benchmarks/benchmark_enhanced_systems.py

#!/usr/bin/env python3
"""
Benchmark Enhanced Felix Framework Systems
 
Comprehensive benchmarking of all five priority enhancement systems:
1. Intelligent Output Chunking & Streaming
2. Dynamic Agent Spawning  
3. Prompt Optimization Pipeline
4. Memory and Persistence Layer
5. Benchmarking & Quality Metrics

This script provides performance metrics and validation results for our enhanced systems.
"""

import sys
import time
import json
import tempfile
import statistics
import os
from pathlib import Path
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, asdict

# Add src to Python path
sys.path.insert(0, str(Path(__file__).parent / "src"))

@dataclass
class BenchmarkResult:
    """Results from a single benchmark test."""
    system_name: str
    test_name: str
    execution_time: float
    success: bool
    metrics: Dict[str, Any]
    error_message: Optional[str] = None

@dataclass
class SystemBenchmark:
    """Benchmark results for an entire system."""
    system_name: str
    total_tests: int
    successful_tests: int
    total_time: float
    average_time: float
    results: List[BenchmarkResult]

def benchmark_chunking_system() -> SystemBenchmark:
    """Benchmark the intelligent output chunking system."""
    print("🧩 Benchmarking Chunking System...")
    
    from src.pipeline.chunking import ProgressiveProcessor, ChunkedResult
    
    results = []
    start_time = time.time()
    
    # Test 1: Basic chunking functionality
    test_start = time.time()
    try:
        processor = ProgressiveProcessor(
            task_id="benchmark_task",
            agent_id="benchmark_agent", 
            full_content="This is test content for chunking. " * 100,  # 500 words
            chunk_size=200
        )
        
        chunks_count = processor.total_chunks
        first_chunk = processor.get_chunk_by_index(0)
        
        success = chunks_count > 1 and first_chunk is not None
        metrics = {
            "total_chunks": chunks_count,
            "content_length": len(processor.full_content),
            "chunk_size": processor.chunk_size,
            "first_chunk_length": len(first_chunk.content_chunk) if first_chunk else 0
        }
        
        results.append(BenchmarkResult(
            system_name="Chunking",
            test_name="basic_chunking",
            execution_time=time.time() - test_start,
            success=success,
            metrics=metrics
        ))
        
    except Exception as e:
        results.append(BenchmarkResult(
            system_name="Chunking",
            test_name="basic_chunking",
            execution_time=time.time() - test_start,
            success=False,
            metrics={},
            error_message=str(e)
        ))
    
    # Test 2: Performance with large content
    test_start = time.time()
    try:
        large_content = "Large content for performance testing. " * 1000  # ~5000 words
        large_processor = ProgressiveProcessor(
            task_id="large_task",
            agent_id="benchmark_agent",
            full_content=large_content,
            chunk_size=500
        )
        
        # Process all chunks
        all_chunks = []
        for i in range(large_processor.total_chunks):
            chunk = large_processor.get_chunk_by_index(i)
            if chunk:
                all_chunks.append(chunk)
        
        success = len(all_chunks) == large_processor.total_chunks
        metrics = {
            "content_length": len(large_content),
            "total_chunks": large_processor.total_chunks,
            "processed_chunks": len(all_chunks),
            "avg_chunk_size": statistics.mean([len(chunk.content_chunk) for chunk in all_chunks]) if all_chunks else 0
        }
        
        results.append(BenchmarkResult(
            system_name="Chunking",
            test_name="large_content_performance",
            execution_time=time.time() - test_start,
            success=success,
            metrics=metrics
        ))
        
    except Exception as e:
        results.append(BenchmarkResult(
            system_name="Chunking",
            test_name="large_content_performance",
            execution_time=time.time() - test_start,
            success=False,
            metrics={},
            error_message=str(e)
        ))
    
    total_time = time.time() - start_time
    successful_tests = sum(1 for r in results if r.success)
    
    return SystemBenchmark(
        system_name="Chunking System",
        total_tests=len(results),
        successful_tests=successful_tests,
        total_time=total_time,
        average_time=total_time / len(results) if results else 0,
        results=results
    )

def benchmark_knowledge_store() -> SystemBenchmark:
    """Benchmark the memory and persistence layer."""
    print("🧠 Benchmarking Knowledge Store...")
    
