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| """ | |
| CASCADE Color Logging Example | |
| Shows how to integrate beautiful colored logs throughout your system. | |
| """ | |
| from .kleene_logger import get_kleene_logger, LogLevel | |
| from .interpretive_logger import get_interpretive_logger, ImpactLevel | |
| def example_data_processing(): | |
| """Example: Data processing with beautiful logs""" | |
| kleene = get_kleene_logger("DataProcessor") | |
| interpretive = get_interpretive_logger("Data Pipeline") | |
| # Start processing | |
| kleene.log(LogLevel.INFO, "load_dataset_start", | |
| state_before={"dataset": "smollm3-blueprint.pdf"}) | |
| interpretive.log(ImpactLevel.LOW, "DataLoader", "Loading dataset", | |
| context="Reading PDF file for analysis", | |
| consequence="Will extract text and metadata", | |
| metrics={"file_size": "1.0MB", "type": "PDF"}) | |
| # Processing steps | |
| kleene.log(LogLevel.DEBUG, "extract_text", | |
| state_before={"page": 1}, | |
| state_after={"pages_processed": 15}) | |
| # Fixed point reached | |
| kleene.log(LogLevel.INFO, "processing_complete", | |
| state_after={"records": 500, "clean": True}, | |
| fixed_point=True, | |
| iterations=3) | |
| interpretive.log(ImpactLevel.MEDIUM, "DataProcessor", "Processing complete", | |
| context="Successfully extracted and cleaned data", | |
| consequence="Ready for forensics analysis", | |
| metrics={"records": 500, "pages": 15, "errors": 0}) | |
| def example_model_observation(): | |
| """Example: Model observation with beautiful logs""" | |
| kleene = get_kleene_logger("ModelObserver") | |
| interpretive = get_interpretive_logger("Model Observatory") | |
| # Model loading | |
| kleene.log(LogLevel.INFO, "model_load_start", | |
| state_before={"model": "mistralai/Mixtral-8x22B-Instruct-v0.1"}) | |
| interpretive.log(ImpactLevel.MEDIUM, "ModelLoader", "Loading Mixtral", | |
| context="Loading 8x22B MoE model for inference", | |
| consequence="Will consume significant VRAM", | |
| metrics={"params": "141B", "active": "39B", "device": "cuda"}) | |
| # Observation | |
| kleene.log(LogLevel.INFO, "observation_start", | |
| state_before={"layers": 0, "hash": "initial"}) | |
| # Fixed point achieved | |
| kleene.log(LogLevel.INFO, "observation_fixed_point", | |
| state_after={"layers": 64, "merkle": "abc123..."}, | |
| fixed_point=True, | |
| iterations=64) | |
| interpretive.log(ImpactLevel.LOW, "CASCADE", "Model observed", | |
| context="Cryptographic proof generated for model execution", | |
| consequence="Merkle root provides verifiable audit trail", | |
| metrics={"model": "Mixtral", "layers": 64, "merkle": "abc123..."}) | |
| def example_error_handling(): | |
| """Example: Error handling with colored logs""" | |
| kleene = get_kleene_logger("ErrorHandler") | |
| interpretive = get_interpretive_logger("System Monitor") | |
| # Error detected | |
| kleene.log(LogLevel.ERROR, "memory_exhaustion", | |
| state_before={"memory": "15.8/16GB", "operation": "inference"}, | |
| fixed_point=False) | |
| interpretive.log(ImpactLevel.HIGH, "MemoryManager", "Out of memory", | |
| context="GPU memory exhausted during model inference", | |
| consequence="Inference failed, system degraded", | |
| metrics={"used": "15.8GB", "total": "16GB", "available": "200MB"}, | |
| recommendation="Enable gradient checkpointing or use smaller batch size") | |
| # Recovery | |
| kleene.log(LogLevel.WARNING, "fallback_activated", | |
| state_after={"mode": "cpu_fallback", "batch_size": 1}) | |
| interpretive.log(ImpactLevel.MEDIUM, "FallbackHandler", "CPU fallback activated", | |
| context="Switched to CPU inference due to memory constraints", | |
| consequence="Performance degraded but functionality preserved", | |
| metrics={"device": "cpu", "batch_size": 1, "slowdown": "10x"}) | |
| # Run all examples | |
| if __name__ == "__main__": | |
| print("\n🎨 CASCADE Color Logging Examples\n") | |
| print("="*60) | |
| example_data_processing() | |
| print("\n" + "="*60) | |
| example_model_observation() | |
| print("\n" + "="*60) | |
| example_error_handling() | |
| print("\n" + "="*60) | |
| print("\n✨ Beautiful logs are ready for production!") | |