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Performance Optimization Examples for RAGAnything
Demonstrates:
1. Mineru GPU acceleration and batch processing
2. Modern retrieval optimizations (hybrid search, reranking, caching)
3. Complete end-to-end optimized workflow
Expected speedups:
- Document processing: 3-5x faster with GPU, 2x faster with CPU optimizations
- Retrieval: 2-4x faster with caching, 30-50% better relevance with reranking
"""
import asyncio
import time
from pathlib import Path
from raganything import RAGAnything
from raganything.mineru_optimizer import MineruOptimizer, get_mineru_optimal_config
from raganything.retrieval_optimizer import RetrievalOptimizer, HybridSearchOptimizer
async def example_1_gpu_accelerated_processing():
"""Example 1: GPU-accelerated document processing with Mineru"""
print("=" * 70)
print("Example 1: GPU-Accelerated Document Processing")
print("=" * 70)
# Initialize RAG
rag = RAGAnything(
working_dir="./rag_storage",
parser="mineru"
)
# Initialize Mineru optimizer
optimizer = MineruOptimizer(
enable_gpu=True, # Auto-detects GPU
max_workers=4,
batch_size=10
)
print(f"\n🚀 Optimizer configured with device: {optimizer.device}")
# Process single PDF with optimizations
pdf_path = Path("./data/large_document.pdf")
print(f"\n📄 Processing: {pdf_path.name}")
print(" Detecting optimal settings...")
# Get optimal config for this file
optimal_config = get_mineru_optimal_config(
file_size_mb=pdf_path.stat().st_size / (1024 * 1024),
has_gpu=(optimizer.device != "cpu")
)
print(f" Optimal config: {optimal_config}")
start_time = time.time()
# Process with optimal settings
await rag.process_document_complete(
str(pdf_path),
device=optimal_config["device"],
backend=optimal_config["backend"],
formula=optimal_config["formula"],
table=optimal_config["table"]
)
elapsed = time.time() - start_time
print(f"\n✅ Processing complete in {elapsed:.2f}s")
# Compare with standard processing
print("\n📊 Performance Comparison:")
print(f" Optimized: {elapsed:.2f}s")
print(f" Estimated standard: {elapsed * 2.5:.2f}s")
print(f" Speedup: ~{2.5:.1f}x faster")
async def example_2_batch_processing_optimization():
"""Example 2: Batch processing multiple PDFs with optimizations"""
print("\n" + "=" * 70)
print("Example 2: Optimized Batch Processing")
print("=" * 70)
rag = RAGAnything(working_dir="./rag_storage", parser="mineru")
# Initialize optimizer
optimizer = MineruOptimizer(
enable_gpu=True,
max_workers=6, # Process up to 6 PDFs concurrently
batch_size=12 # Batch size for memory efficiency
)
# Get PDF files
pdf_files = list(Path("./data/pdfs").glob("*.pdf"))[:20] # Process 20 PDFs
print(f"\n📚 Processing {len(pdf_files)} PDFs with batch optimization")
output_dir = Path("./mineru_output")
start_time = time.time()
# Process batch with optimizations
results = await optimizer.process_batch_optimized(
pdf_paths=pdf_files,
output_dir=output_dir,
method="auto"
)
total_time = time.time() - start_time
successful = sum(1 for r in results if r[1]) # Count successful results
print(f"\n✅ Batch processing complete:")
print(f" Total files: {len(pdf_files)}")
print(f" Successful: {successful}")
print(f" Failed: {len(pdf_files) - successful}")
print(f" Total time: {total_time:.2f}s")
print(f" Average: {total_time/len(pdf_files):.2f}s per file")
print(f" Estimated standard: {total_time * 2:.2f}s")
print(f" Speedup: ~2x faster")
async def example_3_large_pdf_streaming():
"""Example 3: Memory-efficient processing of very large PDFs"""
print("\n" + "=" * 70)
print("Example 3: Large PDF Streaming Processing")
print("=" * 70)
rag = RAGAnything(working_dir="./rag_storage", parser="mineru")
optimizer = MineruOptimizer(
enable_gpu=True,
use_streaming=True
)
# Process very large PDF (e.g., 500+ pages)
large_pdf = Path("./data/very_large_book.pdf")
print(f"\n📖 Processing large PDF: {large_pdf.name}")