    from src.memory.knowledge_store import KnowledgeStore, KnowledgeType, ConfidenceLevel, KnowledgeQuery
    
    results = []
    start_time = time.time()
    
    # Create temporary database
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as temp_file:
        temp_db_path = temp_file.name
    
    try:
        # Test 1: Basic storage and retrieval
        test_start = time.time()
        try:
            store = KnowledgeStore(storage_path=temp_db_path)
            
            # Store test knowledge
            knowledge_id = store.store_knowledge(
                knowledge_type=KnowledgeType.TASK_RESULT,
                content={"task": "benchmark_test", "result": "success", "score": 0.95},
                confidence_level=ConfidenceLevel.HIGH,
                source_agent="benchmark_agent",
                domain="testing",
                tags=["benchmark", "test", "performance"]
            )
            
            # Retrieve knowledge
            query = KnowledgeQuery(
                knowledge_types=[KnowledgeType.TASK_RESULT],
                domains=["testing"]
            )
            retrieved = store.retrieve_knowledge(query)
            
            success = len(retrieved) == 1 and retrieved[0].knowledge_id == knowledge_id
            metrics = {
                "stored_entries": 1,
                "retrieved_entries": len(retrieved),
                "knowledge_id_match": retrieved[0].knowledge_id == knowledge_id if retrieved else False
            }
            
            results.append(BenchmarkResult(
                system_name="KnowledgeStore",
                test_name="basic_storage_retrieval",
                execution_time=time.time() - test_start,
                success=success,
                metrics=metrics
            ))
            
        except Exception as e:
            results.append(BenchmarkResult(
                system_name="KnowledgeStore",
                test_name="basic_storage_retrieval",
                execution_time=time.time() - test_start,
                success=False,
                metrics={},
                error_message=str(e)
            ))
        
        # Test 2: Performance with multiple entries
        test_start = time.time()
        try:
            store = KnowledgeStore(storage_path=temp_db_path)
            
            # Store multiple knowledge entries
            knowledge_ids = []
            for i in range(100):
                kid = store.store_knowledge(
                    knowledge_type=KnowledgeType.AGENT_INSIGHT,
                    content={"insight": f"test_insight_{i}", "value": i * 0.01},
                    confidence_level=ConfidenceLevel.MEDIUM,
                    source_agent=f"agent_{i % 5}",
                    domain="performance_testing",
                    tags=["bulk_test", f"batch_{i // 20}"]
                )
                knowledge_ids.append(kid)
            
            # Query all entries
            query = KnowledgeQuery(domains=["performance_testing"], limit=200)
            all_entries = store.retrieve_knowledge(query)
            
            # Query with filters - batch_2 should have entries 40-59 (20 entries)
            filtered_query = KnowledgeQuery(
                domains=["performance_testing"],
                tags=["batch_2"],
                min_confidence=ConfidenceLevel.MEDIUM
            )
            filtered_entries = store.retrieve_knowledge(filtered_query)
            
            success = len(all_entries) >= 100 and len(filtered_entries) >= 5
            metrics = {
                "stored_entries": len(knowledge_ids),
                "retrieved_all": len(all_entries),
                "retrieved_filtered": len(filtered_entries),
                "storage_success_rate": len([kid for kid in knowledge_ids if kid]) / len(knowledge_ids)
            }
            
            results.append(BenchmarkResult(
                system_name="KnowledgeStore",
                test_name="bulk_storage_performance",
                execution_time=time.time() - test_start,
                success=success,
                metrics=metrics
            ))
            
