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| """ | |
| Test async vs sync query performance | |
| Demonstrates speed improvements from async endpoints and FAISS IVF optimization | |
| """ | |
| import asyncio | |
| import time | |
| from src.core.dual_rag_pipeline import DualStoreRAGPipeline | |
| def test_sync_queries(): | |
| """Test synchronous query performance""" | |
| print("\n" + "="*60) | |
| print("🔄 SYNC QUERY PERFORMANCE TEST") | |
| print("="*60) | |
| pipeline = DualStoreRAGPipeline() | |
| pipeline.load_vector_stores() # Load saved vector stores | |
| test_queries = [ | |
| "How do I track my order?", | |
| "I want to cancel my subscription", | |
| "My payment was declined", | |
| "How do I reset my password?", | |
| "Where is my refund?" | |
| ] | |
| start = time.time() | |
| for i, query in enumerate(test_queries, 1): | |
| q_start = time.time() | |
| result = pipeline.query(query, top_k=3) | |
| q_time = (time.time() - q_start) * 1000 | |
| print(f"\n{i}. Query: {query}") | |
| print(f" Source: {result['source']}") | |
| print(f" Confidence: {result['confidence']:.1%}") | |
| print(f" Latency: {q_time:.0f}ms") | |
| total_time = (time.time() - start) * 1000 | |
| avg_time = total_time / len(test_queries) | |
| print(f"\n{'─'*60}") | |
| print(f"Total Time: {total_time:.0f}ms") | |
| print(f"Average Latency: {avg_time:.0f}ms") | |
| print(f"Queries/sec: {1000/avg_time:.2f}") | |
| return avg_time | |
| async def test_async_queries(): | |
| """Test asynchronous query performance""" | |
| print("\n" + "="*60) | |
| print("⚡ ASYNC QUERY PERFORMANCE TEST") | |
| print("="*60) | |
| pipeline = DualStoreRAGPipeline() | |
| pipeline.load_vector_stores() # Load saved vector stores | |
| test_queries = [ | |
| "How do I track my order?", | |
| "I want to cancel my subscription", | |
| "My payment was declined", | |
| "How do I reset my password?", | |
| "Where is my refund?" | |
| ] | |
| start = time.time() | |
| # Sequential async queries | |
| for i, query in enumerate(test_queries, 1): | |
| q_start = time.time() | |
| result = await pipeline.aquery(query, top_k=3) | |
| q_time = (time.time() - q_start) * 1000 | |
| print(f"\n{i}. Query: {query}") | |
| print(f" Source: {result['source']}") | |
| print(f" Confidence: {result['confidence']:.1%}") | |
| print(f" Latency: {q_time:.0f}ms") | |
| total_time = (time.time() - start) * 1000 | |
| avg_time = total_time / len(test_queries) | |
| print(f"\n{'─'*60}") | |
| print(f"Total Time: {total_time:.0f}ms") | |
| print(f"Average Latency: {avg_time:.0f}ms") | |
| print(f"Queries/sec: {1000/avg_time:.2f}") | |
| return avg_time | |
| async def test_parallel_async_queries(): | |
| """Test parallel async query performance""" | |
| print("\n" + "="*60) | |
| print("🚀 PARALLEL ASYNC QUERY PERFORMANCE TEST") | |
| print("="*60) | |
| pipeline = DualStoreRAGPipeline() | |
| pipeline.load_vector_stores() # Load saved vector stores | |
| test_queries = [ | |
| "How do I track my order?", | |
| "I want to cancel my subscription", | |
| "My payment was declined", | |
| "How do I reset my password?", | |
| "Where is my refund?" | |
| ] | |
| start = time.time() | |
| # Run all queries in parallel | |
| tasks = [pipeline.aquery(query, top_k=3) for query in test_queries] | |
| results = await asyncio.gather(*tasks) | |
| total_time = (time.time() - start) * 1000 | |
| for i, (query, result) in enumerate(zip(test_queries, results), 1): | |
| print(f"\n{i}. Query: {query}") | |
| print(f" Source: {result['source']}") | |
| print(f" Confidence: {result['confidence']:.1%}") | |
| print(f" Latency: {result['latency_ms']:.0f}ms") | |
| avg_time = total_time / len(test_queries) | |
| print(f"\n{'─'*60}") | |
| print(f"Total Time: {total_time:.0f}ms (All 5 queries in parallel!)") | |
| print(f"Average Latency per Query: {avg_time:.0f}ms") | |
| print(f"Effective Throughput: {1000/avg_time:.2f} queries/sec") | |
| return avg_time, total_time | |
| def main(): | |
| """Run all performance tests""" | |
| print("\n" + "🎯 "+"="*58) | |
| print(" RAG PERFORMANCE BENCHMARK") | |
| print(" Dual Vector Stores: 10,580 FAQs + 5,000 Tickets") | |
| print(" FAISS Optimization: IVF with 205/141 clusters") | |
| print(" "+"="*58) | |
| # Test 1: Sync queries | |
| sync_avg = test_sync_queries() | |
| # Test 2: Async queries (sequential) | |
| async_avg = asyncio.run(test_async_queries()) | |
| # Test 3: Async queries (parallel) | |
| parallel_avg, parallel_total = asyncio.run(test_parallel_async_queries()) | |
| # Summary | |
| print("\n" + "🏆 "+"="*58) | |
| print(" PERFORMANCE SUMMARY") | |
| print(" "+"="*58) | |
| print(f"\n Sync Query (baseline): {sync_avg:.0f}ms avg") | |
| print(f" Async Query (sequential): {async_avg:.0f}ms avg") | |
| print(f" Async Query (parallel): {parallel_total:.0f}ms total for 5 queries") | |
| print(f" ({parallel_avg:.0f}ms per query)") | |
| sync_improvement = ((sync_avg - async_avg) / sync_avg) * 100 | |
| parallel_speedup = (sync_avg * 5) / parallel_total | |
| print(f"\n 📊 Improvements:") | |
| print(f" • Async vs Sync: {sync_improvement:.1f}% faster") | |
| print(f" • Parallel Speedup: {parallel_speedup:.1f}x") | |
| print(f" • Target Latency (<300ms): {'✅ ACHIEVED' if async_avg < 300 else '❌ NOT YET'}") | |
| print("\n " + "="*58 + "\n") | |
| if __name__ == "__main__": | |
| main() | |