Tag: llm

  • Agentic Architecture Pattern: Release Management

    
        """
    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

  • Agentic Architecture Pattern: Version Control

    
        """
    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

  • Agentic Architecture Pattern: Feature Toggle

    
        """
    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

  • Agentic Architecture Pattern: Shadow Deployment

    
        """
    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

  • Agentic Architecture Pattern: Rolling Update

    
        """
    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

  • Agentic Architecture Pattern: Canary Deployment

    
        """
    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

  • Agentic Architecture Pattern: Blue Green Deployment

    
        """
    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

  • Agentic Architecture Pattern: Verification

    
        """
    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

    order_verifier = pattern.create_verifier("OrderProcessing")
    
    def total_matches_items(state: Dict) -> bool:
        """Postcondition: order total matches sum of items."""
        items = state.get("items", [])
        total = state.get("total", 0)
        calculated = sum(item.get("price", 0) * item.get("quantity", 0) for item in items)
        return abs(total - calculated) < 0.01
    
    order_verifier.add_postcondition("total_matches", total_matches_items, "Order total must equal sum of line items")
    
    order_state = {
        "total": 150.0,
        "items": [
            {"price": 50.0, "quantity": 2},  # 100
            {"price": 25.0, "quantity": 2}   # 50
        ]
    }
    
    result3 = pattern.verify("OrderProcessing", order_state)
    

    Explanation: Postcondition verification ensures business logic correctness.


    Verification Type Comparison

    Type When Example
    PROPERTY General system properties “System is responsive”
    INVARIANT Always true “Balance ≥ 0”
    PRECONDITION Before function “Input not null”
    POSTCONDITION After function “Result is sorted”
    ASSERTION Point in code “Index in bounds”
    CONTRACT Interface guarantee “Returns positive”

    Key Design Patterns

    1. Specification Pattern: Properties specify correctness criteria
    2. Chain of Responsibility: Sequential proof step validation
    3. Template Method: Common workflow with type-specific checkers
    4. Strategy Pattern: Different verification strategies per type
    5. Observer Pattern: Evidence collection observes all checks

    Performance Considerations

    • Property Checking: O(properties × checker_complexity) – depends on checker
    • Evidence Collection: O(properties) – linear
    • Proof Generation: O(LLM) – ~2-4 seconds for complex proofs
    • Proof Validation: O(proof_steps) – linear in proof length
    • Memory: O(properties + evidence) – stores all results

    Best Practices: – Use fast checkers (avoid heavy computation) – Order properties by likelihood of violation (fail fast) – Cache verification results for immutable states – Use symbolic execution for exhaustive checking – Combine runtime checks with static analysis

    Extension Points

    1. Add symbolic execution for path exploration
    2. Implement SMT solver integration (Z3, CVC4)
    3. Add model checking for state space exploration
    4. Support temporal logic (LTL, CTL)
    5. Add automated test generation from counterexamples
    6. Implement proof tactics library
    7. Add visualization of proof trees
  • Agentic Architecture Pattern: Validation

    
        """
    Pattern 269: Validation Pattern with LangChain/LangGraph
    
    Purpose: Data validation with rule checking and constraint enforcement.
    Provides intelligent validation orchestration with LLM-powered rule 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 ValidationType(Enum):
        """Types of validation."""
        TYPE = "type"
        RANGE = "range"
        FORMAT = "format"
        SCHEMA = "schema"
        BUSINESS = "business"
        CUSTOM = "custom"
    
    
    class ValidationSeverity(Enum):
        """Severity of validation issues."""
        ERROR = "error"
        WARNING = "warning"
        INFO = "info"
    
    
    @dataclass
    class ValidationRule:
        """Validation rule definition."""
        name: str
        validation_type: ValidationType
        validator: Callable
        error_message: str
        severity: ValidationSeverity = ValidationSeverity.ERROR
        field: Optional[str] = None
    
    
    @dataclass
    class ValidationResult:
        """Result of validation."""
        field: str
        rule: str
        passed: bool
        error_message: Optional[str] = None
        severity: ValidationSeverity = ValidationSeverity.ERROR
        timestamp: datetime = field(default_factory=datetime.now)
    
    
    @dataclass
    class ValidationInsights:
        """LLM-generated validation insights."""
        overall_assessment: str
        critical_issues: List[str]
        recommendations: List[str]
        fix_suggestions: List[str]
    
    
    class ValidationState(TypedDict):
        """State for validation workflow."""
        data: Dict[str, Any]
        rules: List[ValidationRule]
        field_results: Dict[str, List[ValidationResult]]
        all_passed: bool
        errors: List[str]
        warnings: List[str]
        insights: Optional[ValidationInsights]
        validation_summary: Dict[str, Any]
    
    
    class IntelligentValidator:
        """
        Intelligent Validator with LangGraph workflow integration.
        
        Features:
        - Multiple validation types
        - LLM-powered rule analysis
        - Intelligent fix suggestions
        - Severity classification
        - Batch validation
        - Custom validators
        - Pattern detection
        - Compliance checking
        """
        
        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)
            
            # Rules
            self.rules: Dict[str, List[ValidationRule]] = {}
            
            # History
            self.validation_history: List[ValidationResult] = []
            
            # Build workflow
            self.workflow = self._build_workflow()
            
            # Statistics
            self.stats = {
                "total_validations": 0,
                "passed": 0,
                "failed": 0,
                "validation_types": {t.value: 0 for t in ValidationType}
            }
        
        def _build_workflow(self) -> StateGraph:
            """Build LangGraph workflow."""
            workflow = StateGraph(ValidationState)
            
            workflow.add_node("parse_rules", self._parse_rules_node)
            workflow.add_node("validate_fields", self._validate_fields_node)
            workflow.add_node("aggregate_results", self._aggregate_results_node)
            workflow.add_node("generate_insights", self._generate_insights_node)
            workflow.add_node("recommend_fixes", self._recommend_fixes_node)
            
            workflow.set_entry_point("parse_rules")
            workflow.add_edge("parse_rules", "validate_fields")
            workflow.add_edge("validate_fields", "aggregate_results")
            workflow.add_edge("aggregate_results", "generate_insights")
            workflow.add_edge("generate_insights", "recommend_fixes")
            workflow.add_edge("recommend_fixes", END)
            
            return workflow.compile()
        
        def _parse_rules_node(self, state: ValidationState) -> ValidationState:
            """Parse and organize validation rules."""
            # Organize rules by field
            all_rules = []
            for field, rules in self.rules.items():
                all_rules.extend(rules)
            
            state["rules"] = all_rules
            state["field_results"] = {}
            
            return state
        
        def _validate_fields_node(self, state: ValidationState) -> ValidationState:
            """Validate all fields."""
            data = state["data"]
            field_results = {}
            
            # Validate each field
            for field, value in data.items():
                if field not in self.rules:
                    continue
                
                field_results[field] = []
                
                for rule in self.rules[field]:
                    self.stats["total_validations"] += 1
                    self.stats["validation_types"][rule.validation_type.value] += 1
                    
                    try:
                        passed = rule.validator(value)
                        
                        result = ValidationResult(
                            field=field,
                            rule=rule.name,
                            passed=passed,
                            error_message=None if passed else rule.error_message,
                            severity=rule.severity
                        )
                        
                        if passed:
                            self.stats["passed"] += 1
                        else:
                            self.stats["failed"] += 1
                        
                        field_results[field].append(result)
                        self.validation_history.append(result)
                    
                    except Exception as e:
                        result = ValidationResult(
                            field=field,
                            rule=rule.name,
                            passed=False,
                            error_message=f"Validation error: {str(e)}",
                            severity=ValidationSeverity.ERROR
                        )
                        field_results[field].append(result)
                        self.validation_history.append(result)
                        self.stats["failed"] += 1
            
            state["field_results"] = field_results
            return state
        
        def _aggregate_results_node(self, state: ValidationState) -> ValidationState:
            """Aggregate validation results."""
            field_results = state["field_results"]
            
            errors = []
            warnings = []
            all_passed = True
            
            for field, results in field_results.items():
                for result in results:
                    if not result.passed:
                        if result.severity == ValidationSeverity.ERROR:
                            errors.append(f"{field}: {result.error_message}")
                            all_passed = False
                        elif result.severity == ValidationSeverity.WARNING:
                            warnings.append(f"{field}: {result.error_message}")
            
            state["errors"] = errors
            state["warnings"] = warnings
            state["all_passed"] = all_passed
            
            # Create summary
            state["validation_summary"] = {
                "total_fields": len(state["data"]),
                "validated_fields": len(field_results),
                "total_rules": sum(len(results) for results in field_results.values()),
                "errors": len(errors),
                "warnings": len(warnings),
                "passed": all_passed
            }
            
            return state
        
        def _generate_insights_node(self, state: ValidationState) -> ValidationState:
            """Generate validation insights using LLM."""
            errors = state["errors"]
            warnings = state["warnings"]
            summary = state["validation_summary"]
            
            if not errors and not warnings:
                state["insights"] = ValidationInsights(
                    overall_assessment="All validations passed successfully",
                    critical_issues=[],
                    recommendations=["Data is valid and ready for processing"],
                    fix_suggestions=[]
                )
                return state
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are an expert in data validation and quality assurance."),
                ("user", """Analyze these validation results and provide insights:
    
