"""
Pattern 148: Error Classification & Routing

Description:
    Implements intelligent error classification and routing to appropriate
    handlers based on error type, severity, and context. This pattern categorizes
    errors and routes them to specialized recovery strategies, enabling more
    sophisticated error handling than simple try-catch blocks.

Components:
    - Error Classifier: Categorizes errors by type and severity
    - Error Router: Routes errors to appropriate handlers
    - Handler Registry: Maps error types to handlers
    - Context Analyzer: Extracts error context
    - Recovery Strategies: Specialized recovery for each error type
    - Escalation Manager: Escalates unhandled errors

Use Cases:
    - Complex agent error handling
    - Multi-service application error management
    - Production system fault tolerance
    - Intelligent error recovery
    - Error analytics and monitoring

Benefits:
    - Targeted error recovery
    - Better error handling granularity
    - Reduced error resolution time
    - Improved system reliability
    - Better debugging and monitoring

Trade-offs:
    - Increased complexity
    - Requires comprehensive error taxonomy
    - Handler maintenance overhead
    - Potential routing errors

LangChain Implementation:
    Uses error classification, pattern matching, and routing to specialized
    handlers for intelligent error management.
"""

import os
import re
import traceback
from typing import Any, Callable, Optional, List, Dict, Type
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
from collections import defaultdict
from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

load_dotenv()


class ErrorCategory(Enum):
    """High-level error categories"""
    TRANSIENT = "transient"  # Temporary, retryable
    PERMANENT = "permanent"  # Persistent, not retryable
    RESOURCE = "resource"  # Resource-related (memory, disk, etc.)
    RATE_LIMIT = "rate_limit"  # API rate limiting
    AUTHENTICATION = "authentication"  # Auth/permission errors
    VALIDATION = "validation"  # Input validation errors
    TIMEOUT = "timeout"  # Timeout errors
    NETWORK = "network"  # Network connectivity
    BUSINESS_LOGIC = "business_logic"  # Business rule violations
    UNKNOWN = "unknown"  # Unclassified


class ErrorSeverity(Enum):
    """Error severity levels"""
    LOW = "low"  # Minor, can be ignored
    MEDIUM = "medium"  # Important, should be handled
    HIGH = "high"  # Critical, requires immediate attention
    CRITICAL = "critical"  # System-level, may need escalation


@dataclass
class ErrorContext:
    """Context information about an error"""
    exception: Exception
    exception_type: str
    message: str
    traceback_str: str
    timestamp: datetime
    operation: Optional[str] = None
    user_id: Optional[str] = None
    request_id: Optional[str] = None
    additional_data: Dict[str, Any] = field(default_factory=dict)


@dataclass
class ClassifiedError:
    """Classified error with category and severity"""
    context: ErrorContext
    category: ErrorCategory
    severity: ErrorSeverity
    retryable: bool
    suggested_action: str
    confidence: float = 1.0


@dataclass
class ErrorStats:
    """Statistics about error handling"""
    total_errors: int = 0
    by_category: Dict[ErrorCategory, int] = field(default_factory=lambda: defaultdict(int))
    by_severity: Dict[ErrorSeverity, int] = field(default_factory=lambda: defaultdict(int))
    by_handler: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
    successful_recoveries: int = 0
    failed_recoveries: int = 0
    escalations: int = 0


class ErrorClassificationRouter:
    """
    Agent that classifies and routes errors to appropriate handlers.
    
    Analyzes errors, categorizes them, and routes to specialized recovery
    strategies for intelligent error management.
    """
    
    def __init__(self, model: str = "gpt-3.5-turbo"):
        """
        Initialize the error classification router.
        
        Args:
            model: LLM model to use
        """
        self.llm = ChatOpenAI(model=model, temperature=0)
        
        self.handlers: Dict[ErrorCategory, Callable] = {}
        self.classification_rules: List[Dict[str, Any]] = []
        self.stats = ErrorStats()
        
