LEANN/benchmarks

🧪 LEANN Benchmarks & Testing

This directory contains performance benchmarks and comprehensive tests for the LEANN system, including backend comparisons and sanity checks across different configurations.

📁 Test Files

diskann_vs_hnsw_speed_comparison.py

Performance comparison between DiskANN and HNSW backends:

• ✅ Search latency comparison with both backends using recompute
• ✅ Index size and build time measurements
• ✅ Score validity testing (ensures no -inf scores)
• ✅ Configurable dataset sizes for different scales

# Quick comparison with 500 docs, 10 queries
python benchmarks/diskann_vs_hnsw_speed_comparison.py

# Large-scale comparison with 2000 docs, 20 queries
python benchmarks/diskann_vs_hnsw_speed_comparison.py 2000 20

test_distance_functions.py

Tests all supported distance functions across DiskANN backend:

• ✅ MIPS (Maximum Inner Product Search)
• ✅ L2 (Euclidean Distance)
• ✅ Cosine (Cosine Similarity)

uv run python tests/sanity_checks/test_distance_functions.py

test_l2_verification.py

Specifically verifies that L2 distance is correctly implemented by:

• Building indices with L2 vs Cosine metrics
• Comparing search results and score ranges
• Validating that different metrics produce expected score patterns

uv run python tests/sanity_checks/test_l2_verification.py

test_sanity_check.py

Comprehensive end-to-end verification including:

• Distance function testing
• Embedding model compatibility
• Search result correctness validation
• Backend integration testing

uv run python tests/sanity_checks/test_sanity_check.py

🎯 What These Tests Verify

✅ Distance Function Support

• All three distance metrics (MIPS, L2, Cosine) work correctly
• Score ranges are appropriate for each metric type
• Different metrics can produce different rankings (as expected)

✅ Backend Integration

• DiskANN backend properly initializes and builds indices
• Graph construction completes without errors
• Search operations return valid results

✅ Embedding Pipeline

• Real-time embedding computation works
• Multiple embedding models are supported
• ZMQ server communication functions correctly

✅ End-to-End Functionality

• Index building → searching → result retrieval pipeline
• Metadata preservation through the entire flow
• Error handling and graceful degradation

🔍 Expected Output

When all tests pass, you should see:

📊 测试结果总结:
  mips      : ✅ 通过
  l2        : ✅ 通过
  cosine    : ✅ 通过

🎉 测试完成!

🐛 Troubleshooting

Common Issues

Import Errors: Ensure you're running from the project root:

cd /path/to/leann
uv run python tests/sanity_checks/test_distance_functions.py

Memory Issues: Reduce graph complexity for resource-constrained systems:

builder = LeannBuilder(
    backend_name="diskann",
    graph_degree=8,  # Reduced from 16
    complexity=16    # Reduced from 32
)

ZMQ Port Conflicts: The tests use different ports to avoid conflicts, but you may need to kill existing processes:

pkill -f "embedding_server"

📊 Performance Expectations

Typical Timing (3 documents, consumer hardware):

• Index Building: 2-5 seconds per distance function
• Search Query: 50-200ms
• Recompute Mode: 5-15 seconds (higher accuracy)

Memory Usage:

• Index Storage: ~1-2 MB per distance function
• Runtime Memory: ~500MB (including model loading)

🔗 Integration with CI/CD

These tests are designed to be run in automated environments:

# GitHub Actions example
- name: Run Sanity Checks
  run: |
    uv run python tests/sanity_checks/test_distance_functions.py
    uv run python tests/sanity_checks/test_l2_verification.py

The tests are deterministic and should produce consistent results across different platforms.