    Validation Summary:
    - Total Fields: {total_fields}
    - Validated Fields: {validated_fields}
    - Total Rules: {total_rules}
    - Errors: {error_count}
    - Warnings: {warning_count}
    
    Errors:
    {errors}
    
    Warnings:
    {warnings}
    
    Provide analysis as JSON with:
    - overall_assessment: Brief assessment of data quality
    - critical_issues: List of most critical issues (2-3 items)
    - recommendations: List of recommendations (2-3 items)
    - fix_suggestions: List of specific fixes (2-3 items)
    
    Focus on actionable insights.""")
            ])
            
            chain = prompt | self.llm
            response = chain.invoke({
                "total_fields": summary["total_fields"],
                "validated_fields": summary["validated_fields"],
                "total_rules": summary["total_rules"],
                "error_count": summary["errors"],
                "warning_count": summary["warnings"],
                "errors": "\n".join(errors) if errors else "None",
                "warnings": "\n".join(warnings) if warnings else "None"
            })
            
            try:
                insights_data = json.loads(response.content)
                state["insights"] = ValidationInsights(
                    overall_assessment=insights_data.get("overall_assessment", "Analysis complete"),
                    critical_issues=insights_data.get("critical_issues", []),
                    recommendations=insights_data.get("recommendations", []),
                    fix_suggestions=insights_data.get("fix_suggestions", [])
                )
            except:
                state["insights"] = ValidationInsights(
                    overall_assessment="Validation completed with issues",
                    critical_issues=errors[:3] if errors else [],
                    recommendations=["Review validation errors", "Fix data issues"],
                    fix_suggestions=["Check field values", "Ensure correct data types"]
                )
            
            return state
        
        def _recommend_fixes_node(self, state: ValidationState) -> ValidationState:
            """Generate fix recommendations."""
            # Insights already contain fix suggestions from LLM
            return state
        
        def add_rule(
            self,
            field: str,
            name: str,
            validation_type: ValidationType,
            validator: Callable,
            error_message: str,
            severity: ValidationSeverity = ValidationSeverity.ERROR
        ):
            """Add validation rule."""
            rule = ValidationRule(
                name=name,
                validation_type=validation_type,
                validator=validator,
                error_message=error_message,
                severity=severity,
                field=field
            )
            
            if field not in self.rules:
                self.rules[field] = []
            
            self.rules[field].append(rule)
        
        def validate(self, data: Dict[str, Any]) -> Dict[str, Any]:
            """Validate data through workflow."""
            initial_state: ValidationState = {
                "data": data,
                "rules": [],
                "field_results": {},
                "all_passed": True,
                "errors": [],
                "warnings": [],
                "insights": None,
                "validation_summary": {}
            }
            
            final_state = self.workflow.invoke(initial_state)
            
            return {
                "valid": final_state["all_passed"],
                "errors": final_state["errors"],
                "warnings": final_state["warnings"],
                "summary": final_state["validation_summary"],
                "insights": final_state["insights"],
                "field_results": final_state["field_results"]
            }
        
        def is_valid(self, data: Dict[str, Any]) -> bool:
            """Check if data is valid."""
            result = self.validate(data)
            return result["valid"]
        
        def get_statistics(self) -> Dict[str, Any]:
            """Get validation statistics."""
            return {
                "name": self.name,
                "rules_count": sum(len(rules) for rules in self.rules.values()),
                **self.stats
            }
    
    
    class ValidationPattern:
        """
        Comprehensive Validation Pattern coordinator with LangChain/LangGraph.
        """
        
        def __init__(self, model_name: str = "gpt-4"):
            self.validators: Dict[str, IntelligentValidator] = {}
            self.model_name = model_name
        
        def create_validator(self, name: str) -> IntelligentValidator:
            """Create a validator."""
            validator = IntelligentValidator(name, model_name=self.model_name)
            self.validators[name] = validator
            return validator
        
        def validate(
            self,
            validator_name: str,
            data: Dict[str, Any]
        ) -> Dict[str, Any]:
            """Run validation."""
            if validator_name not in self.validators:
                return {"error": f"Validator {validator_name} not found"}
            
            validator = self.validators[validator_name]
            result = validator.validate(data)
            
            return {
                "validator": validator_name,
                **result
            }
        
        def get_statistics(self) -> Dict[str, Any]:
            """Get overall statistics."""
            total_validations = sum(v.stats["total_validations"] for v in self.validators.values())
            total_passed = sum(v.stats["passed"] for v in self.validators.values())
            total_failed = sum(v.stats["failed"] for v in self.validators.values())
            
            return {
                "total_validators": len(self.validators),
                "total_validations": total_validations,
                "total_passed": total_passed,
                "total_failed": total_failed
            }
    
    
    if __name__ == "__main__":
        print("=== Validation Pattern with LangChain/LangGraph Demo ===\n")
        
        print("Scenario 1: User Data Validation")
        print("-" * 50)
        pattern = ValidationPattern(model_name="gpt-4")
        
        user_validator = pattern.create_validator("UserValidator")
        
        # Add validation rules
        print("\nAdding validation rules...")
        
        # Type validation
        user_validator.add_rule(
            "age",
            "type_check",
            ValidationType.TYPE,
            lambda x: isinstance(x, int),
            "Age must be an integer",
            ValidationSeverity.ERROR
        )
        
        # Range validation
        user_validator.add_rule(
            "age",
            "range_check",
            ValidationType.RANGE,
            lambda x: 0 <= x  0,
            "Price must be positive",
            ValidationSeverity.ERROR
        )
        
        product_validator.add_rule(
            "price",
            "price_reasonable",
            ValidationType.BUSINESS,
            lambda x: x = 0,
            "Quantity cannot be negative",
            ValidationSeverity.ERROR
        )
        
        product_validator.add_rule(
            "sku",
            "sku_format",
            ValidationType.FORMAT,
            lambda x: isinstance(x, str) and len(x) >= 3,
            "SKU must be at least 3 characters",
            ValidationSeverity.ERROR
        )
        
        print("\nValidation 3: Valid product")
        result3 = pattern.validate("ProductValidator", {
            "price": 29.99,
            "quantity": 100,
            "sku": "PROD-001"
        })
        
        print(f"Valid: {result3['valid']}")
        print(f"Summary: {result3['summary']}")
        
        print("\n" + "=" * 50)
        print("\nValidation 4: Product with warnings")
        result4 = pattern.validate("ProductValidator", {
            "price": 999999.99,  # High price (warning)
            "quantity": 50,
            "sku": "LUXURY-001"
        })
        
        print(f"Valid: {result4['valid']}")
        print(f"Summary: {result4['summary']}")
        if result4['warnings']:
            print(f"\nWarnings:")
            for warning in result4['warnings']:
                print(f"  - {warning}")
        
        print("\n" + "=" * 50)
        print("\nScenario 3: Transaction Validation")
        print("-" * 50)
        
        transaction_validator = pattern.create_validator("TransactionValidator")
        
        # Transaction rules
        transaction_validator.add_rule(
            "amount",
            "amount_positive",
            ValidationType.RANGE,
            lambda x: x > 0,
            "Transaction amount must be positive",
            ValidationSeverity.ERROR
        )
        
        transaction_validator.add_rule(
            "currency",
            "currency_valid",
            ValidationType.FORMAT,
            lambda x: x in ["USD", "EUR", "GBP", "JPY"],
            "Invalid currency code",
            ValidationSeverity.ERROR
        )
        
        transaction_validator.add_rule(
            "account_id",
            "account_format",
            ValidationType.SCHEMA,
            lambda x: isinstance(x, str) and len(x) == 10,
            "Account ID must be 10 characters",
            ValidationSeverity.ERROR
        )
        
        print("\nValidation 5: Valid transaction")
        result5 = pattern.validate("TransactionValidator", {
            "amount": 100.50,
            "currency": "USD",
            "account_id": "ACC1234567"
        })
        
        print(f"Valid: {result5['valid']}")
        print(f"Summary: {result5['summary']}")
        
        print("\n" + "=" * 50)
        print("\nStatistics")
        print("-" * 50)
        
        overall_stats = pattern.get_statistics()
        print(f"\nOverall Statistics:")
        print(f"  Total Validators: {overall_stats['total_validators']}")
        print(f"  Total Validations: {overall_stats['total_validations']}")
        print(f"  Total Passed: {overall_stats['total_passed']}")
        print(f"  Total Failed: {overall_stats['total_failed']}")
        
        print(f"\nUser Validator Statistics:")
        user_stats = user_validator.get_statistics()
        for key, value in user_stats.items():
            print(f"  {key}: {value}")
        
        print(f"\nProduct Validator Statistics:")
        product_stats = product_validator.get_statistics()
        for key, value in product_stats.items():
            print(f"  {key}: {value}")
        
        print("\n" + "=" * 50)
        print("Validation pattern demonstration complete!")
        print("=" * 50)
    
        

    Agentic Architecture Pattern: Validation

    Pattern 269: Validation Pattern – Line-by-Line Explanation

    Overview

    This pattern implements comprehensive data validation with rule checking, constraint enforcement, and LLM-powered fix suggestions. It supports multiple validation types, severity classification, and intelligent error analysis.


    Code Explanation

    Lines 1-15: Imports

    """
    Pattern 269: Validation Pattern with LangChain/LangGraph
    
    Purpose: Data validation with rule checking and constraint enforcement.
    Provides intelligent validation orchestration with LLM-powered rule analysis.
    """
    

    Explanation: Module docstring – pattern for intelligent data validation.

    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 validation with LLM integration.


    Lines 17-33: Enum Definitions

    class ValidationType(Enum):
        """Types of validation."""
        TYPE = "type"
        RANGE = "range"
        FORMAT = "format"
        SCHEMA = "schema"
        BUSINESS = "business"
        CUSTOM = "custom"
    

    Explanation: Six validation types: – TYPE: Check data type (int, str, etc.) – RANGE: Check value within bounds (age between 0-120) – FORMAT: Check pattern (email, phone, regex) – SCHEMA: Check structure (required fields, nested objects) – BUSINESS: Check business rules (order total > line items sum) – CUSTOM: Custom validation functions

    class ValidationSeverity(Enum):
        """Severity of validation issues."""
        ERROR = "error"      # Blocks processing
        WARNING = "warning"  # Should be addressed
        INFO = "info"        # Informational only
    

    Explanation: Three severity levels for validation issues.


    Lines 35-61: Dataclasses

    @dataclass
    class ValidationRule:
        """Validation rule definition."""
        name: str
        validation_type: ValidationType
        validator: Callable
        error_message: str
        severity: ValidationSeverity = ValidationSeverity.ERROR
        field: Optional[str] = None
    

    Explanation: Defines a validation rule: – name: Human-readable identifier – validation_type: Category of validation – validator: Callable that returns True/False – error_message: Message when validation fails – severity: How serious the failure is – field: Optional specific field this rule applies to

    Example: ValidationRule("age_range", ValidationType.RANGE, lambda x: 0 <= x <= 120, "Age must be 0-120")

    @dataclass
    class ValidationResult:
        """Result of validation."""
        field: str
        rule: str
        passed: bool
        error_message: Optional[str] = None
        severity: ValidationSeverity = ValidationSeverity.ERROR
        timestamp: datetime = field(default_factory=datetime.now)
    

    Explanation: Result of applying one rule to one field. Tracks success/failure and error details.

    @dataclass
    class ValidationInsights:
        """LLM-generated validation insights."""
        overall_assessment: str
        critical_issues: List[str]
        recommendations: List[str]
        fix_suggestions: List[str]
    

    Explanation: AI-generated analysis of validation failures with actionable fixes.


    Lines 63-74: TypedDict State

    class ValidationState(TypedDict):
        """State for validation workflow."""
        data: Dict[str, Any]
        rules: List[ValidationRule]
        field_results: Dict[str, List[ValidationResult]]
        all_passed: bool
        errors: List[str]
        warnings: List[str]
        insights: Optional[ValidationInsights]
        validation_summary: Dict[str, Any]
    

    Explanation: Workflow state tracking data, rules, results per field, and AI insights.


    Lines 76-117: IntelligentValidator Initialization

    class IntelligentValidator:
        """
        Intelligent Validator with LangGraph workflow integration.
    
        Features:
        - Multiple validation types
        - LLM-powered rule analysis
        - Intelligent fix suggestions
        - Severity classification
        - Batch validation
        - Custom validators
        - Pattern detection
        - Compliance checking
        """
    

    Explanation: Main validator class with AI-powered analysis.

    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)
    
        # Rules
        self.rules: Dict[str, List[ValidationRule]] = {}
    
        # History
        self.validation_history: List[ValidationResult] = []
    
        # Build workflow
        self.workflow = self._build_workflow()
    
        # Statistics
        self.stats = {
            "total_validations": 0,
            "passed": 0,
            "failed": 0,
            "validation_types": {t.value: 0 for t in ValidationType}
        }
    

    Explanation: Initialize with: – Rules organized by field – Validation history for trend analysis – Workflow for processing – Statistics tracking


    Lines 119-137: Workflow Building

    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow."""
        workflow = StateGraph(ValidationState)
    
        workflow.add_node("parse_rules", self._parse_rules_node)
        workflow.add_node("validate_fields", self._validate_fields_node)
        workflow.add_node("aggregate_results", self._aggregate_results_node)
        workflow.add_node("generate_insights", self._generate_insights_node)
        workflow.add_node("recommend_fixes", self._recommend_fixes_node)
    

    Explanation: Create 5-node workflow: 1. Parse rules: Organize rules by field 2. Validate fields: Apply all rules to data 3. Aggregate results: Collect errors/warnings 4. Generate insights: LLM analyzes failures 5. Recommend fixes: LLM suggests corrections

        workflow.set_entry_point("parse_rules")
        workflow.add_edge("parse_rules", "validate_fields")
        workflow.add_edge("validate_fields", "aggregate_results")
        workflow.add_edge("aggregate_results", "generate_insights")
        workflow.add_edge("generate_insights", "recommend_fixes")
        workflow.add_edge("recommend_fixes", END)
    
        return workflow.compile()
    

    Explanation: Linear flow through all nodes.


    Lines 139-150: Parse Rules Node

    def _parse_rules_node(self, state: ValidationState) -> ValidationState:
        """Parse and organize validation rules."""
        # Organize rules by field
        all_rules = []
        for field, rules in self.rules.items():
            all_rules.extend(rules)
    
        state["rules"] = all_rules
        state["field_results"] = {}
    
        return state
    

    Explanation: Flatten rules from field-organized dictionary into list, initialize results dictionary.


    Lines 152-213: Validate Fields Node

    def _validate_fields_node(self, state: ValidationState) -> ValidationState:
        """Validate all fields."""
        data = state["data"]
        field_results = {}
    
        # Validate each field
        for field, value in data.items():
            if field not in self.rules:
                continue
    
            field_results[field] = []
    

    Explanation: Extract data and initialize results per field. Skip fields without rules.

            for rule in self.rules[field]:
                self.stats["total_validations"] += 1
                self.stats["validation_types"][rule.validation_type.value] += 1
    
                try:
                    passed = rule.validator(value)
    
                    result = ValidationResult(
                        field=field,
                        rule=rule.name,
                        passed=passed,
                        error_message=None if passed else rule.error_message,
                        severity=rule.severity
                    )
    
                    if passed:
                        self.stats["passed"] += 1
                    else:
                        self.stats["failed"] += 1
    
                    field_results[field].append(result)
                    self.validation_history.append(result)
    

    Explanation: For each rule on field: 1. Call validator function with value 2. Create ValidationResult 3. Update statistics 4. Record in field results and history

                except Exception as e:
                    result = ValidationResult(
                        field=field,
                        rule=rule.name,
                        passed=False,
                        error_message=f"Validation error: {str(e)}",
                        severity=ValidationSeverity.ERROR
                    )
                    field_results[field].append(result)
                    self.validation_history.append(result)
                    self.stats["failed"] += 1
    
        state["field_results"] = field_results
        return state
    

    Explanation: Handle exceptions in validator – treat as validation failure. Store results in state.


    Lines 215-247: Aggregate Results Node

    def _aggregate_results_node(self, state: ValidationState) -> ValidationState:
        """Aggregate validation results."""
        field_results = state["field_results"]
    
        errors = []
        warnings = []
    

    Explanation: Initialize error and warning lists.

        # Collect errors and warnings
        for field, results in field_results.items():
            for result in results:
                if not result.passed:
                    message = f"{field}: {result.error_message}"
    
                    if result.severity == ValidationSeverity.ERROR:
                        errors.append(message)
                    elif result.severity == ValidationSeverity.WARNING:
                        warnings.append(message)
    

    Explanation: Iterate through all results, categorize failures by severity.

        # Check if all passed
        all_passed = len(errors) == 0
    
        state["errors"] = errors
        state["warnings"] = warnings
        state["all_passed"] = all_passed
    
        state["validation_summary"] = {
            "total_fields": len(field_results),
            "errors": len(errors),
            "warnings": len(warnings),
            "passed": all_passed
        }
    
        return state
    

    Explanation: Determine overall pass/fail (errors block processing), create summary.


    Lines 249-325: Generate Insights Node (LLM-Powered)

    def _generate_insights_node(self, state: ValidationState) -> ValidationState:
        """Generate LLM-powered validation insights."""
        errors = state["errors"]
        warnings = state["warnings"]
    
        if not errors and not warnings:
            state["insights"] = ValidationInsights(
                overall_assessment="All validations passed",
                critical_issues=[],
                recommendations=[],
                fix_suggestions=[]
            )
            return state
    

    Explanation: If no issues, skip LLM analysis.

        # Prepare data for LLM
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert in data validation and quality assurance."),
            ("user", """Analyze these validation failures:
    
    Validation: {name}
    Errors: {errors}
    Warnings: {warnings}
    
    Data Summary:
    {data_summary}
    
    Provide analysis as JSON with:
    - overall_assessment: Brief assessment of data quality
    - critical_issues: List of most critical problems
    - recommendations: List of recommendations to fix issues
    - fix_suggestions: Specific actionable fixes for each error
    
    Focus on root causes and patterns, not just individual errors.""")
        ])
    

    Explanation: Ask LLM to analyze validation failures, identify patterns, suggest fixes.

        chain = prompt | self.llm
        response = chain.invoke({
            "name": self.name,
            "errors": "\n".join(f"- {e}" for e in errors),
            "warnings": "\n".join(f"- {w}" for w in warnings),
            "data_summary": str(state["validation_summary"])
        })
    

    Explanation: Invoke LLM with error list and summary.

        try:
            insights_data = json.loads(response.content)
    
            state["insights"] = ValidationInsights(
                overall_assessment=insights_data.get("overall_assessment", "Analysis completed"),
                critical_issues=insights_data.get("critical_issues", []),
                recommendations=insights_data.get("recommendations", []),
                fix_suggestions=insights_data.get("fix_suggestions", [])
            )
    
        except Exception as e:
            state["insights"] = ValidationInsights(
                overall_assessment=f"Validation completed with {len(errors)} errors and {len(warnings)} warnings",
                critical_issues=errors[:3],
                recommendations=["Review validation failures and correct data"],
                fix_suggestions=[]
            )
    

    Explanation: Parse JSON response into insights object. Fallback if parsing fails.

        return state
    

    Lines 327-345: Recommend Fixes Node

    def _recommend_fixes_node(self, state: ValidationState) -> ValidationState:
        """Recommend specific fixes."""
        insights = state["insights"]
    
        if not insights or not insights.fix_suggestions:
            return state
    
        # Fixes already generated in insights
        # This node could be extended to apply automatic fixes
    
        return state
    

    Explanation: Placeholder for automatic fix application. Currently fixes are just suggestions from LLM.


    Lines 347-409: Public Methods

    def add_rule(self, field: str, rule: ValidationRule):
        """Add validation rule for field."""
        if field not in self.rules:
            self.rules[field] = []
        self.rules[field].append(rule)
    

    Explanation: Register validation rule for specific field.

    def add_type_rule(self, field: str, expected_type: type, error_message: str = None):
        """Add type validation rule."""
        rule = ValidationRule(
            name=f"{field}_type",
            validation_type=ValidationType.TYPE,
            validator=lambda x: isinstance(x, expected_type),
            error_message=error_message or f"{field} must be {expected_type.__name__}",
            severity=ValidationSeverity.ERROR
        )
        self.add_rule(field, rule)
    

    Explanation: Convenience method for type checking. Example: add_type_rule("age", int).

    def add_range_rule(
        self,
        field: str,
        min_value: Optional[Any] = None,
        max_value: Optional[Any] = None,
        error_message: str = None
    ):
        """Add range validation rule."""
        def range_validator(x):
            if min_value is not None and x < min_value:
                return False
            if max_value is not None and x > max_value:
                return False
            return True
    
        rule = ValidationRule(
            name=f"{field}_range",
            validation_type=ValidationType.RANGE,
            validator=range_validator,
            error_message=error_message or f"{field} must be between {min_value} and {max_value}",
            severity=ValidationSeverity.ERROR
        )
        self.add_rule(field, rule)
    

    Explanation: Convenience method for range checking. Example: add_range_rule("age", 0, 120).

    def validate(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Validate data through workflow."""
        initial_state: ValidationState = {
            "data": data,
            "rules": [],
            "field_results": {},
            "all_passed": False,
            "errors": [],
            "warnings": [],
            "insights": None,
            "validation_summary": {}
        }
    
        final_state = self.workflow.invoke(initial_state)
    
        return {
            "passed": final_state["all_passed"],
            "errors": final_state["errors"],
            "warnings": final_state["warnings"],
            "insights": final_state["insights"],
            "summary": final_state["validation_summary"],
            "field_results": final_state["field_results"]
        }
    

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


    Lines 434-515: Demo Code

    Scenario 1: User Registration Validation

    validator = pattern.create_validator("UserRegistration")
    
    validator.add_type_rule("username", str)
    validator.add_type_rule("age", int)
    validator.add_type_rule("email", str)
    
    validator.add_range_rule("age", min_value=18, max_value=100)
    
    def email_format_validator(email: str) -> bool:
        import re
        pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
        return bool(re.match(pattern, email))
    
    validator.add_format_rule("email", email_format_validator, "Invalid email format")
    

    Explanation: Register multiple validation types: – Type checking (username, age, email) – Range checking (age 18-100) – Format checking (email regex)

    Scenario 2: Valid Data

    valid_data = {
        "username": "john_doe",
        "age": 25,
        "email": "john@example.com"
    }
    
    result1 = pattern.validate("UserRegistration", valid_data)
    

    Explanation: All validations pass, no errors or warnings.

    Scenario 3: Invalid Data

    invalid_data = {
        "username": "jane_doe",
        "age": 150,  # Invalid: too old
        "email": "invalid-email"  # Invalid: bad format
    }
    
    result2 = pattern.validate("UserRegistration", invalid_data)
    

    Explanation: Multiple validation failures trigger LLM analysis and fix suggestions.

    Scenario 4: Business Rule Validation

    order_validator = pattern.create_validator("OrderValidation")
    
    def order_total_matches(data: Dict) -> bool:
        """Business rule: total must match sum of line items."""
        items = data.get("items", [])
        stated_total = data.get("total", 0)
        calculated_total = sum(item.get("price", 0) * item.get("quantity", 0) for item in items)
        return abs(stated_total - calculated_total) < 0.01
    
    order_validator.add_business_rule("order_total", order_total_matches, "Total must match line items")
    
    order_data = {
        "total": 100.0,
        "items": [
            {"price": 10.0, "quantity": 5},  # 50.0
            {"price": 25.0, "quantity": 2}   # 50.0
        ]
    }
    
    result3 = pattern.validate("OrderValidation", order_data)
    

    Explanation: Business rule validation ensures data consistency across fields.


    Validation Type Comparison

    Type Purpose Example
    TYPE Check data type isinstance(x, int)
    RANGE Check bounds 0 <= age <= 120
    FORMAT Check pattern Email regex
    SCHEMA Check structure Required fields
    BUSINESS Check rules Order total = sum
    CUSTOM Custom logic Any function

    Key Design Patterns

    1. Chain of Responsibility: Rules applied sequentially
    2. Strategy Pattern: Different validation types use different strategies
    3. Template Method: Common workflow with type-specific validators
    4. Observer Pattern: Validation history tracks all validations
    5. Specification Pattern: Rules specify validation criteria

    Performance Considerations

    • Rule Application: O(fields × rules_per_field) – linear in rules
    • Type Checking: O(1) – instant with isinstance
    • Range Checking: O(1) – simple comparisons
    • Format (Regex): O(n) – where n is string length
    • Business Rules: O(custom) – depends on complexity
    • LLM Analysis: O(LLM) – ~1-2 seconds for insights

    Best Practices: – Order rules by speed (type → range → format → business) – Fail fast for ERROR severity – Use WARNING for non-blocking issues – Cache validation results for immutable data – Batch validations when possible

    Extension Points

    1. Add schema validation (JSON Schema, XML Schema)
    2. Implement async validators for external checks (API calls)
    3. Add validation caching for expensive checks
    4. Implement automatic fix application
    5. Add cross-field validation (password confirmation)
    6. Support conditional validation (if field X, then validate Y)
    7. Add i18n support for error messages
  • Agentic Architecture Pattern: Benchmarking

    
        """
    Pattern 268: Benchmarking Pattern with LangChain/LangGraph
    
    Purpose: Performance measurement and comparison with statistical analysis.
    Provides intelligent benchmark orchestration with LLM-powered performance insights.
    """
    
    from typing import Dict, Any, List, Optional, Callable, TypedDict
    from dataclasses import dataclass, field
    from datetime import datetime
    from enum import Enum
    import time
    import statistics
    from langchain_core.prompts import ChatPromptTemplate
    from langchain_openai import ChatOpenAI
    from langgraph.graph import StateGraph, END
    import json
    
    
    class BenchmarkType(Enum):
        """Types of benchmarks."""
        EXECUTION_TIME = "execution_time"
        THROUGHPUT = "throughput"
        LATENCY = "latency"
        RESOURCE = "resource"
        COMPARISON = "comparison"
        STRESS = "stress"
    
    
    class PerformanceLevel(Enum):
        """Performance classification."""
        EXCELLENT = "excellent"
        GOOD = "good"
        ACCEPTABLE = "acceptable"
        POOR = "poor"
        CRITICAL = "critical"
    
    
    @dataclass
    class BenchmarkResult:
        """Result of a benchmark run."""
        name: str
        benchmark_type: BenchmarkType
        iterations: int
        mean_time: float
        median_time: float
        min_time: float
        max_time: float
        std_dev: float
        throughput: float
        percentile_95: float
        percentile_99: float
        performance_level: PerformanceLevel
        timestamp: datetime = field(default_factory=datetime.now)
    
    
    @dataclass
    class BenchmarkInsights:
        """LLM-generated benchmark insights."""
        performance_assessment: str
        bottlenecks: List[str]
        recommendations: List[str]
        comparison_summary: str
    
    
    class BenchmarkState(TypedDict):
        """State for benchmark workflow."""
        benchmark_name: str
        function: Optional[Callable]
        iterations: int
        benchmark_type: BenchmarkType
        execution_times: List[float]
        mean_time: float
        median_time: float
        std_dev: float
        throughput: float
        percentile_95: float
        percentile_99: float
        performance_level: PerformanceLevel
        outliers: List[float]
        insights: Optional[BenchmarkInsights]
        comparison_data: Dict[str, Any]
    
    
    class IntelligentBenchmark:
        """
        Intelligent Benchmark with LangGraph workflow integration.
        
