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Artificial intelligence applications increasingly rely on semantic search, recommendation systems, retrieval-augmented generation (RAG), and similarity matching. At the center of these systems lies a new category of infrastructure: vector databases.
Vector Database Development provides a structured, engineering-focused guide to designing and implementing embedding-driven data systems for modern AI applications. This book moves beyond surface-level introductions and explores how vector indexing, similarity search algorithms, and distributed storage architectures operate in production environments.
Inside this book, you will learn: