Overview

Build a solid foundation in AI-powered vector search with Vector Database Mastery - Volume 1. This book guides engineers, data scientists, and AI enthusiasts through the essentials of vector representations, similarity search, and indexing techniques for scalable retrieval systems. Learn how to transform text, images, and structured data into high-dimensional vectors and implement efficient search with cosine, Euclidean, and inner product distances. Explore FAISS, Milvus, and Pinecone, and understand the trade-offs between latency, memory, and recall with practical, production-ready Python code. What You'll Learn: Fundamentals of vector embeddings and similarity search High-dimensional indexing with FAISS HNSW, IVF, and PQ Using managed and self-hosted vector databases Building semantic search engines and recommendation systems Mathematical insights behind performance optimization Key Use Cases: Semantic search beyond keyword queries Personalized recommendations and content retrieval Multimodal search with text and images RAG pipelines for AI applications Start your journey into scalable vector search systems with hands-on examples and theoretical foundations that prepare you for advanced AI retrieval workflows.

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