        Features:
        - Execution time measurement
        - Statistical analysis
        - Outlier detection
        - Percentile calculation
        - LLM-powered insights
        - Performance comparison
        - Bottleneck identification
        - Optimization recommendations
        """
        
        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)
            
            # Results storage
            self.results: Dict[str, BenchmarkResult] = {}
            
            # Build workflow
            self.workflow = self._build_workflow()
            
            # Statistics
            self.stats = {
                "total_benchmarks": 0,
                "benchmark_types": {t.value: 0 for t in BenchmarkType},
                "total_iterations": 0,
                "avg_execution_time": 0.0
            }
        
        def _build_workflow(self) -> StateGraph:
            """Build LangGraph workflow."""
            workflow = StateGraph(BenchmarkState)
            
            workflow.add_node("initialize_benchmark", self._initialize_benchmark_node)
            workflow.add_node("run_iterations", self._run_iterations_node)
            workflow.add_node("analyze_statistics", self._analyze_statistics_node)
            workflow.add_node("detect_outliers", self._detect_outliers_node)
            workflow.add_node("generate_comparison", self._generate_comparison_node)
            
            workflow.set_entry_point("initialize_benchmark")
            workflow.add_edge("initialize_benchmark", "run_iterations")
            workflow.add_edge("run_iterations", "analyze_statistics")
            workflow.add_edge("analyze_statistics", "detect_outliers")
            workflow.add_edge("detect_outliers", "generate_comparison")
            workflow.add_edge("generate_comparison", END)
            
            return workflow.compile()
        
        def _initialize_benchmark_node(self, state: BenchmarkState) -> BenchmarkState:
            """Initialize benchmark state."""
            state["execution_times"] = []
            state["outliers"] = []
            state["comparison_data"] = {}
            return state
        
        def _run_iterations_node(self, state: BenchmarkState) -> BenchmarkState:
            """Run benchmark iterations."""
            function = state["function"]
            iterations = state["iterations"]
            
            if not function:
                return state
            
            times: List[float] = []
            
            # Warm-up run
            try:
                function()
            except:
                pass
            
            # Benchmark iterations
            for _ in range(iterations):
                start = time.perf_counter()
                try:
                    function()
                except:
                    pass
                end = time.perf_counter()
                times.append(end - start)
            
            state["execution_times"] = times
            return state
        
        def _analyze_statistics_node(self, state: BenchmarkState) -> BenchmarkState:
            """Analyze benchmark statistics."""
            times = state["execution_times"]
            
            if not times:
                state["mean_time"] = 0.0
                state["median_time"] = 0.0
                state["std_dev"] = 0.0
                state["throughput"] = 0.0
                state["percentile_95"] = 0.0
                state["percentile_99"] = 0.0
                state["performance_level"] = PerformanceLevel.CRITICAL
                return state
            
            # Calculate statistics
            state["mean_time"] = statistics.mean(times)
            state["median_time"] = statistics.median(times)
            state["std_dev"] = statistics.stdev(times) if len(times) > 1 else 0.0
            state["throughput"] = 1.0 / state["mean_time"] if state["mean_time"] > 0 else 0.0
            
            # Calculate percentiles
            sorted_times = sorted(times)
            p95_idx = int(len(sorted_times) * 0.95)
            p99_idx = int(len(sorted_times) * 0.99)
            state["percentile_95"] = sorted_times[min(p95_idx, len(sorted_times) - 1)]
            state["percentile_99"] = sorted_times[min(p99_idx, len(sorted_times) - 1)]
            
            # Classify performance
            mean_ms = state["mean_time"] * 1000
            if mean_ms < 10:
                state["performance_level"] = PerformanceLevel.EXCELLENT
            elif mean_ms < 50:
                state["performance_level"] = PerformanceLevel.GOOD
            elif mean_ms < 200:
                state["performance_level"] = PerformanceLevel.ACCEPTABLE
            elif mean_ms  BenchmarkState:
            """Detect outlier measurements."""
            times = state["execution_times"]
            
            if not times or len(times)  2 standard deviations)
            outliers = []
            for t in times:
                if abs(t - mean) > 2 * std_dev:
                    outliers.append(t)
            
            state["outliers"] = outliers
            return state
        
        def _generate_comparison_node(self, state: BenchmarkState) -> BenchmarkState:
            """Generate performance insights using LLM."""
            benchmark_name = state["benchmark_name"]
            mean_time = state["mean_time"]
            std_dev = state["std_dev"]
            throughput = state["throughput"]
            performance_level = state["performance_level"]
            outliers = state["outliers"]
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are an expert in performance analysis and optimization."),
                ("user", """Analyze this benchmark result and provide insights:
    
    Benchmark: {benchmark_name}
    Mean Time: {mean_time:.6f}s ({mean_ms:.3f}ms)
    Std Dev: {std_dev:.6f}s
    Throughput: {throughput:.2f} ops/sec
    Performance Level: {performance_level}
    Outliers Detected: {outlier_count}
    
    Provide analysis as JSON with:
    - performance_assessment: Overall performance evaluation
    - bottlenecks: List of potential bottlenecks (2-3 items)
    - recommendations: List of optimization recommendations (2-3 items)
    - comparison_summary: One-line summary for comparison
    
    Focus on actionable insights.""")
            ])
            
            chain = prompt | self.llm
            response = chain.invoke({
                "benchmark_name": benchmark_name,
                "mean_time": mean_time,
                "mean_ms": mean_time * 1000,
                "std_dev": std_dev,
                "throughput": throughput,
                "performance_level": performance_level.value,
                "outlier_count": len(outliers)
            })
            
            try:
                insights_data = json.loads(response.content)
                state["insights"] = BenchmarkInsights(
                    performance_assessment=insights_data.get("performance_assessment", "Performance assessed"),
                    bottlenecks=insights_data.get("bottlenecks", []),
                    recommendations=insights_data.get("recommendations", []),
                    comparison_summary=insights_data.get("comparison_summary", "Benchmark completed")
                )
            except:
                state["insights"] = BenchmarkInsights(
                    performance_assessment=f"Performance level: {performance_level.value}",
                    bottlenecks=["Analysis unavailable"],
                    recommendations=["Re-run benchmark with more iterations"],
                    comparison_summary="Benchmark completed"
                )
            
            return state
        
        def benchmark(
            self,
            name: str,
            function: Callable,
            iterations: int = 100,
            benchmark_type: BenchmarkType = BenchmarkType.EXECUTION_TIME
        ) -> BenchmarkResult:
            """Run benchmark through workflow."""
            initial_state: BenchmarkState = {
                "benchmark_name": name,
                "function": function,
                "iterations": iterations,
                "benchmark_type": benchmark_type,
                "execution_times": [],
                "mean_time": 0.0,
                "median_time": 0.0,
                "std_dev": 0.0,
                "throughput": 0.0,
                "percentile_95": 0.0,
                "percentile_99": 0.0,
                "performance_level": PerformanceLevel.ACCEPTABLE,
                "outliers": [],
                "insights": None,
                "comparison_data": {}
            }
            
            final_state = self.workflow.invoke(initial_state)
            
            # Create result
            result = BenchmarkResult(
                name=name,
                benchmark_type=benchmark_type,
                iterations=iterations,
                mean_time=final_state["mean_time"],
                median_time=final_state["median_time"],
                min_time=min(final_state["execution_times"]) if final_state["execution_times"] else 0.0,
                max_time=max(final_state["execution_times"]) if final_state["execution_times"] else 0.0,
                std_dev=final_state["std_dev"],
                throughput=final_state["throughput"],
                percentile_95=final_state["percentile_95"],
                percentile_99=final_state["percentile_99"],
                performance_level=final_state["performance_level"]
            )
            
            # Store result
            self.results[name] = result
            
            # Update statistics
            self.stats["total_benchmarks"] += 1
            self.stats["benchmark_types"][benchmark_type.value] += 1
            self.stats["total_iterations"] += iterations
            
            # Update average execution time
            total_time = sum(r.mean_time for r in self.results.values())
            self.stats["avg_execution_time"] = total_time / len(self.results)
            
            return result
        
        def compare(
            self,
            implementations: Dict[str, Callable],
            iterations: int = 100
        ) -> Dict[str, BenchmarkResult]:
            """Compare multiple implementations."""
            comparison: Dict[str, BenchmarkResult] = {}
            
            for name, func in implementations.items():
                result = self.benchmark(name, func, iterations, BenchmarkType.COMPARISON)
                comparison[name] = result
            
            return comparison
        
        def generate_report(self, comparison: Dict[str, BenchmarkResult]) -> str:
            """Generate comparison report."""
            if not comparison:
                return "No benchmarks to compare"
            
            report = "\n=== Benchmark Comparison Report ===\n"
            
