Project 5: HNSW (Hierarchical Navigable Small World)

Implement HNSW indexing and search to achieve fast, high-recall approximate nearest neighbor retrieval.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 2-3 weeks
Language Python
Prerequisites Graph data structures, ANN basics
Key Topics HNSW, graph navigation, recall/latency

1. Learning Objectives

By completing this project, you will:

  1. Implement HNSW graph construction.
  2. Navigate multi-layer graphs for search.
  3. Tune ef and M for recall and speed.
  4. Compare HNSW vs brute-force.
  5. Visualize graph layers.

2. Theoretical Foundation

2.1 HNSW Intuition

HNSW builds a multi-layer graph that enables logarithmic-like search by navigating from coarse to fine layers.

3. Project Specification

3.1 What You Will Build

An HNSW-based index that supports add/search operations with tunable recall.

3.2 Functional Requirements

  1. Graph construction with multi-layer nodes.
  2. Search algorithm with ef parameter.
  3. Add/delete support (optional delete).
  4. Benchmarking for recall and latency.
  5. Visualization of graph connectivity.

3.3 Non-Functional Requirements

  • Deterministic seeds for repeatable builds.
  • Configurable parameters (M, ef).
  • Clear evaluation metrics.

4. Solution Architecture

4.1 Components

Component Responsibility
Graph Builder Insert nodes by layer
Searcher Navigate graph layers
Evaluator Recall + latency
Visualizer Graph layer diagrams

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P05-hnsw/
├── src/
│   ├── build.py
│   ├── search.py
│   ├── eval.py
│   └── visualize.py

5.2 Implementation Phases

Phase 1: Graph construction (6-10h)

  • Build layers and neighbor links.
  • Checkpoint: nodes inserted correctly.

Phase 2: Search (6-10h)

  • Implement search from top layer to bottom.
  • Checkpoint: recall vs brute-force measured.

Phase 3: Evaluation (4-8h)

  • Tune M and ef.
  • Checkpoint: report shows tradeoffs.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit graph neighbor counts
Integration search valid top-k results
Regression eval recall stable

6.2 Critical Test Cases

  1. Search returns close neighbors compared to brute-force.
  2. Increasing ef improves recall.
  3. Graph layers are connected.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Disconnected graph search fails enforce neighbor limits
Low recall poor params tune M/ef
Slow insertion high build time batch inserts

8. Extensions & Challenges

Beginner

  • Add small visualization of layers.
  • Add HNSW parameter sweeps.

Intermediate

  • Add delete support.
  • Add persistence for graphs.

Advanced

  • Compare with FAISS HNSW.
  • Add multi-threaded build.

9. Real-World Connections

  • Vector databases use HNSW for fast search.
  • RAG systems depend on HNSW recall.

10. Resources

  • HNSW paper (Malkov & Yashunin)
  • FAISS HNSW docs

11. Self-Assessment Checklist

  • I can implement HNSW indexing.
  • I can tune ef and M.
  • I can measure recall vs latency.

12. Submission / Completion Criteria

Minimum Completion:

  • HNSW index + search

Full Completion:

  • Recall/latency benchmarks

Excellence:

  • Persistence or multi-threaded build

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_VECTOR_DATABASES.md.