"""
Pattern 229: Avatar

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class AvatarConfig:
    """Configuration for Avatar."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class AvatarState:
    """State information for Avatar."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class AvatarAgent:
    """Agent implementing Avatar."""
    
    def __init__(self, name: str, config: AvatarConfig = None):
        self.name = name
        self.config = config or AvatarConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = AvatarState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Avatar."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Avatar. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class AvatarPattern:
    """
    Avatar - Enhanced implementation
    
    This pattern provides comprehensive functionality for avatar
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: AvatarConfig = None):
        self.config = config or AvatarConfig()
        self.agents: Dict[str, AvatarAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Avatar. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: AvatarAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Avatar",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Avatar",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Avatar Demo ===\n")
    
    # Create configuration
    config = AvatarConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = AvatarPattern(config)
    
    # Create and register agents
    agent1 = AvatarAgent("Agent1", config)
    agent2 = AvatarAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate avatar functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Avatar

Pattern 229: 229 Avatar – Line-by-Line Explanation

Overview

Pattern 229: Avatar

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 229
• Pattern Name: 229 Avatar
• File Path: patterns\multi_modal\229_avatar.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 229: Avatar

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 229 Avatar 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

AvatarConfig (Line 18)

Description: Configuration for Avatar.

AvatarState (Line 27)

Description: State information for Avatar.

AvatarAgent (Line 36)

Description: Agent implementing Avatar.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Avatar."""

Purpose: Process input according to Avatar.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

AvatarPattern (Line 85)

Description: Avatar – Enhanced implementation

This pattern provides comprehensive functionality for avatar with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = AvatarConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 228: Virtual Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class VirtualAgentConfig:
    """Configuration for Virtual Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class VirtualAgentState:
    """State information for Virtual Agent."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class VirtualAgentAgent:
    """Agent implementing Virtual Agent."""
    
    def __init__(self, name: str, config: VirtualAgentConfig = None):
        self.name = name
        self.config = config or VirtualAgentConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = VirtualAgentState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Virtual Agent."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Virtual Agent. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class VirtualAgentPattern:
    """
    Virtual Agent - Enhanced implementation
    
    This pattern provides comprehensive functionality for virtual agent
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: VirtualAgentConfig = None):
        self.config = config or VirtualAgentConfig()
        self.agents: Dict[str, VirtualAgentAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Virtual Agent. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: VirtualAgentAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Virtual Agent",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Virtual Agent",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Virtual Agent Demo ===\n")
    
    # Create configuration
    config = VirtualAgentConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = VirtualAgentPattern(config)
    
    # Create and register agents
    agent1 = VirtualAgentAgent("Agent1", config)
    agent2 = VirtualAgentAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate virtual agent functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Virtual Agent

Pattern 228: 228 Virtual Agent – Line-by-Line Explanation

Overview

Pattern 228: Virtual Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 228
• Pattern Name: 228 Virtual Agent
• File Path: patterns\multi_modal\228_virtual_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 228: Virtual Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 228 Virtual Agent 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

VirtualAgentConfig (Line 18)

Description: Configuration for Virtual Agent.

VirtualAgentState (Line 27)

Description: State information for Virtual Agent.

VirtualAgentAgent (Line 36)

Description: Agent implementing Virtual Agent.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Virtual Agent."""

Purpose: Process input according to Virtual Agent.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

VirtualAgentPattern (Line 85)

Description: Virtual Agent – Enhanced implementation

This pattern provides comprehensive functionality for virtual agent with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = VirtualAgentConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 227: Robotic Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class RoboticAgentConfig:
    """Configuration for Robotic Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class RoboticAgentState:
    """State information for Robotic Agent."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class RoboticAgentAgent:
    """Agent implementing Robotic Agent."""
    
    def __init__(self, name: str, config: RoboticAgentConfig = None):
        self.name = name
        self.config = config or RoboticAgentConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = RoboticAgentState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Robotic Agent."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Robotic Agent. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class RoboticAgentPattern:
    """
    Robotic Agent - Enhanced implementation
    
    This pattern provides comprehensive functionality for robotic agent
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: RoboticAgentConfig = None):
        self.config = config or RoboticAgentConfig()
        self.agents: Dict[str, RoboticAgentAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Robotic Agent. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: RoboticAgentAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Robotic Agent",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Robotic Agent",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Robotic Agent Demo ===\n")
    