            # Find fastest
            fastest = min(comparison.values(), key=lambda r: r.mean_time)
            
            for name, result in sorted(comparison.items(), key=lambda x: x[1].mean_time):
                ratio = result.mean_time / fastest.mean_time if fastest.mean_time > 0 else 1.0
                
                report += f"\n{name}:"
                report += f"\n  Mean Time: {result.mean_time*1000:.3f}ms"
                report += f"\n  Median Time: {result.median_time*1000:.3f}ms"
                report += f"\n  Min/Max: {result.min_time*1000:.3f}ms / {result.max_time*1000:.3f}ms"
                report += f"\n  Std Dev: {result.std_dev*1000:.3f}ms"
                report += f"\n  Throughput: {result.throughput:.2f} ops/sec"
                report += f"\n  95th Percentile: {result.percentile_95*1000:.3f}ms"
                report += f"\n  Performance: {result.performance_level.value.upper()}"
                report += f"\n  Relative: {ratio:.2f}x"
                if name == fastest.name:
                    report += " ⚡ FASTEST"
                report += "\n"
            
            return report
        
        def get_statistics(self) -> Dict[str, Any]:
            """Get benchmark statistics."""
            return {
                "name": self.name,
                **self.stats,
                "results_count": len(self.results)
            }
    
    
    class BenchmarkPattern:
        """
        Comprehensive Benchmark Pattern coordinator with LangChain/LangGraph.
        """
        
        def __init__(self, model_name: str = "gpt-4"):
            self.benchmarks: Dict[str, IntelligentBenchmark] = {}
            self.model_name = model_name
        
        def create_benchmark(self, name: str) -> IntelligentBenchmark:
            """Create a benchmark."""
            benchmark = IntelligentBenchmark(name, model_name=self.model_name)
            self.benchmarks[name] = benchmark
            return benchmark
        
        def run(
            self,
            benchmark_name: str,
            test_name: str,
            function: Callable,
            iterations: int = 100
        ) -> Dict[str, Any]:
            """Run a benchmark."""
            if benchmark_name not in self.benchmarks:
                return {"error": f"Benchmark {benchmark_name} not found"}
            
            benchmark = self.benchmarks[benchmark_name]
            result = benchmark.benchmark(test_name, function, iterations)
            
            return {
                "benchmark": benchmark_name,
                "test": test_name,
                "result": {
                    "mean_time": result.mean_time,
                    "median_time": result.median_time,
                    "throughput": result.throughput,
                    "performance_level": result.performance_level.value
                }
            }
        
        def get_statistics(self) -> Dict[str, Any]:
            """Get overall statistics."""
            total_benchmarks = sum(b.stats["total_benchmarks"] for b in self.benchmarks.values())
            total_iterations = sum(b.stats["total_iterations"] for b in self.benchmarks.values())
            
            return {
                "total_benchmark_suites": len(self.benchmarks),
                "total_benchmarks": total_benchmarks,
                "total_iterations": total_iterations
            }
    
    
    if __name__ == "__main__":
        print("=== Benchmark Pattern with LangChain/LangGraph Demo ===\n")
        
        print("Scenario 1: Simple Performance Comparison")
        print("-" * 50)
        pattern = BenchmarkPattern(model_name="gpt-4")
        
        bench = pattern.create_benchmark("PerformanceSuite")
        
        # Define test functions
        def fast_operation():
            """Fast operation."""
            return sum(range(100))
        
        def medium_operation():
            """Medium operation."""
            return sum(range(1000))
        
        def slow_operation():
            """Slow operation."""
            return sum(range(10000))
        
        print("\nRunning individual benchmarks...")
        
        result1 = pattern.run("PerformanceSuite", "FastOp", fast_operation, iterations=1000)
        print(f"\nFastOp:")
        print(f"  Mean: {result1['result']['mean_time']*1000:.3f}ms")
        print(f"  Throughput: {result1['result']['throughput']:.2f} ops/sec")
        print(f"  Performance: {result1['result']['performance_level']}")
        
        result2 = pattern.run("PerformanceSuite", "MediumOp", medium_operation, iterations=1000)
        print(f"\nMediumOp:")
        print(f"  Mean: {result2['result']['mean_time']*1000:.3f}ms")
        print(f"  Throughput: {result2['result']['throughput']:.2f} ops/sec")
        print(f"  Performance: {result2['result']['performance_level']}")
        
        result3 = pattern.run("PerformanceSuite", "SlowOp", slow_operation, iterations=1000)
        print(f"\nSlowOp:")
        print(f"  Mean: {result3['result']['mean_time']*1000:.3f}ms")
        print(f"  Throughput: {result3['result']['throughput']:.2f} ops/sec")
        print(f"  Performance: {result3['result']['performance_level']}")
        
        print("\n" + "=" * 50)
        print("\nScenario 2: Implementation Comparison")
        print("-" * 50)
        
        # Compare implementations
        implementations = {
            "Fast": fast_operation,
            "Medium": medium_operation,
            "Slow": slow_operation
        }
        
        print("\nComparing implementations...")
        comparison = bench.compare(implementations, iterations=500)
        report = bench.generate_report(comparison)
        print(report)
        
        print("=" * 50)
        print("\nScenario 3: List Processing Comparison")
        print("-" * 50)
        
        list_bench = pattern.create_benchmark("ListProcessing")
        
        # Different list processing approaches
        test_list = list(range(1000))
        
        def list_comprehension():
            return [x * 2 for x in test_list]
        
        def map_function():
            return list(map(lambda x: x * 2, test_list))
        
        def for_loop():
            result = []
            for x in test_list:
                result.append(x * 2)
            return result
        
        print("\nBenchmarking list processing approaches...")
        
        list_implementations = {
            "ListComprehension": list_comprehension,
            "MapFunction": map_function,
            "ForLoop": for_loop
        }
        
        list_comparison = list_bench.compare(list_implementations, iterations=500)
        list_report = list_bench.generate_report(list_comparison)
        print(list_report)
        
        print("=" * 50)
        print("\nScenario 4: String Operations Benchmark")
        print("-" * 50)
        
        string_bench = pattern.create_benchmark("StringOperations")
        
        test_strings = ["test" + str(i) for i in range(100)]
        
        def string_join():
            return "".join(test_strings)
        
        def string_concat():
            result = ""
            for s in test_strings:
                result += s
            return result
        
        print("\nBenchmarking string operations...")
        
        string_implementations = {
            "Join": string_join,
            "Concatenation": string_concat
        }
        
        string_comparison = string_bench.compare(string_implementations, iterations=500)
        string_report = string_bench.generate_report(string_comparison)
        print(string_report)
        
        print("=" * 50)
        print("\nStatistics")
        print("-" * 50)
        
        overall_stats = pattern.get_statistics()
        print(f"\nOverall Statistics:")
        print(f"  Total Benchmark Suites: {overall_stats['total_benchmark_suites']}")
        print(f"  Total Benchmarks Run: {overall_stats['total_benchmarks']}")
        print(f"  Total Iterations: {overall_stats['total_iterations']}")
        
        print(f"\nPerformance Suite Statistics:")
        perf_stats = bench.get_statistics()
        for key, value in perf_stats.items():
            print(f"  {key}: {value}")
        
        print("\n" + "=" * 50)
        print("Benchmark pattern demonstration complete!")
        print("=" * 50)
    
        

    Agentic Architecture Pattern: Benchmarking

    Pattern 268: Benchmarking Pattern – Line-by-Line Explanation

    Overview

    This pattern implements comprehensive performance benchmarking with statistical analysis, outlier detection, and LLM-powered insights. It measures execution time, memory usage, and provides detailed performance characteristics.


    Code Explanation

    Lines 1-17: Imports

    """
    Pattern 268: Benchmarking Pattern with LangChain/LangGraph
    
    Purpose: Performance benchmarking with statistical analysis.
    Provides intelligent performance measurement with LLM-powered insights.
    """
    

    Explanation: Module docstring – pattern for measuring and analyzing performance.

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

    Explanation: Includes time for measurements, statistics for analysis.


    Lines 19-28: Enum Definitions

    class BenchmarkType(Enum):
        """Types of benchmarks."""
        PERFORMANCE = "performance"  # Execution time
        MEMORY = "memory"  # Memory usage
        THROUGHPUT = "throughput"  # Operations per second
        LATENCY = "latency"  # Response time
    

    Explanation: Four benchmark types: – PERFORMANCE: How fast does it run? – MEMORY: How much memory does it use? – THROUGHPUT: How many operations per second? – LATENCY: How long until response?

    class BenchmarkStatus(Enum):
        """Status of benchmark."""
        PENDING = "pending"
        RUNNING = "running"
        COMPLETED = "completed"
        FAILED = "failed"
    

    Explanation: Tracks benchmark execution state.


    Lines 30-75: Dataclasses

    @dataclass
    class BenchmarkConfig:
        """Configuration for benchmark."""
        name: str
        function: Callable
        args: tuple = field(default_factory=tuple)
        kwargs: Dict[str, Any] = field(default_factory=dict)
        iterations: int = 100
        warmup_iterations: int = 10
        benchmark_type: BenchmarkType = BenchmarkType.PERFORMANCE
    

    Explanation: Configuration for what to benchmark: – Function to test – Arguments to pass – Number of iterations (default 100) – Warmup iterations (default 10) – discard first runs to account for JIT compilation, cache warming

    @dataclass
    class BenchmarkMetrics:
        """Metrics from benchmark run."""
        mean: float
        median: float
        std_dev: float
        min_value: float
        max_value: float
        percentile_95: float
        percentile_99: float
        total_time: float
        iterations: int
    

    Explanation: Statistical metrics: – Mean: Average execution time – Median: Middle value (less affected by outliers) – Std Dev: Variability – Min/Max: Range – 95th/99th percentile: “Most” requests complete within this time – Total time: Sum of all iterations – Iterations: Number of measurements

    @dataclass
    class BenchmarkResult:
        """Result of benchmark."""
        config: BenchmarkConfig
        metrics: BenchmarkMetrics
        raw_measurements: List[float]
        outliers: List[float]
        status: BenchmarkStatus
        insights: List[str] = field(default_factory=list)
        timestamp: datetime = field(default_factory=datetime.now)
    

    Explanation: Complete benchmark result including raw data, outliers, and AI insights.


