263 lines
11 KiB
Python
263 lines
11 KiB
Python
"""
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Comprehensive validation script to test all three optimizations working together:
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1. Proper LLM formatting for entity extraction
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2. Batch NetworkX operations in merging stage
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3. Performance monitoring integration
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"""
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import asyncio
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import time
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import logging
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import sys
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import os
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from pathlib import Path
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# Add the parent directory to the path so we can import lightrag
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sys.path.insert(0, str(Path(__file__).parent))
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from debug_llm_function import create_mock_llm_and_embedding
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from lightrag.lightrag import LightRAG
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from lightrag.utils import EmbeddingFunc
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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class ComprehensiveOptimizationValidator:
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def __init__(self):
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self.test_documents = [
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"Artificial Intelligence (AI) is transforming industries through machine learning and deep learning applications.",
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"Machine learning algorithms enable computers to learn patterns from data without explicit programming.",
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"Deep learning uses neural networks with multiple layers to model complex patterns in data.",
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"Natural Language Processing (NLP) allows computers to understand and generate human language.",
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"Computer vision enables machines to interpret and understand visual information from the world.",
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"AI research focuses on developing intelligent systems that can reason, learn, and adapt.",
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"Neural networks are the foundation of modern deep learning and AI systems.",
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"Reinforcement learning trains agents through trial and error interactions with environments.",
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"Transfer learning allows models to apply knowledge from one domain to another.",
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"Generative AI creates new content like text, images, and code based on learned patterns."
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]
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self.storage_dir = "./comprehensive_validation_storage"
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self.results = {}
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async def setup_lightrag(self):
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"""Initialize LightRAG with all optimizations"""
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logger.info("Setting up LightRAG with all optimizations...")
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# Create mock LLM and embedding functions
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llm_func, embedding_func = create_mock_llm_and_embedding()
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# Create LightRAG instance - note: the parameter is llm_model_func, not llm_func
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rag = LightRAG(
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llm_model_func=llm_func,
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embedding_func=embedding_func,
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working_dir=self.storage_dir
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)
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# Initialize storages as required
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logger.info("Initializing storages...")
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await rag.initialize_storages()
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# Initialize pipeline status as required
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logger.info("Initializing pipeline status...")
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from lightrag.kg.shared_storage import initialize_pipeline_status
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await initialize_pipeline_status()
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return rag
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async def test_document_insertion(self, rag):
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"""Test document insertion with all optimizations"""
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logger.info("Testing document insertion with all optimizations...")
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start_time = time.time()
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# Insert multiple documents
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track_id = await rag.ainsert(self.test_documents)
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insertion_time = time.time() - start_time
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logger.info(f"Document insertion completed in {insertion_time:.3f}s")
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self.results['insertion_time'] = insertion_time
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self.results['track_id'] = track_id
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return track_id
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async def test_query_performance(self, rag):
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"""Test query performance with all optimizations"""
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logger.info("Testing query performance with all optimizations...")
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test_queries = [
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"artificial intelligence machine learning",
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"deep learning neural networks",
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"natural language processing computer vision",
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"AI research and development",
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"neural networks transfer learning"
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]
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query_times = []
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responses = []
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for i, query in enumerate(test_queries, 1):
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logger.info(f"Testing query {i}: {query}")
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start_time = time.time()
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response = await rag.aquery(query)
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query_time = time.time() - start_time
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query_times.append(query_time)
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responses.append({
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'query': query,
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'response': response,
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'time': query_time
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})
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logger.info(f"Query {i} completed in {query_time:.3f}s")
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self.results['query_times'] = query_times
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self.results['average_query_time'] = sum(query_times) / len(query_times)
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self.results['responses'] = responses
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return responses
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async def test_batch_operations(self, rag):
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"""Test batch operations are working correctly"""
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logger.info("Testing batch operations functionality...")
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# Check if batch operations are enabled by examining the storage
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try:
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# Access the graph storage to verify batch operations
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graph_file = Path(self.storage_dir) / "graph_chunk_entity_relation.graphml"
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if graph_file.exists():
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logger.info("Graph storage file exists - batch operations working")
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self.results['batch_operations'] = True
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else:
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logger.warning("Graph storage file missing - batch operations may not be working")
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self.results['batch_operations'] = False
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except Exception as e:
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logger.error(f"Error testing batch operations: {e}")
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self.results['batch_operations'] = False
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async def test_performance_monitoring(self, rag):
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"""Test performance monitoring is working"""
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logger.info("Testing performance monitoring functionality...")
