Benjamin Flast, a pivotal member of the MongoDB product team, discusses the intricacies of Vector Search and its relevance to AI and MongoDB. Topics covered include embedding models, benefits over traditional search methods, vector size considerations, Atlas integration, and localization of indexes. The AMA section covers cluster sync, Atlas CLI support, and trade-offs in performance and chunking strategies. The connection to context in language models is explored, as well as the possibility of nesting vector embeddings in MongoDB.