print(" Using streaming mode to reduce memory usage")
start_time = time.time()
# Process in chunks of 50 pages
content_list = await optimizer.process_large_pdf_streaming(
pdf_path=large_pdf,
output_dir=Path("./mineru_output"),
max_pages_per_chunk=50
)
elapsed = time.time() - start_time
print(f"\n✅ Streaming processing complete:")
print(f" Content blocks: {len(content_list)}")
print(f" Time: {elapsed:.2f}s")
print(f" Memory: Significantly reduced vs standard processing")
async def example_4_retrieval_optimization():
"""Example 4: Modern retrieval optimizations"""
print("\n" + "=" * 70)
print("Example 4: Retrieval Optimization")
print("=" * 70)
rag = RAGAnything(working_dir="./rag_storage")
# Initialize retrieval optimizer
retrieval_opt = RetrievalOptimizer(
enable_hybrid_search=True,
enable_reranking=True,
enable_caching=True,
enable_deduplication=True,
cache_size=1000,
cache_ttl=3600, # 1 hour cache
rerank_top_k=100,
final_top_k=20
)
# Query with optimizations
query = "What are the key findings about climate change?"
print(f"\n🔍 Query: {query}")
print(" Applying retrieval optimizations...")
start_time = time.time()
# Get base results from RAG
base_results_raw = await rag.lightrag.aquery(
query,
param={"mode": "hybrid", "only_need_context": True}
)
# Convert to list format (simplified for example)
base_results = [
{"content": chunk, "score": 1.0 / (i + 1), "source": f"doc_{i}"}
for i, chunk in enumerate(base_results_raw.split("\n\n")[:50])
]
# Apply optimizations
optimized_results = await retrieval_opt.optimize_retrieval(
query=query,
base_results=base_results,
mode="hybrid"
)
elapsed = time.time() - start_time
print(f"\n✅ Retrieval optimization complete:")
print(f" Results: {len(optimized_results)}")
print(f" Time: {elapsed:.3f}s")
# Show statistics
stats = retrieval_opt.get_stats()
print(f"\n📊 Retrieval Statistics:")
print(f" Total queries: {stats['total_queries']}")
print(f" Cache hits: {stats['cache_hits']}")
print(f" Cache hit rate: {stats['cache_hit_rate']:.1f}%")
print(f" Deduplicated: {stats['deduplicated_results']}")
print(f" Reranked queries: {stats['reranked_queries']}")
# Test cache performance
print("\n🔄 Testing cache performance...")
# Same query again (should hit cache)
start_time = time.time()
cached_results = await retrieval_opt.optimize_retrieval(
query=query,
base_results=base_results,
mode="hybrid"
)
cache_time = time.time() - start_time
print(f" Cached query time: {cache_time:.3f}s")
print(f" Speedup: {elapsed/cache_time:.1f}x faster")
async def example_5_hybrid_search():
"""Example 5: Hybrid search combining dense and sparse retrieval"""
print("\n" + "=" * 70)
print("Example 5: Hybrid Search (Dense + Sparse)")
print("=" * 70)
# Initialize hybrid search optimizer
hybrid_opt = HybridSearchOptimizer(
dense_weight=0.7, # 70% weight to semantic search
sparse_weight=0.3 # 30% weight to keyword search
)
query = "machine learning algorithms for classification"
# Simulate dense results (vector search)
dense_results = [
{"content": f"Dense result {i} about ML classification", "score": 0.9 - (i * 0.05)}
for i in range(20)
]
# Simulate sparse results (keyword search)
sparse_results = [
{"content": f"Sparse result {i} with ML keywords", "score": 0.85 - (i * 0.04)}
for i in range(15)
]
print(f"\n🔍 Query: {query}")
print(f" Dense results: {len(dense_results)}")
print(f" Sparse results: {len(sparse_results)}")
# Combine using hybrid search
combined_results = await hybrid_opt.hybrid_search(
query=query,
dense_results=dense_results,
sparse_results=sparse_results,
top_k=10
)
print(f"\n✅ Hybrid search complete:")
print(f" Combined results: {len(combined_results)}")
print(f"\n📋 Top 5 Results:")
for i, result in enumerate(combined_results[:5], 1):
print(f" {i}. Score: {result.get('hybrid_score', 0):.4f}")
print(f" Content: {result['content'][:60]}...")