        # Set up default classification rules
        self._setup_default_rules()
    
    def _setup_default_rules(self):
        """Set up default error classification rules."""
        self.classification_rules = [
            {
                "patterns": ["rate limit", "429", "too many requests"],
                "category": ErrorCategory.RATE_LIMIT,
                "severity": ErrorSeverity.MEDIUM,
                "retryable": True,
                "action": "Retry with exponential backoff"
            },
            {
                "patterns": ["timeout", "timed out", "deadline exceeded"],
                "category": ErrorCategory.TIMEOUT,
                "severity": ErrorSeverity.MEDIUM,
                "retryable": True,
                "action": "Retry with increased timeout"
            },
            {
                "patterns": ["connection", "network", "unreachable", "503"],
                "category": ErrorCategory.NETWORK,
                "severity": ErrorSeverity.MEDIUM,
                "retryable": True,
                "action": "Check network and retry"
            },
            {
                "patterns": ["authentication", "unauthorized", "401", "403"],
                "category": ErrorCategory.AUTHENTICATION,
                "severity": ErrorSeverity.HIGH,
                "retryable": False,
                "action": "Verify credentials"
            },
            {
                "patterns": ["validation", "invalid", "bad request", "400"],
                "category": ErrorCategory.VALIDATION,
                "severity": ErrorSeverity.MEDIUM,
                "retryable": False,
                "action": "Fix input and retry"
            },
            {
                "patterns": ["out of memory", "memory error", "allocation failed"],
                "category": ErrorCategory.RESOURCE,
                "severity": ErrorSeverity.HIGH,
                "retryable": False,
                "action": "Reduce resource usage or scale up"
            },
            {
                "patterns": ["disk full", "no space left", "quota exceeded"],
                "category": ErrorCategory.RESOURCE,
                "severity": ErrorSeverity.HIGH,
                "retryable": False,
                "action": "Free up space or increase quota"
            }
        ]
    
    def classify_error(self, error: Exception, **context) -> ClassifiedError:
        """
        Classify an error based on type and message.
        
        Args:
            error: Exception to classify
            **context: Additional context
            
        Returns:
            Classified error with category and severity
        """
        self.stats.total_errors += 1
        
        # Create error context
        error_context = ErrorContext(
            exception=error,
            exception_type=type(error).__name__,
            message=str(error),
            traceback_str=traceback.format_exc(),
            timestamp=datetime.now(),
            operation=context.get("operation"),
            user_id=context.get("user_id"),
            request_id=context.get("request_id"),
            additional_data=context
        )
        
        # Try rule-based classification first
        classified = self._classify_by_rules(error_context)
        
        if classified.category == ErrorCategory.UNKNOWN:
            # Fall back to LLM-based classification
            classified = self._classify_by_llm(error_context)
        
        # Update stats
        self.stats.by_category[classified.category] += 1
        self.stats.by_severity[classified.severity] += 1
        
        return classified
    
    def _classify_by_rules(self, context: ErrorContext) -> ClassifiedError:
        """Classify error using rule-based matching."""
        error_str = f"{context.exception_type} {context.message}".lower()
        
        for rule in self.classification_rules:
            if any(pattern in error_str for pattern in rule["patterns"]):
                return ClassifiedError(
                    context=context,
                    category=rule["category"],
                    severity=rule["severity"],
                    retryable=rule["retryable"],
                    suggested_action=rule["action"],
                    confidence=1.0
                )
        
        # No rule matched
        return ClassifiedError(
            context=context,
            category=ErrorCategory.UNKNOWN,
            severity=ErrorSeverity.MEDIUM,
            retryable=False,
            suggested_action="Investigate error",
            confidence=0.0
        )
    
    def _classify_by_llm(self, context: ErrorContext) -> ClassifiedError:
        """Classify error using LLM."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert in error classification. "
                      "Classify the error into one of these categories:\n"
                      "- transient: Temporary, can be retried\n"
                      "- permanent: Persistent, cannot be retried\n"
                      "- resource: Resource-related (memory, disk)\n"
                      "- rate_limit: API rate limiting\n"
                      "- authentication: Auth/permission errors\n"
                      "- validation: Input validation errors\n"
                      "- timeout: Timeout errors\n"
                      "- network: Network connectivity\n"
                      "- business_logic: Business rule violations\n\n"
                      "Also determine severity (low/medium/high/critical) "
                      "and if it's retryable (yes/no).\n\n"
                      "Respond in format: category|severity|retryable|action"),
            ("user", "Error Type: {error_type}\n"
                    "Message: {message}\n"
                    "Context: {operation}\n\n"
                    "Classify this error:")
        ])
        
        try:
            chain = prompt | self.llm | StrOutputParser()
            result = chain.invoke({
                "error_type": context.exception_type,
                "message": context.message,
                "operation": context.operation or "unknown"
            })
            
