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title: "Aurora AI Framework - Troubleshooting Guide | Error Resolution & Support" description: "Complete troubleshooting guide for Aurora AI Framework v1.0.0 - Common issues, error diagnosis, resolution procedures, and support for all 57 integrated systems and 132 API endpoints." keywords: "Aurora AI troubleshooting, AI framework errors, enterprise AI support, error resolution, system diagnostics, AI debugging, performance issues, API troubleshooting" author: "Aurora Development Team" robots: "index, follow" canonical: "https://aurora-ai.github.io/docs/TROUBLESHOOTING.md"

Aurora AI Framework - Complete Troubleshooting Guide

🌟 Overview

This comprehensive troubleshooting guide covers common issues, error diagnosis, and resolution procedures for all 9 core modules and 132 API endpoints in the Aurora AI framework.

🚀 Current System Status: LIVE

  • Web Interface: http://localhost:8081 - ACTIVE
  • Server: Aurora AI Sci-Fi Interface - RUNNING
  • Debug Mode: Enabled (PIN: 343-268-059)
  • API Health: All endpoints responding
  • Last Updated: 2026-05-06

📚 Related Documentation: For system architecture understanding, see our Architecture Guide. For API reference, check our API Documentation.

🚀 Quick Help: For installation issues, see our Installation Guide. For configuration problems, check our Configuration Guide.

🔧 Performance: For performance issues, see our Performance Guide. For monitoring, check our Monitoring Guide.

🚨 Emergency Procedures

System Outage Response

  1. Immediate Assessment

    # Check system status
curl -X GET "http://localhost:8080/api/status"

# Check health endpoint
curl -X GET "http://localhost:8080/api/health"

# Verify core systems
curl -X GET "http://localhost:8080/api/core/components"

  2. Service Recovery

    # Restart web backend
python web_backend/server.py

# Verify database connections
curl -X GET "http://localhost:8080/api/data/inventory"

# Check security systems
curl -X GET "http://localhost:8080/api/security/status"

  3. Data Integrity Check

    # Validate data integrity
curl -X POST "http://localhost:8080/api/validation/quality" \
  -H "Content-Type: application/json" \
  -d '{"scope": "comprehensive", "dataset_id": "emergency_check"}'

🔍 Common Issues and Solutions

1. Connection Issues

Problem: API Connection Timeout

Symptoms:

  • Requests to /api/status timeout
  • Connection refused errors
  • Slow response times

Solutions:

# Check if server is running
ps aux | grep python

# Restart server if needed
python web_backend/server.py

# Check port availability
netstat -tlnp | grep :8080

# Verify firewall settings
sudo ufw status

Debug Code:

import requests
import time

def test_connection():
    try:
        start_time = time.time()
        response = requests.get("http://localhost:8080/api/status", timeout=10)
        duration = time.time() - start_time
        print(f"Connection successful in {duration:.2f}s")
        return True
    except requests.exceptions.Timeout:
        print("Connection timeout - server may be overloaded")
        return False
    except requests.exceptions.ConnectionError:
        print("Connection refused - server not running")
        return False

Problem: Database Connection Issues

Symptoms:

  • Data validation failures
  • Model training errors
  • Pipeline execution failures

Solutions:

# Check database status
curl -X GET "http://localhost:8080/api/data/inventory"

# Verify data pipeline
curl -X GET "http://localhost:8080/api/pipeline/status"

# Run data diagnostics
curl -X POST "http://localhost:8080/api/orchestration/diagnostics" \
  -H "Content-Type: application/json" \
  -d '{"component": "database"}'

2. Performance Issues

Problem: Slow Response Times

Symptoms:

  • API responses > 5 seconds
  • Training pipeline slowdown
  • Inference latency issues

Diagnosis:

# Check performance metrics
curl -X GET "http://localhost:8080/api/monitoring/performance"

# Analyze system resources
curl -X GET "http://localhost:8080/api/resources/status"

# Run performance analysis
curl -X POST "http://localhost:8080/api/optimization/analyze" \
  -H "Content-Type: application/json" \
  -d '{"scope": "full_system", "depth": "comprehensive"}'

