"""
Pattern 153: Shadow Mode Testing

Description:
    Runs new agent versions alongside production without affecting users.
    Captures both production and shadow responses, compares results, and
    identifies differences for validation before full deployment.

Components:
    - Shadow Executor: Runs shadow version in parallel
    - Response Comparator: Compares production vs shadow outputs
    - Difference Analyzer: Identifies and categorizes differences
    - Metrics Collector: Tracks shadow performance
    - Rollback Monitor: Detects degradation signals

Use Cases:
    - Testing new model versions safely
    - Validating prompt changes
    - A/B testing without user impact
    - Gradual feature rollout
    - Performance regression detection

Benefits:
    - Zero user impact during testing
    - Real production traffic testing
    - Early problem detection
    - Confidence before full deployment
    - Comprehensive comparison data

Trade-offs:
    - Doubled computational cost
    - Infrastructure complexity
    - Storage for dual responses
    - Latency considerations
    - Analysis overhead

LangChain Implementation:
    Uses parallel execution with ChatOpenAI, response comparison,
    and comprehensive metrics tracking.
"""

import os
import time
import asyncio
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from dotenv import load_dotenv

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

load_dotenv()


class DifferenceType(Enum):
    """Types of differences between responses"""
    IDENTICAL = "identical"
    MINOR = "minor"  # Small wording differences
    MODERATE = "moderate"  # Different approach, similar meaning
    MAJOR = "major"  # Significantly different
    ERROR = "error"  # One succeeded, one failed


class HealthStatus(Enum):
    """Health status of shadow version"""
    HEALTHY = "healthy"
    WARNING = "warning"
    DEGRADED = "degraded"
    FAILING = "failing"


@dataclass
class ShadowResponse:
    """Response from shadow execution"""
    production_response: str
    shadow_response: str
    production_time: float
    shadow_time: float
    production_error: Optional[str] = None
    shadow_error: Optional[str] = None
    difference_type: Optional[DifferenceType] = None
    similarity_score: float = 0.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class ShadowMetrics:
    """Metrics for shadow testing"""
    total_requests: int = 0
    identical_responses: int = 0
    minor_differences: int = 0
    moderate_differences: int = 0
    major_differences: int = 0
    production_errors: int = 0
    shadow_errors: int = 0
    avg_production_latency: float = 0.0
    avg_shadow_latency: float = 0.0
    shadow_slower_by: float = 0.0  # Percentage
    
    def get_difference_rate(self, diff_type: DifferenceType) -> float:
        """Get rate of specific difference type"""
        if self.total_requests == 0:
            return 0.0
        
        count_map = {
            DifferenceType.IDENTICAL: self.identical_responses,
            DifferenceType.MINOR: self.minor_differences,
            DifferenceType.MODERATE: self.moderate_differences,
            DifferenceType.MAJOR: self.major_differences
        }
        
        return count_map.get(diff_type, 0) / self.total_requests
    
    def get_health_status(self) -> HealthStatus:
        """Determine overall health status"""
        if self.total_requests  0.3 or major_rate > 0.5:
            return HealthStatus.FAILING
        elif error_rate > 0.1 or major_rate > 0.3:
            return HealthStatus.DEGRADED
        elif error_rate > 0.05 or major_rate > 0.15:
            return HealthStatus.WARNING
        else:
            return HealthStatus.HEALTHY


class ShadowModeTester:
    """
    Agent that runs shadow testing by executing production and shadow
    versions in parallel, comparing results without affecting users.
    """
    
    def __init__(self, production_model: str = "gpt-3.5-turbo",
                 shadow_model: str = "gpt-3.5-turbo",
                 production_temp: float = 0.7,
                 shadow_temp: float = 0.7):
        """Initialize shadow mode tester"""
        # Production agent
        self.production_llm = ChatOpenAI(
            model=production_model,
            temperature=production_temp
        )
        
        # Shadow agent (different version or configuration)
        self.shadow_llm = ChatOpenAI(
            model=shadow_model,
            temperature=shadow_temp
        )
        
        # Metrics tracking
        self.metrics = ShadowMetrics()
        self.responses: List[ShadowResponse] = []
        
        # Difference categorization LLM
        self.analyzer_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.0)
        
        # Prompts
        self.production_prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a helpful AI assistant. Provide accurate, clear responses."),
            ("user", "{query}")
        ])
        
        self.shadow_prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a helpful AI assistant. Provide accurate, clear responses."),
            ("user", "{query}")
        ])
        
        self.comparison_prompt = ChatPromptTemplate.from_messages([
            ("system", """Compare two AI responses and categorize the difference.
            