            # Parse result
            parts = result.split("|")
            if len(parts) >= 4:
                category = ErrorCategory(parts[0].strip().lower())
                severity = ErrorSeverity(parts[1].strip().lower())
                retryable = "yes" in parts[2].lower()
                action = parts[3].strip()
                
                return ClassifiedError(
                    context=context,
                    category=category,
                    severity=severity,
                    retryable=retryable,
                    suggested_action=action,
                    confidence=0.8
                )
                
        except Exception as e:
            print(f"LLM classification failed: {e}")
        
        # Fallback
        return ClassifiedError(
            context=context,
            category=ErrorCategory.UNKNOWN,
            severity=ErrorSeverity.MEDIUM,
            retryable=False,
            suggested_action="Manual investigation required",
            confidence=0.5
        )
    
    def register_handler(
        self,
        category: ErrorCategory,
        handler: Callable[[ClassifiedError], Any]
    ):
        """
        Register a handler for an error category.
        
        Args:
            category: Error category
            handler: Handler function
        """
        self.handlers[category] = handler
    
    def route_error(self, classified_error: ClassifiedError) -> Any:
        """
        Route error to appropriate handler.
        
        Args:
            classified_error: Classified error
            
        Returns:
            Handler result
        """
        category = classified_error.category
        
        if category in self.handlers:
            handler = self.handlers[category]
            handler_name = handler.__name__
            
            print(f"\n🔀 Routing to handler: {handler_name}")
            self.stats.by_handler[handler_name] += 1
            
            try:
                result = handler(classified_error)
                self.stats.successful_recoveries += 1
                return result
            except Exception as e:
                print(f"❌ Handler failed: {e}")
                self.stats.failed_recoveries += 1
                return self._default_handler(classified_error)
        else:
            print(f"⚠️  No handler for category: {category.value}")
            return self._default_handler(classified_error)
    
    def _default_handler(self, classified_error: ClassifiedError) -> Dict[str, Any]:
        """Default handler for unhandled errors."""
        if classified_error.severity in [ErrorSeverity.HIGH, ErrorSeverity.CRITICAL]:
            print(f"🚨 Escalating {classified_error.severity.value} severity error")
            self.stats.escalations += 1
        
        return {
            "handled": False,
            "category": classified_error.category.value,
            "severity": classified_error.severity.value,
            "action": classified_error.suggested_action,
            "escalated": classified_error.severity in [ErrorSeverity.HIGH, ErrorSeverity.CRITICAL]
        }
    
    def handle_error(self, error: Exception, **context) -> Any:
        """
        Complete error handling: classify and route.
        
        Args:
            error: Exception to handle
            **context: Additional context
            
        Returns:
            Handler result
        """
        # Classify error
        classified = self.classify_error(error, **context)
        
        print(f"\n📋 Error Classification:")
        print(f"   Category: {classified.category.value}")
        print(f"   Severity: {classified.severity.value}")
        print(f"   Retryable: {classified.retryable}")
        print(f"   Confidence: {classified.confidence:.1%}")
        print(f"   Suggested Action: {classified.suggested_action}")
        
        # Route to handler
        result = self.route_error(classified)
        
        return result
    
    def get_stats_report(self) -> str:
        """Generate error statistics report."""
        report = []
        report.append("\n" + "="*60)
        report.append("ERROR CLASSIFICATION & ROUTING STATISTICS")
        report.append("="*60)
        
        report.append(f"\n📊 Overall Statistics:")
        report.append(f"   Total Errors: {self.stats.total_errors}")
        report.append(f"   Successful Recoveries: {self.stats.successful_recoveries}")
        report.append(f"   Failed Recoveries: {self.stats.failed_recoveries}")
        report.append(f"   Escalations: {self.stats.escalations}")
        
        if self.stats.total_errors > 0:
            recovery_rate = (
                self.stats.successful_recoveries / self.stats.total_errors
            )
            report.append(f"   Recovery Rate: {recovery_rate:.1%}")
        
        report.append(f"\n📂 By Category:")
        for category, count in sorted(
            self.stats.by_category.items(),
            key=lambda x: x[1],
            reverse=True
        ):
            report.append(f"   {category.value}: {count}")
        
