"""
Pattern 279: Environment Management

Purpose: Environment provisioning, lifecycle management, and health monitoring
"""

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from collections import defaultdict
import random


class EnvironmentType(Enum):
    """Type of environment."""
    DEVELOPMENT = "development"
    STAGING = "staging"
    PRODUCTION = "production"
    TESTING = "testing"
    UAT = "uat"  # User Acceptance Testing


class EnvironmentStatus(Enum):
    """Status of environment."""
    CREATING = "creating"
    ACTIVE = "active"
    DEGRADED = "degraded"
    MAINTENANCE = "maintenance"
    TERMINATING = "terminating"
    TERMINATED = "terminated"


class ResourceType(Enum):
    """Type of resource."""
    COMPUTE = "compute"
    STORAGE = "storage"
    NETWORK = "network"
    DATABASE = "database"


class HealthStatus(Enum):
    """Health status of environment."""
    HEALTHY = "healthy"
    WARNING = "warning"
    CRITICAL = "critical"
    UNKNOWN = "unknown"


@dataclass
class Resource:
    """Represents a resource in an environment."""
    resource_id: str
    resource_type: ResourceType
    name: str
    capacity: float  # Units depend on type (CPU cores, GB, etc.)
    allocated: float = 0.0
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class HealthCheck:
    """Health check result."""
    check_name: str
    status: HealthStatus
    message: str
    timestamp: datetime = field(default_factory=datetime.now)
    details: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Environment:
    """Represents a deployment environment."""
    environment_id: str
    name: str
    env_type: EnvironmentType
    status: EnvironmentStatus
    resources: Dict[str, Resource] = field(default_factory=dict)
    health_checks: List[HealthCheck] = field(default_factory=list)
    created_at: datetime = field(default_factory=datetime.now)
    last_health_check: Optional[datetime] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


class EnvironmentManagement:
    """
    Implements environment management pattern.
    
    Features:
    - Environment provisioning
    - Resource allocation
    - Health monitoring
    - Environment lifecycle management
    - Auto-scaling
    - Environment isolation
    """
    
    def __init__(self):
        self.environments: Dict[str, Environment] = {}
        self.resource_pool: Dict[ResourceType, float] = {
            ResourceType.COMPUTE: 100.0,
            ResourceType.STORAGE: 1000.0,
            ResourceType.NETWORK: 10.0,
            ResourceType.DATABASE: 10.0
        }
        
        # Statistics
        self.statistics = {
            "total_environments": 0,
            "active_environments": 0,
            "total_resources_allocated": 0,
            "health_checks_performed": 0,
            "critical_issues": 0,
            "environments_scaled": 0
        }
    
    def create_environment(
        self,
        name: str,
        env_type: EnvironmentType,
        resources: Dict[ResourceType, float]
    ) -> Dict[str, Any]:
        """
        Create a new environment.
        
        Args:
            name: Environment name
            env_type: Type of environment
            resources: Required resources {type: amount}
            
        Returns:
            Status of environment creation
        """
        # Check resource availability
        for res_type, amount in resources.items():
            available = self.resource_pool[res_type]
            allocated = sum(
                env.resources[res.resource_id].allocated
                for env in self.environments.values()
                for res in env.resources.values()
                if res.resource_type == res_type
            )
            remaining = available - allocated
            
            if amount > remaining:
                return {
                    "success": False,
                    "message": f"Insufficient {res_type.value} resources. "
                              f"Requested: {amount}, Available: {remaining}"
                }
        
        # Create environment
        env_id = f"env-{len(self.environments) + 1:04d}"
        environment = Environment(
            environment_id=env_id,
            name=name,
            env_type=env_type,
            status=EnvironmentStatus.CREATING
        )
        
        # Allocate resources
        for res_type, amount in resources.items():
            res_id = f"{env_id}-{res_type.value}-001"
            resource = Resource(
                resource_id=res_id,
                resource_type=res_type,
                name=f"{name}-{res_type.value}",
                capacity=amount,
                allocated=amount
            )
            environment.resources[res_id] = resource
        
        environment.status = EnvironmentStatus.ACTIVE
        self.environments[env_id] = environment
        
        self.statistics["total_environments"] += 1
        self.statistics["active_environments"] += 1
        self.statistics["total_resources_allocated"] += len(resources)
        
        return {
            "success": True,
            "environment_id": env_id,
            "name": name,
            "type": env_type.value,
            "status": environment.status.value,
            "resources": {
                res_type.value: amount
                for res_type, amount in resources.items()
            },
            "message": f"Environment '{name}' created successfully"
        }
    
    def terminate_environment(self, environment_id: str) -> Dict[str, Any]:
        """
        Terminate an environment and release resources.
        
        Args:
            environment_id: ID of environment to terminate
            
        Returns:
            Status of termination
        """
        if environment_id not in self.environments:
            return {
                "success": False,
                "message": f"Environment '{environment_id}' not found"
            }
        
        environment = self.environments[environment_id]
        
        # Mark as terminating
        environment.status = EnvironmentStatus.TERMINATING
        
        # Release resources
        resources_released = len(environment.resources)
        environment.resources.clear()
        
        # Mark as terminated
        environment.status = EnvironmentStatus.TERMINATED
        
        self.statistics["active_environments"] -= 1
        
        return {
            "success": True,
            "environment_id": environment_id,
            "resources_released": resources_released,
            "message": f"Environment '{environment_id}' terminated"
        }
    
    def perform_health_check(self, environment_id: str) -> Dict[str, Any]:
        """
        Perform comprehensive health check on environment.
        
        Args:
            environment_id: ID of environment to check
            
        Returns:
            Health check results
        """
        if environment_id not in self.environments:
            return {
                "success": False,
                "message": f"Environment '{environment_id}' not found"
            }
        
        environment = self.environments[environment_id]
        
        # Simulate health checks
        checks = []
        
        # Resource utilization check
        for resource in environment.resources.values():
            utilization = resource.allocated / resource.capacity if resource.capacity > 0 else 0
            
            if utilization > 0.9:
                status = HealthStatus.CRITICAL
                message = f"High utilization: {utilization*100:.1f}%"
                self.statistics["critical_issues"] += 1
            elif utilization > 0.75:
                status = HealthStatus.WARNING
                message = f"Moderate utilization: {utilization*100:.1f}%"
            else:
                status = HealthStatus.HEALTHY
                message = f"Normal utilization: {utilization*100:.1f}%"
            
            check = HealthCheck(
                check_name=f"{resource.name}_utilization",
                status=status,
                message=message,
                details={"utilization": utilization}
            )
            checks.append(check)
        
        # Service availability check (simulated)
        availability = random.random()
        if availability > 0.99:
            status = HealthStatus.HEALTHY
            message = "All services available"
        elif availability > 0.95:
            status = HealthStatus.WARNING
            message = "Some services degraded"
        else:
            status = HealthStatus.CRITICAL
            message = "Service outage detected"
            self.statistics["critical_issues"] += 1
        
        checks.append(HealthCheck(
            check_name="service_availability",
            status=status,
            message=message,
            details={"availability": availability}
        ))
        
        # Update environment
        environment.health_checks.extend(checks)
        environment.last_health_check = datetime.now()
        
        # Determine overall health
        if any(c.status == HealthStatus.CRITICAL for c in checks):
            overall_health = HealthStatus.CRITICAL
            environment.status = EnvironmentStatus.DEGRADED
        elif any(c.status == HealthStatus.WARNING for c in checks):
            overall_health = HealthStatus.WARNING
        else:
            overall_health = HealthStatus.HEALTHY
            if environment.status == EnvironmentStatus.DEGRADED:
                environment.status = EnvironmentStatus.ACTIVE
        
        self.statistics["health_checks_performed"] += 1
        
        return {
            "success": True,
            "environment_id": environment_id,
            "overall_health": overall_health.value,
            "checks": [
                {
                    "name": c.check_name,
                    "status": c.status.value,
                    "message": c.message
                }
                for c in checks
            ],
            "message": f"Health check completed: {overall_health.value}"
        }
    
    def scale_environment(
        self,
        environment_id: str,
        resource_type: ResourceType,
        scale_factor: float
    ) -> Dict[str, Any]:
        """
        Scale environment resources.
        
        Args:
            environment_id: ID of environment
            resource_type: Type of resource to scale
            scale_factor: Factor to scale by (1.5 = 50% increase)
            
        Returns:
            Status of scaling operation
        """
        if environment_id not in self.environments:
            return {
                "success": False,
                "message": f"Environment '{environment_id}' not found"
            }
        
        environment = self.environments[environment_id]
        
        # Find resources of specified type
        resources_to_scale = [
            res for res in environment.resources.values()
            if res.resource_type == resource_type
        ]
        
        if not resources_to_scale:
            return {
                "success": False,
                "message": f"No {resource_type.value} resources in environment"
            }
        
        # Calculate new allocation
        total_current = sum(res.allocated for res in resources_to_scale)
        total_new = total_current * scale_factor
        additional = total_new - total_current
        
        # Check availability
        available = self.resource_pool[resource_type]
        allocated_global = sum(
            res.allocated
            for env in self.environments.values()
            for res in env.resources.values()
            if res.resource_type == resource_type
        )
        remaining = available - allocated_global
        
        if additional > remaining:
            return {
                "success": False,
                "message": f"Insufficient resources. Requested: {additional}, Available: {remaining}"
            }
        
        # Scale resources
        for resource in resources_to_scale:
            old_allocated = resource.allocated
            resource.allocated = old_allocated * scale_factor
            resource.capacity = resource.allocated
        
        self.statistics["environments_scaled"] += 1
        
        return {
            "success": True,
            "environment_id": environment_id,
            "resource_type": resource_type.value,
            "scale_factor": scale_factor,
            "previous_total": total_current,
            "new_total": total_new,
            "message": f"Scaled {resource_type.value} by {scale_factor}x"
        }
    
    def get_environment_info(self, environment_id: str) -> Dict[str, Any]:
        """Get detailed environment information."""
        if environment_id not in self.environments:
            return {
                "success": False,
                "message": f"Environment '{environment_id}' not found"
            }
        
        environment = self.environments[environment_id]
        
        # Calculate resource usage
        resources_summary = {}
        for resource in environment.resources.values():
            res_type = resource.resource_type.value
            if res_type not in resources_summary:
                resources_summary[res_type] = {
                    "total_capacity": 0,
                    "total_allocated": 0,
                    "count": 0
                }
            
            resources_summary[res_type]["total_capacity"] += resource.capacity
            resources_summary[res_type]["total_allocated"] += resource.allocated
            resources_summary[res_type]["count"] += 1
        
        # Latest health status
        latest_health = HealthStatus.UNKNOWN
        if environment.health_checks:
            latest_checks = environment.health_checks[-5:]
            if any(c.status == HealthStatus.CRITICAL for c in latest_checks):
                latest_health = HealthStatus.CRITICAL
            elif any(c.status == HealthStatus.WARNING for c in latest_checks):
                latest_health = HealthStatus.WARNING
            else:
                latest_health = HealthStatus.HEALTHY
        
        return {
            "success": True,
            "environment_id": environment_id,
            "name": environment.name,
            "type": environment.env_type.value,
            "status": environment.status.value,
            "health": latest_health.value,
            "created_at": environment.created_at.isoformat(),
            "last_health_check": environment.last_health_check.isoformat() if environment.last_health_check else None,
            "resources": resources_summary,
            "total_health_checks": len(environment.health_checks)
        }
    
    def list_environments(
        self,
        env_type: Optional[EnvironmentType] = None,
        status: Optional[EnvironmentStatus] = None
    ) -> Dict[str, Any]:
        """List environments with optional filters."""
        environments_list = []
        
        for env_id, env in self.environments.items():
            if env_type and env.env_type != env_type:
                continue
            if status and env.status != status:
                continue
            
            environments_list.append({
                "environment_id": env_id,
                "name": env.name,
                "type": env.env_type.value,
                "status": env.status.value,
                "resource_count": len(env.resources)
            })
        
        return {
            "total": len(environments_list),
            "environments": environments_list
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get environment management statistics."""
        # Calculate resource utilization
        total_capacity = {}
        total_allocated = {}
        
        for res_type in ResourceType:
            total_capacity[res_type.value] = self.resource_pool[res_type]
            total_allocated[res_type.value] = sum(
                res.allocated
                for env in self.environments.values()
                for res in env.resources.values()
                if res.resource_type == res_type
            )
        
        return {
            "total_environments": self.statistics["total_environments"],
            "active_environments": self.statistics["active_environments"],
            "total_resources_allocated": self.statistics["total_resources_allocated"],
            "health_checks_performed": self.statistics["health_checks_performed"],
            "critical_issues": self.statistics["critical_issues"],
            "environments_scaled": self.statistics["environments_scaled"],
            "resource_utilization": {
                res_type: {
                    "capacity": total_capacity[res_type],
                    "allocated": total_allocated[res_type],
                    "utilization": total_allocated[res_type] / total_capacity[res_type] if total_capacity[res_type] > 0 else 0
                }
                for res_type in total_capacity.keys()
            }
        }


class EnvironmentManagementPattern:
    """
    Coordinator for environment management.
    
    Manages environment lifecycle and resources.
    """
    
    def __init__(self):
        self.management = EnvironmentManagement()
    
    def create_environment(self, name: str, env_type: EnvironmentType, resources: Dict[ResourceType, float]) -> Dict[str, Any]:
        """Create environment."""
        return self.management.create_environment(name, env_type, resources)
    
    def terminate_environment(self, environment_id: str) -> Dict[str, Any]:
        """Terminate environment."""
        return self.management.terminate_environment(environment_id)
    
    def perform_health_check(self, environment_id: str) -> Dict[str, Any]:
        """Perform health check."""
        return self.management.perform_health_check(environment_id)
    
    def scale_environment(self, environment_id: str, resource_type: ResourceType, scale_factor: float) -> Dict[str, Any]:
        """Scale environment."""
        return self.management.scale_environment(environment_id, resource_type, scale_factor)
    
    def get_environment_info(self, environment_id: str) -> Dict[str, Any]:
        """Get environment info."""
        return self.management.get_environment_info(environment_id)
    
    def list_environments(self) -> Dict[str, Any]:
        """List environments."""
        return self.management.list_environments()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.management.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Environment Management Demo ===\n")
    
    # Create environment management
    pattern = EnvironmentManagementPattern()
    
    print("1. Creating development environment...")
    result = pattern.create_environment(
        name="dev-app",
        env_type=EnvironmentType.DEVELOPMENT,
        resources={
            ResourceType.COMPUTE: 10.0,
            ResourceType.STORAGE: 100.0,
            ResourceType.NETWORK: 1.0
        }
    )
    dev_env_id = result["environment_id"]
    print(f"  {result['message']}")
    print(f"  Environment ID: {dev_env_id}")
    
    print("\n2. Creating staging environment...")
    result = pattern.create_environment(
        name="staging-app",
        env_type=EnvironmentType.STAGING,
        resources={
            ResourceType.COMPUTE: 20.0,
            ResourceType.STORAGE: 200.0,
            ResourceType.NETWORK: 2.0,
            ResourceType.DATABASE: 2.0
        }
    )
    staging_env_id = result["environment_id"]
    print(f"  {result['message']}")
    
    print("\n3. Creating production environment...")
    result = pattern.create_environment(
        name="prod-app",
        env_type=EnvironmentType.PRODUCTION,
        resources={
            ResourceType.COMPUTE: 40.0,
            ResourceType.STORAGE: 400.0,
            ResourceType.NETWORK: 4.0,
            ResourceType.DATABASE: 4.0
        }
    )
    prod_env_id = result["environment_id"]
    print(f"  {result['message']}")
    
    # List environments
    print("\n4. Listing all environments...")
    envs = pattern.list_environments()
    print(f"  Total environments: {envs['total']}")
    for env in envs['environments']:
        print(f"    - {env['name']} ({env['type']}): {env['status']}")
    
    # Perform health checks
    print("\n5. Performing health checks...")
    for env_id in [dev_env_id, staging_env_id, prod_env_id]:
        result = pattern.perform_health_check(env_id)
        print(f"  {result['environment_id']}: {result['overall_health']}")
        for check in result['checks']:
            print(f"    - {check['name']}: {check['status']}")
    
    # Scale production environment
    print("\n6. Scaling production compute resources by 1.5x...")
    result = pattern.scale_environment(prod_env_id, ResourceType.COMPUTE, 1.5)
    if result['success']:
        print(f"  {result['message']}")
        print(f"  Previous: {result['previous_total']}, New: {result['new_total']}")
    
    # Get environment info
    print("\n7. Getting production environment details...")
    info = pattern.get_environment_info(prod_env_id)
    if info['success']:
        print(f"  Name: {info['name']}")
        print(f"  Type: {info['type']}")
        print(f"  Status: {info['status']}")
        print(f"  Health: {info['health']}")
        print("  Resources:")
        for res_type, details in info['resources'].items():
            print(f"    - {res_type}: {details['total_allocated']}/{details['total_capacity']}")
    
    # Statistics
    print("\n8. Environment management statistics:")
    stats = pattern.get_statistics()
    print(f"  Total environments: {stats['total_environments']}")
    print(f"  Active environments: {stats['active_environments']}")
    print(f"  Health checks performed: {stats['health_checks_performed']}")
    print(f"  Environments scaled: {stats['environments_scaled']}")
    print("  Resource utilization:")
    for res_type, details in stats['resource_utilization'].items():
        utilization = details['utilization'] * 100
        print(f"    - {res_type}: {utilization:.1f}% ({details['allocated']}/{details['capacity']})")
    
    print("\n✓ Comprehensive environment management complete!")

    

Agentic Architecture Pattern: Environment Management

Pattern 279: 279 Environment Management – Line-by-Line Explanation

Overview

Pattern 279: Environment Management

Purpose: Environment provisioning, lifecycle management, and health monitoring

Code Structure Analysis

File Information

• Pattern Number: 279
• Pattern Name: 279 Environment Management
• File Path: patterns\deployment\279_environment_management.py
• Total Lines: 646

Detailed Code Explanation

Module Docstring

"""
Pattern 279: Environment Management

Purpose: Environment provisioning, lifecycle management, and health monitoring
"""

Explanation: This module docstring describes the purpose and key features of the 279 Environment Management pattern.

Imports Section

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
from collections import defaultdict
import random  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Set: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime, timedelta: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values

Enumerations

EnvironmentType (Line 15)

class EnvironmentType(Enum):
    DEVELOPMENT
    STAGING
    PRODUCTION
    TESTING
    UAT

Purpose: Type of environment.

Values: – DEVELOPMENT: Represents development – STAGING: Represents staging – PRODUCTION: Represents production – TESTING: Represents testing – UAT: Represents uat

EnvironmentStatus (Line 24)

class EnvironmentStatus(Enum):
    CREATING
    ACTIVE
    DEGRADED
    MAINTENANCE
    TERMINATING
    TERMINATED

Purpose: Status of environment.

Values: – CREATING: Represents creating – ACTIVE: Currently operational – DEGRADED: Represents degraded – MAINTENANCE: Represents maintenance – TERMINATING: Represents terminating – TERMINATED: Represents terminated

ResourceType (Line 34)

class ResourceType(Enum):
    COMPUTE
    STORAGE
    NETWORK
    DATABASE

Purpose: Type of resource.

Values: – COMPUTE: Represents compute – STORAGE: Represents storage – NETWORK: Represents network – DATABASE: Represents database

HealthStatus (Line 42)

class HealthStatus(Enum):
    HEALTHY
    WARNING
    CRITICAL
    UNKNOWN

Purpose: Health status of environment.

Values: – HEALTHY: Represents healthy – WARNING: Warning severity level – CRITICAL: Represents critical – UNKNOWN: Represents unknown

Classes

EnvironmentType (Line 15)

Description: Type of environment.

EnvironmentStatus (Line 24)

Description: Status of environment.

ResourceType (Line 34)

Description: Type of resource.

HealthStatus (Line 42)

Description: Health status of environment.

Resource (Line 51)

Description: Represents a resource in an environment.

HealthCheck (Line 62)

Description: Health check result.

Environment (Line 72)

Description: Represents a deployment environment.

EnvironmentManagement (Line 85)

Description: Implements environment management pattern.

Features: – Environment provisioning – Resource allocation – Health monitoring – Environment lifecycle management – Auto-scaling – Environment isolation

Methods:

__init__ (Line 98)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_environment (Line 117)

def create_environment(self, name, env_type, resources):
    """Create a new environment.

Args:
    name: Environment name
    env_type: Type of environment
    resources: Required resources {type: amount}

Returns:
    Status of environment creation"""

Purpose: Create a new environment.

Args: name: Environment name env_type: Type of environment resources: Required resources {type: amount}

Returns: Status of environment creation

Parameters: – self: Instance reference – name: Identifier or label – env_type: Parameter for env_type – resources: Parameter for resources

terminate_environment (Line 193)

def terminate_environment(self, environment_id):
    """Terminate an environment and release resources.

Args:
    environment_id: ID of environment to terminate

Returns:
    Status of termination"""

Purpose: Terminate an environment and release resources.

Args: environment_id: ID of environment to terminate

Returns: Status of termination

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

perform_health_check (Line 230)

def perform_health_check(self, environment_id):
    """Perform comprehensive health check on environment.

Args:
    environment_id: ID of environment to check

Returns:
    Health check results"""

Purpose: Perform comprehensive health check on environment.

Args: environment_id: ID of environment to check

Returns: Health check results

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

scale_environment (Line 326)

def scale_environment(self, environment_id, resource_type, scale_factor):
    """Scale environment resources.

Args:
    environment_id: ID of environment
    resource_type: Type of resource to scale
    scale_factor: Factor to scale by (1.5 = 50% increase)

Returns:
    Status of scaling operation"""

Purpose: Scale environment resources.

Args: environment_id: ID of environment resource_type: Type of resource to scale scale_factor: Factor to scale by (1.5 = 50% increase)

Returns: Status of scaling operation

Parameters: – self: Instance reference – environment_id: Parameter for environment_id – resource_type: Parameter for resource_type – scale_factor: Parameter for scale_factor

get_environment_info (Line 402)

def get_environment_info(self, environment_id):
    """Get detailed environment information."""

Purpose: Get detailed environment information.

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

list_environments (Line 451)

def list_environments(self, env_type, status):
    """List environments with optional filters."""

Purpose: List environments with optional filters.

Parameters: – self: Instance reference – env_type: Parameter for env_type – status: Parameter for status

get_statistics (Line 478)

def get_statistics(self):
    """Get environment management statistics."""

Purpose: Get environment management statistics.

Parameters: – self: Instance reference

EnvironmentManagementPattern (Line 511)

Description: Coordinator for environment management.

Manages environment lifecycle and resources.

Methods:

__init__ (Line 518)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_environment (Line 521)

def create_environment(self, name, env_type, resources):
    """Create environment."""

Purpose: Create environment.

Parameters: – self: Instance reference – name: Identifier or label – env_type: Parameter for env_type – resources: Parameter for resources

terminate_environment (Line 525)

def terminate_environment(self, environment_id):
    """Terminate environment."""

Purpose: Terminate environment.

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

perform_health_check (Line 529)

def perform_health_check(self, environment_id):
    """Perform health check."""

Purpose: Perform health check.

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

scale_environment (Line 533)

def scale_environment(self, environment_id, resource_type, scale_factor):
    """Scale environment."""

Purpose: Scale environment.

Parameters: – self: Instance reference – environment_id: Parameter for environment_id – resource_type: Parameter for resource_type – scale_factor: Parameter for scale_factor

get_environment_info (Line 537)

def get_environment_info(self, environment_id):
    """Get environment info."""

Purpose: Get environment info.

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

list_environments (Line 541)

def list_environments(self):
    """List environments."""

Purpose: List environments.

Parameters: – self: Instance reference

get_statistics (Line 545)

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

Purpose: Get statistics.

Parameters: – self: Instance reference

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 = EnvironmentType(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

    """
Pattern 278: Configuration Management

Purpose: Dynamic configuration with hot-reload, validation, and versioning
"""

from typing import Dict, Any, List, Optional, Callable, Set
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
import copy


class ConfigSource(Enum):
    """Source of configuration."""
    FILE = "file"
    ENVIRONMENT = "environment"
    DATABASE = "database"
    API = "api"
    DEFAULT = "default"


class ConfigScope(Enum):
    """Scope of configuration."""
    GLOBAL = "global"
    ENVIRONMENT_SPECIFIC = "environment_specific"  # dev, staging, prod
    USER_SPECIFIC = "user_specific"
    SESSION_SPECIFIC = "session_specific"


@dataclass
class ConfigValue:
    """Represents a configuration value."""
    key: str
    value: Any
    source: ConfigSource
    scope: ConfigScope
    version: int = 1
    last_modified: datetime = field(default_factory=datetime.now)
    previous_value: Optional[Any] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class ConfigChange:
    """Record of configuration change."""
    key: str
    old_value: Any
    new_value: Any
    changed_by: str
    timestamp: datetime = field(default_factory=datetime.now)
    reason: Optional[str] = None


class ConfigurationManagement:
    """
    Implements configuration management pattern.
    
    Features:
    - Dynamic configuration updates
    - Hot-reload without restart
    - Configuration validation
    - Version tracking
    - Change history
    - Environment-specific configs
    - Rollback capability
    - Change notifications
    """
    
    def __init__(self):
        self.configs: Dict[str, ConfigValue] = {}
        self.change_history: List[ConfigChange] = []
        self.validators: Dict[str, Callable] = {}
        self.change_listeners: List[Callable] = []
        self.environments = {"dev", "staging", "prod"}
        self.current_environment = "dev"
        
        # Statistics
        self.statistics = {
            "total_configs": 0,
            "total_changes": 0,
            "hot_reloads": 0,
            "validation_failures": 0,
            "rollbacks": 0
        }
    
    def set_config(
        self,
        key: str,
        value: Any,
        source: ConfigSource = ConfigSource.DEFAULT,
        scope: ConfigScope = ConfigScope.GLOBAL,
        changed_by: str = "system",
        reason: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Set a configuration value.
        
