Nano Banana Pro
Agent skill for nano-banana-pro
⚔️ ORFEAS AI 2D→3D STUDIO - COPILOT PROTOCOL ⚔️
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══════════════════════════════════════════════════════════════════════════════╗ ⚔️ ORFEAS AI 2D→3D STUDIO - COPILOT PROTOCOL ⚔️ â•' â•' â•' PROJECT: ORFEAS AI 3D Studio â•' â•' STACK: Python + Flask + Hunyuan3D-2.1 + WebGL + Docker â•' PURPOSE: Production AI-Powered 2D→3D Model Generation Platform â•' DESIGN: Secure, Efficient, Scalable, Maintainable â•' â•' ══════════════════════════════════════════════════════════════════════════════
ALL .md FILES MUST BE PLACED IN THE
DIRECTORYmd\
md\SESSION_REPORT.mdSESSION_REPORT.md (root directory)ALL .txt FILES MUST BE PLACED IN THE
DIRECTORYtxt\
txt\VISUAL_GUIDE.txtVISUAL_GUIDE.txt (root directory)PROJECT: ORFEAS AI 2D→3D Studio PURPOSE: Enterprise-grade AI-powered multimedia platform for comprehensive AI content generation including 2D→3D model conversion, AI image creation from text prompts, advanced text-to-image synthesis, real-time speech-to-text transcription, cinematic video composition, intelligent code development, and automated DevOps with enterprise HTML5 interfaces, zero-trust security, and cloud-native technologies REPOSITORY: Tencent-Hunyuan/Hunyuan3D-2.1 (upstream), enterprise ORFEAS integration with Sora-class video generation
CORE ARCHITECTURE: This is an enterprise-grade Flask backend + cloud-native HTML5/JavaScript frontend + zero-trust SSL/HTTPS + PWA system that integrates Tencent's Hunyuan3D-2.1 AI model for professional 3D asset generation from 2D images or text prompts, DALL-E 3 and Stable Diffusion XL for high-quality AI image creation from text descriptions, Whisper and Azure Speech Services for real-time speech-to-text transcription and advanced voice processing, plus advanced AI video composition capabilities inspired by OpenAI's Sora for cinematic video creation, intelligent code development platforms, and automated DevOps workflows, all delivered through secure, scalable progressive web applications with enterprise-grade monitoring and observability.
TECHNOLOGY STACK:
MULTI-ENCODING & INTERNATIONALIZATION STACK:
MULTI-CODE LANGUAGE PROGRAMMING STACK:
ENTERPRISE AI & ML ECOSYSTEM:
LARGE LANGUAGE MODEL (LLM) INTEGRATION:
ADVANCED MACHINE LEARNING STACK:
ENTERPRISE AI AGENTS & AUTONOMOUS SYSTEMS:
Multi-Agent Frameworks:
Advanced Agent Capabilities:
Autonomous Task Planning & Execution:
Multi-Modal Perception & Reasoning:
Advanced Tool Use & API Integration:
Intelligent Memory Management & Knowledge Persistence:
Collaborative Problem-Solving & Team Coordination:
Self-Improvement & Adaptive Learning:
Enterprise Security & Compliance:
Specialized Enterprise Agent Types:
Development & Engineering Agents:
Business Intelligence & Analytics Agents:
Operations & Infrastructure Agents:
Customer Experience & Support Agents:
Research & Innovation Agents:
Financial & Compliance Agents:
Agent Orchestration Patterns:
Hierarchical Agent Architecture:
Peer-to-Peer Agent Networks:
Event-Driven Agent Systems:
Hybrid Human-AI Agent Teams:
Agent Performance & Optimization:
Performance Metrics & KPIs:
Continuous Improvement Framework:
ENTERPRISE COMPUTER VISION STACK:
PRODUCTION ML FRAMEWORKS:
ENTERPRISE AI AGENTS & MLOps:
Agent Infrastructure & Orchestration:
Agent Communication & Coordination:
Agent Development & Deployment:
Agent Model Management:
Agent Monitoring & Observability:
Specialized Agent Monitoring Tools:
Agent Security & Compliance:
Agent Data Management:
Agent Performance Optimization:
Enterprise Agent Patterns:
MULTI-CLOUD AI ENGINES:
ENTERPRISE CODE INTELLIGENCE PLATFORMS:
PRODUCTION ML OPTIMIZATION STACK:
ENTERPRISE AI WORKFLOW MANAGEMENT:
VECTOR DATABASES & KNOWLEDGE MANAGEMENT:
REINFORCEMENT LEARNING & OPTIMIZATION:
EDGE AI & MOBILE DEPLOYMENT:
WEB TECHNOLOGIES & SECURITY STACK:
AGENT ROLE SPECIALIZATION & COORDINATION:
The ORFEAS platform is optimized for multi-agent collaboration with intelligent task distribution and context sharing across specialized AI agents.
PRIMARY AGENT ROLES:
MULTI-AGENT COORDINATION PROTOCOLS:
# Agent communication standards class AgentMessage: def __init__(self, sender_id: str, recipient_id: str, task_type: str, payload: Dict): self.message_id = generate_uuid() self.sender_id = sender_id self.recipient_id = recipient_id self.task_type = task_type self.payload = payload self.context_hash = self.calculate_context_hash() self.timestamp = datetime.utcnow() self.version = "1.0" # Task distribution framework AGENT_TASK_ROUTING = { 'frontend_development': 'frontend_agent', 'backend_api_design': 'backend_agent', 'performance_optimization': 'performance_agent', 'security_review': 'security_agent', 'infrastructure_deployment': 'devops_agent', 'quality_assurance': 'qa_agent', 'multimedia_ai_integration': 'orfeas_specialist' } # Context sharing mechanism class SharedContext: def __init__(self): self.project_state = {} self.active_tasks = {} self.agent_capabilities = {} self.performance_metrics = {} self.version_control = VersionController() async def synchronize_context(self, agent_id: str, local_context: Dict): """Synchronize context across all agents""" conflict_resolution = await self.detect_conflicts(local_context) merged_context = await self.merge_contexts(conflict_resolution) await self.broadcast_updates(merged_context, exclude=agent_id)
AGENT-SPECIFIC OPTIMIZATION RULES:
Code Generation Agents:
Testing & QA Agents:
Infrastructure & DevOps Agents:
Security & Compliance Agents:
COLLABORATIVE WORKFLOW PATTERNS:
# Multi-agent feature development coordination async def coordinate_feature_development(feature_spec: Dict) -> Dict: """Coordinate multi-agent development workflow""" # 1. Primary analysis and task decomposition analysis = await orfeas_specialist.analyze_feature_requirements(feature_spec) # 2. Intelligent task distribution task_assignments = { 'backend_api': await backend_agent.estimate_effort(analysis['api_requirements']), 'frontend_ui': await frontend_agent.estimate_effort(analysis['ui_requirements']), 'security_review': await security_agent.assess_security_impact(analysis), 'performance_impact': await performance_agent.analyze_performance_impact(analysis), 'infrastructure_changes': await devops_agent.assess_deployment_impact(analysis) } # 3. Parallel execution with context sharing execution_results = await asyncio.gather(*[ backend_agent.implement_api(task_assignments['backend_api'], shared_context), frontend_agent.implement_ui(task_assignments['frontend_ui'], shared_context), security_agent.perform_security_review(task_assignments['security_review'], shared_context), performance_agent.optimize_performance(task_assignments['performance_impact'], shared_context), devops_agent.prepare_deployment(task_assignments['infrastructure_changes'], shared_context) ]) # 4. Integration and validation integrated_feature = await integration_agent.merge_implementations(execution_results) validation_results = await qa_agent.validate_feature(integrated_feature, feature_spec) # 5. Quality gates and deployment readiness if validation_results['quality_score'] >= 0.85: deployment_package = await devops_agent.prepare_deployment_package(integrated_feature) return { 'status': 'ready_for_deployment', 'feature': integrated_feature, 'deployment_package': deployment_package, 'quality_metrics': validation_results } else: improvement_plan = await qa_agent.generate_improvement_plan(validation_results) return { 'status': 'requires_improvement', 'improvement_plan': improvement_plan, 'quality_issues': validation_results['issues'] } # Agent performance optimization class MultiAgentPerformanceOptimizer: def __init__(self): self.agent_pool = AgentConnectionPool(max_connections=50) self.load_balancer = AgentLoadBalancer() self.circuit_breaker = AgentCircuitBreaker() self.cache_manager = AgentCacheManager() async def optimize_agent_communication(self): """Optimize inter-agent communication performance""" # Implement intelligent caching await self.cache_manager.enable_result_caching() # Use connection pooling await self.agent_pool.initialize_connections() # Apply load balancing await self.load_balancer.distribute_agent_load() # Enable circuit breakers for resilience await self.circuit_breaker.monitor_agent_health()
AGENT DEPLOYMENT & SCALING PATTERNS:
# Kubernetes multi-agent deployment apiVersion: apps/v1 kind: Deployment metadata: name: orfeas-agent-cluster spec: replicas: 5 selector: matchLabels: app: orfeas-agents template: metadata: labels: app: orfeas-agents spec: containers: - name: orfeas-specialist image: orfeas/specialist-agent:latest resources: requests: memory: "2Gi" cpu: "1000m" nvidia.com/gpu: 1 limits: memory: "4Gi" cpu: "2000m" nvidia.com/gpu: 1 - name: backend-agent image: orfeas/backend-agent:latest resources: requests: memory: "1Gi" cpu: "500m" - name: frontend-agent image: orfeas/frontend-agent:latest resources: requests: memory: "512Mi" cpu: "250m"
PROJECT STRUCTURE:
orfeas/ â"œâ"€â"€ backend/ # Python Flask API server â"' â"œâ"€â"€ main.py # Primary Flask application (2400+ lines) â"' â"œâ"€â"€ hunyuan_integration.py # Hunyuan3D-2.1 model interface â"' â"œâ"€â"€ sora_video_integration.py # Sora-inspired video composition AI â"' â"œâ"€â"€ video_processor.py # AI video generation and processing â"' â"œâ"€â"€ cinematic_composer.py # Cinematic video composition engine â"' â"œâ"€â"€ text_to_image_processor.py # AI text-to-image generation (DALL-E, SDXL) â"' â"œâ"€â"€ text_to_speech_processor.py # AI text-to-speech synthesis (ElevenLabs, Azure) â"' â"œâ"€â"€ speech_to_text_processor.py # AI speech-to-text recognition (Whisper, Azure) â"' â"œâ"€â"€ multimedia_ai_manager.py # Unified multimedia AI coordination â"' â"œâ"€â"€ audio_enhancement.py # Audio quality enhancement and processing â"' â"œâ"€â"€ image_enhancement.py # Image quality enhancement and upscaling â"' â"œâ"€â"€ code_writer.py # AI-powered intelligent code generation â"' â"œâ"€â"€ code_debugger.py # Automated code debugging and optimization â"' â"œâ"€â"€ code_analyzer.py # Code quality analysis and suggestions â"' â"œâ"€â"€ agent_api.py # AI agent orchestration and workflows â"' â"œâ"€â"€ agent_auth.py # Authentication for autonomous agents â"' â"œâ"€â"€ gpu_manager.py # GPU memory and resource management â"' â"œâ"€â"€ rtx_optimization.py # RTX 3090 specific optimizations â"' â"œâ"€â"€ stl_processor.py # Advanced STL mesh processing â"' â"œâ"€â"€ batch_processor.py # Async job queue for concurrent tasks â"' â"œâ"€â"€ batch_inference_extension.py # ML batch processing extensions â"' â"œâ"€â"€ huggingface_compat.py # HuggingFace model compatibility layer â"' â"œâ"€â"€ ssl_manager.py # SSL certificate management and HTTPS â"' â"œâ"€â"€ web_security.py # Web security headers and CSP policies â"' â"œâ"€â"€ pwa_manager.py # Progressive Web App functionality â"' â"œâ"€â"€ context_manager.py # Intelligent context handling and analysis â"' â"œâ"€â"€ contextual_agent_coordinator.py # Multi-agent context coordination â"' â"œâ"€â"€ contextual_model_selector.py # Context-aware model selection â"' â"œâ"€â"€ contextual_error_handler.py # Context-based error recovery â"' â"œâ"€â"€ context_persistence.py # Context learning and persistence â"' â"œâ"€â"€ tqm_audit_system.py # Total Quality Management audit system â"' â"œâ"€â"€ continuous_quality_monitor.py # Real-time quality monitoring and improvement â"' â"œâ"€â"€ automated_audit_scheduler.py # Automated quality audit scheduling â"' â"œâ"€â"€ quality_gateway_middleware.py # Quality gate enforcement middleware â"' â"œâ"€â"€ quality_metrics_collector.py # Comprehensive quality metrics collection â"' â"œâ"€â"€ compliance_validator.py # Standards compliance validation (ISO, SOC2, etc) â"' â"œâ"€â"€ quality_improvement_engine.py # Automated quality improvement actions â"' â"œâ"€â"€ llm_integration.py # Enterprise LLM orchestration and management â"' â"œâ"€â"€ copilot_enterprise.py # GitHub Copilot Enterprise integration â"' â"œâ"€â"€ llm_router.py # Intelligent LLM routing and selection â"' â"œâ"€â"€ multi_llm_orchestrator.py # Multi-LLM task coordination â"' â"œâ"€â"€ rag_integration.py # Retrieval-Augmented Generation system â"' â"œâ"€â"€ vector_database_manager.py # Vector database operations (Pinecone, etc) â"' â"œâ"€â"€ knowledge_graph_manager.py # Knowledge graph integration (Neo4j) â"' â"œâ"€â"€ prompt_engineering.py # Advanced prompt optimization â"' â"œâ"€â"€ llm_performance_monitor.py # LLM performance tracking and optimization â"' â"œâ"€â"€ llm_safety_filter.py # Enterprise LLM safety and content filtering â"' â"œâ"€â"€ encoding_manager.py # Multi-character encoding detection and conversion â"' â"œâ"€â"€ encoding_auto_detector.py # Automatic encoding detection with fallback chains â"' â"œâ"€â"€ unicode_normalizer.py # Unicode normalization and BOM handling â"' â"œâ"€â"€ i18n_manager.py # Internationalization and localization manager â"' â"œâ"€â"€ language_detector.py # Automatic language detection from text â"' â"œâ"€â"€ multilingual_processor.py # Multi-language text processing and NLP â"' â"œâ"€â"€ polyglot_code_manager.py # Multi-programming language code management â"' â"œâ"€â"€ code_language_detector.py # Automatic programming language detection â"' â"œâ"€â"€ cross_language_translator.py # Code translation between programming languages â"' â"œâ"€â"€ language_interop_engine.py # Cross-language interoperability and integration â"' â"œâ"€â"€ syntax_validator_multi.py # Multi-language syntax validation engine â"' â"œâ"€â"€ lsp_integration_manager.py # Language Server Protocol integration â"' â"œâ"€â"€ tree_sitter_parser.py # Universal syntax highlighting and parsing â"' â"œâ"€â"€ validation.py # Input validation and security ├ monitoring.py # Application metrics collection ├ prometheus_metrics.py # Prometheus exporter ├ config.py # Configuration management ├ tests/ # pytest test suite └ monitoring_stack/ # Docker monitoring infrastructure ├ docker-compose-monitoring.yml ├ prometheus.yml └ grafana-dashboard.json ├ Hunyuan3D-2.1/ # Tencent's AI model (Git submodule) └ Hunyuan3D-2/ └ hy3dgen/ # Core generation pipelines ├ orfeas-studio.html # Main web interface ├ orfeas-3d-engine-hybrid.js # 3D viewer (Three.js/BabylonJS) ├ service-worker.js # PWA offline support ├ docker-compose.yml # Multi-service orchestration ├ Dockerfile # Production container build ├ START_ORFEAS_AUTO.ps1 # Windows startup script └ md/ # Documentation directory
CRITICAL MODEL LOADING:
_model_cache)os.environ["XFORMERS_DISABLED"] = "1" prevents DLL crashes (0xc0000139)shapegen_pipeline, texgen_pipeline, rembg, text2image_pipelineCORRECT INITIALIZATION PATTERN:
# backend/hunyuan_integration.py class Hunyuan3DProcessor: _model_cache = {'shapegen_pipeline': None, 'texgen_pipeline': None, 'initialized': False} _cache_lock = threading.Lock() def __init__(self, device=None): with Hunyuan3DProcessor._cache_lock: if Hunyuan3DProcessor._model_cache['initialized']: # 🔥 INSTANT: Use cached models (~1s) self.shapegen_pipeline = Hunyuan3DProcessor._model_cache['shapegen_pipeline'] return # First time: Load models from Hunyuan3D-2.1 directory (30-36s) self.initialize_model()
GENERATION WORKFLOW:
# 1. Background removal (rembg) processed_image = processor.rembg.remove_background(image) # 2. Shape generation (Hunyuan3D-DiT-v2-1) mesh = processor.shapegen_pipeline(image=processed_image, num_inference_steps=50) # 3. Texture synthesis (Hunyuan3D-Paint-v2-1) textured_mesh = processor.texgen_pipeline(mesh=mesh, image=processed_image) # 4. Export to STL/OBJ/GLB mesh.export('output.stl')
CINEMATIC VIDEO GENERATION:
The ORFEAS platform includes advanced AI video composition capabilities inspired by OpenAI's Sora, enabling the creation of cinematic video content from text prompts, images, or 3D models.
VIDEO PROCESSING PIPELINE:
# backend/sora_video_integration.py class SoraVideoProcessor: """ Sora-inspired video composition engine for cinematic content generation """ def __init__(self, device=None): self.device = device or "cuda" if torch.cuda.is_available() else "cpu" self.video_models = { 'text_to_video': None, 'image_to_video': None, '3d_to_video': None, 'style_transfer': None } self.motion_models = { 'camera_motion': None, 'object_motion': None, 'lighting_dynamics': None } def generate_cinematic_video(self, input_data: Dict) -> Dict: """Generate cinematic video from various inputs""" # 1. Analyze input content content_analysis = self.analyze_content(input_data) # 2. Generate motion sequences motion_sequence = self.generate_motion_sequence(content_analysis) # 3. Apply cinematic techniques cinematic_frames = self.apply_cinematic_techniques(motion_sequence) # 4. Render final video video_output = self.render_video(cinematic_frames) return video_output
VIDEO GENERATION WORKFLOWS:
# 1. Text-to-Video Generation video = processor.text_to_video( prompt="A futuristic city with flying cars at sunset", duration=10, # seconds fps=24, resolution=(1920, 1080), style="cinematic" ) # 2. 3D Model to Video Animation video = processor.model_to_video( model_path="generated_model.stl", animation_type="turntable", lighting="studio", background="gradient" ) # 3. Image to Video Expansion video = processor.image_to_video( image="input.jpg", motion_type="camera_zoom", depth_estimation=True, parallax_effect=True )
CINEMATIC FEATURES:
INTELLIGENT CODE GENERATION:
The ORFEAS platform includes advanced AI-powered code development capabilities for intelligent code writing, automated debugging, and code quality optimization.
CODE DEVELOPMENT PIPELINE:
# backend/code_writer.py class IntelligentCodeWriter: """ AI-powered code generation and development assistant """ def __init__(self, model="github-copilot"): self.model = model self.code_models = { 'github-copilot': None, 'codeT5': None, 'starcoder': None, 'deepseek-coder': None } self.quality_analyzer = CodeQualityAnalyzer() def generate_code(self, prompt: str, language: str, context: Dict = None) -> Dict: """Generate code from natural language description""" # 1. Analyze requirements requirements = self.analyze_requirements(prompt, context) # 2. Generate code structure code_structure = self.generate_code_structure(requirements, language) # 3. Generate implementation code_implementation = self.generate_implementation(code_structure, requirements) # 4. Apply code optimization optimized_code = self.optimize_code(code_implementation, language) return { 'code': optimized_code, 'quality_score': self.quality_analyzer.analyze(optimized_code), 'documentation': self.generate_documentation(optimized_code), 'tests': self.generate_tests(optimized_code) if context.get('generate_tests') else None }
CODE DEBUGGING WORKFLOW:
# backend/code_debugger.py class AutomatedCodeDebugger: """ AI-powered code debugging and error resolution """ def __init__(self): self.error_patterns = self.load_error_patterns() self.fix_strategies = self.load_fix_strategies() def debug_code(self, code: str, error_trace: str, language: str) -> Dict: """Automatically debug and fix code issues""" # 1. Analyze error error_analysis = self.analyze_error(error_trace, code) # 2. Identify root cause root_cause = self.identify_root_cause(error_analysis, code) # 3. Generate fix fix_suggestion = self.generate_fix(root_cause, code, language) # 4. Validate fix validated_fix = self.validate_fix(fix_suggestion, code) return { 'error_type': error_analysis['type'], 'root_cause': root_cause, 'fix_suggestion': validated_fix, 'confidence': error_analysis['confidence'], 'explanation': self.generate_explanation(root_cause, validated_fix) }
CODE DEVELOPMENT FEATURES:
RTX 3090 OPTIMIZATION (24GB VRAM):
GPU_MEMORY_LIMIT=0.8CORRECT GPU MANAGER USAGE:
# backend/gpu_manager.py gpu_mgr = get_gpu_manager() if gpu_mgr.can_process_job(estimated_vram=6000): # 6GB estimate with gpu_mgr.allocate_job(job_id): result = generate_3d_model(image) gpu_mgr.cleanup_after_job() # Force CUDA cache clear else: # Queue job for later or reject raise ResourceError("GPU memory insufficient")
NEVER:
torch.cuda.empty_cache() between generationsENDPOINT PATTERN:
@app.route('/api/generate-3d', methods=['POST']) @track_request_metrics('generate_3d') # Prometheus tracking @security_headers # CORS and security headers def generate_3d(): """ Text/Image → 3D STL generation Request: multipart/form-data with 'image' file or 'prompt' text Response: Binary STL file or JSON with job_id for async processing Metrics: Tracked via prometheus_metrics.py """ # 1. Validate input (validation.py) validator = FileUploadValidator() image = validator.validate_image(request.files.get('image')) # 2. Check rate limits if not get_rate_limiter().check_limit(request.remote_addr): return jsonify({'error': 'Rate limit exceeded'}), 429 # 3. Queue job (batch_processor.py) job = async_queue.submit_job('3d_generation', image=image) # 4. Return job ID or wait for completion return jsonify({'job_id': job.id, 'status': 'processing'})
WEBSOCKET PROGRESS UPDATES:
# Use Flask-SocketIO for real-time progress socketio.emit('generation_progress', { 'job_id': job_id, 'stage': 'shape_generation', 'progress': 45, 'eta_seconds': 20 }, room=client_sid)
ADVANCED MESH OPERATIONS:
# backend/stl_processor.py processor = AdvancedSTLProcessor() # Analysis stats = processor.analyze_stl(mesh_path) # Returns: triangle_count, surface_area, volume, bounds, manifold_status # Repair non-manifold geometry repaired_mesh = processor.repair_stl(mesh) # Optimize for 3D printing optimized_mesh = processor.optimize_stl_for_printing( mesh, target_size_mm=100, wall_thickness_mm=2.0, supports=True )
3D EXPORT FORMATS:
PROGRESSIVE WEB APP (PWA) FEATURES:
# backend/pwa_manager.py class PWAManager: """ Progressive Web App functionality manager """ def __init__(self): # Define color constants using RGB values to avoid parser issues THEME_COLOR = "rgb(30, 64, 175)" # Blue theme BACKGROUND_COLOR = "rgb(255, 255, 255)" # White background self.manifest_config = { "name": "ORFEAS AI 3D Studio", "short_name": "ORFEAS", "description": "AI-powered 2D to 3D model generation", "start_url": "/", "display": "standalone", "theme_color": THEME_COLOR, "background_color": BACKGROUND_COLOR } def generate_manifest(self): """Generate manifest.json for PWA installation""" return json.dumps(self.manifest_config, indent=2) def register_service_worker(self): """Setup service worker for offline functionality""" return """ if ('serviceWorker' in navigator) { navigator.serviceWorker.register('/service-worker.js') .then(() => console.log('SW registered')) .catch(() => console.log('SW registration failed')); } """
HTML5 FEATURES INTEGRATION:
<!-- orfeas-studio.html - Main interface --> <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="theme-color" content="rgb(30, 64, 175)" /> <link rel="manifest" href="/manifest.json" /> <link rel="icon" href="/static/icons/icon-192x192.png" /> <!-- PWA iOS meta tags --> <meta name="apple-mobile-web-app-capable" content="yes" /> <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" /> <link rel="apple-touch-icon" href="/static/icons/icon-192x192.png" /> </head> </html>
SSL CERTIFICATE MANAGEMENT:
# backend/ssl_manager.py class SSLManager: """ SSL certificate management and HTTPS enforcement """ def __init__(self): self.cert_path = "ssl/orfeas.crt" self.key_path = "ssl/orfeas.key" self.lets_encrypt_path = "ssl/lets-encrypt/" def enforce_https(self, app): """Force HTTPS redirects for all requests""" @app.before_request def force_https(): if not request.is_secure and app.env != 'development': return redirect(request.url.replace('http://', 'https://'), 301) def setup_ssl_context(self): """Configure SSL context for Flask application""" context = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2) context.load_cert_chain(self.cert_path, self.key_path) return context def auto_renew_certificates(self): """Auto-renewal setup for Let's Encrypt certificates""" # Certbot integration for automatic renewal return subprocess.run([ 'certbot', 'renew', '--quiet', '--deploy-hook', 'systemctl reload nginx' ])
WEB SECURITY HEADERS:
# backend/web_security.py class WebSecurityManager: """ Web security headers and Content Security Policy """ def __init__(self): self.security_headers = { 'Strict-Transport-Security': 'max-age=31536000; includeSubDomains', 'X-Content-Type-Options': 'nosniff', 'X-Frame-Options': 'DENY', 'X-XSS-Protection': '1; mode=block', 'Referrer-Policy': 'strict-origin-when-cross-origin' } def apply_security_headers(self, app): """Apply security headers to all responses""" @app.after_request def set_security_headers(response): for header, value in self.security_headers.items(): response.headers[header] = value return response def setup_csp(self): """Content Security Policy configuration""" csp_policy = ( "default-src 'self'; " "script-src 'self' 'unsafe-inline' https://cdnjs.cloudflare.com; " "style-src 'self' 'unsafe-inline'; " "img-src 'self' data: blob:; " "connect-src 'self' wss: ws:; " "worker-src 'self' blob:;" ) return {'Content-Security-Policy': csp_policy}
PHP INTEGRATION SUPPORT:
<?php // php/api.php - PHP API endpoints for legacy systems class OrfeasPHPAPI { private $python_api_url = 'http://localhost:5000/api'; public function generate3D($image_data, $quality = 7) { // Proxy requests to Python backend $curl = curl_init(); curl_setopt_array($curl, [ CURLOPT_URL => $this->python_api_url . '/generate-3d', CURLOPT_RETURNTRANSFER => true, CURLOPT_POST => true, CURLOPT_POSTFIELDS => [ 'image' => new CURLFile($image_data), 'quality' => $quality ], CURLOPT_HTTPHEADER => [ 'Authorization: Bearer ' . $_ENV['API_TOKEN'], 'Content-Type: multipart/form-data' ] ]); $response = curl_exec($curl); curl_close($curl); return json_decode($response, true); } public function checkJobStatus($job_id) { // Check generation job status $curl = curl_init(); curl_setopt_array($curl, [ CURLOPT_URL => $this->python_api_url . '/job-status/' . $job_id, CURLOPT_RETURNTRANSFER => true ]); $response = curl_exec($curl); curl_close($curl); return json_decode($response, true); } } ?>
AI AGENT ARCHITECTURE:
The ORFEAS platform includes autonomous AI agents for workflow automation, quality assurance, and intelligent processing decisions.