    # Create configuration
    config = RoboticAgentConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = RoboticAgentPattern(config)
    
    # Create and register agents
    agent1 = RoboticAgentAgent("Agent1", config)
    agent2 = RoboticAgentAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate robotic agent functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Robotic Agent

Pattern 227: 227 Robotic Agent – Line-by-Line Explanation

Overview

Pattern 227: Robotic Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 227
• Pattern Name: 227 Robotic Agent
• File Path: patterns\multi_modal\227_robotic_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 227: Robotic Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 227 Robotic Agent 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

RoboticAgentConfig (Line 18)

Description: Configuration for Robotic Agent.

RoboticAgentState (Line 27)

Description: State information for Robotic Agent.

RoboticAgentAgent (Line 36)

Description: Agent implementing Robotic Agent.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Robotic Agent."""

Purpose: Process input according to Robotic Agent.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

RoboticAgentPattern (Line 85)

Description: Robotic Agent – Enhanced implementation

This pattern provides comprehensive functionality for robotic agent with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = RoboticAgentConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 226: Embodied Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class EmbodiedAgentConfig:
    """Configuration for Embodied Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class EmbodiedAgentState:
    """State information for Embodied Agent."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class EmbodiedAgentAgent:
    """Agent implementing Embodied Agent."""
    
    def __init__(self, name: str, config: EmbodiedAgentConfig = None):
        self.name = name
        self.config = config or EmbodiedAgentConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = EmbodiedAgentState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Embodied Agent."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Embodied Agent. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class EmbodiedAgentPattern:
    """
    Embodied Agent - Enhanced implementation
    
    This pattern provides comprehensive functionality for embodied agent
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: EmbodiedAgentConfig = None):
        self.config = config or EmbodiedAgentConfig()
        self.agents: Dict[str, EmbodiedAgentAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Embodied Agent. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: EmbodiedAgentAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Embodied Agent",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Embodied Agent",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Embodied Agent Demo ===\n")
    
    # Create configuration
    config = EmbodiedAgentConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = EmbodiedAgentPattern(config)
    
    # Create and register agents
    agent1 = EmbodiedAgentAgent("Agent1", config)
    agent2 = EmbodiedAgentAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate embodied agent functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Embodied Agent

Pattern 226: 226 Embodied Agent – Line-by-Line Explanation

Overview

Pattern 226: Embodied Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 226
• Pattern Name: 226 Embodied Agent
• File Path: patterns\multi_modal\226_embodied_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 226: Embodied Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 226 Embodied Agent 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

EmbodiedAgentConfig (Line 18)

Description: Configuration for Embodied Agent.

EmbodiedAgentState (Line 27)

Description: State information for Embodied Agent.

EmbodiedAgentAgent (Line 36)

Description: Agent implementing Embodied Agent.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Embodied Agent."""

Purpose: Process input according to Embodied Agent.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

EmbodiedAgentPattern (Line 85)

Description: Embodied Agent – Enhanced implementation

This pattern provides comprehensive functionality for embodied agent with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = EmbodiedAgentConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 225: Sensor Integration

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class SensorIntegrationConfig:
    """Configuration for Sensor Integration."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class SensorIntegrationState:
    """State information for Sensor Integration."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class SensorIntegrationAgent:
    """Agent implementing Sensor Integration."""
    
    def __init__(self, name: str, config: SensorIntegrationConfig = None):
        self.name = name
        self.config = config or SensorIntegrationConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = SensorIntegrationState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Sensor Integration."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Sensor Integration. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class SensorIntegrationPattern:
    """
    Sensor Integration - Enhanced implementation
    
    This pattern provides comprehensive functionality for sensor integration
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: SensorIntegrationConfig = None):
        self.config = config or SensorIntegrationConfig()
        self.agents: Dict[str, SensorIntegrationAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Sensor Integration. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: SensorIntegrationAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Sensor Integration",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Sensor Integration",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Sensor Integration Demo ===\n")
    
    # Create configuration
    config = SensorIntegrationConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = SensorIntegrationPattern(config)
    
    # Create and register agents
    agent1 = SensorIntegrationAgent("Agent1", config)
    agent2 = SensorIntegrationAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate sensor integration functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Sensor Integration

Pattern 225: 225 Sensor Integration – Line-by-Line Explanation

Overview

Pattern 225: Sensor Integration

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 225
• Pattern Name: 225 Sensor Integration
• File Path: patterns\multi_modal\225_sensor_integration.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 225: Sensor Integration

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 225 Sensor Integration 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

SensorIntegrationConfig (Line 18)

Description: Configuration for Sensor Integration.

