fix: mem compare
This commit is contained in:
@@ -10,6 +10,7 @@ import time
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import psutil
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import gc
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import subprocess
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from pathlib import Path
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# Setup logging
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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@@ -95,124 +96,39 @@ def test_faiss_hnsw():
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def test_leann_hnsw():
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"""Test LEANN HNSW Backend"""
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"""Test LEANN HNSW Search Memory (load existing index)"""
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print("\n" + "=" * 50)
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print("TESTING LEANN HNSW BACKEND")
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print("TESTING LEANN HNSW SEARCH MEMORY")
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print("=" * 50)
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tracker = MemoryTracker("LEANN HNSW")
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tracker = MemoryTracker("LEANN HNSW Search")
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# Import and setup
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tracker.checkpoint("Initial")
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from llama_index.core import SimpleDirectoryReader
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from llama_index.core.node_parser import SentenceSplitter
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from leann.api import LeannBuilder, LeannChat
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from pathlib import Path
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from leann.api import LeannSearcher
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tracker.checkpoint("After imports")
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# Load and parse documents
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documents = SimpleDirectoryReader(
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"examples/data",
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recursive=True,
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encoding="utf-8",
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required_exts=[".pdf", ".txt", ".md"],
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).load_data()
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# Find existing LEANN index
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index_paths = [
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"./test_leann_comparison/comparison.leann",
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]
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index_path = None
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for path in index_paths:
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if os.path.exists(path + ".meta.json"):
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index_path = path
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break
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tracker.checkpoint("After document loading")
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if not index_path:
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print("❌ LEANN index not found. Please build it first")
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return {"peak_memory": float("inf"), "error": "Index not found"}
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# Parse into chunks
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node_parser = SentenceSplitter(
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chunk_size=256, chunk_overlap=20, separator=" ", paragraph_separator="\n\n"
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)
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# Load searcher
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searcher = LeannSearcher(index_path)
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tracker.checkpoint("After searcher loading")
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all_texts = []
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for doc in documents:
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nodes = node_parser.get_nodes_from_documents([doc])
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for node in nodes:
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all_texts.append(node.get_content())
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tracker.checkpoint("After text chunking")
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# Build LEANN index
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INDEX_DIR = Path("./test_leann_comparison")
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INDEX_PATH = str(INDEX_DIR / "comparison.leann")
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# Clean up previous index
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import shutil
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if INDEX_DIR.exists():
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shutil.rmtree(INDEX_DIR)
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="facebook/contriever",
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graph_degree=32,
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complexity=64,
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is_compact=True,
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is_recompute=True,
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num_threads=1,
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)
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tracker.checkpoint("After builder setup")
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print("Building LEANN HNSW index...")
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for chunk_text in all_texts:
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builder.add_text(chunk_text)
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builder.build_index(INDEX_PATH)
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tracker.checkpoint("After index building")
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# Ensure any existing embedding server is updated with new data
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print("Ensuring embedding server data synchronization...")
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meta_file = INDEX_DIR / "comparison.leann.meta.json"
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if meta_file.exists():
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from leann.embedding_server_manager import (
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_check_port,
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_update_server_meta_path,
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_check_server_meta_path,
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)
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port = 5557 # Default port for HNSW backend
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if _check_port(port):
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print(f"Updating server meta path to: {meta_file}")
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success = _update_server_meta_path(port, str(meta_file))
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if success:
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print("✅ Server meta path updated successfully")
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else:
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print("❌ Failed to update server meta path")
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# Verify the update and ensure server is ready
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time.sleep(2) # Give server time to reload data
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max_retries = 5
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for retry in range(max_retries):
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if _check_server_meta_path(port, str(meta_file)):
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print("✅ Server meta path verification successful")
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break
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else:
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print(
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f"⏳ Server meta path verification failed (attempt {retry + 1}/{max_retries})"
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)
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if retry < max_retries - 1:
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time.sleep(1)
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else:
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print(
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"❌ Server meta path verification failed after all retries - may cause query issues"
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)
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else:
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print("No existing server found on port 5557")
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# Test queries
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chat = LeannChat(
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index_path=INDEX_PATH, llm_config={"type": "simulated", "model": "test"}
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)
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tracker.checkpoint("After chat setup")
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print("Running queries...")
