"""
Pattern 264: Emulation

Purpose: System emulation with behavior replication and performance testing.
Emulates external systems, APIs, and services for testing purposes.
"""

from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
import time
import random


class EmulationType(Enum):
    """Types of emulation."""
    API = "api"  # API emulation
    DATABASE = "database"  # Database emulation
    SERVICE = "service"  # Service emulation
    HARDWARE = "hardware"  # Hardware emulation
    NETWORK = "network"  # Network conditions


@dataclass
class EmulatedSystem:
    """Emulated system configuration."""
    name: str
    emulation_type: EmulationType
    behavior: Dict[str, Any] = field(default_factory=dict)
    latency_ms: float = 10.0
    failure_rate: float = 0.0  # 0.0 to 1.0
    state: Dict[str, Any] = field(default_factory=dict)


@dataclass
class EmulationRequest:
    """Request to emulated system."""
    system_name: str
    operation: str
    parameters: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EmulationResponse:
    """Response from emulated system."""
    request: EmulationRequest
    success: bool
    data: Any = None
    error: str = ""
    latency_ms: float = 0.0
    timestamp: datetime = field(default_factory=datetime.now)


class EmulationAgent:
    """
    Emulation Agent - System and service emulation.
    
    Features:
    - API emulation
    - Database emulation
    - Service behavior replication
    - Latency simulation
    - Failure injection
    - State management
    - Performance testing
    - Response generation
    """
    
    def __init__(self, name: str):
        self.name = name
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.5)
        
        # Emulated systems
        self.systems: Dict[str, EmulatedSystem] = {}
        self.request_history: List[EmulationRequest] = []
        self.response_history: List[EmulationResponse] = []
        
        # Statistics
        self.statistics: Dict[str, Any] = {
            "total_requests": 0,
            "successful_requests": 0,
            "failed_requests": 0,
            "emulation_types": {t.value: 0 for t in EmulationType},
            "avg_latency_ms": 0.0
        }
    
    def register_system(
        self,
        name: str,
        emulation_type: EmulationType,
        behavior: Dict[str, Any] = None,
        latency_ms: float = 10.0,
        failure_rate: float = 0.0
    ) -> EmulatedSystem:
        """Register an emulated system."""
        
        system = EmulatedSystem(
            name=name,
            emulation_type=emulation_type,
            behavior=behavior or {},
            latency_ms=latency_ms,
            failure_rate=failure_rate
        )
        
        self.systems[name] = system
        
        return system
    
    def emulate_request(
        self,
        request: EmulationRequest
    ) -> EmulationResponse:
        """Emulate a request to a system."""
        
        start_time = time.time()
        
        # Check if system exists
        if request.system_name not in self.systems:
            return EmulationResponse(
                request=request,
                success=False,
                error=f"System {request.system_name} not found",
                latency_ms=0.0
            )
        
        system = self.systems[request.system_name]
        
        # Simulate latency
        time.sleep(system.latency_ms / 1000.0)
        
        # Simulate failure
        if random.random()  Dict[str, Any]:
        """Emulate API response."""
        
        # Check behavior configuration
        if request.operation in system.behavior:
            return system.behavior[request.operation]
        
        # Default API response
        return {
            "status": "success",
            "operation": request.operation,
            "parameters": request.parameters,
            "timestamp": datetime.now().isoformat()
        }
    
    def _emulate_database(
        self,
        system: EmulatedSystem,
        request: EmulationRequest
    ) -> Dict[str, Any]:
        """Emulate database operation."""
        
        operation = request.operation.lower()
        
        if operation == "select":
            # Return mock data
            return {
                "rows": [
                    {"id": 1, "data": "value1"},
                    {"id": 2, "data": "value2"}
                ],
                "count": 2
            }
        elif operation == "insert":
            # Simulate insert
            return {
                "inserted_id": random.randint(1, 1000),
                "affected_rows": 1
            }
        elif operation == "update":
            # Simulate update
            return {
                "affected_rows": random.randint(0, 10)
            }
        elif operation == "delete":
            # Simulate delete
            return {
                "affected_rows": random.randint(0, 5)
            }
        
        return {"status": "executed", "operation": operation}
    
    def _emulate_service(
        self,
        system: EmulatedSystem,
        request: EmulationRequest
    ) -> Dict[str, Any]:
        """Emulate service call."""
        