SensorIntegrationState (Line 27)

Description: State information for Sensor Integration.

SensorIntegrationAgent (Line 36)

Description: Agent implementing Sensor Integration.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Sensor Integration."""

Purpose: Process input according to Sensor Integration.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

SensorIntegrationPattern (Line 85)

Description: Sensor Integration – Enhanced implementation

This pattern provides comprehensive functionality for sensor integration with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = SensorIntegrationConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 224: Cross Modal

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class CrossModalConfig:
    """Configuration for Cross Modal."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class CrossModalState:
    """State information for Cross Modal."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class CrossModalAgent:
    """Agent implementing Cross Modal."""
    
    def __init__(self, name: str, config: CrossModalConfig = None):
        self.name = name
        self.config = config or CrossModalConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = CrossModalState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Cross Modal."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Cross Modal. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class CrossModalPattern:
    """
    Cross Modal - Enhanced implementation
    
    This pattern provides comprehensive functionality for cross modal
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: CrossModalConfig = None):
        self.config = config or CrossModalConfig()
        self.agents: Dict[str, CrossModalAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Cross Modal. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: CrossModalAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Cross Modal",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Cross Modal",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Cross Modal Demo ===\n")
    
    # Create configuration
    config = CrossModalConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = CrossModalPattern(config)
    
    # Create and register agents
    agent1 = CrossModalAgent("Agent1", config)
    agent2 = CrossModalAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate cross modal functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Cross Modal

Pattern 224: 224 Cross Modal – Line-by-Line Explanation

Overview

Pattern 224: Cross Modal

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 224
• Pattern Name: 224 Cross Modal
• File Path: patterns\multi_modal\224_cross_modal.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 224: Cross Modal

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 224 Cross Modal 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

CrossModalConfig (Line 18)

Description: Configuration for Cross Modal.

CrossModalState (Line 27)

Description: State information for Cross Modal.

CrossModalAgent (Line 36)

Description: Agent implementing Cross Modal.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Cross Modal."""

Purpose: Process input according to Cross Modal.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

CrossModalPattern (Line 85)

Description: Cross Modal – Enhanced implementation

This pattern provides comprehensive functionality for cross modal with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = CrossModalConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 223: Multi Modal Fusion

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class MultiModalFusionConfig:
    """Configuration for Multi Modal Fusion."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class MultiModalFusionState:
    """State information for Multi Modal Fusion."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class MultiModalFusionAgent:
    """Agent implementing Multi Modal Fusion."""
    
    def __init__(self, name: str, config: MultiModalFusionConfig = None):
        self.name = name
        self.config = config or MultiModalFusionConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = MultiModalFusionState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Multi Modal Fusion."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Multi Modal Fusion. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class MultiModalFusionPattern:
    """
    Multi Modal Fusion - Enhanced implementation
    
    This pattern provides comprehensive functionality for multi modal fusion
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: MultiModalFusionConfig = None):
        self.config = config or MultiModalFusionConfig()
        self.agents: Dict[str, MultiModalFusionAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Multi Modal Fusion. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: MultiModalFusionAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Multi Modal Fusion",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Multi Modal Fusion",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Multi Modal Fusion Demo ===\n")
    
    # Create configuration
    config = MultiModalFusionConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = MultiModalFusionPattern(config)
    
    # Create and register agents
    agent1 = MultiModalFusionAgent("Agent1", config)
    agent2 = MultiModalFusionAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate multi modal fusion functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Multi Modal Fusion

Pattern 223: 223 Multi Modal Fusion – Line-by-Line Explanation

Overview

Pattern 223: Multi Modal Fusion

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 223
• Pattern Name: 223 Multi Modal Fusion
• File Path: patterns\multi_modal\223_multi_modal_fusion.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 223: Multi Modal Fusion

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 223 Multi Modal Fusion 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

MultiModalFusionConfig (Line 18)

Description: Configuration for Multi Modal Fusion.

MultiModalFusionState (Line 27)

Description: State information for Multi Modal Fusion.

