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Comprehensive Error Handling System Guide

Overview​

The Enterprise RAG Error Handling System transforms technical errors into user-friendly experiences with guided resolution paths. This system provides contextual error messages, intelligent recovery suggestions, escalation management, and comprehensive analytics.

Key Features​

  • User-Friendly Error Classification: Maps technical errors to understandable categories
  • Contextual Error Messages: Explains what happened and why in plain language
  • Intelligent Recovery Suggestions: Provides actionable steps based on user context
  • Automatic Escalation: Routes critical errors to human support when needed
  • Comprehensive Analytics: Tracks error patterns and user satisfaction
  • Feedback Collection: Gathers user input on error message effectiveness

Architecture​

Core Components​

  1. ErrorClassifier: Categorizes technical errors into user-friendly types
  2. ErrorRecoverySystem: Generates intelligent recovery suggestions
  3. ErrorEscalationManager: Handles escalation to human support
  4. ErrorAnalytics: Tracks and analyzes error patterns
  5. ComprehensiveErrorHandler: Orchestrates all components

Error Categories​

  • RETRIEVAL_FAILURE: Issues accessing knowledge base
  • GENERATION_TIMEOUT: Response generation taking too long
  • PERMISSION_DENIED: Access restrictions
  • RATE_LIMIT_EXCEEDED: Too many requests
  • AUTHENTICATION_FAILED: Login/session issues
  • NETWORK_ERROR: Connection problems
  • DATA_NOT_FOUND: No relevant information found
  • CONFIGURATION_ERROR: System configuration issues
  • UNKNOWN_ERROR: Unclassified errors

Error Severity Levels​

  • LOW: Minor issues, minimal impact
  • MEDIUM: Moderate issues, some user impact
  • HIGH: Significant issues, major user impact
  • CRITICAL: System-breaking issues, immediate attention required

Usage​

Basic Error Handling​

from packages.rag.error_handling import create_error_handler, ErrorContext

# Create error handler
handler = create_error_handler()

# Create error context
context = ErrorContext(
    user_id="user123",
    session_id="session456",
    query="How to configure the API?",
    component="api_configuration"
)

# Handle an error
try:
    # Some operation that might fail
    result = risky_operation()
except Exception as e:
    user_error = await handler.handle_error(e, context)
    print(f"User-friendly message: {user_error.message}")
    print(f"Suggested actions: {user_error.suggested_actions}")

Advanced Error Handling with User Context​

# Add user context for better recovery suggestions
user_context = {
    "user_id": "user123",
    "role": "developer",
    "experience_level": "intermediate",
    "preferred_language": "en"
}

user_error = await handler.handle_error(
    error, 
    context, 
    user_context=user_context
)

# Get recovery suggestions tailored to user
recovery_suggestions = user_error.recovery_suggestions

Error Analytics​

from packages.rag.error_analytics_dashboard import create_error_analytics_dashboard

# Create analytics dashboard
dashboard = create_error_analytics_dashboard()

# Add error data
await dashboard.add_error_data(user_error, "resolved")

# Get metrics
metrics = await dashboard.get_error_metrics(time_window_hours=24)
print(f"Total errors: {metrics.total_errors}")
print(f"User satisfaction: {metrics.user_satisfaction['overall_satisfaction']}")

# Generate insights
insights = await dashboard.generate_insights()
for insight in insights:
    print(f"Insight: {insight.title}")
    print(f"Actions: {insight.recommended_actions}")

User Feedback Collection​

# Record user feedback on error message helpfulness
await handler.record_user_feedback(
    error_id="error123",
    user_id="user456",
    helpful=True,
    comments="The error message was very clear and helpful"
)

# Record error resolution
await dashboard.add_resolution_data(
    error_id="error123",
    resolution_method="retry_with_different_parameters",
    resolution_time_minutes=5.0,
    success=True
)

API Endpoints​

Error Handling​

  • POST /errors/handle - Handle an error and get user-friendly response
  • POST /errors/feedback - Submit user feedback on error messages
  • POST /errors/resolution - Track error resolution

Analytics​

  • GET /errors/analytics - Get error metrics and statistics
  • GET /errors/insights - Get actionable insights
  • GET /errors/report - Generate comprehensive error report
  • GET /errors/dashboard - Get dashboard visualization data

Export​

  • GET /errors/export/metrics - Export metrics to JSON
  • GET /errors/export/report - Export report to JSON

Configuration​

Error Classification Patterns​

The system uses regex patterns to classify errors. You can customize these patterns:

# Add custom error pattern
from packages.rag.error_handling import ErrorPattern, ErrorCategory, ErrorSeverity

custom_pattern = ErrorPattern(
    pattern_type="custom_error",
    regex_pattern=r"(?i).*custom.*error.*pattern",
    category=ErrorCategory.UNKNOWN_ERROR,
    severity=ErrorSeverity.MEDIUM,
    confidence=0.8
)

# Add to classifier
classifier = ErrorClassifier()
classifier.patterns.append(custom_pattern)

Escalation Rules​

Configure when errors should be escalated to human support:

escalation_rules = {
    "auto_escalate_conditions": [
        {"severity": ErrorSeverity.CRITICAL, "immediate": True},
        {"severity": ErrorSeverity.HIGH, "count": 3, "time_window_minutes": 10},
        {"category": ErrorCategory.PERMISSION_DENIED, "immediate": True}
    ],
    "escalation_contacts": {
        "technical": "tech-support@company.com",
        "permissions": "it-admin@company.com",
        "general": "support@company.com"
    }
}

Error Message Customization​

Category-Specific Messages​

Each error category has predefined user-friendly messages. You can customize these:

# Customize error messages
category_details = {
    "title": "Custom Error Title",
    "message": "Custom user-friendly message",
    "explanation": "Detailed explanation of what happened",
    "suggested_actions": [ResolutionAction.RETRY, ResolutionAction.CONTACT_SUPPORT],
    "workarounds": ["Custom workaround 1", "Custom workaround 2"],
    "escalation_required": False,
    "recovery_suggestions": ["Custom recovery suggestion"]
}

Localization​

Support multiple languages by customizing error messages:

# Language-specific error messages
error_messages = {
    "en": {
        "title": "Unable to Find Information",
        "message": "I'm having trouble accessing the knowledge base."
    },
    "es": {
        "title": "No se pudo encontrar información",
        "message": "Tengo problemas para acceder a la base de conocimientos."
    }
}

Monitoring and Alerting​

Error Metrics​

Monitor key error metrics:

  • Total Error Count: Track overall error volume
  • Error Rate by Category: Identify problematic areas
  • User Satisfaction: Measure error message effectiveness
  • Resolution Time: Track how quickly errors are resolved
  • Escalation Rate: Monitor escalation frequency

Alerting Thresholds​

Set up alerts for critical metrics:

alert_thresholds = {
    "high_error_rate": {
        "threshold": 50,  # errors per hour
        "action": "email_admin"
    },
    "low_satisfaction": {
        "threshold": 0.7,  # satisfaction rate
        "action": "review_messages"
    },
    "high_escalation_rate": {
        "threshold": 0.3,  # escalation rate
        "action": "investigate_system"
    }
}

Best Practices​

Error Message Design​

  1. Be Clear and Concise: Use simple, jargon-free language
  2. Explain the Impact: Tell users how the error affects them
  3. Provide Next Steps: Give specific actions users can take
  4. Avoid Technical Details: Hide technical information from end users
  5. Be Empathetic: Acknowledge user frustration

Recovery Suggestions​

  1. Context-Aware: Tailor suggestions to user's situation
  2. Actionable: Provide specific, doable steps
  3. Progressive: Start with simple solutions, escalate if needed
  4. Educational: Help users understand and prevent future errors

Analytics Usage​

  1. Regular Monitoring: Check error metrics daily
  2. Trend Analysis: Look for patterns over time
  3. User Feedback: Act on user feedback to improve messages
  4. Continuous Improvement: Use insights to enhance error handling

Troubleshooting​

Common Issues​

Error Classification Not Working

  • Check regex patterns in ErrorClassifier
  • Verify error message format
  • Test with sample error messages

Recovery Suggestions Not Relevant

  • Review user context data
  • Check recovery strategy implementations
  • Test with different user profiles

Escalation Not Triggering

  • Verify escalation rules configuration
  • Check error severity and category
  • Review escalation manager logs

Analytics Data Missing

  • Ensure error data is being recorded
  • Check time window filters
  • Verify data retention settings

Debug Mode​

Enable debug logging for troubleshooting:

import logging

# Enable debug logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("packages.rag.error_handling")
logger.setLevel(logging.DEBUG)

Performance Considerations​

Memory Management​

  • Error history is limited to 10,000 records
  • User feedback data is limited to 1,000 records per error
  • Classification cache is limited to 1,000 entries

Async Operations​

  • All operations are async for better performance
  • Use background tasks for non-critical operations
  • Batch analytics operations when possible

Caching​

  • Error classification results are cached
  • Analytics data is cached for 5 minutes
  • User context is cached for 1 hour

Security Considerations​

Data Privacy​

  • User IDs are hashed in analytics
  • Sensitive error details are filtered
  • Personal information is not logged

Access Control​

  • Error details are restricted by user permissions
  • Escalation contacts are role-based
  • Analytics access requires appropriate privileges

Integration Examples​

FastAPI Integration​

from fastapi import FastAPI, HTTPException
from packages.rag.error_handling import create_error_handler, ErrorContext

app = FastAPI()
handler = create_error_handler()

@app.exception_handler(Exception)
async def global_exception_handler(request: Request, exc: Exception):
    context = ErrorContext(
        user_id=request.headers.get("user-id"),
        query=request.url.path,
        component="api"
    )
    
    user_error = await handler.handle_error(exc, context)
    
    return JSONResponse(
        status_code=500,
        content={
            "error": user_error.message,
            "suggestions": user_error.suggestions,
            "error_id": user_error.error_id
        }
    )

Django Integration​

from django.http import JsonResponse
from packages.rag.error_handling import create_error_handler, ErrorContext

handler = create_error_handler()

def error_view(request):
    try:
        # Some operation
        result = risky_operation()
    except Exception as e:
        context = ErrorContext(
            user_id=request.user.id,
            query=request.GET.get("q", ""),
            component="django_view"
        )
        
        user_error = await handler.handle_error(e, context)
        
        return JsonResponse({
            "error": user_error.message,
            "suggestions": user_error.suggestions
        })

Future Enhancements​

Planned Features​

  1. Machine Learning Classification: Use ML models for better error classification
  2. Predictive Analytics: Predict errors before they occur
  3. Auto-Recovery: Automatically attempt error recovery
  4. Multi-Language Support: Full internationalization
  5. Voice Error Messages: Audio error explanations
  6. Visual Error Guides: Interactive error resolution guides

Extension Points​

  1. Custom Classifiers: Add domain-specific error classifiers
  2. Custom Recovery Strategies: Implement specialized recovery logic
  3. Custom Analytics: Add business-specific metrics
  4. Custom Escalation: Integrate with existing ticketing systems

Support​

For questions, issues, or contributions:

  • Documentation: See this guide and API documentation
  • Issues: Report bugs and feature requests
  • Contributions: Submit pull requests for improvements
  • Support: Contact the development team

License​

This error handling system is part of the Enterprise RAG project and follows the same licensing terms.