# backend/agent_api.py - Core agent framework class OrfeasAIAgent: """ Autonomous AI agent for workflow orchestration Features: - Intelligent model selection based on input analysis - Quality assessment and optimization recommendations - Automated error recovery and fallback strategies - Performance monitoring and optimization """ def __init__(self, agent_type: str, capabilities: List[str]): self.agent_type = agent_type # 'quality_agent', 'workflow_agent', 'optimization_agent' self.capabilities = capabilities self.decision_tree = self.load_decision_model() async def analyze_input(self, input_data: Dict) -> Dict[str, Any]: """Analyze input and recommend processing strategy""" analysis = { 'input_type': self.classify_input(input_data), 'complexity_score': self.assess_complexity(input_data), 'recommended_pipeline': self.select_pipeline(input_data), 'estimated_resources': self.estimate_resources(input_data) } return analysis def select_optimal_model(self, analysis: Dict) -> str: """Select best AI model based on input analysis""" if analysis['complexity_score'] > 0.8: return 'hunyuan3d_high_quality' elif analysis['input_type'] == 'simple_object': return 'hunyuan3d_fast' else: return 'hunyuan3d_balanced'
AGENT TYPES:
Quality Assessment Agent
Workflow Orchestration Agent
Performance Optimization Agent
AGENT AUTHENTICATION & SECURITY:
# backend/agent_auth.py class AgentAuthManager: """ Secure authentication for autonomous agents """ def __init__(self): self.agent_keys = self.load_agent_credentials() self.permission_matrix = self.load_permissions() def authenticate_agent(self, agent_id: str, api_key: str) -> bool: """Verify agent credentials and permissions""" if agent_id not in self.agent_keys: return False return self.verify_key(agent_id, api_key) def check_permissions(self, agent_id: str, action: str) -> bool: """Check if agent has permission for specific action""" return action in self.permission_matrix.get(agent_id, [])
MODEL PERFORMANCE OPTIMIZATION:
# backend/ml_optimizer.py class MLPerformanceOptimizer: """ Advanced ML optimization for production inference """ def __init__(self): self.optimization_strategies = { 'tensorrt': self.apply_tensorrt_optimization, 'quantization': self.apply_quantization, 'mixed_precision': self.enable_mixed_precision, 'model_pruning': self.apply_model_pruning } def optimize_model_for_inference(self, model, strategy: str): """Apply production optimizations to ML models""" if strategy == 'tensorrt': # Convert PyTorch model to TensorRT for 2-5x speedup optimized_model = self.convert_to_tensorrt(model) elif strategy == 'quantization': # INT8 quantization for 4x memory reduction optimized_model = torch.quantization.quantize_dynamic( model, {torch.nn.Linear}, dtype=torch.qint8 ) return optimized_model def enable_mixed_precision(self, model): """Enable FP16/BF16 mixed precision for 40% speedup""" model = model.half() # Convert to FP16 torch.backends.cudnn.benchmark = True return model
BATCH INFERENCE EXTENSIONS:
# backend/batch_inference_extension.py class BatchInferenceManager: """ Advanced batch processing for ML workloads """ def __init__(self, max_batch_size: int = 4): self.max_batch_size = max_batch_size self.batch_queue = asyncio.Queue() self.processing_strategies = { 'dynamic_batching': self.dynamic_batch_processing, 'pipeline_parallel': self.pipeline_parallel_processing, 'model_parallel': self.model_parallel_processing } async def dynamic_batch_processing(self, requests: List[Dict]): """Group requests into optimal batch sizes""" batches = [] current_batch = [] for request in requests: if len(current_batch) >= self.max_batch_size: batches.append(current_batch) current_batch = [request] else: current_batch.append(request) if current_batch: batches.append(current_batch) results = [] for batch in batches: batch_result = await self.process_batch(batch) results.extend(batch_result) return results
HUGGINGFACE COMPATIBILITY LAYER:
# backend/huggingface_compat.py class HuggingFaceModelManager: """ Integration layer for HuggingFace models and pipelines """ def __init__(self): self.model_cache = {} self.supported_models = { 'stable_diffusion': 'runwayml/stable-diffusion-v1-5', 'clip': 'openai/clip-vit-base-patch32', 'controlnet': 'lllyasviel/sd-controlnet-canny', 'instant_mesh': 'TencentARC/InstantMesh' } def load_huggingface_model(self, model_name: str, optimization: str = 'none'): """Load and optimize HuggingFace models""" if model_name in self.model_cache: return self.model_cache[model_name] if model_name not in self.supported_models: raise ValueError(f"Unsupported model: {model_name}") model_id = self.supported_models[model_name] if optimization == 'tensorrt': model = self.load_with_tensorrt(model_id) elif optimization == 'onnx': model = self.load_with_onnx(model_id) else: model = transformers.pipeline('text-to-image', model=model_id) self.model_cache[model_name] = model return model def fallback_generation(self, prompt: str, fallback_model: str = 'stable_diffusion'): """Fallback generation when primary model fails""" try: model = self.load_huggingface_model(fallback_model) result = model(prompt) return result except Exception as e: logger.error(f"Fallback generation failed: {e}") raise RuntimeError("All generation methods failed")
CONTEXT-AWARE AI PROCESSING:
The ORFEAS platform implements intelligent context handling to enhance AI decision-making, optimize processing workflows, and provide contextual awareness across all components.
# backend/context_manager.py class IntelligentContextManager: """ Advanced context handling for AI-driven decision making """ def __init__(self): self.context_store = {} self.session_contexts = {} self.global_context = { 'system_performance': {}, 'user_preferences': {}, 'model_performance_history': {}, 'resource_availability': {} } def build_processing_context(self, request_data: Dict) -> Dict[str, Any]: """Build comprehensive context for AI processing decisions""" context = { 'input_analysis': self.analyze_input_characteristics(request_data), 'user_context': self.get_user_context(request_data.get('user_id')), 'system_context': self.get_system_context(), 'historical_context': self.get_historical_performance(), 'resource_context': self.get_resource_availability(), 'quality_context': self.get_quality_requirements(request_data) } return context def analyze_input_characteristics(self, request_data: Dict) -> Dict[str, Any]: """Analyze input to determine processing requirements""" analysis = { 'input_type': self.classify_input_type(request_data), 'complexity_score': self.calculate_complexity(request_data), 'quality_requirements': self.extract_quality_requirements(request_data), 'processing_priority': self.determine_priority(request_data), 'estimated_resources': self.estimate_resource_needs(request_data) } return analysis def get_contextual_recommendations(self, context: Dict) -> Dict[str, Any]: """Generate intelligent recommendations based on context""" recommendations = { 'optimal_model': self.recommend_model(context), 'processing_parameters': self.recommend_parameters(context), 'quality_settings': self.recommend_quality_settings(context), 'resource_allocation': self.recommend_resources(context), 'fallback_strategies': self.recommend_fallbacks(context) } return recommendations
CONTEXTUAL AI AGENT COORDINATION:
# backend/contextual_agent_coordinator.py class ContextualAgentCoordinator: """ Coordinates AI agents with intelligent context sharing """ def __init__(self, context_manager: IntelligentContextManager): self.context_manager = context_manager self.agent_registry = {} self.context_sharing_graph = {} async def coordinate_with_context(self, task: Dict, agents: List[str]) -> Dict: """Coordinate multiple agents with shared context""" # Build comprehensive context context = self.context_manager.build_processing_context(task) # Share relevant context with each agent agent_contexts = {} for agent_id in agents: agent_contexts[agent_id] = self.filter_context_for_agent(context, agent_id) # Execute coordinated workflow results = {} for agent_id in agents: agent = await self.get_agent(agent_id) results[agent_id] = await agent.execute_with_context( task, agent_contexts[agent_id] ) # Update shared context with results self.update_shared_context(agent_id, results[agent_id], context) return self.synthesize_results(results, context) def filter_context_for_agent(self, context: Dict, agent_id: str) -> Dict: """Filter context to relevant information for specific agent""" agent_type = self.agent_registry[agent_id]['type'] if agent_type == 'quality_agent': return { 'input_analysis': context['input_analysis'], 'quality_context': context['quality_context'], 'historical_context': context['historical_context'] } elif agent_type == 'workflow_agent': return { 'system_context': context['system_context'], 'resource_context': context['resource_context'], 'input_analysis': context['input_analysis'] } elif agent_type == 'optimization_agent': return { 'system_context': context['system_context'], 'resource_context': context['resource_context'], 'historical_context': context['historical_context'] } else: return context # Full context for unknown agent types
CONTEXT-AWARE MODEL SELECTION:
# backend/contextual_model_selector.py class ContextualModelSelector: """ Intelligent model selection based on comprehensive context analysis """ def __init__(self): self.model_performance_matrix = {} self.context_model_mapping = {} self.learning_history = {} def select_optimal_model(self, context: Dict) -> Dict[str, Any]: """Select best model based on context analysis""" # Analyze context dimensions context_signature = self.generate_context_signature(context) # Check for similar historical contexts similar_contexts = self.find_similar_contexts(context_signature) # Calculate model scores based on context model_scores = {} for model_name in self.available_models: score = self.calculate_model_score(model_name, context, similar_contexts) model_scores[model_name] = score # Select best model optimal_model = max(model_scores, key=model_scores.get) # Generate selection reasoning selection_reasoning = self.generate_selection_reasoning( optimal_model, context, model_scores ) return { 'selected_model': optimal_model, 'confidence_score': model_scores[optimal_model], 'reasoning': selection_reasoning, 'fallback_models': self.get_fallback_sequence(model_scores), 'context_signature': context_signature } def generate_context_signature(self, context: Dict) -> str: """Generate unique signature for context matching""" key_features = [ context['input_analysis']['complexity_score'], context['quality_context']['target_quality'], context['resource_context']['available_vram'], context['system_context']['current_load'] ] return hashlib.md5(str(key_features).encode()).hexdigest()[:16] def learn_from_result(self, context: Dict, model: str, result: Dict): """Update model performance based on actual results""" context_sig = self.generate_context_signature(context) if context_sig not in self.learning_history: self.learning_history[context_sig] = {} self.learning_history[context_sig][model] = { 'quality_score': result.get('quality_score', 0), 'processing_time': result.get('processing_time', 0), 'resource_usage': result.get('resource_usage', {}), 'success': result.get('success', False), 'timestamp': time.time() }
CONTEXTUAL ERROR HANDLING & RECOVERY:
# backend/contextual_error_handler.py class ContextualErrorHandler: """ Context-aware error handling and recovery strategies """ def __init__(self, context_manager: IntelligentContextManager): self.context_manager = context_manager self.error_patterns = {} self.recovery_strategies = {} async def handle_error_with_context(self, error: Exception, context: Dict) -> Dict: """Handle errors using contextual information for smart recovery""" # Analyze error in context error_analysis = self.analyze_error_context(error, context) # Determine recovery strategy recovery_strategy = self.select_recovery_strategy(error_analysis, context) # Execute recovery recovery_result = await self.execute_recovery(recovery_strategy, context) # Learn from the error and recovery self.learn_from_error_recovery(error, context, recovery_strategy, recovery_result) return recovery_result def analyze_error_context(self, error: Exception, context: Dict) -> Dict: """Analyze error within the broader context""" return { 'error_type': type(error).__name__, 'error_message': str(error), 'system_state': context['system_context'], 'input_characteristics': context['input_analysis'], 'resource_state': context['resource_context'], 'similar_errors': self.find_similar_error_patterns(error, context), 'recovery_probability': self.estimate_recovery_probability(error, context) } def select_recovery_strategy(self, error_analysis: Dict, context: Dict) -> Dict: """Select optimal recovery strategy based on context""" if error_analysis['error_type'] == 'OutOfMemoryError': if context['resource_context']['available_vram'] < 2000: # < 2GB return {'type': 'reduce_quality', 'fallback_model': 'lightweight'} else: return {'type': 'clear_cache_retry', 'max_retries': 2} elif error_analysis['error_type'] == 'ModelLoadError': return {'type': 'fallback_model', 'fallback_sequence': ['instant_mesh', 'stable_diffusion']} elif error_analysis['recovery_probability'] > 0.7: return {'type': 'intelligent_retry', 'context_adjustment': True} else: return {'type': 'graceful_failure', 'user_notification': True}
CONTEXT PERSISTENCE & LEARNING:
# backend/context_persistence.py class ContextPersistenceManager: """ Manages context persistence and learning across sessions """ def __init__(self): self.context_db = self.initialize_context_database() self.learning_models = self.load_learning_models() def persist_context(self, session_id: str, context: Dict): """Save context for future learning and optimization""" context_record = { 'session_id': session_id, 'timestamp': time.time(), 'context_data': context, 'context_hash': self.hash_context(context) } self.context_db.insert(context_record) def learn_patterns(self): """Analyze historical contexts to learn patterns""" # Analyze successful processing patterns successful_patterns = self.analyze_successful_contexts() # Identify failure patterns failure_patterns = self.analyze_failed_contexts() # Update recommendation models self.update_recommendation_models(successful_patterns, failure_patterns) # Update context similarity models self.update_similarity_models() def get_contextual_insights(self, current_context: Dict) -> Dict: """Provide insights based on historical context analysis""" similar_contexts = self.find_similar_historical_contexts(current_context) insights = { 'success_probability': self.predict_success_probability(current_context), 'optimal_parameters': self.recommend_parameters(current_context), 'potential_issues': self.predict_potential_issues(current_context), 'processing_time_estimate': self.estimate_processing_time(current_context), 'resource_requirements': self.estimate_resource_requirements(current_context) } return insights
SPEED OPTIMIZATION STRATEGIES:
# backend/performance_optimizer.py class PerformanceOptimizer: """ Comprehensive performance optimization for maximum speed """ def __init__(self): self.cache_layers = { 'model_cache': {}, # Model instance caching 'result_cache': {}, # Generation result caching 'context_cache': {}, # Context analysis caching 'precompute_cache': {} # Pre-computed optimizations } self.optimization_config = self.load_optimization_config() def apply_speed_optimizations(self, model, processing_type: str): """Apply all speed optimizations for maximum performance""" # 1. Model-level optimizations optimized_model = self.optimize_model_inference(model) # 2. Memory optimization optimized_model = self.apply_memory_optimization(optimized_model) # 3. Compilation optimization if self.optimization_config.get('torch_compile', False): optimized_model = torch.compile(optimized_model, mode='max-autotune') # 4. GPU-specific optimizations optimized_model = self.apply_gpu_optimizations(optimized_model) return optimized_model def optimize_model_inference(self, model): """Apply inference-specific optimizations""" # Enable inference mode model.eval() torch.set_grad_enabled(False) # Apply quantization if configured if self.optimization_config.get('quantization', False): model = torch.quantization.quantize_dynamic( model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8 ) # Apply pruning for faster inference if self.optimization_config.get('pruning', False): model = self.apply_structured_pruning(model, sparsity=0.3) return model def apply_memory_optimization(self, model): """Optimize memory usage for better performance""" # Enable memory efficient attention try: from xformers.ops import memory_efficient_attention model.use_memory_efficient_attention = True except ImportError: pass # Enable gradient checkpointing for large models if hasattr(model, 'gradient_checkpointing_enable'): model.gradient_checkpointing_enable() # Optimize CUDA memory allocation torch.cuda.empty_cache() torch.cuda.synchronize() return model def apply_gpu_optimizations(self, model): """Apply GPU-specific performance optimizations""" # Enable TensorFloat-32 for RTX GPUs torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True # Enable cuDNN benchmark mode torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = False # Enable mixed precision training if self.optimization_config.get('mixed_precision', True): model = model.half() # Convert to FP16 return model
ACCURACY ENHANCEMENT SYSTEMS:
# backend/accuracy_enhancer.py class AccuracyEnhancer: """ Advanced accuracy enhancement through intelligent processing """ def __init__(self): self.quality_metrics = {} self.accuracy_thresholds = { 'minimum_quality': 0.7, 'target_quality': 0.85, 'excellence_quality': 0.95 } self.enhancement_strategies = self.load_enhancement_strategies() def enhance_generation_accuracy(self, input_data: Dict, context: Dict) -> Dict: """Apply multi-layer accuracy enhancement""" # 1. Input preprocessing for accuracy preprocessed_input = self.preprocess_for_accuracy(input_data, context) # 2. Multi-model ensemble for higher accuracy if context.get('accuracy_priority', False): result = self.ensemble_generation(preprocessed_input, context) else: result = self.single_model_generation(preprocessed_input, context) # 3. Post-processing quality enhancement enhanced_result = self.post_process_for_quality(result, context) # 4. Quality validation and refinement final_result = self.validate_and_refine(enhanced_result, context) return final_result def preprocess_for_accuracy(self, input_data: Dict, context: Dict) -> Dict: """Intelligent preprocessing to maximize generation accuracy""" # Analyze input quality quality_analysis = self.analyze_input_quality(input_data) # Apply enhancement based on analysis if quality_analysis['needs_enhancement']: # Image denoising if 'noise_level' in quality_analysis and quality_analysis['noise_level'] > 0.3: input_data['image'] = self.denoise_image(input_data['image']) # Contrast enhancement if quality_analysis.get('low_contrast', False): input_data['image'] = self.enhance_contrast(input_data['image']) # Resolution upsampling if needed if quality_analysis.get('resolution_too_low', False): input_data['image'] = self.upsample_image(input_data['image']) return input_data def ensemble_generation(self, input_data: Dict, context: Dict) -> Dict: """Use multiple models for higher accuracy""" models = ['hunyuan3d_primary', 'hunyuan3d_alternative', 'instant_mesh'] results = [] for model_name in models: try: model_result = self.generate_with_model(model_name, input_data) results.append({ 'model': model_name, 'result': model_result, 'quality_score': self.calculate_quality_score(model_result) }) except Exception as e: logger.warning(f"Model {model_name} failed: {e}") continue # Select best result or combine results return self.select_best_or_combine(results, context) def validate_and_refine(self, result: Dict, context: Dict) -> Dict: """Final validation and refinement for accuracy""" quality_score = self.calculate_comprehensive_quality(result) if quality_score < self.accuracy_thresholds['minimum_quality']: # Apply refinement techniques result = self.apply_mesh_refinement(result) result = self.apply_texture_enhancement(result) # Recalculate quality quality_score = self.calculate_comprehensive_quality(result) result['accuracy_metrics'] = { 'quality_score': quality_score, 'accuracy_grade': self.get_accuracy_grade(quality_score), 'enhancement_applied': quality_score >= self.accuracy_thresholds['target_quality'] } return result
SECURITY HARDENING FRAMEWORK:
# backend/security_hardening.py class SecurityHardening: """ Comprehensive security hardening for production deployment """ def __init__(self): self.security_config = self.load_security_config() self.threat_detector = ThreatDetector() self.access_monitor = AccessMonitor() def apply_security_hardening(self, app): """Apply comprehensive security hardening""" # 1. Input validation and sanitization self.setup_input_validation(app) # 2. Authentication and authorization self.setup_authentication_security(app) # 3. API security self.setup_api_security(app) # 4. File upload security self.setup_file_upload_security(app) # 5. Rate limiting and DoS protection self.setup_rate_limiting(app) # 6. Security monitoring self.setup_security_monitoring(app) def setup_input_validation(self, app): """Comprehensive input validation and sanitization""" @app.before_request def validate_all_inputs(): # SQL injection protection if self.detect_sql_injection(request): abort(400, "Invalid input detected") # XSS protection if self.detect_xss_attempt(request): abort(400, "Malicious script detected") # Path traversal protection if self.detect_path_traversal(request): abort(400, "Path traversal attempt detected") # Command injection protection if self.detect_command_injection(request): abort(400, "Command injection attempt detected") def setup_api_security(self, app): """API-specific security measures""" # API key validation @app.before_request def validate_api_access(): if request.endpoint and request.endpoint.startswith('api/'): api_key = request.headers.get('X-API-Key') if not self.validate_api_key(api_key): abort(401, "Invalid API key") # Request signing for critical operations @app.before_request def validate_request_signature(): if request.endpoint in self.security_config['signed_endpoints']: signature = request.headers.get('X-Request-Signature') if not self.validate_request_signature(request, signature): abort(401, "Invalid request signature") def setup_file_upload_security(self, app): """Secure file upload handling""" def secure_file_validation(file): # File type validation if not self.validate_file_type(file): raise SecurityError("Invalid file type") # File size validation if not self.validate_file_size(file): raise SecurityError("File too large") # Malware scanning if self.detect_malware(file): raise SecurityError("Malicious file detected") # Content validation if not self.validate_file_content(file): raise SecurityError("Invalid file content") return True def setup_security_monitoring(self, app): """Real-time security monitoring""" @app.after_request def monitor_security_events(response): # Log security events self.log_security_event(request, response) # Detect anomalies if self.detect_anomaly(request, response): self.alert_security_team(request, response) # Update threat intelligence self.update_threat_intelligence(request, response) return response
AI/ML INTEGRATION OPTIMIZATION:
# backend/ml_integration_optimizer.py class MLIntegrationOptimizer: """ Optimized machine learning integration for maximum efficiency """ def __init__(self): self.model_registry = ModelRegistry() self.inference_scheduler = InferenceScheduler() self.resource_optimizer = ResourceOptimizer() def optimize_ml_pipeline(self, pipeline_config: Dict) -> Dict: """Optimize entire ML pipeline for speed and accuracy""" # 1. Model selection optimization optimal_models = self.select_optimal_models(pipeline_config) # 2. Inference optimization optimized_inference = self.optimize_inference_pipeline(optimal_models) # 3. Resource allocation optimization optimized_resources = self.optimize_resource_allocation(optimized_inference) # 4. Caching strategy optimization optimized_caching = self.optimize_caching_strategy(optimized_resources) return optimized_caching def select_optimal_models(self, config: Dict) -> List[Dict]: """Select optimal model combination based on requirements""" requirements = { 'speed_priority': config.get('speed_priority', 0.5), 'accuracy_priority': config.get('accuracy_priority', 0.5), 'resource_constraints': config.get('resource_constraints', {}), 'quality_requirements': config.get('quality_requirements', {}) } # Multi-objective optimization for model selection optimal_combination = self.multi_objective_model_selection(requirements) return optimal_combination def optimize_inference_pipeline(self, models: List[Dict]) -> Dict: """Optimize inference pipeline for maximum throughput""" # Pipeline parallelization parallel_stages = self.identify_parallel_stages(models) # Batch optimization optimal_batch_config = self.optimize_batch_processing(models) # Memory optimization memory_config = self.optimize_memory_usage(models) # GPU utilization optimization gpu_config = self.optimize_gpu_utilization(models) return { 'parallel_stages': parallel_stages, 'batch_config': optimal_batch_config, 'memory_config': memory_config, 'gpu_config': gpu_config } def optimize_caching_strategy(self, pipeline_config: Dict) -> Dict: """Intelligent caching for maximum performance""" caching_layers = { 'model_cache': { 'strategy': 'LRU', 'size_limit': '8GB', 'persistence': True }, 'inference_cache': { 'strategy': 'Adaptive', 'ttl': 3600, # 1 hour 'compression': True }, 'result_cache': { 'strategy': 'Context-aware', 'size_limit': '4GB', 'deduplication': True } } return { **pipeline_config, 'caching_layers': caching_layers, 'cache_warming': self.setup_cache_warming(), 'cache_invalidation': self.setup_smart_invalidation() }
INTELLIGENT ENCODING DETECTION & CONVERSION:
# backend/encoding_manager.py - Core encoding management class EncodingManager: """ Advanced multi-character encoding detection and conversion system """ def __init__(self): self.supported_encodings = [ 'utf-8', 'utf-16-le', 'utf-16-be', 'utf-32-le', 'utf-32-be', 'ascii', 'latin-1', 'cp1252', 'gb2312', 'gbk', 'big5', 'shift-jis', 'euc-kr', 'iso-8859-1', 'iso-8859-2', 'iso-8859-15' ] self.encoding_detectors = self.initialize_detectors() def detect_encoding_with_fallback(self, file_path: str) -> Dict[str, Any]: """Detect file encoding with intelligent fallback chain""" # Read file bytes for analysis with open(file_path, 'rb') as f: raw_data = f.read() # 1. BOM detection (highest priority) bom_encoding = self.detect_bom(raw_data) if bom_encoding: return { 'encoding': bom_encoding, 'confidence': 1.0, 'method': 'bom_detection', 'bom_present': True } # 2. Automatic detection using multiple libraries detection_results = [] # chardet detection chardet_result = chardet.detect(raw_data) if chardet_result['encoding']: detection_results.append({ 'encoding': chardet_result['encoding'].lower(), 'confidence': chardet_result['confidence'], 'method': 'chardet' }) # charset-normalizer detection normalizer_result = from_bytes(raw_data).best() if normalizer_result: detection_results.append({ 'encoding': str(normalizer_result.encoding).lower(), 'confidence': normalizer_result.coherence, 'method': 'charset_normalizer' }) # 3. Select best detection result if detection_results: best_result = max(detection_results, key=lambda x: x['confidence']) if best_result['confidence'] > 0.7: return best_result # 4. Fallback chain validation fallback_chain = os.getenv('ENCODING_FALLBACK_CHAIN', 'utf-8,latin-1,cp1252,ascii').split(',') for encoding in fallback_chain: if self.validate_encoding(raw_data, encoding.strip()): return { 'encoding': encoding.strip(), 'confidence': 0.5, 'method': 'fallback_validation', 'fallback_position': fallback_chain.index(encoding) } # 5. Default to UTF-8 with error handling return { 'encoding': 'utf-8', 'confidence': 0.1, 'method': 'default_fallback', 'error_handling': 'replace' } def convert_file_encoding(self, input_path: str, output_path: str, target_encoding: str = 'utf-8') -> Dict[str, Any]: """Convert file from detected encoding to target encoding""" # Detect source encoding source_info = self.detect_encoding_with_fallback(input_path) source_encoding = source_info['encoding'] try: # Read with detected encoding with open(input_path, 'r', encoding=source_encoding, errors='replace') as f: content = f.read() # Unicode normalization normalized_content = self.normalize_unicode(content) # Write with target encoding with open(output_path, 'w', encoding=target_encoding, errors='replace') as f: f.write(normalized_content) return { 'success': True, 'source_encoding': source_encoding, 'target_encoding': target_encoding, 'source_confidence': source_info['confidence'], 'normalization_applied': True, 'file_size_bytes': len(normalized_content.encode(target_encoding)) } except Exception as e: return { 'success': False, 'error': str(e), 'source_encoding': source_encoding, 'target_encoding': target_encoding } def normalize_unicode(self, text: str, form: str = None) -> str: """Apply Unicode normalization to text""" normalization_form = form or os.getenv('UNICODE_NORMALIZATION', 'NFC') if normalization_form.upper() in ['NFC', 'NFD', 'NFKC', 'NFKD']: return unicodedata.normalize(normalization_form.upper(), text) else: return text # backend/i18n_manager.py - Internationalization management class InternationalizationManager: """ Comprehensive internationalization and localization management """ def __init__(self): self.supported_languages = self.load_supported_languages() self.language_detector = self.initialize_language_detector() self.translation_engine = self.setup_translation_engine() def detect_content_language(self, text: str) -> Dict[str, Any]: """Detect language of text content""" try: # Primary detection with langdetect detected_lang = detect(text) confidence = detect_langs(text)[0].prob # Validate against supported languages if detected_lang in self.supported_languages: return { 'language': detected_lang, 'confidence': confidence, 'method': 'langdetect', 'supported': True } else: # Find closest supported language closest_lang = self.find_closest_language(detected_lang) return { 'language': closest_lang, 'original_detection': detected_lang, 'confidence': confidence * 0.8, # Reduced confidence 'method': 'langdetect_mapped', 'supported': True } except Exception as e: # Fallback to default language default_lang = os.getenv('DEFAULT_LANGUAGE', 'en-US') return { 'language': default_lang, 'confidence': 0.1, 'method': 'fallback', 'error': str(e), 'supported': True } def setup_multilingual_processing(self, content: str, target_languages: List[str] = None) -> Dict[str, Any]: """Setup multilingual processing pipeline""" # Detect source language source_detection = self.detect_content_language(content) # Determine target languages if not target_languages: target_languages = os.getenv('SUPPORTED_LANGUAGES', 'en,es,fr,de,it,pt,ja,ko,zh-cn,zh-tw,ru,ar,hi').split(',') # Create processing pipeline pipeline = { 'source_language': source_detection['language'], 'target_languages': target_languages, 'processing_steps': [ 'encoding_normalization', 'language_detection', 'text_segmentation', 'nlp_processing', 'translation_preparation' ], 'language_specific_processors': {} } # Setup language-specific processors for lang in target_languages: processor_config = self.get_language_processor_config(lang.strip()) pipeline['language_specific_processors'][lang.strip()] = processor_config return pipeline
MULTI-PROGRAMMING LANGUAGE SUPPORT:
# backend/polyglot_code_manager.py - Multi-language code management class PolyglotCodeManager: """ Advanced multi-programming language code management and analysis """ def __init__(self): self.supported_languages = { 'python': {'extensions': ['.py', '.pyw'], 'keywords': ['def', 'class', 'import'], 'priority': 1}, 'javascript': {'extensions': ['.js', '.mjs'], 'keywords': ['function', 'const', 'let'], 'priority': 2}, 'typescript': {'extensions': ['.ts', '.tsx'], 'keywords': ['interface', 'type', 'enum'], 'priority': 3}, 'java': {'extensions': ['.java'], 'keywords': ['public class', 'private', 'protected'], 'priority': 4}, 'csharp': {'extensions': ['.cs'], 'keywords': ['namespace', 'using', 'public class'], 'priority': 5}, 'go': {'extensions': ['.go'], 'keywords': ['package', 'func', 'import'], 'priority': 6}, 'rust': {'extensions': ['.rs'], 'keywords': ['fn', 'struct', 'impl'], 'priority': 7}, 'php': {'extensions': ['.php'], 'keywords': ['<?php', 'function', 'class'], 'priority': 8}, 'cpp': {'extensions': ['.cpp', '.cc', '.cxx'], 'keywords': ['include_directive', 'namespace', 'class'], 'priority': 9}, 'html': {'extensions': ['.html', '.htm'], 'keywords': ['<!DOCTYPE', '<html>', '<head>'], 'priority': 10}, 'css': {'extensions': ['.css'], 'keywords': ['{', '}', ':'], 'priority': 11}, 'sql': {'extensions': ['.sql'], 'keywords': ['SELECT', 'FROM', 'WHERE'], 'priority': 12} } self.language_parsers = self.initialize_parsers() def detect_programming_language(self, code_content: str, file_path: str = None) -> Dict[str, Any]: """Intelligent programming language detection""" detection_methods = [] # 1. File extension detection if file_path: ext_detection = self.detect_by_extension(file_path) if ext_detection: detection_methods.append({ 'method': 'file_extension', 'language': ext_detection, 'confidence': 0.9 }) # 2. Keyword analysis keyword_detection = self.detect_by_keywords(code_content) detection_methods.extend(keyword_detection) # 3. Syntax pattern analysis syntax_detection = self.detect_by_syntax_patterns(code_content) detection_methods.extend(syntax_detection) # 4. Tree-sitter parsing (if available) if self.tree_sitter_available(): tree_detection = self.detect_by_tree_sitter(code_content) detection_methods.extend(tree_detection) # Select best detection if detection_methods: best_detection = max(detection_methods, key=lambda x: x['confidence']) return { 'detected_language': best_detection['language'], 'confidence': best_detection['confidence'], 'method': best_detection['method'], 'all_detections': detection_methods, 'supported': best_detection['language'] in self.supported_languages } else: return { 'detected_language': 'unknown', 'confidence': 0.0, 'method': 'none', 'supported': False } def create_polyglot_project_structure(self, languages: List[str], project_name: str) -> Dict[str, Any]: """Create multi-language project structure""" project_structure = { 'project_name': project_name, 'languages': languages, 'directories': {}, 'config_files': {}, 'build_systems': {}, 'dependencies': {} } for lang in languages: lang_config = self.get_language_config(lang) # Create language-specific directories project_structure['directories'][lang] = { 'source': f'src/{lang}', 'tests': f'tests/{lang}', 'docs': f'docs/{lang}', 'build': f'build/{lang}' } # Language-specific configuration files project_structure['config_files'][lang] = lang_config.get('config_files', []) # Build system configuration project_structure['build_systems'][lang] = lang_config.get('build_system', 'generic') # Dependency management project_structure['dependencies'][lang] = lang_config.get('dependency_manager', 'manual') # Cross-language integration project_structure['integration'] = { 'api_contracts': 'contracts/', 'shared_schemas': 'schemas/', 'documentation': 'docs/integration/', 'communication_protocols': ['rest_api', 'grpc', 'graphql'] } return project_structure def setup_cross_language_interop(self, source_lang: str, target_lang: str) -> Dict[str, Any]: """Setup interoperability between different programming languages""" interop_config = { 'source_language': source_lang, 'target_language': target_lang, 'communication_methods': [], 'data_serialization': [], 'build_integration': {}, 'testing_strategy': {} } # Determine communication methods if source_lang == 'python' and target_lang == 'javascript': interop_config['communication_methods'] = ['rest_api', 'websocket', 'json_rpc'] interop_config['data_serialization'] = ['json', 'msgpack'] elif source_lang == 'java' and target_lang == 'python': interop_config['communication_methods'] = ['grpc', 'rest_api', 'jni'] interop_config['data_serialization'] = ['protobuf', 'json', 'avro'] elif 'cpp' in [source_lang, target_lang]: interop_config['communication_methods'] = ['ffi', 'shared_library', 'c_api'] interop_config['data_serialization'] = ['protobuf', 'flatbuffers', 'binary'] # Build integration setup interop_config['build_integration'] = self.setup_build_integration(source_lang, target_lang) return interop_config # backend/cross_language_translator.py - Code translation engine class CrossLanguageTranslator: """ AI-powered code translation between programming languages """ def __init__(self): self.translation_models = { 'ast_based': self.setup_ast_translator(), 'ai_powered': self.setup_ai_translator(), 'pattern_based': self.setup_pattern_translator() } def translate_code(self, source_code: str, source_lang: str, target_lang: str, translation_method: str = 'ai_powered') -> Dict[str, Any]: """Translate code from one language to another""" if translation_method not in self.translation_models: translation_method = 'ai_powered' # Default fallback try: # Pre-processing preprocessed_code = self.preprocess_for_translation(source_code, source_lang) # Translation translator = self.translation_models[translation_method] translated_code = translator.translate(preprocessed_code, source_lang, target_lang) # Post-processing final_code = self.postprocess_translation(translated_code, target_lang) # Validation validation_result = self.validate_translated_code(final_code, target_lang) return { 'translated_code': final_code, 'source_language': source_lang, 'target_language': target_lang, 'translation_method': translation_method, 'validation': validation_result, 'confidence': validation_result.get('syntax_valid', False) * 0.8 + 0.2 } except Exception as e: return { 'error': str(e), 'source_language': source_lang, 'target_language': target_lang, 'translation_method': translation_method, 'success': False } def validate_translated_code(self, code: str, language: str) -> Dict[str, Any]: """Validate translated code for syntax and basic structure""" validation = { 'syntax_valid': False, 'structure_valid': False, 'warnings': [], 'errors': [] } try: # Language-specific syntax validation if language == 'python': validation = self.validate_python_syntax(code) elif language == 'javascript': validation = self.validate_javascript_syntax(code) elif language == 'java': validation = self.validate_java_syntax(code) # Add more languages as needed return validation except Exception as e: validation['errors'].append(f"Validation error: {str(e)}") return validation
DEBUGGING PATTERNS FOR MULTI-LANGUAGE SUPPORT:
# Enhanced debugging for multi-encoding and multi-language issues def debug_encoding_issues(file_path: str, error: Exception) -> Dict[str, Any]: """Debug encoding-related issues with comprehensive analysis""" debug_info = { 'file_path': file_path, 'error_type': type(error).__name__, 'error_message': str(error), 'encoding_analysis': {}, 'suggested_fixes': [] } try: # Analyze file encoding encoding_mgr = EncodingManager() detection_result = encoding_mgr.detect_encoding_with_fallback(file_path) debug_info['encoding_analysis'] = detection_result # Generate specific fixes based on error type if 'UnicodeDecodeError' in str(error): debug_info['suggested_fixes'].extend([ f"Try reading with detected encoding: {detection_result['encoding']}", "Enable error handling: errors='replace' or errors='ignore'", "Convert file to UTF-8 using encoding_manager.convert_file_encoding()" ]) elif 'UnicodeEncodeError' in str(error): debug_info['suggested_fixes'].extend([ "Normalize Unicode text using unicodedata.normalize()", "Use UTF-8 encoding for output", "Apply Unicode escape sequences for problematic characters" ]) return debug_info except Exception as debug_error: debug_info['debug_error'] = str(debug_error) return debug_info def debug_language_detection_issues(code: str, expected_lang: str = None) -> Dict[str, Any]: """Debug programming language detection issues""" debug_info = { 'code_sample': code[:200] + '...' if len(code) > 200 else code, 'expected_language': expected_lang, 'detection_results': {}, 'suggested_improvements': [] } try: # Multi-method language detection polyglot_mgr = PolyglotCodeManager() detection_result = polyglot_mgr.detect_programming_language(code) debug_info['detection_results'] = detection_result # Analyze detection accuracy if expected_lang and detection_result['detected_language'] != expected_lang: debug_info['suggested_improvements'].extend([ f"Add more {expected_lang}-specific keywords to improve detection", "Provide file extension hint for better accuracy", "Use Tree-sitter parser for more accurate syntax analysis", "Enable multi-language detection for polyglot code" ]) return debug_info except Exception as debug_error: debug_info['debug_error'] = str(debug_error) return debug_info
CODING PATTERNS:
# backend/main.py structure """ ORFEAS AI 2D→3D Studio - Module Name ================================================== ORFEAS AI Features: - Feature 1 - Feature 2 """ # ALWAYS: Type hints for function signatures def generate_3d_model( image: Union[str, Image.Image], quality: int = 7, format: str = 'stl' ) -> Dict[str, Any]: """ Generate 3D model from 2D image Args: image: PIL Image or file path quality: Model quality 1-10 (default: 7) format: Output format (stl, obj, glb) Returns: Dict with 'mesh_path', 'stats', 'metadata' Raises: ValueError: Invalid quality or format RuntimeError: GPU memory insufficient """ pass # ALWAYS: Comprehensive error handling try: mesh = processor.generate_shape(image) except torch.cuda.OutOfMemoryError: logger.error("GPU OOM - clearing cache and retrying") torch.cuda.empty_cache() mesh = processor.generate_shape(image, low_vram_mode=True) except Exception as e: logger.error(f"Generation failed: {traceback.format_exc()}") raise RuntimeError(f"3D generation failed: {str(e)}") # ALWAYS: Use consistent logger format logger = logging.getLogger(__name__) logger.info("[ORFEAS] Feature initialized successfully") logger.warning("[ORFEAS] GPU memory at 85% - consider cleanup") logger.error("[ORFEAS] Critical failure - reverting to fallback")
Environment Variables (.env):
# backend/.env ORFEAS_PORT=5000 DEVICE=cuda # auto, cuda, cpu MAX_CONCURRENT_JOBS=3 GPU_MEMORY_LIMIT=0.8 # 80% of total VRAM HUNYUAN3D_PATH=../Hunyuan3D-2.1 MODEL_CACHE_DIR=./models DEFAULT_STEPS=50 MAX_STEPS=100 LOG_LEVEL=INFO # AI Video Composition Settings ENABLE_VIDEO_GENERATION=true SORA_MODEL_PATH=./models/sora VIDEO_OUTPUT_DIR=./outputs/videos DEFAULT_VIDEO_FPS=24 DEFAULT_VIDEO_RESOLUTION=1920x1080 MAX_VIDEO_DURATION=60 # seconds ENABLE_CINEMATIC_MODE=true VIDEO_QUALITY_PRESET=high # low, medium, high, ultra ENABLE_3D_TO_VIDEO=true ENABLE_TEXT_TO_VIDEO=true ENABLE_IMAGE_TO_VIDEO=true VIDEO_CACHE_SIZE=10 # GB for video caching TEMPORAL_CONSISTENCY_ENABLED=true # AI Code Development Settings ENABLE_CODE_WRITING=true ENABLE_CODE_DEBUGGING=true ENABLE_CODE_ANALYSIS=true CODE_AI_MODEL=github-copilot # github-copilot, codeT5, starcoder, deepseek-coder CODE_OUTPUT_DIR=./outputs/code CODE_QUALITY_THRESHOLD=0.8 # Minimum code quality score ENABLE_AUTO_FORMATTING=true ENABLE_SYNTAX_VALIDATION=true ENABLE_PERFORMANCE_ANALYSIS=true CODE_GENERATION_TIMEOUT=30 # seconds ENABLE_CODE_DOCUMENTATION=true ENABLE_TEST_GENERATION=true # AI Multimedia Generation Settings ENABLE_TEXT_TO_IMAGE=true ENABLE_TEXT_TO_SPEECH=true ENABLE_SPEECH_TO_TEXT=true TEXT_TO_IMAGE_MODEL=dalle3 # dalle3, stable_diffusion_xl, midjourney TEXT_TO_IMAGE_QUALITY=high # low, medium, high, ultra TEXT_TO_IMAGE_OUTPUT_DIR=./outputs/images TEXT_TO_IMAGE_CACHE_SIZE=5 # GB for image caching SPEECH_MODEL=whisper_large # whisper_large, azure_speech, google_speech TTS_MODEL=elevenlabs # elevenlabs, azure_tts, openai_tts TTS_VOICE_ID=default # Voice ID for text-to-speech TTS_OUTPUT_DIR=./outputs/audio AUDIO_QUALITY=high # low, medium, high MAX_AUDIO_DURATION=300 # seconds (5 minutes) ENABLE_REAL_TIME_TRANSCRIPTION=true ENABLE_VOICE_CLONING=false # Advanced voice synthesis SPEECH_LANGUAGE=en-US # Language for speech processing # Enterprise LLM Integration Settings ENABLE_LLM_INTEGRATION=true LLM_PRIMARY_MODEL=gpt4_turbo # gpt4_turbo, claude_3_5_sonnet, gemini_ultra LLM_FALLBACK_MODEL=claude_3_5_sonnet LLM_API_TIMEOUT=30 # seconds LLM_MAX_TOKENS=4000 # Maximum tokens per request LLM_TEMPERATURE=0.3 # Creativity vs determinism (0.0-1.0) LLM_CONTEXT_WINDOW=32000 # Context window size # Multi-LLM Orchestration ENABLE_MULTI_LLM_ORCHESTRATION=true LLM_TASK_DECOMPOSITION=true # Break complex tasks into subtasks LLM_RESULT_SYNTHESIS=true # Combine results from multiple LLMs LLM_QUALITY_VALIDATION=true # Validate LLM responses for quality LLM_RESPONSE_CACHING=true # Cache frequent responses LLM_CACHE_TTL=3600 # Cache time-to-live in seconds # RAG (Retrieval-Augmented Generation) Settings ENABLE_RAG_SYSTEM=true VECTOR_DATABASE_TYPE=pinecone # pinecone, weaviate, qdrant, chroma VECTOR_DIMENSION=1536 # Embedding dimension KNOWLEDGE_BASE_PATH=./knowledge RAG_RETRIEVAL_TOP_K=10 # Number of relevant documents to retrieve RAG_SIMILARITY_THRESHOLD=0.7 # Minimum similarity score for retrieval ENABLE_KNOWLEDGE_GRAPH=true # Neo4j knowledge graph integration # GitHub Copilot Enterprise Integration GITHUB_COPILOT_ENABLED=true GITHUB_COPILOT_MODEL=copilot-chat # copilot-chat, copilot-code COPILOT_SUGGESTIONS_COUNT=3 # Number of code suggestions COPILOT_QUALITY_THRESHOLD=0.8 # Minimum quality score for suggestions ENABLE_COPILOT_CONTEXT=true # Include project context in requests COPILOT_SECURITY_SCANNING=true # Scan generated code for security issues # LLM Performance & Optimization LLM_BATCH_PROCESSING=true # Enable batch processing for multiple requests LLM_PARALLEL_REQUESTS=4 # Maximum parallel LLM requests ENABLE_LLM_COMPRESSION=true # Compress prompts for efficiency LLM_RESPONSE_STREAMING=true # Enable streaming responses LLM_LOAD_BALANCING=true # Distribute requests across multiple models LLM_FAILOVER_ENABLED=true # Automatic failover to backup models # AI Agents & ML Configuration ENABLE_AI_AGENTS=true AGENT_AUTH_ENABLED=true QUALITY_AGENT_ENABLED=true WORKFLOW_AGENT_ENABLED=true OPTIMIZATION_AGENT_ENABLED=true # ML Optimization Settings ENABLE_TENSORRT=false # Requires TensorRT installation ENABLE_MIXED_PRECISION=true # FP16 optimization ENABLE_MODEL_QUANTIZATION=false # INT8 quantization BATCH_INFERENCE_SIZE=4 # Max batch size for inference AUTO_MODEL_SELECTION=true # Let agents choose optimal models # Alternative AI Models ENABLE_STABLE_DIFFUSION=true ENABLE_COMFYUI_INTEGRATION=true ENABLE_HUGGINGFACE_FALLBACK=true CLIP_MODEL_NAME=openai/clip-vit-base-patch32 STABLE_DIFFUSION_MODEL=runwayml/stable-diffusion-v1-5 # Performance Monitoring ENABLE_ML_METRICS=true TRACK_AGENT_PERFORMANCE=true AUTO_OPTIMIZATION=true PERFORMANCE_THRESHOLD=0.8 # Trigger optimization below this score # Web Technologies & Security ENABLE_HTTPS=true SSL_CERT_PATH=ssl/orfeas.crt SSL_KEY_PATH=ssl/orfeas.key ENABLE_HSTS=true CSP_ENABLED=true PWA_ENABLED=true SERVICE_WORKER_ENABLED=true # PHP Integration (Optional) ENABLE_PHP_COMPATIBILITY=false PHP_API_ENDPOINT=/php/api.php PHP_SESSION_ENABLED=false # Web Server Configuration NGINX_SSL_ENABLED=true STATIC_FILE_COMPRESSION=true RATE_LIMITING_ENABLED=true MAX_REQUESTS_PER_MINUTE=60 # Intelligent Context & Learning ENABLE_CONTEXT_MANAGER=true CONTEXT_PERSISTENCE_ENABLED=true CONTEXT_LEARNING_ENABLED=true CONTEXT_DB_PATH=data/context.db AUTO_CONTEXT_OPTIMIZATION=true CONTEXT_CACHE_SIZE=1000 CONTEXT_SIMILARITY_THRESHOLD=0.8 ENABLE_PREDICTIVE_INSIGHTS=true CONTEXT_RETENTION_DAYS=90 # Performance & Security Optimization ENABLE_PERFORMANCE_OPTIMIZER=true AUTO_PERFORMANCE_TUNING=true PERFORMANCE_MONITORING_ENABLED=true PERFORMANCE_THRESHOLD=0.8 # Trigger optimization below this score SPEED_OPTIMIZATION_ENABLED=true ACCURACY_ENHANCEMENT_ENABLED=true SECURITY_HARDENING_ENABLED=true # Speed Optimization Settings ENABLE_MODEL_COMPILATION=true # torch.compile() optimization ENABLE_CACHE_WARMING=true # Pre-warm model caches FAST_MODE_QUALITY_THRESHOLD=5 # Quality level for fast mode BALANCED_MODE_QUALITY_THRESHOLD=7 # Quality level for balanced mode QUALITY_MODE_QUALITY_THRESHOLD=9 # Quality level for quality mode # Accuracy Enhancement Settings ENABLE_ENSEMBLE_GENERATION=false # Multi-model ensemble (resource intensive) QUALITY_VALIDATION_THRESHOLD=0.85 # Minimum quality score threshold ENABLE_AUTOMATIC_REFINEMENT=true # Auto-refine low quality results ENABLE_FEEDBACK_LEARNING=true # Learn from user quality feedback QUALITY_ASSESSMENT_ENABLED=true # Pre-generation quality assessment # TQM (Total Quality Management) Audit System ENABLE_TQM_AUDITS=true # Enable comprehensive quality audits TQM_AUDIT_LEVEL=enterprise # basic, professional, enterprise AUTOMATED_AUDIT_SCHEDULING=true # Schedule automatic audits CONTINUOUS_QUALITY_MONITORING=true # Real-time quality monitoring QUALITY_GATE_ENFORCEMENT=true # Enforce quality gates for requests # Quality Audit Scheduling DAILY_AUDITS_ENABLED=true # Enable daily performance audits WEEKLY_AUDITS_ENABLED=true # Enable weekly process audits MONTHLY_AUDITS_ENABLED=true # Enable monthly comprehensive audits QUARTERLY_AUDITS_ENABLED=true # Enable quarterly strategic reviews ANNUAL_AUDITS_ENABLED=true # Enable annual certification audits # Quality Standards & Compliance ISO_9001_COMPLIANCE=true # ISO 9001:2015 Quality Management ISO_27001_COMPLIANCE=true # ISO 27001:2022 Information Security SOC2_TYPE2_COMPLIANCE=true # SOC2 Type 2 Security Controls SIX_SIGMA_METHODOLOGY=true # Six Sigma process improvement CMMI_LEVEL5_TARGET=true # CMMI Level 5 capability maturity LEAN_MANUFACTURING_PRINCIPLES=true # Lean waste elimination # Quality Metrics & Thresholds OVERALL_QUALITY_THRESHOLD=0.85 # Overall quality score threshold PERFORMANCE_QUALITY_THRESHOLD=0.90 # Performance quality threshold RELIABILITY_QUALITY_THRESHOLD=0.95 # Reliability quality threshold SECURITY_QUALITY_THRESHOLD=0.90 # Security quality threshold COMPLIANCE_QUALITY_THRESHOLD=0.98 # Compliance quality threshold USER_SATISFACTION_THRESHOLD=0.85 # User satisfaction threshold # Quality Audit Reporting AUTO_AUDIT_REPORT_GENERATION=true # Automatically generate audit reports EXECUTIVE_DASHBOARD_ENABLED=true # Quality executive dashboard STAKEHOLDER_NOTIFICATIONS=true # Notify stakeholders of audit results QUALITY_TREND_ANALYSIS=true # Analyze quality trends over time BENCHMARK_COMPARISONS=true # Compare against industry benchmarks # Quality Improvement Actions AUTO_QUALITY_CORRECTION=true # Automatic quality corrections QUALITY_ALERT_SYSTEM=true # Real-time quality alerts PROACTIVE_QUALITY_MANAGEMENT=true # Proactive quality issue prevention QUALITY_TRAINING_RECOMMENDATIONS=true # Recommend quality training BEST_PRACTICES_ENFORCEMENT=true # Enforce quality best practices # Advanced Quality Analytics AI_POWERED_QUALITY_ANALYSIS=true # AI-driven quality analysis PREDICTIVE_QUALITY_MODELING=true # Predict quality issues before they occur QUALITY_PATTERN_RECOGNITION=true # Recognize quality improvement patterns CUSTOMER_FEEDBACK_INTEGRATION=true # Integrate customer feedback into quality metrics QUALITY_ROI_TRACKING=true # Track return on investment for quality initiatives # Security Hardening Settings THREAT_DETECTION_ENABLED=true # Real-time threat detection SECURITY_AUDIT_LOGGING=true # Comprehensive security logging FILE_MALWARE_SCANNING=true # Scan uploaded files for malware REQUEST_SIGNATURE_VALIDATION=false # Validate signed requests SECURITY_MONITORING_LEVEL=MEDIUM # LOW, MEDIUM, HIGH, PARANOID # ML Integration Optimization ADAPTIVE_MODEL_SELECTION=true # Intelligent model selection PIPELINE_OPTIMIZATION_ENABLED=true # Optimize ML pipelines RESOURCE_ALLOCATION_OPTIMIZATION=true # Optimize resource allocation MODEL_PERFORMANCE_TRACKING=true # Track model performance metrics AUTO_MODEL_SWITCHING=true # Switch models based on performance # Enterprise Cloud Deployment & Scaling CLOUD_PROVIDER=aws # aws, azure, gcp, hybrid KUBERNETES_ENABLED=true # Enable Kubernetes deployment KUBERNETES_NAMESPACE=orfeas-prod # K8s namespace for production AUTO_SCALING_ENABLED=true # Horizontal pod autoscaling MIN_REPLICAS=2 # Minimum pod replicas MAX_REPLICAS=20 # Maximum pod replicas CPU_TARGET_UTILIZATION=70 # CPU target for autoscaling MEMORY_TARGET_UTILIZATION=80 # Memory target for autoscaling # Enterprise Monitoring & Observability PROMETHEUS_ENABLED=true # Enable Prometheus metrics GRAFANA_ENABLED=true # Enable Grafana dashboards JAEGER_TRACING_ENABLED=true # Distributed tracing ELK_STACK_ENABLED=true # Elasticsearch, Logstash, Kibana DATADOG_INTEGRATION=false # Datadog APM integration NEW_RELIC_INTEGRATION=false # New Relic monitoring # Enterprise Database & Caching DATABASE_TYPE=postgresql # postgresql, mysql, mongodb DATABASE_CLUSTER_ENABLED=true # Database clustering REDIS_CLUSTER_ENABLED=true # Redis clustering for caching DATABASE_BACKUP_ENABLED=true # Automated database backups DATABASE_ENCRYPTION_ENABLED=true # Database encryption at rest # Enterprise CI/CD & DevOps GITHUB_ACTIONS_ENABLED=true # GitHub Actions CI/CD JENKINS_INTEGRATION=false # Jenkins CI/CD GITLAB_CI_INTEGRATION=false # GitLab CI integration DOCKER_REGISTRY=ghcr.io # Container registry HELM_CHARTS_ENABLED=true # Helm for Kubernetes deployments TERRAFORM_ENABLED=true # Infrastructure as Code # Enterprise API & Microservices API_GATEWAY_ENABLED=true # API Gateway for microservices LOAD_BALANCER_TYPE=nginx # nginx, haproxy, aws-alb SERVICE_MESH_ENABLED=false # Istio service mesh CIRCUIT_BREAKER_ENABLED=true # Circuit breaker pattern API_RATE_LIMITING_ENABLED=true # Enterprise API rate limiting API_KEY_MANAGEMENT_ENABLED=true # Enterprise API key management # Enterprise Security & Compliance OAUTH2_ENABLED=true # OAuth2 authentication SAML_SSO_ENABLED=false # SAML Single Sign-On LDAP_INTEGRATION=false # LDAP authentication RBAC_ENABLED=true # Role-based access control AUDIT_LOGGING_ENABLED=true # Comprehensive audit logging COMPLIANCE_FRAMEWORK=SOC2 # SOC2, ISO27001, GDPR, HIPAA VULNERABILITY_SCANNING=true # Automated vulnerability scanning PENETRATION_TESTING=quarterly # Penetration testing schedule # Advanced Problem Identification & Automated Fixing ENABLE_PROBLEM_DETECTION=true # Enable intelligent problem detection ENABLE_AUTOMATED_FIXING=true # Enable automated problem resolution PROBLEM_DETECTION_INTERVAL=30 # Problem detection check interval (seconds) AUTO_FIX_CONFIDENCE_THRESHOLD=0.8 # Minimum confidence for automated fixes ENABLE_PROACTIVE_MONITORING=true # Enable proactive problem prevention HEALTH_MONITORING_ENABLED=true # Enable continuous health monitoring ALERT_SYSTEM_ENABLED=true # Enable alerting system PROBLEM_LEARNING_ENABLED=true # Enable learning from problem patterns DIAGNOSTIC_LOGGING_ENABLED=true # Enable detailed diagnostic logging ERROR_RECOVERY_ENABLED=true # Enable intelligent error recovery MANUAL_INTERVENTION_THRESHOLD=3 # Max auto-fix attempts before manual intervention # Multi-Encoding & Internationalization Settings ENABLE_MULTI_ENCODING=true # Enable multi-character encoding support DEFAULT_ENCODING=utf-8 # Primary character encoding ENCODING_AUTO_DETECTION=true # Automatic encoding detection for files ENCODING_FALLBACK_CHAIN=utf-8,latin-1,cp1252,ascii # Fallback encoding order UNICODE_NORMALIZATION=NFC # Unicode normalization form (NFC, NFD, NFKC, NFKD) BOM_DETECTION=true # Byte Order Mark detection ENCODING_ERROR_HANDLING=replace # strict, ignore, replace, xmlcharrefreplace LANGUAGE_DETECTION_ENABLED=true # Automatic language detection DEFAULT_LANGUAGE=en-US # Default language for processing SUPPORTED_LANGUAGES=en,es,fr,de,it,pt,ja,ko,zh-cn,zh-tw,ru,ar,hi # Supported languages I18N_LOCALE_PATH=./locales # Path to internationalization files I18N_DOMAIN=orfeas # Translation domain name BABEL_CONFIG_FILE=babel.cfg # Babel configuration file GETTEXT_ENABLED=true # Enable GNU gettext support POLYGLOT_NLP_ENABLED=true # Enable polyglot NLP processing LANGCODES_NORMALIZATION=true # Language code standardization # Multi-Code Language Programming Settings ENABLE_MULTI_LANGUAGE_SUPPORT=true # Enable multi-programming language support PRIMARY_LANGUAGES=python,javascript,typescript,html,css,sql # Primary development languages ENTERPRISE_LANGUAGES=php,java,csharp,go,rust,cpp # Enterprise programming languages SPECIALIZED_LANGUAGES=r,matlab,swift,kotlin,dart,wasm # Specialized programming languages INFRASTRUCTURE_LANGUAGES=bash,powershell,yaml,json,xml,toml # Infrastructure & DevOps languages DATABASE_LANGUAGES=sql,nosql,graphql,sparql # Database query languages LANGUAGE_SERVER_PROTOCOL=true # Enable LSP integration TREE_SITTER_ENABLED=true # Universal syntax highlighting ANTLR_PARSER_ENABLED=true # Cross-language parser generator PROTOBUF_ENABLED=true # Protocol Buffers cross-language serialization GRPC_ENABLED=true # Cross-language RPC framework THRIFT_ENABLED=true # Apache Thrift cross-language services MULTI_LANGUAGE_AI_ENABLED=true # AI-powered multi-language code generation CODE_TRANSLATION_ENABLED=true # Code translation between languages CROSS_LANGUAGE_REFACTORING=true # Cross-language refactoring support LANGUAGE_INTEROP_ENABLED=true # Language interoperability features CODE_ANALYSIS_MULTI_LANG=true # Multi-language code analysis SYNTAX_VALIDATION_MULTI_LANG=true # Multi-language syntax validation AUTO_LANGUAGE_DETECTION=true # Automatic programming language detection LANGUAGE_SPECIFIC_OPTIMIZATION=true # Language-specific performance optimizations POLYGLOT_PROJECT_SUPPORT=true # Support for polyglot programming projects LANGUAGE_MIGRATION_TOOLS=true # Tools for migrating code between languages
Config Access Pattern:
# backend/config.py class Config: def __init__(self): self.config = { "processing": { "device": os.getenv("DEVICE", "auto"), "max_concurrent_jobs": int(os.getenv("MAX_CONCURRENT_JOBS", 3)) } } # Usage in modules from config import Config config = Config() device = config.get("processing.device") # auto, cuda, or cpu
ERROR HANDLING PHILOSOPHY:
ENHANCED PATTERN WITH AUTOMATED PROBLEM DETECTION:
@app.route('/api/generate-3d') def generate_3d(): try: # Primary path: Hunyuan3D-2.1 mesh = hunyuan_processor.generate_shape(image) except torch.cuda.OutOfMemoryError as e: # Automatic problem detection and fixing error_context = { 'error_type': 'GPU_MEMORY', 'error_message': str(e), 'system_state': get_gpu_stats() } problem_detector = IntelligentProblemDetector() fix_result = problem_detector.auto_fix_problems([{ 'category': 'GPU_MEMORY', 'automated_fix': True, 'solutions': ['clear_cache', 'reduce_batch_size'] }]) if fix_result['successful_fixes']: logger.info("[ORFEAS] GPU OOM auto-fixed, retrying generation") mesh = hunyuan_processor.generate_shape(image) else: logger.warning("[ORFEAS] GPU OOM - using fallback processor") mesh = FallbackProcessor().generate_shape(image) except FileNotFoundError as e: # Auto-detect and fix model file issues error_context = { 'error_type': 'MODEL_FILES', 'error_message': str(e), 'file_path': getattr(e, 'filename', 'unknown') } problem_detector = IntelligentProblemDetector() detection_result = problem_detector.detect_and_classify_problems(error_context) if detection_result['automated_fixes_available']: fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems']) if fix_result['successful_fixes']: logger.info("[ORFEAS] Model files auto-fixed, retrying generation") mesh = hunyuan_processor.generate_shape(image) else: return jsonify({ 'error': 'AI model not initialized', 'details': 'Please run setup_full_ai.py first', 'diagnostic_info': detection_result, 'auto_fix_attempted': True }), 500 else: logger.error(f"[ORFEAS] Model files missing: {e}") return jsonify({ 'error': 'AI model not initialized', 'details': 'Please run setup_full_ai.py first' }), 500 except Exception as e: # Comprehensive error handling with problem detection error_context = { 'error_type': type(e).__name__, 'error_message': str(e), 'traceback': traceback.format_exc(), 'request_data': request.get_json() if request.is_json else None, 'system_state': get_system_state(), 'timestamp': datetime.utcnow().isoformat() } # Attempt automated problem detection and fixing problem_detector = IntelligentProblemDetector() detection_result = problem_detector.detect_and_classify_problems(error_context) fix_result = None if detection_result['automated_fixes_available']: fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems']) if fix_result['successful_fixes']: logger.info(f"[ORFEAS] Automated fix applied: {fix_result['successful_fixes']}") try: # Retry the operation after fix mesh = hunyuan_processor.generate_shape(image) except Exception as retry_error: logger.error(f"[ORFEAS] Retry after fix failed: {retry_error}") logger.error(f"[ORFEAS] Unexpected error: {traceback.format_exc()}") socketio.emit('generation_error', { 'message': 'Generation failed', 'recoverable': bool(fix_result and fix_result['successful_fixes']), 'diagnostic_info': detection_result, 'auto_fix_attempted': fix_result is not None }) return jsonify({ 'error': str(e), 'error_id': generate_error_id(), 'diagnostic_info': detection_result, 'auto_fix_result': fix_result, 'support_guidance': generate_user_guidance(detection_result) }), 500
Batch Processing for Long Operations:
# backend/batch_processor.py from batch_processor import AsyncJobQueue async_queue = AsyncJobQueue(max_workers=3, gpu_mgr=gpu_manager) # Submit job job = async_queue.submit_job( job_type='3d_generation', image=image_data, quality=7, callback=on_complete ) # Track progress @socketio.on('check_job_status') def check_status(job_id): status = async_queue.get_job_status(job_id) emit('job_status', { 'progress': status.progress, 'stage': status.current_stage, 'eta': status.eta_seconds })
Initial Setup:
# 1. Clone repository git clone --recursive https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1.git cd orfeas # 2. Create Python virtual environment python -m venv .venv .venv\Scripts\Activate.ps1 # Windows source .venv/bin/activate # Linux/Mac # 3. Install dependencies cd backend pip install -r requirements.txt pip install -r requirements-production.txt # For production features # 4. Download Hunyuan3D-2.1 models python download_models.py # 5. Configure environment copy .env.example .env # Edit .env with your settings # 6. Test installation python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"
Backend Server:
# Start backend (development) cd backend python main.py # Start backend (production mode) $env:FLASK_ENV="production" python main.py # Start with monitoring docker-compose -f monitoring_stack/docker-compose-monitoring.yml up -d python main.py # Run tests pytest # All tests pytest -m unit # Unit tests only pytest -m integration # Integration tests pytest --cov=. # With coverage
Docker Deployment:
# Full stack with GPU support docker-compose up -d # Build and restart docker-compose up -d --build # View logs docker-compose logs -f backend docker-compose logs -f frontend # Stop all services docker-compose down # Monitor GPU usage docker exec orfeas-backend-production nvidia-smi
Test Organization:
backend/tests/ ├ conftest.py # Shared fixtures ├ unit/ # Fast isolated tests ├ test_validation.py # Input validation ├ test_config.py # Configuration └ test_utils.py # Utility functions ├ integration/ # API integration tests ├ test_api_endpoints.py # All endpoints ├ test_workflow.py # Complete workflows └ test_formats.py # Format conversion ├ security/ # Security tests ├ test_security_fixes.py # Security fixes (26 tests) └ test_vulnerabilities.py # Attack vectors └ performance/ # Load/benchmark tests ├ test_load.py # Concurrent requests └ test_benchmarks.py # Performance metrics
Run Specific Tests:
# Security validation pytest tests/security/test_security_fixes.py -v # API integration pytest tests/integration/test_api_endpoints.py # Load testing pytest tests/performance/test_load.py -v -s # Coverage report pytest --cov=backend --cov-report=html start htmlcov/index.html # View coverage
COMPREHENSIVE QUALITY AUDIT FRAMEWORK:
The ORFEAS platform implements enterprise-grade TQM audit techniques to ensure continuous quality improvement, compliance adherence, and operational excellence across all AI-powered multimedia generation processes.
# backend/tqm_audit_system.py class TQMAuditSystem: """ Advanced Total Quality Management audit system for enterprise operations """ def __init__(self): self.audit_standards = { 'iso_9001_2015': 'Quality Management Systems', 'iso_27001_2022': 'Information Security Management', 'soc2_type2': 'Security and Availability Controls', 'six_sigma': 'Process Improvement and Defect Reduction', 'cmmi_level5': 'Capability Maturity Model Integration', 'lean_manufacturing': 'Waste Elimination and Efficiency' } self.quality_dimensions = { 'performance': 'Speed, accuracy, and reliability metrics', 'features': 'Functionality and capability assessment', 'reliability': 'Consistency and error rate analysis', 'conformance': 'Standards compliance verification', 'durability': 'Long-term operation stability', 'serviceability': 'Support and maintenance quality', 'aesthetics': 'User experience and interface quality', 'perceived_quality': 'Customer satisfaction metrics' } def conduct_comprehensive_quality_audit(self, audit_scope: str) -> Dict[str, Any]: """Execute comprehensive TQM audit across all quality dimensions""" audit_results = { 'audit_metadata': { 'audit_id': self.generate_audit_id(), 'audit_scope': audit_scope, 'audit_date': datetime.utcnow().isoformat(), 'auditor_team': self.get_audit_team(), 'audit_standards': list(self.audit_standards.keys()) }, 'quality_assessments': {}, 'compliance_verification': {}, 'performance_benchmarks': {}, 'improvement_recommendations': {}, 'risk_assessments': {} } # 1. Performance Quality Audit audit_results['quality_assessments']['performance'] = self.audit_performance_quality() # 2. Process Quality Audit audit_results['quality_assessments']['process'] = self.audit_process_quality() # 3. Product Quality Audit audit_results['quality_assessments']['product'] = self.audit_product_quality() # 4. Service Quality Audit audit_results['quality_assessments']['service'] = self.audit_service_quality() # 5. Compliance Quality Audit audit_results['compliance_verification'] = self.audit_compliance_status() # 6. Security Quality Audit audit_results['quality_assessments']['security'] = self.audit_security_quality() # 7. AI Model Quality Audit audit_results['quality_assessments']['ai_models'] = self.audit_ai_model_quality() return audit_results def audit_performance_quality(self) -> Dict[str, Any]: """Audit system performance against quality benchmarks""" performance_metrics = { 'response_time_analysis': { 'api_endpoints': self.measure_api_response_times(), 'ai_generation_speed': self.measure_generation_performance(), 'database_query_performance': self.measure_db_performance(), 'quality_targets': { 'api_p95_response_time': '<500ms', 'generation_completion_time': '<60s', 'database_query_time': '<100ms' } }, 'throughput_analysis': { 'concurrent_users_supported': self.measure_concurrent_capacity(), 'requests_per_second': self.measure_rps_capacity(), 'resource_utilization': self.measure_resource_efficiency(), 'quality_targets': { 'concurrent_users': '>1000', 'requests_per_second': '>100', 'cpu_utilization_efficiency': '>80%' } }, 'reliability_metrics': { 'uptime_percentage': self.calculate_system_uptime(), 'error_rates': self.analyze_error_patterns(), 'mean_time_to_recovery': self.calculate_mttr(), 'quality_targets': { 'system_uptime': '>99.95%', 'error_rate': '<0.1%', 'mttr': '<15min' } } } return { 'metrics': performance_metrics, 'quality_score': self.calculate_performance_quality_score(performance_metrics), 'compliance_status': self.verify_performance_compliance(performance_metrics), 'improvement_areas': self.identify_performance_improvements(performance_metrics) } def audit_process_quality(self) -> Dict[str, Any]: """Audit business processes for quality, efficiency, and compliance""" process_audit = { 'development_processes': { 'code_review_compliance': self.audit_code_review_process(), 'testing_coverage_analysis': self.audit_testing_processes(), 'deployment_process_quality': self.audit_deployment_practices(), 'documentation_standards': self.audit_documentation_quality() }, 'operational_processes': { 'incident_management': self.audit_incident_processes(), 'change_management': self.audit_change_control(), 'monitoring_effectiveness': self.audit_monitoring_processes(), 'backup_recovery_procedures': self.audit_disaster_recovery() }, 'quality_control_processes': { 'input_validation_procedures': self.audit_input_validation(), 'output_quality_verification': self.audit_output_quality(), 'automated_testing_pipeline': self.audit_ci_cd_quality(), 'user_acceptance_testing': self.audit_uat_processes() } } return { 'process_assessments': process_audit, 'process_maturity_level': self.assess_process_maturity(), 'compliance_gaps': self.identify_process_gaps(), 'optimization_opportunities': self.identify_process_optimizations() } def audit_ai_model_quality(self) -> Dict[str, Any]: """Comprehensive audit of AI model quality and performance""" ai_model_audit = { 'model_accuracy_assessment': { 'hunyuan3d_accuracy': self.measure_3d_generation_accuracy(), 'video_generation_quality': self.measure_video_quality(), 'code_generation_accuracy': self.measure_code_quality(), 'benchmark_comparisons': self.compare_against_benchmarks() }, 'model_bias_analysis': { 'demographic_bias_testing': self.test_demographic_bias(), 'cultural_bias_assessment': self.assess_cultural_bias(), 'fairness_metrics': self.calculate_fairness_metrics(), 'bias_mitigation_effectiveness': self.assess_bias_mitigation() }, 'model_robustness_testing': { 'adversarial_input_resistance': self.test_adversarial_robustness(), 'edge_case_handling': self.test_edge_cases(), 'input_variation_stability': self.test_input_stability(), 'error_handling_quality': self.test_error_scenarios() }, 'model_explainability': { 'decision_transparency': self.assess_model_transparency(), 'output_justification': self.test_explanation_quality(), 'interpretability_scores': self.measure_interpretability(), 'user_understanding_metrics': self.measure_user_comprehension() } } return { 'ai_quality_metrics': ai_model_audit, 'overall_ai_quality_score': self.calculate_ai_quality_score(ai_model_audit), 'regulatory_compliance': self.verify_ai_compliance(), 'ethical_ai_assessment': self.assess_ethical_compliance() } def generate_quality_audit_report(self, audit_results: Dict) -> Dict[str, Any]: """Generate comprehensive TQM audit report with actionable insights""" executive_summary = { 'overall_quality_score': self.calculate_overall_quality_score(audit_results), 'compliance_status': self.summarize_compliance_status(audit_results), 'critical_findings': self.identify_critical_issues(audit_results), 'improvement_priorities': self.prioritize_improvements(audit_results), 'investment_recommendations': self.recommend_quality_investments(audit_results) } detailed_findings = { 'quality_dimension_scores': self.score_quality_dimensions(audit_results), 'process_maturity_assessment': self.assess_process_maturity_levels(audit_results), 'risk_analysis': self.analyze_quality_risks(audit_results), 'benchmark_comparisons': self.compare_industry_benchmarks(audit_results), 'trend_analysis': self.analyze_quality_trends(audit_results) } action_plan = { 'immediate_actions': self.define_immediate_actions(audit_results), 'short_term_improvements': self.plan_short_term_improvements(audit_results), 'long_term_strategic_initiatives': self.plan_strategic_initiatives(audit_results), 'resource_requirements': self.estimate_resource_needs(audit_results), 'success_metrics': self.define_success_metrics(audit_results) } return { 'report_metadata': { 'report_id': self.generate_report_id(), 'generation_date': datetime.utcnow().isoformat(), 'report_version': '1.0', 'next_audit_due': self.calculate_next_audit_date() }, 'executive_summary': executive_summary, 'detailed_findings': detailed_findings, 'action_plan': action_plan, 'appendices': { 'raw_audit_data': audit_results, 'compliance_evidence': self.compile_compliance_evidence(), 'quality_metrics_history': self.retrieve_historical_metrics() } }
CONTINUOUS QUALITY MONITORING:
# backend/continuous_quality_monitor.py class ContinuousQualityMonitor: """ Real-time quality monitoring and continuous improvement system """ def __init__(self): self.quality_thresholds = { 'performance': {'warning': 0.8, 'critical': 0.6}, 'reliability': {'warning': 0.95, 'critical': 0.90}, 'security': {'warning': 0.9, 'critical': 0.8}, 'compliance': {'warning': 0.98, 'critical': 0.95} } def real_time_quality_monitoring(self): """Continuous monitoring of quality metrics with real-time alerting""" while True: try: # Collect real-time quality metrics current_metrics = { 'performance_metrics': self.collect_performance_metrics(), 'reliability_metrics': self.collect_reliability_metrics(), 'security_metrics': self.collect_security_metrics(), 'compliance_metrics': self.collect_compliance_metrics(), 'user_satisfaction_metrics': self.collect_satisfaction_metrics() } # Analyze quality trends quality_trends = self.analyze_quality_trends(current_metrics) # Detect quality degradation quality_issues = self.detect_quality_degradation(current_metrics) # Trigger automated responses if quality_issues: self.trigger_quality_response(quality_issues) # Generate quality alerts alerts = self.generate_quality_alerts(current_metrics, quality_trends) if alerts: self.send_quality_alerts(alerts) # Update quality dashboard self.update_quality_dashboard(current_metrics, quality_trends) time.sleep(30) # Monitor every 30 seconds except Exception as e: logger.error(f"[TQM] Quality monitoring error: {e}") time.sleep(60) def automated_quality_improvement(self, quality_metrics: Dict) -> Dict: """Automated quality improvement based on continuous monitoring""" improvement_actions = { 'performance_optimizations': [], 'reliability_enhancements': [], 'security_strengthening': [], 'compliance_adjustments': [] } # Performance improvements if quality_metrics['performance_score'] < self.quality_thresholds['performance']['warning']: improvement_actions['performance_optimizations'].extend([ 'enable_additional_caching', 'optimize_database_queries', 'scale_horizontal_instances', 'implement_connection_pooling' ]) # Reliability improvements if quality_metrics['reliability_score'] < self.quality_thresholds['reliability']['warning']: improvement_actions['reliability_enhancements'].extend([ 'implement_circuit_breakers', 'enhance_error_handling', 'improve_health_checks', 'strengthen_monitoring' ]) # Security improvements if quality_metrics['security_score'] < self.quality_thresholds['security']['warning']: improvement_actions['security_strengthening'].extend([ 'update_security_policies', 'enhance_access_controls', 'strengthen_encryption', 'improve_audit_logging' ]) return improvement_actions
QUALITY AUDIT AUTOMATION:
# backend/automated_audit_scheduler.py class AutomatedAuditScheduler: """ Automated scheduling and execution of quality audits """ def __init__(self): self.audit_schedule = { 'daily_audits': ['performance', 'security_baseline'], 'weekly_audits': ['process_compliance', 'ai_model_quality'], 'monthly_audits': ['comprehensive_tqm', 'regulatory_compliance'], 'quarterly_audits': ['strategic_quality_review', 'external_audit_prep'], 'annual_audits': ['full_certification_audit', 'quality_system_review'] } def schedule_automated_audits(self): """Schedule and execute automated quality audits""" scheduler = BackgroundScheduler() # Daily audits scheduler.add_job( func=self.execute_performance_audit, trigger='cron', hour=2, # 2 AM daily minute=0, id='daily_performance_audit' ) # Weekly audits scheduler.add_job( func=self.execute_comprehensive_audit, trigger='cron', day_of_week='sun', hour=3, # 3 AM Sunday minute=0, id='weekly_comprehensive_audit' ) # Monthly audits scheduler.add_job( func=self.execute_tqm_audit, trigger='cron', day=1, # First day of month hour=4, # 4 AM minute=0, id='monthly_tqm_audit' ) scheduler.start()
Enable Debug Logging:
# backend/main.py logging.basicConfig(level=logging.DEBUG) # Or via environment $env:LOG_LEVEL="DEBUG" python main.py
GPU Debugging:
# Check GPU availability import torch print(f"CUDA available: {torch.cuda.is_available()}") print(f"GPU device: {torch.cuda.get_device_name(0)}") print(f"VRAM total: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB") # Monitor GPU memory from gpu_manager import get_gpu_manager gpu_mgr = get_gpu_manager() print(gpu_mgr.get_gpu_stats())
Common Issues with Automated Detection:
# Issue: XFORMERS DLL crash (0xc0000139) # Fix: Already handled via XFORMERS_DISABLED=1 in hunyuan_integration.py # Auto-Detection: Pattern 'xformers.*DLL.*0xc0000139' triggers automatic XFORMERS disabling # Issue: GPU out of memory # Fix: Reduce concurrent jobs or enable low_vram_mode $env:MAX_CONCURRENT_JOBS=1 $env:GPU_MEMORY_LIMIT=0.7 # Auto-Detection: OutOfMemoryError triggers automatic cache clearing and job reduction # Issue: Models not found # Fix: Verify Hunyuan3D-2.1 path python backend/download_models.py # Auto-Detection: FileNotFoundError with 'hunyuan3d' triggers automatic model download # Issue: WebSocket disconnects # Fix: Check CORS origins and ping timeout $env:CORS_ORIGINS="*" $env:WS_PING_TIMEOUT=60 # Auto-Detection: ConnectionError with 'websocket' triggers automatic CORS fix and restart # Issue: Context processing failures # Fix: Clear context cache and reset learning system $env:CONTEXT_CACHE_SIZE=500 $env:CONTEXT_SIMILARITY_THRESHOLD=0.9 # Auto-Detection: Context-related errors trigger cache cleanup and threshold adjustment # Issue: AI agent coordination failures # Fix: Restart agent coordinator and verify authentication $env:AGENT_AUTH_ENABLED=true $env:QUALITY_AGENT_ENABLED=true # Auto-Detection: Agent communication errors trigger automatic agent restart # Issue: Text-to-Image generation failures # Fix: Switch to alternative model and verify API keys $env:TEXT_TO_IMAGE_MODEL=stable_diffusion_xl $env:DALLE3_API_KEY=verify_key $env:TEXT_TO_IMAGE_QUALITY=medium # Auto-Detection: Image generation errors trigger model switching # Issue: Text-to-Speech synthesis failures # Fix: Switch TTS engine and verify voice models $env:TTS_MODEL=azure_tts $env:TTS_VOICE_ID=default $env:AUDIO_QUALITY=medium # Auto-Detection: TTS errors trigger engine switching and voice validation # Issue: Speech-to-Text transcription errors # Fix: Switch recognition engine and adjust audio quality $env:SPEECH_MODEL=azure_speech $env:SPEECH_LANGUAGE=en-US $env:ENABLE_REAL_TIME_TRANSCRIPTION=false # Auto-Detection: STT errors trigger engine switching and language detection # Issue: Audio/Image file format compatibility # Fix: Enable format conversion and validate file types $env:ENABLE_AUDIO_CONVERSION=true $env:ENABLE_IMAGE_CONVERSION=true $env:MAX_AUDIO_DURATION=180 # Auto-Detection: Format errors trigger automatic conversion # Issue: Multimedia model loading failures # Fix: Download missing models and verify storage space python backend/download_multimedia_models.py $env:MULTIMEDIA_CACHE_SIZE=10 $env:MODEL_DOWNLOAD_RETRY=3 # Auto-Detection: Model loading errors trigger automatic download # Issue: Model performance degradation # Fix: Trigger model optimization and selection $env:AUTO_MODEL_SWITCHING=true $env:ADAPTIVE_MODEL_SELECTION=true # Auto-Detection: Performance metrics below threshold trigger model optimization # Issue: Security policy violations # Fix: Update security configuration and audit logs $env:SECURITY_MONITORING_LEVEL=HIGH $env:AUDIT_LOGGING_ENABLED=true # Auto-Detection: Security violations trigger automatic policy enforcement
AGENT-SPECIFIC DEBUGGING PATTERNS:
# Issue: Agent authentication failures # Fix: Verify agent credentials and regenerate API keys $env:AGENT_AUTH_ENABLED=true $env:AGENT_API_KEY_REGENERATION=true $env:AGENT_SECURITY_LEVEL="enterprise" # Auto-Detection: 401/403 errors from agent endpoints trigger authentication debugging # Issue: Agent communication timeouts # Fix: Adjust timeout settings and check network connectivity $env:AGENT_COMMUNICATION_TIMEOUT=30 $env:AGENT_RETRY_ATTEMPTS=3 $env:AGENT_HEARTBEAT_INTERVAL=10 # Auto-Detection: Communication timeout errors trigger automatic retry with extended timeout # Issue: Agent load balancing failures # Fix: Restart load balancer and redistribute agent instances $env:AGENT_LOAD_BALANCING=true $env:AGENT_HEALTH_CHECK_INTERVAL=5 $env:AGENT_FAILOVER_ENABLED=true # Auto-Detection: Agent health check failures trigger automatic load redistribution # Issue: Agent performance degradation # Fix: Scale agent instances and optimize resource allocation $env:AGENT_AUTO_SCALING=true $env:AGENT_PERFORMANCE_THRESHOLD=0.8 $env:AGENT_RESOURCE_OPTIMIZATION=true # Auto-Detection: Agent performance below 80% triggers automatic scaling # Issue: Agent workflow orchestration failures # Fix: Reset workflow state and restart orchestration engine $env:WORKFLOW_ORCHESTRATION_ENABLED=true $env:WORKFLOW_STATE_RECOVERY=true $env:WORKFLOW_CHECKPOINT_INTERVAL=30 # Auto-Detection: Workflow interruption triggers automatic state recovery # Issue: Agent memory leaks or resource exhaustion # Fix: Restart agents and implement resource cleanup $env:AGENT_MEMORY_LIMIT="2Gi" $env:AGENT_RESOURCE_CLEANUP_INTERVAL=300 $env:AGENT_GRACEFUL_SHUTDOWN_TIMEOUT=30 # Auto-Detection: Agent memory usage above threshold triggers restart # Issue: Agent learning system failures # Fix: Reset learning state and rebuild experience buffer $env:AGENT_LEARNING_ENABLED=true $env:AGENT_EXPERIENCE_BUFFER_SIZE=1000 $env:AGENT_LEARNING_CHECKPOINT_INTERVAL=60 # Auto-Detection: Learning system errors trigger experience buffer reset # Issue: Agent deployment failures in Kubernetes # Fix: Check deployment manifests and resource availability $env:KUBERNETES_AGENT_NAMESPACE="orfeas-agents" $env:KUBERNETES_AGENT_REPLICAS=3 $env:KUBERNETES_AGENT_AUTO_RECOVERY=true # Auto-Detection: Kubernetes deployment errors trigger manifest validation # Issue: Agent service mesh connectivity issues # Fix: Restart service mesh components and verify configuration $env:AGENT_SERVICE_MESH_ENABLED=true $env:ISTIO_SIDECAR_INJECTION=true $env:SERVICE_MESH_TELEMETRY=true # Auto-Detection: Service mesh errors trigger sidecar restart # Issue: Agent monitoring and observability failures # Fix: Restart monitoring agents and verify metric collection $env:AGENT_MONITORING_ENABLED=true $env:PROMETHEUS_AGENT_METRICS=true $env:GRAFANA_AGENT_DASHBOARDS=true # Auto-Detection: Missing agent metrics trigger monitoring system restart
AGENT DEBUGGING INTEGRATION:
# Enable agent-specific debugging in development from agent_api import OrfeasAIAgent from agent_monitoring import AgentPerformanceMonitor from agent_discovery import AgentServiceDiscovery from problem_detector import IntelligentProblemDetector # Initialize agent debugging system agent_monitor = AgentPerformanceMonitor() agent_discovery = AgentServiceDiscovery() problem_detector = IntelligentProblemDetector() # Manual agent debugging def debug_with_agent_analysis(agent_issue_or_exception): """Debug with comprehensive agent analysis""" # Build agent context agent_context = { 'issue_type': type(agent_issue_or_exception).__name__, 'issue_message': str(agent_issue_or_exception), 'agent_states': agent_monitor.get_agent_states(), 'agent_health': agent_discovery.check_agent_health(), 'system_state': get_system_state(), 'timestamp': datetime.utcnow().isoformat() } # Analyze agent performance performance_analysis = agent_monitor.analyze_agent_performance() print(f"[AGENT-DEBUG] Agent Performance Analysis:") print(f" - Total Agents: {performance_analysis['total_agents']}") print(f" - Healthy Agents: {performance_analysis['healthy_agents']}") print(f" - Average Response Time: {performance_analysis['avg_response_time']:.2f}ms") print(f" - Success Rate: {performance_analysis['success_rate']:.2f}%") # Check agent discovery agent_status = agent_discovery.get_discovery_status() print(f"[AGENT-DEBUG] Agent Discovery Status:") for agent_type, agents in agent_status.items(): print(f" - {agent_type}: {len(agents)} instances") for agent in agents: print(f" * {agent['id']}: {agent['status']} ({agent['last_seen']})") # Detect agent-specific problems agent_problems = problem_detector.detect_agent_problems(agent_context) print(f"[AGENT-DEBUG] Detected Agent Problems: {len(agent_problems)}") for problem in agent_problems: print(f" - {problem['category']}: {problem['description']}") # Apply automated agent fixes if agent_problems: print("[AGENT-DEBUG] Applying automated agent fixes...") fix_results = problem_detector.auto_fix_agent_problems(agent_problems) print(f"[AGENT-DEBUG] Applied fixes: {fix_results}") return { 'performance_analysis': performance_analysis, 'agent_status': agent_status, 'detected_problems': agent_problems, 'fix_results': fix_results if 'fix_results' in locals() else None } # Agent-specific problem detection patterns def detect_agent_specific_issues(): """Detect agent-specific issues with automated patterns""" agent_issues = [] # Check agent authentication status auth_status = agent_discovery.check_agent_authentication() for agent_id, auth_result in auth_status.items(): if not auth_result['authenticated']: agent_issues.append({ 'category': 'AGENT_AUTHENTICATION', 'severity': 'HIGH', 'description': f"Agent {agent_id} authentication failed", 'automated_fix': True, 'solutions': ['regenerate_api_key', 'verify_credentials'] }) # Check agent communication comm_status = agent_discovery.check_agent_communication() for agent_id, comm_result in comm_status.items(): if comm_result['response_time'] > 5000: # > 5 seconds agent_issues.append({ 'category': 'AGENT_COMMUNICATION', 'severity': 'MEDIUM', 'description': f"Agent {agent_id} slow communication: {comm_result['response_time']}ms", 'automated_fix': True, 'solutions': ['restart_agent', 'optimize_network'] }) # Check agent resource usage resource_status = agent_monitor.check_agent_resources() for agent_id, resource_usage in resource_status.items(): if resource_usage['memory_percent'] > 90: agent_issues.append({ 'category': 'AGENT_RESOURCE', 'severity': 'HIGH', 'description': f"Agent {agent_id} high memory usage: {resource_usage['memory_percent']}%", 'automated_fix': True, 'solutions': ['restart_agent', 'increase_memory_limit'] }) return agent_issues # Auto-fix agent issues def auto_fix_agent_issues(agent_issues: List[Dict]) -> Dict[str, Any]: """Automatically fix agent-specific issues""" fix_results = { 'attempted_fixes': [], 'successful_fixes': [], 'failed_fixes': [] } for issue in agent_issues: if issue.get('automated_fix', False): try: category = issue['category'] solutions = issue.get('solutions', []) for solution in solutions: if category == 'AGENT_AUTHENTICATION': result = fix_agent_authentication_issue(solution, issue) elif category == 'AGENT_COMMUNICATION': result = fix_agent_communication_issue(solution, issue) elif category == 'AGENT_RESOURCE': result = fix_agent_resource_issue(solution, issue) else: result = fix_generic_agent_issue(solution, issue) if result['success']: fix_results['successful_fixes'].append({ 'issue': issue, 'solution': solution, 'result': result }) break # Success, no need to try other solutions if not any(fix['issue'] == issue for fix in fix_results['successful_fixes']): fix_results['failed_fixes'].append(issue) except Exception as e: logger.error(f"[AGENT-DEBUG] Failed to fix agent issue {issue['category']}: {e}") fix_results['failed_fixes'].append(issue) return fix_results def fix_agent_authentication_issue(solution: str, issue: Dict) -> Dict[str, Any]: """Fix agent authentication issues""" if solution == 'regenerate_api_key': # Regenerate API key for agent agent_id = issue.get('agent_id') new_api_key = agent_discovery.regenerate_agent_api_key(agent_id) return {'success': True, 'details': f'New API key generated for {agent_id}'} elif solution == 'verify_credentials': # Verify and update agent credentials agent_id = issue.get('agent_id') verification_result = agent_discovery.verify_agent_credentials(agent_id) return {'success': verification_result, 'details': 'Credentials verified and updated'} return {'success': False, 'details': f'Unknown authentication solution: {solution}'} def fix_agent_communication_issue(solution: str, issue: Dict) -> Dict[str, Any]: """Fix agent communication issues""" if solution == 'restart_agent': # Restart specific agent agent_id = issue.get('agent_id') restart_result = agent_discovery.restart_agent(agent_id) return {'success': restart_result, 'details': f'Agent {agent_id} restarted'} elif solution == 'optimize_network': # Optimize network configuration for agent network_optimization = agent_discovery.optimize_agent_network() return {'success': True, 'details': 'Agent network optimized'} return {'success': False, 'details': f'Unknown communication solution: {solution}'} def fix_agent_resource_issue(solution: str, issue: Dict) -> Dict[str, Any]: """Fix agent resource issues""" if solution == 'restart_agent': # Restart agent to free resources agent_id = issue.get('agent_id') restart_result = agent_discovery.restart_agent(agent_id) return {'success': restart_result, 'details': f'Agent {agent_id} restarted for resource cleanup'} elif solution == 'increase_memory_limit': # Increase memory limit for agent agent_id = issue.get('agent_id') limit_result = agent_discovery.increase_agent_memory_limit(agent_id) return {'success': limit_result, 'details': f'Memory limit increased for {agent_id}'} return {'success': False, 'details': f'Unknown resource solution: {solution}'}
Problem Detection Integration:
# Enable automated problem detection in development from problem_detector import IntelligentProblemDetector from problem_prevention import ProactiveProblemPrevention # Initialize problem detection system problem_detector = IntelligentProblemDetector() prevention_system = ProactiveProblemPrevention() # Start proactive monitoring if os.getenv('ENABLE_PROACTIVE_MONITORING', 'true').lower() == 'true': prevention_system.start_continuous_monitoring() # Manual problem detection for debugging def debug_with_problem_detection(error_or_exception): """Debug with intelligent problem detection""" error_context = { 'error_type': type(error_or_exception).__name__, 'error_message': str(error_or_exception), 'traceback': traceback.format_exc() if hasattr(error_or_exception, '__traceback__') else None, 'system_state': get_system_state(), 'timestamp': datetime.utcnow().isoformat() } # Detect problems detection_result = problem_detector.detect_and_classify_problems(error_context) print(f"[DEBUG] Detected Problems: {len(detection_result['detected_problems'])}") for problem in detection_result['detected_problems']: print(f" - {problem['category']}: {problem['pattern']} (Confidence: {problem.get('confidence', 0):.2f})") # Attempt fixes if detection_result['automated_fixes_available']: fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems']) print(f"[DEBUG] Fix Results: {fix_result}") return detection_result
INTELLIGENT PROBLEM DETECTION SYSTEM:
# backend/problem_detector.py class IntelligentProblemDetector: """ Advanced problem identification and automated fixing system """ def __init__(self): self.problem_patterns = self.load_problem_database() self.solution_engine = AutomatedSolutionEngine() self.learning_system = ProblemLearningSystem() def detect_and_classify_problems(self, error_context: Dict) -> Dict[str, Any]: """Detect and classify problems with confidence scoring""" detected_problems = [] # 1. Error pattern matching pattern_matches = self.match_error_patterns(error_context) # 2. System state analysis system_issues = self.analyze_system_state() # 3. Performance degradation detection performance_issues = self.detect_performance_issues() # 4. Resource constraint analysis resource_issues = self.analyze_resource_constraints() # 5. Integration point failures integration_issues = self.check_integration_points() all_issues = { 'error_patterns': pattern_matches, 'system_state': system_issues, 'performance': performance_issues, 'resources': resource_issues, 'integrations': integration_issues } # Classify and prioritize problems classified_problems = self.classify_problems(all_issues) return { 'detected_problems': classified_problems, 'confidence_scores': self.calculate_confidence_scores(classified_problems), 'severity_levels': self.assess_severity(classified_problems), 'automated_fixes_available': self.check_automated_fixes(classified_problems) } def match_error_patterns(self, error_context: Dict) -> List[Dict]: """Match errors against known problem patterns""" patterns = [ { 'pattern': 'torch.cuda.OutOfMemoryError', 'category': 'GPU_MEMORY', 'severity': 'HIGH', 'automated_fix': True, 'solutions': ['reduce_batch_size', 'enable_cpu_fallback', 'clear_cache'] }, { 'pattern': 'FileNotFoundError.*hunyuan3d', 'category': 'MODEL_FILES', 'severity': 'CRITICAL', 'automated_fix': True, 'solutions': ['download_models', 'verify_paths', 'check_permissions'] }, { 'pattern': 'ConnectionError.*websocket', 'category': 'NETWORK', 'severity': 'MEDIUM', 'automated_fix': True, 'solutions': ['restart_websocket', 'check_cors', 'verify_ports'] }, { 'pattern': 'xformers.*DLL.*0xc0000139', 'category': 'DEPENDENCY', 'severity': 'HIGH', 'automated_fix': True, 'solutions': ['disable_xformers', 'reinstall_dependencies'] } ] matches = [] error_message = error_context.get('error_message', '') for pattern in patterns: if re.search(pattern['pattern'], error_message, re.IGNORECASE): matches.append({ **pattern, 'matched_text': error_message, 'confidence': self.calculate_pattern_confidence(pattern, error_context) }) return matches def auto_fix_problems(self, detected_problems: List[Dict]) -> Dict[str, Any]: """Automatically fix detected problems""" fix_results = { 'attempted_fixes': [], 'successful_fixes': [], 'failed_fixes': [], 'manual_intervention_required': [] } for problem in detected_problems: if problem.get('automated_fix', False): fix_result = self.attempt_automated_fix(problem) fix_results['attempted_fixes'].append({ 'problem': problem, 'result': fix_result }) if fix_result['success']: fix_results['successful_fixes'].append(problem) else: fix_results['failed_fixes'].append(problem) else: fix_results['manual_intervention_required'].append(problem) return fix_results def attempt_automated_fix(self, problem: Dict) -> Dict[str, Any]: """Attempt to automatically fix a specific problem""" category = problem['category'] solutions = problem.get('solutions', []) for solution in solutions: try: if category == 'GPU_MEMORY': result = self.fix_gpu_memory_issue(solution) elif category == 'MODEL_FILES': result = self.fix_model_files_issue(solution) elif category == 'NETWORK': result = self.fix_network_issue(solution) elif category == 'DEPENDENCY': result = self.fix_dependency_issue(solution) else: result = self.fix_generic_issue(solution, problem) if result['success']: return { 'success': True, 'solution_applied': solution, 'details': result['details'] } except Exception as e: logger.warning(f"[ORFEAS] Failed to apply solution {solution}: {e}") continue return { 'success': False, 'attempted_solutions': solutions, 'error': 'All automated solutions failed' } def fix_gpu_memory_issue(self, solution: str) -> Dict[str, Any]: """Fix GPU memory related issues""" if solution == 'reduce_batch_size': # Reduce concurrent processing os.environ['MAX_CONCURRENT_JOBS'] = '1' return {'success': True, 'details': 'Reduced concurrent jobs to 1'} elif solution == 'enable_cpu_fallback': # Enable CPU fallback mode os.environ['ENABLE_CPU_FALLBACK'] = 'true' return {'success': True, 'details': 'Enabled CPU fallback processing'} elif solution == 'clear_cache': # Clear GPU cache import torch if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.synchronize() return {'success': True, 'details': 'Cleared GPU cache'} return {'success': False, 'details': f'Unknown solution: {solution}'} def fix_model_files_issue(self, solution: str) -> Dict[str, Any]: """Fix model files related issues""" if solution == 'download_models': # Attempt to download missing models try: subprocess.run(['python', 'backend/download_models.py'], check=True) return {'success': True, 'details': 'Models downloaded successfully'} except subprocess.CalledProcessError as e: return {'success': False, 'details': f'Model download failed: {e}'} elif solution == 'verify_paths': # Verify and fix model paths hunyuan_path = os.getenv('HUNYUAN3D_PATH', '../Hunyuan3D-2.1') if os.path.exists(hunyuan_path): return {'success': True, 'details': 'Model paths verified'} else: return {'success': False, 'details': f'Model path not found: {hunyuan_path}'} return {'success': False, 'details': f'Unknown solution: {solution}'} def fix_network_issue(self, solution: str) -> Dict[str, Any]: """Fix network and connectivity issues""" if solution == 'restart_websocket': # Restart WebSocket connections try: # Implementation depends on your WebSocket framework return {'success': True, 'details': 'WebSocket connections restarted'} except Exception as e: return {'success': False, 'details': f'WebSocket restart failed: {e}'} elif solution == 'check_cors': # Fix CORS configuration os.environ['CORS_ORIGINS'] = '*' return {'success': True, 'details': 'CORS origins updated'} return {'success': False, 'details': f'Unknown solution: {solution}'} def fix_dependency_issue(self, solution: str) -> Dict[str, Any]: """Fix dependency and environment issues""" if solution == 'disable_xformers': # Disable XFORMERS to prevent DLL crashes os.environ['XFORMERS_DISABLED'] = '1' return {'success': True, 'details': 'XFORMERS disabled'} elif solution == 'reinstall_dependencies': # Attempt to reinstall problematic dependencies try: subprocess.run(['pip', 'install', '--force-reinstall', 'torch', 'torchvision'], check=True) return {'success': True, 'details': 'Dependencies reinstalled'} except subprocess.CalledProcessError as e: return {'success': False, 'details': f'Dependency reinstall failed: {e}'} return {'success': False, 'details': f'Unknown solution: {solution}'}
PROACTIVE PROBLEM PREVENTION:
# backend/problem_prevention.py class ProactiveProblemPrevention: """ Proactive problem prevention and system health monitoring """ def __init__(self): self.health_monitors = self.setup_health_monitors() self.prevention_rules = self.load_prevention_rules() self.alert_system = AlertSystem() def continuous_health_monitoring(self): """Continuously monitor system health and prevent problems""" while True: try: # 1. Monitor GPU health gpu_health = self.monitor_gpu_health() if gpu_health['risk_level'] > 0.7: self.prevent_gpu_issues(gpu_health) # 2. Monitor memory usage memory_health = self.monitor_memory_usage() if memory_health['usage_percent'] > 85: self.prevent_memory_issues(memory_health) # 3. Monitor model performance model_health = self.monitor_model_performance() if model_health['degradation_detected']: self.prevent_performance_degradation(model_health) # 4. Monitor file system fs_health = self.monitor_file_system() if fs_health['issues_detected']: self.prevent_file_system_issues(fs_health) # 5. Monitor network connectivity network_health = self.monitor_network_health() if network_health['connectivity_issues']: self.prevent_network_issues(network_health) time.sleep(30) # Check every 30 seconds except Exception as e: logger.error(f"[ORFEAS] Health monitoring error: {e}") time.sleep(60) # Wait longer on error def prevent_gpu_issues(self, gpu_health: Dict): """Prevent GPU-related problems before they occur""" if gpu_health['memory_usage'] > 0.9: # Preemptively clear cache import torch torch.cuda.empty_cache() logger.info("[ORFEAS] Preemptive GPU cache clear performed") if gpu_health['temperature'] > 80: # Reduce processing intensity os.environ['GPU_INTENSITY_LIMIT'] = '0.7' self.alert_system.send_alert('GPU temperature high', 'warning') if gpu_health['utilization'] > 0.95: # Queue new requests instead of processing immediately self.activate_request_queuing() def smart_error_recovery(self, error: Exception, context: Dict) -> Dict[str, Any]: """Intelligent error recovery with learning capabilities""" # 1. Analyze error context error_analysis = self.analyze_error_context(error, context) # 2. Check historical recovery patterns historical_solutions = self.get_historical_solutions(error_analysis) # 3. Apply most successful historical solution first if historical_solutions: recovery_result = self.apply_historical_solution( historical_solutions[0], error, context ) if recovery_result['success']: return recovery_result # 4. Attempt intelligent recovery strategies recovery_strategies = self.generate_recovery_strategies(error_analysis) for strategy in recovery_strategies: try: recovery_result = self.execute_recovery_strategy(strategy, error, context) if recovery_result['success']: # Learn from successful recovery self.learn_from_recovery(error_analysis, strategy, recovery_result) return recovery_result except Exception as recovery_error: logger.warning(f"[ORFEAS] Recovery strategy failed: {recovery_error}") continue # 5. Escalate to manual intervention return { 'success': False, 'recovery_attempted': True, 'escalation_required': True, 'manual_intervention_steps': self.generate_manual_steps(error_analysis) }
AUTOMATED DIAGNOSTICS INTEGRATION:
# Integration with existing error handling @app.errorhandler(Exception) def handle_exception(error): """Enhanced error handler with automated problem detection and fixing""" # 1. Capture comprehensive error context error_context = { 'error_type': type(error).__name__, 'error_message': str(error), 'traceback': traceback.format_exc(), 'request_data': request.get_json() if request.is_json else None, 'system_state': get_system_state(), 'timestamp': datetime.utcnow().isoformat() } # 2. Detect and classify problems problem_detector = IntelligentProblemDetector() detection_result = problem_detector.detect_and_classify_problems(error_context) # 3. Attempt automated fixes if detection_result['automated_fixes_available']: fix_result = problem_detector.auto_fix_problems(detection_result['detected_problems']) if fix_result['successful_fixes']: logger.info(f"[ORFEAS] Automated fix applied: {fix_result['successful_fixes']}") # Retry the operation if fix was successful return retry_operation_after_fix(fix_result) # 4. Smart error recovery prevention_system = ProactiveProblemPrevention() recovery_result = prevention_system.smart_error_recovery(error, error_context) if recovery_result['success']: return recovery_result['response'] # 5. Enhanced error response with diagnostic information return jsonify({ 'error': str(error), 'error_id': generate_error_id(), 'diagnostic_info': { 'detected_problems': detection_result['detected_problems'], 'attempted_fixes': fix_result if 'fix_result' in locals() else None, 'recovery_attempted': recovery_result['recovery_attempted'], 'escalation_required': recovery_result.get('escalation_required', False) }, 'user_guidance': generate_user_guidance(detection_result), 'support_info': generate_support_information(error_context) }), 500
TQM-SPECIFIC DEBUGGING COMMANDS:
# TQM Quality Issue Detection and Resolution # Issue: Quality audit failures or low quality scores # Fix: Trigger comprehensive quality audit and optimization $env:ENABLE_TQM_AUDITS="true" $env:TQM_AUDIT_LEVEL="enterprise" $env:CONTINUOUS_QUALITY_MONITORING="true" # Auto-Detection: Quality scores below threshold trigger automatic audit and improvement # Issue: Performance quality degradation # Fix: Enable performance optimization and monitoring $env:ENABLE_PERFORMANCE_OPTIMIZER="true" $env:AUTO_PERFORMANCE_TUNING="true" $env:PERFORMANCE_THRESHOLD="0.8" # Auto-Detection: Performance metrics below 80% trigger automatic optimization # Issue: Reliability quality issues # Fix: Enhance error handling and monitoring $env:RELIABILITY_QUALITY_THRESHOLD="0.95" $env:ERROR_RECOVERY_ENABLED="true" $env:HEALTH_MONITORING_ENABLED="true" # Auto-Detection: Error rates above 5% trigger reliability enhancement # Issue: Security quality violations # Fix: Strengthen security controls and audit logging $env:SECURITY_HARDENING_ENABLED="true" $env:SECURITY_MONITORING_LEVEL="HIGH" $env:AUDIT_LOGGING_ENABLED="true" # Auto-Detection: Security violations trigger automatic hardening # Issue: Compliance quality failures # Fix: Update compliance framework and validation $env:ISO_9001_COMPLIANCE="true" $env:SOC2_TYPE2_COMPLIANCE="true" $env:COMPLIANCE_QUALITY_THRESHOLD="0.98" # Auto-Detection: Compliance failures trigger automatic validation update # Issue: User satisfaction quality issues # Fix: Enable user feedback integration and satisfaction monitoring $env:USER_SATISFACTION_THRESHOLD="0.85" $env:CUSTOMER_FEEDBACK_INTEGRATION="true" $env:QUALITY_ROI_TRACKING="true" # Auto-Detection: User satisfaction below 85% trigger improvement actions # Issue: Quality audit scheduling failures # Fix: Reset audit scheduler and verify configuration $env:AUTOMATED_AUDIT_SCHEDULING="true" $env:DAILY_AUDITS_ENABLED="true" $env:QUALITY_ALERT_SYSTEM="true" # Auto-Detection: Audit scheduling errors trigger automatic scheduler restart # Issue: Quality metrics collection failures # Fix: Restart metrics collector and verify database connection $env:QUALITY_METRICS_COLLECTOR="enabled" $env:QUALITY_TREND_ANALYSIS="true" $env:BENCHMARK_COMPARISONS="true" # Auto-Detection: Metrics collection errors trigger collector restart # Issue: Quality improvement actions not applied # Fix: Enable automated quality correction and alert system $env:AUTO_QUALITY_CORRECTION="true" $env:PROACTIVE_QUALITY_MANAGEMENT="true" $env:QUALITY_IMPROVEMENT_ENGINE="enabled" # Auto-Detection: Quality degradation triggers automatic improvement actions # Issue: TQM audit report generation failures # Fix: Reset report generator and verify template access $env:AUTO_AUDIT_REPORT_GENERATION="true" $env:EXECUTIVE_DASHBOARD_ENABLED="true" $env:STAKEHOLDER_NOTIFICATIONS="true" # Auto-Detection: Report generation errors trigger template verification # Issue: Quality standards compliance violations # Fix: Update compliance validation and trigger corrective actions $env:SIX_SIGMA_METHODOLOGY="true" $env:CMMI_LEVEL5_TARGET="true" $env:LEAN_MANUFACTURING_PRINCIPLES="true" # Auto-Detection: Standards violations trigger compliance update # Issue: Quality gate enforcement failures # Fix: Reset quality gateway middleware and verify thresholds $env:QUALITY_GATE_ENFORCEMENT="true" $env:OVERALL_QUALITY_THRESHOLD="0.85" $env:QUALITY_ALERT_SYSTEM="true" # Auto-Detection: Gate enforcement errors trigger middleware restart # Issue: Predictive quality modeling failures # Fix: Retrain models and update prediction algorithms $env:AI_POWERED_QUALITY_ANALYSIS="true" $env:PREDICTIVE_QUALITY_MODELING="true" $env:QUALITY_PATTERN_RECOGNITION="true" # Auto-Detection: Modeling failures trigger automatic model retraining
TQM Quality Debugging Integration:
# Enable TQM debugging in development from tqm_audit_system import TQMAuditSystem from continuous_quality_monitor import ContinuousQualityMonitor from problem_detector import IntelligentProblemDetector # Initialize TQM debugging system tqm_audit = TQMAuditSystem() quality_monitor = ContinuousQualityMonitor() problem_detector = IntelligentProblemDetector() # Manual TQM quality debugging def debug_with_tqm_quality_analysis(quality_issue_or_exception): """Debug with comprehensive TQM quality analysis""" # Build quality context quality_context = { 'issue_type': type(quality_issue_or_exception).__name__, 'issue_message': str(quality_issue_or_exception), 'quality_metrics': quality_monitor.collect_current_metrics(), 'audit_status': tqm_audit.get_audit_status(), 'system_state': get_system_state(), 'timestamp': datetime.utcnow().isoformat() } # Conduct comprehensive quality audit audit_results = tqm_audit.conduct_comprehensive_quality_audit('debug_session') print(f"[TQM-DEBUG] Quality Audit Results:") print(f" - Overall Quality Score: {audit_results['executive_summary']['overall_quality_score']:.2f}") print(f" - Performance Quality: {audit_results['quality_assessments']['performance']['quality_score']:.2f}") print(f" - Reliability Quality: {audit_results['quality_assessments']['reliability']['quality_score']:.2f}") print(f" - Security Quality: {audit_results['quality_assessments']['security']['quality_score']:.2f}") # Identify quality improvement opportunities improvements = audit_results['improvement_recommendations'] print(f"[TQM-DEBUG] Quality Improvement Opportunities: {len(improvements)}") for improvement in improvements[:5]: # Show top 5 print(f" - {improvement['category']}: {improvement['description']}") # Apply automated quality corrections if audit_results['executive_summary']['overall_quality_score'] < 0.8: print("[TQM-DEBUG] Applying automated quality corrections...") quality_corrections = quality_monitor.automated_quality_improvement(quality_context) print(f"[TQM-DEBUG] Applied corrections: {quality_corrections}") return audit_results # TQM quality issue detection patterns def detect_tqm_quality_issues(): """Detect TQM-specific quality issues""" quality_issues = [] # Check audit compliance audit_status = tqm_audit.get_audit_status() if audit_status.get('overdue_audits', 0) > 0: quality_issues.append({ 'category': 'AUDIT_COMPLIANCE', 'severity': 'HIGH', 'description': f"{audit_status['overdue_audits']} audits overdue", 'automated_fix': True, 'solutions': ['schedule_immediate_audit', 'update_audit_calendar'] }) # Check quality thresholds current_quality = quality_monitor.assess_current_quality() for dimension, score in current_quality.items(): if score < 0.8: # Below quality threshold quality_issues.append({ 'category': f'QUALITY_{dimension.upper()}', 'severity': 'MEDIUM' if score > 0.6 else 'HIGH', 'description': f"{dimension} quality below threshold: {score:.2f}", 'automated_fix': True, 'solutions': [f'improve_{dimension}_quality', 'trigger_quality_review'] }) # Check compliance status compliance_status = tqm_audit.check_compliance_status() for standard, status in compliance_status.items(): if not status.get('compliant', True): quality_issues.append({ 'category': 'COMPLIANCE_VIOLATION', 'severity': 'CRITICAL', 'description': f"{standard} compliance violation detected", 'automated_fix': True, 'solutions': ['update_compliance_controls', 'schedule_compliance_audit'] }) return quality_issues # Auto-fix TQM quality issues def auto_fix_tqm_quality_issues(quality_issues: List[Dict]) -> Dict[str, Any]: """Automatically fix TQM quality issues""" fix_results = { 'attempted_fixes': [], 'successful_fixes': [], 'failed_fixes': [] } for issue in quality_issues: if issue.get('automated_fix', False): try: category = issue['category'] solutions = issue.get('solutions', []) for solution in solutions: if category.startswith('AUDIT_'): result = fix_audit_issue(solution, issue) elif category.startswith('QUALITY_'): result = fix_quality_issue(solution, issue) elif category.startswith('COMPLIANCE_'): result = fix_compliance_issue(solution, issue) else: result = fix_generic_quality_issue(solution, issue) if result['success']: fix_results['successful_fixes'].append({ 'issue': issue, 'solution': solution, 'result': result }) break # Success, no need to try other solutions if not any(fix['issue'] == issue for fix in fix_results['successful_fixes']): fix_results['failed_fixes'].append(issue) except Exception as e: logger.error(f"[TQM-DEBUG] Failed to fix quality issue {issue['category']}: {e}") fix_results['failed_fixes'].append(issue) return fix_results def fix_audit_issue(solution: str, issue: Dict) -> Dict[str, Any]: """Fix audit-related quality issues""" if solution == 'schedule_immediate_audit': # Schedule immediate audit audit_scheduler = AutomatedAuditScheduler() audit_scheduler.schedule_immediate_audit('comprehensive_tqm') return {'success': True, 'details': 'Immediate audit scheduled'} elif solution == 'update_audit_calendar': # Update audit calendar audit_scheduler = AutomatedAuditScheduler() audit_scheduler.update_audit_schedule() return {'success': True, 'details': 'Audit calendar updated'} return {'success': False, 'details': f'Unknown audit solution: {solution}'} def fix_quality_issue(solution: str, issue: Dict) -> Dict[str, Any]: """Fix quality dimension issues""" if solution.startswith('improve_') and solution.endswith('_quality'): dimension = solution.replace('improve_', '').replace('_quality', '') # Apply dimension-specific improvements improvement_actions = quality_monitor.automated_quality_improvement({ f'{dimension}_score': issue.get('current_score', 0.5) }) return {'success': True, 'details': f'Quality improvement applied for {dimension}'} elif solution == 'trigger_quality_review': # Trigger comprehensive quality review review_result = tqm_audit.conduct_quality_review(issue) return {'success': True, 'details': 'Quality review triggered'} return {'success': False, 'details': f'Unknown quality solution: {solution}'} def fix_compliance_issue(solution: str, issue: Dict) -> Dict[str, Any]: """Fix compliance-related quality issues""" if solution == 'update_compliance_controls': # Update compliance controls compliance_manager = ComplianceManager() compliance_manager.update_controls_for_standard(issue.get('standard')) return {'success': True, 'details': 'Compliance controls updated'} elif solution == 'schedule_compliance_audit': # Schedule compliance-specific audit audit_scheduler = AutomatedAuditScheduler() audit_scheduler.schedule_compliance_audit(issue.get('standard')) return {'success': True, 'details': 'Compliance audit scheduled'} return {'success': False, 'details': f'Unknown compliance solution: {solution}'}
CRITICAL PERFORMANCE OPTIMIZATION:
Hunyuan3D models take 30-36 seconds to load initially. The
_model_cache singleton pattern reduces subsequent loads to <1 second (94% speed improvement).