MultiModalFusionAgent (Line 36)

Description: Agent implementing Multi Modal Fusion.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Multi Modal Fusion."""

Purpose: Process input according to Multi Modal Fusion.

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

get_statistics (Line 75)

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

Purpose: Get agent statistics.

Parameters: – self: Instance reference

MultiModalFusionPattern (Line 85)

Description: Multi Modal Fusion – Enhanced implementation

This pattern provides comprehensive functionality for multi modal fusion with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

def register_agent(self, agent):
    """Register an agent with the pattern."""

Purpose: Register an agent with the pattern.

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

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

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

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

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

get_statistics (Line 192)

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

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

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 = MultiModalFusionConfig(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 222: Audio-Text Pattern

Multi-modal agent that processes both audio and textual information,
enabling speech understanding, emotion detection, and contextual responses.
"""

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 AudioFeatureType(Enum):
    """Types of audio features."""
    SPEECH = "speech"
    EMOTION = "emotion"
    AMBIENT = "ambient"
    MUSIC = "music"


@dataclass
class AudioInput:
    """Represents audio input (simulated)."""
    transcript: str  # In real impl, would come from ASR
    duration: float  # seconds
    features: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class AudioFeatures:
    """Extracted audio features."""
    speech_content: str
    emotion: str  # happy, sad, angry, neutral, etc.
    tone: str  # formal, casual, urgent, etc.
    pace: str  # fast, normal, slow
    confidence: float = 0.0


class AudioAnalyzer:
    """Analyzes audio characteristics."""
    
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm
    
    def analyze(self, audio: AudioInput) -> AudioFeatures:
        """Extract audio features from transcript."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an audio analysis expert. Analyze the speech for:
            1. Emotional tone (happy, sad, angry, neutral, excited, etc.)
            2. Speaking tone (formal, casual, urgent, calm, etc.)
            3. Pace (fast, normal, slow)"""),
            ("human", "Speech transcript: '{transcript}'\nProvide analysis.")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"transcript": audio.transcript})
        content = str(response.content).lower()
        
        # Parse response
        emotion = "neutral"
        if "happy" in content or "joy" in content:
            emotion = "happy"
        elif "sad" in content or "melancholy" in content:
            emotion = "sad"
        elif "angry" in content or "frustrated" in content:
            emotion = "angry"
        elif "excited" in content:
            emotion = "excited"
        
        tone = "casual" if "casual" in content else "formal" if "formal" in content else "neutral"
        pace = "fast" if "fast" in content else "slow" if "slow" in content else "normal"
        
        return AudioFeatures(
            speech_content=audio.transcript,
            emotion=emotion,
            tone=tone,
            pace=pace,
            confidence=0.85
        )


class AudioTextFusion:
    """Fuses audio features with text understanding."""
    
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm
    
    def fuse(self, audio_features: AudioFeatures, text_query: Optional[str] = None) -> Dict[str, Any]:
        """Create unified audio-text understanding."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Combine audio characteristics with text content for contextual understanding."),
            ("human", """Speech: "{speech}"
Emotion: {emotion}
Tone: {tone}
Pace: {pace}
{query_part}

Provide contextual understanding considering both content and delivery.""")
        ])
        
        query_part = f"\nAdditional query: {text_query}" if text_query else ""
        
        chain = prompt | self.llm
        response = chain.invoke({
            "speech": audio_features.speech_content,
            "emotion": audio_features.emotion,
            "tone": audio_features.tone,
            "pace": audio_features.pace,
            "query_part": query_part
        })
        
        return {
            "understanding": str(response.content),
            "emotion": audio_features.emotion,
            "tone": audio_features.tone
        }


class AudioTextPattern:
    """
    Audio-Text Pattern - Processes combined audio and textual inputs.
    
    Enables speech understanding with emotion and prosody awareness,
    suitable for voice assistants and conversational AI.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.audio_analyzer = AudioAnalyzer(self.llm)
        self.fusion = AudioTextFusion(self.llm)
        self.conversation_history: List[Dict[str, Any]] = []
    
    def process(self, audio: AudioInput, text_query: Optional[str] = None) -> Dict[str, Any]:
        """Process audio with optional text query."""
        
