Building Vector Search Engines from Scratch

Reactive PublishingDiscover the inner workings of modern AI-powered search with Building Vector Search Engines from Scratch.As vector embeddings transform how we store, retrieve, and interact with data, building a high-performance vector search system has become an essential skill for AI engineers, machine learning practitioners, and backend developers. This book takes you step-by-step through the complete process of designing and implementing your own vector search engine, starting from fundamental concepts and progressing to production-ready architectures.What You'll Learn: - Core Storage Strategies: Explore efficient data structures and databases optimized for high-dimensional vector data, including flat indexes, graph-based methods, and quantization techniques.- Indexing Techniques: Master inverted files, product quantization, hierarchical navigable small world (HNSW) graphs, and other state-of-the-art indexing approaches that balance speed, memory usage, and accuracy.- High-Performance Retrieval: Implement approximate nearest neighbor (ANN) search, similarity metrics, reranking, and hybrid search systems that deliver sub-millisecond query times at scale.- AI System Integration: Learn how to integrate vector search into real-world applications such as semantic search, recommendation engines, retrieval-augmented generation (RAG) for LLMs, and multimodal AI systems.- Performance Optimization and Scaling: Address challenges in distributed systems, hardware acceleration (CPU/GPU), monitoring, and maintaining relevance as your data grows.Written with clear explanations, practical code examples in Python, and architectural diagrams, this hands-on guide emphasizes building systems that are both theoretically sound and practically deployable. Whether you're developing the…