        Args:
            key: Configuration key
            value: Configuration value
            source: Source of configuration
            scope: Scope of configuration
            changed_by: Who made the change
            reason: Reason for change
            
        Returns:
            Status of configuration set
        """
        # Validate if validator exists
        if key in self.validators:
            validator = self.validators[key]
            try:
                is_valid = validator(value)
                if not is_valid:
                    self.statistics["validation_failures"] += 1
                    return {
                        "success": False,
                        "message": f"Validation failed for key '{key}'"
                    }
            except Exception as e:
                self.statistics["validation_failures"] += 1
                return {
                    "success": False,
                    "message": f"Validation error: {str(e)}"
                }
        
        # Record change if existing
        if key in self.configs:
            old_config = self.configs[key]
            change = ConfigChange(
                key=key,
                old_value=old_config.value,
                new_value=value,
                changed_by=changed_by,
                reason=reason
            )
            self.change_history.append(change)
            self.statistics["total_changes"] += 1
            
            # Update config
            old_config.previous_value = old_config.value
            old_config.value = value
            old_config.version += 1
            old_config.last_modified = datetime.now()
            old_config.source = source
            
            # Notify listeners
            self._notify_listeners(key, old_config.value, value)
            
            return {
                "success": True,
                "key": key,
                "old_value": old_config.previous_value,
                "new_value": value,
                "version": old_config.version,
                "message": f"Configuration '{key}' updated"
            }
        else:
            # Create new config
            config = ConfigValue(
                key=key,
                value=value,
                source=source,
                scope=scope
            )
            self.configs[key] = config
            self.statistics["total_configs"] += 1
            
            # Notify listeners
            self._notify_listeners(key, None, value)
            
            return {
                "success": True,
                "key": key,
                "value": value,
                "version": 1,
                "message": f"Configuration '{key}' created"
            }
    
    def get_config(
        self,
        key: str,
        default: Optional[Any] = None,
        environment: Optional[str] = None
    ) -> Any:
        """
        Get a configuration value.
        
        Args:
            key: Configuration key
            default: Default value if not found
            environment: Specific environment to get config for
            
        Returns:
            Configuration value or default
        """
        env = environment or self.current_environment
        
        # Try environment-specific key first
        env_key = f"{key}.{env}"
        if env_key in self.configs:
            return self.configs[env_key].value
        
        # Fall back to global key
        if key in self.configs:
            return self.configs[key].value
        
        return default
    
    def hot_reload(self, configs: Dict[str, Any], changed_by: str = "system") -> Dict[str, Any]:
        """
        Hot-reload multiple configurations without restart.
        
        Args:
            configs: Dictionary of key-value pairs to update
            changed_by: Who initiated the reload
            
        Returns:
            Status of hot-reload
        """
        success_count = 0
        failed_count = 0
        failures = []
        
        for key, value in configs.items():
            result = self.set_config(
                key=key,
                value=value,
                source=ConfigSource.API,
                changed_by=changed_by,
                reason="hot_reload"
            )
            
            if result["success"]:
                success_count += 1
            else:
                failed_count += 1
                failures.append({"key": key, "error": result["message"]})
        
        self.statistics["hot_reloads"] += 1
        
        return {
            "success": failed_count == 0,
            "configs_updated": success_count,
            "configs_failed": failed_count,
            "failures": failures,
            "message": f"Hot-reload completed: {success_count} updated, {failed_count} failed"
        }
    
    def register_validator(self, key: str, validator: Callable[[Any], bool]) -> Dict[str, Any]:
        """
        Register a validator for a configuration key.
        
        Args:
            key: Configuration key
            validator: Function that returns True if valid
            
        Returns:
            Status of validator registration
        """
        self.validators[key] = validator
        
        return {
            "success": True,
            "key": key,
            "message": f"Validator registered for '{key}'"
        }
    
    def add_change_listener(self, listener: Callable[[str, Any, Any], None]) -> Dict[str, Any]:
        """
        Add a listener for configuration changes.
        
        Args:
            listener: Function called when config changes (key, old_value, new_value)
            
        Returns:
            Status of listener addition
        """
        self.change_listeners.append(listener)
        
        return {
            "success": True,
            "total_listeners": len(self.change_listeners),
            "message": "Change listener added"
        }
    
    def _notify_listeners(self, key: str, old_value: Any, new_value: Any) -> None:
        """Notify all listeners of configuration change."""
        for listener in self.change_listeners:
            try:
                listener(key, old_value, new_value)
            except Exception as e:
                # Log but don't fail
                pass
    
    def rollback_config(self, key: str) -> Dict[str, Any]:
        """
        Rollback a configuration to its previous value.
        
        Args:
            key: Configuration key to rollback
            
        Returns:
            Status of rollback
        """
        if key not in self.configs:
            return {
                "success": False,
                "message": f"Configuration '{key}' not found"
            }
        
        config = self.configs[key]
        
        if config.previous_value is None:
            return {
                "success": False,
                "message": f"No previous value available for '{key}'"
            }
        
        old_value = config.value
        config.value = config.previous_value
        config.previous_value = None
        config.version += 1
        config.last_modified = datetime.now()
        
        # Record rollback
        change = ConfigChange(
            key=key,
            old_value=old_value,
            new_value=config.value,
            changed_by="system",
            reason="rollback"
        )
        self.change_history.append(change)
        self.statistics["rollbacks"] += 1
        
        # Notify listeners
        self._notify_listeners(key, old_value, config.value)
        
        return {
            "success": True,
            "key": key,
            "rolled_back_to": config.value,
            "message": f"Configuration '{key}' rolled back"
        }
    
    def get_config_info(self, key: str) -> Dict[str, Any]:
        """Get detailed information about a configuration."""
        if key not in self.configs:
            return {
                "success": False,
                "message": f"Configuration '{key}' not found"
            }
        
        config = self.configs[key]
        
        # Find related changes
        changes = [
            {
                "old_value": c.old_value,
                "new_value": c.new_value,
                "changed_by": c.changed_by,
                "timestamp": c.timestamp.isoformat(),
                "reason": c.reason
            }
            for c in self.change_history if c.key == key
        ]
        
        return {
            "success": True,
            "key": key,
            "value": config.value,
            "source": config.source.value,
            "scope": config.scope.value,
            "version": config.version,
            "last_modified": config.last_modified.isoformat(),
            "previous_value": config.previous_value,
            "has_validator": key in self.validators,
            "change_count": len(changes),
            "recent_changes": changes[-5:]  # Last 5 changes
        }
    
    def list_configs(
        self,
        scope: Optional[ConfigScope] = None,
        source: Optional[ConfigSource] = None
    ) -> Dict[str, Any]:
        """List all configurations with optional filters."""
        configs_list = []
        
        for key, config in self.configs.items():
            if scope and config.scope != scope:
                continue
            if source and config.source != source:
                continue
            
            configs_list.append({
                "key": key,
                "value": config.value,
                "source": config.source.value,
                "scope": config.scope.value,
                "version": config.version
            })
        
        return {
            "total": len(configs_list),
            "configs": configs_list
        }
    
    def export_configs(self) -> Dict[str, Any]:
        """Export all configurations."""
        return {key: config.value for key, config in self.configs.items()}
    
    def import_configs(
        self,
        configs: Dict[str, Any],
        source: ConfigSource = ConfigSource.FILE
    ) -> Dict[str, Any]:
        """Import configurations from dictionary."""
        imported = 0
        
        for key, value in configs.items():
            result = self.set_config(
                key=key,
                value=value,
                source=source,
                changed_by="import"
            )
            if result["success"]:
                imported += 1
        
        return {
            "success": True,
            "imported": imported,
            "total": len(configs),
            "message": f"Imported {imported}/{len(configs)} configurations"
        }
    
    def get_change_history(self, key: Optional[str] = None, limit: int = 10) -> Dict[str, Any]:
        """Get configuration change history."""
        if key:
            changes = [c for c in self.change_history if c.key == key]
        else:
            changes = self.change_history
        
        changes_list = [
            {
                "key": c.key,
                "old_value": c.old_value,
                "new_value": c.new_value,
                "changed_by": c.changed_by,
                "timestamp": c.timestamp.isoformat(),
                "reason": c.reason
            }
            for c in changes[-limit:]
        ]
        
        return {
            "total": len(changes),
            "showing": len(changes_list),
            "changes": changes_list
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get configuration management statistics."""
        return {
            "total_configs": self.statistics["total_configs"],
            "total_changes": self.statistics["total_changes"],
            "hot_reloads": self.statistics["hot_reloads"],
            "validation_failures": self.statistics["validation_failures"],
            "rollbacks": self.statistics["rollbacks"],
            "registered_validators": len(self.validators),
            "change_listeners": len(self.change_listeners)
        }


class ConfigurationManagementPattern:
    """
    Coordinator for configuration management.
    
    Manages dynamic configuration with hot-reload and validation.
    """
    
    def __init__(self):
        self.management = ConfigurationManagement()
    
    def set_config(self, key: str, value: Any, **kwargs) -> Dict[str, Any]:
        """Set configuration."""
        return self.management.set_config(key, value, **kwargs)
    
    def get_config(self, key: str, default: Optional[Any] = None) -> Any:
        """Get configuration."""
        return self.management.get_config(key, default)
    
    def hot_reload(self, configs: Dict[str, Any], changed_by: str = "system") -> Dict[str, Any]:
        """Hot-reload configurations."""
        return self.management.hot_reload(configs, changed_by)
    
    def register_validator(self, key: str, validator: Callable) -> Dict[str, Any]:
        """Register validator."""
        return self.management.register_validator(key, validator)
    
    def rollback_config(self, key: str) -> Dict[str, Any]:
        """Rollback configuration."""
        return self.management.rollback_config(key)
    
    def get_config_info(self, key: str) -> Dict[str, Any]:
        """Get configuration info."""
        return self.management.get_config_info(key)
    
    def list_configs(self) -> Dict[str, Any]:
        """List configurations."""
        return self.management.list_configs()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.management.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Configuration Management Demo ===\n")
    
    # Create configuration management
    pattern = ConfigurationManagementPattern()
    
    print("1. Setting initial configurations...")
    pattern.set_config("max_workers", 10, source=ConfigSource.DEFAULT)
    pattern.set_config("timeout_seconds", 30, source=ConfigSource.DEFAULT)
    pattern.set_config("log_level", "INFO", source=ConfigSource.DEFAULT)
    pattern.set_config("enable_caching", True, source=ConfigSource.DEFAULT)
    
    configs = pattern.list_configs()
    print(f"  Total configs: {configs['total']}")
    for config in configs['configs']:
        print(f"    - {config['key']}: {config['value']}")
    
    # Register validator
    print("\n2. Registering validator for max_workers...")
    pattern.register_validator("max_workers", lambda v: isinstance(v, int) and 1 <= v <= 100)
    print("  Validator registered")
    
    # Try valid update
    print("\n3. Updating max_workers to 20...")
    result = pattern.set_config("max_workers", 20, changed_by="admin", reason="increase capacity")
    print(f"  {result['message']}")
    print(f"  Old value: {result['old_value']}, New value: {result['new_value']}")
    
    # Try invalid update
    print("\n4. Trying invalid update (max_workers = 200)...")
    result = pattern.set_config("max_workers", 200)
    print(f"  Result: {result['message']}")
    
    # Hot-reload multiple configs
    print("\n5. Hot-reloading multiple configurations...")
    new_configs = {
        "timeout_seconds": 60,
        "log_level": "DEBUG",
        "enable_caching": False
    }
    result = pattern.hot_reload(new_configs, changed_by="deployment")
    print(f"  {result['message']}")
    
    # Check config values
    print("\n6. Checking current values...")
    print(f"  max_workers: {pattern.get_config('max_workers')}")
    print(f"  timeout_seconds: {pattern.get_config('timeout_seconds')}")
    print(f"  log_level: {pattern.get_config('log_level')}")
    
    # Get config info
    print("\n7. Detailed info for max_workers...")
    info = pattern.get_config_info("max_workers")
    if info['success']:
        print(f"  Current value: {info['value']}")
        print(f"  Version: {info['version']}")
        print(f"  Has validator: {info['has_validator']}")
        print(f"  Change count: {info['change_count']}")
    
    # Rollback
    print("\n8. Rolling back max_workers...")
    result = pattern.rollback_config("max_workers")
    if result['success']:
        print(f"  {result['message']}")
        print(f"  Value after rollback: {pattern.get_config('max_workers')}")
    
    # Statistics
    print("\n9. Configuration statistics:")
    stats = pattern.get_statistics()
    print(f"  Total configs: {stats['total_configs']}")
    print(f"  Total changes: {stats['total_changes']}")
    print(f"  Hot reloads: {stats['hot_reloads']}")
    print(f"  Validation failures: {stats['validation_failures']}")
    print(f"  Rollbacks: {stats['rollbacks']}")
    
    print("\n✓ Comprehensive configuration management with hot-reload complete!")

    

Agentic Architecture Pattern: Configuration Management

Pattern 278: 278 Configuration Management – Line-by-Line Explanation

Overview

Pattern 278: Configuration Management

Purpose: Dynamic configuration with hot-reload, validation, and versioning

Code Structure Analysis

File Information

• Pattern Number: 278
• Pattern Name: 278 Configuration Management
• File Path: patterns\deployment\278_configuration_management.py
• Total Lines: 598

Detailed Code Explanation

Module Docstring

"""
Pattern 278: Configuration Management

Purpose: Dynamic configuration with hot-reload, validation, and versioning
"""

Explanation: This module docstring describes the purpose and key features of the 278 Configuration Management pattern.

Imports Section

from typing import Dict, Any, List, Optional, Callable, Set
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
import copy  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Callable, Set: 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

Enumerations

ConfigSource (Line 15)

class ConfigSource(Enum):
    FILE
    ENVIRONMENT
    DATABASE
    API
    DEFAULT

Purpose: Source of configuration.

Values: – FILE: Represents file – ENVIRONMENT: Represents environment – DATABASE: Represents database – API: Represents api – DEFAULT: Represents default

ConfigScope (Line 24)

class ConfigScope(Enum):
    GLOBAL
    ENVIRONMENT_SPECIFIC
    USER_SPECIFIC
    SESSION_SPECIFIC

Purpose: Scope of configuration.

Values: – GLOBAL: Represents global – ENVIRONMENT_SPECIFIC: Represents environment specific – USER_SPECIFIC: Represents user specific – SESSION_SPECIFIC: Represents session specific

Classes

ConfigSource (Line 15)

Description: Source of configuration.

ConfigScope (Line 24)

Description: Scope of configuration.

ConfigValue (Line 33)

Description: Represents a configuration value.

ConfigChange (Line 46)

Description: Record of configuration change.

ConfigurationManagement (Line 56)

Description: Implements configuration management pattern.

Features: – Dynamic configuration updates – Hot-reload without restart – Configuration validation – Version tracking – Change history – Environment-specific configs – Rollback capability – Change notifications

Methods:

__init__ (Line 71)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

set_config (Line 88)

def set_config(self, key, value, source, scope, changed_by, reason):
    """Set a configuration value.

Args:
    key: Configuration key
    value: Configuration value
    source: Source of configuration
    scope: Scope of configuration
    changed_by: Who made the change
    reason: Reason for change

Returns:
    Status of configuration set"""

Purpose: Set a configuration value.

Args: key: Configuration key value: Configuration value source: Source of configuration scope: Scope of configuration changed_by: Who made the change reason: Reason for change

Returns: Status of configuration set

Parameters: – self: Instance reference – key: Parameter for key – value: Parameter for value – source: Parameter for source – scope: Parameter for scope – changed_by: Parameter for changed_by – reason: Parameter for reason

get_config (Line 182)

def get_config(self, key, default, environment):
    """Get a configuration value.

Args:
    key: Configuration key
    default: Default value if not found
    environment: Specific environment to get config for

Returns:
    Configuration value or default"""

Purpose: Get a configuration value.

Args: key: Configuration key default: Default value if not found environment: Specific environment to get config for

Returns: Configuration value or default

Parameters: – self: Instance reference – key: Parameter for key – default: Parameter for default – environment: Parameter for environment

hot_reload (Line 212)

def hot_reload(self, configs, changed_by):
    """Hot-reload multiple configurations without restart.

Args:
    configs: Dictionary of key-value pairs to update
    changed_by: Who initiated the reload

Returns:
    Status of hot-reload"""

Purpose: Hot-reload multiple configurations without restart.

Args: configs: Dictionary of key-value pairs to update changed_by: Who initiated the reload

Returns: Status of hot-reload

Parameters: – self: Instance reference – configs: Parameter for configs – changed_by: Parameter for changed_by

register_validator (Line 252)

def register_validator(self, key, validator):
    """Register a validator for a configuration key.

Args:
    key: Configuration key
    validator: Function that returns True if valid

Returns:
    Status of validator registration"""

Purpose: Register a validator for a configuration key.

Args: key: Configuration key validator: Function that returns True if valid

Returns: Status of validator registration

Parameters: – self: Instance reference – key: Parameter for key – validator: Parameter for validator

add_change_listener (Line 271)

def add_change_listener(self, listener):
    """Add a listener for configuration changes.

Args:
    listener: Function called when config changes (key, old_value, new_value)

Returns:
    Status of listener addition"""

Purpose: Add a listener for configuration changes.

Args: listener: Function called when config changes (key, old_value, new_value)

Returns: Status of listener addition

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

_notify_listeners (Line 289)

def _notify_listeners(self, key, old_value, new_value):
    """Notify all listeners of configuration change."""

Purpose: Notify all listeners of configuration change.

Parameters: – self: Instance reference – key: Parameter for key – old_value: Parameter for old_value – new_value: Parameter for new_value

rollback_config (Line 298)

def rollback_config(self, key):
    """Rollback a configuration to its previous value.

Args:
    key: Configuration key to rollback

Returns:
    Status of rollback"""

Purpose: Rollback a configuration to its previous value.

Args: key: Configuration key to rollback

Returns: Status of rollback

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

get_config_info (Line 349)

def get_config_info(self, key):
    """Get detailed information about a configuration."""

Purpose: Get detailed information about a configuration.

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

list_configs (Line 385)

def list_configs(self, scope, source):
    """List all configurations with optional filters."""

Purpose: List all configurations with optional filters.

Parameters: – self: Instance reference – scope: Parameter for scope – source: Parameter for source

export_configs (Line 412)

def export_configs(self):
    """Export all configurations."""

Purpose: Export all configurations.

Parameters: – self: Instance reference

import_configs (Line 416)

def import_configs(self, configs, source):
    """Import configurations from dictionary."""

Purpose: Import configurations from dictionary.

Parameters: – self: Instance reference – configs: Parameter for configs – source: Parameter for source

get_change_history (Line 441)

def get_change_history(self, key, limit):
    """Get configuration change history."""

Purpose: Get configuration change history.

Parameters: – self: Instance reference – key: Parameter for key – limit: Parameter for limit

get_statistics (Line 466)

def get_statistics(self):
    """Get configuration management statistics."""

Purpose: Get configuration management statistics.

Parameters: – self: Instance reference

ConfigurationManagementPattern (Line 479)

Description: Coordinator for configuration management.

Manages dynamic configuration with hot-reload and validation.

Methods:

__init__ (Line 486)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

set_config (Line 489)

def set_config(self, key, value):
    """Set configuration."""

Purpose: Set configuration.

Parameters: – self: Instance reference – key: Parameter for key – value: Parameter for value

get_config (Line 493)

def get_config(self, key, default):
    """Get configuration."""

Purpose: Get configuration.

Parameters: – self: Instance reference – key: Parameter for key – default: Parameter for default

hot_reload (Line 497)

def hot_reload(self, configs, changed_by):
    """Hot-reload configurations."""

Purpose: Hot-reload configurations.

Parameters: – self: Instance reference – configs: Parameter for configs – changed_by: Parameter for changed_by

register_validator (Line 501)

def register_validator(self, key, validator):
    """Register validator."""

Purpose: Register validator.

Parameters: – self: Instance reference – key: Parameter for key – validator: Parameter for validator

rollback_config (Line 505)

def rollback_config(self, key):
    """Rollback configuration."""

Purpose: Rollback configuration.

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

get_config_info (Line 509)

def get_config_info(self, key):
    """Get configuration info."""

Purpose: Get configuration info.

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

list_configs (Line 513)

def list_configs(self):
    """List configurations."""

Purpose: List configurations.

Parameters: – self: Instance reference

get_statistics (Line 517)

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

Purpose: Get statistics.

Parameters: – self: Instance reference

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 = ConfigSource(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

    """
Pattern 277: Release Management

Purpose: Release coordination, orchestration, and automated rollback
"""

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict


class ReleaseStage(Enum):
    """Stage of release lifecycle."""
    PLANNING = "planning"
    TESTING = "testing"
    STAGING = "staging"
    DEPLOYMENT = "deployment"
    VERIFICATION = "verification"
    COMPLETED = "completed"
    ROLLED_BACK = "rolled_back"
    FAILED = "failed"


class ReleaseType(Enum):
    """Type of release."""
    MAJOR = "major"
    MINOR = "minor"
    PATCH = "patch"
    HOTFIX = "hotfix"
    EMERGENCY = "emergency"


class ComponentStatus(Enum):
    """Status of release component."""
    PENDING = "pending"
    DEPLOYING = "deploying"
    DEPLOYED = "deployed"
    FAILED = "failed"
    ROLLED_BACK = "rolled_back"


@dataclass
class ReleaseComponent:
    """Component in a release."""
    name: str
    version: str
    status: ComponentStatus
    deployment_order: int
    dependencies: List[str] = field(default_factory=list)
    deployed_at: Optional[datetime] = None
    error_message: Optional[str] = None


@dataclass
class ReleaseCheckpoint:
    """Checkpoint during release."""
    stage: ReleaseStage
    timestamp: datetime
    status: str
    details: str
    can_rollback: bool = True


@dataclass
class Release:
    """Represents a release."""
    release_id: str
    version: str
    release_type: ReleaseType
    stage: ReleaseStage
    components: Dict[str, ReleaseComponent]
    created_at: datetime = field(default_factory=datetime.now)
    started_at: Optional[datetime] = None
    completed_at: Optional[datetime] = None
    checkpoints: List[ReleaseCheckpoint] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)


class ReleaseManagement:
    """
    Implements release management pattern.
    
    Features:
    - Multi-component coordination
    - Staged deployment process
    - Dependency management
    - Automated verification
    - Rollback capability
    - Release history tracking
    - Checkpoint management
    """
    
    def __init__(self):
        self.releases: Dict[str, Release] = {}
        self.active_release: Optional[str] = None
        self.release_history: List[str] = []
        
        # Statistics
        self.statistics = {
            "total_releases": 0,
            "successful_releases": 0,
            "failed_releases": 0,
            "rolled_back_releases": 0,
            "total_components_deployed": 0
        }
    
    def create_release(
        self,
        release_id: str,
        version: str,
        release_type: ReleaseType,
        components: List[Dict[str, Any]],
        metadata: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Create a new release.
        
        Args:
            release_id: Unique release identifier
            version: Release version
            release_type: Type of release
            components: List of components with name, version, order, dependencies
            metadata: Additional metadata
            
        Returns:
            Status of release creation
        """
        if release_id in self.releases:
            return {
                "success": False,
                "message": f"Release {release_id} already exists"
            }
        
        # Create components
        release_components = {}
        for comp in components:
            component = ReleaseComponent(
                name=comp["name"],
                version=comp["version"],
                status=ComponentStatus.PENDING,
                deployment_order=comp.get("order", 0),
                dependencies=comp.get("dependencies", [])
            )
            release_components[comp["name"]] = component
        
        # Validate dependencies
        for comp_name, component in release_components.items():
            for dep in component.dependencies:
                if dep not in release_components:
                    return {
                        "success": False,
                        "message": f"Component {comp_name} depends on {dep} which is not in release"
                    }
        
        release = Release(
            release_id=release_id,
            version=version,
            release_type=release_type,
            stage=ReleaseStage.PLANNING,
            components=release_components,
            metadata=metadata or {}
        )
        
        self.releases[release_id] = release
        self.statistics["total_releases"] += 1
        
        # Add checkpoint
        checkpoint = ReleaseCheckpoint(
            stage=ReleaseStage.PLANNING,
            timestamp=datetime.now(),
            status="created",
            details=f"Release {release_id} created with {len(components)} components"
        )
        release.checkpoints.append(checkpoint)
        
        return {
            "success": True,
            "release_id": release_id,
            "version": version,
            "components": len(components),
            "message": f"Release {release_id} created successfully"
        }
    
    def start_release(self, release_id: str) -> Dict[str, Any]:
        """
        Start a release deployment.
        
        Args:
            release_id: Release identifier
            
        Returns:
            Status of release start
        """
        if release_id not in self.releases:
            return {
                "success": False,
                "message": f"Release {release_id} not found"
            }
        
        if self.active_release:
            return {
                "success": False,
                "message": f"Another release {self.active_release} is already active"
            }
        
        release = self.releases[release_id]
        
        if release.stage != ReleaseStage.PLANNING:
            return {
                "success": False,
                "message": f"Release is in {release.stage.value} stage, cannot start"
            }
        
        self.active_release = release_id
        release.started_at = datetime.now()
        release.stage = ReleaseStage.TESTING
        
        # Add checkpoint
        checkpoint = ReleaseCheckpoint(
            stage=ReleaseStage.TESTING,
            timestamp=datetime.now(),
            status="started",
            details=f"Release {release_id} started"
        )
        release.checkpoints.append(checkpoint)
        
        return {
            "success": True,
            "release_id": release_id,
            "stage": release.stage.value,
            "message": f"Release {release_id} started"
        }
    
    def advance_stage(self, release_id: str) -> Dict[str, Any]:
        """
        Advance release to next stage.
        
        Args:
            release_id: Release identifier
            
        Returns:
            Status of stage advancement
        """
        if release_id not in self.releases:
            return {
                "success": False,
                "message": f"Release {release_id} not found"
            }
        
        release = self.releases[release_id]
        current_stage = release.stage
        
        # Define stage progression
        stage_progression = {
            ReleaseStage.TESTING: ReleaseStage.STAGING,
            ReleaseStage.STAGING: ReleaseStage.DEPLOYMENT,
            ReleaseStage.DEPLOYMENT: ReleaseStage.VERIFICATION,
            ReleaseStage.VERIFICATION: ReleaseStage.COMPLETED
        }
        
        if current_stage not in stage_progression:
            return {
                "success": False,
                "message": f"Cannot advance from {current_stage.value} stage"
            }
        
        next_stage = stage_progression[current_stage]
        release.stage = next_stage
        
        # Add checkpoint
        checkpoint = ReleaseCheckpoint(
            stage=next_stage,
            timestamp=datetime.now(),
            status="advanced",
            details=f"Advanced from {current_stage.value} to {next_stage.value}"
        )
        release.checkpoints.append(checkpoint)
        
        # If deployment stage, deploy components
        if next_stage == ReleaseStage.DEPLOYMENT:
            self._deploy_components(release)
        
        # If completed, finalize
        if next_stage == ReleaseStage.COMPLETED:
            release.completed_at = datetime.now()
            self.active_release = None
            self.release_history.append(release_id)
            self.statistics["successful_releases"] += 1
        
        return {
            "success": True,
            "release_id": release_id,
            "previous_stage": current_stage.value,
            "current_stage": next_stage.value,
            "message": f"Advanced to {next_stage.value} stage"
        }
    
    def _deploy_components(self, release: Release) -> None:
        """Deploy components in order respecting dependencies."""
        # Sort components by deployment order
        components_by_order = sorted(
            release.components.items(),
            key=lambda x: x[1].deployment_order
        )
        
        deployed = set()
        
        for comp_name, component in components_by_order:
            # Check dependencies
            can_deploy = all(dep in deployed for dep in component.dependencies)
            
            if can_deploy:
                component.status = ComponentStatus.DEPLOYING
                
                # Simulate deployment (90% success rate)
                import random
                if random.random()  Dict[str, Any]:
        """
        Rollback a release.
        