Categories:
- identical: Same or nearly identical responses
- minor: Small wording differences, same meaning
- moderate: Different approach but similar quality
- major: Significantly different responses or quality

Response 1 (Production):
{response1}

Response 2 (Shadow):
{response2}

Return only the category name."""),
            ("user", "Categorize the difference between these responses.")
        ])
    
    def execute_shadow_request(self, query: str) -> ShadowResponse:
        """
        Execute query on both production and shadow versions
        
        Args:
            query: User query to execute
            
        Returns:
            ShadowResponse with both results and comparison
        """
        # Execute in parallel using threads
        with ThreadPoolExecutor(max_workers=2) as executor:
            # Submit both requests
            prod_future = executor.submit(self._execute_production, query)
            shadow_future = executor.submit(self._execute_shadow, query)
            
            # Wait for both to complete
            prod_result, prod_time, prod_error = prod_future.result()
            shadow_result, shadow_time, shadow_error = shadow_future.result()
        
        # Compare responses
        if prod_error is None and shadow_error is None:
            diff_type, similarity = self._compare_responses(prod_result, shadow_result)
        else:
            diff_type = DifferenceType.ERROR
            similarity = 0.0
        
        # Create response object
        response = ShadowResponse(
            production_response=prod_result,
            shadow_response=shadow_result,
            production_time=prod_time,
            shadow_time=shadow_time,
            production_error=prod_error,
            shadow_error=shadow_error,
            difference_type=diff_type,
            similarity_score=similarity
        )
        
        # Update metrics
        self._update_metrics(response)
        
        # Store response
        self.responses.append(response)
        
        # Return production result to user (shadow is hidden)
        return response
    
    def _execute_production(self, query: str) -> Tuple[str, float, Optional[str]]:
        """Execute on production system"""
        start_time = time.time()
        
        try:
            chain = self.production_prompt | self.production_llm | StrOutputParser()
            result = chain.invoke({"query": query})
            execution_time = time.time() - start_time
            return result, execution_time, None
            
        except Exception as e:
            execution_time = time.time() - start_time
            return "", execution_time, str(e)
    
    def _execute_shadow(self, query: str) -> Tuple[str, float, Optional[str]]:
        """Execute on shadow system"""
        start_time = time.time()
        
        try:
            chain = self.shadow_prompt | self.shadow_llm | StrOutputParser()
            result = chain.invoke({"query": query})
            execution_time = time.time() - start_time
            return result, execution_time, None
            
        except Exception as e:
            execution_time = time.time() - start_time
            return "", execution_time, str(e)
    
    def _compare_responses(self, prod_response: str, 
                          shadow_response: str) -> Tuple[DifferenceType, float]:
        """Compare two responses and categorize difference"""
        # Quick check for identical
        if prod_response.strip() == shadow_response.strip():
            return DifferenceType.IDENTICAL, 1.0
        
        # Calculate basic similarity
        similarity = self._calculate_similarity(prod_response, shadow_response)
        
        # Use LLM for categorization
        try:
            chain = self.comparison_prompt | self.analyzer_llm | StrOutputParser()
            category_str = chain.invoke({
                "response1": prod_response[:500],  # Truncate for efficiency
                "response2": shadow_response[:500]
            }).strip().lower()
            