        except Exception as e:
            results.append(BenchmarkResult(
                system_name="KnowledgeStore",
                test_name="bulk_storage_performance",
                execution_time=time.time() - test_start,
                success=False,
                metrics={},
                error_message=str(e)
            ))
        
    finally:
        # Cleanup temporary database
        if os.path.exists(temp_db_path):
            os.unlink(temp_db_path)
    
    total_time = time.time() - start_time
    successful_tests = sum(1 for r in results if r.success)
    
    return SystemBenchmark(
        system_name="Knowledge Store",
        total_tests=len(results),
        successful_tests=successful_tests,
        total_time=total_time,
        average_time=total_time / len(results) if results else 0,
        results=results
    )

def benchmark_quality_metrics() -> SystemBenchmark:
    """Benchmark the quality metrics calculation system."""
    print("📊 Benchmarking Quality Metrics...")
    
    from src.comparison.quality_metrics import QualityMetricsCalculator, DomainType
    
    results = []
    start_time = time.time()
    
    # Test 1: Basic quality assessment
    test_start = time.time()
    try:
        calculator = QualityMetricsCalculator()
        
        test_text = """
        This comprehensive analysis examines artificial intelligence development trends. 
        First, we establish the foundational concepts. Furthermore, advanced machine learning 
        techniques demonstrate significant improvements in accuracy and efficiency. Research 
        indicates that 95% of systems show enhanced performance. Therefore, these methodologies 
        provide substantial value for implementation.
        """
        
        quality_score = calculator.calculate_quality_score(test_text, DomainType.TECHNICAL)
        
        success = (
            0 <= quality_score.overall_score <= 1 and
            quality_score.coherence_score > 0 and
            quality_score.accuracy_score > 0 and
            quality_score.word_count > 0
        )
        
        metrics = {
            "overall_score": quality_score.overall_score,
            "coherence_score": quality_score.coherence_score,
            "accuracy_score": quality_score.accuracy_score,
            "completeness_score": quality_score.completeness_score,
            "clarity_score": quality_score.clarity_score,
            "word_count": quality_score.word_count,
            "sentence_count": quality_score.sentence_count,
            "has_bleu_score": quality_score.bleu_score is not None
        }
        
        results.append(BenchmarkResult(
            system_name="QualityMetrics",
            test_name="basic_quality_assessment",
            execution_time=time.time() - test_start,
            success=success,
            metrics=metrics
        ))
        
    except Exception as e:
        results.append(BenchmarkResult(
            system_name="QualityMetrics",
            test_name="basic_quality_assessment",
            execution_time=time.time() - test_start,
            success=False,
            metrics={},
            error_message=str(e)
        ))
    
    # Test 2: Performance with multiple texts
    test_start = time.time()
    try:
        calculator = QualityMetricsCalculator()
        
        test_texts = [
            "High quality technical documentation with research backing.",
            "Some random text without much structure or clarity here.",
            "This demonstrates excellent coherence. Furthermore, the analysis provides substantial evidence. Research indicates clear patterns.",
            "Poor quality text with no structure clarity issues many problems",
            "Comprehensive examination reveals innovative approaches. Studies show 92% effectiveness rates."
        ]
        
        batch_scores = calculator.batch_calculate_scores(test_texts, DomainType.GENERAL)
        
        success = len(batch_scores) == len(test_texts) and all(0 <= score.overall_score <= 1 for score in batch_scores)
        metrics = {
            "texts_processed": len(batch_scores),
            "average_overall_score": statistics.mean([score.overall_score for score in batch_scores]),
            "score_range": max([score.overall_score for score in batch_scores]) - min([score.overall_score for score in batch_scores]),
            "all_valid_scores": all(0 <= score.overall_score <= 1 for score in batch_scores)
        }
        
        results.append(BenchmarkResult(
            system_name="QualityMetrics",
            test_name="batch_processing_performance",
            execution_time=time.time() - test_start,
            success=success,
            metrics=metrics
        ))
        
