UnrealBlueprintMCP

# Batch Operations Examples This directory demonstrates efficient batch operations for the UnrealBlueprintMCP system, including bulk blueprint creation, mass property updates, and parallel processing patterns. ## 📋 Examples Overview ### 🏗️ Bulk Blueprint Creation **File**: `bulk_blueprint_creator.py` - Create multiple blueprints from templates - Parallel processing for improved performance - Progress tracking and error handling - Support for different blueprint types ### 🔄 Mass Property Updates **File**: `mass_property_updater.py` - Update properties across multiple blueprints - Batch property modifications - Rollback capabilities for failed operations - Property validation and type checking ### 📦 Asset Pipeline Automation **File**: `asset_pipeline.py` - Complete asset processing workflows - Batch import and setup operations - Automated asset organization - Quality assurance checks ### 🎯 Performance Optimizer **File**: `performance_optimizer.py` - Batch performance optimization operations - Memory usage analysis - Asset optimization suggestions - Performance benchmarking ## 🚀 Quick Start 1. **Setup Environment** ```bash cd examples/advanced/batch_operations pip install -r requirements.txt ``` 2. **Configure Settings** ```python # Edit config if needed from ..config import DEFAULT_BATCH_SIZE, MCP_SERVER_URL ``` 3. **Run Basic Example** ```bash python bulk_blueprint_creator.py --count 10 --type character ``` ## 📊 Performance Considerations - **Batch Size**: Optimal size is 5-10 operations per batch - **Concurrent Connections**: Limit to 3-5 simultaneous connections - **Memory Usage**: Monitor memory when processing large batches - **Error Recovery**: Always implement rollback mechanisms ## 🔧 Configuration Options ```python BATCH_SETTINGS = { "max_batch_size": 10, "max_concurrent": 5, "retry_attempts": 3, "retry_delay": 1.0, "progress_callback": True, "error_logging": True } ``` ## 📚 Usage Patterns ### Simple Batch Creation ```python from bulk_blueprint_creator import BatchBlueprintCreator creator = BatchBlueprintCreator() results = await creator.create_batch([ {"name": "Enemy1", "type": "character"}, {"name": "Enemy2", "type": "character"}, {"name": "Weapon1", "type": "weapon"} ]) ``` ### Advanced Batch Processing ```python from asset_pipeline import AssetPipeline pipeline = AssetPipeline() await pipeline.process_batch( assets=asset_list, operations=["import", "optimize", "validate"], parallel=True, on_progress=lambda x: print(f"Progress: {x}%") ) ``` ## ⚠️ Important Notes - Always test batch operations in development environment first - Use progress callbacks for long-running operations - Implement proper error handling and rollback mechanisms - Monitor system resources during large batch operations - Consider rate limiting for very large batches