        # Use LLM to generate realistic response
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are emulating a {request.operation} service. "
                      f"Generate a realistic JSON response."),
            ("human", f"Parameters: {request.parameters}\n\n"
                     f"Generate response (JSON format):")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        # Parse response (simplified)
        return {
            "service_response": str(response.content)[:200],
            "status": "completed"
        }
    
    def set_system_state(
        self,
        system_name: str,
        state: Dict[str, Any]
    ):
        """Set state for emulated system."""
        if system_name in self.systems:
            self.systems[system_name].state.update(state)
    
    def configure_network_conditions(
        self,
        system_name: str,
        latency_ms: float,
        failure_rate: float
    ):
        """Configure network conditions for emulation."""
        if system_name in self.systems:
            self.systems[system_name].latency_ms = latency_ms
            self.systems[system_name].failure_rate = failure_rate
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get emulation statistics."""
        
        success_rate = 0.0
        if self.statistics["total_requests"] > 0:
            success_rate = (
                self.statistics["successful_requests"] / 
                self.statistics["total_requests"]
            ) * 100
        
        return {
            "name": self.name,
            **self.statistics,
            "success_rate": f"{success_rate:.1f}%"
        }


class EmulationPattern:
    """
    Emulation Pattern - Manage emulation agents.
    
    Coordinates emulation of multiple systems.
    """
    
    def __init__(self):
        self.agents: Dict[str, EmulationAgent] = {}
    
    def create_agent(self, name: str) -> EmulationAgent:
        """Create a new emulation agent."""
        agent = EmulationAgent(name)
        self.agents[name] = agent
        print(f"   Created emulation agent: {name}")
        return agent
    
    def run(
        self,
        agent_name: str,
        request: EmulationRequest
    ) -> Dict[str, Any]:
        """Execute emulation request."""
        if agent_name not in self.agents:
            return {"error": f"Agent {agent_name} not found"}
        
        agent = self.agents[agent_name]
        response = agent.emulate_request(request)
        
        print(f"\n=== Emulation by {agent_name} ===")
        print(f"System: {request.system_name}")
        print(f"Operation: {request.operation}")
        print(f"Success: {response.success}")
        print(f"Latency: {response.latency_ms:.2f}ms")
        
        if response.success:
            print(f"Data: {str(response.data)[:100]}")
        else:
            print(f"Error: {response.error}")
        
        return {
            "agent": agent_name,
            "success": response.success,
            "latency_ms": response.latency_ms,
            "statistics": agent.get_statistics()
        }


# Example usage
if __name__ == "__main__":
    print("=== Emulation Agent Pattern Demo ===\n")
    
    # Create pattern
    pattern = EmulationPattern()
    
    # Create agent
    print("1. Setting up emulation agent...")
    emulator = pattern.create_agent("SystemEmulator")
    
    # Register emulated systems
    print("\n2. Registering emulated systems...")
    
    emulator.register_system(
        "PaymentAPI",
        EmulationType.API,
        behavior={
            "process_payment": {"status": "approved", "transaction_id": "TXN123"}
        },
        latency_ms=50.0,
        failure_rate=0.1
    )
    
    emulator.register_system(
        "UserDB",
        EmulationType.DATABASE,
        latency_ms=20.0,
        failure_rate=0.05
    )
    
    # Execute emulations
    print("\n3. Executing emulation requests...")
    
    request1 = EmulationRequest(
        system_name="PaymentAPI",
        operation="process_payment",
        parameters={"amount": 100.0, "currency": "USD"}
    )
    
    result1 = pattern.run("SystemEmulator", request1)
    
    print("\n" + "="*60 + "\n")
    
    request2 = EmulationRequest(
        system_name="UserDB",
        operation="select",
        parameters={"query": "SELECT * FROM users WHERE id=1"}
    )
    
    result2 = pattern.run("SystemEmulator", request2)
    
    # Print final statistics
    print("\n" + "="*60)
    print("\n4. Final Statistics:")
    stats = result2["statistics"]
    for key, value in stats.items():
        print(f"   {key}: {value}")
    
    print("\n✓ Comprehensive system emulation with behavior replication complete!")