    except Exception as e:
        results.append(BenchmarkResult(
            system_name="QualityMetrics",
            test_name="batch_processing_performance",
            execution_time=time.time() - test_start,
            success=False,
            metrics={},
            error_message=str(e)
        ))
    
    total_time = time.time() - start_time
    successful_tests = sum(1 for r in results if r.success)
    
    return SystemBenchmark(
        system_name="Quality Metrics",
        total_tests=len(results),
        successful_tests=successful_tests,
        total_time=total_time,
        average_time=total_time / len(results) if results else 0,
        results=results
    )

def benchmark_prompt_optimization() -> SystemBenchmark:
    """Benchmark the prompt optimization system."""
    print("🎯 Benchmarking Prompt Optimization...")
    
    from src.agents.prompt_optimization import PromptOptimizer
    
    results = []
    start_time = time.time()
    
    # Create temporary database
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as temp_file:
        temp_db_path = temp_file.name
    
    try:
        # Test 1: Basic prompt optimization
        test_start = time.time()
        try:
            optimizer = PromptOptimizer()
            
            base_prompt = "Analyze the following content and provide insights"
            context = {"domain": "technical", "task_type": "analysis"}
            
            optimized_prompt = optimizer.optimize_prompt(base_prompt, context)
            
            success = (
                optimized_prompt is not None and
                len(optimized_prompt.prompt_text) > len(base_prompt) and
                optimized_prompt.prompt_id is not None
            )
            
            metrics = {
                "base_prompt_length": len(base_prompt),
                "optimized_prompt_length": len(optimized_prompt.prompt_text),
                "improvement_ratio": len(optimized_prompt.prompt_text) / len(base_prompt),
                "has_prompt_id": optimized_prompt.prompt_id is not None,
                "has_context": len(optimized_prompt.context) > 0
            }
            
            results.append(BenchmarkResult(
                system_name="PromptOptimizer",
                test_name="basic_optimization",
                execution_time=time.time() - test_start,
                success=success,
                metrics=metrics
            ))
            
        except Exception as e:
            results.append(BenchmarkResult(
                system_name="PromptOptimizer",
                test_name="basic_optimization",
                execution_time=time.time() - test_start,
                success=False,
                metrics={},
                error_message=str(e)
            ))
        
        # Test 2: Performance tracking
        test_start = time.time()
        try:
            optimizer = PromptOptimizer()
            
            # Create and track multiple prompts
            prompt_performance_data = []
            for i in range(10):
                prompt_text = f"Test prompt {i} for analysis task"
                optimized = optimizer.optimize_prompt(prompt_text, {"iteration": i})
                
                # Record performance
                optimizer.record_prompt_performance(
                    prompt_id=optimized.prompt_id,
                    success_rate=0.7 + (i * 0.02),  # Simulated improvement
                    quality_metrics={"coherence": 0.8 + (i * 0.01)},
                    context={"test_iteration": i}
                )
                
                performance = optimizer.get_prompt_performance(optimized.prompt_id)
                prompt_performance_data.append(len(performance))
            
            success = (
                len(prompt_performance_data) == 10 and
                all(count > 0 for count in prompt_performance_data)
            )
            
            metrics = {
                "prompts_created": len(prompt_performance_data),
                "average_performance_records": statistics.mean(prompt_performance_data),
                "all_recorded_performance": all(count > 0 for count in prompt_performance_data)
            }
            
            results.append(BenchmarkResult(
                system_name="PromptOptimizer",
                test_name="performance_tracking",
                execution_time=time.time() - test_start,
                success=success,
                metrics=metrics
            ))
            
        except Exception as e:
            results.append(BenchmarkResult(
                system_name="PromptOptimizer",
                test_name="performance_tracking",
                execution_time=time.time() - test_start,
                success=False,
                metrics={},
                error_message=str(e)
            ))
        