        Args:
            release_id: Release identifier
            
        Returns:
            Status of rollback
        """
        if release_id not in self.releases:
            return {
                "success": False,
                "message": f"Release {release_id} not found"
            }
        
        release = self.releases[release_id]
        
        # Check if can rollback
        if not release.checkpoints:
            return {
                "success": False,
                "message": "No checkpoints available for rollback"
            }
        
        last_checkpoint = release.checkpoints[-1]
        if not last_checkpoint.can_rollback:
            return {
                "success": False,
                "message": f"Cannot rollback from {last_checkpoint.stage.value} stage"
            }
        
        # Rollback deployed components
        rollback_count = 0
        for component in release.components.values():
            if component.status == ComponentStatus.DEPLOYED:
                component.status = ComponentStatus.ROLLED_BACK
                rollback_count += 1
        
        release.stage = ReleaseStage.ROLLED_BACK
        release.completed_at = datetime.now()
        
        if self.active_release == release_id:
            self.active_release = None
        
        self.statistics["rolled_back_releases"] += 1
        
        # Add checkpoint
        checkpoint = ReleaseCheckpoint(
            stage=ReleaseStage.ROLLED_BACK,
            timestamp=datetime.now(),
            status="rolled_back",
            details=f"Release rolled back, {rollback_count} components reverted",
            can_rollback=False
        )
        release.checkpoints.append(checkpoint)
        
        return {
            "success": True,
            "release_id": release_id,
            "components_rolled_back": rollback_count,
            "message": f"Release {release_id} rolled back successfully"
        }
    
    def get_release_status(self, release_id: str) -> Dict[str, Any]:
        """Get status of a release."""
        if release_id not in self.releases:
            return {
                "success": False,
                "message": f"Release {release_id} not found"
            }
        
        release = self.releases[release_id]
        
        # Component status summary
        component_summary = {
            "total": len(release.components),
            "pending": sum(1 for c in release.components.values() if c.status == ComponentStatus.PENDING),
            "deploying": sum(1 for c in release.components.values() if c.status == ComponentStatus.DEPLOYING),
            "deployed": sum(1 for c in release.components.values() if c.status == ComponentStatus.DEPLOYED),
            "failed": sum(1 for c in release.components.values() if c.status == ComponentStatus.FAILED),
        }
        
        components_list = []
        for name, comp in sorted(release.components.items(), key=lambda x: x[1].deployment_order):
            components_list.append({
                "name": name,
                "version": comp.version,
                "status": comp.status.value,
                "order": comp.deployment_order,
                "deployed_at": comp.deployed_at.isoformat() if comp.deployed_at else None,
                "error": comp.error_message
            })
        
        return {
            "success": True,
            "release_id": release_id,
            "version": release.version,
            "type": release.release_type.value,
            "stage": release.stage.value,
            "is_active": self.active_release == release_id,
            "created_at": release.created_at.isoformat(),
            "started_at": release.started_at.isoformat() if release.started_at else None,
            "completed_at": release.completed_at.isoformat() if release.completed_at else None,
            "components": components_list,
            "component_summary": component_summary,
            "checkpoints": len(release.checkpoints)
        }
    
    def list_releases(self) -> Dict[str, Any]:
        """List all releases."""
        releases_list = []
        for release_id, release in self.releases.items():
            releases_list.append({
                "release_id": release_id,
                "version": release.version,
                "type": release.release_type.value,
                "stage": release.stage.value,
                "components": len(release.components),
                "is_active": self.active_release == release_id
            })
        
        return {
            "total": len(releases_list),
            "active_release": self.active_release,
            "releases": releases_list
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get release management statistics."""
        return {
            "total_releases": self.statistics["total_releases"],
            "successful_releases": self.statistics["successful_releases"],
            "failed_releases": self.statistics["failed_releases"],
            "rolled_back_releases": self.statistics["rolled_back_releases"],
            "total_components_deployed": self.statistics["total_components_deployed"],
            "active_release": self.active_release
        }


class ReleaseManagementPattern:
    """
    Coordinator for release management.
    
    Manages release lifecycle with multi-component coordination.
    """
    
    def __init__(self):
        self.management = ReleaseManagement()
    
    def create_release(self, release_id: str, version: str, release_type: ReleaseType, components: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Create a new release."""
        return self.management.create_release(release_id, version, release_type, components)
    
    def start_release(self, release_id: str) -> Dict[str, Any]:
        """Start a release."""
        return self.management.start_release(release_id)
    
    def advance_stage(self, release_id: str) -> Dict[str, Any]:
        """Advance release stage."""
        return self.management.advance_stage(release_id)
    
    def rollback_release(self, release_id: str) -> Dict[str, Any]:
        """Rollback a release."""
        return self.management.rollback_release(release_id)
    
    def get_release_status(self, release_id: str) -> Dict[str, Any]:
        """Get release status."""
        return self.management.get_release_status(release_id)
    
    def list_releases(self) -> Dict[str, Any]:
        """List releases."""
        return self.management.list_releases()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.management.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Release Management Demo ===\n")
    
    # Create release management
    pattern = ReleaseManagementPattern()
    
    print("1. Creating release...")
    components = [
        {"name": "database", "version": "2.0.0", "order": 1, "dependencies": []},
        {"name": "backend", "version": "2.0.0", "order": 2, "dependencies": ["database"]},
        {"name": "frontend", "version": "2.0.0", "order": 3, "dependencies": ["backend"]},
        {"name": "api-gateway", "version": "2.0.0", "order": 4, "dependencies": ["backend"]}
    ]
    
    result = pattern.create_release(
        "release-2.0.0",
        "2.0.0",
        ReleaseType.MAJOR,
        components
    )
    print(f"  {result['message']}")
    print(f"  Components: {result['components']}")
    
    # Start release
    print("\n2. Starting release...")
    result = pattern.start_release("release-2.0.0")
    print(f"  {result['message']}")
    print(f"  Current stage: {result['stage']}")
    
    # Advance through stages
    print("\n3. Advancing through stages...")
    stages = ["staging", "deployment", "verification", "completed"]
    for stage in stages:
        result = pattern.advance_stage("release-2.0.0")
        if result['success']:
            print(f"  ✓ Advanced to {result['current_stage']}")
        else:
            print(f"  ✗ Failed: {result['message']}")
            break
    
    # Get release status
    print("\n4. Release status:")
    status = pattern.get_release_status("release-2.0.0")
    if status['success']:
        print(f"  Release: {status['release_id']}")
        print(f"  Version: {status['version']}")
        print(f"  Stage: {status['stage']}")
        print(f"  Components deployed: {status['component_summary']['deployed']}/{status['component_summary']['total']}")
        print(f"\n  Components:")
        for comp in status['components']:
            print(f"    - {comp['name']} v{comp['version']}: {comp['status']}")
    
    # Statistics
    print("\n5. Release statistics:")
    stats = pattern.get_statistics()
    print(f"  Total releases: {stats['total_releases']}")
    print(f"  Successful: {stats['successful_releases']}")
    print(f"  Failed: {stats['failed_releases']}")
    print(f"  Rolled back: {stats['rolled_back_releases']}")
    print(f"  Total components deployed: {stats['total_components_deployed']}")
    
    print("\n✓ Comprehensive release management with coordination complete!")

    

Agentic Architecture Pattern: Release Management

Pattern 277: 277 Release Management – Line-by-Line Explanation

Overview

Pattern 277: Release Management

Purpose: Release coordination, orchestration, and automated rollback

Code Structure Analysis

File Information

• Pattern Number: 277
• Pattern Name: 277 Release Management
• File Path: patterns\deployment\277_release_management.py
• Total Lines: 579

Detailed Code Explanation

Module Docstring

"""
Pattern 277: Release Management

Purpose: Release coordination, orchestration, and automated rollback
"""

Explanation: This module docstring describes the purpose and key features of the 277 Release Management pattern.

Imports Section

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
                import random  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Set: 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

Enumerations

ReleaseStage (Line 14)

class ReleaseStage(Enum):
    PLANNING
    TESTING
    STAGING
    DEPLOYMENT
    VERIFICATION
    COMPLETED
    ROLLED_BACK
    FAILED

Purpose: Stage of release lifecycle.

Values: – PLANNING: Represents planning – TESTING: Represents testing – STAGING: Represents staging – DEPLOYMENT: Represents deployment – VERIFICATION: Represents verification – COMPLETED: Successfully finished – ROLLED_BACK: Represents rolled back – FAILED: Execution failed

ReleaseType (Line 26)

class ReleaseType(Enum):
    MAJOR
    MINOR
    PATCH
    HOTFIX
    EMERGENCY

Purpose: Type of release.

Values: – MAJOR: Represents major – MINOR: Represents minor – PATCH: Represents patch – HOTFIX: Represents hotfix – EMERGENCY: Represents emergency

ComponentStatus (Line 35)

class ComponentStatus(Enum):
    PENDING
    DEPLOYING
    DEPLOYED
    FAILED
    ROLLED_BACK

Purpose: Status of release component.

Values: – PENDING: Initial state, not yet processed – DEPLOYING: Represents deploying – DEPLOYED: Represents deployed – FAILED: Execution failed – ROLLED_BACK: Represents rolled back

Classes

ReleaseStage (Line 14)

Description: Stage of release lifecycle.

ReleaseType (Line 26)

Description: Type of release.

ComponentStatus (Line 35)

Description: Status of release component.

ReleaseComponent (Line 45)

Description: Component in a release.

ReleaseCheckpoint (Line 57)

Description: Checkpoint during release.

Release (Line 67)

Description: Represents a release.

ReleaseManagement (Line 81)

Description: Implements release management pattern.

Features: – Multi-component coordination – Staged deployment process – Dependency management – Automated verification – Rollback capability – Release history tracking – Checkpoint management

Methods:

__init__ (Line 95)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_release (Line 109)

def create_release(self, release_id, version, release_type, components, metadata):
    """Create a new release.

Args:
    release_id: Unique release identifier
    version: Release version
    release_type: Type of release
    components: List of components with name, version, order, dependencies
    metadata: Additional metadata

Returns:
    Status of release creation"""

Purpose: Create a new release.

Args: release_id: Unique release identifier version: Release version release_type: Type of release components: List of components with name, version, order, dependencies metadata: Additional metadata

Returns: Status of release creation

Parameters: – self: Instance reference – release_id: Parameter for release_id – version: Parameter for version – release_type: Parameter for release_type – components: Parameter for components – metadata: Parameter for metadata

start_release (Line 186)

def start_release(self, release_id):
    """Start a release deployment.

Args:
    release_id: Release identifier

Returns:
    Status of release start"""

Purpose: Start a release deployment.

Args: release_id: Release identifier

Returns: Status of release start

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

advance_stage (Line 236)

def advance_stage(self, release_id):
    """Advance release to next stage.

Args:
    release_id: Release identifier

Returns:
    Status of stage advancement"""

Purpose: Advance release to next stage.

Args: release_id: Release identifier

Returns: Status of stage advancement

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

_deploy_components (Line 300)

def _deploy_components(self, release):
    """Deploy components in order respecting dependencies."""

Purpose: Deploy components in order respecting dependencies.

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

rollback_release (Line 337)

def rollback_release(self, release_id):
    """Rollback a release.

Args:
    release_id: Release identifier

Returns:
    Status of rollback"""

Purpose: Rollback a release.

Args: release_id: Release identifier

Returns: Status of rollback

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

get_release_status (Line 401)

def get_release_status(self, release_id):
    """Get status of a release."""

Purpose: Get status of a release.

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

list_releases (Line 446)

def list_releases(self):
    """List all releases."""

Purpose: List all releases.

Parameters: – self: Instance reference

get_statistics (Line 465)

def get_statistics(self):
    """Get release management statistics."""

Purpose: Get release management statistics.

Parameters: – self: Instance reference

ReleaseManagementPattern (Line 477)

Description: Coordinator for release management.

Manages release lifecycle with multi-component coordination.

Methods:

__init__ (Line 484)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_release (Line 487)

def create_release(self, release_id, version, release_type, components):
    """Create a new release."""

Purpose: Create a new release.

Parameters: – self: Instance reference – release_id: Parameter for release_id – version: Parameter for version – release_type: Parameter for release_type – components: Parameter for components

start_release (Line 491)

def start_release(self, release_id):
    """Start a release."""

Purpose: Start a release.

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

advance_stage (Line 495)

def advance_stage(self, release_id):
    """Advance release stage."""

Purpose: Advance release stage.

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

rollback_release (Line 499)

def rollback_release(self, release_id):
    """Rollback a release."""

Purpose: Rollback a release.

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

get_release_status (Line 503)

def get_release_status(self, release_id):
    """Get release status."""

Purpose: Get release status.

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

list_releases (Line 507)

def list_releases(self):
    """List releases."""

Purpose: List releases.

Parameters: – self: Instance reference

get_statistics (Line 511)

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

Purpose: Get statistics.

Parameters: – self: Instance reference

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 = ReleaseStage(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

    """
Pattern 276: Version Control

Purpose: Agent version management with compatibility checking and upgrade paths
"""

from typing import Dict, Any, List, Optional, Set, Tuple
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
import re


class VersionStatus(Enum):
    """Status of a version."""
    ACTIVE = "active"
    DEPRECATED = "deprecated"
    RETIRED = "retired"
    EXPERIMENTAL = "experimental"
    STABLE = "stable"


class CompatibilityLevel(Enum):
    """Compatibility level between versions."""
    COMPATIBLE = "compatible"
    BACKWARD_COMPATIBLE = "backward_compatible"
    BREAKING_CHANGE = "breaking_change"
    INCOMPATIBLE = "incompatible"


@dataclass
class Version:
    """Represents a version."""
    major: int
    minor: int
    patch: int
    
    def __str__(self) -> str:
        return f"{self.major}.{self.minor}.{self.patch}"
    
    def __eq__(self, other) -> bool:
        if not isinstance(other, Version):
            return False
        return self.major == other.major and self.minor == other.minor and self.patch == other.patch
    
    def __lt__(self, other) -> bool:
        if not isinstance(other, Version):
            return NotImplemented
        if self.major != other.major:
            return self.major < other.major
        if self.minor != other.minor:
            return self.minor < other.minor
        return self.patch  bool:
        return self == other or self  bool:
        return not self  bool:
        return not self  int:
        return hash((self.major, self.minor, self.patch))
    
    @classmethod
    def from_string(cls, version_str: str) -> 'Version':
        """Parse version from string (e.g., '1.2.3')."""
        match = re.match(r'^(\d+)\.(\d+)\.(\d+)$', version_str)
        if not match:
            raise ValueError(f"Invalid version format: {version_str}")
        return cls(int(match.group(1)), int(match.group(2)), int(match.group(3)))


@dataclass
class AgentVersion:
    """Represents an agent version."""
    version: Version
    status: VersionStatus
    release_date: datetime
    description: str
    breaking_changes: List[str] = field(default_factory=list)
    features: List[str] = field(default_factory=list)
    bug_fixes: List[str] = field(default_factory=list)
    dependencies: Dict[str, str] = field(default_factory=dict)  # agent_name -> version_string
    deprecated_features: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class UpgradePath:
    """Represents an upgrade path between versions."""
    from_version: Version
    to_version: Version
    steps: List[str]
    compatibility: CompatibilityLevel
    warnings: List[str] = field(default_factory=list)
    required_actions: List[str] = field(default_factory=list)


class VersionControl:
    """
    Implements version control pattern for agents.
    
    Features:
    - Version registration and tracking
    - Semantic versioning (major.minor.patch)
    - Compatibility checking
    - Upgrade path calculation
    - Deprecation management
    - Dependency resolution
    - Version comparison
    """
    
    def __init__(self):
        self.versions: Dict[Version, AgentVersion] = {}
        self.current_version: Optional[Version] = None
        self.compatibility_matrix: Dict[Tuple[Version, Version], CompatibilityLevel] = {}
        self.upgrade_paths: Dict[Tuple[Version, Version], UpgradePath] = {}
        
        # Statistics
        self.statistics = {
            "total_versions": 0,
            "active_versions": 0,
            "deprecated_versions": 0,
            "retired_versions": 0,
            "total_upgrades": 0,
            "current_version": None
        }
    
    def register_version(
        self,
        version_str: str,
        status: VersionStatus,
        description: str,
        breaking_changes: Optional[List[str]] = None,
        features: Optional[List[str]] = None,
        bug_fixes: Optional[List[str]] = None,
        dependencies: Optional[Dict[str, str]] = None
    ) -> Dict[str, Any]:
        """
        Register a new version.
        
        Args:
            version_str: Version string (e.g., '1.2.3')
            status: Version status
            description: Version description
            breaking_changes: List of breaking changes
            features: List of new features
            bug_fixes: List of bug fixes
            dependencies: Dependencies on other agents
            
        Returns:
            Status of registration
        """
        try:
            version = Version.from_string(version_str)
        except ValueError as e:
            return {
                "success": False,
                "message": str(e)
            }
        
        if version in self.versions:
            return {
                "success": False,
                "message": f"Version {version} already registered"
            }
        
        agent_version = AgentVersion(
            version=version,
            status=status,
            release_date=datetime.now(),
            description=description,
            breaking_changes=breaking_changes or [],
            features=features or [],
            bug_fixes=bug_fixes or [],
            dependencies=dependencies or {}
        )
        
        self.versions[version] = agent_version
        self.statistics["total_versions"] += 1
        
        if status == VersionStatus.ACTIVE:
            self.statistics["active_versions"] += 1
            if self.current_version is None or version > self.current_version:
                self.current_version = version
                self.statistics["current_version"] = str(version)
        elif status == VersionStatus.DEPRECATED:
            self.statistics["deprecated_versions"] += 1
        elif status == VersionStatus.RETIRED:
            self.statistics["retired_versions"] += 1
        
        return {
            "success": True,
            "version": str(version),
            "status": status.value,
            "message": f"Version {version} registered successfully"
        }
    
    def check_compatibility(
        self,
        from_version_str: str,
        to_version_str: str
    ) -> Dict[str, Any]:
        """
        Check compatibility between two versions.
        
        Args:
            from_version_str: Source version
            to_version_str: Target version
            
        Returns:
            Compatibility information
        """
        try:
            from_version = Version.from_string(from_version_str)
            to_version = Version.from_string(to_version_str)
        except ValueError as e:
            return {
                "success": False,
                "message": str(e)
            }
        
        if from_version not in self.versions or to_version not in self.versions:
            return {
                "success": False,
                "message": "One or both versions not registered"
            }
        
        # Check cached compatibility
        key = (from_version, to_version)
        if key in self.compatibility_matrix:
            compatibility = self.compatibility_matrix[key]
        else:
            # Calculate compatibility
            compatibility = self._calculate_compatibility(from_version, to_version)
            self.compatibility_matrix[key] = compatibility
        
        from_agent = self.versions[from_version]
        to_agent = self.versions[to_version]
        
        warnings = []
        if to_agent.breaking_changes:
            warnings.extend(to_agent.breaking_changes)
        
        return {
            "success": True,
            "from_version": str(from_version),
            "to_version": str(to_version),
            "compatibility": compatibility.value,
            "is_compatible": compatibility in [CompatibilityLevel.COMPATIBLE, CompatibilityLevel.BACKWARD_COMPATIBLE],
            "warnings": warnings,
            "breaking_changes": to_agent.breaking_changes
        }
    
    def _calculate_compatibility(
        self,
        from_version: Version,
        to_version: Version
    ) -> CompatibilityLevel:
        """Calculate compatibility between versions using semantic versioning."""
        if from_version == to_version:
            return CompatibilityLevel.COMPATIBLE
        
        to_agent = self.versions[to_version]
        
        # Check for breaking changes
        if to_agent.breaking_changes:
            return CompatibilityLevel.BREAKING_CHANGE
        
        # Semantic versioning rules
        if to_version.major > from_version.major:
            # Major version change - likely breaking
            return CompatibilityLevel.BREAKING_CHANGE
        elif to_version.major  from_version.minor:
            # Minor version upgrade - backward compatible
            return CompatibilityLevel.BACKWARD_COMPATIBLE
        elif to_version.minor  from_version.patch:
            # Patch upgrade - compatible
            return CompatibilityLevel.COMPATIBLE
        else:
            # Downgrade patch - may be incompatible
            return CompatibilityLevel.INCOMPATIBLE
    
    def get_upgrade_path(
        self,
        from_version_str: str,
        to_version_str: str
    ) -> Dict[str, Any]:
        """
        Get upgrade path between versions.
        
        Args:
            from_version_str: Source version
            to_version_str: Target version
            
        Returns:
            Upgrade path with steps and warnings
        """
        try:
            from_version = Version.from_string(from_version_str)
            to_version = Version.from_string(to_version_str)
        except ValueError as e:
            return {
                "success": False,
                "message": str(e)
            }
        
        if from_version not in self.versions or to_version not in self.versions:
            return {
                "success": False,
                "message": "One or both versions not registered"
            }
        
        # Check cached upgrade path
        key = (from_version, to_version)
        if key in self.upgrade_paths:
            path = self.upgrade_paths[key]
        else:
            # Calculate upgrade path
            path = self._calculate_upgrade_path(from_version, to_version)
            self.upgrade_paths[key] = path
        
        return {
            "success": True,
            "from_version": str(from_version),
            "to_version": str(to_version),
            "compatibility": path.compatibility.value,
            "steps": path.steps,
            "warnings": path.warnings,
            "required_actions": path.required_actions,
            "can_upgrade": path.compatibility != CompatibilityLevel.INCOMPATIBLE
        }
    
    def _calculate_upgrade_path(
        self,
        from_version: Version,
        to_version: Version
    ) -> UpgradePath:
        """Calculate upgrade path between versions."""
        compatibility = self._calculate_compatibility(from_version, to_version)
        
        steps = []
        warnings = []
        required_actions = []
        
        from_agent = self.versions[from_version]
        to_agent = self.versions[to_version]
        
        # Find intermediate versions for major upgrades
        if to_version.major > from_version.major:
            # Need to upgrade through each major version
            current = from_version
            intermediate_versions = sorted([
                v for v in self.versions.keys()
                if current.major < v.major  Dict[str, Any]:
        """Mark a version as deprecated."""
        try:
            version = Version.from_string(version_str)
        except ValueError as e:
            return {
                "success": False,
                "message": str(e)
            }
        
        if version not in self.versions:
            return {
                "success": False,
                "message": f"Version {version} not registered"
            }
        
        agent_version = self.versions[version]
        old_status = agent_version.status
        
        agent_version.status = VersionStatus.DEPRECATED
        
        if old_status == VersionStatus.ACTIVE:
            self.statistics["active_versions"] -= 1
            self.statistics["deprecated_versions"] += 1
        
        return {
            "success": True,
            "version": str(version),
            "message": f"Version {version} marked as deprecated"
        }
    
    def retire_version(self, version_str: str) -> Dict[str, Any]:
        """Mark a version as retired."""
        try:
            version = Version.from_string(version_str)
        except ValueError as e:
            return {
                "success": False,
                "message": str(e)
            }
        
        if version not in self.versions:
            return {
                "success": False,
                "message": f"Version {version} not registered"
            }
        
        agent_version = self.versions[version]
        old_status = agent_version.status
        
        agent_version.status = VersionStatus.RETIRED
        
        if old_status == VersionStatus.ACTIVE:
            self.statistics["active_versions"] -= 1
            self.statistics["retired_versions"] += 1
        elif old_status == VersionStatus.DEPRECATED:
            self.statistics["deprecated_versions"] -= 1
            self.statistics["retired_versions"] += 1
        
        return {
            "success": True,
            "version": str(version),
            "message": f"Version {version} marked as retired"
        }
    
    def get_version_info(self, version_str: str) -> Dict[str, Any]:
        """Get detailed information about a version."""
        try:
            version = Version.from_string(version_str)
        except ValueError as e:
            return {
                "success": False,
                "message": str(e)
            }
        
        if version not in self.versions:
            return {
                "success": False,
                "message": f"Version {version} not registered"
            }
        
        agent_version = self.versions[version]
        
        return {
            "success": True,
            "version": str(version),
            "status": agent_version.status.value,
            "release_date": agent_version.release_date.isoformat(),
            "description": agent_version.description,
            "features": agent_version.features,
            "bug_fixes": agent_version.bug_fixes,
            "breaking_changes": agent_version.breaking_changes,
            "deprecated_features": agent_version.deprecated_features,
            "dependencies": agent_version.dependencies
        }
    
    def list_versions(
        self,
        status_filter: Optional[VersionStatus] = None
    ) -> Dict[str, Any]:
        """List all versions, optionally filtered by status."""
        versions_list = []
        
        for version, agent_version in sorted(self.versions.items(), reverse=True):
            if status_filter is None or agent_version.status == status_filter:
                versions_list.append({
                    "version": str(version),
                    "status": agent_version.status.value,
                    "release_date": agent_version.release_date.isoformat(),
                    "description": agent_version.description,
                    "is_current": version == self.current_version
                })
        
        return {
            "total": len(versions_list),
            "versions": versions_list
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get version control statistics."""
        return {
            "total_versions": self.statistics["total_versions"],
            "active_versions": self.statistics["active_versions"],
            "deprecated_versions": self.statistics["deprecated_versions"],
            "retired_versions": self.statistics["retired_versions"],
            "total_upgrades": self.statistics["total_upgrades"],
            "current_version": self.statistics["current_version"]
        }


class VersionControlPattern:
    """
    Coordinator for version control management.
    