            # Map to enum
            category_map = {
                "identical": DifferenceType.IDENTICAL,
                "minor": DifferenceType.MINOR,
                "moderate": DifferenceType.MODERATE,
                "major": DifferenceType.MAJOR
            }
            
            diff_type = category_map.get(category_str, DifferenceType.MODERATE)
            
        except Exception as e:
            # Fallback to similarity-based categorization
            if similarity > 0.9:
                diff_type = DifferenceType.MINOR
            elif similarity > 0.7:
                diff_type = DifferenceType.MODERATE
            else:
                diff_type = DifferenceType.MAJOR
        
        return diff_type, similarity
    
    def _calculate_similarity(self, text1: str, text2: str) -> float:
        """Calculate simple word-overlap similarity"""
        words1 = set(text1.lower().split())
        words2 = set(text2.lower().split())
        
        if not words1 or not words2:
            return 0.0
        
        intersection = words1.intersection(words2)
        union = words1.union(words2)
        
        return len(intersection) / len(union) if union else 0.0
    
    def _update_metrics(self, response: ShadowResponse):
        """Update metrics based on response"""
        self.metrics.total_requests += 1
        
        # Count difference types
        if response.difference_type == DifferenceType.IDENTICAL:
            self.metrics.identical_responses += 1
        elif response.difference_type == DifferenceType.MINOR:
            self.metrics.minor_differences += 1
        elif response.difference_type == DifferenceType.MODERATE:
            self.metrics.moderate_differences += 1
        elif response.difference_type == DifferenceType.MAJOR:
            self.metrics.major_differences += 1
        
        # Count errors
        if response.production_error:
            self.metrics.production_errors += 1
        if response.shadow_error:
            self.metrics.shadow_errors += 1
        
        # Update latency metrics (running average)
        n = self.metrics.total_requests
        self.metrics.avg_production_latency = (
            (self.metrics.avg_production_latency * (n - 1) + response.production_time) / n
        )
        self.metrics.avg_shadow_latency = (
            (self.metrics.avg_shadow_latency * (n - 1) + response.shadow_time) / n
        )
        
        # Calculate relative performance
        if self.metrics.avg_production_latency > 0:
            self.metrics.shadow_slower_by = (
                (self.metrics.avg_shadow_latency - self.metrics.avg_production_latency) 
                / self.metrics.avg_production_latency * 100
            )
    
    def batch_test(self, queries: List[str], 
                   show_progress: bool = True) -> List[ShadowResponse]:
        """
        Test multiple queries in shadow mode
        
        Args:
            queries: List of queries to test
            show_progress: Whether to show progress
            
        Returns:
            List of shadow responses
        """
        print(f"\n🔄 Starting shadow mode testing for {len(queries)} queries...")
        
        results = []
        for i, query in enumerate(queries, 1):
            if show_progress:
                print(f"   Processing {i}/{len(queries)}: {query[:50]}...")
            
            response = self.execute_shadow_request(query)
            results.append(response)
            
            # Show inline status
            if show_progress:
                status = "✓" if response.difference_type in [DifferenceType.IDENTICAL, DifferenceType.MINOR] else "⚠"
                print(f"   {status} Difference: {response.difference_type.value}, Similarity: {response.similarity_score:.2f}")
        
        return results
    
    def get_health_status(self) -> HealthStatus:
        """Get current health status of shadow version"""
        return self.metrics.get_health_status()
    
    def get_divergent_examples(self, min_difference: DifferenceType = DifferenceType.MODERATE,
                               limit: int = 10) -> List[ShadowResponse]:
        """Get examples where responses diverged significantly"""
        divergent = [
            r for r in self.responses
            if r.difference_type in [DifferenceType.MODERATE, DifferenceType.MAJOR]
        ]
        
        # Sort by similarity (lowest first = most divergent)
        divergent.sort(key=lambda r: r.similarity_score)
        
        return divergent[:limit]
    
    def generate_report(self) -> str:
        """Generate comprehensive shadow testing report"""
        metrics = self.metrics
        health = self.get_health_status()
        
