt vector search and related functionalities. The blog also highlights MongoDB's role in this ecosystem, offering tools like Atlas Vector Search that leverage vector embeddings and advanced search processes to transform information retrieval. The growing adoption of vector search and LLMs reflects a broader industry shift toward AI-driven applications, with companies like SuperDuperDB, Algomo, and Source Digital using these technologies to enhance their products and services across various domains.