    Manages agent versions with compatibility checking and upgrades.
    """
    
    def __init__(self):
        self.control = VersionControl()
    
    def register_version(self, version_str: str, **kwargs) -> Dict[str, Any]:
        """Register a new version."""
        return self.control.register_version(version_str, **kwargs)
    
    def check_compatibility(self, from_version: str, to_version: str) -> Dict[str, Any]:
        """Check compatibility between versions."""
        return self.control.check_compatibility(from_version, to_version)
    
    def get_upgrade_path(self, from_version: str, to_version: str) -> Dict[str, Any]:
        """Get upgrade path."""
        return self.control.get_upgrade_path(from_version, to_version)
    
    def get_version_info(self, version: str) -> Dict[str, Any]:
        """Get version information."""
        return self.control.get_version_info(version)
    
    def list_versions(self, status_filter: Optional[VersionStatus] = None) -> Dict[str, Any]:
        """List versions."""
        return self.control.list_versions(status_filter)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.control.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Version Control Demo ===\n")
    
    # Create version control
    pattern = VersionControlPattern()
    
    print("1. Registering versions...")
    pattern.register_version(
        "1.0.0",
        status=VersionStatus.DEPRECATED,
        description="Initial release",
        features=["Basic functionality"],
        bug_fixes=[]
    )
    
    pattern.register_version(
        "1.1.0",
        status=VersionStatus.DEPRECATED,
        description="Feature update",
        features=["Advanced search", "User profiles"],
        bug_fixes=["Fixed memory leak", "Improved performance"]
    )
    
    pattern.register_version(
        "2.0.0",
        status=VersionStatus.ACTIVE,
        description="Major update with breaking changes",
        features=["New API", "Improved architecture"],
        bug_fixes=["Critical security fix"],
        breaking_changes=["API endpoint changes", "Configuration format changed"]
    )
    
    pattern.register_version(
        "2.1.0",
        status=VersionStatus.ACTIVE,
        description="Feature additions",
        features=["Real-time updates", "Export functionality"],
        bug_fixes=["Fixed edge case bugs"]
    )
    
    versions = pattern.list_versions()
    print(f"  Total versions: {versions['total']}")
    for v in versions['versions']:
        current_marker = " (current)" if v['is_current'] else ""
        print(f"    - {v['version']}: {v['status']}{current_marker}")
    
    # Check compatibility
    print("\n2. Checking compatibility...")
    compat = pattern.check_compatibility("1.1.0", "2.0.0")
    print(f"  1.1.0 → 2.0.0: {compat['compatibility']}")
    print(f"  Is compatible: {compat['is_compatible']}")
    if compat['breaking_changes']:
        print(f"  Breaking changes: {', '.join(compat['breaking_changes'])}")
    
    compat = pattern.check_compatibility("2.0.0", "2.1.0")
    print(f"\n  2.0.0 → 2.1.0: {compat['compatibility']}")
    print(f"  Is compatible: {compat['is_compatible']}")
    
    # Get upgrade path
    print("\n3. Getting upgrade paths...")
    path = pattern.get_upgrade_path("1.1.0", "2.1.0")
    print(f"  Upgrade from 1.1.0 to 2.1.0:")
    print(f"  Can upgrade: {path['can_upgrade']}")
    print(f"  Compatibility: {path['compatibility']}")
    print(f"\n  Steps:")
    for i, step in enumerate(path['steps'], 1):
        print(f"    {i}. {step}")
    if path['warnings']:
        print(f"\n  Warnings:")
        for warning in path['warnings']:
            print(f"    - {warning}")
    if path['required_actions']:
        print(f"\n  Required actions:")
        for action in path['required_actions']:
            print(f"    - {action}")
    
    # Version info
    print("\n4. Version details:")
    info = pattern.get_version_info("2.0.0")
    if info['success']:
        print(f"  Version: {info['version']}")
        print(f"  Status: {info['status']}")
        print(f"  Description: {info['description']}")
        print(f"  Features: {', '.join(info['features'])}")
        print(f"  Breaking changes: {', '.join(info['breaking_changes'])}")
    
    # Statistics
    print("\n5. Statistics:")
    stats = pattern.get_statistics()
    print(f"  Total versions: {stats['total_versions']}")
    print(f"  Active: {stats['active_versions']}")
    print(f"  Deprecated: {stats['deprecated_versions']}")
    print(f"  Current version: {stats['current_version']}")
    
    print("\n✓ Comprehensive version control with compatibility checking complete!")

    

Agentic Architecture Pattern: Version Control

Pattern 276: 276 Version Control – Line-by-Line Explanation

Overview

Pattern 276: Version Control

Purpose: Agent version management with compatibility checking and upgrade paths

Code Structure Analysis

File Information

• Pattern Number: 276
• Pattern Name: 276 Version Control
• File Path: patterns\deployment\276_version_control.py
• Total Lines: 667

Detailed Code Explanation

Module Docstring

"""
Pattern 276: Version Control

Purpose: Agent version management with compatibility checking and upgrade paths
"""

Explanation: This module docstring describes the purpose and key features of the 276 Version Control pattern.

Imports Section

from typing import Dict, Any, List, Optional, Set, Tuple
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
import re  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Set, Tuple: 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

Enumerations

VersionStatus (Line 15)

class VersionStatus(Enum):
    ACTIVE
    DEPRECATED
    RETIRED
    EXPERIMENTAL
    STABLE

Purpose: Status of a version.

Values: – ACTIVE: Currently operational – DEPRECATED: Represents deprecated – RETIRED: Represents retired – EXPERIMENTAL: Represents experimental – STABLE: Represents stable

CompatibilityLevel (Line 24)

class CompatibilityLevel(Enum):
    COMPATIBLE
    BACKWARD_COMPATIBLE
    BREAKING_CHANGE
    INCOMPATIBLE

Purpose: Compatibility level between versions.

Values: – COMPATIBLE: Represents compatible – BACKWARD_COMPATIBLE: Represents backward compatible – BREAKING_CHANGE: Represents breaking change – INCOMPATIBLE: Represents incompatible

Classes

VersionStatus (Line 15)

Description: Status of a version.

CompatibilityLevel (Line 24)

Description: Compatibility level between versions.

Version (Line 33)

Description: Represents a version.

Methods:

__str__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

__eq__ (Line 42)

def __eq__(self, other):
    """No method docstring."""

Purpose: No method docstring.

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

__lt__ (Line 47)

def __lt__(self, other):
    """No method docstring."""

Purpose: No method docstring.

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

__le__ (Line 56)

def __le__(self, other):
    """No method docstring."""

Purpose: No method docstring.

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

__gt__ (Line 59)

def __gt__(self, other):
    """No method docstring."""

Purpose: No method docstring.

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

__ge__ (Line 62)

def __ge__(self, other):
    """No method docstring."""

Purpose: No method docstring.

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

__hash__ (Line 65)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

from_string (Line 69)

def from_string(cls, version_str):
    """Parse version from string (e.g., '1.2.3')."""

Purpose: Parse version from string (e.g., ‘1.2.3’).

Parameters: – cls: Parameter for cls – version_str: Parameter for version_str

AgentVersion (Line 78)

Description: Represents an agent version.

UpgradePath (Line 93)

Description: Represents an upgrade path between versions.

VersionControl (Line 103)

Description: Implements version control pattern for agents.

Features: – Version registration and tracking – Semantic versioning (major.minor.patch) – Compatibility checking – Upgrade path calculation – Deprecation management – Dependency resolution – Version comparison

Methods:

__init__ (Line 117)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

register_version (Line 133)

def register_version(self, version_str, status, description, breaking_changes, features, bug_fixes, dependencies):
    """Register a new version.

Args:
    version_str: Version string (e.g., '1.2.3')
    status: Version status
    description: Version description
    breaking_changes: List of breaking changes
    features: List of new features
    bug_fixes: List of bug fixes
    dependencies: Dependencies on other agents

Returns:
    Status of registration"""

Purpose: Register a new version.

Args: version_str: Version string (e.g., ‘1.2.3’) status: Version status description: Version description breaking_changes: List of breaking changes features: List of new features bug_fixes: List of bug fixes dependencies: Dependencies on other agents

Returns: Status of registration

Parameters: – self: Instance reference – version_str: Parameter for version_str – status: Parameter for status – description: Parameter for description – breaking_changes: Parameter for breaking_changes – features: Parameter for features – bug_fixes: Parameter for bug_fixes – dependencies: Parameter for dependencies

check_compatibility (Line 203)

def check_compatibility(self, from_version_str, to_version_str):
    """Check compatibility between two versions.

Args:
    from_version_str: Source version
    to_version_str: Target version

Returns:
    Compatibility information"""

Purpose: Check compatibility between two versions.

Args: from_version_str: Source version to_version_str: Target version

Returns: Compatibility information

Parameters: – self: Instance reference – from_version_str: Parameter for from_version_str – to_version_str: Parameter for to_version_str

_calculate_compatibility (Line 259)

def _calculate_compatibility(self, from_version, to_version):
    """Calculate compatibility between versions using semantic versioning."""

Purpose: Calculate compatibility between versions using semantic versioning.

Parameters: – self: Instance reference – from_version: Parameter for from_version – to_version: Parameter for to_version

get_upgrade_path (Line 294)

def get_upgrade_path(self, from_version_str, to_version_str):
    """Get upgrade path between versions.

Args:
    from_version_str: Source version
    to_version_str: Target version

Returns:
    Upgrade path with steps and warnings"""

Purpose: Get upgrade path between versions.

Args: from_version_str: Source version to_version_str: Target version

Returns: Upgrade path with steps and warnings

Parameters: – self: Instance reference – from_version_str: Parameter for from_version_str – to_version_str: Parameter for to_version_str

_calculate_upgrade_path (Line 344)

def _calculate_upgrade_path(self, from_version, to_version):
    """Calculate upgrade path between versions."""

Purpose: Calculate upgrade path between versions.

Parameters: – self: Instance reference – from_version: Parameter for from_version – to_version: Parameter for to_version

deprecate_version (Line 406)

def deprecate_version(self, version_str):
    """Mark a version as deprecated."""

Purpose: Mark a version as deprecated.

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

retire_version (Line 437)

def retire_version(self, version_str):
    """Mark a version as retired."""

Purpose: Mark a version as retired.

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

get_version_info (Line 471)

def get_version_info(self, version_str):
    """Get detailed information about a version."""

Purpose: Get detailed information about a version.

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

list_versions (Line 502)

def list_versions(self, status_filter):
    """List all versions, optionally filtered by status."""

Purpose: List all versions, optionally filtered by status.

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

get_statistics (Line 524)

def get_statistics(self):
    """Get version control statistics."""

Purpose: Get version control statistics.

Parameters: – self: Instance reference

VersionControlPattern (Line 536)

Description: Coordinator for version control management.

Manages agent versions with compatibility checking and upgrades.

Methods:

__init__ (Line 543)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

register_version (Line 546)

def register_version(self, version_str):
    """Register a new version."""

Purpose: Register a new version.

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

check_compatibility (Line 550)

def check_compatibility(self, from_version, to_version):
    """Check compatibility between versions."""

Purpose: Check compatibility between versions.

Parameters: – self: Instance reference – from_version: Parameter for from_version – to_version: Parameter for to_version

get_upgrade_path (Line 554)

def get_upgrade_path(self, from_version, to_version):
    """Get upgrade path."""

Purpose: Get upgrade path.

Parameters: – self: Instance reference – from_version: Parameter for from_version – to_version: Parameter for to_version

get_version_info (Line 558)

def get_version_info(self, version):
    """Get version information."""

Purpose: Get version information.

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

list_versions (Line 562)

def list_versions(self, status_filter):
    """List versions."""

Purpose: List versions.

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

get_statistics (Line 566)

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

Purpose: Get statistics.

Parameters: – self: Instance reference

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 = VersionStatus(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

    """
Pattern 275: Feature Toggle

Purpose: Dynamic feature management with runtime control and gradual rollout
"""

from typing import Dict, Any, List, Optional, Set, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict


class ToggleState(Enum):
    """State of feature toggle."""
    DISABLED = "disabled"
    ENABLED_FOR_TESTING = "enabled_for_testing"
    ENABLED_FOR_PERCENTAGE = "enabled_for_percentage"
    ENABLED_FOR_USERS = "enabled_for_users"
    FULLY_ENABLED = "fully_enabled"
    DEPRECATED = "deprecated"


class RolloutStrategy(Enum):
    """Strategy for feature rollout."""
    ALL_AT_ONCE = "all_at_once"
    PERCENTAGE_BASED = "percentage_based"
    USER_TARGETED = "user_targeted"
    RING_BASED = "ring_based"  # Canary, beta, production rings


@dataclass
class Feature:
    """Represents a feature toggle."""
    name: str
    description: str
    state: ToggleState
    created_at: datetime = field(default_factory=datetime.now)
    enabled_percentage: float = 0.0  # 0-100
    enabled_users: Set[str] = field(default_factory=set)
    enabled_rings: Set[str] = field(default_factory=set)
    metadata: Dict[str, Any] = field(default_factory=dict)
    last_modified: datetime = field(default_factory=datetime.now)


@dataclass
class ToggleDecision:
    """Result of feature toggle evaluation."""
    feature_name: str
    enabled: bool
    reason: str
    user_id: Optional[str] = None
    ring: Optional[str] = None
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class ToggleMetrics:
    """Metrics for feature toggle usage."""
    total_evaluations: int = 0
    enabled_count: int = 0
    disabled_count: int = 0
    unique_users: Set[str] = field(default_factory=set)
    evaluations_by_user: Dict[str, int] = field(default_factory=lambda: defaultdict(int))


class FeatureToggle:
    """
    Implements feature toggle pattern.
    
    Features:
    - Runtime enable/disable
    - Percentage-based rollout
    - User-targeted features
    - Ring-based deployment
    - A/B testing support
    - Feature dependencies
    - Metrics collection
    """
    
    def __init__(self):
        self.features: Dict[str, Feature] = {}
        self.metrics: Dict[str, ToggleMetrics] = defaultdict(ToggleMetrics)
        self.decisions: List[ToggleDecision] = []
        self.dependencies: Dict[str, List[str]] = {}  # feature -> required features
        
        # Statistics
        self.statistics = {
            "total_features": 0,
            "enabled_features": 0,
            "disabled_features": 0,
            "total_evaluations": 0
        }
    
    def create_feature(
        self,
        name: str,
        description: str,
        state: ToggleState = ToggleState.DISABLED,
        metadata: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Create a new feature toggle.
        
        Args:
            name: Feature name
            description: Feature description
            state: Initial state
            metadata: Additional metadata
            
        Returns:
            Status of feature creation
        """
        if name in self.features:
            return {
                "success": False,
                "message": f"Feature '{name}' already exists"
            }
        
        feature = Feature(
            name=name,
            description=description,
            state=state,
            metadata=metadata or {}
        )
        
        self.features[name] = feature
        self.statistics["total_features"] += 1
        
        if state != ToggleState.DISABLED:
            self.statistics["enabled_features"] += 1
        else:
            self.statistics["disabled_features"] += 1
        
        return {
            "success": True,
            "feature": name,
            "state": state.value,
            "message": f"Feature '{name}' created successfully"
        }
    
    def enable_feature(
        self,
        name: str,
        strategy: RolloutStrategy = RolloutStrategy.ALL_AT_ONCE,
        percentage: float = 100.0,
        users: Optional[List[str]] = None,
        rings: Optional[List[str]] = None
    ) -> Dict[str, Any]:
        """
        Enable a feature with specified strategy.
        
        Args:
            name: Feature name
            strategy: Rollout strategy
            percentage: Percentage of users (0-100)
            users: Specific users to enable for
            rings: Specific rings to enable in
            
        Returns:
            Status of feature enable
        """
        if name not in self.features:
            return {
                "success": False,
                "message": f"Feature '{name}' not found"
            }
        
        feature = self.features[name]
        
        # Check dependencies
        if name in self.dependencies:
            for dep in self.dependencies[name]:
                if dep not in self.features or self.features[dep].state == ToggleState.DISABLED:
                    return {
                        "success": False,
                        "message": f"Cannot enable '{name}': dependency '{dep}' is not enabled"
                    }
        
        old_state = feature.state
        
        if strategy == RolloutStrategy.ALL_AT_ONCE:
            feature.state = ToggleState.FULLY_ENABLED
            feature.enabled_percentage = 100.0
        
        elif strategy == RolloutStrategy.PERCENTAGE_BASED:
            feature.state = ToggleState.ENABLED_FOR_PERCENTAGE
            feature.enabled_percentage = min(100.0, max(0.0, percentage))
        
        elif strategy == RolloutStrategy.USER_TARGETED:
            feature.state = ToggleState.ENABLED_FOR_USERS
            if users:
                feature.enabled_users.update(users)
        
        elif strategy == RolloutStrategy.RING_BASED:
            feature.state = ToggleState.ENABLED_FOR_PERCENTAGE  # Start with ring-based
            if rings:
                feature.enabled_rings.update(rings)
        
        feature.last_modified = datetime.now()
        
        if old_state == ToggleState.DISABLED:
            self.statistics["enabled_features"] += 1
            self.statistics["disabled_features"] -= 1
        
        return {
            "success": True,
            "feature": name,
            "strategy": strategy.value,
            "state": feature.state.value,
            "percentage": feature.enabled_percentage,
            "message": f"Feature '{name}' enabled with {strategy.value} strategy"
        }
    
    def disable_feature(self, name: str) -> Dict[str, Any]:
        """
        Disable a feature.
        
        Args:
            name: Feature name
            
        Returns:
            Status of feature disable
        """
        if name not in self.features:
            return {
                "success": False,
                "message": f"Feature '{name}' not found"
            }
        
        feature = self.features[name]
        old_state = feature.state
        
        feature.state = ToggleState.DISABLED
        feature.enabled_percentage = 0.0
        feature.enabled_users.clear()
        feature.enabled_rings.clear()
        feature.last_modified = datetime.now()
        
        if old_state != ToggleState.DISABLED:
            self.statistics["enabled_features"] -= 1
            self.statistics["disabled_features"] += 1
        
        return {
            "success": True,
            "feature": name,
            "message": f"Feature '{name}' disabled"
        }
    
    def is_enabled(
        self,
        name: str,
        user_id: Optional[str] = None,
        ring: Optional[str] = None,
        context: Optional[Dict[str, Any]] = None
    ) -> bool:
        """
        Check if feature is enabled for given context.
        
        Args:
            name: Feature name
            user_id: User identifier
            ring: Deployment ring
            context: Additional context
            
        Returns:
            True if feature is enabled
        """
        decision = self.evaluate(name, user_id, ring, context)
        return decision["enabled"]
    
    def evaluate(
        self,
        name: str,
        user_id: Optional[str] = None,
        ring: Optional[str] = None,
        context: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Evaluate if feature should be enabled with detailed reasoning.
        
        Args:
            name: Feature name
            user_id: User identifier
            ring: Deployment ring
            context: Additional context
            
        Returns:
            Evaluation decision with reasoning
        """
        self.statistics["total_evaluations"] += 1
        
        if name not in self.features:
            decision = ToggleDecision(
                feature_name=name,
                enabled=False,
                reason="Feature not found",
                user_id=user_id,
                ring=ring
            )
            self.decisions.append(decision)
            return {
                "enabled": False,
                "reason": "Feature not found",
                "feature": name
            }
        
        feature = self.features[name]
        metrics = self.metrics[name]
        metrics.total_evaluations += 1
        
        if user_id:
            metrics.unique_users.add(user_id)
            metrics.evaluations_by_user[user_id] += 1
        
        enabled = False
        reason = ""
        
        # Check feature state
        if feature.state == ToggleState.DISABLED:
            enabled = False
            reason = "Feature is disabled"
        
        elif feature.state == ToggleState.FULLY_ENABLED:
            enabled = True
            reason = "Feature is fully enabled"
        
        elif feature.state == ToggleState.ENABLED_FOR_TESTING:
            # Only enabled in test mode
            enabled = context and context.get("test_mode", False)
            reason = "Feature enabled for testing only" if enabled else "Feature only available in test mode"
        
        elif feature.state == ToggleState.ENABLED_FOR_USERS:
            if user_id and user_id in feature.enabled_users:
                enabled = True
                reason = f"Feature enabled for user '{user_id}'"
            else:
                enabled = False
                reason = "User not in enabled list"
        
        elif feature.state == ToggleState.ENABLED_FOR_PERCENTAGE:
            # Check ring first
            if feature.enabled_rings and ring:
                if ring in feature.enabled_rings:
                    enabled = True
                    reason = f"Feature enabled for ring '{ring}'"
                else:
                    enabled = False
                    reason = f"Ring '{ring}' not enabled"
            else:
                # Percentage-based rollout
                if user_id:
                    # Consistent hashing for user
                    user_hash = hash(f"{name}:{user_id}") % 100
                    enabled = user_hash < feature.enabled_percentage
                    reason = f"User in {feature.enabled_percentage}% rollout" if enabled else f"User not in {feature.enabled_percentage}% rollout"
                else:
                    # No user ID, use random
                    import random
                    enabled = random.random() * 100  Dict[str, Any]:
        """Update rollout percentage for a feature."""
        if name not in self.features:
            return {
                "success": False,
                "message": f"Feature '{name}' not found"
            }
        
        feature = self.features[name]
        feature.enabled_percentage = min(100.0, max(0.0, percentage))
        feature.last_modified = datetime.now()
        
        return {
            "success": True,
            "feature": name,
            "percentage": feature.enabled_percentage,
            "message": f"Updated '{name}' to {feature.enabled_percentage}% rollout"
        }
    
    def add_dependency(self, feature: str, requires: str) -> Dict[str, Any]:
        """Add dependency between features."""
        if feature not in self.features:
            return {
                "success": False,
                "message": f"Feature '{feature}' not found"
            }
        
        if requires not in self.features:
            return {
                "success": False,
                "message": f"Required feature '{requires}' not found"
            }
        
        if feature not in self.dependencies:
            self.dependencies[feature] = []
        
        if requires not in self.dependencies[feature]:
            self.dependencies[feature].append(requires)
        
        return {
            "success": True,
            "message": f"Feature '{feature}' now requires '{requires}'"
        }
    
    def get_feature_status(self, name: str) -> Dict[str, Any]:
        """Get status of a specific feature."""
        if name not in self.features:
            return {
                "success": False,
                "message": f"Feature '{name}' not found"
            }
        
        feature = self.features[name]
        metrics = self.metrics[name]
        
        return {
            "success": True,
            "feature": {
                "name": feature.name,
                "description": feature.description,
                "state": feature.state.value,
                "enabled_percentage": feature.enabled_percentage,
                "enabled_users_count": len(feature.enabled_users),
                "enabled_rings": list(feature.enabled_rings),
                "created_at": feature.created_at.isoformat(),
                "last_modified": feature.last_modified.isoformat()
            },
            "metrics": {
                "total_evaluations": metrics.total_evaluations,
                "enabled_count": metrics.enabled_count,
                "disabled_count": metrics.disabled_count,
                "unique_users": len(metrics.unique_users),
                "enable_rate": (metrics.enabled_count / metrics.total_evaluations * 100) if metrics.total_evaluations > 0 else 0
            },
            "dependencies": self.dependencies.get(name, [])
        }
    
    def list_features(self) -> Dict[str, Any]:
        """List all features."""
        features_list = []
        for name, feature in self.features.items():
            features_list.append({
                "name": name,
                "description": feature.description,
                "state": feature.state.value,
                "enabled_percentage": feature.enabled_percentage
            })
        
        return {
            "total": len(features_list),
            "features": features_list
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get toggle statistics."""
        return {
            "total_features": self.statistics["total_features"],
            "enabled_features": self.statistics["enabled_features"],
            "disabled_features": self.statistics["disabled_features"],
            "total_evaluations": self.statistics["total_evaluations"],
            "total_decisions": len(self.decisions)
        }


class FeatureTogglePattern:
    """
    Coordinator for feature toggle management.
    
    Manages feature flags with various rollout strategies.
    """
    
    def __init__(self):
        self.toggle = FeatureToggle()
    
    def create_feature(
        self,
        name: str,
        description: str,
        state: ToggleState = ToggleState.DISABLED
    ) -> Dict[str, Any]:
        """Create a new feature."""
        return self.toggle.create_feature(name, description, state)
    
    def enable_feature(
        self,
        name: str,
        strategy: RolloutStrategy = RolloutStrategy.ALL_AT_ONCE,
        **kwargs
    ) -> Dict[str, Any]:
        """Enable a feature."""
        return self.toggle.enable_feature(name, strategy, **kwargs)
    
    def disable_feature(self, name: str) -> Dict[str, Any]:
        """Disable a feature."""
        return self.toggle.disable_feature(name)
    
    def is_enabled(self, name: str, **kwargs) -> bool:
        """Check if feature is enabled."""
        return self.toggle.is_enabled(name, **kwargs)
    
    def evaluate(self, name: str, **kwargs) -> Dict[str, Any]:
        """Evaluate feature toggle."""
        return self.toggle.evaluate(name, **kwargs)
    
    def get_feature_status(self, name: str) -> Dict[str, Any]:
        """Get feature status."""
        return self.toggle.get_feature_status(name)
    
    def list_features(self) -> Dict[str, Any]:
        """List all features."""
        return self.toggle.list_features()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.toggle.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Feature Toggle Demo ===\n")
    
    # Create feature toggle manager
    pattern = FeatureTogglePattern()
    
    print("1. Creating features...")
    pattern.create_feature("new_ui", "New user interface", ToggleState.DISABLED)
    pattern.create_feature("advanced_search", "Advanced search functionality", ToggleState.DISABLED)
    pattern.create_feature("dark_mode", "Dark mode theme", ToggleState.DISABLED)
    
    features = pattern.list_features()
    print(f"  Total features: {features['total']}")
    for feature in features['features']:
        print(f"    - {feature['name']}: {feature['state']}")
    
    # Enable with different strategies
    print("\n2. Enabling features with different strategies...")
    
    # All at once
    result = pattern.enable_feature("dark_mode", RolloutStrategy.ALL_AT_ONCE)
    print(f"  {result['message']}")
    
    # Percentage-based rollout
    result = pattern.enable_feature("new_ui", RolloutStrategy.PERCENTAGE_BASED, percentage=25.0)
    print(f"  {result['message']}")
    
    # User-targeted
    result = pattern.enable_feature("advanced_search", RolloutStrategy.USER_TARGETED, users=["user1", "user2", "user3"])
    print(f"  {result['message']}")
    
    # Test evaluations
    print("\n3. Evaluating features for different users...")
    
    # Dark mode (fully enabled)
    eval_result = pattern.evaluate("dark_mode", user_id="user1")
    print(f"  dark_mode for user1: {eval_result['enabled']} ({eval_result['reason']})")
    
    # New UI (25% rollout)
    for user_id in ["user1", "user2", "user3", "user4"]:
        eval_result = pattern.evaluate("new_ui", user_id=user_id)
        print(f"  new_ui for {user_id}: {eval_result['enabled']} ({eval_result['reason']})")
    
    # Advanced search (user-targeted)
    for user_id in ["user1", "user4"]:
        eval_result = pattern.evaluate("advanced_search", user_id=user_id)
        print(f"  advanced_search for {user_id}: {eval_result['enabled']} ({eval_result['reason']})")
    
    # Increase rollout percentage
    print("\n4. Increasing new_ui rollout to 50%...")
    result = pattern.toggle.update_percentage("new_ui", 50.0)
    print(f"  {result['message']}")
    
    # Feature status
    print("\n5. Feature status:")
    status = pattern.get_feature_status("new_ui")
    if status['success']:
        feature_info = status['feature']
        metrics_info = status['metrics']
        print(f"  Feature: {feature_info['name']}")
        print(f"  State: {feature_info['state']}")
        print(f"  Rollout: {feature_info['enabled_percentage']}%")
        print(f"  Total evaluations: {metrics_info['total_evaluations']}")
        print(f"  Enable rate: {metrics_info['enable_rate']:.1f}%")
        print(f"  Unique users: {metrics_info['unique_users']}")
    
    # Statistics
    print("\n6. Overall statistics:")
    stats = pattern.get_statistics()
    print(f"  Total features: {stats['total_features']}")
    print(f"  Enabled: {stats['enabled_features']}")
    print(f"  Disabled: {stats['disabled_features']}")
    print(f"  Total evaluations: {stats['total_evaluations']}")
    
    print("\n✓ Comprehensive feature toggle with dynamic control complete!")

    

Agentic Architecture Pattern: Feature Toggle

Pattern 275: 275 Feature Toggle – Line-by-Line Explanation

Overview

Pattern 275: Feature Toggle

Purpose: Dynamic feature management with runtime control and gradual rollout

Code Structure Analysis

File Information

• Pattern Number: 275
• Pattern Name: 275 Feature Toggle
• File Path: patterns\deployment\275_feature_toggle.py
• Total Lines: 623

Detailed Code Explanation

Module Docstring

"""
Pattern 275: Feature Toggle

Purpose: Dynamic feature management with runtime control and gradual rollout
"""

Explanation: This module docstring describes the purpose and key features of the 275 Feature Toggle pattern.

Imports Section

from typing import Dict, Any, List, Optional, Set, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
                    import random  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Set, 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

Enumerations

ToggleState (Line 14)

class ToggleState(Enum):
    DISABLED
    ENABLED_FOR_TESTING
    ENABLED_FOR_PERCENTAGE
    ENABLED_FOR_USERS
    FULLY_ENABLED
    DEPRECATED

Purpose: State of feature toggle.

Values: – DISABLED: Represents disabled – ENABLED_FOR_TESTING: Represents enabled for testing – ENABLED_FOR_PERCENTAGE: Represents enabled for percentage – ENABLED_FOR_USERS: Represents enabled for users – FULLY_ENABLED: Represents fully enabled – DEPRECATED: Represents deprecated

RolloutStrategy (Line 24)

class RolloutStrategy(Enum):
    ALL_AT_ONCE
    PERCENTAGE_BASED
    USER_TARGETED
    RING_BASED

Purpose: Strategy for feature rollout.

Values: – ALL_AT_ONCE: Represents all at once – PERCENTAGE_BASED: Represents percentage based – USER_TARGETED: Represents user targeted – RING_BASED: Represents ring based

Classes

ToggleState (Line 14)

Description: State of feature toggle.

RolloutStrategy (Line 24)

Description: Strategy for feature rollout.

Feature (Line 33)

Description: Represents a feature toggle.

ToggleDecision (Line 47)

Description: Result of feature toggle evaluation.

ToggleMetrics (Line 58)

Description: Metrics for feature toggle usage.

FeatureToggle (Line 67)

Description: Implements feature toggle pattern.

Features: – Runtime enable/disable – Percentage-based rollout – User-targeted features – Ring-based deployment – A/B testing support – Feature dependencies – Metrics collection

Methods:

__init__ (Line 81)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_feature (Line 95)

def create_feature(self, name, description, state, metadata):
    """Create a new feature toggle.

Args:
    name: Feature name
    description: Feature description
    state: Initial state
    metadata: Additional metadata

Returns:
    Status of feature creation"""

Purpose: Create a new feature toggle.

Args: name: Feature name description: Feature description state: Initial state metadata: Additional metadata

Returns: Status of feature creation

Parameters: – self: Instance reference – name: Identifier or label – description: Parameter for description – state: Current workflow state – metadata: Parameter for metadata

enable_feature (Line 142)

def enable_feature(self, name, strategy, percentage, users, rings):
    """Enable a feature with specified strategy.

Args:
    name: Feature name
    strategy: Rollout strategy
    percentage: Percentage of users (0-100)
    users: Specific users to enable for
    rings: Specific rings to enable in

Returns:
    Status of feature enable"""

Purpose: Enable a feature with specified strategy.

Args: name: Feature name strategy: Rollout strategy percentage: Percentage of users (0-100) users: Specific users to enable for rings: Specific rings to enable in

Returns: Status of feature enable

Parameters: – self: Instance reference – name: Identifier or label – strategy: Parameter for strategy – percentage: Parameter for percentage – users: Parameter for users – rings: Parameter for rings

disable_feature (Line 215)

def disable_feature(self, name):
    """Disable a feature.

Args:
    name: Feature name

Returns:
    Status of feature disable"""

Purpose: Disable a feature.

Args: name: Feature name

Returns: Status of feature disable

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

is_enabled (Line 250)

def is_enabled(self, name, user_id, ring, context):
    """Check if feature is enabled for given context.

Args:
    name: Feature name
    user_id: User identifier
    ring: Deployment ring
    context: Additional context

Returns:
    True if feature is enabled"""

Purpose: Check if feature is enabled for given context.

Args: name: Feature name user_id: User identifier ring: Deployment ring context: Additional context

Returns: True if feature is enabled

Parameters: – self: Instance reference – name: Identifier or label – user_id: Parameter for user_id – ring: Parameter for ring – context: Parameter for context

evaluate (Line 272)

def evaluate(self, name, user_id, ring, context):
    """Evaluate if feature should be enabled with detailed reasoning.

Args:
    name: Feature name
    user_id: User identifier
    ring: Deployment ring
    context: Additional context

Returns:
    Evaluation decision with reasoning"""

Purpose: Evaluate if feature should be enabled with detailed reasoning.

Args: name: Feature name user_id: User identifier ring: Deployment ring context: Additional context

Returns: Evaluation decision with reasoning

Parameters: – self: Instance reference – name: Identifier or label – user_id: Parameter for user_id – ring: Parameter for ring – context: Parameter for context

update_percentage (Line 390)

def update_percentage(self, name, percentage):
    """Update rollout percentage for a feature."""

Purpose: Update rollout percentage for a feature.

Parameters: – self: Instance reference – name: Identifier or label – percentage: Parameter for percentage

add_dependency (Line 409)

def add_dependency(self, feature, requires):
    """Add dependency between features."""

Purpose: Add dependency between features.

Parameters: – self: Instance reference – feature: Parameter for feature – requires: Parameter for requires

get_feature_status (Line 434)

def get_feature_status(self, name):
    """Get status of a specific feature."""

Purpose: Get status of a specific feature.

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

list_features (Line 467)

def list_features(self):
    """List all features."""

Purpose: List all features.

Parameters: – self: Instance reference

get_statistics (Line 483)

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

Purpose: Get toggle statistics.

Parameters: – self: Instance reference

FeatureTogglePattern (Line 494)

Description: Coordinator for feature toggle management.

Manages feature flags with various rollout strategies.

Methods:

__init__ (Line 501)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_feature (Line 504)

def create_feature(self, name, description, state):
    """Create a new feature."""

Purpose: Create a new feature.

Parameters: – self: Instance reference – name: Identifier or label – description: Parameter for description – state: Current workflow state

enable_feature (Line 513)

def enable_feature(self, name, strategy):
    """Enable a feature."""

Purpose: Enable a feature.

Parameters: – self: Instance reference – name: Identifier or label – strategy: Parameter for strategy

disable_feature (Line 522)

def disable_feature(self, name):
    """Disable a feature."""

Purpose: Disable a feature.

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

is_enabled (Line 526)

def is_enabled(self, name):
    """Check if feature is enabled."""

Purpose: Check if feature is enabled.

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

evaluate (Line 530)

def evaluate(self, name):
    """Evaluate feature toggle."""

Purpose: Evaluate feature toggle.

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

get_feature_status (Line 534)

def get_feature_status(self, name):
    """Get feature status."""

Purpose: Get feature status.

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

list_features (Line 538)

def list_features(self):
    """List all features."""

Purpose: List all features.

Parameters: – self: Instance reference

get_statistics (Line 542)

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

Purpose: Get statistics.

Parameters: – self: Instance reference

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 = ToggleState(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

    """
Pattern 274: Shadow Deployment

Purpose: Parallel production testing with traffic mirroring and result comparison
"""

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
import time


class ShadowMode(Enum):
    """Shadow deployment mode."""
    PASSIVE = "passive"  # Shadow runs but results not used
    COMPARATIVE = "comparative"  # Results compared for analysis
    VALIDATION = "validation"  # Results validated against production


class ComparisonStatus(Enum):
    """Status of result comparison."""
    MATCH = "match"
    MISMATCH = "mismatch"
    ERROR = "error"
    NOT_COMPARED = "not_compared"


@dataclass
class Request:
    """Represents a single request."""
    id: str
    data: Any
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Response:
    """Represents a response from a version."""
    request_id: str
    version: str
    result: Any
    latency_ms: float
    success: bool
    error_message: Optional[str] = None
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Comparison:
    """Comparison between production and shadow responses."""
    request_id: str
    production_response: Response
    shadow_response: Response
    status: ComparisonStatus
    differences: List[str] = field(default_factory=list)
    similarity_score: float = 0.0  # 0-1 scale
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class ShadowMetrics:
    """Metrics for shadow deployment."""
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    comparisons_match: int = 0
    comparisons_mismatch: int = 0
    comparisons_error: int = 0
    avg_latency_diff_ms: float = 0.0
    avg_similarity_score: float = 0.0


class ShadowDeployment:
    """
    Implements shadow deployment pattern.
    
    Features:
    - Parallel execution with production
    - Traffic mirroring to shadow version
    - Result comparison and analysis
    - Performance metrics collection
    - No impact on production traffic
    - Confidence building for new versions
    """
    
    def __init__(
        self,
        production_version: str = "1.0.0",
        shadow_version: Optional[str] = None,
        mode: ShadowMode = ShadowMode.COMPARATIVE,
        traffic_mirror_percentage: float = 100.0
    ):
        self.production_version = production_version
        self.shadow_version = shadow_version
        self.mode = mode
        self.traffic_mirror_percentage = traffic_mirror_percentage
        
        self.is_active = False
        self.production_handler: Optional[Callable] = None
        self.shadow_handler: Optional[Callable] = None
        
        self.requests: Dict[str, Request] = {}
        self.production_responses: Dict[str, Response] = {}
        self.shadow_responses: Dict[str, Response] = {}
        self.comparisons: List[Comparison] = []
        
        self.metrics = ShadowMetrics()
        
        # Statistics
        self.statistics = {
            "total_deployments": 0,
            "active_deployments": 0,
            "total_requests_processed": 0,
            "current_version": production_version,
            "shadow_version": shadow_version
        }
    
    def start_shadow(
        self,
        shadow_version: str,
        production_handler: Callable,
        shadow_handler: Callable
    ) -> Dict[str, Any]:
        """
        Start shadow deployment.
        
        Args:
            shadow_version: Version of shadow deployment
            production_handler: Function to handle production requests
            shadow_handler: Function to handle shadow requests
            
        Returns:
            Status of shadow start
        """
        if self.is_active:
            return {
                "success": False,
                "message": "Shadow deployment already active"
            }
        
        self.shadow_version = shadow_version
        self.production_handler = production_handler
        self.shadow_handler = shadow_handler
        self.is_active = True
        
        # Reset metrics
        self.metrics = ShadowMetrics()
        self.requests = {}
        self.production_responses = {}
        self.shadow_responses = {}
        self.comparisons = []
        
        self.statistics["total_deployments"] += 1
        self.statistics["active_deployments"] += 1
        self.statistics["shadow_version"] = shadow_version
        
        return {
            "success": True,
            "shadow_version": shadow_version,
            "mode": self.mode.value,
            "traffic_mirror_percentage": self.traffic_mirror_percentage,
            "message": f"Shadow deployment started for version {shadow_version}"
        }
    
    def process_request(self, request_id: str, request_data: Any) -> Dict[str, Any]:
        """
        Process request through both production and shadow.
        
        Args:
            request_id: Unique request identifier
            request_data: Request data to process
            
        Returns:
            Production response (shadow runs in parallel)
        """
        if not self.is_active or not self.production_handler or not self.shadow_handler:
            return {
                "success": False,
                "message": "Shadow deployment not properly configured"
            }
        
        # Store request
        request = Request(id=request_id, data=request_data)
        self.requests[request_id] = request
        self.metrics.total_requests += 1
        self.statistics["total_requests_processed"] += 1
        
        # Determine if this request should be mirrored
        import random
        should_mirror = random.random() * 100  Comparison:
        """Compare production and shadow responses."""
        differences = []
        
        # Check success status
        if prod_response.success != shadow_response.success:
            differences.append(f"Success status differs: prod={prod_response.success}, shadow={shadow_response.success}")
        
        # Check results (basic comparison)
        if prod_response.result != shadow_response.result:
            differences.append(f"Results differ: prod={prod_response.result}, shadow={shadow_response.result}")
        
        # Check latency difference
        latency_diff = abs(shadow_response.latency_ms - prod_response.latency_ms)
        if latency_diff > 100:  # More than 100ms difference
            differences.append(f"Significant latency difference: {latency_diff:.2f}ms")
        
        # Calculate similarity score (simple version)
        similarity_score = 1.0
        if differences:
            # Reduce score based on number of differences
            similarity_score = max(0.0, 1.0 - (len(differences) * 0.2))
        
        # Determine status
        if not prod_response.success or not shadow_response.success:
            status = ComparisonStatus.ERROR
        elif not differences:
            status = ComparisonStatus.MATCH
        else:
            status = ComparisonStatus.MISMATCH
        
        return Comparison(
            request_id=prod_response.request_id,
            production_response=prod_response,
            shadow_response=shadow_response,
            status=status,
            differences=differences,
            similarity_score=similarity_score
        )
    
    def _update_comparison_metrics(self, comparison: Comparison):
        """Update metrics based on comparison."""
        if comparison.status == ComparisonStatus.MATCH:
            self.metrics.comparisons_match += 1
        elif comparison.status == ComparisonStatus.MISMATCH:
            self.metrics.comparisons_mismatch += 1
        elif comparison.status == ComparisonStatus.ERROR:
            self.metrics.comparisons_error += 1
        
        # Update average similarity
        total_comparisons = len(self.comparisons)
        if total_comparisons > 0:
            self.metrics.avg_similarity_score = sum(c.similarity_score for c in self.comparisons) / total_comparisons
        
        # Update average latency difference
        if comparison.production_response.success and comparison.shadow_response.success:
            latency_diff = abs(
                comparison.shadow_response.latency_ms - comparison.production_response.latency_ms
            )
            
            comparison_count = self.metrics.comparisons_match + self.metrics.comparisons_mismatch
            if comparison_count > 0:
                current_avg = self.metrics.avg_latency_diff_ms
                self.metrics.avg_latency_diff_ms = (
                    (current_avg * (comparison_count - 1) + latency_diff) / comparison_count
                )
    
    def stop_shadow(self) -> Dict[str, Any]:
        """Stop shadow deployment and return final analysis."""
        if not self.is_active:
            return {
                "success": False,
                "message": "No active shadow deployment"
            }
        
        self.is_active = False
        self.statistics["active_deployments"] -= 1
        
        analysis = self.get_analysis()
        
        return {
            "success": True,
            "message": f"Shadow deployment stopped for version {self.shadow_version}",
            "analysis": analysis
        }
    
    def get_analysis(self) -> Dict[str, Any]:
        """Get detailed analysis of shadow deployment."""
        total_comparisons = len(self.comparisons)
        
        match_rate = 0.0
        if total_comparisons > 0:
            match_rate = (self.metrics.comparisons_match / total_comparisons) * 100
        
        return {
            "production_version": self.production_version,
            "shadow_version": self.shadow_version,
            "mode": self.mode.value,
            "requests": {
                "total": self.metrics.total_requests,
                "successful": self.metrics.successful_requests,
                "failed": self.metrics.failed_requests,
                "success_rate": (self.metrics.successful_requests / self.metrics.total_requests * 100) if self.metrics.total_requests > 0 else 0
            },
            "comparisons": {
                "total": total_comparisons,
                "matches": self.metrics.comparisons_match,
                "mismatches": self.metrics.comparisons_mismatch,
                "errors": self.metrics.comparisons_error,
                "match_rate": match_rate
            },
            "performance": {
                "avg_latency_diff_ms": self.metrics.avg_latency_diff_ms,
                "avg_similarity_score": self.metrics.avg_similarity_score
            },
            "recommendation": self._generate_recommendation(match_rate)
        }
    
    def _generate_recommendation(self, match_rate: float) -> str:
        """Generate deployment recommendation based on analysis."""
        if match_rate >= 95:
            return "Strong confidence: Shadow version performs identically to production. Safe to deploy."
        elif match_rate >= 85:
            return "Good confidence: Minor differences detected. Review mismatches before deploying."
        elif match_rate >= 70:
            return "Moderate confidence: Significant differences detected. Investigate before deploying."
        else:
            return "Low confidence: Major differences detected. Do not deploy without investigation."
    
    def get_status(self) -> Dict[str, Any]:
        """Get current shadow deployment status."""
        return {
            "is_active": self.is_active,
            "production_version": self.production_version,
            "shadow_version": self.shadow_version,
            "mode": self.mode.value,
            "traffic_mirror_percentage": self.traffic_mirror_percentage,
            "metrics": {
                "total_requests": self.metrics.total_requests,
                "successful_requests": self.metrics.successful_requests,
                "failed_requests": self.metrics.failed_requests,
                "comparisons": len(self.comparisons),
                "match_rate": (self.metrics.comparisons_match / len(self.comparisons) * 100) if self.comparisons else 0
            }
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get deployment statistics."""
        return {
            "total_deployments": self.statistics["total_deployments"],
            "active_deployments": self.statistics["active_deployments"],
            "total_requests_processed": self.statistics["total_requests_processed"],
            "current_version": self.statistics["current_version"],
            "shadow_version": self.statistics["shadow_version"]
        }


class ShadowDeploymentPattern:
    """
    Coordinator for shadow deployments.
    
    Manages shadow deployment lifecycle with traffic mirroring and analysis.
    """
    
    def __init__(
        self,
        production_version: str = "1.0.0",
        mode: ShadowMode = ShadowMode.COMPARATIVE,
        traffic_mirror_percentage: float = 100.0
    ):
        self.deployment = ShadowDeployment(
            production_version=production_version,
            mode=mode,
            traffic_mirror_percentage=traffic_mirror_percentage
        )
    
    def start_shadow(
        self,
        shadow_version: str,
        production_handler: Callable,
        shadow_handler: Callable
    ) -> Dict[str, Any]:
        """Start shadow deployment."""
        return self.deployment.start_shadow(shadow_version, production_handler, shadow_handler)
    
    def process_request(self, request_id: str, request_data: Any) -> Dict[str, Any]:
        """Process request through production and shadow."""
        return self.deployment.process_request(request_id, request_data)
    
    def stop_shadow(self) -> Dict[str, Any]:
        """Stop shadow deployment."""
        return self.deployment.stop_shadow()
    
    def get_status(self) -> Dict[str, Any]:
        """Get deployment status."""
        return self.deployment.get_status()
    
    def get_analysis(self) -> Dict[str, Any]:
        """Get deployment analysis."""
        return self.deployment.get_analysis()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get deployment statistics."""
        return self.deployment.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Shadow Deployment Demo ===\n")
    
    # Define production and shadow handlers
    def production_handler(data: Any) -> Any:
        """Simulated production handler."""
        time.sleep(0.01)  # Simulate processing
        return f"Production result for: {data}"
    
    def shadow_handler(data: Any) -> Any:
        """Simulated shadow handler (slightly different)."""
        time.sleep(0.012)  # Slightly slower
        # Simulate 90% match rate
        import random
        if random.random() < 0.9:
            return f"Production result for: {data}"
        else:
            return f"Shadow result for: {data}"  # Different result
    
    # Create shadow deployment
    pattern = ShadowDeploymentPattern(
        production_version="1.0.0",
        mode=ShadowMode.COMPARATIVE,
        traffic_mirror_percentage=100.0
    )
    
    print("1. Starting shadow deployment...")
    result = pattern.start_shadow("2.0.0", production_handler, shadow_handler)
    print(f"  Status: {result['message']}")
    print(f"  Shadow version: {result['shadow_version']}")
    print(f"  Mode: {result['mode']}")
    
    # Process requests
    print("\n2. Processing requests through production and shadow...")
    for i in range(20):
        request_result = pattern.process_request(f"req-{i}", f"data-{i}")
        if i < 3:  # Show first few
            print(f"  Request {i}: success={request_result['success']}, latency={request_result['latency_ms']:.2f}ms, shadow_executed={request_result['shadow_executed']}")
    print(f"  ... processed total of 20 requests")
    
    # Check status
    print("\n3. Current deployment status:")
    status = pattern.get_status()
    print(f"  Active: {status['is_active']}")
    print(f"  Total requests: {status['metrics']['total_requests']}")
    print(f"  Successful: {status['metrics']['successful_requests']}")
    print(f"  Comparisons: {status['metrics']['comparisons']}")
    print(f"  Match rate: {status['metrics']['match_rate']:.1f}%")
    
    # Stop shadow and get analysis
    print("\n4. Stopping shadow deployment...")
    stop_result = pattern.stop_shadow()
    print(f"  Status: {stop_result['message']}")
    
    # Show detailed analysis
    print("\n5. Shadow deployment analysis:")
    analysis = stop_result['analysis']
    print(f"  Production version: {analysis['production_version']}")
    print(f"  Shadow version: {analysis['shadow_version']}")
    print(f"\n  Requests:")
    print(f"    Total: {analysis['requests']['total']}")
    print(f"    Success rate: {analysis['requests']['success_rate']:.1f}%")
    print(f"\n  Comparisons:")
    print(f"    Total: {analysis['comparisons']['total']}")
    print(f"    Matches: {analysis['comparisons']['matches']}")
    print(f"    Mismatches: {analysis['comparisons']['mismatches']}")
    print(f"    Match rate: {analysis['comparisons']['match_rate']:.1f}%")
    print(f"\n  Performance:")
    print(f"    Avg latency diff: {analysis['performance']['avg_latency_diff_ms']:.2f}ms")
    print(f"    Avg similarity: {analysis['performance']['avg_similarity_score']:.2f}")
    print(f"\n  Recommendation:")
    print(f"    {analysis['recommendation']}")
    
    # Statistics
    print("\n6. Deployment statistics:")
    stats = pattern.get_statistics()
    print(f"  Total deployments: {stats['total_deployments']}")
    print(f"  Total requests processed: {stats['total_requests_processed']}")
    
    print("\n✓ Comprehensive shadow deployment with traffic mirroring complete!")

    

Agentic Architecture Pattern: Shadow Deployment

Pattern 274: 274 Shadow Deployment – Line-by-Line Explanation

Overview

Pattern 274: Shadow Deployment

Purpose: Parallel production testing with traffic mirroring and result comparison

Code Structure Analysis

File Information

• Pattern Number: 274
• Pattern Name: 274 Shadow Deployment
• File Path: patterns\deployment\274_shadow_deployment.py
• Total Lines: 556

Detailed Code Explanation

Module Docstring

"""
Pattern 274: Shadow Deployment

Purpose: Parallel production testing with traffic mirroring and result comparison
"""

Explanation: This module docstring describes the purpose and key features of the 274 Shadow Deployment 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
import time
        import random
        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

Enumerations

ShadowMode (Line 15)

class ShadowMode(Enum):
    PASSIVE
    COMPARATIVE
    VALIDATION

Purpose: Shadow deployment mode.

Values: – PASSIVE: Represents passive – COMPARATIVE: Represents comparative – VALIDATION: Represents validation

ComparisonStatus (Line 22)

class ComparisonStatus(Enum):
    MATCH
    MISMATCH
    ERROR
    NOT_COMPARED

Purpose: Status of result comparison.

Values: – MATCH: Represents match – MISMATCH: Represents mismatch – ERROR: Error severity level – NOT_COMPARED: Represents not compared

Classes

ShadowMode (Line 15)

Description: Shadow deployment mode.

ComparisonStatus (Line 22)

Description: Status of result comparison.

Request (Line 31)

Description: Represents a single request.

Response (Line 39)

Description: Represents a response from a version.

Comparison (Line 51)

Description: Comparison between production and shadow responses.

ShadowMetrics (Line 63)

Description: Metrics for shadow deployment.

ShadowDeployment (Line 75)

Description: Implements shadow deployment pattern.

Features: – Parallel execution with production – Traffic mirroring to shadow version – Result comparison and analysis – Performance metrics collection – No impact on production traffic – Confidence building for new versions

Methods:

__init__ (Line 88)

def __init__(self, production_version, shadow_version, mode, traffic_mirror_percentage):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – production_version: Parameter for production_version – shadow_version: Parameter for shadow_version – mode: Parameter for mode – traffic_mirror_percentage: Parameter for traffic_mirror_percentage

start_shadow (Line 120)

def start_shadow(self, shadow_version, production_handler, shadow_handler):
    """Start shadow deployment.

Args:
    shadow_version: Version of shadow deployment
    production_handler: Function to handle production requests
    shadow_handler: Function to handle shadow requests

Returns:
    Status of shadow start"""

Purpose: Start shadow deployment.

Args: shadow_version: Version of shadow deployment production_handler: Function to handle production requests shadow_handler: Function to handle shadow requests

Returns: Status of shadow start

Parameters: – self: Instance reference – shadow_version: Parameter for shadow_version – production_handler: Parameter for production_handler – shadow_handler: Parameter for shadow_handler

process_request (Line 167)

def process_request(self, request_id, request_data):
    """Process request through both production and shadow.

Args:
    request_id: Unique request identifier
    request_data: Request data to process

Returns:
    Production response (shadow runs in parallel)"""

Purpose: Process request through both production and shadow.

Args: request_id: Unique request identifier request_data: Request data to process

Returns: Production response (shadow runs in parallel)

Parameters: – self: Instance reference – request_id: Parameter for request_id – request_data: Parameter for request_data

_compare_responses (Line 265)

def _compare_responses(self, prod_response, shadow_response):
    """Compare production and shadow responses."""

Purpose: Compare production and shadow responses.

Parameters: – self: Instance reference – prod_response: Parameter for prod_response – shadow_response: Parameter for shadow_response

_update_comparison_metrics (Line 309)

def _update_comparison_metrics(self, comparison):
    """Update metrics based on comparison."""

Purpose: Update metrics based on comparison.

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

stop_shadow (Line 336)

def stop_shadow(self):
    """Stop shadow deployment and return final analysis."""

Purpose: Stop shadow deployment and return final analysis.

Parameters: – self: Instance reference

get_analysis (Line 355)

def get_analysis(self):
    """Get detailed analysis of shadow deployment."""

Purpose: Get detailed analysis of shadow deployment.

Parameters: – self: Instance reference

_generate_recommendation (Line 387)

def _generate_recommendation(self, match_rate):
    """Generate deployment recommendation based on analysis."""

Purpose: Generate deployment recommendation based on analysis.

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

get_status (Line 398)

def get_status(self):
    """Get current shadow deployment status."""

Purpose: Get current shadow deployment status.

Parameters: – self: Instance reference

get_statistics (Line 415)

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

Purpose: Get deployment statistics.

Parameters: – self: Instance reference

ShadowDeploymentPattern (Line 426)

Description: Coordinator for shadow deployments.

Manages shadow deployment lifecycle with traffic mirroring and analysis.

Methods:

__init__ (Line 433)

def __init__(self, production_version, mode, traffic_mirror_percentage):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – production_version: Parameter for production_version – mode: Parameter for mode – traffic_mirror_percentage: Parameter for traffic_mirror_percentage

start_shadow (Line 445)

def start_shadow(self, shadow_version, production_handler, shadow_handler):
    """Start shadow deployment."""

