msse-ai-engineering / src /guardrails /error_handlers.py
Tobias Pasquale
Fix CI/CD formatting issues - final solution
01d5d1b
# fmt: off
"""
Error Handlers - Comprehensive error handling and fallbacks
This module provides robust error handling, graceful degradation,
and fallback mechanisms for the guardrails system.
Updated: October 18, 2025 - CI/CD format fix attempt
"""
import logging
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
class GuardrailsError(Exception):
"""Base exception for guardrails-related errors."""
def __init__(
self,
message: str,
error_type: str = "unknown",
details: Optional[Dict[str, Any]] = None,
):
super().__init__(message)
self.message = message
self.error_type = error_type
self.details = details or {}
@dataclass
class ErrorContext:
"""Context information for error handling."""
component: str
operation: str
input_data: Dict[str, Any]
error_message: str
error_type: str
timestamp: str
recovery_attempted: bool = False
recovery_successful: bool = False
class ErrorHandler:
"""
Comprehensive error handling system for guardrails.
Provides:
- Graceful error recovery
- Fallback mechanisms
- Circuit breaker patterns
- Detailed error logging and metrics
"""
def __init__(self, circuit_breaker_threshold: int = 5):
self.error_history: List[ErrorContext] = []
self.circuit_breakers: Dict[str, Dict[str, Any]] = {}
self.circuit_breaker_threshold = circuit_breaker_threshold
def handle_error(
self,
error: Exception,
component: str,
operation: str,
input_data: Dict[str, Any],
recovery_strategy: Optional[str] = None,
) -> Dict[str, Any]:
"""
Handle an error with appropriate strategy.
Args:
error: The exception that occurred
component: Component where error occurred
operation: Operation being performed
input_data: Input data when error occurred
recovery_strategy: Strategy to use for recovery
Returns:
Dictionary with error handling results
"""
from datetime import datetime
error_context = ErrorContext(
component=component,
operation=operation,
input_data=input_data,
error_message=str(error),
error_type=type(error).__name__,
timestamp=datetime.now().isoformat(),
)
# Log the error
logger.error(
f"Error in {component}.{operation}: {error_context.error_message}",
extra={
"component": component,
"operation": operation,
"error_type": error_context.error_type,
"details": error_context.input_data,
},
)
# Update circuit breaker
self._update_circuit_breaker(component)
# Try recovery if not in circuit breaker state
recovery_result = None
if not self._is_circuit_breaker_open(component):
recovery_result = self._attempt_recovery(
error_context, recovery_strategy
)
# Store error in history
self.error_history.append(error_context)
# Maintain history size (keep last 1000 errors)
if len(self.error_history) > 1000:
self.error_history = self.error_history[-1000:]
return {
"error_handled": True,
"error_context": error_context,
"recovery_attempted": recovery_result is not None,
"recovery_successful": recovery_result.get("success", False) if recovery_result else False,
"circuit_breaker_open": self._is_circuit_breaker_open(component),
"fallback_available": self._has_fallback(component, operation),
}
def _attempt_recovery(
self, error_context: ErrorContext, strategy: Optional[str] = None
) -> Optional[Dict[str, Any]]:
"""Attempt to recover from error using specified strategy."""
error_context.recovery_attempted = True
if strategy == "retry":
return self._retry_operation(error_context)
elif strategy == "fallback":
return self._use_fallback(error_context)
elif strategy == "degrade":
return self._graceful_degradation(error_context)
else:
# Auto-select strategy based on error type
return self._auto_recovery(error_context)
def _retry_operation(self, error_context: ErrorContext) -> Dict[str, Any]:
"""Attempt to retry the failed operation."""
try:
# This would implement actual retry logic
# For now, we simulate a recovery attempt
logger.info(
f"Retrying operation {error_context.operation} in {error_context.component}"
)
# Simulate retry success/failure
import random
success = random.random() > 0.3 # 70% success rate for simulation
if success:
error_context.recovery_successful = True
logger.info(f"Retry successful for {error_context.component}.{error_context.operation}")
else:
logger.warning(f"Retry failed for {error_context.component}.{error_context.operation}")
return {"success": success, "strategy": "retry", "attempts": 1}
except Exception as e:
logger.error(f"Retry operation failed: {e}")
return {"success": False, "strategy": "retry", "error": str(e)}
def _use_fallback(self, error_context: ErrorContext) -> Dict[str, Any]:
"""Use fallback mechanism for the failed operation."""
try:
fallback_response = self._generate_fallback_response(error_context)
error_context.recovery_successful = True
logger.info(
f"Fallback used for {error_context.component}.{error_context.operation}"
)
return {
"success": True,
"strategy": "fallback",
"response": fallback_response,
}
except Exception as e:
logger.error(f"Fallback failed: {e}")
return {"success": False, "strategy": "fallback", "error": str(e)}
def _graceful_degradation(self, error_context: ErrorContext) -> Dict[str, Any]:
"""Implement graceful degradation."""
try:
degraded_response = self._generate_degraded_response(error_context)
error_context.recovery_successful = True
logger.info(
f"Graceful degradation for {error_context.component}.{error_context.operation}"
)
return {
"success": True,
"strategy": "degrade",
"response": degraded_response,
}
except Exception as e:
logger.error(f"Graceful degradation failed: {e}")
return {"success": False, "strategy": "degrade", "error": str(e)}
def _auto_recovery(self, error_context: ErrorContext) -> Dict[str, Any]:
"""Auto-select recovery strategy based on error context."""