    finally:
        # Cleanup temporary database
        if os.path.exists(temp_db_path):
            os.unlink(temp_db_path)
    
    total_time = time.time() - start_time
    successful_tests = sum(1 for r in results if r.success)
    
    return SystemBenchmark(
        system_name="Prompt Optimization",
        total_tests=len(results),
        successful_tests=successful_tests,
        total_time=total_time,
        average_time=total_time / len(results) if results else 0,
        results=results
    )

def benchmark_integration_scenario() -> SystemBenchmark:
    """Benchmark integration of multiple enhanced systems."""
    print("🔗 Benchmarking Integration Scenarios...")
    
    results = []
    start_time = time.time()
    
    # Test 1: Chunking + Quality Metrics integration
    test_start = time.time()
    try:
        from src.pipeline.chunking import ProgressiveProcessor
        from src.comparison.quality_metrics import QualityMetricsCalculator, DomainType
        
        # Create content for chunking
        blog_content = """
        # AI Ethics in Healthcare: A Comprehensive Analysis
        
        The integration of artificial intelligence into healthcare systems presents unprecedented 
        opportunities and challenges. This analysis examines key ethical considerations, regulatory 
        frameworks, and implementation strategies for responsible AI deployment in medical contexts.
        
        ## Current State and Challenges
        
        Healthcare AI systems currently face several critical ethical challenges. First, ensuring 
        patient privacy while enabling valuable medical research requires sophisticated data handling. 
        Furthermore, algorithmic bias in diagnostic tools can perpetuate healthcare disparities.
        
        ## Regulatory and Implementation Frameworks
        
        Effective governance structures must balance innovation with patient safety. Research indicates 
        that 87% of healthcare institutions require comprehensive ethical review processes for AI systems.
        
        ## Future Directions
        
        Therefore, successful AI integration demands collaborative approaches between technologists, 
        clinicians, ethicists, and policymakers to ensure beneficial outcomes for all patients.
        """
        
        # Chunk the content
        processor = ProgressiveProcessor(
            task_id="integration_test",
            agent_id="integration_agent",
            full_content=blog_content,
            chunk_size=400
        )
        
        # Calculate quality metrics for each chunk
        quality_calculator = QualityMetricsCalculator()
        chunk_quality_scores = []
        
        for i in range(processor.total_chunks):
            chunk = processor.get_chunk_by_index(i)
            if chunk and chunk.content_chunk:
                quality_score = quality_calculator.calculate_quality_score(
                    chunk.content_chunk, 
                    DomainType.TECHNICAL
                )
                chunk_quality_scores.append(quality_score.overall_score)
        
        # Calculate overall quality metrics
        overall_quality = quality_calculator.calculate_quality_score(blog_content, DomainType.TECHNICAL)
        
        success = (
            processor.total_chunks > 1 and
            len(chunk_quality_scores) == processor.total_chunks and
            overall_quality.overall_score > 0.5 and
            all(0 <= score <= 1 for score in chunk_quality_scores)
        )
        
        metrics = {
            "total_chunks": processor.total_chunks,
            "chunks_with_quality": len(chunk_quality_scores),
            "average_chunk_quality": statistics.mean(chunk_quality_scores) if chunk_quality_scores else 0,
            "overall_quality_score": overall_quality.overall_score,
            "quality_consistency": statistics.stdev(chunk_quality_scores) if len(chunk_quality_scores) > 1 else 0
        }
        
        results.append(BenchmarkResult(
            system_name="Integration",
            test_name="chunking_quality_metrics",
            execution_time=time.time() - test_start,
            success=success,
            metrics=metrics
        ))
        
    except Exception as e:
        results.append(BenchmarkResult(
            system_name="Integration",
            test_name="chunking_quality_metrics",
            execution_time=time.time() - test_start,
            success=False,
            metrics={},
            error_message=str(e)
        ))
    
