Understanding Vector Databases: Semantic Search and AI

Vector databases are introduced as a solution to the limitations of traditional databases when handling unstructured data by representing it as vector embeddings, which are numerical arrays capturing semantic meaning. These databases enable similarity searches based on conceptual relationships rather than exact matches. Embedding models, trained on vast datasets, generate these vector embeddings, and vector indexing techniques like HNSW and IVF ensure efficient searching within the high-dimensional vector space. A key application highlighted is Retrieval Augmented Generation (RAG), where vector databases store knowledge for Large Language Models to access and ground their responses.