DISTRIBUTED AI AND ML SYSTEMS / C04

Vector Databases & Semantic Retrieval Infrastructure

Master the data infrastructure powering RAG and multimodal AI: high-dimensional vector search, HNSW graph indexing, Inverted File (IVF) partitioning, Product Quantization, and hybrid metadata filtering.

1 topic360 min estimatedPublished

Retrieval-Augmented Generation (RAG) and semantic intelligence require querying billions of dense floating-point embedding vectors in milliseconds. Traditional B-Tree and relational indexes fail in high-dimensional vector spaces due to the curse of dimensionality. This module teaches the algorithmic and systems architecture behind modern vector databases: metric spaces, approximate nearest neighbor graphs, vector quantization, and distributed hybrid query engines.

You will analyze the recall-versus-latency Pareto frontier of Hierarchical Navigable Small World (HNSW) graphs, Inverted File (IVF) Voronoi partitions, and DiskANN SSD-resident indexes, calculate exact memory savings from Scalar (SQ8), Binary, and Product Quantization (PQ) codebooks, and architect single-stage pre-filtered graph traversal for multi-tenant metadata isolation.

What you will learn

  • Dissect the mathematical mechanics of Approximate Nearest Neighbor (ANN) search algorithms (HNSW, IVF, DiskANN)
  • Calculate memory footprints and compression ratios for FP32 vs. Scalar Quantization (SQ8) vs. Product Quantization (PQ)
  • Compare distance metrics (Cosine Similarity, Euclidean L2, Inner Product) and their hardware SIMD acceleration
  • Evaluate hybrid filtering architectures (pre-filtering, post-filtering, single-stage filtered graph traversal) in distributed vector stores
STAGE TOPICS

Topics in this chapter

Step 85Vector Search Foundations: HNSW, IVF, and Product QuantizationDeep dive into high-dimensional vector search: the curse of dimensionality, Hierarchical Navigable Small World (HNSW) graph layers, Inverted File (IVF) Voronoi cells, Product Quantization (PQ), and production vector DB architecture.45 min ↗