    # Test 2: Knowledge Store + Quality Metrics integration
    test_start = time.time()
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as temp_file:
        temp_db_path = temp_file.name
    
    try:
        from src.memory.knowledge_store import KnowledgeStore, KnowledgeType, ConfidenceLevel
        from src.comparison.quality_metrics import QualityMetricsCalculator, DomainType
        
        store = KnowledgeStore(storage_path=temp_db_path)
        calculator = QualityMetricsCalculator()
        
        # Store knowledge with quality assessments
        test_content = [
            "High quality technical analysis with comprehensive research backing and statistical evidence. This demonstrates sophisticated understanding of complex systems with detailed methodological approach and rigorous validation procedures.",
            "Poor quality text with unclear structure and limited substance or insight.",
            "Excellent coherence demonstrated through logical flow. Furthermore, evidence supports conclusions with comprehensive analysis and detailed reasoning throughout the investigation."
        ]
        
        quality_assessments = []
        stored_ids = []
        
        for i, content in enumerate(test_content):
            # Calculate quality
            quality_score = calculator.calculate_quality_score(content, DomainType.TECHNICAL)
            quality_assessments.append(quality_score.overall_score)
            
            # Store knowledge with quality metadata
            knowledge_id = store.store_knowledge(
                knowledge_type=KnowledgeType.TASK_RESULT,
                content={
                    "text": content,
                    "quality_assessment": {
                        "overall_score": quality_score.overall_score,
                        "coherence": quality_score.coherence_score,
                        "accuracy": quality_score.accuracy_score
                    }
                },
                confidence_level=ConfidenceLevel.HIGH if quality_score.overall_score > 0.7 else ConfidenceLevel.MEDIUM,
                source_agent="integration_test",
                domain="quality_testing",
                tags=["integration", "quality", f"batch_{i}"]
            )
            stored_ids.append(knowledge_id)
        
        # Query high-quality knowledge
        from src.memory.knowledge_store import KnowledgeQuery
        high_quality_query = KnowledgeQuery(
            domains=["quality_testing"],
            min_confidence=ConfidenceLevel.MEDIUM,
            content_keywords=["quality"]
        )
        high_quality_entries = store.retrieve_knowledge(high_quality_query)
        
        success = (
            len(stored_ids) == len(test_content) and
            len(quality_assessments) == len(test_content) and
            len(high_quality_entries) > 0 and
            all(entry.content.get("quality_assessment") for entry in high_quality_entries)
        )
        
        metrics = {
            "content_pieces": len(test_content),
            "stored_entries": len(stored_ids),
            "quality_assessments": len(quality_assessments),
            "high_quality_retrieved": len(high_quality_entries),
            "average_quality": statistics.mean(quality_assessments),
            "quality_range": max(quality_assessments) - min(quality_assessments)
        }
        
        results.append(BenchmarkResult(
            system_name="Integration",
            test_name="knowledge_quality_integration",
            execution_time=time.time() - test_start,
            success=success,
            metrics=metrics
        ))
        
    except Exception as e:
        results.append(BenchmarkResult(
            system_name="Integration",
            test_name="knowledge_quality_integration",
            execution_time=time.time() - test_start,
            success=False,
            metrics={},
            error_message=str(e)
        ))
    
    finally:
        if os.path.exists(temp_db_path):
            os.unlink(temp_db_path)
    
    total_time = time.time() - start_time
    successful_tests = sum(1 for r in results if r.success)
    
    return SystemBenchmark(
        system_name="Integration Scenarios",
        total_tests=len(results),
        successful_tests=successful_tests,
        total_time=total_time,
        average_time=total_time / len(results) if results else 0,
        results=results
    )

def generate_benchmark_report(benchmarks: List[SystemBenchmark]) -> Dict[str, Any]:
    """Generate comprehensive benchmark report."""
    
