This audiobook is narrated by a digital voice.

"Vector Databases for Generative AI Applications" explores the intersection of two cutting-edge fields: vector databases and generative artificial intelligence (AI). The book provides a comprehensive overview of how vector databases, a specialized form of database optimized for vector similarity search, can enhance various generative AI applications.

The first part of the book introduces the fundamentals of vector databases, including key concepts such as vector indexing, similarity search algorithms, and performance optimizations. Readers are guided through the architecture and functionality of vector databases, with a focus on how they differ from traditional relational databases and their suitability for handling high-dimensional data.

In the second part, the book delves into the application of vector databases in generative AI. It explores how vector databases can be leveraged to store and retrieve large collections of high-dimensional vectors, which are prevalent in generative AI tasks such as natural language processing, computer vision, and recommender systems. Through real-world examples and case studies, the book demonstrates how vector databases can accelerate the training and inference processes of generative AI models by efficiently managing vector representations of data points.