The History of Weaviate

Weaviate is an open-source search engine that uses natural language processing (NLP) and vector storage for semantic search and classification. It was inspired by word embeddings, a machine learning technique that represents words as vectors in a high-dimensional space. The concept of Weaviate was born out of the need to store data objects semantically without relying on naming conventions or standards. Its architecture is based on RDF structures and uses GraphQL for data representation. Today, Weaviate is used in various industries for tasks such as invoice classification, document search, site search, product knowledge graphs, and more. The future of Weaviate includes continued growth and development of new features, with a focus on maintaining its open-source nature.