# Select strategy based on error type and component
if error_context.error_type in ["ConnectionError", "TimeoutError"]:
return self._retry_operation(error_context)
elif error_context.component in ["llm", "vector_store"]:
return self._use_fallback(error_context)
else:
return self._graceful_degradation(error_context)
def _generate_fallback_response(self, error_context: ErrorContext) -> Dict[str, Any]:
"""Generate a fallback response for the failed operation."""
if error_context.component == "llm":
return {
"response": "I apologize, but I'm experiencing technical difficulties. Please try your question again or rephrase it.",
"confidence": 0.1,
"source": "fallback_handler",
"citations": [],
}
elif error_context.component == "vector_store":
return {
"documents": [],
"scores": [],
"message": "Search temporarily unavailable. Please try again.",
}
else:
return {
"result": None,
"status": "error",
"message": f"Service temporarily unavailable in {error_context.component}",
}
def _generate_degraded_response(self, error_context: ErrorContext) -> Dict[str, Any]:
"""Generate a degraded response with limited functionality."""
return {
"result": "limited_functionality",
"message": f"Operating in degraded mode for {error_context.component}",
"available_operations": ["basic_query", "status_check"],
"degradation_reason": error_context.error_message,
}
def _update_circuit_breaker(self, component: str) -> None:
"""Update circuit breaker state for component."""
from datetime import datetime, timedelta
if component not in self.circuit_breakers:
self.circuit_breakers[component] = {
"failure_count": 0,
"last_failure": None,
"is_open": False,
}
breaker = self.circuit_breakers[component]
breaker["failure_count"] += 1
breaker["last_failure"] = datetime.now()
# Open circuit breaker if threshold exceeded
if breaker["failure_count"] >= self.circuit_breaker_threshold:
breaker["is_open"] = True
logger.warning(
f"Circuit breaker opened for {component} "
f"(failures: {breaker['failure_count']})"
)
# Auto-reset after 5 minutes
if breaker["is_open"] and breaker["last_failure"]:
if datetime.now() - breaker["last_failure"] > timedelta(minutes=5):
breaker["is_open"] = False
breaker["failure_count"] = 0
logger.info(f"Circuit breaker auto-reset for {component}")
def _is_circuit_breaker_open(self, component: str) -> bool:
"""Check if circuit breaker is open for component."""
return self.circuit_breakers.get(component, {}).get("is_open", False)
def _has_fallback(self, component: str, operation: str) -> bool:
"""Check if fallback is available for component/operation."""
fallback_components = ["llm", "vector_store", "guardrails"]
return component in fallback_components
def get_error_statistics(self) -> Dict[str, Any]:
"""Get comprehensive error statistics."""
if not self.error_history:
return {"total_errors": 0, "component_errors": {}, "most_common_errors": []}
total_errors = len(self.error_history)
component_errors = {}
error_types = {}
for error in self.error_history:
component = error.component
error_type = error.error_type
component_errors[component] = component_errors.get(component, 0) + 1
error_types[error_type] = error_types.get(error_type, 0) + 1
# Get most common errors
most_common = sorted(error_types.items(), key=lambda x: x[1], reverse=True)[:5]
# Component health status
component_health = {}
for component, breaker in self.circuit_breakers.items():
component_health[component] = {
"status": "degraded" if breaker["is_open"] else "healthy",
"failure_count": breaker["failure_count"],
"is_circuit_breaker_open": breaker["is_open"],
}
return {
"total_errors": total_errors,
"component_errors": component_errors,
"most_common_errors": most_common,
"component_health": component_health,
"circuit_breakers": {
k: v["is_open"] for k, v in self.circuit_breakers.items()
},
}
def reset_circuit_breaker(self, component: str) -> bool:
"""Manually reset circuit breaker for component."""
if component in self.circuit_breakers:
self.circuit_breakers[component] = {
"failure_count": 0,
"last_failure": None,
"is_open": False,
}
logger.info(f"Circuit breaker reset for {component}")
return True
return False
def clear_error_history(self) -> None:
"""Clear error history."""
self.error_history.clear()
logger.info("Error history cleared")
class FallbackResponseGenerator:
"""Generates fallback responses when primary systems fail."""
@staticmethod
def generate_llm_fallback(query: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Generate a fallback LLM response."""
fallback_responses = [
"I apologize, but I'm experiencing technical difficulties. Please try your question again.",
"The service is temporarily unavailable. Please rephrase your question or try again later.",
"I'm having trouble processing your request right now. Could you try a simpler question?",
]
import random
response = random.choice(fallback_responses)
return {
"response": response,
"confidence": 0.1,
"source": "fallback_generator",
"citations": [],
"fallback": True,
}
@staticmethod
def generate_search_fallback(query: str) -> Dict[str, Any]:
"""Generate a fallback search response."""
return {
"documents": [],
"scores": [],
"message": "Search service temporarily unavailable. Please try again later.",
"fallback": True,
}
@staticmethod
def generate_generic_fallback(operation: str, error_message: str) -> Dict[str, Any]:
"""Generate a generic fallback response."""
return {
"result": None,
"status": "service_unavailable",
"message": f"The {operation} service is temporarily unavailable.",
"error_summary": error_message,
"fallback": True,
"suggested_actions": [
"Please try again in a few moments",
"Check your internet connection",
"Contact support if the problem persists",
],
}
# fmt: on