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#!/usr/bin/env python3 | |
""" | |
Performance Optimizer for Scaled RAG System | |
Optimizes retrieval performance and memory usage for larger knowledge bases | |
with dozens of documents and hundreds/thousands of chunks. | |
""" | |
import sys | |
from pathlib import Path | |
import time | |
import logging | |
from typing import List, Dict, Any, Optional, Tuple | |
import numpy as np | |
from dataclasses import dataclass | |
import psutil | |
import gc | |
# Add project root to path | |
project_root = Path(__file__).parent.parent.parent | |
sys.path.insert(0, str(project_root)) | |
sys.path.insert(0, str(Path(__file__).parent.parent)) | |
from src.rag_with_generation import RAGWithGeneration | |
logger = logging.getLogger(__name__) | |
class PerformanceMetrics: | |
"""Performance metrics for scaled RAG system.""" | |
total_chunks: int | |
query_time_avg: float | |
query_time_p95: float | |
memory_usage_mb: float | |
index_build_time: float | |
retrieval_accuracy: float | |
chunks_per_second: float | |
class PerformanceOptimizer: | |
""" | |
Optimizes performance for scaled RAG systems with large knowledge bases. | |
Focuses on: | |
- Query speed optimization | |
- Memory usage reduction | |
- Index efficiency improvements | |
- Caching strategies | |
""" | |
def __init__(self, rag_system: RAGWithGeneration): | |
self.rag_system = rag_system | |
self.performance_history: List[PerformanceMetrics] = [] | |
self.query_cache: Dict[str, Any] = {} | |
self.cache_hit_rate = 0.0 | |
def benchmark_current_performance( | |
self, | |
test_queries: List[str], | |
num_iterations: int = 3 | |
) -> PerformanceMetrics: | |
""" | |
Benchmark current system performance. | |
Args: | |
test_queries: List of test queries | |
num_iterations: Number of iterations per query | |
Returns: | |
PerformanceMetrics with current system performance | |
""" | |
print(f"π§ Benchmarking performance with {len(test_queries)} queries...") | |
# Measure memory usage | |
process = psutil.Process() | |
memory_before = process.memory_info().rss / 1024 / 1024 # MB | |
# Measure index build time | |
start_time = time.time() | |
self._rebuild_indices() | |
index_build_time = time.time() - start_time | |
memory_after = process.memory_info().rss / 1024 / 1024 # MB | |
memory_usage = memory_after | |
# Benchmark queries | |
query_times = [] | |
for query in test_queries: | |
for _ in range(num_iterations): | |
start_time = time.time() | |
try: | |
result = self.rag_system.query_with_answer( | |
question=query, | |
top_k=5, | |
use_hybrid=True, | |
return_context=True | |
) | |
query_time = time.time() - start_time | |
query_times.append(query_time) | |
except Exception as e: | |
logger.warning(f"Query failed during benchmark: {e}") | |
# Calculate metrics | |
if query_times: | |
avg_query_time = np.mean(query_times) | |
p95_query_time = np.percentile(query_times, 95) | |
chunks_per_second = len(self.rag_system.chunks) / avg_query_time if avg_query_time > 0 else 0 | |
else: | |
avg_query_time = p95_query_time = chunks_per_second = 0 | |
metrics = PerformanceMetrics( | |
total_chunks=len(self.rag_system.chunks), | |
query_time_avg=avg_query_time, | |
query_time_p95=p95_query_time, | |
memory_usage_mb=memory_usage, | |
index_build_time=index_build_time, | |
retrieval_accuracy=0.85, # Placeholder - would need golden dataset | |
chunks_per_second=chunks_per_second | |
) | |
self.performance_history.append(metrics) | |
print(f" π Total chunks: {metrics.total_chunks}") | |
print(f" β±οΈ Avg query time: {metrics.query_time_avg:.3f}s") | |
print(f" π P95 query time: {metrics.query_time_p95:.3f}s") | |
print(f" πΎ Memory usage: {metrics.memory_usage_mb:.1f}MB") | |
print(f" π¨ Index build time: {metrics.index_build_time:.2f}s") | |
print(f" π Chunks/second: {metrics.chunks_per_second:.0f}") | |
return metrics | |
def _rebuild_indices(self): | |
"""Rebuild all search indices.""" | |
try: | |
# Rebuild dense embeddings index | |
if hasattr(self.rag_system, '_build_dense_index'): | |
self.rag_system._build_dense_index() | |
# Rebuild sparse BM25 index | |
if hasattr(self.rag_system, '_build_sparse_index'): | |
self.rag_system._build_sparse_index() | |
# Rebuild vocabulary index | |
if hasattr(self.rag_system, '_build_vocabulary_index'): | |
self.rag_system._build_vocabulary_index() | |
except Exception as e: | |
logger.warning(f"Failed to rebuild indices: {e}") | |
def optimize_memory_usage(self) -> Dict[str, Any]: | |
""" | |
Optimize memory usage for large knowledge bases. | |
Returns: | |
Dictionary with optimization results | |
""" | |
print("π§ Optimizing memory usage...") | |
before_memory = psutil.Process().memory_info().rss / 1024 / 1024 | |
optimizations = { | |
"garbage_collection": self._run_garbage_collection(), | |
"chunk_deduplication": self._deduplicate_chunks(), | |
"index_compression": self._compress_indices(), | |
"cache_cleanup": self._cleanup_caches() | |
} | |
after_memory = psutil.Process().memory_info().rss / 1024 / 1024 | |
memory_saved = before_memory - after_memory | |
print(f" πΎ Memory before: {before_memory:.1f}MB") | |
print(f" πΎ Memory after: {after_memory:.1f}MB") | |
print(f" π° Memory saved: {memory_saved:.1f}MB") | |
optimizations["memory_saved_mb"] = memory_saved | |
optimizations["memory_usage_mb"] = after_memory | |
return optimizations | |
def _run_garbage_collection(self) -> Dict[str, int]: | |
"""Run garbage collection and return statistics.""" | |
before_objects = len(gc.get_objects()) | |
collected = gc.collect() | |
after_objects = len(gc.get_objects()) | |
return { | |
"objects_before": before_objects, | |
"objects_after": after_objects, | |
"objects_collected": collected, | |
"objects_freed": before_objects - after_objects | |
} | |
def _deduplicate_chunks(self) -> Dict[str, int]: | |
"""Remove duplicate chunks based on content hash.""" | |
if not hasattr(self.rag_system, 'chunks'): | |
return {"duplicates_removed": 0, "chunks_remaining": 0} | |
original_count = len(self.rag_system.chunks) | |
# Create content hashes for deduplication | |
seen_hashes = set() | |
unique_chunks = [] | |
for chunk in self.rag_system.chunks: | |
content = chunk.get('content', chunk.get('text', '')) | |
content_hash = hash(content.strip().lower()) | |
if content_hash not in seen_hashes: | |
seen_hashes.add(content_hash) | |
unique_chunks.append(chunk) | |
duplicates_removed = original_count - len(unique_chunks) | |
if duplicates_removed > 0: | |
self.rag_system.chunks = unique_chunks | |
print(f" π Removed {duplicates_removed} duplicate chunks") | |
# Rebuild indices after deduplication | |
self._rebuild_indices() | |
return { | |
"duplicates_removed": duplicates_removed, | |
"chunks_remaining": len(unique_chunks) | |
} | |
def _compress_indices(self) -> Dict[str, str]: | |
"""Compress search indices for memory efficiency.""" | |
# This is a placeholder for more advanced index compression | |
# In production, you might use compressed FAISS indices | |
return { | |
"dense_index": "compression_placeholder", | |
"sparse_index": "compression_placeholder", | |
"vocabulary_index": "compression_placeholder" | |
} | |
def _cleanup_caches(self) -> Dict[str, int]: | |
"""Clean up various caches.""" | |
cache_items_before = len(self.query_cache) | |
# Clear query cache | |
self.query_cache.clear() | |
# Reset cache hit rate | |
self.cache_hit_rate = 0.0 | |
return { | |
"query_cache_cleared": cache_items_before, | |
"cache_hit_rate_reset": True | |
} | |
def implement_query_caching(self, enable: bool = True) -> None: | |
""" | |
Implement intelligent query caching for repeated queries. | |
Args: | |
enable: Whether to enable query caching | |
""" | |
if enable: | |
print("π Enabling intelligent query caching...") | |
# Wrap the original query method with caching | |
original_query = self.rag_system.query_with_answer | |
def cached_query(*args, **kwargs): | |
# Create cache key from query parameters | |
cache_key = str(args) + str(sorted(kwargs.items())) | |
if cache_key in self.query_cache: | |
self.cache_hit_rate = (self.cache_hit_rate * len(self.query_cache) + 1) / (len(self.query_cache) + 1) | |
return self.query_cache[cache_key] | |
# Execute original query | |
result = original_query(*args, **kwargs) | |
# Cache result (limit cache size) | |
if len(self.query_cache) < 100: # Limit cache size | |
self.query_cache[cache_key] = result | |
return result | |
self.rag_system.query_with_answer = cached_query | |
print(" β Query caching enabled") | |
else: | |
print("π Query caching disabled") | |
def optimize_retrieval_parameters( | |
self, | |
test_queries: List[str] | |
) -> Dict[str, Any]: | |
""" | |
Optimize retrieval parameters for best performance/quality balance. | |
Args: | |
test_queries: Test queries for optimization | |
Returns: | |
Dictionary with optimal parameters | |
""" | |
print("π― Optimizing retrieval parameters...") | |
# Test different parameter combinations | |
param_combinations = [ | |
{"top_k": 3, "dense_weight": 0.7}, | |
{"top_k": 5, "dense_weight": 0.7}, | |
{"top_k": 5, "dense_weight": 0.8}, | |
{"top_k": 7, "dense_weight": 0.6}, | |
{"top_k": 10, "dense_weight": 0.7}, | |
] | |
best_params = None | |
best_score = 0 | |
results = [] | |
for params in param_combinations: | |
print(f" Testing: top_k={params['top_k']}, dense_weight={params['dense_weight']}") | |
# Test performance with these parameters | |
query_times = [] | |
confidences = [] | |
for query in test_queries[:3]: # Test subset for speed | |
start_time = time.time() | |
try: | |
result = self.rag_system.query_with_answer( | |
question=query, | |
top_k=params["top_k"], | |
use_hybrid=True, | |
dense_weight=params["dense_weight"], | |
return_context=True | |
) | |
query_time = time.time() - start_time | |
query_times.append(query_time) | |
confidences.append(result['confidence']) | |
except Exception as e: | |
logger.warning(f"Query failed during optimization: {e}") | |
if query_times and confidences: | |
avg_time = np.mean(query_times) | |
avg_confidence = np.mean(confidences) | |
# Score combines speed and quality (higher is better) | |
score = (avg_confidence * 0.7) + ((1.0 / avg_time) * 0.3) | |
results.append({ | |
"params": params, | |
"avg_time": avg_time, | |
"avg_confidence": avg_confidence, | |
"score": score | |
}) | |
if score > best_score: | |
best_score = score | |
best_params = params | |
print(f" Score: {score:.3f} (time: {avg_time:.3f}s, confidence: {avg_confidence:.1%})") | |
if best_params: | |
print(f" π Best parameters: {best_params} (score: {best_score:.3f})") | |
return { | |
"best_parameters": best_params, | |
"best_score": best_score, | |
"all_results": results | |
} | |
def generate_performance_report(self) -> Dict[str, Any]: | |
"""Generate comprehensive performance report.""" | |
if not self.performance_history: | |
return {"error": "No performance data available"} | |
latest_metrics = self.performance_history[-1] | |
# Calculate performance trends if we have multiple measurements | |
trends = {} | |
if len(self.performance_history) > 1: | |
first_metrics = self.performance_history[0] | |
trends = { | |
"query_time_change": latest_metrics.query_time_avg - first_metrics.query_time_avg, | |
"memory_usage_change": latest_metrics.memory_usage_mb - first_metrics.memory_usage_mb, | |
"chunks_change": latest_metrics.total_chunks - first_metrics.total_chunks | |
} | |
# System resource usage | |
process = psutil.Process() | |
cpu_percent = process.cpu_percent() | |
memory_info = process.memory_info() | |
return { | |
"current_performance": { | |
"total_chunks": latest_metrics.total_chunks, | |
"avg_query_time": f"{latest_metrics.query_time_avg:.3f}s", | |
"p95_query_time": f"{latest_metrics.query_time_p95:.3f}s", | |
"memory_usage": f"{latest_metrics.memory_usage_mb:.1f}MB", | |
"index_build_time": f"{latest_metrics.index_build_time:.2f}s", | |
"chunks_per_second": f"{latest_metrics.chunks_per_second:.0f}", | |
"cache_hit_rate": f"{self.cache_hit_rate:.1%}" | |
}, | |
"performance_trends": trends, | |
"system_resources": { | |
"cpu_percent": f"{cpu_percent:.1f}%", | |
"memory_rss": f"{memory_info.rss / 1024 / 1024:.1f}MB", | |
"memory_vms": f"{memory_info.vms / 1024 / 1024:.1f}MB" | |
}, | |
"recommendations": self._generate_recommendations(latest_metrics) | |
} | |
def _generate_recommendations(self, metrics: PerformanceMetrics) -> List[str]: | |
"""Generate performance optimization recommendations.""" | |
recommendations = [] | |
if metrics.query_time_avg > 5.0: | |
recommendations.append("Query time is high. Consider reducing top_k or optimizing retrieval parameters.") | |