    Lines 77-90: TypedDict State

    class BenchmarkState(TypedDict):
        """State for benchmarking workflow."""
        config: BenchmarkConfig
        raw_measurements: List[float]
        metrics: Optional[BenchmarkMetrics]
        outliers: List[float]
        status: BenchmarkStatus
        insights: List[str]
        error: Optional[str]
    

    Explanation: Workflow state tracking measurements, metrics, outliers, and status.


    Lines 92-130: IntelligentBenchmark Initialization

    class IntelligentBenchmark:
        """
        Intelligent Benchmarking with LangGraph workflow integration.
    
        Features:
        - Multiple benchmark types (performance, memory, throughput, latency)
        - Statistical analysis (mean, median, std dev, percentiles)
        - Outlier detection
        - LLM-powered performance insights
        - Comparison reports
        """
    

    Explanation: Main benchmarking class with statistical analysis and AI insights.

    def __init__(self, model_name: str = "gpt-4", temperature: float = 0.5):
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
    
        # Storage
        self.results: Dict[str, BenchmarkResult] = {}
    
        # Build workflow
        self.workflow = self._build_workflow()
    
        # Statistics
        self.stats = {
            "total_benchmarks": 0,
            "total_measurements": 0,
            "total_outliers": 0
        }
    

    Explanation: Initialize LLM, results storage, workflow, and tracking statistics.


    Lines 132-150: Workflow Building

    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow."""
        workflow = StateGraph(BenchmarkState)
    
        workflow.add_node("warmup", self._warmup_node)
        workflow.add_node("measure", self._measure_node)
        workflow.add_node("analyze_statistics", self._analyze_statistics_node)
        workflow.add_node("detect_outliers", self._detect_outliers_node)
        workflow.add_node("generate_insights", self._generate_insights_node)
    

    Explanation: Create 5-node workflow: 1. Warmup: Run iterations that will be discarded 2. Measure: Collect timing data 3. Analyze: Calculate statistics 4. Detect outliers: Find anomalies 5. Generate insights: Use LLM to interpret results

        workflow.set_entry_point("warmup")
        workflow.add_edge("warmup", "measure")
        workflow.add_edge("measure", "analyze_statistics")
        workflow.add_edge("analyze_statistics", "detect_outliers")
        workflow.add_edge("detect_outliers", "generate_insights")
        workflow.add_edge("generate_insights", END)
    
        return workflow.compile()
    

    Explanation: Linear flow through all nodes.


    Lines 152-174: Warmup Node

    def _warmup_node(self, state: BenchmarkState) -> BenchmarkState:
        """Warm up before measurement."""
        config = state["config"]
    
        state["status"] = BenchmarkStatus.RUNNING
    
        # Run warmup iterations
        for _ in range(config.warmup_iterations):
            try:
                config.function(*config.args, **config.kwargs)
            except Exception as e:
                state["status"] = BenchmarkStatus.FAILED
                state["error"] = f"Warmup failed: {str(e)}"
                return state
    
        state["insights"].append(f"Completed {config.warmup_iterations} warmup iterations")
    
        return state
    

    Explanation: Execute function multiple times before actual measurement. This accounts for: – JIT compilation warmup (in languages like Python/Java) – Cache warming (file system, CPU cache) – Connection pool initialization – Eliminates “cold start” bias from measurements


    Lines 176-217: Measure Node

    def _measure_node(self, state: BenchmarkState) -> BenchmarkState:
        """Measure performance."""
        config = state["config"]
        measurements: List[float] = []
    

    Explanation: Initialize measurement list.

        for i in range(config.iterations):
            if config.benchmark_type == BenchmarkType.PERFORMANCE:
                # Measure execution time
                start_time = time.perf_counter()
                try:
                    config.function(*config.args, **config.kwargs)
                except Exception as e:
                    state["status"] = BenchmarkStatus.FAILED
                    state["error"] = f"Measurement failed at iteration {i}: {str(e)}"
                    return state
                end_time = time.perf_counter()
    
                measurement = (end_time - start_time) * 1000  # Convert to milliseconds
                measurements.append(measurement)
    

    Explanation: PERFORMANCE measurement: – Use time.perf_counter() – highest resolution timer available – Calculate elapsed time – Convert to milliseconds for readability – Append to measurements list

            elif config.benchmark_type == BenchmarkType.LATENCY:
                # Similar to performance but may include network time
                start_time = time.perf_counter()
                try:
                    config.function(*config.args, **config.kwargs)
                except Exception as e:
                    state["status"] = BenchmarkStatus.FAILED
                    state["error"] = f"Measurement failed at iteration {i}: {str(e)}"
                    return state
                end_time = time.perf_counter()
    
                measurement = (end_time - start_time) * 1000
                measurements.append(measurement)
    

    Explanation: LATENCY – same as performance, but semantic difference (latency often includes network/IO).

        state["raw_measurements"] = measurements
        self.stats["total_measurements"] += len(measurements)
    
        return state
    

    Explanation: Store measurements and update statistics.


    Lines 219-257: Analyze Statistics Node

    def _analyze_statistics_node(self, state: BenchmarkState) -> BenchmarkState:
        """Analyze benchmark statistics."""
        measurements = state["raw_measurements"]
    
        if not measurements:
            state["status"] = BenchmarkStatus.FAILED
            state["error"] = "No measurements to analyze"
            return state
    

    Explanation: Validate we have data to analyze.

        # Calculate statistics
        sorted_measurements = sorted(measurements)
    
        metrics = BenchmarkMetrics(
            mean=statistics.mean(measurements),
            median=statistics.median(measurements),
            std_dev=statistics.stdev(measurements) if len(measurements) > 1 else 0.0,
            min_value=min(measurements),
            max_value=max(measurements),
            percentile_95=sorted_measurements[int(len(sorted_measurements) * 0.95)],
            percentile_99=sorted_measurements[int(len(sorted_measurements) * 0.99)],
            total_time=sum(measurements),
            iterations=len(measurements)
        )
    

    Explanation: Calculate comprehensive statistics: – Mean: Average performance (sum / count) – Median: Middle value when sorted (robust against outliers) – Std Dev: Standard deviation (how spread out values are) – Min/Max: Best and worst cases – 95th percentile: 95% of requests faster than this – 99th percentile: 99% of requests faster than this (tail latency) – Total time: Sum of all measurements – Iterations: Count

    Percentiles are key for SLA definitions: “99% of requests complete within 100ms”.

        state["metrics"] = metrics
        state["insights"].append(
            f"Mean: {metrics.mean:.2f}ms, Median: {metrics.median:.2f}ms, "
            f"Std Dev: {metrics.std_dev:.2f}ms, 95th: {metrics.percentile_95:.2f}ms"
        )
    
        return state
    

    Explanation: Store metrics and add summary insight.


    Lines 259-299: Detect Outliers Node

    def _detect_outliers_node(self, state: BenchmarkState) -> BenchmarkState:
        """Detect outliers in measurements."""
        measurements = state["raw_measurements"]
        metrics = state["metrics"]
    
        if not metrics or len(measurements) < 3:
            state["outliers"] = []
            return state
    

    Explanation: Need at least 3 measurements for outlier detection.

        # Use IQR method for outlier detection
        sorted_measurements = sorted(measurements)
        q1_idx = int(len(sorted_measurements) * 0.25)
        q3_idx = int(len(sorted_measurements) * 0.75)
    
        q1 = sorted_measurements[q1_idx]
        q3 = sorted_measurements[q3_idx]
        iqr = q3 - q1
    

    Explanation: IQR (Interquartile Range) Method: – Q1 = 25th percentile – Q3 = 75th percentile – IQR = Q3 – Q1 (middle 50% of data)

    Standard statistical method for outlier detection.

        # Outliers are values beyond Q1 - 1.5*IQR or Q3 + 1.5*IQR
        lower_bound = q1 - 1.5 * iqr
        upper_bound = q3 + 1.5 * iqr
    
        outliers = [m for m in measurements if m < lower_bound or m > upper_bound]
    

    Explanation: Classic outlier definition: – Below Q1 – 1.5 × IQR (unusually fast) – Above Q3 + 1.5 × IQR (unusually slow)

    1.5 is standard multiplier from Tukey’s box plot method.

        state["outliers"] = outliers
        self.stats["total_outliers"] += len(outliers)
    
        if outliers:
            state["insights"].append(
                f"Detected {len(outliers)} outliers ({len(outliers)/len(measurements)*100:.1f}%)"
            )
    
        return state
    

    Explanation: Store outliers and report percentage.


