Project 4: Product Quantization (PQ)

Implement product quantization to compress vectors and speed up approximate search.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 16-24 hours
Language Python
Prerequisites K-means, vector quantization
Key Topics PQ, compression, distance approximation

1. Learning Objectives

By completing this project, you will:

  1. Split vectors into subspaces for PQ.
  2. Train codebooks with k-means.
  3. Encode vectors into compact codes.
  4. Approximate distances using lookup tables.
  5. Measure recall vs compression ratio.

2. Theoretical Foundation

2.1 PQ Intuition

Product quantization compresses vectors by quantizing subspaces, enabling fast distance approximations.

3. Project Specification

3.1 What You Will Build

A PQ-based index that compresses vectors and performs approximate nearest neighbor search.

3.2 Functional Requirements

  1. Subspace splitting for vectors.
  2. Codebook training per subspace.
  3. Encoding of vectors into codes.
  4. Approximate search with lookup tables.
  5. Evaluation of recall and compression.

3.3 Non-Functional Requirements

  • Deterministic training with fixed seeds.
  • Configurable subspace sizes.
  • Clear metrics for compression and accuracy.

4. Solution Architecture

4.1 Components

Component Responsibility
Splitter Divide vectors into subspaces
Codebooks Train k-means per subspace
Encoder Encode vectors into codes
Searcher Approximate distance via LUTs

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P04-pq/
├── src/
│   ├── split.py
│   ├── train.py
│   ├── encode.py
│   ├── search.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Subspaces + codebooks (6-8h)

  • Split vectors and train codebooks.
  • Checkpoint: codebooks converge.

Phase 2: Encoding + search (6-8h)

  • Encode vectors and build LUTs.
  • Checkpoint: approximate search returns neighbors.

Phase 3: Evaluation (4-8h)

  • Measure recall and compression ratios.
  • Checkpoint: report shows tradeoffs.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit encoding code lengths correct
Integration search outputs match baseline
Regression eval recall stable

6.2 Critical Test Cases

  1. Encoded vectors use expected code lengths.
  2. Approximate search returns top-k neighbors.
  3. Compression ratio matches configuration.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Poor recall inaccurate search increase codebook size
Misaligned subspaces errors check vector dims
Slow encoding long build time vectorize operations

8. Extensions & Challenges

Beginner

  • Add PQ visualizations.
  • Add CSV export for codes.

Intermediate

  • Add OPQ (optimized PQ).
  • Compare PQ vs IVF-PQ.

Advanced

  • Add GPU acceleration.
  • Integrate with FAISS PQ.

9. Real-World Connections

  • Large-scale search uses PQ for compression.
  • Edge deployments depend on smaller indexes.

10. Resources

  • Product Quantization papers
  • FAISS PQ documentation

11. Self-Assessment Checklist

  • I can implement PQ encoding.
  • I can compute approximate distances.
  • I can measure recall vs compression.

12. Submission / Completion Criteria

Minimum Completion:

  • PQ encoding + search

Full Completion:

  • Recall and compression report

Excellence:

  • OPQ or FAISS integration

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