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Vector Databases — Theory, Engineering, and Frontiers

Welcome to the open-source technical book on vector database internals. This resource covers everything from the mathematical foundations of high-dimensional search to production-grade distributed system architectures.

What You'll Learn

  • Part I — Foundations

    High-dimensional geometry, ANN algorithms (HNSW, LSH, PQ), index–storage trade-offs, query semantics, and vectorization pipelines.

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  • Part II — System Architecture

    Core components, storage engines, distributed sharding, hybrid search, hardware acceleration (SIMD/GPU/FPGA), and observability.

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  • Part III — Implementation Deep-Dive

    Build an HNSW store in Rust, GPU-accelerated PQ-IVF, transactional vector inserts, elastic scaling, and benchmark harnesses.

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  • Part IV — Advanced Topics & Research Frontiers

    Privacy-preserving search, continual learning, Gen-AI agent pipelines, and future directions (learned indexing, vector SQL).

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Who This Book Is For

  • ML/AI Engineers building retrieval-augmented generation (RAG) pipelines
  • Database Engineers designing or evaluating vector storage systems
  • Systems Programmers interested in high-performance search infrastructure
  • Researchers exploring the frontier of approximate nearest neighbor search
  • Students seeking a rigorous yet practical treatment of the field

How to Use This Book

Each chapter is self-contained — you can read linearly or jump to any topic of interest. Chapters include:

  • Mathematical notation rendered with MathJax ($L_2$, cosine similarity, etc.)
  • Code examples in Python, Rust, and C++ with copy-to-clipboard
  • Architecture diagrams built with Mermaid
  • Admonitions highlighting key insights, warnings, and practical tips

Contributing

This is an open-source project. Contributions are welcome! See the GitHub repository for how to contribute.