        report.append(f"\n⚠️  By Severity:")
        for severity, count in sorted(
            self.stats.by_severity.items(),
            key=lambda x: x[1],
            reverse=True
        ):
            report.append(f"   {severity.value}: {count}")
        
        if self.stats.by_handler:
            report.append(f"\n🔧 By Handler:")
            for handler, count in sorted(
                self.stats.by_handler.items(),
                key=lambda x: x[1],
                reverse=True
            ):
                report.append(f"   {handler}: {count}")
        
        report.append("\n" + "="*60)
        
        return "\n".join(report)


def demonstrate_error_classification_routing():
    """Demonstrate the Error Classification & Routing pattern."""
    print("="*60)
    print("ERROR CLASSIFICATION & ROUTING PATTERN DEMONSTRATION")
    print("="*60)
    
    router = ErrorClassificationRouter()
    
    # Example 1: Register handlers for different error types
    print("\n" + "="*60)
    print("Example 1: Register Specialized Error Handlers")
    print("="*60)
    
    def handle_rate_limit(error: ClassifiedError) -> Dict:
        """Handler for rate limit errors."""
        print("   💤 Handling rate limit: waiting before retry...")
        return {"action": "retry_after_delay", "delay": 60}
    
    def handle_authentication(error: ClassifiedError) -> Dict:
        """Handler for authentication errors."""
        print("   🔐 Handling auth error: refreshing credentials...")
        return {"action": "refresh_credentials"}
    
    def handle_validation(error: ClassifiedError) -> Dict:
        """Handler for validation errors."""
        print("   ✏️  Handling validation error: correcting input...")
        return {"action": "fix_input", "message": error.context.message}
    
    def handle_network(error: ClassifiedError) -> Dict:
        """Handler for network errors."""
        print("   🌐 Handling network error: checking connection...")
        return {"action": "check_network_and_retry"}
    
    # Register handlers
    router.register_handler(ErrorCategory.RATE_LIMIT, handle_rate_limit)
    router.register_handler(ErrorCategory.AUTHENTICATION, handle_authentication)
    router.register_handler(ErrorCategory.VALIDATION, handle_validation)
    router.register_handler(ErrorCategory.NETWORK, handle_network)
    
    print("✅ Registered 4 specialized handlers")
    
    # Example 2: Classify and route various errors
    print("\n" + "="*60)
    print("Example 2: Classify and Route Various Errors")
    print("="*60)
    
    test_errors = [
        (Exception("Rate limit exceeded (429)"), "api_call"),
        (Exception("Connection timeout after 30s"), "database_query"),
        (Exception("Invalid API key provided"), "authentication"),
        (Exception("Invalid input: email format incorrect"), "user_registration"),
        (Exception("Network unreachable"), "external_service"),
    ]
    
    for error, operation in test_errors:
        print(f"\n{'='*60}")
        print(f"Error: {error}")
        print(f"Operation: {operation}")
        
        result = router.handle_error(error, operation=operation)
        print(f"Result: {result}")
    
    # Example 3: LLM-based classification for unknown errors
    print("\n" + "="*60)
    print("Example 3: LLM-Based Classification")
    print("="*60)
    
    unknown_error = Exception(
        "Failed to process payment: insufficient funds in account"
    )
    
    print(f"\nClassifying unknown error: {unknown_error}")
    classified = router.classify_error(
        unknown_error,
        operation="payment_processing"
    )
    
    print(f"\n📋 LLM Classification:")
    print(f"   Category: {classified.category.value}")
    print(f"   Severity: {classified.severity.value}")
    print(f"   Confidence: {classified.confidence:.1%}")
    print(f"   Suggested Action: {classified.suggested_action}")
    
    # Example 4: Error severity-based routing
    print("\n" + "="*60)
    print("Example 4: Severity-Based Escalation")
    print("="*60)
    
    severity_errors = [
        (Exception("Minor warning: cache miss"), ErrorSeverity.LOW),
        (Exception("API timeout on retry"), ErrorSeverity.MEDIUM),
        (Exception("Database connection pool exhausted"), ErrorSeverity.HIGH),
        (Exception("Critical system failure: all services down"), ErrorSeverity.CRITICAL),
    ]
    
    print("\nProcessing errors by severity:")
    for error, expected_severity in severity_errors:
        classified = router.classify_error(error)
        severity = classified.severity
        
        status = "🔴" if severity in [ErrorSeverity.HIGH, ErrorSeverity.CRITICAL] else "🟡"
        print(f"\n{status} {severity.value.upper()}: {error}")
        
        if severity in [ErrorSeverity.HIGH, ErrorSeverity.CRITICAL]:
            print(f"   ⚠️  Escalating to on-call team")
    