    

Agentic Architecture Pattern: Emulation

Pattern 264: 264 Emulation – Line-by-Line Explanation

Overview

Pattern 264: Emulation

Purpose: System emulation with behavior replication and performance testing. Emulates external systems, APIs, and services for testing purposes.

Code Structure Analysis

File Information

• Pattern Number: 264
• Pattern Name: 264 Emulation
• File Path: patterns\testing_validation\264_emulation.py
• Total Lines: 413

Detailed Code Explanation

Module Docstring

"""
Pattern 264: Emulation

Purpose: System emulation with behavior replication and performance testing.
Emulates external systems, APIs, and services for testing purposes.
"""

Explanation: This module docstring describes the purpose and key features of the 264 Emulation pattern.

Imports Section

from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
import time
import random  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Callable: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Enumerations

EmulationType (Line 19)

class EmulationType(Enum):
    API
    DATABASE
    SERVICE
    HARDWARE
    NETWORK

Purpose: Types of emulation.

Values: – API: Represents api – DATABASE: Represents database – SERVICE: Represents service – HARDWARE: Represents hardware – NETWORK: Represents network

Classes

EmulationType (Line 19)

Description: Types of emulation.

EmulatedSystem (Line 29)

Description: Emulated system configuration.

EmulationRequest (Line 40)

Description: Request to emulated system.

EmulationResponse (Line 49)

Description: Response from emulated system.

EmulationAgent (Line 59)

Description: Emulation Agent – System and service emulation.

Features: – API emulation – Database emulation – Service behavior replication – Latency simulation – Failure injection – State management – Performance testing – Response generation

Methods:

__init__ (Line 74)

def __init__(self, name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label

register_system (Line 92)

def register_system(self, name, emulation_type, behavior, latency_ms, failure_rate):
    """Register an emulated system."""

Purpose: Register an emulated system.

Parameters: – self: Instance reference – name: Identifier or label – emulation_type: Parameter for emulation_type – behavior: Parameter for behavior – latency_ms: Parameter for latency_ms – failure_rate: Parameter for failure_rate

emulate_request (Line 114)

def emulate_request(self, request):
    """Emulate a request to a system."""

Purpose: Emulate a request to a system.

Parameters: – self: Instance reference – request: Parameter for request

_emulate_api (Line 188)

def _emulate_api(self, system, request):
    """Emulate API response."""

Purpose: Emulate API response.

Parameters: – self: Instance reference – system: Parameter for system – request: Parameter for request

_emulate_database (Line 207)

def _emulate_database(self, system, request):
    """Emulate database operation."""

Purpose: Emulate database operation.

Parameters: – self: Instance reference – system: Parameter for system – request: Parameter for request

_emulate_service (Line 244)

def _emulate_service(self, system, request):
    """Emulate service call."""

Purpose: Emulate service call.

Parameters: – self: Instance reference – system: Parameter for system – request: Parameter for request

set_system_state (Line 268)

def set_system_state(self, system_name, state):
    """Set state for emulated system."""

Purpose: Set state for emulated system.

Parameters: – self: Instance reference – system_name: Parameter for system_name – state: Current workflow state

configure_network_conditions (Line 277)

def configure_network_conditions(self, system_name, latency_ms, failure_rate):
    """Configure network conditions for emulation."""

Purpose: Configure network conditions for emulation.

Parameters: – self: Instance reference – system_name: Parameter for system_name – latency_ms: Parameter for latency_ms – failure_rate: Parameter for failure_rate

get_statistics (Line 288)

def get_statistics(self):
    """Get emulation statistics."""

Purpose: Get emulation statistics.

Parameters: – self: Instance reference

EmulationPattern (Line 305)

Description: Emulation Pattern – Manage emulation agents.

Coordinates emulation of multiple systems.

Methods:

__init__ (Line 312)

def __init__(self):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference

create_agent (Line 315)

def create_agent(self, name):
    """Create a new emulation agent."""

Purpose: Create a new emulation agent.

Parameters: – self: Instance reference – name: Identifier or label

run (Line 322)

def run(self, agent_name, request):
    """Execute emulation request."""

Purpose: Execute emulation request.

Parameters: – self: Instance reference – agent_name: Parameter for agent_name – request: Parameter for request

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = EmulationType(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities