Exploring AI Semantic Search with Vector Databases

A recent video by Lev Selector delves into how AI can locate relevant information using semantic search, even when different words are used. The process involves converting text into numerical vectors through embeddings, allowing AI to identify similar meanings by proximity in vector space. This method is crucial for AI systems to retrieve pertinent data from vector databases, which is then used in Retrieval-Augmented Generation (RAG) systems to enhance language model responses.