Vector Search Indexing: PQ, LSH, and HNSW Algorithms

As modern AI applications and Large Language Models expand, searching through millions of high-dimensional vector embeddings quickly and accurately has become a critical engineering challenge. Traditional search databases fail at this scale, making specialized vector indexing algorithms essential for modern retrieval-augmented generation (RAG) and recommendation systems. This text-based course guides you through the inner workings of the three most important vector indexing algorithms: Product Quantization (PQ), Locality-Sensitive Hashing (LSH), and Hierarchical Navigable Small World (HNSW). By completing this course, you will transition from understanding basic vector space concepts to confidently selecting, configuring, and optimizing indexes for production-grade vector search engines. You will gain the theoretical clarity needed to make informed architecture decisions in any AI-driven application. What you'll learn: - Understand the foundational concepts of vector embeddings, dimensionality, and similarity metrics. - Analyze how Locality-Sensitive Hashing (LSH) groups similar vectors using specialized hash functions. - Explore Product Quantization (PQ) to compress high-dimensional vectors and dramatically reduce memory footprints. - Master Hierarchical Navigable Small World (HNSW) graphs for highly efficient nearest-neighbor routing. - Evaluate key engineering trade-offs between search latency, index build time, memory usage, and recall accuracy. - Apply these indexing strategies to design robust retrieval pipelines for modern AI and search systems. This course begins with essential terminology, basic geometric concepts, and foundational definitions of vector spaces before diving deep into the mechanics of each indexing algorithm. Through clear written explanations, conceptual walkthroughs, and step-by-step pseudocode analysis, you will learn how to evaluate and implement these algorithms in real-world scenarios. This course is designed for software engineers, data analysts, and aspiring AI developers who want to understand the backend machinery of vector databases. No prior background in advanced indexing or database internals is required. Start reading today to unlock the power of high-performance vector search.