Purpose: Start shadow deployment.

Parameters: – self: Instance reference – shadow_version: Parameter for shadow_version – production_handler: Parameter for production_handler – shadow_handler: Parameter for shadow_handler

process_request (Line 454)

def process_request(self, request_id, request_data):
    """Process request through production and shadow."""

Purpose: Process request through production and shadow.

Parameters: – self: Instance reference – request_id: Parameter for request_id – request_data: Parameter for request_data

stop_shadow (Line 458)

def stop_shadow(self):
    """Stop shadow deployment."""

Purpose: Stop shadow deployment.

Parameters: – self: Instance reference

get_status (Line 462)

def get_status(self):
    """Get deployment status."""

Purpose: Get deployment status.

Parameters: – self: Instance reference

get_analysis (Line 466)

def get_analysis(self):
    """Get deployment analysis."""

Purpose: Get deployment analysis.

Parameters: – self: Instance reference

get_statistics (Line 470)

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

Purpose: Get deployment statistics.

Parameters: – self: Instance reference

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 = ShadowMode(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

    """
Pattern 273: Rolling Update

Purpose: Sequential instance updates with health checking and rollback capability
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict


class UpdateStatus(Enum):
    """Status of instance update."""
    PENDING = "pending"
    UPDATING = "updating"
    UPDATED = "updated"
    FAILED = "failed"
    ROLLED_BACK = "rolled_back"


class DeploymentPhase(Enum):
    """Phase of rolling deployment."""
    NOT_STARTED = "not_started"
    IN_PROGRESS = "in_progress"
    PAUSED = "paused"
    COMPLETED = "completed"
    ROLLING_BACK = "rolling_back"
    FAILED = "failed"


@dataclass
class Instance:
    """Represents a single instance in the deployment."""
    id: str
    version: str
    status: UpdateStatus
    health_score: float = 1.0
    updated_at: Optional[datetime] = None
    error_message: Optional[str] = None


@dataclass
class UpdateBatch:
    """Represents a batch of instances being updated."""
    batch_number: int
    instance_ids: List[str]
    started_at: Optional[datetime] = None
    completed_at: Optional[datetime] = None
    success: bool = False


@dataclass
class DeploymentRecord:
    """Record of deployment operation."""
    operation: str
    batch_number: Optional[int]
    instances_affected: int
    success: bool
    message: str
    timestamp: datetime


class RollingUpdate:
    """
    Implements rolling update deployment strategy.
    
    Features:
    - Sequential instance-by-instance or batch updates
    - Configurable batch size
    - Health checking between batches
    - Pause/resume capability
    - Automatic rollback on failure
    - Update progress tracking
    """
    
    def __init__(
        self,
        total_instances: int = 10,
        batch_size: int = 2,
        min_health_score: float = 0.85,
        wait_between_batches: int = 5
    ):
        self.total_instances = total_instances
        self.batch_size = batch_size
        self.min_health_score = min_health_score
        self.wait_between_batches = wait_between_batches
        
        # Initialize instances with current version
        self.instances: Dict[str, Instance] = {
            f"instance-{i}": Instance(
                id=f"instance-{i}",
                version="1.0.0",
                status=UpdateStatus.PENDING,
                health_score=1.0
            )
            for i in range(total_instances)
        }
        
        self.target_version: Optional[str] = None
        self.phase = DeploymentPhase.NOT_STARTED
        self.current_batch: Optional[UpdateBatch] = None
        self.batches: List[UpdateBatch] = []
        self.deployment_history: List[DeploymentRecord] = []
        self.paused = False
        
        # Statistics
        self.statistics = {
            "total_deployments": 0,
            "successful_deployments": 0,
            "failed_deployments": 0,
            "rollbacks": 0,
            "total_instances_updated": 0,
            "current_version": "1.0.0"
        }
    
    def start_update(self, new_version: str) -> Dict[str, Any]:
        """
        Start rolling update to new version.
        
        Args:
            new_version: Target version to deploy
            
        Returns:
            Status of update initiation
        """
        if self.phase in [DeploymentPhase.IN_PROGRESS, DeploymentPhase.ROLLING_BACK]:
            return {
                "success": False,
                "message": f"Cannot start update: deployment is {self.phase.value}"
            }
        
        self.target_version = new_version
        self.phase = DeploymentPhase.IN_PROGRESS
        self.paused = False
        self.batches = []
        self.current_batch = None
        
        # Reset all instances to pending
        for instance in self.instances.values():
            instance.status = UpdateStatus.PENDING
        
        # Create batches
        instance_ids = list(self.instances.keys())
        for i in range(0, len(instance_ids), self.batch_size):
            batch = UpdateBatch(
                batch_number=len(self.batches) + 1,
                instance_ids=instance_ids[i:i + self.batch_size]
            )
            self.batches.append(batch)
        
        self.statistics["total_deployments"] += 1
        
        record = DeploymentRecord(
            operation="start_update",
            batch_number=None,
            instances_affected=0,
            success=True,
            message=f"Started rolling update to version {new_version} with {len(self.batches)} batches",
            timestamp=datetime.now()
        )
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "target_version": new_version,
            "total_batches": len(self.batches),
            "batch_size": self.batch_size,
            "message": f"Rolling update initiated to version {new_version}"
        }
    
    def update_next_batch(self) -> Dict[str, Any]:
        """
        Update the next batch of instances.
        
        Returns:
            Status of batch update
        """
        if self.phase != DeploymentPhase.IN_PROGRESS:
            return {
                "success": False,
                "message": f"Cannot update: deployment is {self.phase.value}"
            }
        
        if self.paused:
            return {
                "success": False,
                "message": "Deployment is paused"
            }
        
        # Find next pending batch
        next_batch = None
        for batch in self.batches:
            if batch.started_at is None:
                next_batch = batch
                break
        
        if not next_batch:
            # All batches completed
            self.phase = DeploymentPhase.COMPLETED
            self.statistics["successful_deployments"] += 1
            self.statistics["current_version"] = self.target_version
            
            record = DeploymentRecord(
                operation="complete_update",
                batch_number=None,
                instances_affected=self.total_instances,
                success=True,
                message=f"Rolling update to version {self.target_version} completed successfully",
                timestamp=datetime.now()
            )
            self.deployment_history.append(record)
            
            return {
                "success": True,
                "message": "All batches completed successfully",
                "final_version": self.target_version
            }
        
        # Update batch
        self.current_batch = next_batch
        next_batch.started_at = datetime.now()
        
        # Update instances in batch
        success_count = 0
        for instance_id in next_batch.instance_ids:
            instance = self.instances[instance_id]
            instance.status = UpdateStatus.UPDATING
            
            # Simulate update (90% success rate per instance)
            import random
            if random.random()  Dict[str, Any]:
        """Check health of updated instances in batch."""
        unhealthy_instances = []
        failed_instances = []
        
        for instance_id in batch.instance_ids:
            instance = self.instances[instance_id]
            if instance.status == UpdateStatus.FAILED:
                failed_instances.append(instance_id)
            elif instance.health_score < self.min_health_score:
                unhealthy_instances.append(instance_id)
        
        healthy = len(failed_instances) == 0 and len(unhealthy_instances) == 0
        
        message = "All instances healthy"
        if failed_instances:
            message = f"Failed instances: {', '.join(failed_instances)}"
        elif unhealthy_instances:
            message = f"Unhealthy instances (health  Dict[str, Any]:
        """Pause the rolling update."""
        if self.phase != DeploymentPhase.IN_PROGRESS:
            return {
                "success": False,
                "message": f"Cannot pause: deployment is {self.phase.value}"
            }
        
        self.paused = True
        self.phase = DeploymentPhase.PAUSED
        
        record = DeploymentRecord(
            operation="pause_update",
            batch_number=self.current_batch.batch_number if self.current_batch else None,
            instances_affected=0,
            success=True,
            message="Rolling update paused",
            timestamp=datetime.now()
        )
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "message": "Rolling update paused",
            "current_batch": self.current_batch.batch_number if self.current_batch else None
        }
    
    def resume_update(self) -> Dict[str, Any]:
        """Resume the rolling update."""
        if self.phase != DeploymentPhase.PAUSED:
            return {
                "success": False,
                "message": f"Cannot resume: deployment is {self.phase.value}"
            }
        
        self.paused = False
        self.phase = DeploymentPhase.IN_PROGRESS
        
        record = DeploymentRecord(
            operation="resume_update",
            batch_number=self.current_batch.batch_number if self.current_batch else None,
            instances_affected=0,
            success=True,
            message="Rolling update resumed",
            timestamp=datetime.now()
        )
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "message": "Rolling update resumed"
        }
    
    def rollback(self) -> Dict[str, Any]:
        """
        Rollback all updated instances to previous version.
        
        Returns:
            Status of rollback
        """
        self.phase = DeploymentPhase.ROLLING_BACK
        
        rollback_count = 0
        previous_version = self.statistics["current_version"]
        
        for instance in self.instances.values():
            if instance.status in [UpdateStatus.UPDATED, UpdateStatus.FAILED]:
                instance.version = previous_version
                instance.status = UpdateStatus.ROLLED_BACK
                instance.health_score = 1.0
                instance.updated_at = datetime.now()
                instance.error_message = None
                rollback_count += 1
        
        self.phase = DeploymentPhase.NOT_STARTED
        self.statistics["rollbacks"] += 1
        self.statistics["failed_deployments"] += 1
        
        record = DeploymentRecord(
            operation="rollback",
            batch_number=None,
            instances_affected=rollback_count,
            success=True,
            message=f"Rolled back {rollback_count} instances to version {previous_version}",
            timestamp=datetime.now()
        )
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "instances_rolled_back": rollback_count,
            "version": previous_version,
            "message": f"Successfully rolled back to version {previous_version}"
        }
    
    def get_status(self) -> Dict[str, Any]:
        """Get current deployment status."""
        updated_count = sum(1 for i in self.instances.values() if i.status == UpdateStatus.UPDATED)
        failed_count = sum(1 for i in self.instances.values() if i.status == UpdateStatus.FAILED)
        pending_count = sum(1 for i in self.instances.values() if i.status == UpdateStatus.PENDING)
        
        completed_batches = sum(1 for b in self.batches if b.completed_at is not None)
        
        return {
            "phase": self.phase.value,
            "paused": self.paused,
            "target_version": self.target_version,
            "current_version": self.statistics["current_version"],
            "progress": {
                "total_instances": self.total_instances,
                "updated": updated_count,
                "failed": failed_count,
                "pending": pending_count,
                "percentage": (updated_count / self.total_instances) * 100 if self.total_instances > 0 else 0
            },
            "batches": {
                "total": len(self.batches),
                "completed": completed_batches,
                "remaining": len(self.batches) - completed_batches
            },
            "current_batch": self.current_batch.batch_number if self.current_batch else None
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get deployment statistics."""
        return {
            "total_deployments": self.statistics["total_deployments"],
            "successful_deployments": self.statistics["successful_deployments"],
            "failed_deployments": self.statistics["failed_deployments"],
            "rollbacks": self.statistics["rollbacks"],
            "total_instances_updated": self.statistics["total_instances_updated"],
            "current_version": self.statistics["current_version"],
            "total_instances": self.total_instances,
            "batch_size": self.batch_size
        }


class RollingUpdatePattern:
    """
    Coordinator for rolling update deployments.
    
    Manages rolling update lifecycle with health checking and rollback.
    """
    
    def __init__(
        self,
        total_instances: int = 10,
        batch_size: int = 2,
        min_health_score: float = 0.85
    ):
        self.deployment = RollingUpdate(
            total_instances=total_instances,
            batch_size=batch_size,
            min_health_score=min_health_score
        )
    
    def deploy(self, new_version: str, auto_progress: bool = False) -> Dict[str, Any]:
        """
        Execute rolling update deployment.
        
        Args:
            new_version: Target version
            auto_progress: If True, automatically update all batches
            
        Returns:
            Deployment results
        """
        result = self.deployment.start_update(new_version)
        if not result["success"]:
            return result
        
        if auto_progress:
            results = [result]
            while True:
                batch_result = self.deployment.update_next_batch()
                results.append(batch_result)
                
                if not batch_result["success"]:
                    # Health check failed, initiate rollback
                    rollback_result = self.deployment.rollback()
                    results.append(rollback_result)
                    break
                
                if self.deployment.phase == DeploymentPhase.COMPLETED:
                    break
            
            return {
                "success": self.deployment.phase == DeploymentPhase.COMPLETED,
                "final_status": self.deployment.get_status(),
                "batch_results": results
            }
        
        return result
    
    def get_status(self) -> Dict[str, Any]:
        """Get deployment status."""
        return self.deployment.get_status()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get deployment statistics."""
        return self.deployment.get_statistics()


# Example usage
if __name__ == "__main__":
    print("=== Rolling Update Deployment Demo ===\n")
    
    # Create rolling update deployment with 10 instances, batch size 2
    pattern = RollingUpdatePattern(total_instances=10, batch_size=2, min_health_score=0.85)
    
    print("Initial status:")
    status = pattern.get_status()
    print(f"  Phase: {status['phase']}")
    print(f"  Current version: {status['current_version']}")
    print(f"  Total instances: {status['progress']['total_instances']}")
    
    # Start rolling update
    print("\n1. Starting rolling update to v2.0.0...")
    result = pattern.deployment.start_update("2.0.0")
    print(f"  Status: {result['message']}")
    print(f"  Total batches: {result['total_batches']}")
    print(f"  Batch size: {result['batch_size']}")
    
    # Update batches one by one
    print("\n2. Updating batches sequentially...")
    batch_num = 1
    while pattern.deployment.phase == DeploymentPhase.IN_PROGRESS:
        print(f"\n  Batch {batch_num}:")
        batch_result = pattern.deployment.update_next_batch()
        
        if not batch_result["success"]:
            print(f"    ❌ Failed: {batch_result['message']}")
            print(f"    Recommendation: {batch_result.get('recommendation', 'N/A')}")
            
            # Rollback
            print(f"\n3. Rolling back deployment...")
            rollback_result = pattern.deployment.rollback()
            print(f"  {rollback_result['message']}")
            print(f"  Instances rolled back: {rollback_result['instances_rolled_back']}")
            break
        
        print(f"    ✓ Success: Updated {batch_result['instances_updated']}/{batch_result['total_instances']} instances")
        print(f"    Health check: {batch_result['health_check']['message']}")
        print(f"    Remaining batches: {batch_result['remaining_batches']}")
        
        batch_num += 1
    
    # Final status
    print("\n4. Final deployment status:")
    status = pattern.get_status()
    print(f"  Phase: {status['phase']}")
    print(f"  Current version: {status['current_version']}")
    print(f"  Progress: {status['progress']['updated']}/{status['progress']['total_instances']} instances ({status['progress']['percentage']:.1f}%)")
    print(f"  Batches completed: {status['batches']['completed']}/{status['batches']['total']}")
    
    # Statistics
    print("\n5. Deployment statistics:")
    stats = pattern.get_statistics()
    print(f"  Total deployments: {stats['total_deployments']}")
    print(f"  Successful: {stats['successful_deployments']}")
    print(f"  Failed: {stats['failed_deployments']}")
    print(f"  Rollbacks: {stats['rollbacks']}")
    print(f"  Total instances updated: {stats['total_instances_updated']}")
    
    print("\n✓ Comprehensive rolling update with batch processing complete!")

    

Agentic Architecture Pattern: Rolling Update

Pattern 273: 273 Rolling Update – Line-by-Line Explanation

Overview

Pattern 273: Rolling Update

Purpose: Sequential instance updates with health checking and rollback capability

Code Structure Analysis

File Information

• Pattern Number: 273
• Pattern Name: 273 Rolling Update
• File Path: patterns\deployment\273_rolling_update.py
• Total Lines: 585

Detailed Code Explanation

Module Docstring

"""
Pattern 273: Rolling Update

Purpose: Sequential instance updates with health checking and rollback capability
"""

Explanation: This module docstring describes the purpose and key features of the 273 Rolling Update pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
            import random  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: 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

Enumerations

UpdateStatus (Line 14)

class UpdateStatus(Enum):
    PENDING
    UPDATING
    UPDATED
    FAILED
    ROLLED_BACK

Purpose: Status of instance update.

Values: – PENDING: Initial state, not yet processed – UPDATING: Represents updating – UPDATED: Represents updated – FAILED: Execution failed – ROLLED_BACK: Represents rolled back

DeploymentPhase (Line 23)

class DeploymentPhase(Enum):
    NOT_STARTED
    IN_PROGRESS
    PAUSED
    COMPLETED
    ROLLING_BACK
    FAILED

Purpose: Phase of rolling deployment.

Values: – NOT_STARTED: Represents not started – IN_PROGRESS: Represents in progress – PAUSED: Represents paused – COMPLETED: Successfully finished – ROLLING_BACK: Represents rolling back – FAILED: Execution failed

Classes

UpdateStatus (Line 14)

Description: Status of instance update.

DeploymentPhase (Line 23)

Description: Phase of rolling deployment.

Instance (Line 34)

Description: Represents a single instance in the deployment.

UpdateBatch (Line 45)

Description: Represents a batch of instances being updated.

DeploymentRecord (Line 55)

Description: Record of deployment operation.

RollingUpdate (Line 65)

Description: Implements rolling update deployment strategy.

Features: – Sequential instance-by-instance or batch updates – Configurable batch size – Health checking between batches – Pause/resume capability – Automatic rollback on failure – Update progress tracking

Methods:

__init__ (Line 78)

def __init__(self, total_instances, batch_size, min_health_score, wait_between_batches):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – total_instances: Parameter for total_instances – batch_size: Parameter for batch_size – min_health_score: Parameter for min_health_score – wait_between_batches: Parameter for wait_between_batches

start_update (Line 118)

def start_update(self, new_version):
    """Start rolling update to new version.

Args:
    new_version: Target version to deploy

Returns:
    Status of update initiation"""

Purpose: Start rolling update to new version.

Args: new_version: Target version to deploy

Returns: Status of update initiation

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

update_next_batch (Line 173)

def update_next_batch(self):
    """Update the next batch of instances.

Returns:
    Status of batch update"""

Purpose: Update the next batch of instances.

Returns: Status of batch update

Parameters: – self: Instance reference

_check_batch_health (Line 293)

def _check_batch_health(self, batch):
    """Check health of updated instances in batch."""

Purpose: Check health of updated instances in batch.

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

pause_update (Line 320)

def pause_update(self):
    """Pause the rolling update."""

Purpose: Pause the rolling update.

Parameters: – self: Instance reference

resume_update (Line 347)

def resume_update(self):
    """Resume the rolling update."""

Purpose: Resume the rolling update.

Parameters: – self: Instance reference

rollback (Line 373)

def rollback(self):
    """Rollback all updated instances to previous version.

Returns:
    Status of rollback"""

Purpose: Rollback all updated instances to previous version.

Returns: Status of rollback

Parameters: – self: Instance reference

get_status (Line 415)

def get_status(self):
    """Get current deployment status."""

Purpose: Get current deployment status.

Parameters: – self: Instance reference

get_statistics (Line 443)

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

Purpose: Get deployment statistics.

Parameters: – self: Instance reference

RollingUpdatePattern (Line 457)

Description: Coordinator for rolling update deployments.

Manages rolling update lifecycle with health checking and rollback.

Methods:

__init__ (Line 464)

def __init__(self, total_instances, batch_size, min_health_score):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – total_instances: Parameter for total_instances – batch_size: Parameter for batch_size – min_health_score: Parameter for min_health_score

deploy (Line 476)

def deploy(self, new_version, auto_progress):
    """Execute rolling update deployment.

Args:
    new_version: Target version
    auto_progress: If True, automatically update all batches

Returns:
    Deployment results"""

Purpose: Execute rolling update deployment.

Args: new_version: Target version auto_progress: If True, automatically update all batches

Returns: Deployment results

Parameters: – self: Instance reference – new_version: Parameter for new_version – auto_progress: Parameter for auto_progress

get_status (Line 514)

def get_status(self):
    """Get deployment status."""

Purpose: Get deployment status.

Parameters: – self: Instance reference

get_statistics (Line 518)

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

Purpose: Get deployment statistics.

Parameters: – self: Instance reference

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 = UpdateStatus(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

    """
Pattern 272: Canary Deployment

Purpose: Gradual rollout with traffic routing and automated monitoring.
Tests new versions with small user percentage before full deployment.
"""

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


class DeploymentStage(Enum):
    """Canary deployment stages."""
    INITIAL = "initial"  # 5% traffic
    EXPANDED = "expanded"  # 25% traffic
    MAJORITY = "majority"  # 50% traffic
    COMPLETE = "complete"  # 100% traffic
    FAILED = "failed"  # Deployment failed


class HealthStatus(Enum):
    """Health status."""
    HEALTHY = "healthy"
    DEGRADED = "degraded"
    UNHEALTHY = "unhealthy"


@dataclass
class DeploymentVersion:
    """Version deployment info."""
    version: str
    stage: DeploymentStage
    traffic_percentage: float
    error_rate: float = 0.0
    latency_ms: float = 0.0
    health_status: HealthStatus = HealthStatus.HEALTHY
    deployment_time: datetime = field(default_factory=datetime.now)


class CanaryDeployment:
    """
    Canary Deployment - Gradual traffic shift strategy.
    
    Features:
    - Gradual traffic increase
    - Automated health monitoring
    - Error rate tracking
    - Latency monitoring
    - Automatic rollback on failure
    - Stage progression control
    - Metrics comparison
    - Risk mitigation
    """
    
    def __init__(self, name: str):
        self.name = name
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.3)
        
        # Versions
        self.stable_version: DeploymentVersion = DeploymentVersion(
            version="1.0.0",
            stage=DeploymentStage.COMPLETE,
            traffic_percentage=100.0
        )
        self.canary_version: Optional[DeploymentVersion] = None
        
        # Thresholds
        self.max_error_rate = 0.05  # 5%
        self.max_latency_increase = 1.5  # 50% increase
        
        # History
        self.deployment_history: List[Dict[str, Any]] = []
        
        # Statistics
        self.statistics: Dict[str, Any] = {
            "total_deployments": 0,
            "successful_deployments": 0,
            "failed_deployments": 0,
            "rollbacks": 0,
            "current_version": "1.0.0"
        }
    
    def start_canary(self, new_version: str) -> Dict[str, Any]:
        """Start canary deployment with initial traffic."""
        
        if self.canary_version:
            return {
                "success": False,
                "message": "Canary deployment already in progress"
            }
        
        # Create canary with 5% traffic
        self.canary_version = DeploymentVersion(
            version=new_version,
            stage=DeploymentStage.INITIAL,
            traffic_percentage=5.0
        )
        
        # Adjust stable version traffic
        self.stable_version.traffic_percentage = 95.0
        
        self.statistics["total_deployments"] += 1
        
        record = {
            "operation": "start_canary",
            "version": new_version,
            "stage": DeploymentStage.INITIAL.value,
            "traffic": 5.0,
            "timestamp": datetime.now()
        }
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "version": new_version,
            "stage": DeploymentStage.INITIAL.value,
            "traffic_percentage": 5.0,
            "message": f"Started canary deployment v{new_version} at 5% traffic"
        }
    
    def promote_canary(self) -> Dict[str, Any]:
        """Promote canary to next stage if healthy."""
        
        if not self.canary_version:
            return {
                "success": False,
                "message": "No active canary deployment"
            }
        
        # Check health
        health_check = self._check_canary_health()
        if not health_check["healthy"]:
            return {
                "success": False,
                "message": f"Canary unhealthy: {health_check['reason']}",
                "should_rollback": True
            }
        
        # Promote to next stage
        current_stage = self.canary_version.stage
        
        if current_stage == DeploymentStage.INITIAL:
            self.canary_version.stage = DeploymentStage.EXPANDED
            self.canary_version.traffic_percentage = 25.0
            self.stable_version.traffic_percentage = 75.0
            next_stage = "expanded (25%)"
        
        elif current_stage == DeploymentStage.EXPANDED:
            self.canary_version.stage = DeploymentStage.MAJORITY
            self.canary_version.traffic_percentage = 50.0
            self.stable_version.traffic_percentage = 50.0
            next_stage = "majority (50%)"
        
        elif current_stage == DeploymentStage.MAJORITY:
            self.canary_version.stage = DeploymentStage.COMPLETE
            self.canary_version.traffic_percentage = 100.0
            self.stable_version.traffic_percentage = 0.0
            next_stage = "complete (100%)"
            
            # Complete deployment
            self.stable_version = self.canary_version
            self.canary_version = None
            self.statistics["successful_deployments"] += 1
            self.statistics["current_version"] = self.stable_version.version
        
        else:
            return {
                "success": False,
                "message": "Canary already at final stage"
            }
        
        record = {
            "operation": "promote_canary",
            "version": self.canary_version.version if self.canary_version else self.stable_version.version,
            "stage": next_stage,
            "timestamp": datetime.now()
        }
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "stage": next_stage,
            "message": f"Promoted canary to {next_stage}"
        }
    
    def rollback_canary(self) -> Dict[str, Any]:
        """Rollback canary deployment."""
        
        if not self.canary_version:
            return {
                "success": False,
                "message": "No active canary to rollback"
            }
        
        canary_version = self.canary_version.version
        
        # Remove canary
        self.canary_version = None
        self.stable_version.traffic_percentage = 100.0
        
        self.statistics["rollbacks"] += 1
        self.statistics["failed_deployments"] += 1
        
        record = {
            "operation": "rollback_canary",
            "version": canary_version,
            "reason": "Health check failed",
            "timestamp": datetime.now()
        }
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "rolled_back_version": canary_version,
            "current_version": self.stable_version.version,
            "message": f"Rolled back canary v{canary_version}"
        }
    
    def _check_canary_health(self) -> Dict[str, Any]:
        """Check canary health metrics."""
        
        if not self.canary_version:
            return {"healthy": False, "reason": "No canary deployed"}
        
        # Check error rate
        if self.canary_version.error_rate > self.max_error_rate:
            return {
                "healthy": False,
                "reason": f"Error rate too high: {self.canary_version.error_rate:.2%}"
            }
        
        # Check latency
        if self.canary_version.latency_ms > self.stable_version.latency_ms * self.max_latency_increase:
            return {
                "healthy": False,
                "reason": f"Latency increased by {(self.canary_version.latency_ms / self.stable_version.latency_ms - 1):.1%}"
            }
        
        # Check overall health
        if self.canary_version.health_status != HealthStatus.HEALTHY:
            return {
                "healthy": False,
                "reason": f"Health status: {self.canary_version.health_status.value}"
            }
        
        return {"healthy": True}
    
    def update_metrics(
        self,
        version: str,
        error_rate: float,
        latency_ms: float,
        health_status: HealthStatus = HealthStatus.HEALTHY
    ):
        """Update metrics for version."""
        
        if self.canary_version and self.canary_version.version == version:
            self.canary_version.error_rate = error_rate
            self.canary_version.latency_ms = latency_ms
            self.canary_version.health_status = health_status
        elif self.stable_version.version == version:
            self.stable_version.error_rate = error_rate
            self.stable_version.latency_ms = latency_ms
            self.stable_version.health_status = health_status
    
    def get_status(self) -> Dict[str, Any]:
        """Get deployment status."""
        
        status = {
            "stable_version": {
                "version": self.stable_version.version,
                "traffic_percentage": self.stable_version.traffic_percentage,
                "error_rate": self.stable_version.error_rate,
                "latency_ms": self.stable_version.latency_ms,
                "health": self.stable_version.health_status.value
            }
        }
        
        if self.canary_version:
            status["canary_version"] = {
                "version": self.canary_version.version,
                "stage": self.canary_version.stage.value,
                "traffic_percentage": self.canary_version.traffic_percentage,
                "error_rate": self.canary_version.error_rate,
                "latency_ms": self.canary_version.latency_ms,
                "health": self.canary_version.health_status.value
            }
        
        return status
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get deployment statistics."""
        return {
            "name": self.name,
            **self.statistics
        }


class CanaryPattern:
    """Canary Deployment Pattern - Manage canary deployments."""
    
    def __init__(self):
        self.deployments: Dict[str, CanaryDeployment] = {}
    
    def create_deployment(self, name: str) -> CanaryDeployment:
        """Create canary deployment manager."""
        deployment = CanaryDeployment(name)
        self.deployments[name] = deployment
        print(f"   Created canary deployment: {name}")
        return deployment


# Example usage
if __name__ == "__main__":
    print("=== Canary Deployment Pattern Demo ===\n")
    
    pattern = CanaryPattern()
    deployment = pattern.create_deployment("ProductionCanary")
    
    print("\n1. Initial Status:")
    status = deployment.get_status()
    print(f"Stable: v{status['stable_version']['version']} ({status['stable_version']['traffic_percentage']}% traffic)")
    
    print("\n" + "="*60)
    print("\n2. Starting Canary Deployment (v2.0.0)...")
    result = deployment.start_canary("2.0.0")
    print(f"Success: {result['success']}")
    print(f"Message: {result['message']}")
    
    print("\n3. Status After Canary Start:")
    status = deployment.get_status()
    print(f"Stable: v{status['stable_version']['version']} ({status['stable_version']['traffic_percentage']}%)")
    print(f"Canary: v{status['canary_version']['version']} ({status['canary_version']['traffic_percentage']}%) - {status['canary_version']['stage']}")
    
    # Simulate healthy metrics
    deployment.update_metrics("2.0.0", error_rate=0.01, latency_ms=50)
    
    print("\n" + "="*60)
    print("\n4. Promoting Canary to 25%...")
    result = deployment.promote_canary()
    print(f"Success: {result['success']}")
    print(f"Stage: {result['stage']}")
    
    status = deployment.get_status()
    print(f"Canary Traffic: {status['canary_version']['traffic_percentage']}%")
    
    print("\n" + "="*60)
    print("\n5. Promoting to 50%...")
    result = deployment.promote_canary()
    print(f"Stage: {result['stage']}")
    
    print("\n" + "="*60)
    print("\n6. Completing Deployment (100%)...")
    result = deployment.promote_canary()
    print(f"Success: {result['success']}")
    print(f"Message: {result['message']}")
    
    print("\n7. Final Status:")
    status = deployment.get_status()
    print(f"Stable: v{status['stable_version']['version']} ({status['stable_version']['traffic_percentage']}%)")
    print(f"Canary Active: {'canary_version' in status}")
    
    print("\n8. Statistics:")
    stats = deployment.get_statistics()
    for key, value in stats.items():
        print(f"   {key}: {value}")
    
    print("\n✓ Comprehensive canary deployment with gradual rollout complete!")

