Project 3: Inverted File Index (IVF)

Implement an IVF index that clusters vectors and narrows search to relevant partitions.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 12-18 hours
Language Python
Prerequisites K-means basics, vector search
Key Topics clustering, coarse quantization, ANN

1. Learning Objectives

By completing this project, you will:

  1. Implement k-means for coarse clustering.
  2. Build IVF partitions from centroids.
  3. Query only top-n clusters.
  4. Measure recall vs latency.
  5. Compare IVF to brute-force.

2. Theoretical Foundation

2.1 IVF Intuition

Clustering reduces search space by probing only the most relevant partitions.

3. Project Specification

3.1 What You Will Build

An IVF-based vector index with configurable cluster count and probe count.

3.2 Functional Requirements

  1. K-means clustering to build centroids.
  2. Partitioned index storing vectors per centroid.
  3. Query pipeline probing top-n clusters.
  4. Recall evaluation vs brute-force.
  5. Benchmarking for latency.

3.3 Non-Functional Requirements

  • Deterministic clustering with fixed seeds.
  • Configurable parameters (nlist, nprobe).
  • Clear metrics reporting.

4. Solution Architecture

4.1 Components

Component Responsibility
Clustering Build centroids
Partitions Store vectors by cluster
Query Engine Probe clusters
Evaluator Measure recall

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P03-ivf/
├── src/
│   ├── cluster.py
│   ├── index.py
│   ├── query.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Clustering (4-6h)

  • Implement k-means for centroids.
  • Checkpoint: centroids converge.

Phase 2: Indexing (4-6h)

  • Assign vectors to partitions.
  • Checkpoint: partitions built with counts.

Phase 3: Querying + eval (4-6h)

  • Probe clusters and compute recall.
  • Checkpoint: tradeoff report generated.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit clustering stable centroids
Integration query correct partitions
Regression eval recall stable

6.2 Critical Test Cases

  1. Probing more clusters increases recall.
  2. Index returns results similar to brute-force.
  3. Partition sizes sum to total vectors.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Poor clusters low recall increase k or iterations
Slow queries no speedup reduce probe count
Imbalanced partitions hotspots re-cluster or use k-means++

8. Extensions & Challenges

Beginner

  • Add k-means++ initialization.
  • Add cluster visualization.

Intermediate

  • Add residual quantization.
  • Add IVF + flat comparison.

Advanced

  • Add inverted multi-index.
  • Compare with FAISS IVF.

9. Real-World Connections

  • Vector DBs use IVF for large-scale search.
  • ANN systems rely on partitioning.

10. Resources

  • FAISS IVF docs
  • K-means references

11. Self-Assessment Checklist

  • I can build an IVF index.
  • I can tune nlist and nprobe.
  • I can measure recall vs latency.

12. Submission / Completion Criteria

Minimum Completion:

  • IVF index with query support

Full Completion:

  • Recall + latency benchmarks

Excellence:

  • Multi-index or FAISS comparison

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