        report = [
            "\n" + "="*70,
            "SHADOW MODE TESTING REPORT",
            "="*70,
            f"\nTotal Requests: {metrics.total_requests}",
            f"Health Status: {health.value.upper()} {'🟢' if health == HealthStatus.HEALTHY else '🟡' if health == HealthStatus.WARNING else '🟠' if health == HealthStatus.DEGRADED else '🔴'}",
            "\nResponse Comparison:",
            f"  Identical: {metrics.identical_responses} ({metrics.get_difference_rate(DifferenceType.IDENTICAL)*100:.1f}%)",
            f"  Minor Differences: {metrics.minor_differences} ({metrics.get_difference_rate(DifferenceType.MINOR)*100:.1f}%)",
            f"  Moderate Differences: {metrics.moderate_differences} ({metrics.get_difference_rate(DifferenceType.MODERATE)*100:.1f}%)",
            f"  Major Differences: {metrics.major_differences} ({metrics.get_difference_rate(DifferenceType.MAJOR)*100:.1f}%)",
            "\nError Rates:",
            f"  Production Errors: {metrics.production_errors} ({metrics.production_errors/metrics.total_requests*100:.1f}%)",
            f"  Shadow Errors: {metrics.shadow_errors} ({metrics.shadow_errors/metrics.total_requests*100:.1f}%)",
            "\nPerformance:",
            f"  Avg Production Latency: {metrics.avg_production_latency:.3f}s",
            f"  Avg Shadow Latency: {metrics.avg_shadow_latency:.3f}s",
            f"  Shadow Performance: {metrics.shadow_slower_by:+.1f}% {'(slower)' if metrics.shadow_slower_by > 0 else '(faster)'}",
        ]
        
        # Add recommendation
        report.append("\nRecommendation:")
        if health == HealthStatus.HEALTHY:
            report.append("  ✅ Shadow version looks good for promotion to production")
        elif health == HealthStatus.WARNING:
            report.append("  ⚠️  Monitor shadow version closely before promotion")
        elif health == HealthStatus.DEGRADED:
            report.append("  ⚠️  Shadow version shows degradation - investigate before promotion")
        else:
            report.append("  ❌ Shadow version failing - DO NOT promote to production")
        
        report.append("="*70)
        return "\n".join(report)
    
    def show_divergent_examples(self, limit: int = 5):
        """Show examples where responses diverged"""
        divergent = self.get_divergent_examples(limit=limit)
        
        if not divergent:
            print("\n✅ No significant divergent examples found")
            return
        
        print(f"\n{'='*70}")
        print(f"DIVERGENT EXAMPLES (Top {len(divergent)})")
        print(f"{'='*70}")
        
        for i, response in enumerate(divergent, 1):
            print(f"\nExample {i}:")
            print(f"Difference Type: {response.difference_type.value}")
            print(f"Similarity: {response.similarity_score:.2f}")
            print(f"\nProduction Response:")
            print(f"  {response.production_response[:200]}...")
            print(f"\nShadow Response:")
            print(f"  {response.shadow_response[:200]}...")
            print("-" * 70)


def demonstrate_shadow_mode():
    """Demonstrate shadow mode testing"""
    print("="*70)
    print("Pattern 153: Shadow Mode Testing")
    print("="*70)
    
    # Create tester with different configurations
    # In real scenario, these would be different model versions or prompts
    tester = ShadowModeTester(
        production_model="gpt-3.5-turbo",
        shadow_model="gpt-3.5-turbo",
        production_temp=0.7,
        shadow_temp=0.9  # Different temperature for shadow
    )
    
    # Test queries
    test_queries = [
        "What is Python?",
        "Explain machine learning in simple terms",
        "How do I learn programming?",
        "What's the difference between AI and ML?",
        "Write a haiku about coding",
        "What are best practices for API design?",
        "Explain recursion with an example",
        "How does garbage collection work?",
        "What is the difference between REST and GraphQL?",
        "Explain async/await in JavaScript"
    ]
    
    print("\n" + "="*70)
    print("BATCH SHADOW TESTING")
    print("="*70)
    print(f"Production: GPT-3.5-Turbo (temp=0.7)")
    print(f"Shadow: GPT-3.5-Turbo (temp=0.9)")
    
    # Run batch test
    results = tester.batch_test(test_queries, show_progress=True)
    
    # Generate report
    print(tester.generate_report())
    
    # Show divergent examples
    tester.show_divergent_examples(limit=3)
    
    # Test individual request
    print("\n" + "="*70)
    print("INDIVIDUAL REQUEST EXAMPLE")
    print("="*70)
    
    query = "Explain quantum computing"
    print(f"\nQuery: {query}")
    
    response = tester.execute_shadow_request(query)
    
    print(f"\n📊 Results:")
    print(f"  Difference Type: {response.difference_type.value}")
    print(f"  Similarity Score: {response.similarity_score:.2f}")
    print(f"  Production Time: {response.production_time:.3f}s")
    print(f"  Shadow Time: {response.shadow_time:.3f}s")
    
    print(f"\n🏭 Production Response:")
    print(f"  {response.production_response[:150]}...")
    
    print(f"\n🔬 Shadow Response:")
    print(f"  {response.shadow_response[:150]}...")
    