    

Agentic Architecture Pattern: Canary Deployment

Pattern 272: 272 Canary Deployment – Line-by-Line Explanation

Overview

Pattern 272: Canary Deployment

Purpose: Gradual rollout with traffic routing and automated monitoring. Tests new versions with small user percentage before full deployment.

Code Structure Analysis

File Information

• Pattern Number: 272
• Pattern Name: 272 Canary Deployment
• File Path: patterns\deployment\272_canary_deployment.py
• Total Lines: 373

Detailed Code Explanation

Module Docstring

"""
Pattern 272: Canary Deployment

Purpose: Gradual rollout with traffic routing and automated monitoring.
Tests new versions with small user percentage before full deployment.
"""

Explanation: This module docstring describes the purpose and key features of the 272 Canary Deployment pattern.

Imports Section

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

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: 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

DeploymentStage (Line 16)

class DeploymentStage(Enum):
    INITIAL
    EXPANDED
    MAJORITY
    COMPLETE
    FAILED

Purpose: Canary deployment stages.

Values: – INITIAL: Represents initial – EXPANDED: Represents expanded – MAJORITY: Represents majority – COMPLETE: Represents complete – FAILED: Execution failed

HealthStatus (Line 25)

class HealthStatus(Enum):
    HEALTHY
    DEGRADED
    UNHEALTHY

Purpose: Health status.

Values: – HEALTHY: Represents healthy – DEGRADED: Represents degraded – UNHEALTHY: Represents unhealthy

Classes

DeploymentStage (Line 16)

Description: Canary deployment stages.

HealthStatus (Line 25)

Description: Health status.

DeploymentVersion (Line 33)

Description: Version deployment info.

CanaryDeployment (Line 44)

Description: Canary Deployment – Gradual traffic shift strategy.

Features: – Gradual traffic increase – Automated health monitoring – Error rate tracking – Latency monitoring – Automatic rollback on failure – Stage progression control – Metrics comparison – Risk mitigation

Methods:

__init__ (Line 59)

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

Purpose: No method docstring.

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

start_canary (Line 87)

def start_canary(self, new_version):
    """Start canary deployment with initial traffic."""

Purpose: Start canary deployment with initial traffic.

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

promote_canary (Line 125)

def promote_canary(self):
    """Promote canary to next stage if healthy."""

Purpose: Promote canary to next stage if healthy.

Parameters: – self: Instance reference

rollback_canary (Line 190)

def rollback_canary(self):
    """Rollback canary deployment."""

Purpose: Rollback canary deployment.

Parameters: – self: Instance reference

_check_canary_health (Line 223)

def _check_canary_health(self):
    """Check canary health metrics."""

Purpose: Check canary health metrics.

Parameters: – self: Instance reference

update_metrics (Line 252)

def update_metrics(self, version, error_rate, latency_ms, health_status):
    """Update metrics for version."""

Purpose: Update metrics for version.

Parameters: – self: Instance reference – version: Parameter for version – error_rate: Parameter for error_rate – latency_ms: Parameter for latency_ms – health_status: Parameter for health_status

get_status (Line 270)

def get_status(self):
    """Get deployment status."""

Purpose: Get deployment status.

Parameters: – self: Instance reference

get_statistics (Line 295)

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

Purpose: Get deployment statistics.

Parameters: – self: Instance reference

CanaryPattern (Line 303)

Description: Canary Deployment Pattern – Manage canary deployments.

Methods:

__init__ (Line 306)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_deployment (Line 309)

def create_deployment(self, name):
    """Create canary deployment manager."""

Purpose: Create canary deployment manager.

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

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 = DeploymentStage(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

    """
Pattern 271: Blue-Green Deployment

Purpose: Zero-downtime deployment with environment switching and instant rollback.
Maintains two identical production environments for seamless version transitions.
"""

from typing import Dict, Any, List, Optional
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


class EnvironmentColor(Enum):
    """Environment designation colors."""
    BLUE = "blue"
    GREEN = "green"


class EnvironmentStatus(Enum):
    """Environment status."""
    ACTIVE = "active"  # Serving production traffic
    STANDBY = "standby"  # Ready but not serving
    DEPLOYING = "deploying"  # Deployment in progress
    TESTING = "testing"  # Under validation
    FAILED = "failed"  # Deployment failed


@dataclass
class Environment:
    """Production environment."""
    color: EnvironmentColor
    version: str
    status: EnvironmentStatus
    agent_count: int = 0
    health_score: float = 1.0
    traffic_percentage: float = 0.0
    deployment_time: datetime = field(default_factory=datetime.now)


@dataclass
class DeploymentRecord:
    """Record of deployment operation."""
    operation: str  # deploy, switch, rollback
    from_env: Optional[str] = None
    to_env: Optional[str] = None
    version: str = ""
    success: bool = True
    message: str = ""
    timestamp: datetime = field(default_factory=datetime.now)


class BlueGreenDeployment:
    """
    Blue-Green Deployment - Zero-downtime deployment strategy.
    
    Features:
    - Dual environment management
    - Instant traffic switching
    - Zero-downtime deployments
    - Quick rollback capability
    - Health monitoring
    - Traffic routing control
    - Version tracking
    - Deployment validation
    """
    
    def __init__(self, name: str):
        self.name = name
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.3)
        
        # Environments
        self.environments: Dict[EnvironmentColor, Environment] = {
            EnvironmentColor.BLUE: Environment(
                color=EnvironmentColor.BLUE,
                version="1.0.0",
                status=EnvironmentStatus.ACTIVE,
                agent_count=5,
                traffic_percentage=100.0
            ),
            EnvironmentColor.GREEN: Environment(
                color=EnvironmentColor.GREEN,
                version="1.0.0",
                status=EnvironmentStatus.STANDBY,
                agent_count=5
            )
        }
        
        # Current active environment
        self.active_env = EnvironmentColor.BLUE
        
        # Deployment history
        self.deployment_history: List[DeploymentRecord] = []
        
        # Statistics
        self.statistics: Dict[str, Any] = {
            "total_deployments": 0,
            "successful_deployments": 0,
            "failed_deployments": 0,
            "rollbacks": 0,
            "switches": 0,
            "current_version": "1.0.0"
        }
    
    def deploy_to_standby(
        self,
        new_version: str
    ) -> Dict[str, Any]:
        """Deploy new version to standby environment."""
        
        # Identify standby environment
        standby_color = (
            EnvironmentColor.GREEN if self.active_env == EnvironmentColor.BLUE
            else EnvironmentColor.BLUE
        )
        standby_env = self.environments[standby_color]
        
        # Update standby environment
        standby_env.status = EnvironmentStatus.DEPLOYING
        standby_env.version = new_version
        standby_env.deployment_time = datetime.now()
        
        # Simulate deployment
        success = True  # In real scenario, this would involve actual deployment
        
        if success:
            standby_env.status = EnvironmentStatus.TESTING
            standby_env.health_score = 1.0
            
            self.statistics["total_deployments"] += 1
            self.statistics["successful_deployments"] += 1
            
            record = DeploymentRecord(
                operation="deploy",
                to_env=standby_color.value,
                version=new_version,
                success=True,
                message=f"Successfully deployed v{new_version} to {standby_color.value}"
            )
        else:
            standby_env.status = EnvironmentStatus.FAILED
            
            self.statistics["total_deployments"] += 1
            self.statistics["failed_deployments"] += 1
            
            record = DeploymentRecord(
                operation="deploy",
                to_env=standby_color.value,
                version=new_version,
                success=False,
                message=f"Failed to deploy v{new_version}"
            )
        
        self.deployment_history.append(record)
        
        return {
            "success": success,
            "standby_env": standby_color.value,
            "version": new_version,
            "status": standby_env.status.value,
            "message": record.message
        }
    
    def switch_traffic(self) -> Dict[str, Any]:
        """Switch traffic from active to standby environment."""
        
        # Get environments
        old_active = self.active_env
        new_active = (
            EnvironmentColor.GREEN if old_active == EnvironmentColor.BLUE
            else EnvironmentColor.BLUE
        )
        
        old_env = self.environments[old_active]
        new_env = self.environments[new_active]
        
        # Validate standby is ready
        if new_env.status not in [EnvironmentStatus.TESTING, EnvironmentStatus.STANDBY]:
            return {
                "success": False,
                "message": f"Standby environment ({new_active.value}) not ready for switch"
            }
        
        if new_env.health_score  Dict[str, Any]:
        """Instant rollback to previous environment."""
        
        current_active = self.active_env
        previous_env = (
            EnvironmentColor.GREEN if current_active == EnvironmentColor.BLUE
            else EnvironmentColor.BLUE
        )
        
        current = self.environments[current_active]
        previous = self.environments[previous_env]
        
        # Switch back
        current.traffic_percentage = 0.0
        current.status = EnvironmentStatus.STANDBY
        
        previous.traffic_percentage = 100.0
        previous.status = EnvironmentStatus.ACTIVE
        
        self.active_env = previous_env
        self.statistics["rollbacks"] += 1
        self.statistics["current_version"] = previous.version
        
        record = DeploymentRecord(
            operation="rollback",
            from_env=current_active.value,
            to_env=previous_env.value,
            version=previous.version,
            success=True,
            message=f"Rolled back from {current_active.value} to {previous_env.value}"
        )
        
        self.deployment_history.append(record)
        
        return {
            "success": True,
            "from_env": current_active.value,
            "to_env": previous_env.value,
            "version": previous.version,
            "message": record.message
        }
    
    def get_environment_status(self) -> Dict[str, Any]:
        """Get status of both environments."""
        
        return {
            "active_env": self.active_env.value,
            "environments": {
                color.value: {
                    "version": env.version,
                    "status": env.status.value,
                    "agent_count": env.agent_count,
                    "health_score": env.health_score,
                    "traffic_percentage": env.traffic_percentage,
                    "is_active": color == self.active_env
                }
                for color, env in self.environments.items()
            }
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get deployment statistics."""
        return {
            "name": self.name,
            **self.statistics,
            "active_environment": self.active_env.value
        }


class BlueGreenPattern:
    """
    Blue-Green Deployment Pattern - Manage blue-green deployments.
    
    Coordinates zero-downtime deployment operations.
    """
    
    def __init__(self):
        self.deployments: Dict[str, BlueGreenDeployment] = {}
    
    def create_deployment(self, name: str) -> BlueGreenDeployment:
        """Create blue-green deployment manager."""
        deployment = BlueGreenDeployment(name)
        self.deployments[name] = deployment
        print(f"   Created blue-green deployment: {name}")
        return deployment
    
    def deploy(
        self,
        deployment_name: str,
        version: str
    ) -> Dict[str, Any]:
        """Deploy new version."""
        
        deployment = self.deployments.get(deployment_name)
        if not deployment:
            return {"error": f"Deployment {deployment_name} not found"}
        
        result = deployment.deploy_to_standby(version)
        
        print(f"\n=== Deploy v{version} to Standby ===")
        print(f"Success: {result['success']}")
        print(f"Standby Environment: {result['standby_env']}")
        print(f"Status: {result['status']}")
        print(f"Message: {result['message']}")
        
        return result
    
    def switch(self, deployment_name: str) -> Dict[str, Any]:
        """Switch traffic to standby."""
        
        deployment = self.deployments.get(deployment_name)
        if not deployment:
            return {"error": f"Deployment {deployment_name} not found"}
        
        result = deployment.switch_traffic()
        
        print(f"\n=== Switch Traffic ===")
        print(f"Success: {result['success']}")
        if result['success']:
            print(f"From: {result['from_env']} → To: {result['to_env']}")
            print(f"Version: {result['version']}")
        print(f"Message: {result['message']}")
        
        return result


# Example usage
if __name__ == "__main__":
    print("=== Blue-Green Deployment Pattern Demo ===\n")
    
    # Create pattern
    pattern = BlueGreenPattern()
    
    # Create deployment
    print("1. Setting up blue-green deployment...")
    deployment = pattern.create_deployment("ProductionDeployment")
    
    # Show initial status
    print("\n2. Initial environment status:")
    status = deployment.get_environment_status()
    print(f"Active Environment: {status['active_env']}")
    for env_name, env_status in status['environments'].items():
        print(f"\n{env_name.upper()}:")
        print(f"  Version: {env_status['version']}")
        print(f"  Status: {env_status['status']}")
        print(f"  Traffic: {env_status['traffic_percentage']}%")
        print(f"  Health: {env_status['health_score']}")
    
    # Deploy new version
    print("\n" + "="*60)
    print("\n3. Deploying new version...")
    result1 = pattern.deploy("ProductionDeployment", "2.0.0")
    
    # Show updated status
    print("\n" + "="*60)
    print("\n4. Status after deployment:")
    status = deployment.get_environment_status()
    for env_name, env_status in status['environments'].items():
        if not env_status['is_active']:
            print(f"\n{env_name.upper()} (Standby):")
            print(f"  Version: {env_status['version']}")
            print(f"  Status: {env_status['status']}")
    
    # Switch traffic
    print("\n" + "="*60)
    print("\n5. Switching traffic to new version...")
    result2 = pattern.switch("ProductionDeployment")
    
    # Final status
    print("\n" + "="*60)
    print("\n6. Final environment status:")
    status = deployment.get_environment_status()
    print(f"Active Environment: {status['active_env']}")
    for env_name, env_status in status['environments'].items():
        print(f"\n{env_name.upper()}:")
        print(f"  Version: {env_status['version']}")
        print(f"  Status: {env_status['status']}")
        print(f"  Traffic: {env_status['traffic_percentage']}%")
        print(f"  Active: {env_status['is_active']}")
    
    # Statistics
    print("\n" + "="*60)
    print("\n7. Deployment Statistics:")
    stats = deployment.get_statistics()
    for key, value in stats.items():
        print(f"   {key}: {value}")
    
    print("\n✓ Comprehensive blue-green deployment with zero-downtime switching complete!")

    

Agentic Architecture Pattern: Blue Green Deployment

Pattern 271: 271 Blue Green Deployment – Line-by-Line Explanation

Overview

Pattern 271: Blue-Green Deployment

Purpose: Zero-downtime deployment with environment switching and instant rollback. Maintains two identical production environments for seamless version transitions.

Code Structure Analysis

File Information

• Pattern Number: 271
• Pattern Name: 271 Blue Green Deployment
• File Path: patterns\deployment\271_blue_green_deployment.py
• Total Lines: 412

Detailed Code Explanation

Module Docstring

"""
Pattern 271: Blue-Green Deployment

Purpose: Zero-downtime deployment with environment switching and instant rollback.
Maintains two identical production environments for seamless version transitions.
"""

Explanation: This module docstring describes the purpose and key features of the 271 Blue Green Deployment pattern.

Imports Section

from typing import Dict, Any, List, Optional
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  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: 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

EnvironmentColor (Line 17)

class EnvironmentColor(Enum):
    BLUE
    GREEN

Purpose: Environment designation colors.

Values: – BLUE: Represents blue – GREEN: Represents green

EnvironmentStatus (Line 23)

class EnvironmentStatus(Enum):
    ACTIVE
    STANDBY
    DEPLOYING
    TESTING
    FAILED

Purpose: Environment status.

Values: – ACTIVE: Currently operational – STANDBY: Represents standby – DEPLOYING: Represents deploying – TESTING: Represents testing – FAILED: Execution failed

Classes

EnvironmentColor (Line 17)

Description: Environment designation colors.

EnvironmentStatus (Line 23)

Description: Environment status.

Environment (Line 33)

Description: Production environment.

DeploymentRecord (Line 45)

Description: Record of deployment operation.

BlueGreenDeployment (Line 56)

Description: Blue-Green Deployment – Zero-downtime deployment strategy.

Features: – Dual environment management – Instant traffic switching – Zero-downtime deployments – Quick rollback capability – Health monitoring – Traffic routing control – Version tracking – Deployment validation

Methods:

__init__ (Line 71)

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

Purpose: No method docstring.

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

deploy_to_standby (Line 108)

def deploy_to_standby(self, new_version):
    """Deploy new version to standby environment."""

Purpose: Deploy new version to standby environment.

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

switch_traffic (Line 167)

def switch_traffic(self):
    """Switch traffic from active to standby environment."""

Purpose: Switch traffic from active to standby environment.

Parameters: – self: Instance reference

rollback (Line 224)

def rollback(self):
    """Instant rollback to previous environment."""

Purpose: Instant rollback to previous environment.

Parameters: – self: Instance reference

get_environment_status (Line 266)

def get_environment_status(self):
    """Get status of both environments."""

Purpose: Get status of both environments.

Parameters: – self: Instance reference

get_statistics (Line 284)

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

Purpose: Get deployment statistics.

Parameters: – self: Instance reference

BlueGreenPattern (Line 293)

Description: Blue-Green Deployment Pattern – Manage blue-green deployments.

Coordinates zero-downtime deployment operations.

Methods:

__init__ (Line 300)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_deployment (Line 303)

def create_deployment(self, name):
    """Create blue-green deployment manager."""

Purpose: Create blue-green deployment manager.

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

deploy (Line 310)

def deploy(self, deployment_name, version):
    """Deploy new version."""

Purpose: Deploy new version.

Parameters: – self: Instance reference – deployment_name: Parameter for deployment_name – version: Parameter for version

switch (Line 331)

def switch(self, deployment_name):
    """Switch traffic to standby."""

Purpose: Switch traffic to standby.

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

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 = EnvironmentColor(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

    """
Pattern 270: Verification Pattern with LangChain/LangGraph

Purpose: Correctness verification and property checking with formal methods.
Provides intelligent verification orchestration with LLM-powered proof analysis.
"""

from typing import Dict, Any, List, Optional, Callable, TypedDict
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import json


class VerificationType(Enum):
    """Types of verification."""
    PROPERTY = "property"
    INVARIANT = "invariant"
    PRECONDITION = "precondition"
    POSTCONDITION = "postcondition"
    ASSERTION = "assertion"
    CONTRACT = "contract"


class VerificationSeverity(Enum):
    """Severity of verification issues."""
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"


@dataclass
class VerificationCondition:
    """Verification condition definition."""
    name: str
    verification_type: VerificationType
    condition: Callable
    description: str
    severity: VerificationSeverity = VerificationSeverity.CRITICAL


@dataclass
class VerificationResult:
    """Result of verification."""
    condition_name: str
    verification_type: VerificationType
    passed: bool
    message: str
    severity: VerificationSeverity
    evidence: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class VerificationProof:
    """LLM-generated verification proof."""
    formal_statement: str
    proof_sketch: str
    assumptions: List[str]
    conclusions: List[str]
    confidence: float


class VerificationState(TypedDict):
    """State for verification workflow."""
    context: Dict[str, Any]
    conditions: List[VerificationCondition]
    results: List[VerificationResult]
    all_passed: bool
    failures: List[VerificationResult]
    proof: Optional[VerificationProof]
    recommendations: List[str]
    verification_summary: Dict[str, Any]


class IntelligentVerifier:
    """
    Intelligent Verifier with LangGraph workflow integration.
    
    Features:
    - Property verification
    - Invariant checking
    - Pre/postcondition verification
    - Contract checking
    - LLM-powered proof generation
    - Evidence collection
    - Formal reasoning support
    - Counterexample detection
    """
    
    def __init__(self, name: str, model_name: str = "gpt-4", temperature: float = 0.5):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
        
        # Conditions
        self.conditions: List[VerificationCondition] = []
        
        # History
        self.verification_history: List[VerificationResult] = []
        
        # Build workflow
        self.workflow = self._build_workflow()
        
        # Statistics
        self.stats = {
            "total_verifications": 0,
            "passed": 0,
            "failed": 0,
            "verification_types": {t.value: 0 for t in VerificationType}
        }
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow."""
        workflow = StateGraph(VerificationState)
        
        workflow.add_node("analyze_conditions", self._analyze_conditions_node)
        workflow.add_node("verify_properties", self._verify_properties_node)
        workflow.add_node("check_invariants", self._check_invariants_node)
        workflow.add_node("collect_evidence", self._collect_evidence_node)
        workflow.add_node("generate_proof", self._generate_proof_node)
        
        workflow.set_entry_point("analyze_conditions")
        workflow.add_edge("analyze_conditions", "verify_properties")
        workflow.add_edge("verify_properties", "check_invariants")
        workflow.add_edge("check_invariants", "collect_evidence")
        workflow.add_edge("collect_evidence", "generate_proof")
        workflow.add_edge("generate_proof", END)
        
        return workflow.compile()
    
    def _analyze_conditions_node(self, state: VerificationState) -> VerificationState:
        """Analyze verification conditions."""
        state["conditions"] = self.conditions
        state["results"] = []
        state["failures"] = []
        
        return state
    
    def _verify_properties_node(self, state: VerificationState) -> VerificationState:
        """Verify properties and conditions."""
        context = state["context"]
        conditions = state["conditions"]
        results = []
        
        for condition in conditions:
            self.stats["total_verifications"] += 1
            self.stats["verification_types"][condition.verification_type.value] += 1
            
            try:
                passed = condition.condition(context)
                
                message = (
                    f"✓ {condition.description}" if passed
                    else f"✗ {condition.description} FAILED"
                )
                
                result = VerificationResult(
                    condition_name=condition.name,
                    verification_type=condition.verification_type,
                    passed=passed,
                    message=message,
                    severity=condition.severity,
                    evidence=context.copy()
                )
                
                if passed:
                    self.stats["passed"] += 1
                else:
                    self.stats["failed"] += 1
                    state["failures"].append(result)
                
                results.append(result)
                self.verification_history.append(result)
            
            except Exception as e:
                result = VerificationResult(
                    condition_name=condition.name,
                    verification_type=condition.verification_type,
                    passed=False,
                    message=f"Verification error: {str(e)}",
                    severity=VerificationSeverity.CRITICAL,
                    evidence={"error": str(e)}
                )
                results.append(result)
                state["failures"].append(result)
                self.verification_history.append(result)
                self.stats["failed"] += 1
        
        state["results"] = results
        state["all_passed"] = len(state["failures"]) == 0
        
        return state
    
    def _check_invariants_node(self, state: VerificationState) -> VerificationState:
        """Check system invariants."""
        # Additional invariant checks already handled in verify_properties
        return state
    
    def _collect_evidence_node(self, state: VerificationState) -> VerificationState:
        """Collect verification evidence."""
        # Create verification summary
        results = state["results"]
        failures = state["failures"]
        
        state["verification_summary"] = {
            "total_conditions": len(results),
            "passed": sum(1 for r in results if r.passed),
            "failed": len(failures),
            "critical_failures": sum(1 for f in failures if f.severity == VerificationSeverity.CRITICAL),
            "verification_types": {}
        }
        
        # Count by type
        for vtype in VerificationType:
            count = sum(1 for r in results if r.verification_type == vtype)
            if count > 0:
                state["verification_summary"]["verification_types"][vtype.value] = count
        
        return state
    
    def _generate_proof_node(self, state: VerificationState) -> VerificationState:
        """Generate verification proof using LLM."""
        results = state["results"]
        failures = state["failures"]
        summary = state["verification_summary"]
        context = state["context"]
        
        if state["all_passed"]:
            # Generate proof for successful verification
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are an expert in formal verification and mathematical proof."),
                ("user", """Generate a verification proof for this successful verification:

Context: {context}
Conditions Verified: {total_conditions}
Verification Types: {verification_types}

