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fix

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This commit is contained in:
Andy Lee
2025-11-24 08:01:42 +00:00
parent 253680043a
commit cd1d853a46
1 changed files with 2 additions and 53 deletions
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55
issue_159.py
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@@ -2,10 +2,9 @@
""" """
Test script to reproduce issue #159: Slow search performance Test script to reproduce issue #159: Slow search performance
Configuration: Configuration:
- GPU: 4090×1 - GPU: A10
- embedding_model: BAAI/bge-large-zh-v1.5 - embedding_model: BAAI/bge-large-zh-v1.5
- data size: 180M text (~90K chunks) - data size: 180M text (~90K chunks)
- beam_width: 10 (though this is mainly for DiskANN, not HNSW)
- backend: hnsw - backend: hnsw
""" """
@@ -13,7 +12,7 @@ import os
import time import time
from pathlib import Path from pathlib import Path
from leann.api import LeannBuilder, LeannSearcher, SearchResult from leann.api import LeannBuilder, LeannSearcher
os.environ["LEANN_LOG_LEVEL"] = "DEBUG" os.environ["LEANN_LOG_LEVEL"] = "DEBUG"
@@ -83,42 +82,6 @@ def test_search_performance():
test_query = "LEANN向量数据库存储优化" test_query = "LEANN向量数据库存储优化"
# Test with default complexity (64)
print("\n Test 1: Default complexity (64) `1 ")
print(f" Query: '{test_query}'")
start_time = time.time()
results: list[SearchResult] = searcher.search(test_query, top_k=10, complexity=64)
search_time = time.time() - start_time
print(f" ✓ Search completed in {search_time:.2f} seconds")
print(f" Results: {len(results)} items")
# Test with default complexity (64)
print("\n Test 1: Default complexity (64)")
print(f" Query: '{test_query}'")
start_time = time.time()
results = searcher.search(test_query, top_k=10, complexity=64)
search_time = time.time() - start_time
print(f" ✓ Search completed in {search_time:.2f} seconds")
print(f" Results: {len(results)} items")
# Test with lower complexity (32)
print("\n Test 2: Lower complexity (32)")
print(f" Query: '{test_query}'")
start_time = time.time()
results = searcher.search(test_query, top_k=10, complexity=32)
search_time = time.time() - start_time
print(f" ✓ Search completed in {search_time:.2f} seconds")
print(f" Results: {len(results)} items")
# Test with even lower complexity (16)
print("\n Test 3: Lower complexity (16)")
print(f" Query: '{test_query}'")
start_time = time.time()
results = searcher.search(test_query, top_k=10, complexity=16)
search_time = time.time() - start_time
print(f" ✓ Search completed in {search_time:.2f} seconds")
print(f" Results: {len(results)} items")
# Test with minimal complexity (8) # Test with minimal complexity (8)
print("\n Test 4: Minimal complexity (8)") print("\n Test 4: Minimal complexity (8)")
print(f" Query: '{test_query}'") print(f" Query: '{test_query}'")
@@ -129,20 +92,6 @@ def test_search_performance():
print(f" Results: {len(results)} items") print(f" Results: {len(results)} items")
print("\n" + "=" * 80) print("\n" + "=" * 80)
print("Performance Analysis:")
print("=" * 80)
print("\nKey Findings:")
print("1. beam_width parameter is mainly for DiskANN backend, not HNSW")
print("2. For HNSW, the main parameter affecting search speed is 'complexity'")
print("3. Lower complexity values (16-32) should provide faster search")
print("4. The paper mentions ~2 seconds, which likely uses:")
print(" - Smaller embedding model (~100M params vs 300M for bge-large)")
print(" - Lower complexity (16-32)")
print(" - Possibly DiskANN backend for better performance")
print("\nRecommendations:")
print("- Try complexity=16 or complexity=32 for faster search")
print("- Consider using DiskANN backend for better performance on large datasets")
print("- Or use a smaller embedding model if speed is critical")
if __name__ == "__main__": if __name__ == "__main__":