Project 6: IVF-PQ Composite Index

Combine IVF and PQ to build a scalable index that balances recall, latency, and memory.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 2-3 weeks
Language Python
Prerequisites Projects 3-5
Key Topics composite indexes, compression, ANN tuning

1. Learning Objectives

By completing this project, you will:

  1. Integrate IVF partitioning with PQ compression.
  2. Tune nlist, nprobe, and PQ code sizes.
  3. Measure recall, latency, and memory tradeoffs.
  4. Compare IVF-PQ vs IVF and PQ alone.
  5. Build a tuning report for parameters.

2. Theoretical Foundation

2.1 Why IVF-PQ

IVF reduces search space, PQ compresses vectors. Together they scale to massive datasets.

3. Project Specification

3.1 What You Will Build

A composite IVF-PQ index with configurable parameters and benchmark reporting.

3.2 Functional Requirements

  1. IVF clustering for partitions.
  2. PQ encoding for vectors within partitions.
  3. Query pipeline with probes and LUTs.
  4. Evaluation of recall and memory.
  5. Parameter sweeps for tuning.

3.3 Non-Functional Requirements

  • Deterministic training with fixed seeds.
  • Clear benchmark outputs.
  • Stable index persistence.

4. Solution Architecture

4.1 Components

Component Responsibility
IVF Builder Create partitions
PQ Encoder Compress vectors
Searcher Probe partitions and LUTs
Evaluator Measure metrics

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P06-ivf-pq/
├── src/
│   ├── ivf.py
│   ├── pq.py
│   ├── search.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: IVF + PQ integration (8-12h)

  • Build IVF partitions and PQ encodings.
  • Checkpoint: vectors stored as codes per partition.

Phase 2: Query pipeline (6-10h)

  • Probe partitions and compute LUT distances.
  • Checkpoint: results returned with recall metrics.

Phase 3: Tuning (6-10h)

  • Sweep parameters and report tradeoffs.
  • Checkpoint: tuning report generated.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit encoding PQ codes correct
Integration search IVF-PQ query works
Regression tuning stable metrics

6.2 Critical Test Cases

  1. IVF-PQ search returns neighbors vs brute-force.
  2. Memory usage lower than IVF-only.
  3. Parameter sweeps show recall/latency tradeoffs.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Poor recall low accuracy increase nprobe or codebook size
Large memory weak compression reduce PQ code size
Slow search no speedup tune partition count

8. Extensions & Challenges

Beginner

  • Add parameter grid search.
  • Add visual charts for metrics.

Intermediate

  • Add OPQ before PQ.
  • Compare with FAISS IVF-PQ.

Advanced

  • Add sharded IVF-PQ.
  • Add GPU acceleration.

9. Real-World Connections

  • Large-scale search uses IVF-PQ in production.
  • Vector DBs tune these parameters for SLAs.

10. Resources

  • FAISS IVF-PQ docs
  • PQ research papers

11. Self-Assessment Checklist

  • I can build an IVF-PQ composite index.
  • I can tune parameters for recall vs latency.
  • I can measure memory savings.

12. Submission / Completion Criteria

Minimum Completion:

  • IVF-PQ index with search

Full Completion:

  • Benchmark report
  • Parameter tuning

Excellence:

  • OPQ or GPU integration
  • Sharded IVF-PQ

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