    # Example 5: Context-aware classification
    print("\n" + "="*60)
    print("Example 5: Context-Aware Error Classification")
    print("="*60)
    
    context_error = Exception("Operation failed")
    
    contexts = [
        {"operation": "payment_processing", "user_id": "user123", "amount": 1000},
        {"operation": "cache_refresh", "service": "recommendations"},
        {"operation": "user_login", "user_id": "admin", "ip": "192.168.1.1"}
    ]
    
    print("\nSame error in different contexts:")
    for ctx in contexts:
        print(f"\nContext: {ctx['operation']}")
        classified = router.classify_error(context_error, **ctx)
        print(f"   Severity: {classified.severity.value}")
        print(f"   Action: {classified.suggested_action}")
    
    # Example 6: Error pattern analysis
    print("\n" + "="*60)
    print("Example 6: Error Pattern Analysis")
    print("="*60)
    
    # Simulate multiple errors
    simulated_errors = [
        Exception("Rate limit exceeded"),
        Exception("Rate limit exceeded"),
        Exception("Rate limit exceeded"),
        Exception("Connection timeout"),
        Exception("Connection timeout"),
        Exception("Invalid credentials"),
    ]
    
    print("\nProcessing batch of errors:")
    for i, error in enumerate(simulated_errors, 1):
        router.handle_error(error, request_id=f"req-{i}")
    
    # Generate statistics
    stats_report = router.get_stats_report()
    print(stats_report)
    
    # Identify patterns
    print("\n💡 Pattern Analysis:")
    if router.stats.by_category[ErrorCategory.RATE_LIMIT] >= 3:
        print("   ⚠️  High rate of rate limit errors detected")
        print("   💡 Recommendation: Implement request throttling")
    
    if router.stats.by_category[ErrorCategory.NETWORK] >= 2:
        print("   ⚠️  Multiple network errors detected")
        print("   💡 Recommendation: Check service health")
    
    # Summary
    print("\n" + "="*60)
    print("SUMMARY")
    print("="*60)
    print("""
The Error Classification & Routing pattern demonstrates:

1. Intelligent Classification: Categorizes errors by type and severity
2. Specialized Handlers: Routes errors to appropriate recovery strategies
3. Rule-Based + LLM: Combines rule matching with AI classification
4. Context Awareness: Considers operation context in classification
5. Escalation Logic: Escalates severe errors automatically

Key Benefits:
- Targeted error recovery (80-95% automation typical)
- Reduced mean time to recovery (MTTR)
- Better error visibility and monitoring
- Intelligent escalation of critical issues
- Comprehensive error analytics

Error Categories:
- Transient: Temporary, retry with backoff
- Permanent: Persistent, manual intervention
- Rate Limit: Backoff and respect limits
- Authentication: Refresh credentials
- Validation: Fix input data
- Resource: Scale or optimize
- Network: Check connectivity
- Timeout: Increase limits or optimize

Handler Strategies:
- Retry: For transient errors
- Refresh: For authentication issues
- Validate: For input errors
- Scale: For resource errors
- Alert: For critical errors
- Log: For analysis and debugging

Best Practices:
- Maintain comprehensive error taxonomy
- Use rule-based classification first (fast)
- Fall back to LLM for unknown errors
- Log all errors with full context
- Monitor error patterns over time
- Update handlers based on learnings
- Test handlers thoroughly
- Document recovery procedures

Severity Escalation:
- Low: Log only
- Medium: Automated recovery
- High: Alert and automated recovery
- Critical: Immediate escalation to on-call

Use Cases:
- Production system error handling
- Multi-service applications
- API gateway error management
- Microservices fault tolerance
- Distributed system coordination
    """)


if __name__ == "__main__":
    demonstrate_error_classification_routing()

    

Agentic Design Pattern: Error Classification

Pattern 148: Error Classification – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the Error Classification pattern implementation.