    # Health check
    health = tester.get_health_status()
    print(f"\n🏥 Shadow Health Status: {health.value.upper()}")
    
    # Summary
    print("\n" + "="*70)
    print("SUMMARY")
    print("="*70)
    print("""
Shadow Mode Testing Pattern provides:

1. Zero-Impact Testing:
   - Tests run parallel to production
   - Users only see production responses
   - Shadow results collected for analysis
   - No service disruption

2. Comprehensive Comparison:
   - Response similarity analysis
   - Difference categorization
   - Performance comparison
   - Error rate tracking

3. Health Monitoring:
   - Automatic health status assessment
   - Divergence detection
   - Performance regression identification
   - Error rate monitoring

4. Deployment Confidence:
   - Real production traffic testing
   - Early problem detection
   - Data-driven promotion decisions
   - Risk mitigation

5. Use Cases:
   - New model version validation
   - Prompt engineering testing
   - Configuration changes
   - Performance optimization
   - Feature rollout validation

This pattern is essential for safely testing changes to production
AI systems before full deployment.
    """)


if __name__ == "__main__":
    demonstrate_shadow_mode()

    

Agentic Design Pattern: Shadow Mode

Pattern 153: Shadow Mode – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the Shadow Mode pattern implementation.

---

Pattern Description

Pattern 153: Shadow Mode Testing

Description: Runs new agent versions alongside production without affecting users. Captures both production and shadow responses, compares results, and identifies differences for validation before full deployment.

Components: – Shadow Executor: Runs shadow version in parallel – Response Comparator: Compares production vs shadow outputs – Difference Analyzer: Identifies and categorizes differences – Metrics Collector: Tracks shadow performance – Rollback Monitor: Detects degradation signals

Use Cases: – Testing new model versions safely – Validating prompt changes – A/B testing without user impact – Gradual feature rollout – Performance regression detection

Benefits: – Zero user impact during testing – Real production traffic testing – Early problem detection – Confidence before full deployment – Comprehensive comparison data

Trade-offs: – Doubled computational cost – Infrastructure complexity – Storage for dual responses – Latency considerations – Analysis overhead

LangChain Implementation: Uses parallel execution with ChatOpenAI, response comparison, and comprehensive metrics tracking.

---

File Statistics

• Total Lines: 584
• Classes: 5
• Functions: 1
• Constants: 0

---

Module Documentation

"""
Pattern 153: Shadow Mode Testing

Description:
    Runs new agent versions alongside production without affecting users.
    Captures both production and shadow responses, compares results, and
    identifies differences for validation before full deployment.

Components:
    - Shadow Executor: Runs shadow version in parallel
    - Response Comparator: Compares production vs shadow outputs
    - Difference Analyzer: Identifies and categorizes differences
    - Metrics Collector: Tracks shadow performance
    - Rollback Monitor: Detects degradation signals

Use Cases:
    - Testing new model versions safely
    - Validating prompt changes
    - A/B testing without user impact
    - Gradual feature rollout
    - Performance regression detection

Benefits:
    - Zero user impact during testing
    - Real production traffic testing
    - Early problem detection
    - Confidence before full deployment
    - Comprehensive comparison data

Trade-offs:
    - Doubled computational cost
    - Infrastructure complexity
    - Storage for dual responses
    - Latency considerations
    - Analysis overhead

LangChain Implementation:
    Uses parallel execution with ChatOpenAI, response comparison,
    and comprehensive metrics tracking.
"""

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

---

Import Statements

Standard Library Imports

import asyncio
import collections.defaultdict
import concurrent.futures.ThreadPoolExecutor
import concurrent.futures.as_completed
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 time
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Tuple

Purpose: Core Python functionality: – asyncio: Asynchronous I/O operations – collections.defaultdict: Specialized container datatypes – concurrent.futures.ThreadPoolExecutor: Regular expression operations – concurrent.futures.as_completed: Regular expression operations – 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 – time: Time-related functions – typing.Any: 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.Tuple: 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

Purpose: Additional dependencies for specialized functionality.