# CORRECT: Check cache before loading class Hunyuan3DProcessor: _model_cache = {'shapegen_pipeline': None, 'initialized': False} _cache_lock = threading.Lock() def __init__(self, device=None): with Hunyuan3DProcessor._cache_lock: if Hunyuan3DProcessor._model_cache['initialized']: # Instant load from cache self.shapegen_pipeline = self._model_cache['shapegen_pipeline'] return self.initialize_model() # First-time load # WRONG: Loading models every time def generate_3d(): processor = Hunyuan3DProcessor() # 30s load every request! return processor.generate(image)
ALWAYS clean up GPU memory after generation:
# CORRECT: Proper cleanup def generate_3d_model(image): try: mesh = processor.generate_shape(image) return mesh finally: torch.cuda.empty_cache() # CRITICAL: Free unused memory # WRONG: Memory leak def generate_3d_model(image): mesh = processor.generate_shape(image) return mesh # GPU memory not released!
Monitor GPU before processing:
from gpu_manager import get_gpu_manager gpu_mgr = get_gpu_manager() if gpu_mgr.can_process_job(estimated_vram=6000): with gpu_mgr.allocate_job(job_id): result = generate() else: raise ResourceError("Insufficient GPU memory")
Long-running operations MUST use AsyncJobQueue:
# CORRECT: Async processing for 3D generation from batch_processor import get_async_queue async_queue = get_async_queue() job = async_queue.submit_job( job_type='3d_generation', image=image, quality=7 ) # Return job ID immediately return jsonify({'job_id': job.id, 'status': 'queued'}) # WRONG: Blocking request (30-60s timeout) @app.route('/generate-3d') def generate_3d(): mesh = processor.generate_shape(image) # Blocks for 30-60s! return send_file(mesh)
ALWAYS validate STL files before sending:
from stl_processor import AdvancedSTLProcessor processor = AdvancedSTLProcessor() # Analyze mesh quality stats = processor.analyze_stl(mesh_path) if not stats['manifold']: # Repair non-manifold geometry mesh = processor.repair_stl(mesh) # Optimize for 3D printing mesh = processor.optimize_stl_for_printing( mesh, target_size_mm=100, wall_thickness_mm=2.0 ) # Export with validation mesh.export('output.stl')
Track ALL API requests and generations:
from production_metrics import track_request, GenerationTracker @app.route('/api/generate-3d') @track_request('generate_3d') # Automatic request tracking def generate_3d(): tracker = GenerationTracker('3d_generation') try: mesh = processor.generate(image) tracker.success(duration=elapsed, quality=8) except Exception as e: tracker.failure(reason=str(e), duration=elapsed) raise
Access metrics:
ENTERPRISE AGENT AUTHENTICATION & SECURITY:
from agent_auth import AgentAuthManager from agent_security import AgentSecurityManager auth_mgr = AgentAuthManager() security_mgr = AgentSecurityManager() @app.route('/api/agent/quality-check', methods=['POST']) @security_mgr.agent_security_wrapper def agent_quality_check(): """Enterprise-grade agent endpoint with comprehensive security""" agent_id = request.headers.get('X-Agent-ID') api_key = request.headers.get('X-Agent-Key') request_signature = request.headers.get('X-Request-Signature') # Multi-layer authentication if not auth_mgr.authenticate_agent(agent_id, api_key): security_mgr.log_security_event('unauthorized_agent_access', agent_id) return jsonify({'error': 'Unauthorized agent'}), 401 # Permission validation if not auth_mgr.check_permissions(agent_id, 'quality_assessment'): security_mgr.log_security_event('insufficient_permissions', agent_id) return jsonify({'error': 'Insufficient permissions'}), 403 # Request signature validation for critical operations if not security_mgr.validate_request_signature(request, request_signature, agent_id): security_mgr.log_security_event('invalid_signature', agent_id) return jsonify({'error': 'Invalid request signature'}), 401 # Rate limiting for agents if not auth_mgr.check_agent_rate_limits(agent_id, 'quality_assessment'): return jsonify({'error': 'Agent rate limit exceeded'}), 429 return process_quality_check(request.get_json())
ADVANCED AGENT ORCHESTRATION WORKFLOW:
from agent_api import OrfeasAIAgent, AgentOrchestrator from agent_communication import AgentMessageBus from agent_monitoring import AgentPerformanceMonitor # Initialize enterprise agent orchestration orchestrator = AgentOrchestrator() message_bus = AgentMessageBus() performance_monitor = AgentPerformanceMonitor() class EnterpriseAgentWorkflow: """ Advanced agent workflow for complex multi-step processing """ def __init__(self): self.quality_agent = OrfeasAIAgent('quality_agent', [ 'image_analysis', 'complexity_assessment', 'quality_prediction' ]) self.workflow_agent = OrfeasAIAgent('workflow_agent', [ 'pipeline_selection', 'resource_optimization', 'error_recovery' ]) self.optimization_agent = OrfeasAIAgent('optimization_agent', [ 'performance_tuning', 'model_selection', 'parameter_optimization' ]) async def execute_intelligent_3d_generation(self, input_data: Dict) -> Dict: """Execute intelligent 3D generation with multi-agent coordination""" # Phase 1: Intelligent input analysis with performance_monitor.track_agent_performance('quality_agent'): quality_analysis = await self.quality_agent.analyze_input({ 'image': input_data['image'], 'requested_quality': input_data.get('quality', 7), 'user_preferences': input_data.get('user_preferences', {}), 'deadline_constraints': input_data.get('deadline') }) # Phase 2: Workflow optimization with performance_monitor.track_agent_performance('workflow_agent'): workflow_plan = await self.workflow_agent.create_optimal_workflow({ 'quality_analysis': quality_analysis, 'system_resources': self.get_system_resources(), 'current_load': self.get_current_system_load() }) # Phase 3: Parameter optimization with performance_monitor.track_agent_performance('optimization_agent'): optimized_params = await self.optimization_agent.optimize_parameters({ 'workflow_plan': workflow_plan, 'quality_requirements': quality_analysis['quality_requirements'], 'performance_constraints': workflow_plan['performance_constraints'] }) # Phase 4: Coordinated execution with real-time monitoring execution_result = await self.execute_with_monitoring( workflow_plan, optimized_params, input_data ) # Phase 5: Quality validation and feedback final_result = await self.validate_and_enhance_result( execution_result, quality_analysis, optimized_params ) return final_result async def execute_with_monitoring(self, workflow_plan: Dict, params: Dict, input_data: Dict) -> Dict: """Execute generation with real-time agent monitoring and adaptation""" # Initialize execution context execution_context = { 'start_time': time.time(), 'workflow_plan': workflow_plan, 'optimized_params': params, 'agent_states': {} } try: # Select optimal model based on agent analysis if workflow_plan['recommended_model'] == 'hunyuan3d_ultra': processor = UltraQualityHunyuan3DProcessor(params) elif workflow_plan['recommended_model'] == 'hunyuan3d_fast': processor = FastHunyuan3DProcessor(params) elif workflow_plan['recommended_model'] == 'hunyuan3d_balanced': processor = BalancedHunyuan3DProcessor(params) else: # Agent-recommended fallback processor = AdaptiveHunyuan3DProcessor(params) # Execute with agent-driven resource management with self.agent_resource_manager(workflow_plan['resource_allocation']): result = await processor.generate_with_monitoring( input_data, monitoring_callback=self.agent_monitoring_callback ) return result except Exception as e: # Agent-driven error recovery recovery_result = await self.workflow_agent.handle_execution_error( error=e, execution_context=execution_context, recovery_strategies=workflow_plan.get('recovery_strategies', []) ) if recovery_result['recovery_successful']: return recovery_result['recovered_result'] else: raise RuntimeError(f"Agent workflow execution failed: {e}") # Initialize enterprise workflow enterprise_workflow = EnterpriseAgentWorkflow() # Usage in API endpoints @app.route('/api/intelligent-generate-3d', methods=['POST']) async def intelligent_3d_generation(): """AI-driven intelligent 3D generation with agent orchestration""" try: input_data = { 'image': request.files.get('image'), 'quality': int(request.form.get('quality', 7)), 'user_preferences': request.get_json().get('preferences', {}), 'deadline': request.form.get('deadline'), 'priority': request.form.get('priority', 'normal') } # Execute with enterprise agent workflow result = await enterprise_workflow.execute_intelligent_3d_generation(input_data) return jsonify({ 'generation_result': result['output'], 'agent_analysis': result['agent_insights'], 'performance_metrics': result['performance_data'], 'quality_score': result['quality_assessment'] }) except Exception as e: logger.error(f"[ORFEAS] Intelligent generation failed: {e}") return jsonify({'error': str(e)}), 500
AGENT COMMUNICATION & MESSAGE PASSING:
from agent_communication import AgentMessageBus, AgentProtocol class AgentCommunicationPatterns: """ Advanced agent communication patterns for distributed processing """ def __init__(self): self.message_bus = AgentMessageBus() self.protocol = AgentProtocol() async def setup_agent_communication_network(self): """Setup inter-agent communication network""" # Register message handlers for each agent type await self.message_bus.register_handler( 'quality_assessment_request', self.handle_quality_assessment_message ) await self.message_bus.register_handler( 'workflow_optimization_request', self.handle_workflow_optimization_message ) await self.message_bus.register_handler( 'parameter_optimization_request', self.handle_parameter_optimization_message ) # Setup agent discovery and heartbeat await self.setup_agent_discovery() async def coordinate_multi_agent_task(self, task: Dict) -> Dict: """Coordinate complex task across multiple agents""" # Create task coordination context coordination_context = { 'task_id': self.generate_task_id(), 'task_type': task['type'], 'involved_agents': [], 'coordination_strategy': self.select_coordination_strategy(task), 'communication_graph': self.build_communication_graph(task) } # Phase 1: Task decomposition and agent assignment subtasks = await self.decompose_task_for_agents(task, coordination_context) # Phase 2: Parallel agent execution with coordination agent_results = {} async with self.coordination_context(coordination_context): for agent_id, subtask in subtasks.items(): agent_results[agent_id] = await self.execute_agent_subtask( agent_id, subtask, coordination_context ) # Phase 3: Result synthesis and validation synthesized_result = await self.synthesize_agent_results( agent_results, coordination_context ) return synthesized_result # Agent message handling patterns async def handle_quality_assessment_message(message: Dict) -> Dict: """Handle quality assessment requests from other agents""" quality_agent = await get_agent('quality_assessment') assessment_result = await quality_agent.assess_quality({ 'input_data': message['payload']['input_data'], 'assessment_criteria': message['payload']['criteria'], 'requesting_agent': message['sender_id'] }) return { 'message_type': 'quality_assessment_response', 'recipient_id': message['sender_id'], 'payload': assessment_result, 'correlation_id': message['correlation_id'] }
AGENT LEARNING & ADAPTATION PATTERNS:
from agent_learning import AgentLearningSystem, ExperienceBuffer from agent_adaptation import AdaptiveAgent, PerformanceFeedback class IntelligentAdaptiveAgent(OrfeasAIAgent): """ Self-improving agent with learning and adaptation capabilities """ def __init__(self, agent_type: str, capabilities: List[str]): super().__init__(agent_type, capabilities) self.learning_system = AgentLearningSystem() self.experience_buffer = ExperienceBuffer() self.performance_history = {} async def execute_with_learning(self, task: Dict, context: Dict) -> Dict: """Execute task with learning and adaptation""" # Pre-execution: Retrieve relevant experience relevant_experience = self.experience_buffer.get_relevant_experience( task_signature=self.generate_task_signature(task), context_similarity_threshold=0.8 ) # Apply learned optimizations if relevant_experience: optimized_task = self.apply_learned_optimizations(task, relevant_experience) else: optimized_task = task # Execute with performance monitoring start_time = time.time() try: result = await self.execute_core_logic(optimized_task, context) # Calculate performance metrics execution_time = time.time() - start_time performance_score = self.calculate_performance_score(result, execution_time) # Store experience for future learning experience = { 'task': task, 'context': context, 'optimizations_applied': optimized_task != task, 'result': result, 'performance_score': performance_score, 'execution_time': execution_time, 'success': True, 'timestamp': time.time() } self.experience_buffer.store_experience(experience) # Trigger learning if enough new experiences accumulated if self.experience_buffer.should_trigger_learning(): await self.learning_system.update_agent_knowledge(self, self.experience_buffer) return result except Exception as e: # Learn from failures failure_experience = { 'task': task, 'context': context, 'error': str(e), 'error_type': type(e).__name__, 'success': False, 'timestamp': time.time() } self.experience_buffer.store_experience(failure_experience) # Attempt intelligent error recovery recovery_result = await self.attempt_learned_recovery(e, task, context) if recovery_result['recovered']: return recovery_result['result'] else: raise def apply_learned_optimizations(self, task: Dict, experiences: List[Dict]) -> Dict: """Apply optimizations learned from previous similar tasks""" optimized_task = task.copy() # Extract optimization patterns from successful experiences successful_experiences = [exp for exp in experiences if exp['success']] if successful_experiences: # Learn optimal parameters optimal_params = self.learning_system.extract_optimal_parameters( successful_experiences ) optimized_task.update(optimal_params) # Learn optimal preprocessing steps preprocessing_optimizations = self.learning_system.extract_preprocessing_patterns( successful_experiences ) optimized_task['preprocessing'] = preprocessing_optimizations return optimized_task # Agent performance optimization patterns class PerformanceOptimizedAgent(IntelligentAdaptiveAgent): """ Agent optimized for maximum performance with intelligent caching """ def __init__(self, agent_type: str, capabilities: List[str]): super().__init__(agent_type, capabilities) self.result_cache = self.initialize_intelligent_cache() self.performance_optimizer = AgentPerformanceOptimizer() async def execute_with_performance_optimization(self, task: Dict, context: Dict) -> Dict: """Execute with comprehensive performance optimization""" # Check intelligent cache cache_key = self.generate_cache_key(task, context) cached_result = await self.result_cache.get_cached_result(cache_key) if cached_result and self.is_cache_valid(cached_result, context): # Update cache hit metrics self.performance_optimizer.record_cache_hit(cache_key) return cached_result['result'] # No cache hit - execute with optimization optimized_execution = await self.performance_optimizer.optimize_execution( agent=self, task=task, context=context ) result = await self.execute_with_learning(task, context) # Cache result for future use await self.result_cache.cache_result( cache_key, result, ttl=self.calculate_cache_ttl(task, result) ) return result
ENTERPRISE AGENT DEPLOYMENT PATTERNS:
from kubernetes import client, config from agent_deployment import AgentDeploymentManager, AgentScaler class EnterpriseAgentDeployment: """ Enterprise-grade agent deployment and scaling patterns """ def __init__(self): self.deployment_manager = AgentDeploymentManager() self.scaler = AgentScaler() self.k8s_client = self.initialize_kubernetes_client() def deploy_agent_cluster(self, agent_config: Dict) -> Dict: """Deploy agent cluster with auto-scaling and load balancing""" deployment_spec = { 'agent_type': agent_config['type'], 'replicas': agent_config.get('initial_replicas', 3), 'resource_requirements': { 'cpu': agent_config.get('cpu_request', '500m'), 'memory': agent_config.get('memory_request', '1Gi'), 'gpu': agent_config.get('gpu_request', 0) }, 'auto_scaling': { 'min_replicas': agent_config.get('min_replicas', 2), 'max_replicas': agent_config.get('max_replicas', 10), 'target_cpu_utilization': agent_config.get('target_cpu', 70), 'target_memory_utilization': agent_config.get('target_memory', 80) }, 'health_checks': { 'readiness_probe': '/health/ready', 'liveness_probe': '/health/live', 'startup_probe': '/health/startup' } } # Deploy to Kubernetes deployment_result = self.deployment_manager.deploy_to_kubernetes( deployment_spec, namespace=agent_config.get('namespace', 'orfeas-agents') ) # Setup service mesh integration service_mesh_config = self.setup_agent_service_mesh(deployment_result) # Configure monitoring and observability monitoring_config = self.setup_agent_monitoring(deployment_result) return { 'deployment_id': deployment_result['deployment_id'], 'service_endpoints': deployment_result['endpoints'], 'service_mesh_config': service_mesh_config, 'monitoring_config': monitoring_config, 'scaling_config': deployment_spec['auto_scaling'] } def setup_agent_circuit_breaker(self, agent_service: str) -> Dict: """Setup circuit breaker pattern for agent resilience""" circuit_breaker_config = { 'failure_threshold': 5, # Trip after 5 failures 'recovery_timeout': 30, # Try recovery after 30 seconds 'success_threshold': 3, # Close after 3 successes 'request_timeout': 10, # 10 second request timeout 'monitoring_window': 60 # 60 second monitoring window } return self.deployment_manager.configure_circuit_breaker( agent_service, circuit_breaker_config ) # Agent load balancing patterns @app.route('/api/agent-proxy/<agent_type>', methods=['POST']) async def intelligent_agent_proxy(agent_type: str): """Intelligent agent proxy with load balancing and failover""" try: # Get available agent instances available_agents = await agent_discovery.get_healthy_agents(agent_type) if not available_agents: return jsonify({'error': f'No healthy {agent_type} agents available'}), 503 # Select optimal agent based on current load selected_agent = agent_load_balancer.select_optimal_agent( available_agents, request_size=len(request.get_data()), request_complexity=analyze_request_complexity(request) ) # Execute request with circuit breaker protection with agent_circuit_breaker.protected_execution(selected_agent['id']): result = await forward_to_agent(selected_agent, request.get_json()) return jsonify(result) except CircuitBreakerOpenError: # Circuit breaker is open - try fallback fallback_result = await execute_fallback_agent_logic(agent_type, request.get_json()) return jsonify(fallback_result) except Exception as e: logger.error(f"[ORFEAS] Agent proxy error: {e}") return jsonify({'error': 'Agent execution failed'}), 500
ENTERPRISE QUALITY AUDIT WORKFLOW:
Always integrate TQM (Total Quality Management) audit techniques into development and production workflows:
# CORRECT: Integrated quality audit patterns from tqm_audit_system import TQMAuditSystem from continuous_quality_monitor import ContinuousQualityMonitor # Initialize TQM system at application startup tqm_audit = TQMAuditSystem() quality_monitor = ContinuousQualityMonitor() # Start continuous quality monitoring quality_monitor.start_real_time_monitoring() @app.before_request def quality_gate_check(): """Quality gate before processing requests""" # Pre-request quality assessment quality_score = quality_monitor.assess_current_quality() if quality_score < 0.8: # Below quality threshold # Trigger quality improvement actions improvement_actions = quality_monitor.automated_quality_improvement({ 'performance_score': quality_score, 'timestamp': datetime.utcnow() }) logger.warning(f"[TQM] Quality below threshold: {quality_score}, actions: {improvement_actions}") @app.after_request def quality_metrics_collection(response): """Collect quality metrics after each request""" # Post-request quality tracking quality_monitor.track_request_quality({ 'endpoint': request.endpoint, 'response_time': g.get('response_time', 0), 'status_code': response.status_code, 'error_occurred': response.status_code >= 400 }) return response # WRONG: No quality monitoring or audit integration @app.route('/api/generate-3d') def generate_without_quality_checks(): return process_generation() # No quality gates or monitoring!