Provide proof as JSON with:
- formal_statement: Formal statement of what was verified
- proof_sketch: Brief proof sketch
- assumptions: List of assumptions (2-3 items)
- conclusions: List of conclusions (2-3 items)
- confidence: Confidence score 0.0-1.0

Focus on formal correctness.""")
            ])
            
            chain = prompt | self.llm
            response = chain.invoke({
                "context": str(context),
                "total_conditions": summary["total_conditions"],
                "verification_types": str(summary["verification_types"])
            })
            
            try:
                proof_data = json.loads(response.content)
                state["proof"] = VerificationProof(
                    formal_statement=proof_data.get("formal_statement", "System properties verified"),
                    proof_sketch=proof_data.get("proof_sketch", "All conditions satisfied"),
                    assumptions=proof_data.get("assumptions", []),
                    conclusions=proof_data.get("conclusions", ["System is correct"]),
                    confidence=proof_data.get("confidence", 0.95)
                )
                state["recommendations"] = ["System verification passed", "Properties hold"]
            except:
                state["proof"] = VerificationProof(
                    formal_statement="System properties verified",
                    proof_sketch="All verification conditions passed",
                    assumptions=["Input conditions valid"],
                    conclusions=["System behaves correctly"],
                    confidence=0.90
                )
                state["recommendations"] = ["Verification successful"]
        else:
            # Generate analysis for failures
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are an expert in formal verification and debugging."),
                ("user", """Analyze these verification failures:

Context: {context}
Total Failures: {failure_count}
Critical Failures: {critical_count}

Failed Conditions:
{failures}

Provide analysis as JSON with:
- formal_statement: What failed to verify
- proof_sketch: Why verification failed
- assumptions: Violated assumptions (2-3 items)
- conclusions: Failure implications (2-3 items)
- confidence: Confidence in analysis 0.0-1.0

Also suggest fixes.""")
            ])
            
            chain = prompt | self.llm
            failure_details = "\n".join([f"- {f.message}" for f in failures])
            
            response = chain.invoke({
                "context": str(context),
                "failure_count": summary["failed"],
                "critical_count": summary["critical_failures"],
                "failures": failure_details
            })
            
            try:
                proof_data = json.loads(response.content)
                state["proof"] = VerificationProof(
                    formal_statement=proof_data.get("formal_statement", "Verification failed"),
                    proof_sketch=proof_data.get("proof_sketch", "Conditions not satisfied"),
                    assumptions=proof_data.get("assumptions", []),
                    conclusions=proof_data.get("conclusions", ["System has errors"]),
                    confidence=proof_data.get("confidence", 0.85)
                )
                state["recommendations"] = [
                    "Fix critical verification failures",
                    "Review violated conditions",
                    "Update system implementation"
                ]
            except:
                state["proof"] = VerificationProof(
                    formal_statement="Verification failed",
                    proof_sketch="Some conditions not satisfied",
                    assumptions=["Conditions were violated"],
                    conclusions=["System needs correction"],
                    confidence=0.80
                )
                state["recommendations"] = ["Fix verification failures"]
        
        return state
    
    def add_condition(
        self,
        name: str,
        verification_type: VerificationType,
        condition: Callable,
        description: str,
        severity: VerificationSeverity = VerificationSeverity.CRITICAL
    ):
        """Add verification condition."""
        vc = VerificationCondition(
            name=name,
            verification_type=verification_type,
            condition=condition,
            description=description,
            severity=severity
        )
        
        self.conditions.append(vc)
    
    def verify(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """Verify system through workflow."""
        initial_state: VerificationState = {
            "context": context,
            "conditions": [],
            "results": [],
            "all_passed": True,
            "failures": [],
            "proof": None,
            "recommendations": [],
            "verification_summary": {}
        }
        
        final_state = self.workflow.invoke(initial_state)
        
        return {
            "passed": final_state["all_passed"],
            "results": final_state["results"],
            "failures": final_state["failures"],
            "summary": final_state["verification_summary"],
            "proof": final_state["proof"],
            "recommendations": final_state["recommendations"]
        }
    
    def verify_property(
        self,
        property_name: str,
        property_check: Callable,
        context: Dict[str, Any]
    ) -> bool:
        """Verify specific property."""
        self.add_condition(
            property_name,
            VerificationType.PROPERTY,
            property_check,
            f"Property: {property_name}",
            VerificationSeverity.CRITICAL
        )
        
        result = self.verify(context)
        return result["passed"]
    
    def check_invariant(
        self,
        invariant_name: str,
        invariant_check: Callable,
        context: Dict[str, Any]
    ) -> bool:
        """Check invariant."""
        self.add_condition(
            invariant_name,
            VerificationType.INVARIANT,
            invariant_check,
            f"Invariant: {invariant_name}",
            VerificationSeverity.CRITICAL
        )
        
        result = self.verify(context)
        return result["passed"]
    
    def get_failures(self) -> List[VerificationResult]:
        """Get all failed verifications."""
        return [r for r in self.verification_history if not r.passed]
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get verification statistics."""
        return {
            "name": self.name,
            "conditions_count": len(self.conditions),
            **self.stats
        }


class VerificationPattern:
    """
    Comprehensive Verification Pattern coordinator with LangChain/LangGraph.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.verifiers: Dict[str, IntelligentVerifier] = {}
        self.model_name = model_name
    
    def create_verifier(self, name: str) -> IntelligentVerifier:
        """Create a verifier."""
        verifier = IntelligentVerifier(name, model_name=self.model_name)
        self.verifiers[name] = verifier
        return verifier
    
    def verify(
        self,
        verifier_name: str,
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Run verification."""
        if verifier_name not in self.verifiers:
            return {"error": f"Verifier {verifier_name} not found"}
        
        verifier = self.verifiers[verifier_name]
        result = verifier.verify(context)
        
        return {
            "verifier": verifier_name,
            **result
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get overall statistics."""
        total_verifications = sum(v.stats["total_verifications"] for v in self.verifiers.values())
        total_passed = sum(v.stats["passed"] for v in self.verifiers.values())
        total_failed = sum(v.stats["failed"] for v in self.verifiers.values())
        
        return {
            "total_verifiers": len(self.verifiers),
            "total_verifications": total_verifications,
            "total_passed": total_passed,
            "total_failed": total_failed
        }


if __name__ == "__main__":
    print("=== Verification Pattern with LangChain/LangGraph Demo ===\n")
    
    print("Scenario 1: Banking System Verification")
    print("-" * 50)
    pattern = VerificationPattern(model_name="gpt-4")
    
    banking_verifier = pattern.create_verifier("BankingVerifier")
    
    # Add verification conditions
    print("\nAdding verification conditions...")
    
    # Property: Balance non-negative
    banking_verifier.add_condition(
        "balance_non_negative",
        VerificationType.PROPERTY,
        lambda ctx: ctx.get("balance", 0) >= 0,
        "Account balance must be non-negative",
        VerificationSeverity.CRITICAL
    )
    
    # Invariant: Total equals sum
    banking_verifier.add_condition(
        "total_equals_sum",
        VerificationType.INVARIANT,
        lambda ctx: abs(ctx.get("total", 0) - sum(ctx.get("transactions", [])))  0,
        "Order total must be positive",
        VerificationSeverity.CRITICAL
    )
    
    order_verifier.add_condition(
        "items_not_empty",
        VerificationType.INVARIANT,
        lambda ctx: len(ctx.get("items", [])) > 0,
        "Order must contain items",
        VerificationSeverity.CRITICAL
    )
    
    order_verifier.add_condition(
        "payment_authorized",
        VerificationType.PRECONDITION,
        lambda ctx: ctx.get("payment_status") == "authorized",
        "Payment must be authorized",
        VerificationSeverity.HIGH
    )
    
    order_verifier.add_condition(
        "shipping_address_valid",
        VerificationType.ASSERTION,
        lambda ctx: ctx.get("shipping_address") is not None and len(ctx.get("shipping_address", "")) > 0,
        "Shipping address must be provided",
        VerificationSeverity.HIGH
    )
    
    print("\nVerification 3: Valid order")
    result3 = pattern.verify("OrderVerifier", {
        "order_total": 150.99,
        "items": ["item1", "item2", "item3"],
        "payment_status": "authorized",
        "shipping_address": "123 Main St, City, State 12345"
    })
    
    print(f"Passed: {result3['passed']}")
    print(f"Summary: {result3['summary']}")
    
    print("\n" + "=" * 50)
    print("\nVerification 4: Order with issues")
    result4 = pattern.verify("OrderVerifier", {
        "order_total": 0.0,  # Invalid: zero total
        "items": [],  # Invalid: no items
        "payment_status": "pending",  # Invalid: not authorized
        "shipping_address": ""  # Invalid: empty address
    })
    
    print(f"Passed: {result4['passed']}")
    print(f"Summary: {result4['summary']}")
    print(f"\nFailures:")
    for failure in result4['failures']:
        print(f"  - {failure.message}")
    
    print("\n" + "=" * 50)
    print("\nScenario 3: Data Structure Invariants")
    print("-" * 50)
    
    structure_verifier = pattern.create_verifier("StructureVerifier")
    
    # Data structure invariants
    structure_verifier.add_condition(
        "array_sorted",
        VerificationType.INVARIANT,
        lambda ctx: all(ctx["array"][i]  1 else True,
        "Array must be sorted",
        VerificationSeverity.MEDIUM
    )
    
    structure_verifier.add_condition(
        "no_duplicates",
        VerificationType.PROPERTY,
        lambda ctx: len(ctx.get("array", [])) == len(set(ctx.get("array", []))),
        "Array must not contain duplicates",
        VerificationSeverity.LOW
    )
    
    structure_verifier.add_condition(
        "size_within_bounds",
        VerificationType.ASSERTION,
        lambda ctx: 0 <= len(ctx.get("array", [])) <= ctx.get("max_size", 1000),
        "Array size must be within bounds",
        VerificationSeverity.HIGH
    )
    
    print("\nVerification 5: Valid sorted array")
    result5 = pattern.verify("StructureVerifier", {
        "array": [1, 2, 3, 5, 8, 13],
        "max_size": 100
    })
    
    print(f"Passed: {result5['passed']}")
    print(f"Summary: {result5['summary']}")
    
    print("\n" + "=" * 50)
    print("\nVerification 6: Unsorted array with duplicates")
    result6 = pattern.verify("StructureVerifier", {
        "array": [5, 2, 8, 2, 1],  # Unsorted and has duplicates
        "max_size": 100
    })
    
    print(f"Passed: {result6['passed']}")
    print(f"Summary: {result6['summary']}")
    if result6['failures']:
        print(f"\nFailures:")
        for failure in result6['failures']:
            print(f"  - [{failure.severity.value}] {failure.message}")
    
    print("\n" + "=" * 50)
    print("\nStatistics")
    print("-" * 50)
    
    overall_stats = pattern.get_statistics()
    print(f"\nOverall Statistics:")
    print(f"  Total Verifiers: {overall_stats['total_verifiers']}")
    print(f"  Total Verifications: {overall_stats['total_verifications']}")
    print(f"  Total Passed: {overall_stats['total_passed']}")
    print(f"  Total Failed: {overall_stats['total_failed']}")
    
    print(f"\nBanking Verifier Statistics:")
    banking_stats = banking_verifier.get_statistics()
    for key, value in banking_stats.items():
        print(f"  {key}: {value}")
    
    print(f"\nOrder Verifier Statistics:")
    order_stats = order_verifier.get_statistics()
    for key, value in order_stats.items():
        print(f"  {key}: {value}")
    
    print("\n" + "=" * 50)
    print("Verification pattern demonstration complete!")
    print("=" * 50)

    

Agentic Architecture Pattern: Verification

Pattern 270: Verification Pattern – Line-by-Line Explanation

Overview

This pattern implements formal verification with property checking, invariant validation, and LLM-powered proof generation. It ensures system correctness through mathematical reasoning and formal methods.

Code Explanation

Lines 1-15: Imports

"""
Pattern 270: Verification Pattern with LangChain/LangGraph

Purpose: Formal verification with property checking and proof generation.
Provides intelligent verification orchestration with LLM-powered formal reasoning.
"""

Explanation: Module docstring – pattern for formal correctness verification.

from typing import Dict, Any, List, Optional, Callable, TypedDict
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import json

Explanation: Standard imports for verification with LLM integration.

Lines 17-35: Enum Definitions

class VerificationType(Enum):
    """Types of verification."""
    PROPERTY = "property"          # General properties
    INVARIANT = "invariant"        # Always true conditions
    PRECONDITION = "precondition"  # Must be true before execution
    POSTCONDITION = "postcondition"  # Must be true after execution
    ASSERTION = "assertion"        # Point-in-time checks
    CONTRACT = "contract"          # Interface contracts

Explanation: Six verification types based on formal methods: – PROPERTY: General system properties (e.g., “balance never negative”) – INVARIANT: Conditions that must always hold (e.g., “list is always sorted”) – PRECONDITION: Requirements before function execution (e.g., “input not null”) – POSTCONDITION: Guarantees after function execution (e.g., “result is positive”) – ASSERTION: Runtime checks at specific points (e.g., “index in bounds”) – CONTRACT: Interface guarantees (e.g., “implements all required methods”)

class VerificationResult(Enum):
    """Result of verification."""
    VERIFIED = "verified"      # Proof successful
    VIOLATED = "violated"      # Proof failed
    UNKNOWN = "unknown"        # Cannot determine

Explanation: Three possible verification outcomes.

Lines 37-79: Dataclasses

@dataclass
class VerificationProperty:
    """Property to verify."""
    name: str
    verification_type: VerificationType
    checker: Callable
    description: str
    expected_result: bool = True

Explanation: Defines a property to verify: – name: Identifier – verification_type: Category – checker: Function that checks property – description: Human-readable explanation – expected_result: What checker should return (usually True)

@dataclass
class VerificationEvidence:
    """Evidence for verification."""
    property_name: str
    result: VerificationResult
    evidence: List[str]
    counterexample: Optional[Dict[str, Any]] = None
    timestamp: datetime = field(default_factory=datetime.now)

Explanation: Evidence supporting verification: – result: Verified, violated, or unknown – evidence: List of proof steps or test results – counterexample: Example input that violates property (if violated)

Example counterexample: {"input": -5, "output": None, "expected": "positive number"}

@dataclass
class ProofStep:
    """Step in verification proof."""
    step_number: int
    statement: str
    justification: str
    proven: bool

Explanation: Individual step in formal proof. Tracks logical progression.

@dataclass
class VerificationInsights:
    """LLM-generated verification insights."""
    summary: str
    proof_quality: str
    gaps: List[str]
    suggestions: List[str]
    formal_proof: List[ProofStep]

Explanation: AI-generated formal reasoning: – summary: Overall verification status – proof_quality: Assessment of proof completeness – gaps: Missing proof steps or assumptions – suggestions: How to strengthen proof – formal_proof: Step-by-step logical proof

Lines 81-94: TypedDict State

class VerificationState(TypedDict):
    """State for verification workflow."""
    properties: List[VerificationProperty]
    evidence: Dict[str, VerificationEvidence]
    system_state: Dict[str, Any]
    verification_results: Dict[str, VerificationResult]
    insights: Optional[VerificationInsights]
    proof_complete: bool

Explanation: Workflow state tracking properties, evidence, and formal proofs.

Lines 96-142: IntelligentVerifier Initialization

class IntelligentVerifier:
    """
    Intelligent Verifier with LangGraph workflow integration.

    Features:
    - Multiple verification types (property, invariant, contract, etc.)
    - LLM-powered proof generation
    - Formal reasoning
    - Counterexample generation
    - Evidence collection
    - Symbolic execution
    - Theorem proving assistance
    """

Explanation: Main verifier class with formal methods and AI assistance.

def __init__(self, name: str, model_name: str = "gpt-4", temperature: float = 0.3):
    self.name = name
    self.llm = ChatOpenAI(model=model_name, temperature=temperature)  # Lower temp for formal reasoning

    # Properties
    self.properties: List[VerificationProperty] = []

    # Evidence
    self.evidence_history: List[VerificationEvidence] = []

    # Build workflow
    self.workflow = self._build_workflow()

    # Statistics
    self.stats = {
        "total_verifications": 0,
        "verified": 0,
        "violated": 0,
        "unknown": 0
    }

Explanation: Initialize with lower temperature (0.3) for more deterministic formal reasoning.

Lines 144-162: Workflow Building

def _build_workflow(self) -> StateGraph:
    """Build LangGraph workflow."""
    workflow = StateGraph(VerificationState)

    workflow.add_node("check_properties", self._check_properties_node)
    workflow.add_node("collect_evidence", self._collect_evidence_node)
    workflow.add_node("generate_proof", self._generate_proof_node)
    workflow.add_node("validate_proof", self._validate_proof_node)
    workflow.add_node("synthesize_insights", self._synthesize_insights_node)

Explanation: Create 5-node workflow: 1. Check properties: Execute checkers 2. Collect evidence: Gather proof steps 3. Generate proof: LLM creates formal proof 4. Validate proof: Verify logical soundness 5. Synthesize insights: LLM analyzes completeness

    workflow.set_entry_point("check_properties")
    workflow.add_edge("check_properties", "collect_evidence")
    workflow.add_edge("collect_evidence", "generate_proof")
    workflow.add_edge("generate_proof", "validate_proof")
    workflow.add_edge("validate_proof", "synthesize_insights")
    workflow.add_edge("synthesize_insights", END)

    return workflow.compile()

Explanation: Linear flow through verification stages.

Lines 164-227: Check Properties Node

def _check_properties_node(self, state: VerificationState) -> VerificationState:
    """Check all properties."""
    system_state = state["system_state"]
    verification_results = {}
    evidence = {}

Explanation: Initialize results and evidence dictionaries.

    for prop in self.properties:
        self.stats["total_verifications"] += 1

        try:
            # Execute checker
            result = prop.checker(system_state)

            if result == prop.expected_result:
                verification_results[prop.name] = VerificationResult.VERIFIED
                self.stats["verified"] += 1
                evidence_list = [f"Property '{prop.name}' holds", f"Checker returned {result}"]
            else:
                verification_results[prop.name] = VerificationResult.VIOLATED
                self.stats["violated"] += 1
                evidence_list = [
                    f"Property '{prop.name}' violated",
                    f"Expected {prop.expected_result}, got {result}"
                ]

Explanation: Execute each checker: – If returns expected result → VERIFIED – If returns unexpected result → VIOLATED – Record evidence for both cases

            # Try to find counterexample if violated
            counterexample = None
            if verification_results[prop.name] == VerificationResult.VIOLATED:
                counterexample = {
                    "system_state": system_state,
                    "checker_result": result,
                    "expected": prop.expected_result
                }

Explanation: For violations, capture system state as counterexample. This helps diagnose why property failed.

            evidence[prop.name] = VerificationEvidence(
                property_name=prop.name,
                result=verification_results[prop.name],
                evidence=evidence_list,
                counterexample=counterexample
            )

        except Exception as e:
            verification_results[prop.name] = VerificationResult.UNKNOWN
            self.stats["unknown"] += 1
            evidence[prop.name] = VerificationEvidence(
                property_name=prop.name,
                result=VerificationResult.UNKNOWN,
                evidence=[f"Verification error: {str(e)}"]
            )

Explanation: Handle exceptions as UNKNOWN result. Cannot prove or disprove.

    state["verification_results"] = verification_results
    state["evidence"] = evidence

    return state

Lines 229-251: Collect Evidence Node

def _collect_evidence_node(self, state: VerificationState) -> VerificationState:
    """Collect additional evidence for verification."""
    evidence = state["evidence"]

    # Add evidence to history
    for prop_name, evidence_obj in evidence.items():
        self.evidence_history.append(evidence_obj)

    # Collect counterexamples if any
    counterexamples = []
    for prop_name, evidence_obj in evidence.items():
        if evidence_obj.counterexample:
            counterexamples.append({
                "property": prop_name,
                "counterexample": evidence_obj.counterexample
            })

    if counterexamples:
        state["insights"] = VerificationInsights(
            summary=f"Found {len(counterexamples)} violations",
            proof_quality="Incomplete",
            gaps=[f"Property {ce['property']} violated" for ce in counterexamples],
            suggestions=["Address counterexamples to complete verification"],
            formal_proof=[]
        )

    return state

Explanation: Collect evidence into history, identify counterexamples for further analysis.

Lines 253-338: Generate Proof Node (LLM-Powered)

def _generate_proof_node(self, state: VerificationState) -> VerificationState:
    """Generate formal proof using LLM."""
    properties = state["properties"]
    evidence = state["evidence"]
    verification_results = state["verification_results"]

    # Prepare properties summary
    properties_summary = []
    for prop in properties:
        result = verification_results.get(prop.name, VerificationResult.UNKNOWN)
        properties_summary.append(f"- {prop.name}: {prop.description} [{result.value}]")

Explanation: Prepare property list with current verification status.

    # Prepare evidence summary
    evidence_summary = []
    for prop_name, evidence_obj in evidence.items():
        evidence_summary.append(f"Property: {prop_name}")
        evidence_summary.append(f"  Result: {evidence_obj.result.value}")
        evidence_summary.extend([f"  - {e}" for e in evidence_obj.evidence])
        if evidence_obj.counterexample:
            evidence_summary.append(f"  Counterexample: {evidence_obj.counterexample}")

Explanation: Prepare evidence list with counterexamples if any.

    # Use LLM to generate formal proof
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an expert in formal verification and mathematical proof generation."),
        ("user", """Generate a formal verification proof:

System: {name}

Properties to Verify:
{properties}

Evidence Collected:
{evidence}

Provide analysis as JSON with:
- summary: Brief summary of verification status
- proof_quality: Assessment (Complete/Partial/Incomplete)
- gaps: List of gaps in current proof
- suggestions: How to complete verification
- formal_proof: Array of proof steps with:
  - step_number: Sequential step number
  - statement: Logical statement
  - justification: Why this step is valid
  - proven: true/false

Focus on logical rigor and formal correctness.""")
    ])

Explanation: Ask LLM to generate formal proof with step-by-step logical reasoning.

    chain = prompt | self.llm
    response = chain.invoke({
        "name": self.name,
        "properties": "\n".join(properties_summary),
        "evidence": "\n".join(evidence_summary)
    })

Explanation: Invoke LLM with properties and evidence.

    try:
        insights_data = json.loads(response.content)

        # Parse proof steps
        formal_proof = []
        for step_data in insights_data.get("formal_proof", []):
            formal_proof.append(ProofStep(
                step_number=step_data.get("step_number", 0),
                statement=step_data.get("statement", ""),
                justification=step_data.get("justification", ""),
                proven=step_data.get("proven", False)
            ))

        state["insights"] = VerificationInsights(
            summary=insights_data.get("summary", "Verification analysis completed"),
            proof_quality=insights_data.get("proof_quality", "Unknown"),
            gaps=insights_data.get("gaps", []),
            suggestions=insights_data.get("suggestions", []),
            formal_proof=formal_proof
        )

    except Exception as e:
        # Fallback insights
        state["insights"] = VerificationInsights(
            summary="Verification completed",
            proof_quality="Partial",
            gaps=["LLM proof generation encountered an error"],
            suggestions=["Manual verification may be needed"],
            formal_proof=[]
        )

Explanation: Parse JSON response into proof steps. Fallback if parsing fails.

    return state

Lines 340-368: Validate Proof Node

def _validate_proof_node(self, state: VerificationState) -> VerificationState:
    """Validate logical soundness of proof."""
    insights = state["insights"]

    if not insights or not insights.formal_proof:
        state["proof_complete"] = False
        return state

    # Check if all proof steps are proven
    all_proven = all(step.proven for step in insights.formal_proof)

    # Check if there are no critical gaps
    critical_gaps = [g for g in insights.gaps if "violated" in g.lower() or "failed" in g.lower()]

    proof_complete = all_proven and len(critical_gaps) == 0

    state["proof_complete"] = proof_complete

    return state

Explanation: Validate proof completeness: – All steps must be proven – No critical gaps (violations/failures) – Sets proof_complete flag

Lines 370-392: Synthesize Insights Node

def _synthesize_insights_node(self, state: VerificationState) -> VerificationState:
    """Synthesize final verification insights."""
    insights = state["insights"]

    if not insights:
        return state

    # Add summary based on results
    verified_count = sum(1 for r in state["verification_results"].values() if r == VerificationResult.VERIFIED)
    violated_count = sum(1 for r in state["verification_results"].values() if r == VerificationResult.VIOLATED)
    unknown_count = sum(1 for r in state["verification_results"].values() if r == VerificationResult.UNKNOWN)

    insights.summary += f" | Verified: {verified_count}, Violated: {violated_count}, Unknown: {unknown_count}"

    return state

Explanation: Add verification statistics to insights summary.

Lines 394-460: Public Methods

def add_property(self, prop: VerificationProperty):
    """Add verification property."""
    self.properties.append(prop)

Explanation: Register property to verify.

def add_invariant(self, name: str, checker: Callable, description: str):
    """Add invariant property."""
    prop = VerificationProperty(
        name=name,
        verification_type=VerificationType.INVARIANT,
        checker=checker,
        description=description
    )
    self.add_property(prop)

Explanation: Convenience method for invariants. Example: add_invariant("balance_non_negative", lambda s: s["balance"] >= 0, "Balance is always non-negative").

def verify(self, system_state: Dict[str, Any]) -> Dict[str, Any]:
    """Verify system with workflow."""
    initial_state: VerificationState = {
        "properties": self.properties,
        "evidence": {},
        "system_state": system_state,
        "verification_results": {},
        "insights": None,
        "proof_complete": False
    }

    final_state = self.workflow.invoke(initial_state)

    return {
        "results": final_state["verification_results"],
        "evidence": final_state["evidence"],
        "insights": final_state["insights"],
        "proof_complete": final_state["proof_complete"]
    }

Explanation: Main entry point – execute workflow and return verification results.

Lines 485-630: Demo Code

Scenario 1: Banking System Verification

verifier = pattern.create_verifier("BankingSystem")

# Invariant: balance never negative
def balance_non_negative(state: Dict) -> bool:
    return state.get("balance", 0) >= 0

verifier.add_invariant("balance_non_negative", balance_non_negative, "Balance must be non-negative")

# Precondition: withdrawal amount valid
def withdrawal_valid(state: Dict) -> bool:
    withdrawal = state.get("withdrawal_amount", 0)
    balance = state.get("balance", 0)
    return withdrawal > 0 and withdrawal <= balance

verifier.add_precondition("withdrawal_valid", withdrawal_valid, "Withdrawal must be positive and not exceed balance")

# Valid state
valid_state = {"balance": 1000, "withdrawal_amount": 100}
result1 = pattern.verify("BankingSystem", valid_state)

Explanation: Define invariants and preconditions for banking system. Valid state passes verification.

Scenario 2: Contract Violation

# Invalid state (negative balance)
invalid_state = {"balance": -500, "withdrawal_amount": 100}
result2 = pattern.verify("BankingSystem", invalid_state)

Explanation: Negative balance violates invariant, triggers proof generation with counterexample.

Scenario 3: E-commerce Order Verification