Project 1: Brute Force k-NN and Distance Metrics

Implement brute-force nearest neighbor search and compare cosine, dot, and Euclidean metrics.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 8-12 hours
Language Python
Prerequisites Linear algebra basics
Key Topics similarity metrics, k-NN, benchmarking

1. Learning Objectives

By completing this project, you will:

  1. Implement brute-force k-NN search.
  2. Compare cosine, dot, and Euclidean distances.
  3. Measure accuracy vs speed for each metric.
  4. Build a baseline for future ANN indexes.
  5. Visualize nearest neighbors for sample queries.

2. Theoretical Foundation

2.1 Baseline Importance

Brute-force search is the gold standard for accuracy and the baseline for evaluating approximate methods.

3. Project Specification

3.1 What You Will Build

A CLI tool that loads vectors and returns top-k neighbors with different similarity metrics.

3.2 Functional Requirements

  1. Vector loader for dataset.
  2. Distance metrics implementations.
  3. Top-k retrieval for queries.
  4. Benchmark mode for latency.
  5. Visualization of neighbors (optional).

3.3 Non-Functional Requirements

  • Deterministic results with fixed seeds.
  • Clear metric comparisons in output.
  • Handles 100k vectors on CPU.

4. Solution Architecture

4.1 Components

Component Responsibility
Loader Read vectors
Metrics Compute distances
Searcher Top-k retrieval
Benchmark Measure latency

5. Implementation Guide

5.1 Project Structure

LEARN_VECTOR_DATABASES/P01-brute-force-knn/
├── src/
│   ├── load.py
│   ├── metrics.py
│   ├── search.py
│   └── benchmark.py

5.2 Implementation Phases

Phase 1: Metrics (3-4h)

  • Implement cosine, dot, Euclidean.
  • Checkpoint: metrics match reference values.

Phase 2: k-NN search (3-4h)

  • Compute top-k neighbors.
  • Checkpoint: results consistent across runs.

Phase 3: Benchmarking (2-4h)

  • Measure latency per metric.
  • Checkpoint: report shows comparisons.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit metrics known distances
Integration search correct top-k
Regression benchmark stable results

6.2 Critical Test Cases

  1. Cosine and dot produce expected ranking.
  2. Euclidean distance handles normalized vectors.
  3. Top-k results match brute-force baseline.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Wrong normalization odd cosine results normalize inputs
Slow search high latency use vectorized ops
Metric confusion inconsistent ordering document metric definitions

8. Extensions & Challenges

Beginner

  • Add CSV import.
  • Add batch query support.

Intermediate

  • Add vector normalization options.
  • Add top-k recall report.

Advanced

  • Add SIMD acceleration.
  • Compare with FAISS brute force.

9. Real-World Connections

  • ANN benchmarks rely on brute-force ground truth.
  • Embedding quality depends on metric choice.

10. Resources

  • Vector similarity references
  • FAISS docs

11. Self-Assessment Checklist

  • I can implement k-NN search from scratch.
  • I can compare distance metrics reliably.
  • I can benchmark latency.

12. Submission / Completion Criteria

Minimum Completion:

  • Brute-force k-NN with 2 metrics

Full Completion:

  • All 3 metrics + benchmarks

Excellence:

  • SIMD optimization or FAISS comparison

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