Solutions:

# Execute optimization
curl -X POST "http://localhost:8080/api/optimization/execute" \
  -H "Content-Type: application/json" \
  -d '{"plan": "auto", "level": "conservative"}'

# Optimize resources
curl -X POST "http://localhost:8080/api/resources/optimize" \
  -H "Content-Type: application/json" \
  -d '{"scope": "full_system", "strategy": "balanced"}'

# Scale inference service
curl -X POST "http://localhost:8080/api/inference/scale" \
  -H "Content-Type: application/json" \
  -d '{"target_instances": 3, "scaling_policy": "auto"}'

Problem: High Memory Usage

Symptoms:

  • System memory > 80%
  • Out of memory errors
  • Service crashes

Diagnosis:

# Check resource status
curl -X GET "http://localhost:8080/api/resources/status"

# Monitor memory usage
curl -X GET "http://localhost:8080/api/monitoring/metrics"

Solutions:

# Clean up unused data
curl -X POST "http://localhost:8080/api/data/cleanup" \
  -H "Content-Type: application/json" \
  -d '{"cleanup_type": "aggressive", "retention_days": 7}'

# Optimize resource allocation
curl -X POST "http://localhost:8080/api/resources/allocate" \
  -H "Content-Type: application/json" \
  -d '{"type": "optimization", "priority": "high"}'

3. Data Issues

Problem: Data Validation Failures

Symptoms:

  • Schema validation errors
  • Data quality issues
  • Training data corruption

Diagnosis:

# Run comprehensive data validation
curl -X POST "http://localhost:8080/api/validation/quality" \
  -H "Content-Type: application/json" \
  -d '{"scope": "comprehensive", "dataset_id": "problem_dataset"}'

# Check schema validation
curl -X POST "http://localhost:8080/api/validation/schema" \
  -H "Content-Type: application/json" \
  -d '{"schema_type": "json_schema", "data": {"test": "data"}}'

# Statistical validation
curl -X POST "http://localhost:8080/api/validation/statistical" \
  -H "Content-Type: application/json" \
  -d '{"type": "comprehensive", "confidence": 0.95}'

Solutions:

# Clean and repair data
curl -X POST "http://localhost:8080/api/data/cleanup" \
  -H "Content-Type: application/json" \
  -d '{"cleanup_type": "repair", "auto_fix": true}'

# Restore from backup
curl -X POST "http://localhost:8080/api/data/backup" \
  -H "Content-Type: application/json" \
  -d '{"backup_type": "restore", "source": "latest_backup"}'

Problem: Model Training Failures

Symptoms:

  • Training pipeline errors
  • Model accuracy degradation
  • Training timeout issues

Diagnosis:

# Check training status
curl -X GET "http://localhost:8080/api/training/status"

# Analyze training errors
curl -X GET "http://localhost:8080/api/errors/history"

# Check model repository
curl -X GET "http://localhost:8080/api/models/repository"

Solutions:

# Restart training with optimized parameters
curl -X POST "http://localhost:8080/api/training/enhanced" \
  -H "Content-Type: application/json" \
  -d '{"algorithm": "RandomForest", "optimization": true, "timeout": 3600}'

# Compare with previous models
curl -X POST "http://localhost:8080/api/models/compare" \
  -H "Content-Type: application/json" \
  -d '{"model_ids": ["MDL-001", "MDL-002"], "metrics": ["accuracy", "performance"]}'

# Use hyperparameter optimization
curl -X POST "http://localhost:8080/api/training/hyperopt" \
  -H "Content-Type: application/json" \
  -d '{"algorithm": "RandomForest", "optimization_method": "bayesian"}'

4. Security Issues

Problem: Authentication Failures

Symptoms:

  • 401 Unauthorized errors
  • JWT token issues
  • Access denied errors

Diagnosis:

# Check security status
curl -X GET "http://localhost:8080/api/security/status"

# Test encryption
curl -X POST "http://localhost:8080/api/security/encrypt" \
  -H "Content-Type: application/json" \
  -d '{"action": "test", "data": "test_data"}'

Solutions:

# Regenerate secrets
curl -X POST "http://localhost:8080/api/config/secrets" \
  -H "Content-Type: application/json" \
  -d '{"action": "regenerate", "scope": "authentication"}'

# Validate configuration
curl -X POST "http://localhost:8080/api/config/validate" \
  -H "Content-Type: application/json" \
  -d '{"validate_security": true}'

Problem: Data Encryption Issues

Symptoms:

  • Encryption failures
  • Data corruption
  • Performance degradation

Diagnosis:

# Test encryption functionality
curl -X POST "http://localhost:8080/api/security/encrypt" \
  -H "Content-Type: application/json" \
  -d '{"action": "encrypt", "data": "test_data", "algorithm": "AES-256"}'

Solutions:

# Reset encryption keys
curl -X POST "http://localhost:8080/api/config/secrets" \
  -H "Content-Type: application/json" \
  -d '{"action": "reset_keys", "algorithm": "AES-256"}'

5. Integration Issues

Problem: External System Integration Failures

Symptoms:

  • API connection failures
  • Data synchronization issues
  • Third-party service errors

Diagnosis:

# Run integration tests
curl -X POST "http://localhost:8080/api/integration/test" \
  -H "Content-Type: application/json" \
  -d '{"scope": "external_systems", "type": "connectivity"}'

# Validate system compatibility
curl -X POST "http://localhost:8080/api/integration/validate" \
  -H "Content-Type: application/json" \
  -d '{"level": "comprehensive", "compatibility": true}'

Solutions:

# Reconfigure integration
curl -X POST "http://localhost:8080/api/config/merge" \
  -H "Content-Type: application/json" \
  -d '{"config_files": ["integration_config.yaml"], "validate": true}'

📊 Error Codes and Solutions

HTTP Status Codes

  • 200 OK: Request successful
  • 400 Bad Request: Invalid request format
  • 401 Unauthorized: Authentication required
  • 403 Forbidden: Insufficient permissions
  • 404 Not Found: Endpoint not found
  • 500 Internal Server Error: Server error
  • 503 Service Unavailable: System temporarily unavailable

Aurora-Specific Error Codes

  • QUANTUM_VALIDATION_ERROR: Data validation failed
  • QUANTUM_TRAINING_ERROR: Model training error
  • QUANTUM_SECURITY_ERROR: Security system error
  • QUANTUM_RESOURCE_ERROR: Resource allocation error
  • QUANTUM_INTEGRATION_ERROR: Integration failure

🛠️ Debug Tools and Utilities

System Health Monitor

import requests
import time
from datetime import datetime

class AuroraHealthMonitor:
    def __init__(self, base_url="http://localhost:8080"):
        self.base_url = base_url
        self.health_status = {}
    
    def check_all_systems(self):
        """Check health of all Aurora systems"""
        endpoints = [
            ('Core System', '/api/status'),
            ('Training Pipeline', '/api/training/status'),
            ('Security System', '/api/security/status'),
            ('Data Pipeline', '/api/pipeline/status'),
            ('Inference Service', '/api/inference/status'),
            ('Resource Management', '/api/resources/status'),
            ('Monitoring System', '/api/monitoring/advanced')
        ]
        
        for name, endpoint in endpoints:
            try:
                response = requests.get(f"{self.base_url}{endpoint}", timeout=5)
                self.health_status[name] = {
                    'status': 'HEALTHY' if response.status_code == 200 else 'UNHEALTHY',
                    'response_time': response.elapsed.total_seconds(),
                    'last_check': datetime.now().isoformat()
                }
            except Exception as e:
                self.health_status[name] = {
                    'status': 'ERROR',
                    'error': str(e),
                    'last_check': datetime.now().isoformat()
                }
        
        return self.health_status
    
    def generate_health_report(self):
        """Generate comprehensive health report"""
        health_data = self.check_all_systems()
        
        report = {
            'timestamp': datetime.now().isoformat(),
            'overall_health': 'HEALTHY' if all(
                status['status'] == 'HEALTHY' for status in health_data.values()
            ) else 'DEGRADED',
            'systems': health_data,
            'recommendations': []
        }
        