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print("Running search queries...")
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queries = [
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"什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面,任务令一般在什么城市颁发",
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"What is LEANN and how does it work?",
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@@ -221,18 +137,20 @@ def test_leann_hnsw():
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for i, query in enumerate(queries):
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start_time = time.time()
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_ = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True, complexity=32)
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# Use same parameters as Faiss: top_k=20, ef=120 (complexity parameter)
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_ = searcher.search(query, top_k=20, ef=120)
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query_time = time.time() - start_time
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print(f"Query {i + 1} time: {query_time:.3f}s")
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tracker.checkpoint(f"After query {i + 1}")
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peak_memory = tracker.summary()
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# Get storage size before cleanup - only index files (exclude text data)
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# Get storage size before cleanup
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storage_size = 0
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INDEX_DIR = Path(index_path).parent
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if INDEX_DIR.exists():
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total_size = 0
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for dirpath, dirnames, filenames in os.walk(str(INDEX_DIR)):
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for dirpath, _, filenames in os.walk(str(INDEX_DIR)):
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for filename in filenames:
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# Only count actual index files, skip text data and backups
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if filename.endswith((".old", ".tmp", ".bak", ".jsonl", ".json")):
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@@ -243,20 +161,19 @@ def test_leann_hnsw():
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total_size += os.path.getsize(filepath)
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storage_size = total_size / (1024 * 1024) # Convert to MB
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# Clean up (but keep directory for storage size comparison)
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del chat, builder
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# Clean up
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del searcher
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gc.collect()
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return {
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"peak_memory": peak_memory,
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"storage_size": storage_size,
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"tracker": tracker,
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}
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def main():
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"""Run comparison tests"""
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print("Memory Usage Comparison: Faiss HNSW vs LEANN HNSW")
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print("Storage + Search Memory Comparison: Faiss HNSW vs LEANN HNSW")
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print("=" * 60)
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# Test Faiss HNSW
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@@ -271,7 +188,7 @@ def main():
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# Final comparison
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print("\n" + "=" * 60)
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print("FINAL COMPARISON")
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print("STORAGE + SEARCH MEMORY COMPARISON")
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print("=" * 60)
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# Get storage sizes
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@@ -281,33 +198,37 @@ def main():
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# Get Faiss storage size using Python
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if os.path.exists("./storage_faiss"):
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total_size = 0
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for dirpath, dirnames, filenames in os.walk("./storage_faiss"):
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for dirpath, _, filenames in os.walk("./storage_faiss"):
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for filename in filenames:
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filepath = os.path.join(dirpath, filename)
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total_size += os.path.getsize(filepath)
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faiss_storage_size = total_size / (1024 * 1024) # Convert to MB
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# LEANN storage size is already captured in leann_results
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print(f"Faiss HNSW:")
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print("Faiss HNSW:")
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if "error" in faiss_results:
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print(f" ❌ Failed: {faiss_results['error']}")
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else:
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print(f" Peak Memory: {faiss_results['peak_memory']:.1f} MB")
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print(f" Search Memory: {faiss_results['peak_memory']:.1f} MB")
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print(f" Storage Size: {faiss_storage_size:.1f} MB")
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print(f"\nLEANN HNSW:")
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print(f" Peak Memory: {leann_results['peak_memory']:.1f} MB")
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print(f" Storage Size: {leann_storage_size:.1f} MB")
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print("\nLEANN HNSW:")
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if "error" in leann_results:
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print(f" ❌ Failed: {leann_results['error']}")
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else:
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print(f" Search Memory: {leann_results['peak_memory']:.1f} MB")
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print(f" Storage Size: {leann_storage_size:.1f} MB")
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# Calculate improvements only if Faiss test succeeded
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if "error" not in faiss_results:
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# Calculate improvements only if both tests succeeded
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if "error" not in faiss_results and "error" not in leann_results:
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memory_ratio = faiss_results["peak_memory"] / leann_results["peak_memory"]
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print(f"\nLEANN vs Faiss:")
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print(f" Memory Usage: {memory_ratio:.1f}x less")
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print("\nLEANN vs Faiss Performance:")
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memory_saving = faiss_results["peak_memory"] - leann_results["peak_memory"]
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print(
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f" Search Memory: {memory_ratio:.1f}x less ({memory_saving:.1f} MB saved)"
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)
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# Storage comparison - be clear about which is larger
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# Storage comparison
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if leann_storage_size > faiss_storage_size:
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storage_ratio = leann_storage_size / faiss_storage_size
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print(
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@@ -319,20 +240,16 @@ def main():
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f" Storage Size: {storage_ratio:.1f}x smaller (LEANN uses less storage)"
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)
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else:
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print(f" Storage Size: similar")
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print(f"\nSavings:")
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memory_saving = faiss_results["peak_memory"] - leann_results["peak_memory"]
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storage_diff = faiss_storage_size - leann_storage_size
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print(f" Memory: {memory_saving:.1f} MB")
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if storage_diff >= 0:
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print(f" Storage: {storage_diff:.1f} MB saved")
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else:
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print(f" Storage: {abs(storage_diff):.1f} MB additional used")
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print(" Storage Size: similar")
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else:
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print(f"\n✅ LEANN HNSW ran successfully!")
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print(f"📊 LEANN Memory Usage: {leann_results['peak_memory']:.1f} MB")
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print(f"📊 LEANN Storage Size: {leann_storage_size:.1f} MB")
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if "error" not in leann_results:
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print("\n✅ LEANN HNSW completed successfully!")
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print(f"📊 Search Memory: {leann_results['peak_memory']:.1f} MB")
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print(f"📊 Storage Size: {leann_storage_size:.1f} MB")
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if "error" not in faiss_results:
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print("\n✅ Faiss HNSW completed successfully!")
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print(f"📊 Search Memory: {faiss_results['peak_memory']:.1f} MB")
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print(f"📊 Storage Size: {faiss_storage_size:.1f} MB")
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if __name__ == "__main__":
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@@ -1,14 +1,17 @@
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#!/usr/bin/env python3
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"""Test only Faiss HNSW"""
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import sys
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import time
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import psutil
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import gc
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def get_memory_usage():
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process = psutil.Process()
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return process.memory_info().rss / 1024 / 1024
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class MemoryTracker:
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def __init__(self, name: str):
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self.name = name
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@@ -27,9 +30,21 @@ class MemoryTracker:
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print(f"Peak Memory: {peak_mem:.1f} MB")
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return peak_mem
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def main():
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import faiss
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from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, StorageContext, Settings
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try:
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import faiss
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except ImportError:
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print("Faiss is not installed.")
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print("Please install it with `uv pip install faiss-cpu`")
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sys.exit(1)
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from llama_index.core import (
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SimpleDirectoryReader,
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VectorStoreIndex,
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StorageContext,
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Settings,
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)
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from llama_index.vector_stores.faiss import FaissVectorStore
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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@@ -45,7 +60,12 @@ def main():
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faiss_index.hnsw.efConstruction = 64
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tracker.checkpoint("After Faiss index creation")
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documents = SimpleDirectoryReader("examples/data", recursive=True, encoding="utf-8", required_exts=[".pdf", ".txt", ".md"]).load_data()
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documents = SimpleDirectoryReader(
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"examples/data",
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recursive=True,
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encoding="utf-8",
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required_exts=[".pdf", ".txt", ".md"],
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).load_data()
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tracker.checkpoint("After document loading")
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print("Building Faiss HNSW index...")
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@@ -74,5 +94,6 @@ def main():
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peak_memory = tracker.summary()
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print(f"Peak Memory: {peak_memory:.1f} MB")
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if __name__ == "__main__":
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main()
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main()
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