---

Classes

Class: DifferenceType

Lines 60-66

Inherits from: Enum

Purpose:

Types of differences between responses

---

Class: HealthStatus

Lines 69-74

Inherits from: Enum

Purpose:

Health status of shadow version

---

Class: ShadowResponse

Lines 78-88

Purpose:

Response from shadow execution

---

Class: ShadowMetrics

Lines 92-134

Purpose:

Metrics for shadow testing

Methods:

get_difference_rate(self, diff_type)

Lines 105-117

Purpose:

Get rate of specific difference type

Parameters: – diff_type: Diff type

Returns: float

get_health_status(self)

Lines 119-134

Purpose:

Determine overall health status

Parameters:

Returns: HealthStatus

---

Class: ShadowModeTester

Lines 137-466

Purpose:

Agent that runs shadow testing by executing production and shadow
versions in parallel, comparing results without affecting users.

Methods:

__init__(self, production_model, shadow_model, production_temp, shadow_temp)

Lines 143-195

Purpose:

Initialize shadow mode tester

Parameters: – production_model: Language model instance – shadow_model: Language model instance – production_temp: Production temp – shadow_temp: Shadow temp

execute_shadow_request(self, query)

Lines 197-243

Purpose:

Execute query on both production and shadow versions

Args:
    query: User query to execute

Returns:
    ShadowResponse with both results and comparison

Parameters: – query: Input query or question

Returns: ShadowResponse

_execute_production(self, query)

Lines 245-257

Purpose:

Execute on production system

Parameters: – query: Input query or question

Returns: <ast.Subscript object at 0x000001A06D7C9910>

_execute_shadow(self, query)

Lines 259-271

Purpose:

Execute on shadow system

Parameters: – query: Input query or question

Returns: <ast.Subscript object at 0x000001A06D6380D0>

_compare_responses(self, prod_response, shadow_response)

Lines 273-310

Purpose:

Compare two responses and categorize difference

Parameters: – prod_response: Prod response – shadow_response: Shadow response

Returns: <ast.Subscript object at 0x000001A06D85A710>

_calculate_similarity(self, text1, text2)

Lines 312-323

Purpose:

Calculate simple word-overlap similarity

Parameters: – text1: Text1 – text2: Text2

Returns: float

_update_metrics(self, response)

Lines 325-359

Purpose:

Update metrics based on response

Parameters: – response: Response

batch_test(self, queries, show_progress)

Lines 361-388

Purpose:

Test multiple queries in shadow mode

Args:
    queries: List of queries to test
    show_progress: Whether to show progress

Returns:
    List of shadow responses

Parameters: – queries: Queries – show_progress: Show progress

Returns: <ast.Subscript object at 0x000001A06D17C050>

get_health_status(self)

Lines 390-392

Purpose:

Get current health status of shadow version

Parameters:

Returns: HealthStatus

get_divergent_examples(self, min_difference, limit)

Lines 394-405

Purpose:

Get examples where responses diverged significantly

Parameters: – min_difference: Min difference – limit: Limit

Returns: <ast.Subscript object at 0x000001A06D702490>

generate_report(self)

Lines 407-444

Purpose:

Generate comprehensive shadow testing report

Parameters:

Returns: str

show_divergent_examples(self, limit)

Lines 446-466

Purpose:

Show examples where responses diverged

Parameters: – limit: Limit

---

Functions

demonstrate_shadow_mode()

Lines 469-579

Purpose:

Demonstrate shadow mode testing

---

Key Concepts Summary

Pattern Overview

The Shadow Mode 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)

---

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

---

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

---

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

---

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

---

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

---

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

---

Related Patterns

• Pattern 152
• Pattern 154

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

---

Summary

The Shadow Mode 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/

---

File: 153_shadow_mode.py
Total Lines: 584
Pattern Category: AI Agent Design Pattern #153

---

Generated from actual implementation code
For educational and reference purposes