    total_tests = sum(b.total_tests for b in benchmarks)
    total_successful = sum(b.successful_tests for b in benchmarks)
    total_time = sum(b.total_time for b in benchmarks)
    
    report = {
        "benchmark_timestamp": time.time(),
        "summary": {
            "total_systems": len(benchmarks),
            "total_tests": total_tests,
            "successful_tests": total_successful,
            "success_rate": total_successful / total_tests if total_tests > 0 else 0,
            "total_execution_time": total_time,
            "average_time_per_test": total_time / total_tests if total_tests > 0 else 0
        },
        "system_results": []
    }
    
    for benchmark in benchmarks:
        system_result = {
            "system_name": benchmark.system_name,
            "tests": benchmark.total_tests,
            "successful": benchmark.successful_tests,
            "success_rate": benchmark.successful_tests / benchmark.total_tests if benchmark.total_tests > 0 else 0,
            "total_time": benchmark.total_time,
            "average_time": benchmark.average_time,
            "detailed_results": []
        }
        
        for result in benchmark.results:
            system_result["detailed_results"].append({
                "test_name": result.test_name,
                "success": result.success,
                "execution_time": result.execution_time,
                "metrics": result.metrics,
                "error": result.error_message
            })
        
        report["system_results"].append(system_result)
    
    return report

def main():
    """Run comprehensive benchmarks of all enhanced systems."""
    print("🚀 Felix Framework Enhanced Systems Benchmark")
    print("=" * 60)
    
    benchmarks = []
    
    # Run individual system benchmarks
    benchmarks.append(benchmark_chunking_system())
    benchmarks.append(benchmark_knowledge_store())
    benchmarks.append(benchmark_quality_metrics())
    benchmarks.append(benchmark_prompt_optimization())
    benchmarks.append(benchmark_integration_scenario())
    
    print("\n" + "=" * 60)
    print("📊 BENCHMARK RESULTS SUMMARY")
    print("=" * 60)
    
    # Generate and display report
    report = generate_benchmark_report(benchmarks)
    
    print(f"Total Systems Tested: {report['summary']['total_systems']}")
    print(f"Total Tests: {report['summary']['total_tests']}")
    print(f"Successful Tests: {report['summary']['successful_tests']}")
    print(f"Overall Success Rate: {report['summary']['success_rate']:.1%}")
    print(f"Total Execution Time: {report['summary']['total_execution_time']:.3f}s")
    print(f"Average Time per Test: {report['summary']['average_time_per_test']:.3f}s")
    
    print("\n" + "-" * 60)
    print("DETAILED SYSTEM RESULTS")
    print("-" * 60)
    
    for system_result in report["system_results"]:
        print(f"\n🔧 {system_result['system_name']}")
        print(f"   Tests: {system_result['successful']}/{system_result['tests']} "
              f"({system_result['success_rate']:.1%} success)")
        print(f"   Time: {system_result['total_time']:.3f}s "
              f"(avg: {system_result['average_time']:.3f}s)")
        
        for test_result in system_result["detailed_results"]:
            status = "✅" if test_result["success"] else "❌"
            print(f"   {status} {test_result['test_name']}: {test_result['execution_time']:.3f}s")
            if not test_result["success"] and test_result["error"]:
                print(f"      Error: {test_result['error']}")
    
    # Save detailed report
    report_path = Path("benchmark_results.json")
    with open(report_path, 'w') as f:
        json.dump(report, f, indent=2)
    
    print(f"\n💾 Detailed results saved to: {report_path}")
    
    print("\n" + "=" * 60)
    print("✨ BENCHMARK COMPLETE")
    print("=" * 60)
    
    if report['summary']['success_rate'] >= 0.8:
        print("🎉 EXCELLENT: All enhanced systems performing well!")
    elif report['summary']['success_rate'] >= 0.6:
        print("✅ GOOD: Most enhanced systems working correctly")
    else:
        print("⚠️  WARNING: Some systems need attention")
    
    return report

if __name__ == "__main__":
    main()