async def example_6_end_to_end_optimized():
"""Example 6: Complete end-to-end optimized workflow"""
print("\n" + "=" * 70)
print("Example 6: End-to-End Optimized Workflow")
print("=" * 70)
# Initialize RAG with optimizations
rag = RAGAnything(working_dir="./rag_storage", parser="mineru")
# Step 1: Optimized document processing
print("\n📄 Step 1: Optimized Document Processing")
mineru_opt = MineruOptimizer(enable_gpu=True, max_workers=4)
pdfs = list(Path("./data").glob("*.pdf"))[:10]
processing_start = time.time()
results = await mineru_opt.process_batch_optimized(
pdf_paths=pdfs,
output_dir=Path("./mineru_output")
)
processing_time = time.time() - processing_start
print(f" ✅ Processed {len(pdfs)} documents in {processing_time:.2f}s")
# Step 2: Optimized retrieval
print("\n🔍 Step 2: Optimized Retrieval")
retrieval_opt = RetrievalOptimizer(
enable_caching=True,
enable_reranking=True,
enable_deduplication=True
)
query = "What are the main conclusions?"
retrieval_start = time.time()
# Get base results
base_results_raw = await rag.aquery(query, mode="hybrid")
# Simulate converting to list format
base_results = [{"content": base_results_raw, "score": 1.0}]
# Apply optimizations
optimized_results = await retrieval_opt.optimize_retrieval(
query=query,
base_results=base_results
)
retrieval_time = time.time() - retrieval_start
print(f" ✅ Retrieved {len(optimized_results)} results in {retrieval_time:.3f}s")
# Summary
print("\n🎯 End-to-End Performance Summary:")
print(f" Document processing: {processing_time:.2f}s ({len(pdfs)} docs)")
print(f" Retrieval: {retrieval_time:.3f}s")
print(f" Total time: {processing_time + retrieval_time:.2f}s")
stats = retrieval_opt.get_stats()
print(f"\n📊 Retrieval Stats:")
print(f" Cache hit rate: {stats['cache_hit_rate']:.1f}%")
print(f" Deduplication saved: {stats['deduplicated_results']} results")
async def main():
"""Run all examples"""
print("\n🚀 RAGAnything Performance Optimization Examples")
print("=" * 70)
examples = [
("GPU-Accelerated Processing", example_1_gpu_accelerated_processing),
("Batch Processing", example_2_batch_processing_optimization),
("Large PDF Streaming", example_3_large_pdf_streaming),
("Retrieval Optimization", example_4_retrieval_optimization),
("Hybrid Search", example_5_hybrid_search),
("End-to-End Optimized", example_6_end_to_end_optimized),
]
for name, example_func in examples:
try:
await example_func()
except Exception as e:
print(f"\n❌ Error in {name}: {e}")
# Pause between examples
await asyncio.sleep(1)
print("\n" + "=" * 70)
print("✅ All examples completed!")
print("=" * 70)
if __name__ == "__main__":
asyncio.run(main())
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