        # Analyze audio
        audio_features = self.audio_analyzer.analyze(audio)
        
        # Fuse with text
        fused = self.fusion.fuse(audio_features, text_query)
        
        # Generate response considering audio context
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an empathetic audio-text assistant. Respond appropriately 
            considering both the content and the emotional/tonal delivery."""),
            ("human", """Understanding: {understanding}
Detected emotion: {emotion}
Speaking tone: {tone}

Generate an appropriate response.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "understanding": fused["understanding"],
            "emotion": fused["emotion"],
            "tone": fused["tone"]
        })
        
        result = {
            "response": str(response.content),
            "detected_emotion": audio_features.emotion,
            "detected_tone": audio_features.tone,
            "speech_pace": audio_features.pace,
            "confidence": audio_features.confidence
        }
        
        self.conversation_history.append(result)
        return result
    
    def run(self, speech_transcript: str, duration: float = 3.0, text_query: Optional[str] = None) -> Dict[str, Any]:
        """Main execution method."""
        audio = AudioInput(
            transcript=speech_transcript,
            duration=duration
        )
        
        result = self.process(audio, text_query)
        
        return {
            "pattern": "Audio-Text Pattern",
            **result
        }


# Example usage
if __name__ == "__main__":
    print("=== Audio-Text Pattern Demo ===\n")
    
    pattern = AudioTextPattern()
    
    # Example 1: Excited speech
    result1 = pattern.run(
        speech_transcript="I just got the job! This is amazing! I can't believe it!",
        duration=3.5
    )
    
    print("Example 1: Excited announcement")
    print(f"Detected emotion: {result1['detected_emotion']}")
    print(f"Detected tone: {result1['detected_tone']}")
    print(f"Response: {result1['response'][:150]}...\n")
    
    # Example 2: Urgent request
    result2 = pattern.run(
        speech_transcript="I need help right away. The server is down and customers can't access the site.",
        duration=4.0,
        text_query="What should I do first?"
    )
    
    print("Example 2: Urgent technical issue")
    print(f"Detected emotion: {result2['detected_emotion']}")
    print(f"Detected tone: {result2['detected_tone']}")
    print(f"Response: {result2['response'][:150]}...\n")
    
    # Example 3: Calm inquiry
    result3 = pattern.run(
        speech_transcript="Could you please explain how the new system works? I'd like to understand it better.",
        duration=3.0
    )
    
    print("Example 3: Calm inquiry")
    print(f"Detected emotion: {result3['detected_emotion']}")
    print(f"Detected tone: {result3['detected_tone']}")
    print(f"Response: {result3['response'][:150]}...\n")
    
    print(f"Total conversations: {len(pattern.conversation_history)}")

    

Agentic Architecture Pattern: Audio Text

Pattern 222: 222 Audio Text – Line-by-Line Explanation

Overview

Pattern 222: Audio-Text Pattern

Multi-modal agent that processes both audio and textual information, enabling speech understanding, emotion detection, and contextual responses.

Code Structure Analysis

File Information

• Pattern Number: 222
• Pattern Name: 222 Audio Text
• File Path: patterns\multi_modal\222_audio_text.py
• Total Lines: 231

Detailed Code Explanation

Module Docstring

"""
Pattern 222: Audio-Text Pattern

Multi-modal agent that processes both audio and textual information,
enabling speech understanding, emotion detection, and contextual responses.
"""

Explanation: This module docstring describes the purpose and key features of the 222 Audio Text 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

AudioFeatureType (Line 16)

class AudioFeatureType(Enum):
    SPEECH
    EMOTION
    AMBIENT
    MUSIC

Purpose: Types of audio features.

Values: – SPEECH: Represents speech – EMOTION: Represents emotion – AMBIENT: Represents ambient – MUSIC: Represents music

Classes

AudioFeatureType (Line 16)

Description: Types of audio features.

AudioInput (Line 25)

Description: Represents audio input (simulated).

AudioFeatures (Line 34)

Description: Extracted audio features.

AudioAnalyzer (Line 43)

Description: Analyzes audio characteristics.

Methods:

__init__ (Line 46)

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

Purpose: No method docstring.

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

analyze (Line 49)

def analyze(self, audio):
    """Extract audio features from transcript."""

Purpose: Extract audio features from transcript.

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

AudioTextFusion (Line 86)

Description: Fuses audio features with text understanding.

Methods:

__init__ (Line 89)

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

Purpose: No method docstring.