---

Pattern Description

Pattern 148: Error Classification & Routing

Description: Implements intelligent error classification and routing to appropriate handlers based on error type, severity, and context. This pattern categorizes errors and routes them to specialized recovery strategies, enabling more sophisticated error handling than simple try-catch blocks.

Components: – Error Classifier: Categorizes errors by type and severity – Error Router: Routes errors to appropriate handlers – Handler Registry: Maps error types to handlers – Context Analyzer: Extracts error context – Recovery Strategies: Specialized recovery for each error type – Escalation Manager: Escalates unhandled errors

Use Cases: – Complex agent error handling – Multi-service application error management – Production system fault tolerance – Intelligent error recovery – Error analytics and monitoring

Benefits: – Targeted error recovery – Better error handling granularity – Reduced error resolution time – Improved system reliability – Better debugging and monitoring

Trade-offs: – Increased complexity – Requires comprehensive error taxonomy – Handler maintenance overhead – Potential routing errors

LangChain Implementation: Uses error classification, pattern matching, and routing to specialized handlers for intelligent error management.

---

File Statistics

• Total Lines: 680
• Classes: 6
• Functions: 1
• Constants: 0

---

Module Documentation

"""
Pattern 148: Error Classification & Routing

Description:
    Implements intelligent error classification and routing to appropriate
    handlers based on error type, severity, and context. This pattern categorizes
    errors and routes them to specialized recovery strategies, enabling more
    sophisticated error handling than simple try-catch blocks.

Components:
    - Error Classifier: Categorizes errors by type and severity
    - Error Router: Routes errors to appropriate handlers
    - Handler Registry: Maps error types to handlers
    - Context Analyzer: Extracts error context
    - Recovery Strategies: Specialized recovery for each error type
    - Escalation Manager: Escalates unhandled errors

Use Cases:
    - Complex agent error handling
    - Multi-service application error management
    - Production system fault tolerance
    - Intelligent error recovery
    - Error analytics and monitoring

Benefits:
    - Targeted error recovery
    - Better error handling granularity
    - Reduced error resolution time
    - Improved system reliability
    - Better debugging and monitoring

Trade-offs:
    - Increased complexity
    - Requires comprehensive error taxonomy
    - Handler maintenance overhead
    - Potential routing errors

LangChain Implementation:
    Uses error classification, pattern matching, and routing to specialized
    handlers for intelligent error management.
"""

Purpose: This module implements the Error Classification pattern, providing a structured approach to agent design and implementation.

---

Import Statements

Standard Library Imports

import collections.defaultdict
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import re
import typing.Any
import typing.Callable
import typing.Dict
import typing.List
import typing.Optional
import typing.Type

Purpose: Core Python functionality: – collections.defaultdict: Specialized container datatypes – dataclasses.dataclass: Simplified class definitions with @dataclass – dataclasses.field: Simplified class definitions with @dataclass – datetime.datetime: Date and time handling – enum.Enum: Enumeration support – langchain_core.output_parsers.StrOutputParser: Regular expression operations – langchain_core.prompts.ChatPromptTemplate: Regular expression operations – os: Environment variable access and system operations – re: Regular expression operations – typing.Any: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional) – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Type: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows

Other Imports

from dotenv.load_dotenv
from traceback

Purpose: Additional dependencies for specialized functionality.

---

Classes

Class: ErrorCategory

Lines 60-71

Inherits from: Enum

Purpose:

High-level error categories

---

Class: ErrorSeverity

Lines 74-79

Inherits from: Enum

Purpose:

Error severity levels

---

Class: ErrorContext

Lines 83-93

Purpose:

Context information about an error

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Class: ClassifiedError

Lines 97-104

Purpose:

Classified error with category and severity

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Class: ErrorStats

Lines 108-116

Purpose:

Statistics about error handling

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Class: ErrorClassificationRouter

Lines 119-453

Purpose:

Agent that classifies and routes errors to appropriate handlers.

Analyzes errors, categorizes them, and routes to specialized recovery
strategies for intelligent error management.

Methods:

__init__(self, model)

Lines 127-141

Purpose:

Initialize the error classification router.

Args:
    model: LLM model to use

Parameters: – model: Language model instance

_setup_default_rules(self)

Lines 143-195

Purpose:

Set up default error classification rules.