        # Add recommendations based on health status
        for name, status in health_data.items():
            if status['status'] != 'HEALTHY':
                report['recommendations'].append(
                    f"Check {name} - {status.get('error', 'Unknown error')}"
                )
        
        return report

Performance Analyzer

class AuroraPerformanceAnalyzer:
    def __init__(self, base_url="http://localhost:8080"):
        self.base_url = base_url
    
    def analyze_performance(self):
        """Analyze system performance"""
        try:
            # Get performance metrics
            response = requests.get(f"{self.base_url}/api/monitoring/performance")
            perf_data = response.json()
            
            # Get resource status
            resource_response = requests.get(f"{self.base_url}/api/resources/status")
            resource_data = resource_response.json()
            
            analysis = {
                'timestamp': datetime.now().isoformat(),
                'performance_metrics': perf_data,
                'resource_utilization': resource_data,
                'bottlenecks': [],
                'recommendations': []
            }
            
            # Identify bottlenecks
            if resource_data.get('system_resources', {}).get('cpu', {}).get('utilization', 0) > 80:
                analysis['bottlenecks'].append('High CPU utilization')
            
            if resource_data.get('system_resources', {}).get('memory', {}).get('utilization', 0) > 85:
                analysis['bottlenecks'].append('High memory utilization')
            
            # Generate recommendations
            if analysis['bottlenecks']:
                analysis['recommendations'].append('Consider resource optimization')
                analysis['recommendations'].append('Review system scaling requirements')
            
            return analysis
            
        except Exception as e:
            return {'error': f'Performance analysis failed: {str(e)}'}

📞 Support Escalation

Level 1 Support (Self-Service)

  • Use this troubleshooting guide
  • Check system logs: /api/logs/errors
  • Run diagnostics: /api/orchestration/diagnostics
  • Review documentation

Level 2 Support (Advanced Issues)

  • Complex integration problems
  • Performance optimization
  • Security configuration
  • Custom development issues

Level 3 Support (Critical Issues)

  • System outages
  • Data corruption
  • Security breaches
  • Complete system failures

🔄 Preventive Maintenance

Daily Checks

# System health check
curl -X GET "http://localhost:8080/api/status"

# Resource monitoring
curl -X GET "http://localhost:8080/api/resources/status"

# Error log review
curl -X GET "http://localhost:8080/api/logs/errors"

Weekly Maintenance

# Comprehensive system validation
curl -X POST "http://localhost:8080/api/integration/validate" \
  -H "Content-Type: application/json" \
  -d '{"level": "comprehensive"}'

# Performance optimization
curl -X POST "http://localhost:8080/api/optimization/execute" \
  -H "Content-Type: application/json" \
  -d '{"plan": "auto", "level": "conservative"}'

# Data backup verification
curl -X POST "http://localhost:8080/api/data/backup" \
  -H "Content-Type: application/json" \
  -d '{"backup_type": "verify"}'

Monthly Maintenance

# Full system benchmarking
curl -X POST "http://localhost:8080/api/integration/benchmark" \
  -H "Content-Type: application/json" \
  -d '{"type": "comprehensive", "load": "normal"}'

# Security audit
curl -X GET "http://localhost:8080/api/security/status"

# Configuration review
curl -X POST "http://localhost:8080/api/config/validate" \
  -H "Content-Type: application/json" \
  -d '{"validate_all": true}'

Aurora AI Troubleshooting Guide
Comprehensive Error Resolution • System Diagnostics • Support Procedures

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