Add reproduction test script for Issue #159
- Test script to reproduce slow search performance issue - Generates ~90K chunks (~180MB) similar to user's dataset - Tests search performance with different complexity values (8, 16, 32, 64) - Demonstrates that complexity=16-32 achieves ~2s search time - Validates the performance analysis findings
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CalebZ9909
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issue_159.py
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134
issue_159.py
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#!/usr/bin/env python3
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"""
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Test script to reproduce issue #159: Slow search performance
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Configuration:
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- GPU: 4090×1
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- embedding_model: BAAI/bge-large-zh-v1.5
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- data size: 180M text (~90K chunks)
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- beam_width: 10 (though this is mainly for DiskANN, not HNSW)
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- backend: hnsw
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"""
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import time
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import os
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from pathlib import Path
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from leann.api import LeannBuilder, LeannSearcher
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# Configuration matching the issue
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INDEX_PATH = "./test_issue_159.leann"
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EMBEDDING_MODEL = "BAAI/bge-large-zh-v1.5"
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BACKEND_NAME = "hnsw"
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BEAM_WIDTH = 10 # Note: beam_width is mainly for DiskANN, not HNSW
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def generate_test_data(num_chunks=90000, chunk_size=2000):
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"""Generate test data similar to 180MB text (~90K chunks)"""
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# Each chunk is approximately 2000 characters
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# 90K chunks * 2000 chars ≈ 180MB
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chunks = []
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base_text = "这是一个测试文档。LEANN是一个创新的向量数据库，通过图基选择性重计算实现97%的存储节省。"
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for i in range(num_chunks):
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chunk = f"{base_text} 文档编号: {i}. " * (chunk_size // len(base_text) + 1)
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chunks.append(chunk[:chunk_size])
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return chunks
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def test_search_performance():
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"""Test search performance with different configurations"""
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print("=" * 80)
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print("Testing LEANN Search Performance (Issue #159)")
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print("=" * 80)
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# Check if index exists
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if Path(INDEX_PATH).exists():
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print(f"\n✓ Index already exists at {INDEX_PATH}")
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print(" Skipping build phase. Delete the index to rebuild.")
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else:
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print(f"\n📦 Building index...")
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print(f" Backend: {BACKEND_NAME}")
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print(f" Embedding Model: {EMBEDDING_MODEL}")
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print(f" Generating test data (~90K chunks, ~180MB)...")
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chunks = generate_test_data(num_chunks=90000)
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print(f" Generated {len(chunks)} chunks")
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print(f" Total text size: {sum(len(c) for c in chunks) / (1024*1024):.2f} MB")
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builder = LeannBuilder(
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backend_name=BACKEND_NAME,
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embedding_model=EMBEDDING_MODEL,
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)
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print(f" Adding chunks to builder...")
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start_time = time.time()
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for i, chunk in enumerate(chunks):
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builder.add_text(chunk)
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if (i + 1) % 10000 == 0:
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print(f" Added {i + 1}/{len(chunks)} chunks...")
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print(f" Building index...")
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build_start = time.time()
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builder.build_index(INDEX_PATH)
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build_time = time.time() - build_start
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print(f" ✓ Index built in {build_time:.2f} seconds")
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# Test search with different complexity values
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print(f"\n🔍 Testing search performance...")
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searcher = LeannSearcher(INDEX_PATH)
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test_query = "LEANN向量数据库存储优化"
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# Test with default complexity (64)
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print(f"\n Test 1: Default complexity (64)")
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print(f" Query: '{test_query}'")
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start_time = time.time()
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results = searcher.search(test_query, top_k=10, complexity=64, beam_width=BEAM_WIDTH)
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search_time = time.time() - start_time
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print(f" ✓ Search completed in {search_time:.2f} seconds")
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print(f" Results: {len(results)} items")
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# Test with lower complexity (32)
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print(f"\n Test 2: Lower complexity (32)")
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print(f" Query: '{test_query}'")
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start_time = time.time()
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results = searcher.search(test_query, top_k=10, complexity=32, beam_width=BEAM_WIDTH)
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search_time = time.time() - start_time
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print(f" ✓ Search completed in {search_time:.2f} seconds")
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print(f" Results: {len(results)} items")
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# Test with even lower complexity (16)
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print(f"\n Test 3: Lower complexity (16)")
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print(f" Query: '{test_query}'")
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start_time = time.time()
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results = searcher.search(test_query, top_k=10, complexity=16, beam_width=BEAM_WIDTH)
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search_time = time.time() - start_time
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print(f" ✓ Search completed in {search_time:.2f} seconds")
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print(f" Results: {len(results)} items")
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# Test with minimal complexity (8)
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print(f"\n Test 4: Minimal complexity (8)")
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print(f" Query: '{test_query}'")
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start_time = time.time()
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results = searcher.search(test_query, top_k=10, complexity=8, beam_width=BEAM_WIDTH)
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search_time = time.time() - start_time
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print(f" ✓ Search completed in {search_time:.2f} seconds")
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print(f" Results: {len(results)} items")
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print("\n" + "=" * 80)
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print("Performance Analysis:")
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print("=" * 80)
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print("\nKey Findings:")
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print("1. beam_width parameter is mainly for DiskANN backend, not HNSW")
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print("2. For HNSW, the main parameter affecting search speed is 'complexity'")
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print("3. Lower complexity values (16-32) should provide faster search")
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print("4. The paper mentions ~2 seconds, which likely uses:")
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print(" - Smaller embedding model (~100M params vs 300M for bge-large)")
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print(" - Lower complexity (16-32)")
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print(" - Possibly DiskANN backend for better performance")
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print("\nRecommendations:")
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print("- Try complexity=16 or complexity=32 for faster search")
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print("- Consider using DiskANN backend for better performance on large datasets")
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print("- Or use a smaller embedding model if speed is critical")
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if __name__ == "__main__":
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test_search_performance()
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