Parameters:

classify_error(self, error)

Lines 197-234

Purpose:

Classify an error based on type and message.

Args:
    error: Exception to classify
    **context: Additional context

Returns:
    Classified error with category and severity

Parameters: – error: Error

Returns: ClassifiedError

_classify_by_rules(self, context)

Lines 236-259

Purpose:

Classify error using rule-based matching.

Parameters: – context: Context

Returns: ClassifiedError

_classify_by_llm(self, context)

Lines 261-320

Purpose:

Classify error using LLM.

Parameters: – context: Context

Returns: ClassifiedError

register_handler(self, category, handler)

Lines 322-334

Purpose:

Register a handler for an error category.

Args:
    category: Error category
    handler: Handler function

Parameters: – category: Category – handler: Handler

route_error(self, classified_error)

Lines 336-365

Purpose:

Route error to appropriate handler.

Args:
    classified_error: Classified error

Returns:
    Handler result

Parameters: – classified_error: Classified error

Returns: Any

_default_handler(self, classified_error)

Lines 367-379

Purpose:

Default handler for unhandled errors.

Parameters: – classified_error: Classified error

Returns: <ast.Subscript object at 0x000001A06D7CEBD0>

handle_error(self, error)

Lines 381-405

Purpose:

Complete error handling: classify and route.

Args:
    error: Exception to handle
    **context: Additional context

Returns:
    Handler result

Parameters: – error: Error

Returns: Any

get_stats_report(self)

Lines 407-453

Purpose:

Generate error statistics report.

Parameters:

Returns: str

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Functions

demonstrate_error_classification_routing()

Lines 456-675

Purpose:

Demonstrate the Error Classification & Routing pattern.

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Key Concepts Summary

Pattern Overview

The Error Classification pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

1. Agent Class: Main implementation of the pattern logic
2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
3. Tool Integration: Optional tools for extended functionality
4. Memory Management: State handling across interactions
5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

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Best Practices Demonstrated

1. Environment Configuration

• ✅ Use .env files for API keys
• ✅ Never hardcode credentials
• ✅ Validate environment variables on startup

2. Error Handling

• ✅ Graceful degradation on failures
• ✅ Informative error messages
• ✅ Logging for debugging

3. Prompt Engineering

• ✅ Clear, specific instructions
• ✅ Structured output formats
• ✅ Few-shot examples when beneficial

4. Code Organization

• ✅ Separation of concerns (class-based design)
• ✅ Type hints for clarity
• ✅ Docstrings for documentation

5. Performance Optimization

• ✅ Appropriate temperature settings
• ✅ Token budget management
• ✅ Caching where applicable

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LangChain Components Used

• ChatOpenAI: OpenAI model interface
• Chains: Sequential component composition

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

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Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

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Performance Considerations

Computational Cost

• LLM Calls: Primary cost factor (based on tokens)
• Latency: Depends on model and input size
• Memory: Minimal for most patterns

Optimization Strategies

1. Prompt Optimization: Minimize tokens while maintaining clarity
2. Caching: Cache repeated queries/responses
3. Model Selection: Use smaller models when appropriate
4. Batch Processing: Group similar requests
5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

• GPT-3.5-turbo: ~$0.002 per request
• GPT-4: ~$0.03 per request
• GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

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Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

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Further Improvements

Production Enhancements

1. Monitoring: Add logging, metrics, tracing
2. Validation: Input/output schema validation
3. Rate Limiting: Implement request throttling
4. Caching: Redis or in-memory cache for responses
5. Async Support: Non-blocking operations for scalability

Feature Extensions

1. Multi-Model Support: Switch between providers
2. Fallback Models: Graceful degradation strategy
3. Custom Tools: Domain-specific function calling
4. Advanced Memory: Vector stores for long-term memory
5. Evaluation: Automated testing and benchmarking

Code Quality

1. Testing: Unit tests, integration tests
2. Documentation: API docs, usage examples
3. Type Safety: Stricter type hints
4. Configuration: Externalized config files

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Related Patterns

• Pattern 147
• Pattern 149

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

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Summary

The Error Classification pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

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File: 148_error_classification.py
Total Lines: 680
Pattern Category: AI Agent Design Pattern #148

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Generated from actual implementation code
For educational and reference purposes