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13min chapter
Vector Databases, Embeddings, and a history of Deep Learning with Leo Dirac
Practically Intelligent
Embeddings and Choosing a Vector Database
This chapter discusses the concept of embeddings and the considerations for choosing a vector database. It explains the importance of efficiency in storing and retrieval, logical separations for organization, and baseline considerations like price and speed. The chapter also explores the potential of vector databases in AI infrastructure and their role in handling large-scale data and performing similarity searches.
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