Understanding n-Dimensional Vectors in Generative AI

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This chapter explores the role of n-dimensional vectors in representing complex data within generative AI. It discusses the significance of dimensionality and embeddings, showcasing how they allow for enhanced content comparison and semantic search. The chapter also examines various embedding models from tech companies and the considerations required for effective data management and storage.

MongoDB Atlas is a managed NoSQL database that uses JSON-like documents with optional schemas. The platform recently released new vector search capabilities to facilitate building AI capabilities.

Ben Flast is the Director of Product Management at MongoDB. He joins the show to talk about the company’s developments with vector search.

This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments.

Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.

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