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

fuse (Line 92)

def fuse(self, audio_features, text_query):
    """Create unified audio-text understanding."""

Purpose: Create unified audio-text understanding.

Parameters: – self: Instance reference – audio_features: Parameter for audio_features – text_query: Parameter for text_query

AudioTextPattern (Line 123)

Description: Audio-Text Pattern – Processes combined audio and textual inputs.

Enables speech understanding with emotion and prosody awareness, suitable for voice assistants and conversational AI.

Methods:

__init__ (Line 131)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

process (Line 137)

def process(self, audio, text_query):
    """Process audio with optional text query."""

Purpose: Process audio with optional text query.

Parameters: – self: Instance reference – audio: Parameter for audio – text_query: Parameter for text_query

run (Line 175)

def run(self, speech_transcript, duration, text_query):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – speech_transcript: Parameter for speech_transcript – duration: Parameter for duration – text_query: Parameter for text_query

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 = AudioFeatureType(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 221: Vision-Language Pattern

Multi-modal agent that processes both visual and textual information to understand
and respond to combined image-text inputs. Integrates computer vision with language
models for unified understanding.
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


class ModalityType(Enum):
    """Types of input modalities."""
    VISION = "vision"
    LANGUAGE = "language"
    COMBINED = "combined"


@dataclass
class MultiModalInput:
    """Input combining vision and language."""
    image_description: str  # In real impl, would be image data/embeddings
    text: str
    modality: ModalityType
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class VisionFeature:
    """Extracted visual features."""
    objects: List[str]
    scene: str
    attributes: Dict[str, Any]
    confidence: float = 0.0


@dataclass
class GroundedResponse:
    """Response grounded in both vision and language."""
    text_response: str
    visual_references: List[str]
    confidence: float
    reasoning: str


class VisionEncoder:
    """Simulates vision encoding (in real impl, would use vision model)."""
    
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm
    
    def encode(self, image_description: str) -> VisionFeature:
        """Extract visual features from image description."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a vision feature extractor. Extract objects, scene type, and key attributes."),
            ("human", "Image description: {desc}\n\nExtract: objects (list), scene (string), key attributes")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"desc": image_description})
        content = str(response.content)
        
        # Parse response (simplified)
        objects = [obj.strip() for obj in content.split("Objects:")[1].split("Scene:")[0].strip().split(",") if obj.strip()][:5]
        scene = content.split("Scene:")[1].split("\n")[0].strip() if "Scene:" in content else "general"
        
        return VisionFeature(
            objects=objects,
            scene=scene,
            attributes={"description": image_description},
            confidence=0.85
        )


class LanguageEncoder:
    """Encodes language understanding."""
    
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm
    
    def encode(self, text: str) -> Dict[str, Any]:
        """Extract linguistic features."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Analyze this text for intent, entities, and key concepts."),
            ("human", "{text}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"text": text})
        
        return {
            "text": text,
            "analysis": str(response.content),
            "confidence": 0.9
        }


class VisionLanguageFusion:
    """Fuses vision and language representations."""
    
    def __init__(self, llm: ChatOpenAI):
        self.llm = llm
    
    def fuse(self, vision: VisionFeature, language: Dict[str, Any]) -> Dict[str, Any]:
        """Create unified representation."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are fusing visual and textual information into unified understanding."),
            ("human", """Visual info:
Objects: {objects}
Scene: {scene}

Textual info:
{text}

Provide a unified understanding that grounds the text in the visual context.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "objects": ", ".join(vision.objects),
            "scene": vision.scene,
            "text": language["text"]
        })
        
        return {
            "unified": str(response.content),
            "visual_elements": vision.objects,
            "scene_context": vision.scene
        }


class VisionLanguagePattern:
    """
    Vision-Language Pattern - Processes combined visual and textual inputs.
    
    Integrates computer vision and NLP for multi-modal understanding,
    enabling agents to reason about images and text together.
    """
    
    def __init__(self, model_name: str = "gpt-4-vision-preview"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.vision_encoder = VisionEncoder(self.llm)
        self.language_encoder = LanguageEncoder(self.llm)
        self.fusion = VisionLanguageFusion(self.llm)
        self.history: List[GroundedResponse] = []
    
    def process(self, input_data: MultiModalInput) -> GroundedResponse:
        """Process multi-modal input."""
        