    Lines 301-365: Generate Insights Node (LLM-Powered)

    def _generate_insights_node(self, state: BenchmarkState) -> BenchmarkState:
        """Generate LLM-powered performance insights."""
        config = state["config"]
        metrics = state["metrics"]
        outliers = state["outliers"]
    
        if not metrics:
            return state
    

    Explanation: Validate we have metrics to analyze.

        # Prepare data for LLM
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert in performance optimization and benchmarking analysis."),
            ("user", """Analyze these benchmark results:
    
    Benchmark: {name}
    Type: {benchmark_type}
    Iterations: {iterations}
    
    Statistics:
    - Mean: {mean:.2f}ms
    - Median: {median:.2f}ms
    - Std Dev: {std_dev:.2f}ms
    - Min: {min_value:.2f}ms
    - Max: {max_value:.2f}ms
    - 95th percentile: {percentile_95:.2f}ms
    - 99th percentile: {percentile_99:.2f}ms
    
    Outliers: {outlier_count} ({outlier_percent:.1f}%)
    
    Provide analysis as JSON with:
    - performance_grade: Letter grade A-F based on consistency and speed
    - bottlenecks: List of potential bottlenecks
    - recommendations: List of optimization recommendations
    - consistency_analysis: How consistent is the performance?
    - outlier_impact: Impact of outliers on overall performance""")
        ])
    

    Explanation: Comprehensive prompt asking LLM to analyze: – Overall performance grade – Potential bottlenecks causing slowness – Specific optimization recommendations – Performance consistency (low std dev = good) – Whether outliers are problematic

        chain = prompt | self.llm
        response = chain.invoke({
            "name": config.name,
            "benchmark_type": config.benchmark_type.value,
            "iterations": metrics.iterations,
            "mean": metrics.mean,
            "median": metrics.median,
            "std_dev": metrics.std_dev,
            "min_value": metrics.min_value,
            "max_value": metrics.max_value,
            "percentile_95": metrics.percentile_95,
            "percentile_99": metrics.percentile_99,
            "outlier_count": len(outliers),
            "outlier_percent": (len(outliers) / metrics.iterations * 100) if metrics.iterations > 0 else 0
        })
    

    Explanation: Invoke LLM with all statistical data.

        try:
            insights_data = json.loads(response.content)
    
            state["insights"].append(f"Performance Grade: {insights_data.get('performance_grade', 'N/A')}")
            state["insights"].append(f"Consistency: {insights_data.get('consistency_analysis', 'N/A')}")
    
            bottlenecks = insights_data.get('bottlenecks', [])
            if bottlenecks:
                state["insights"].append(f"Potential Bottlenecks: {', '.join(bottlenecks)}")
    
            recommendations = insights_data.get('recommendations', [])
            if recommendations:
                state["insights"].extend([f"Recommendation: {rec}" for rec in recommendations])
    
        except Exception as e:
            state["insights"].append(f"LLM analysis completed (JSON parsing note: {str(e)})")
    

    Explanation: Parse JSON response and add insights to state. Fallback if parsing fails.

        state["status"] = BenchmarkStatus.COMPLETED
        return state
    

    Lines 367-405: Public Benchmark Method

    def benchmark(self, config: BenchmarkConfig) -> BenchmarkResult:
        """Run benchmark with workflow."""
        initial_state: BenchmarkState = {
            "config": config,
            "raw_measurements": [],
            "metrics": None,
            "outliers": [],
            "status": BenchmarkStatus.PENDING,
            "insights": [],
            "error": None
        }
    

    Explanation: Initialize workflow state.

        final_state = self.workflow.invoke(initial_state)
    

    Explanation: Execute workflow (warmup → measure → analyze → detect outliers → generate insights).

        # Create result
        result = BenchmarkResult(
            config=config,
            metrics=final_state["metrics"],
            raw_measurements=final_state["raw_measurements"],
            outliers=final_state["outliers"],
            status=final_state["status"],
            insights=final_state["insights"]
        )
    
        self.results[config.name] = result
        self.stats["total_benchmarks"] += 1
    
        return result
    

    Explanation: Package results, store in history, update statistics, and return.


    Lines 430-500: BenchmarkingPattern Coordinator

    class BenchmarkingPattern:
        """
        Comprehensive Benchmarking Pattern coordinator with LangChain/LangGraph.
        """
    
        def __init__(self, model_name: str = "gpt-4"):
            self.benchmarker = IntelligentBenchmark(model_name=model_name)
    

    Explanation: Simple coordinator wrapping benchmarker.

        def benchmark_function(
            self,
            name: str,
            function: Callable,
            args: tuple = (),
            kwargs: Dict[str, Any] = None,
            iterations: int = 100,
            warmup: int = 10,
            benchmark_type: BenchmarkType = BenchmarkType.PERFORMANCE
        ) -> BenchmarkResult:
            """Benchmark a function."""
            config = BenchmarkConfig(
                name=name,
                function=function,
                args=args,
                kwargs=kwargs or {},
                iterations=iterations,
                warmup_iterations=warmup,
                benchmark_type=benchmark_type
            )
    
            return self.benchmarker.benchmark(config)
    

    Explanation: Convenience method to benchmark function with explicit parameters.

        def compare_results(self, names: List[str]) -> Dict[str, Any]:
            """Compare multiple benchmark results."""
            results = {name: self.benchmarker.results.get(name) for name in names if name in self.benchmarker.results}
    
            if not results:
                return {"error": "No results found for comparison"}
    
            comparison = {
                "summary": {},
                "rankings": {},
                "relative_performance": {}
            }
    
            # Calculate rankings by mean
            sorted_by_mean = sorted(results.items(), key=lambda x: x[1].metrics.mean if x[1].metrics else float('inf'))
    
            for rank, (name, result) in enumerate(sorted_by_mean, 1):
                comparison["rankings"][name] = rank
    
            # Calculate relative performance (vs fastest)
            fastest_mean = sorted_by_mean[0][1].metrics.mean if sorted_by_mean else 1.0
    
            for name, result in results.items():
                if result.metrics:
                    comparison["relative_performance"][name] = {
                        "mean_ratio": result.metrics.mean / fastest_mean,
                        "slower_by": f"{((result.metrics.mean / fastest_mean - 1) * 100):.1f}%"
                    }
    
            return comparison
    

    Explanation: Compare multiple benchmarks: – Rank by mean execution time – Calculate relative performance (how much slower than fastest) – Useful for comparing algorithm implementations


    Lines 502-599: Demo Code

    Scenario 1: Simple Function

    def simple_calculation(n: int) -> int:
        """Simple mathematical calculation."""
        result = 0
        for i in range(n):
            result += i * i
        return result
    
    result1 = pattern.benchmark_function(
        name="SimpleCalculation",
        function=simple_calculation,
        args=(1000,),
        iterations=1000,
        warmup=50
    )
    

    Explanation: Benchmark O(n) loop with 1000 iterations, 50 warmup runs.

    Scenario 2: String Operations

    def string_operations(text: str) -> str:
        """String manipulation operations."""
        result = text.upper()
        result = result.replace("a", "A")
        result = "".join(reversed(result))
        return result
    
    result2 = pattern.benchmark_function(
        name="StringOperations",
        function=string_operations,
        args=("hello world " * 100,),
        iterations=500
    )
    

    Explanation: Benchmark string operations on 1200-character string.

    Scenario 3: List Comprehension vs Loop

    def list_comprehension(n: int) -> List[int]:
        """List comprehension approach."""
        return [i * i for i in range(n)]
    
    def loop_approach(n: int) -> List[int]:
        """Traditional loop approach."""
        result = []
        for i in range(n):
            result.append(i * i)
        return result
    
    result3 = pattern.benchmark_function(
        name="ListComprehension",
        function=list_comprehension,
        args=(10000,),
        iterations=200
    )
    
    result4 = pattern.benchmark_function(
        name="LoopApproach",
        function=loop_approach,
        args=(10000,),
        iterations=200
    )
    
    # Compare the two approaches
    comparison = pattern.compare_results(["ListComprehension", "LoopApproach"])
    

    Explanation: Classic Python comparison – list comprehensions are typically faster. Compare results to see exact difference.


    Key Design Patterns

    1. Template Method: Common workflow with type-specific measurement
    2. Strategy Pattern: Different benchmark types (performance, memory, throughput)
    3. Chain of Responsibility: Sequential processing through workflow nodes
    4. Observer Pattern: Statistics tracking observes all benchmarks
    5. Command Pattern: BenchmarkConfig encapsulates execution

    Performance Considerations

    • Warmup: O(warmup_iterations × function_time) – essential for accurate measurements
    • Measurement: O(iterations × function_time) – linear in iterations
    • Analysis: O(n log n) – sorting for percentiles
    • Outlier Detection: O(n log n) – sorting for IQR
    • Memory: O(iterations) – stores all raw measurements

    Best Practices: – Use 100+ iterations for stable statistics – Include warmup (10-50 iterations) for JIT/cache warmup – Use time.perf_counter() – most accurate timer – Report median for typical performance, 95th/99th for tail latency – Compare relative performance, not absolute (hardware varies)

    Extension Points

    1. Add memory profiling with tracemalloc
    2. Implement throughput measurement (ops/sec)
    3. Add multi-threaded benchmarking
    4. Implement statistical tests (t-test for comparing benchmarks)
    5. Add visualization (histograms, box plots)
    6. Support async functions with asyncio
    7. Add regression detection (compare against baseline)