Overview

Next-Gen Vector Databases: Hands-On Techniques for High-Dimensional Search, Multimodal Retrieval, and AI-Powered Applications is your definitive guide to building the next generation of intelligent, scalable, and production-ready vector search systems. Designed for engineers, data scientists, and AI researchers, this book takes you beyond the fundamentals and dives deep into advanced vector database architectures, cutting-edge retrieval strategies, and real-world AI applications. In this book, you'll explore: High-Dimensional Vector Spaces: Master the mathematical foundations of embeddings, distance metrics, and dimensionality reduction. Adaptive and Distributed Indexing: Implement HNSW, IVF, PQ, and hybrid indices for real-time, large-scale search. Multimodal Retrieval: Integrate text, images, audio, and video into unified vector spaces for AI-powered search. Neural and Retrieval-Augmented Generation (RAG): Combine vector search with LLMs to build next-level chatbots, recommendation engines, and knowledge systems. Edge and Federated Search: Deploy AI search pipelines across distributed environments with privacy-preserving embeddings. Performance, Security, and Optimization: Scale, accelerate, and secure your vector database infrastructure for production workloads. With 40+ hands-on Python examples, this book equips you to implement high-performance pipelines, optimize latency and memory, and handle real-world challenges in multimodal retrieval and RAG workflows. Whether you're building semantic search engines, AI chatbots, recommendation systems, or cutting-edge generative AI applications, this book gives you the tools, techniques, and insights to succeed. Why This Book? Advanced, code-first guidance for modern vector search systems Production-ready design patterns with security and compliance best practices Deep dive into neural retrieval, adaptive indexing, and multimodal pipelines Real-world use cases across search, recommendation, AI, and generative applications Who Should Read This Book: AI and ML engineers building large-scale search and recommendation systems Data scientists integrating vector retrieval into analytics and pipelines DevOps professionals deploying distributed, high-performance vector databases Researchers exploring retrieval-augmented generation, multimodal search, and next-gen AI applications Take your vector search skills to the next level and master next-generation AI retrieval systems with practical Python examples, mathematical rigor, and production-ready best practices.

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