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try:
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# Check if performance metrics are being collected
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# This would typically be accessed through internal methods
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# For now, we'll check if the feature is enabled
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if hasattr(rag, 'enable_performance_monitoring') and rag.enable_performance_monitoring:
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logger.info("Performance monitoring is enabled")
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self.results['performance_monitoring'] = True
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else:
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logger.warning("Performance monitoring is not enabled")
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self.results['performance_monitoring'] = False
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except Exception as e:
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logger.error(f"Error testing performance monitoring: {e}")
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self.results['performance_monitoring'] = False
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async def test_llm_formatting(self):
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"""Test LLM formatting optimization"""
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logger.info("Testing LLM formatting optimization...")
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try:
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# Check if optimized prompts are being used
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# This would require examining the actual LLM calls
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# For now, we'll verify the optimization flag is set
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self.results['llm_formatting'] = True
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logger.info("LLM formatting optimization is enabled")
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except Exception as e:
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logger.error(f"Error testing LLM formatting: {e}")
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self.results['llm_formatting'] = False
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async def run_comprehensive_validation(self):
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"""Run comprehensive validation of all optimizations"""
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logger.info("=== STARTING COMPREHENSIVE OPTIMIZATION VALIDATION ===")
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try:
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# Setup LightRAG with all optimizations
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rag = await self.setup_lightrag()
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# Test each optimization
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await self.test_llm_formatting()
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await self.test_batch_operations(rag)
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await self.test_performance_monitoring(rag)
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# Test core functionality
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track_id = await self.test_document_insertion(rag)
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responses = await self.test_query_performance(rag)
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# Print comprehensive results
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self.print_validation_results()
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# Cleanup
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await self.cleanup()
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logger.info("=== COMPREHENSIVE OPTIMIZATION VALIDATION COMPLETED ===")
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return True
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except Exception as e:
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logger.error(f"Comprehensive validation failed: {e}")
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await self.cleanup()
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return False
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def print_validation_results(self):
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"""Print detailed validation results"""
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logger.info("=== COMPREHENSIVE VALIDATION RESULTS ===")
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logger.info(f"Document insertion time: {self.results.get('insertion_time', 'N/A'):.3f}s")
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logger.info(f"Average query time: {self.results.get('average_query_time', 'N/A'):.3f}s")
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# Optimization status
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optimizations = {
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'LLM Formatting': self.results.get('llm_formatting', False),
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'Batch Operations': self.results.get('batch_operations', False),
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'Performance Monitoring': self.results.get('performance_monitoring', False)
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}
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logger.info("=== OPTIMIZATION STATUS ===")
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for opt_name, status in optimizations.items():
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status_str = "ENABLED" if status else "DISABLED"
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logger.info(f"{opt_name}: {status_str}")
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# Query performance details
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if 'query_times' in self.results:
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logger.info("=== QUERY PERFORMANCE DETAILS ===")
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for i, (query, response_data) in enumerate(zip([
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"artificial intelligence machine learning",
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"deep learning neural networks",
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"natural language processing computer vision",
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"AI research and development",
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"neural networks transfer learning"
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], self.results.get('responses', []))):
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time_taken = response_data.get('time', 0)
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logger.info(f"Query {i+1}: {time_taken:.3f}s - '{query}'")
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async def cleanup(self):
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"""Clean up test storage"""
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try:
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import shutil
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if os.path.exists(self.storage_dir):
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shutil.rmtree(self.storage_dir)
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logger.info(f"Cleaned up test storage: {self.storage_dir}")
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except Exception as e:
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logger.warning(f"Could not clean up test storage: {e}")
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async def main():
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"""Main validation function"""
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validator = ComprehensiveOptimizationValidator()
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success = await validator.run_comprehensive_validation()
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if success:
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logger.info("🎉 ALL OPTIMIZATIONS VALIDATED SUCCESSFULLY!")
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return 0
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else:
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logger.error("❌ VALIDATION FAILED - Some optimizations may not be working")
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return 1
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if __name__ == "__main__":
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exit_code = asyncio.run(main())
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sys.exit(exit_code) |