AUTOMATED QUALITY AUDIT INTEGRATION:
# CORRECT: Comprehensive quality audit workflow from automated_audit_scheduler import AutomatedAuditScheduler audit_scheduler = AutomatedAuditScheduler() def schedule_quality_audits(): """Schedule automated quality audits based on TQM principles""" # Daily performance audits audit_scheduler.schedule_audit('performance', 'daily', { 'focus_areas': ['response_times', 'throughput', 'error_rates'], 'quality_targets': { 'api_response_p95': '<500ms', 'error_rate': '<0.1%', 'uptime': '>99.9%' } }) # Weekly process quality audits audit_scheduler.schedule_audit('process_quality', 'weekly', { 'focus_areas': ['code_review', 'testing_coverage', 'deployment_quality'], 'compliance_standards': ['iso_9001', 'cmmi_level5'] }) # Monthly comprehensive TQM audits audit_scheduler.schedule_audit('comprehensive_tqm', 'monthly', { 'focus_areas': ['all_quality_dimensions'], 'audit_standards': ['iso_9001_2015', 'six_sigma', 'lean_manufacturing'], 'generate_report': True, 'stakeholder_notification': True }) # Execute quality audit after major releases def post_deployment_quality_audit(deployment_info: Dict): """Execute comprehensive quality audit after deployment""" audit_results = tqm_audit.conduct_comprehensive_quality_audit('post_deployment') # Generate executive report audit_report = tqm_audit.generate_quality_audit_report(audit_results) # Check for critical quality issues if audit_report['executive_summary']['overall_quality_score'] < 0.85: # Trigger immediate quality improvement actions logger.critical(f"[TQM] Post-deployment quality below threshold") trigger_quality_improvement_plan(audit_report) return audit_report
CONTINUOUS QUALITY MONITORING PATTERNS:
# CORRECT: Real-time quality monitoring with automated responses class QualityGatewayMiddleware: """Middleware for continuous quality monitoring and control""" def __init__(self): self.quality_monitor = ContinuousQualityMonitor() self.quality_thresholds = { 'performance': 0.8, 'reliability': 0.95, 'security': 0.9, 'user_satisfaction': 0.85 } def before_request(self): """Quality gate before request processing""" current_quality = self.quality_monitor.assess_real_time_quality() # Check quality dimensions for dimension, threshold in self.quality_thresholds.items(): if current_quality.get(dimension, 0) < threshold: # Quality below threshold - take corrective action self.trigger_quality_correction(dimension, current_quality[dimension]) # Store quality context for post-processing analysis g.quality_context = current_quality def after_request(self, response): """Quality measurement and learning after request""" processing_time = time.time() - g.get('start_time', time.time()) # Update quality metrics quality_update = { 'request_quality': g.get('quality_context', {}), 'response_quality': { 'processing_time': processing_time, 'success': response.status_code < 400, 'response_size': len(response.get_data()) } } # Feed quality data to continuous improvement system self.quality_monitor.update_quality_metrics(quality_update) return response def trigger_quality_correction(self, dimension: str, current_score: float): """Automated quality correction based on dimension""" if dimension == 'performance': # Performance quality correction self.enable_performance_optimizations() elif dimension == 'reliability': # Reliability quality correction self.enhance_error_handling() elif dimension == 'security': # Security quality correction self.strengthen_security_controls() logger.info(f"[TQM] Quality correction applied for {dimension}: {current_score}")
QUALITY METRICS INTEGRATION:
# CORRECT: Comprehensive quality metrics collection from prometheus_client import Counter, Histogram, Gauge # TQM-specific metrics quality_audit_counter = Counter( 'tqm_audits_total', 'Total number of TQM audits executed', ['audit_type', 'audit_scope'] ) quality_score_gauge = Gauge( 'tqm_quality_score', 'Current TQM quality score', ['quality_dimension'] ) quality_improvement_histogram = Histogram( 'tqm_improvement_actions_duration', 'Time taken for quality improvement actions', ['improvement_type'] ) def track_quality_metrics(func): """Decorator to track quality metrics for any function""" def wrapper(*args, **kwargs): start_time = time.time() try: result = func(*args, **kwargs) # Track successful quality operation quality_score = calculate_operation_quality_score(result) quality_score_gauge.labels(quality_dimension=func.__name__).set(quality_score) return result except Exception as e: # Track quality failure quality_score_gauge.labels(quality_dimension=func.__name__).set(0) raise finally: # Track operation duration duration = time.time() - start_time quality_improvement_histogram.labels( improvement_type=func.__name__ ).observe(duration) return wrapper # Usage in core functions @track_quality_metrics def generate_3d_with_quality_tracking(image): """3D generation with integrated quality tracking""" # Pre-generation quality assessment input_quality = assess_input_quality(image) if input_quality < 0.7: # Enhance input quality before processing image = enhance_image_quality(image) # Generation with quality monitoring result = processor.generate_shape(image) # Post-generation quality validation output_quality = assess_output_quality(result) if output_quality < 0.8: # Apply quality improvement techniques result = apply_quality_enhancements(result) return result
ENTERPRISE LLM INTEGRATION WORKFLOW:
# backend/llm_integration.py - Core LLM orchestration from langchain_enterprise import LangChainManager from llm_router import LLMRouter from context_manager import IntelligentContextManager class EnterpriseLLMManager: """ Enterprise-grade Large Language Model integration and orchestration """ def __init__(self): self.llm_router = LLMRouter() self.context_manager = IntelligentContextManager() self.active_models = { 'gpt4_turbo': None, 'claude_3_5_sonnet': None, 'gemini_ultra': None, 'llama_3_1_405b': None, 'mistral_8x22b': None } self.model_capabilities = self.load_model_capabilities() def select_optimal_llm(self, task_context: Dict) -> str: """Select best LLM based on task requirements and context""" task_type = task_context.get('task_type', 'general') complexity = task_context.get('complexity_score', 0.5) latency_requirement = task_context.get('max_latency_ms', 5000) # Task-specific model selection if task_type == 'code_generation': if complexity > 0.8: return 'gpt4_turbo' # Best for complex coding tasks else: return 'claude_3_5_sonnet' # Fast and accurate for simple code elif task_type == 'reasoning_analysis': return 'gpt4_turbo' # Superior reasoning capabilities elif task_type == 'content_creation': return 'claude_3_5_sonnet' # Excellent creative writing elif task_type == 'multimodal_understanding': return 'gemini_ultra' # Best multimodal capabilities elif task_type == 'real_time_chat': if latency_requirement < 1000: # < 1 second return 'mistral_8x22b' # Fastest response time else: return 'claude_3_5_sonnet' else: # General purpose return 'gpt4_turbo' # Default to most capable model async def process_with_llm(self, prompt: str, context: Dict, model_override: str = None) -> Dict: """Process request with optimal LLM selection and context awareness""" # Build comprehensive context processing_context = self.context_manager.build_llm_context(context) # Select optimal model selected_model = model_override or self.select_optimal_llm(processing_context) # Apply context-aware prompt enhancement enhanced_prompt = self.enhance_prompt_with_context(prompt, processing_context) # Execute with selected LLM try: result = await self.execute_llm_request(selected_model, enhanced_prompt, processing_context) # Apply post-processing and validation validated_result = self.validate_and_enhance_response(result, processing_context) # Update model performance metrics self.update_model_performance(selected_model, result, processing_context) return validated_result except Exception as e: # Intelligent fallback to alternative models return await self.fallback_llm_processing(prompt, processing_context, failed_model=selected_model) def enhance_prompt_with_context(self, prompt: str, context: Dict) -> str: """Enhance prompt with intelligent context injection""" context_template = """ SYSTEM CONTEXT: - Platform: ORFEAS AI 2D→3D Studio Enterprise - User Expertise: {user_expertise} - Task Complexity: {complexity_score} - Previous Context: {previous_interactions} - Quality Requirements: {quality_requirements} USER REQUEST: {original_prompt} ENHANCED INSTRUCTIONS: - Provide enterprise-grade responses with technical accuracy - Include code examples when relevant (Python/JavaScript preferred) - Consider 3D modeling, AI, and multimedia context - Ensure responses align with ORFEAS platform capabilities """ return context_template.format( user_expertise=context.get('user_expertise', 'intermediate'), complexity_score=context.get('complexity_score', 0.5), previous_interactions=context.get('previous_context', 'None'), quality_requirements=context.get('quality_requirements', 'high'), original_prompt=prompt )
GITHUB COPILOT ENTERPRISE INTEGRATION:
# backend/copilot_enterprise.py - GitHub Copilot Enterprise integration class GitHubCopilotEnterprise: """ GitHub Copilot Enterprise integration for advanced code generation """ def __init__(self): self.copilot_api = self.initialize_copilot_api() self.code_context_manager = CodeContextManager() self.quality_validator = CodeQualityValidator() async def generate_code_with_copilot(self, requirements: str, context: Dict) -> Dict: """Generate code using GitHub Copilot Enterprise with context awareness""" # Build code generation context code_context = { 'project_type': 'orfeas_ai_platform', 'language': context.get('language', 'python'), 'framework': 'flask_pytorch_enterprise', 'coding_standards': 'orfeas_enterprise_standards', 'security_level': 'enterprise_grade', 'existing_codebase': self.code_context_manager.get_relevant_code(requirements) } # Generate code with Copilot copilot_response = await self.copilot_api.generate_code( prompt=self.build_copilot_prompt(requirements, code_context), max_tokens=2000, temperature=0.2, # Low temperature for more deterministic code include_context=True ) # Validate and enhance generated code validated_code = self.quality_validator.validate_and_enhance( code=copilot_response['generated_code'], requirements=requirements, context=code_context ) return { 'generated_code': validated_code['code'], 'quality_score': validated_code['quality_score'], 'suggestions': validated_code['improvement_suggestions'], 'tests': await self.generate_tests_for_code(validated_code['code']), 'documentation': self.generate_code_documentation(validated_code['code']) } def build_copilot_prompt(self, requirements: str, context: Dict) -> str: """Build optimized prompt for GitHub Copilot""" prompt_template = """ # ORFEAS AI Enterprise Platform - Code Generation Request ## Project Context: - Platform: ORFEAS AI 2D→3D Studio Enterprise - Framework: Flask + PyTorch + Hunyuan3D-2.1 - Language: {language} - Security: Enterprise-grade with input validation - Performance: GPU-optimized for RTX 3090 - Quality: Production-ready with comprehensive error handling ## Requirements: {requirements} ## Technical Constraints: - Follow ORFEAS coding patterns and conventions - Include comprehensive error handling and logging - Implement proper input validation and security checks - Use type hints and docstrings - Optimize for GPU memory management - Include monitoring and metrics collection ## Expected Output: - Clean, production-ready code - Comprehensive error handling - Performance optimizations - Security best practices - Proper documentation """ return prompt_template.format( language=context.get('language', 'python'), requirements=requirements )
MULTI-LLM ORCHESTRATION PATTERNS:
# Multi-LLM coordination for complex tasks class MultiLLMOrchestrator: """ Orchestrate multiple LLMs for complex multi-step tasks """ def __init__(self): self.llm_manager = EnterpriseLLMManager() self.task_decomposer = TaskDecomposer() self.result_synthesizer = ResultSynthesizer() async def execute_complex_task(self, task_description: str, context: Dict) -> Dict: """Execute complex task using multiple specialized LLMs""" # 1. Decompose complex task into subtasks subtasks = self.task_decomposer.decompose_task(task_description, context) # 2. Assign optimal LLM for each subtask llm_assignments = {} for subtask in subtasks: optimal_llm = self.llm_manager.select_optimal_llm(subtask['context']) llm_assignments[subtask['id']] = optimal_llm # 3. Execute subtasks in parallel or sequence results = {} for subtask in subtasks: assigned_llm = llm_assignments[subtask['id']] # Execute with assigned LLM result = await self.llm_manager.process_with_llm( prompt=subtask['prompt'], context=subtask['context'], model_override=assigned_llm ) results[subtask['id']] = { 'result': result, 'llm_used': assigned_llm, 'execution_time': result.get('execution_time', 0) } # 4. Synthesize results from multiple LLMs final_result = self.result_synthesizer.synthesize_results( results, task_description, context ) return { 'final_result': final_result, 'execution_breakdown': results, 'llm_assignments': llm_assignments, 'total_execution_time': sum(r['execution_time'] for r in results.values()) }
RAG (RETRIEVAL-AUGMENTED GENERATION) PATTERNS:
# backend/rag_integration.py - Advanced RAG implementation class EnterpriseRAGSystem: """ Enterprise-grade Retrieval-Augmented Generation system """ def __init__(self): self.vector_store = self.initialize_vector_store() self.knowledge_graph = self.initialize_knowledge_graph() self.retrieval_engine = AdvancedRetrievalEngine() self.generation_engine = LLMGenerationEngine() def initialize_vector_store(self): """Initialize enterprise vector database""" from pinecone_enterprise import PineconeEnterprise return PineconeEnterprise( index_name="orfeas_knowledge_base", dimension=1536, # OpenAI embedding dimension metric="cosine", shards=4, # Enterprise scaling replicas=2 # High availability ) async def rag_enhanced_generation(self, query: str, context: Dict) -> Dict: """Generate response using RAG with multiple knowledge sources""" # 1. Multi-source knowledge retrieval retrieved_knowledge = await self.retrieve_relevant_knowledge(query, context) # 2. Knowledge synthesis and ranking synthesized_knowledge = self.synthesize_knowledge_sources(retrieved_knowledge) # 3. Context-aware prompt construction enhanced_prompt = self.build_rag_prompt(query, synthesized_knowledge, context) # 4. LLM generation with retrieved context generated_response = await self.generation_engine.generate_with_context( prompt=enhanced_prompt, context=context, knowledge_context=synthesized_knowledge ) # 5. Response validation and citation validated_response = self.validate_and_cite_response( response=generated_response, sources=retrieved_knowledge, original_query=query ) return { 'response': validated_response['text'], 'confidence_score': validated_response['confidence'], 'citations': validated_response['citations'], 'knowledge_sources': retrieved_knowledge, 'retrieval_quality': self.assess_retrieval_quality(retrieved_knowledge, query) } async def retrieve_relevant_knowledge(self, query: str, context: Dict) -> List[Dict]: """Retrieve relevant knowledge from multiple sources""" retrieval_sources = [ 'orfeas_documentation', 'technical_specifications', 'code_examples', 'best_practices', 'troubleshooting_guides', 'api_references' ] all_retrieved = [] for source in retrieval_sources: # Vector similarity search vector_results = await self.vector_store.similarity_search( query=query, namespace=source, top_k=5, filter=self.build_retrieval_filter(context, source) ) # Knowledge graph traversal graph_results = await self.knowledge_graph.find_related_concepts( query=query, source=source, max_depth=2, relationship_types=['related_to', 'implements', 'extends'] ) # Combine and score results combined_results = self.combine_retrieval_results(vector_results, graph_results) all_retrieved.extend(combined_results) # Rank and deduplicate ranked_results = self.rank_retrieval_results(all_retrieved, query, context) return ranked_results[:10] # Return top 10 most relevant
LLM API ENDPOINTS:
# LLM-powered API endpoints for the ORFEAS platform @app.route('/api/llm/generate-code', methods=['POST']) @track_request('llm_code_generation') def api_llm_generate_code(): """Generate code using enterprise LLM capabilities""" try: data = request.get_json() requirements = data.get('requirements', '') language = data.get('language', 'python') context = data.get('context', {}) if not requirements: return jsonify({'error': 'No requirements provided'}), 400 # Generate code with GitHub Copilot Enterprise copilot = GitHubCopilotEnterprise() result = await copilot.generate_code_with_copilot(requirements, { 'language': language, 'user_context': context }) return jsonify({ 'generated_code': result['generated_code'], 'quality_score': result['quality_score'], 'suggestions': result['suggestions'], 'tests': result['tests'], 'documentation': result['documentation'] }) except Exception as e: logger.error(f"[ORFEAS] LLM code generation failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/llm/intelligent-analysis', methods=['POST']) @track_request('llm_intelligent_analysis') def api_llm_intelligent_analysis(): """Perform intelligent analysis using multiple LLMs""" try: data = request.get_json() analysis_request = data.get('analysis_request', '') analysis_type = data.get('analysis_type', 'general') context = data.get('context', {}) # Use multi-LLM orchestration for complex analysis orchestrator = MultiLLMOrchestrator() result = await orchestrator.execute_complex_task( task_description=f"Perform {analysis_type} analysis: {analysis_request}", context=context ) return jsonify({ 'analysis_result': result['final_result'], 'llm_breakdown': result['execution_breakdown'], 'execution_time': result['total_execution_time'], 'confidence_score': result.get('confidence_score', 0.85) }) except Exception as e: logger.error(f"[ORFEAS] LLM intelligent analysis failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/llm/rag-query', methods=['POST']) @track_request('llm_rag_query') def api_llm_rag_query(): """Answer questions using RAG (Retrieval-Augmented Generation)""" try: data = request.get_json() query = data.get('query', '') context = data.get('context', {}) if not query: return jsonify({'error': 'No query provided'}), 400 # Use RAG system for knowledge-enhanced responses rag_system = EnterpriseRAGSystem() result = await rag_system.rag_enhanced_generation(query, context) return jsonify({ 'response': result['response'], 'confidence_score': result['confidence_score'], 'citations': result['citations'], 'knowledge_sources': result['knowledge_sources'], 'retrieval_quality': result['retrieval_quality'] }) except Exception as e: logger.error(f"[ORFEAS] LLM RAG query failed: {e}") return jsonify({'error': str(e)}), 500
HuggingFace fallback patterns:
from huggingface_compat import HuggingFaceModelManager hf_mgr = HuggingFaceModelManager() try: # Primary: Hunyuan3D-2.1 mesh = hunyuan_processor.generate_3d(image) except Exception as e: logger.warning(f"[ORFEAS] Primary model failed: {e}") try: # Fallback: InstantMesh via HuggingFace mesh = hf_mgr.fallback_generation(prompt, 'instant_mesh') except Exception as e2: logger.error(f"[ORFEAS] All models failed: {e2}") return jsonify({'error': 'Generation failed'}), 500
ComfyUI workflow integration:
# backend/comfyui_integration.py class ComfyUIWorkflowManager: def __init__(self): self.api_url = "http://localhost:8188" self.workflows = { 'text_to_3d': 'workflows/text_to_3d.json', 'image_to_3d': 'workflows/image_to_3d.json', 'style_transfer_3d': 'workflows/style_transfer_3d.json' } async def execute_workflow(self, workflow_type: str, inputs: Dict): """Execute ComfyUI workflow as alternative generation method""" workflow_path = self.workflows.get(workflow_type) if not workflow_path: raise ValueError(f"Unknown workflow: {workflow_type}") # Load and customize workflow with open(workflow_path) as f: workflow = json.load(f) # Update inputs self.update_workflow_inputs(workflow, inputs) # Submit to ComfyUI API response = await self.submit_workflow(workflow) return response
VIDEO GENERATION WORKFLOW:
# backend/video_processor.py - Core video composition from sora_video_integration import SoraVideoProcessor from cinematic_composer import CinematicComposer video_processor = SoraVideoProcessor() cinematic_composer = CinematicComposer() def generate_cinematic_video_from_3d(model_path: str, style: str = "cinematic") -> Dict: """Generate cinematic video from 3D model""" # 1. Analyze 3D model for optimal camera movements model_analysis = video_processor.analyze_3d_model(model_path) # 2. Generate camera motion sequence camera_sequence = cinematic_composer.generate_camera_movements( model_analysis, style=style ) # 3. Apply lighting and environment scene_setup = cinematic_composer.setup_cinematic_scene( model_path, camera_sequence ) # 4. Render video with Sora-inspired generation video_output = video_processor.render_cinematic_video( scene_setup, fps=24, resolution=(1920, 1080) ) return video_output # Text-to-Video generation workflow def generate_video_from_text(prompt: str, duration: int = 10) -> Dict: """Generate cinematic video from text prompt""" # 1. Analyze prompt for visual elements prompt_analysis = video_processor.analyze_text_prompt(prompt) # 2. Generate initial frames key_frames = video_processor.generate_key_frames( prompt, frame_count=duration * 4 # 4 key frames per second ) # 3. Apply temporal consistency consistent_frames = video_processor.apply_temporal_consistency(key_frames) # 4. Generate intermediate frames with motion full_video = video_processor.interpolate_motion( consistent_frames, target_fps=24 ) return full_video
VIDEO API ENDPOINTS:
# Video generation API endpoints @app.route('/api/generate-video-from-3d', methods=['POST']) def api_generate_video_from_3d(): """Generate video from 3D model""" try: model_file = request.files.get('model') style = request.form.get('style', 'cinematic') duration = int(request.form.get('duration', 10)) # Validate 3D model file if not model_file: return jsonify({'error': 'No 3D model provided'}), 400 # Save uploaded model model_path = save_uploaded_file(model_file, 'models') # Generate video video_result = generate_cinematic_video_from_3d(model_path, style) return jsonify({ 'video_path': video_result['output_path'], 'duration': video_result['duration'], 'fps': video_result['fps'], 'resolution': video_result['resolution'] }) except Exception as e: logger.error(f"[ORFEAS] Video generation failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/generate-video-from-text', methods=['POST']) def api_generate_video_from_text(): """Generate video from text prompt""" try: data = request.get_json() prompt = data.get('prompt', '') duration = data.get('duration', 10) style = data.get('style', 'cinematic') if not prompt: return jsonify({'error': 'No prompt provided'}), 400 # Generate video video_result = generate_video_from_text(prompt, duration) return jsonify({ 'video_path': video_result['output_path'], 'prompt': prompt, 'duration': video_result['duration'], 'style': style }) except Exception as e: logger.error(f"[ORFEAS] Text-to-video generation failed: {e}") return jsonify({'error': str(e)}), 500
CINEMATIC FEATURES IMPLEMENTATION:
# backend/cinematic_composer.py - Advanced cinematic features class CinematicComposer: """Advanced cinematic video composition engine""" def __init__(self): self.camera_presets = { 'turntable': self.generate_turntable_motion, 'fly_around': self.generate_orbital_motion, 'dolly_zoom': self.generate_dolly_zoom, 'hero_shot': self.generate_hero_shot } def generate_turntable_motion(self, model_analysis: Dict) -> List[Dict]: """Generate smooth turntable camera motion""" frames = [] center = model_analysis['bounding_box']['center'] radius = model_analysis['bounding_box']['radius'] * 2.5 for frame in range(240): # 10 seconds at 24fps angle = (frame / 240) * 360 # Full rotation camera_pos = { 'x': center['x'] + radius * math.cos(math.radians(angle)), 'y': center['y'] + radius * 0.3, # Slight elevation 'z': center['z'] + radius * math.sin(math.radians(angle)), 'target': center, 'fov': 45 } frames.append(camera_pos) return frames def apply_cinematic_lighting(self, scene: Dict, style: str) -> Dict: """Apply cinematic lighting setup""" # Define color values using RGB to avoid parser issues WHITE = 'rgb(255, 255, 255)' LIGHT_BLUE = 'rgb(240, 240, 255)' WARM_WHITE = 'rgb(255, 248, 225)' WARM_YELLOW = 'rgb(255, 238, 170)' DARK_GRAY = 'rgb(74, 85, 104)' WARM_ORANGE = 'rgb(255, 107, 53)' lighting_presets = { 'studio': { 'key_light': {'intensity': 1.0, 'color': WHITE, 'position': (2, 3, 2)}, 'fill_light': {'intensity': 0.4, 'color': LIGHT_BLUE, 'position': (-1, 2, 1)}, 'rim_light': {'intensity': 0.8, 'color': WARM_WHITE, 'position': (0, 1, -2)} }, 'dramatic': { 'key_light': {'intensity': 1.2, 'color': WARM_YELLOW, 'position': (3, 4, 1)}, 'fill_light': {'intensity': 0.2, 'color': DARK_GRAY, 'position': (-2, 1, 0)}, 'rim_light': {'intensity': 1.0, 'color': WARM_ORANGE, 'position': (-1, 2, -3)} } } scene['lighting'] = lighting_presets.get(style, lighting_presets['studio']) return scene
TEXT-TO-IMAGE GENERATION WORKFLOW:
# backend/text_to_image_processor.py - Core text-to-image generation from text_to_image_ai import TextToImageProcessor from image_enhancement import ImageEnhancer class TextToImageProcessor: """Advanced text-to-image generation with multiple AI engines""" def __init__(self): self.models = { 'dalle3': self.setup_dalle3(), 'stable_diffusion_xl': self.setup_sdxl(), 'midjourney': self.setup_midjourney() } self.enhancer = ImageEnhancer() async def generate_image_from_text(self, prompt: str, style: str = "photorealistic") -> Dict: """Generate high-quality image from text prompt""" # 1. Analyze and enhance prompt enhanced_prompt = self.enhance_prompt(prompt, style) # 2. Select optimal model based on prompt analysis optimal_model = self.select_optimal_model(enhanced_prompt) # 3. Generate image with selected model generated_image = await self.models[optimal_model].generate(enhanced_prompt) # 4. Apply post-processing enhancements enhanced_image = self.enhancer.enhance_image(generated_image) return { 'image': enhanced_image, 'model_used': optimal_model, 'prompt': enhanced_prompt, 'style': style }
TEXT-TO-SPEECH GENERATION WORKFLOW:
# backend/text_to_speech_processor.py - Core TTS generation from speech_synthesis import SpeechSynthesizer from voice_cloning import VoiceCloningEngine class TextToSpeechProcessor: """Enterprise-grade text-to-speech generation""" def __init__(self): self.engines = { 'elevenlabs': self.setup_elevenlabs(), 'azure_tts': self.setup_azure_tts(), 'openai_tts': self.setup_openai_tts() } self.voice_cloner = VoiceCloningEngine() async def synthesize_speech(self, text: str, voice_id: str = "default") -> Dict: """Generate natural speech from text""" # 1. Preprocess text for optimal synthesis processed_text = self.preprocess_text(text) # 2. Select optimal TTS engine optimal_engine = self.select_tts_engine(processed_text, voice_id) # 3. Generate speech audio audio_data = await self.engines[optimal_engine].synthesize( text=processed_text, voice_id=voice_id, quality='high' ) # 4. Apply audio enhancements enhanced_audio = self.enhance_audio_quality(audio_data) return { 'audio_data': enhanced_audio, 'duration': self.get_audio_duration(enhanced_audio), 'engine_used': optimal_engine, 'voice_id': voice_id }
SPEECH-TO-TEXT PROCESSING WORKFLOW:
# backend/speech_to_text_processor.py - Core STT processing from speech_recognition import SpeechRecognizer from audio_preprocessing import AudioPreprocessor class SpeechToTextProcessor: """Advanced speech-to-text with real-time capabilities""" def __init__(self): self.recognizers = { 'whisper_large': self.setup_whisper(), 'azure_speech': self.setup_azure_stt(), 'google_speech': self.setup_google_stt() } self.preprocessor = AudioPreprocessor() async def transcribe_audio(self, audio_data: bytes, language: str = "en-US") -> Dict: """Convert speech audio to text""" # 1. Preprocess audio for optimal recognition processed_audio = self.preprocessor.enhance_audio(audio_data) # 2. Select optimal recognition engine optimal_recognizer = self.select_stt_engine(processed_audio, language) # 3. Perform speech recognition transcription = await self.recognizers[optimal_recognizer].transcribe( audio=processed_audio, language=language, enable_punctuation=True, enable_timestamps=True ) # 4. Post-process transcription enhanced_transcription = self.enhance_transcription(transcription) return { 'text': enhanced_transcription['text'], 'confidence': enhanced_transcription['confidence'], 'timestamps': enhanced_transcription['timestamps'], 'language': language, 'engine_used': optimal_recognizer } async def real_time_transcription(self, audio_stream) -> AsyncGenerator[str, None]: """Real-time speech-to-text streaming""" async for audio_chunk in audio_stream: partial_result = await self.transcribe_audio_chunk(audio_chunk) yield partial_result['text']
MULTIMEDIA API ENDPOINTS:
# Multimedia AI API endpoints @app.route('/api/generate-image-from-text', methods=['POST']) def api_text_to_image(): """Generate image from text prompt""" try: data = request.get_json() prompt = data.get('prompt', '') style = data.get('style', 'photorealistic') if not prompt: return jsonify({'error': 'No prompt provided'}), 400 # Generate image result = text_to_image_processor.generate_image_from_text(prompt, style) return jsonify({ 'image_url': result['image_url'], 'prompt': result['prompt'], 'style': result['style'], 'model_used': result['model_used'] }) except Exception as e: logger.error(f"[ORFEAS] Text-to-image generation failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/synthesize-speech', methods=['POST']) def api_text_to_speech(): """Convert text to speech""" try: data = request.get_json() text = data.get('text', '') voice_id = data.get('voice_id', 'default') if not text: return jsonify({'error': 'No text provided'}), 400 # Generate speech result = tts_processor.synthesize_speech(text, voice_id) return jsonify({ 'audio_url': result['audio_url'], 'duration': result['duration'], 'voice_id': result['voice_id'], 'engine_used': result['engine_used'] }) except Exception as e: logger.error(f"[ORFEAS] Text-to-speech generation failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/transcribe-audio', methods=['POST']) def api_speech_to_text(): """Convert speech to text""" try: audio_file = request.files.get('audio') language = request.form.get('language', 'en-US') if not audio_file: return jsonify({'error': 'No audio file provided'}), 400 # Transcribe audio result = stt_processor.transcribe_audio(audio_file.read(), language) return jsonify({ 'text': result['text'], 'confidence': result['confidence'], 'timestamps': result['timestamps'], 'language': result['language'], 'engine_used': result['engine_used'] }) except Exception as e: logger.error(f"[ORFEAS] Speech-to-text processing failed: {e}") return jsonify({'error': str(e)}), 500
INTELLIGENT CODE GENERATION WORKFLOW:
# backend/code_writer.py - Core code generation from code_writer import IntelligentCodeWriter from code_debugger import AutomatedCodeDebugger code_writer = IntelligentCodeWriter() code_debugger = AutomatedCodeDebugger() def generate_code_from_requirements(requirements: str, language: str = "python") -> Dict: """Generate code from natural language requirements""" # 1. Analyze requirements and extract context analysis = code_writer.analyze_requirements(requirements, { 'target_language': language, 'code_style': 'clean', 'include_tests': True, 'include_docs': True }) # 2. Generate code structure code_structure = code_writer.generate_code_structure(analysis, language) # 3. Generate implementation implementation = code_writer.generate_implementation(code_structure, analysis) # 4. Validate and optimize optimized_code = code_writer.optimize_code(implementation, language) return { 'code': optimized_code, 'structure': code_structure, 'documentation': code_writer.generate_documentation(optimized_code), 'tests': code_writer.generate_tests(optimized_code), 'quality_score': code_writer.quality_analyzer.analyze(optimized_code) } # Code debugging workflow def debug_and_fix_code(code: str, error_message: str, language: str = "python") -> Dict: """Debug code and provide automated fixes""" # 1. Analyze error and code context debug_result = code_debugger.debug_code(code, error_message, language) # 2. Apply automated fixes if confidence is high if debug_result['confidence'] > 0.8: fixed_code = code_debugger.apply_fix(code, debug_result['fix_suggestion']) # 3. Validate the fix validation_result = code_debugger.validate_fix(fixed_code, code) return { 'original_code': code, 'fixed_code': fixed_code, 'error_analysis': debug_result, 'validation': validation_result, 'auto_applied': True } else: return { 'original_code': code, 'error_analysis': debug_result, 'suggested_fixes': debug_result['fix_suggestion'], 'auto_applied': False }
CODE DEVELOPMENT API ENDPOINTS:
# Code generation and debugging API endpoints @app.route('/api/generate-code', methods=['POST']) def api_generate_code(): """Generate code from natural language description""" try: data = request.get_json() requirements = data.get('requirements', '') language = data.get('language', 'python') context = data.get('context', {}) if not requirements: return jsonify({'error': 'No requirements provided'}), 400 # Generate code result = generate_code_from_requirements(requirements, language) return jsonify({ 'generated_code': result['code'], 'documentation': result['documentation'], 'tests': result['tests'], 'quality_score': result['quality_score'], 'language': language }) except Exception as e: logger.error(f"[ORFEAS] Code generation failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/debug-code', methods=['POST']) def api_debug_code(): """Debug code and provide automated fixes""" try: data = request.get_json() code = data.get('code', '') error_message = data.get('error_message', '') language = data.get('language', 'python') if not code or not error_message: return jsonify({'error': 'Code and error message required'}), 400 # Debug and fix code debug_result = debug_and_fix_code(code, error_message, language) return jsonify({ 'debug_analysis': debug_result['error_analysis'], 'fixed_code': debug_result.get('fixed_code'), 'auto_applied': debug_result['auto_applied'], 'suggested_fixes': debug_result.get('suggested_fixes'), 'validation': debug_result.get('validation') }) except Exception as e: logger.error(f"[ORFEAS] Code debugging failed: {e}") return jsonify({'error': str(e)}), 500 @app.route('/api/analyze-code-quality', methods=['POST']) def api_analyze_code_quality(): """Analyze code quality and provide suggestions""" try: data = request.get_json() code = data.get('code', '') language = data.get('language', 'python') if not code: return jsonify({'error': 'No code provided'}), 400 # Analyze code quality quality_analysis = code_writer.quality_analyzer.analyze_comprehensive(code, language) return jsonify({ 'quality_score': quality_analysis['overall_score'], 'metrics': quality_analysis['metrics'], 'suggestions': quality_analysis['suggestions'], 'refactoring_opportunities': quality_analysis['refactoring'], 'language': language }) except Exception as e: logger.error(f"[ORFEAS] Code quality analysis failed: {e}") return jsonify({'error': str(e)}), 500
CODE DEVELOPMENT FEATURES IMPLEMENTATION:
# backend/code_analyzer.py - Advanced code analysis class CodeQualityAnalyzer: """Advanced code quality analysis and suggestions""" def __init__(self): self.analysis_engines = { 'syntax': self.analyze_syntax, 'style': self.analyze_style, 'complexity': self.analyze_complexity, 'performance': self.analyze_performance, 'security': self.analyze_security } def analyze_comprehensive(self, code: str, language: str) -> Dict: """Perform comprehensive code analysis""" results = {} for analysis_type, analyzer in self.analysis_engines.items(): try: results[analysis_type] = analyzer(code, language) except Exception as e: logger.warning(f"[ORFEAS] {analysis_type} analysis failed: {e}") results[analysis_type] = {'score': 0, 'issues': [str(e)]} # Calculate overall score overall_score = self.calculate_overall_score(results) return { 'overall_score': overall_score, 'metrics': results, 'suggestions': self.generate_suggestions(results), 'refactoring': self.identify_refactoring_opportunities(code, results) } def generate_suggestions(self, analysis_results: Dict) -> List[str]: """Generate actionable suggestions for code improvement""" suggestions = [] for analysis_type, result in analysis_results.items(): if result['score'] < 0.7: # Below acceptable threshold suggestions.extend(result.get('suggestions', [])) return suggestions def identify_refactoring_opportunities(self, code: str, analysis: Dict) -> List[Dict]: """Identify specific refactoring opportunities""" opportunities = [] # Complexity-based refactoring if analysis.get('complexity', {}).get('score', 1) < 0.6: opportunities.append({ 'type': 'extract_method', 'description': 'Consider extracting complex logic into separate methods', 'priority': 'high' }) # Performance-based refactoring if analysis.get('performance', {}).get('score', 1) < 0.7: opportunities.append({ 'type': 'optimize_loops', 'description': 'Optimize loops and data structures for better performance', 'priority': 'medium' }) return opportunities
CONTEXT-AWARE PROCESSING WORKFLOW:
# Always build context before processing from context_manager import IntelligentContextManager context_mgr = IntelligentContextManager() @app.route('/api/generate-3d', methods=['POST']) def generate_3d_with_context(): """Context-aware 3D generation with intelligent decision making""" # 1. Build comprehensive processing context context = context_mgr.build_processing_context({ 'image': request.files.get('image'), 'user_id': request.headers.get('X-User-ID'), 'quality_preference': request.form.get('quality', 7), 'deadline': request.form.get('deadline'), 'priority': request.form.get('priority', 'normal') }) # 2. Get contextual recommendations recommendations = context_mgr.get_contextual_recommendations(context) # 3. Select optimal model based on context from contextual_model_selector import ContextualModelSelector model_selector = ContextualModelSelector() model_choice = model_selector.select_optimal_model(context) # 4. Execute with context-aware error handling try: result = execute_generation_with_context( model_choice['selected_model'], context, recommendations ) # 5. Learn from successful execution model_selector.learn_from_result(context, model_choice['selected_model'], result) return jsonify(result) except Exception as e: # 6. Context-aware error recovery from contextual_error_handler import ContextualErrorHandler error_handler = ContextualErrorHandler(context_mgr) recovery_result = await error_handler.handle_error_with_context(e, context) return jsonify(recovery_result)
AGENT COORDINATION WITH CONTEXT:
# Coordinate multiple agents with shared context from contextual_agent_coordinator import ContextualAgentCoordinator async def intelligent_3d_generation_workflow(image_data: Dict): """Multi-agent workflow with intelligent context sharing""" coordinator = ContextualAgentCoordinator(context_mgr) # Define agents for the workflow workflow_agents = [ 'quality_assessment_agent', 'model_selection_agent', 'resource_optimization_agent', 'generation_execution_agent' ] # Execute coordinated workflow with context task = { 'type': '3d_generation', 'input_data': image_data, 'quality_requirements': {'min_quality': 7, 'max_time': 60} } results = await coordinator.coordinate_with_context(task, workflow_agents) return results
CONTEXT-AWARE MODEL SELECTION:
# Intelligent model selection based on context analysis def select_generation_model_with_context(request_data: Dict) -> str: """Select optimal generation model based on comprehensive context""" # Build context context = context_mgr.build_processing_context(request_data) # Analyze input characteristics input_analysis = context['input_analysis'] # Context-based decision tree if input_analysis['complexity_score'] > 0.8: if context['resource_context']['available_vram'] > 16000: # > 16GB return 'hunyuan3d_ultra_quality' else: return 'hunyuan3d_high_quality_optimized' elif input_analysis['input_type'] == 'simple_object': if context['user_context']['speed_preference'] == 'fast': return 'hunyuan3d_fast' else: return 'hunyuan3d_balanced' elif context['system_context']['current_load'] > 0.8: # High system load - use lighter model return 'instant_mesh_lightweight' else: # Default to balanced model with context optimization return 'hunyuan3d_context_optimized'
CONTEXT PERSISTENCE AND LEARNING:
# Persist context for continuous learning from context_persistence import ContextPersistenceManager persistence_mgr = ContextPersistenceManager() def process_with_learning(session_id: str, request_data: Dict): """Process request with context learning and persistence""" # Build context context = context_mgr.build_processing_context(request_data) # Get historical insights insights = persistence_mgr.get_contextual_insights(context) # Apply learned optimizations if insights['success_probability'] > 0.9: # High confidence - use recommended parameters processing_params = insights['optimal_parameters'] else: # Lower confidence - use conservative parameters processing_params = get_conservative_parameters(context) # Execute processing start_time = time.time() try: result = execute_generation(processing_params) # Record successful context success_context = { **context, 'result_quality': result['quality_score'], 'processing_time': time.time() - start_time, 'success': True } persistence_mgr.persist_context(session_id, success_context) return result except Exception as e: # Record failure context for learning failure_context = { **context, 'error_type': type(e).__name__, 'error_message': str(e), 'processing_time': time.time() - start_time, 'success': False } persistence_mgr.persist_context(session_id, failure_context) raise
CONTEXT-AWARE RESOURCE ALLOCATION:
# Intelligent resource allocation based on context def allocate_resources_with_context(context: Dict) -> Dict: """Allocate computational resources based on context analysis""" allocation = { 'gpu_memory': 8000, # Default 8GB 'cpu_threads': 4, # Default 4 threads 'batch_size': 1, # Default single processing 'quality_level': 7 # Default quality } # Adjust based on input complexity if context['input_analysis']['complexity_score'] > 0.8: allocation['gpu_memory'] = 12000 # 12GB for complex inputs allocation['quality_level'] = 9 # Adjust based on system load if context['system_context']['current_load'] > 0.7: allocation['cpu_threads'] = 2 # Reduce CPU usage allocation['batch_size'] = 1 # No batching under high load # Adjust based on user priority if context.get('user_context', {}).get('priority') == 'high': allocation['gpu_memory'] = min(allocation['gpu_memory'] * 1.5, 16000) allocation['cpu_threads'] = min(allocation['cpu_threads'] * 2, 8) # Adjust based on deadline constraints deadline = context.get('quality_context', {}).get('deadline') if deadline and deadline < 30: # Less than 30 seconds allocation['quality_level'] = 5 # Reduce quality for speed allocation['gpu_memory'] = 6000 # Use less memory for faster processing return allocation
SPEED OPTIMIZATION WORKFLOW:
# Apply comprehensive speed optimizations from performance_optimizer import PerformanceOptimizer optimizer = PerformanceOptimizer() # Model-level optimizations @app.before_first_request def optimize_models(): """Apply optimizations before serving requests""" global hunyuan_processor # Apply speed optimizations to all models hunyuan_processor.shapegen_pipeline = optimizer.apply_speed_optimizations( hunyuan_processor.shapegen_pipeline, 'shape_generation' ) hunyuan_processor.texgen_pipeline = optimizer.apply_speed_optimizations( hunyuan_processor.texgen_pipeline, 'texture_generation' ) # Pre-warm caches optimizer.warm_model_caches() logger.info("[ORFEAS] Performance optimizations applied successfully") # Request-level optimizations @app.before_request def optimize_request(): """Apply per-request optimizations""" # Cache request context for faster processing g.start_time = time.time() g.optimization_context = optimizer.build_optimization_context(request) # Apply request-specific optimizations if g.optimization_context.get('enable_fast_mode', False): g.processing_mode = 'fast' elif g.optimization_context.get('enable_quality_mode', False): g.processing_mode = 'quality' else: g.processing_mode = 'balanced' @app.after_request def track_performance(response): """Track and optimize based on performance metrics""" processing_time = time.time() - g.get('start_time', time.time()) # Update performance metrics optimizer.update_performance_metrics( endpoint=request.endpoint, processing_time=processing_time, mode=g.get('processing_mode', 'unknown'), success=response.status_code < 400 ) # Trigger auto-optimization if performance degrades if optimizer.should_auto_optimize(processing_time): optimizer.trigger_auto_optimization() return response
ACCURACY ENHANCEMENT PATTERNS:
# Quality-first generation workflow from accuracy_enhancer import AccuracyEnhancer enhancer = AccuracyEnhancer() def generate_high_accuracy_3d(image_data: bytes, quality_requirements: Dict) -> Dict: """Generate 3D model with maximum accuracy focus""" # Build accuracy-focused context context = { 'accuracy_priority': True, 'quality_requirements': quality_requirements, 'enable_ensemble': quality_requirements.get('enable_ensemble', False), 'enable_refinement': quality_requirements.get('enable_refinement', True) } # Multi-stage accuracy enhancement try: # Stage 1: Input analysis and preprocessing preprocessed_input = enhancer.preprocess_for_accuracy( {'image': image_data}, context ) # Stage 2: Enhanced generation result = enhancer.enhance_generation_accuracy(preprocessed_input, context) # Stage 3: Quality validation if result['accuracy_metrics']['quality_score'] < 0.85: logger.warning("[ORFEAS] Quality below threshold, applying refinement") result = enhancer.apply_advanced_refinement(result, context) return result except Exception as e: logger.error(f"[ORFEAS] High-accuracy generation failed: {e}") # Fallback to standard generation with basic enhancement return enhancer.generate_with_fallback_enhancement(preprocessed_input) # Quality monitoring and feedback loop @app.route('/api/quality-feedback', methods=['POST']) def submit_quality_feedback(): """Receive user quality feedback for continuous improvement""" feedback = request.get_json() # Update accuracy models based on feedback enhancer.update_quality_models( generation_id=feedback['generation_id'], user_rating=feedback['rating'], specific_issues=feedback.get('issues', []), improvements_suggested=feedback.get('improvements', []) ) # Trigger model retraining if needed if enhancer.should_retrain_quality_models(): enhancer.schedule_quality_model_retraining() return jsonify({'status': 'feedback_received', 'quality_updated': True})
SECURITY OPTIMIZATION PATTERNS:
# Multi-layer security validation from security_hardening import SecurityHardening security = SecurityHardening() def secure_3d_generation_workflow(image_file, user_context: Dict) -> Dict: """Secure 3D generation with comprehensive protection""" # Layer 1: Input validation and sanitization try: validated_file = security.comprehensive_file_validation(image_file) except SecurityError as e: logger.security(f"[ORFEAS-SECURITY] File validation failed: {e}") raise ValidationError("File failed security validation") # Layer 2: User authentication and authorization if not security.validate_user_permissions(user_context, 'generate_3d'): logger.security(f"[ORFEAS-SECURITY] Unauthorized access attempt from {user_context}") raise AuthorizationError("Insufficient permissions") # Layer 3: Rate limiting and abuse protection if not security.check_rate_limits(user_context['user_id'], 'generation'): logger.security(f"[ORFEAS-SECURITY] Rate limit exceeded for user {user_context['user_id']}") raise RateLimitError("Generation rate limit exceeded") # Layer 4: Secure processing environment with security.secure_processing_context() as secure_env: result = process_generation_securely(validated_file, secure_env) # Layer 5: Output sanitization sanitized_result = security.sanitize_generation_output(result) # Layer 6: Audit logging security.log_secure_operation( operation='3d_generation', user=user_context['user_id'], input_hash=security.hash_input(validated_file), output_hash=security.hash_output(sanitized_result), success=True ) return sanitized_result # Real-time threat detection @app.before_request def detect_threats(): """Real-time threat detection and mitigation""" threat_level = security.assess_request_threat_level(request) if threat_level >= security.THREAT_HIGH: # Block immediately and alert security.block_request_and_alert(request, threat_level) abort(403, "Request blocked due to security threat") elif threat_level >= security.THREAT_MEDIUM: # Enhanced monitoring security.enable_enhanced_monitoring(request) g.security_enhanced = True # Log all security assessments security.log_threat_assessment(request, threat_level)
ML INTEGRATION OPTIMIZATION PATTERNS:
# Optimized ML pipeline execution from ml_integration_optimizer import MLIntegrationOptimizer ml_optimizer = MLIntegrationOptimizer() def optimized_ai_generation_pipeline(input_data: Dict, performance_profile: str) -> Dict: """Execute AI generation with optimal ML integration""" # Select optimal pipeline configuration pipeline_config = ml_optimizer.get_optimal_pipeline_config(performance_profile) # Apply ML-specific optimizations optimized_pipeline = ml_optimizer.optimize_ml_pipeline(pipeline_config) # Execute with intelligent resource management with ml_optimizer.managed_ml_execution(optimized_pipeline) as executor: # Pre-execution optimizations executor.prepare_models(input_data) executor.allocate_optimal_resources() # Parallel execution stages if optimized_pipeline['parallel_stages']: results = executor.execute_parallel_stages( input_data, optimized_pipeline['parallel_stages'] ) else: results = executor.execute_sequential_pipeline(input_data) # Post-execution cleanup executor.cleanup_resources() return results # Adaptive model selection based on real-time performance class AdaptiveModelSelector: def __init__(self): self.performance_history = {} self.model_rankings = {} def select_optimal_model(self, task_context: Dict) -> str: """Select best model based on current conditions""" # Analyze current system state system_state = self.analyze_system_state() # Consider task requirements task_requirements = self.extract_task_requirements(task_context) # Historical performance analysis historical_performance = self.get_historical_performance( task_requirements, system_state ) # Multi-objective model selection model_scores = {} for model_name in self.available_models: scores = { 'speed_score': self.calculate_speed_score(model_name, system_state), 'accuracy_score': self.calculate_accuracy_score(model_name, task_context), 'resource_score': self.calculate_resource_score(model_name, system_state), 'reliability_score': self.calculate_reliability_score(model_name) } # Weighted combination based on priorities final_score = self.combine_scores(scores, task_requirements) model_scores[model_name] = final_score # Select best model optimal_model = max(model_scores, key=model_scores.get) # Update selection history self.update_selection_history(optimal_model, task_context, system_state) return optimal_model # Initialize adaptive components adaptive_selector = AdaptiveModelSelector() @app.route('/api/adaptive-generate', methods=['POST']) def adaptive_3d_generation(): """AI generation with adaptive optimization""" # Extract request context task_context = { 'image_complexity': analyze_image_complexity(request.files['image']), 'quality_preference': request.form.get('quality', 7), 'speed_preference': request.form.get('speed_preference', 'balanced'), 'user_priority': get_user_priority(request.headers.get('X-User-ID')) } # Select optimal model adaptively optimal_model = adaptive_selector.select_optimal_model(task_context) # Execute with selected optimization profile performance_profile = determine_performance_profile(task_context) result = optimized_ai_generation_pipeline( {'image': request.files['image']}, performance_profile ) # Update performance tracking adaptive_selector.update_performance_tracking(optimal_model, result) return jsonify(result)
Model Location:
Hunyuan3D-2.1/Hunyuan3D-2/ ├ hunyuan3d-dit-v2-1/ # Shape generation (3.3GB) ├ hunyuan3d-paintpbr-v2-1/ # Texture generation (2GB) └ hy3dgen/ # Core generation pipelines
VRAM Requirements:
Environment Variables:
HUNYUAN3D_PATH=../Hunyuan3D-2.1 DEVICE=cuda # auto, cuda, cpu GPU_MEMORY_LIMIT=0.8 # 80% safety margin XFORMERS_DISABLED=1 # Prevent DLL crash
CRITICAL: NVIDIA Container Runtime Required
# docker-compose.yml services: backend: deploy: resources: reservations: devices: - driver: nvidia count: 1 capabilities: [gpu]
Verify GPU access:
docker exec orfeas-backend-production nvidia-smi
Progress Updates Pattern:
# Backend: Emit progress during generation from flask_socketio import emit @socketio.on('start_generation') def handle_generation(data): for progress in range(0, 100, 10): emit('generation_progress', { 'job_id': job_id, 'progress': progress, 'stage': 'shape_generation', 'eta_seconds': estimated_remaining })
// Frontend: Listen for updates socket.on("generation_progress", (data) => { updateProgressBar(data.progress); updateETA(data.eta_seconds); });
Security-Critical Patterns:
from validation import FileUploadValidator validator = FileUploadValidator() # ALWAYS validate before processing image = validator.validate_image(request.files['file']) # Checks: file type, size, dimensions, malicious content # NEVER trust user input filename = secure_filename(request.files['file'].filename)
Allowed formats:
MAX_FILE_SIZE)Agent Service Discovery:
# backend/agent_discovery.py class AgentServiceDiscovery: def __init__(self): self.registered_agents = {} self.agent_capabilities = {} def register_agent(self, agent_id: str, capabilities: List[str], endpoint: str): """Register AI agent with its capabilities""" self.registered_agents[agent_id] = endpoint self.agent_capabilities[agent_id] = capabilities def find_agents_for_task(self, required_capability: str) -> List[str]: """Find agents capable of handling specific task""" capable_agents = [] for agent_id, capabilities in self.agent_capabilities.items(): if required_capability in capabilities: capable_agents.append(agent_id) return capable_agents
Agent Communication Protocol:
# Inter-agent communication for complex workflows async def coordinate_generation_workflow(image_data: bytes, quality_requirements: Dict): """Coordinate multiple AI agents for optimal 3D generation""" # 1. Quality assessment agent analyzes input quality_agent = await get_agent('quality_assessment') analysis = await quality_agent.analyze_input(image_data) # 2. Workflow agent selects optimal pipeline workflow_agent = await get_agent('workflow_orchestration') pipeline = await workflow_agent.select_pipeline(analysis) # 3. Optimization agent configures parameters optimization_agent = await get_agent('performance_optimization') optimized_params = await optimization_agent.optimize_parameters(pipeline, analysis) # 4. Execute generation with optimized settings result = await execute_generation_pipeline(pipeline, optimized_params) return result
Multi-Model Fallback Chain:
# backend/multi_model_manager.py class MultiModelGenerationManager: def __init__(self): self.model_chain = [ 'hunyuan3d_2_1', # Primary model 'instant_mesh', # HuggingFace fallback 'stable_diffusion_3d', # SD-based 3D generation 'comfyui_workflow' # ComfyUI alternative ] async def generate_with_fallback(self, input_data: Dict) -> Dict: """Try models in order until one succeeds""" last_error = None for model_name in self.model_chain: try: logger.info(f"[ORFEAS] Attempting generation with {model_name}") result = await self.generate_with_model(model_name, input_data) # Validate result quality if self.validate_generation_quality(result): logger.info(f"[ORFEAS] Successful generation with {model_name}") return result else: logger.warning(f"[ORFEAS] {model_name} produced low-quality result") continue except Exception as e: logger.warning(f"[ORFEAS] {model_name} failed: {e}") last_error = e continue # All models failed logger.error(f"[ORFEAS] All generation models failed. Last error: {last_error}") raise RuntimeError("All generation methods exhausted")
Model Performance Monitoring:
# Track performance metrics for each AI model from production_metrics import ModelPerformanceTracker perf_tracker = ModelPerformanceTracker() @perf_tracker.track_model_performance async def generate_with_model(model_name: str, input_data: Dict): """Generate 3D model with performance tracking""" start_time = time.time() try: model = await load_model(model_name) result = await model.generate(input_data) # Track success metrics duration = time.time() - start_time perf_tracker.record_success(model_name, duration, result.quality_score) return result except Exception as e: # Track failure metrics duration = time.time() - start_time perf_tracker.record_failure(model_name, duration, str(e)) raise
CONTEXT DATABASE SETUP:
# backend/context_database.py class ContextDatabase: """ Context persistence and retrieval system """ def __init__(self, db_path: str = "data/context.db"): self.db_path = db_path self.connection = self.initialize_database() def initialize_database(self): """Create context tables if they don't exist""" conn = sqlite3.connect(self.db_path) conn.execute(""" CREATE TABLE IF NOT EXISTS context_history ( id INTEGER PRIMARY KEY AUTOINCREMENT, session_id TEXT NOT NULL, timestamp REAL NOT NULL, context_data TEXT NOT NULL, context_hash TEXT NOT NULL, success BOOLEAN NOT NULL, processing_time REAL, quality_score REAL, model_used TEXT, resource_usage TEXT ) """) conn.execute(""" CREATE INDEX IF NOT EXISTS idx_context_hash ON context_history(context_hash); """) conn.execute(""" CREATE INDEX IF NOT EXISTS idx_timestamp ON context_history(timestamp); """) return conn
CONTEXT-AWARE INITIALIZATION:
# Always initialize context manager at startup from context_manager import IntelligentContextManager from contextual_agent_coordinator import ContextualAgentCoordinator def initialize_context_system(): """Initialize intelligent context handling system""" # Initialize core context manager context_mgr = IntelligentContextManager() # Initialize agent coordinator with context agent_coordinator = ContextualAgentCoordinator(context_mgr) # Register available agents agent_coordinator.register_agent( 'quality_assessment', ['image_analysis', 'complexity_scoring'], 'http://localhost:5001/quality' ) agent_coordinator.register_agent( 'model_selection', ['model_recommendation', 'performance_prediction'], 'http://localhost:5002/model-select' ) # Start context learning background task if os.getenv('CONTEXT_LEARNING_ENABLED') == 'true': context_mgr.start_learning_daemon() return context_mgr, agent_coordinator # Initialize at application startup context_manager, agent_coordinator = initialize_context_system()
CONTEXT MIDDLEWARE INTEGRATION:
# Flask middleware for automatic context building from flask import g @app.before_request def build_request_context(): """Build context for every request automatically""" if not hasattr(g, 'context'): request_data = { 'endpoint': request.endpoint, 'method': request.method, 'user_agent': request.headers.get('User-Agent'), 'ip_address': request.remote_addr, 'timestamp': time.time() } # Add file data if present if request.files: request_data['files'] = { name: { 'filename': file.filename, 'size': len(file.read()), 'content_type': file.content_type } for name, file in request.files.items() } # Reset file streams for file in request.files.values(): file.seek(0) # Build comprehensive context g.context = context_manager.build_processing_context(request_data) g.recommendations = context_manager.get_contextual_recommendations(g.context) @app.after_request def update_context_learning(response): """Update context learning based on request outcome""" if hasattr(g, 'context') and hasattr(g, 'processing_result'): # Update learning based on request success/failure success = response.status_code < 400 learning_data = { 'context': g.context, 'success': success, 'status_code': response.status_code, 'processing_time': getattr(g, 'processing_time', 0), 'response_size': len(response.get_data()) } context_manager.update_learning(learning_data) return response
CONTEXT-BASED MONITORING:
# Enhanced monitoring with context awareness from prometheus_client import Counter, Histogram, Gauge # Context-aware metrics context_processing_time = Histogram( 'context_processing_seconds', 'Time spent on context processing', ['context_type', 'complexity_level'] ) context_accuracy = Gauge( 'context_prediction_accuracy', 'Accuracy of context-based predictions', ['prediction_type'] ) context_cache_hits = Counter( 'context_cache_hits_total', 'Number of context cache hits', ['cache_type'] ) def track_context_performance(func): """Decorator to track context-aware performance""" def wrapper(*args, **kwargs): context = g.get('context', {}) complexity = context.get('input_analysis', {}).get('complexity_score', 0) complexity_level = 'simple' if complexity < 0.3 else 'medium' if complexity < 0.7 else 'complex' with context_processing_time.labels( context_type=context.get('input_analysis', {}).get('input_type', 'unknown'), complexity_level=complexity_level ).time(): result = func(*args, **kwargs) return result return wrapper
COMPREHENSIVE PRODUCTION DEPLOYMENT GUIDE:
For complete production deployment, enterprise sales enablement, industry partnerships, and competitive market positioning documentation, see:
📄 md/ORFEAS_PRODUCTION_DEPLOYMENT_OPTIMIZATION.md
This comprehensive document includes:
Key Production Metrics:
Enterprise Deployment Capabilities:
══════════════════════════════════════════════════════════════════════════════╗ ⚔️ END OF ORFEAS COPILOT INSTRUCTIONS ⚔️ â•' â•' GITHUB COPILOT: Use these instructions for all ORFEAS AI 2D→3D Studio work â•' AGENT BEHAVIOR: Autonomous, precise, project-specific code generation â•' â•' CODE QUALITY: Python best practices, GPU optimization, security-first â•' â•' â•' â•' ══════════════════════════════════════════════════════════════════════════════