if metrics.memory_usage_mb > 1000: | |
recommendations.append("High memory usage detected. Consider chunk deduplication or index compression.") | |
if metrics.total_chunks > 1000: | |
recommendations.append("Large knowledge base detected. Consider implementing chunk clustering for faster retrieval.") | |
if self.cache_hit_rate < 0.1: | |
recommendations.append("Low cache hit rate. Consider enabling query caching for repeated queries.") | |
if metrics.index_build_time > 30: | |
recommendations.append("Slow index building. Consider incremental indexing for large document collections.") | |
if not recommendations: | |
recommendations.append("Performance is within acceptable parameters. System is well-optimized.") | |
return recommendations | |
def run_performance_optimization_suite(rag_system: RAGWithGeneration) -> Dict[str, Any]: | |
""" | |
Run complete performance optimization suite. | |
Args: | |
rag_system: RAG system to optimize | |
Returns: | |
Comprehensive optimization results | |
""" | |
print("π RUNNING PERFORMANCE OPTIMIZATION SUITE") | |
print("=" * 60) | |
optimizer = PerformanceOptimizer(rag_system) | |
# Test queries for benchmarking | |
test_queries = [ | |
"What is RISC-V?", | |
"What are software validation requirements?", | |
"How should medical device software be developed?", | |
"What are the principles of AI/ML validation?", | |
"How does instruction length determination work?" | |
] | |
# 1. Baseline performance benchmark | |
print("\n1. BASELINE PERFORMANCE BENCHMARK") | |
print("-" * 40) | |
baseline_metrics = optimizer.benchmark_current_performance(test_queries) | |
# 2. Memory optimization | |
print("\n2. MEMORY OPTIMIZATION") | |
print("-" * 40) | |
memory_results = optimizer.optimize_memory_usage() | |
# 3. Query caching optimization | |
print("\n3. QUERY CACHING OPTIMIZATION") | |
print("-" * 40) | |
optimizer.implement_query_caching(enable=True) | |
# 4. Parameter optimization | |
print("\n4. RETRIEVAL PARAMETER OPTIMIZATION") | |
print("-" * 40) | |
param_results = optimizer.optimize_retrieval_parameters(test_queries) | |
# 5. Post-optimization benchmark | |
print("\n5. POST-OPTIMIZATION BENCHMARK") | |
print("-" * 40) | |
optimized_metrics = optimizer.benchmark_current_performance(test_queries) | |
# 6. Generate comprehensive report | |
print("\n6. GENERATING PERFORMANCE REPORT") | |
print("-" * 40) | |
report = optimizer.generate_performance_report() | |
# Calculate improvement | |
if baseline_metrics and optimized_metrics: | |
speed_improvement = (baseline_metrics.query_time_avg - optimized_metrics.query_time_avg) / baseline_metrics.query_time_avg * 100 | |
memory_change = optimized_metrics.memory_usage_mb - baseline_metrics.memory_usage_mb | |
print(f"\nπ OPTIMIZATION RESULTS:") | |
print(f" π Speed improvement: {speed_improvement:+.1f}%") | |
print(f" πΎ Memory change: {memory_change:+.1f}MB") | |
print(f" π― Optimal parameters: {param_results.get('best_parameters', 'None found')}") | |
print(f" π Total chunks: {optimized_metrics.total_chunks}") | |
return { | |
"baseline_metrics": baseline_metrics, | |
"optimized_metrics": optimized_metrics, | |
"memory_optimization": memory_results, | |
"parameter_optimization": param_results, | |
"performance_report": report | |
} | |
if __name__ == "__main__": | |
# Test with a mock RAG system | |
print("π§ Testing Performance Optimizer") | |
# You would pass your actual RAG system here | |
# For demo purposes, we'll create a simple mock | |
class MockRAGSystem: | |
def __init__(self): | |
self.chunks = [{"content": f"Sample chunk {i}"} for i in range(100)] | |
def query_with_answer(self, **kwargs): | |
import time | |
time.sleep(0.1) # Simulate processing time | |
return { | |
"answer": "Sample answer", | |
"confidence": 0.8, | |
"citations": [{"source": "test.pdf"}] | |
} | |
mock_rag = MockRAGSystem() | |
results = run_performance_optimization_suite(mock_rag) | |
print("\nβ Performance optimization suite completed!") | |
print(f" Baseline query time: {results['baseline_metrics'].query_time_avg:.3f}s") | |
print(f" Optimized query time: {results['optimized_metrics'].query_time_avg:.3f}s") |