        # Encode vision
        vision_features = self.vision_encoder.encode(input_data.image_description)
        
        # Encode language
        language_features = self.language_encoder.encode(input_data.text)
        
        # Fuse modalities
        fused = self.fusion.fuse(vision_features, language_features)
        
        # Generate grounded response
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are a vision-language agent. Generate responses that are grounded in both 
            the visual scene and the textual query. Reference specific visual elements."""),
            ("human", """Scene: {scene}
Objects visible: {objects}
Question/Text: {text}
Unified understanding: {unified}

Provide a detailed response that references specific visual elements.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "scene": fused["scene_context"],
            "objects": ", ".join(fused["visual_elements"]),
            "text": input_data.text,
            "unified": fused["unified"]
        })
        
        grounded_response = GroundedResponse(
            text_response=str(response.content),
            visual_references=fused["visual_elements"],
            confidence=0.85,
            reasoning=fused["unified"]
        )
        
        self.history.append(grounded_response)
        return grounded_response
    
    def run(self, image_description: str, text_query: str) -> Dict[str, Any]:
        """Main execution method."""
        input_data = MultiModalInput(
            image_description=image_description,
            text=text_query,
            modality=ModalityType.COMBINED
        )
        
        response = self.process(input_data)
        
        return {
            "pattern": "Vision-Language Pattern",
            "response": response.text_response,
            "visual_references": response.visual_references,
            "confidence": response.confidence,
            "reasoning": response.reasoning
        }


# Example usage
if __name__ == "__main__":
    print("=== Vision-Language Pattern Demo ===\n")
    
    pattern = VisionLanguagePattern()
    
    # Example 1: Image understanding with question
    result1 = pattern.run(
        image_description="A sunny park with children playing on swings, a dog running, and people sitting on benches under trees",
        text_query="What activities are people doing in this scene?"
    )
    
    print(f"Query: {result1['response'][:200]}...")
    print(f"Visual References: {result1['visual_references']}")
    print(f"Confidence: {result1['confidence']}\n")
    
    # Example 2: Object localization
    result2 = pattern.run(
        image_description="A kitchen counter with a red apple, a glass of milk, a sandwich, and a newspaper",
        text_query="Where can I find something healthy to eat?"
    )
    
    print(f"Query: {result2['response'][:200]}...")
    print(f"Visual References: {result2['visual_references']}")
    print(f"Confidence: {result2['confidence']}\n")
    
    print(f"Total interactions: {len(pattern.history)}")

    

Agentic Architecture Pattern: Vision Language

Pattern 221: 221 Vision Language – Line-by-Line Explanation

Overview

Pattern 221: Vision-Language Pattern

Multi-modal agent that processes both visual and textual information to understand and respond to combined image-text inputs. Integrates computer vision with language models for unified understanding.

Code Structure Analysis

File Information

• Pattern Number: 221
• Pattern Name: 221 Vision Language
• File Path: patterns\multi_modal\221_vision_language.py
• Total Lines: 242

Detailed Code Explanation

Module Docstring

"""
Pattern 221: Vision-Language Pattern

Multi-modal agent that processes both visual and textual information to understand
and respond to combined image-text inputs. Integrates computer vision with language
models for unified understanding.
"""

Explanation: This module docstring describes the purpose and key features of the 221 Vision Language 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Enumerations

ModalityType (Line 19)

class ModalityType(Enum):
    VISION
    LANGUAGE
    COMBINED

Purpose: Types of input modalities.

Values: – VISION: Represents vision – LANGUAGE: Represents language – COMBINED: Represents combined

Classes

ModalityType (Line 19)

Description: Types of input modalities.

MultiModalInput (Line 27)

Description: Input combining vision and language.

VisionFeature (Line 36)

Description: Extracted visual features.

GroundedResponse (Line 45)

Description: Response grounded in both vision and language.

VisionEncoder (Line 53)

Description: Simulates vision encoding (in real impl, would use vision model).

Methods:

__init__ (Line 56)

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

Purpose: No method docstring.

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

encode (Line 59)

def encode(self, image_description):
    """Extract visual features from image description."""

Purpose: Extract visual features from image description.

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

LanguageEncoder (Line 82)

Description: Encodes language understanding.

Methods:

__init__ (Line 85)

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

Purpose: No method docstring.

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

encode (Line 88)

def encode(self, text):
    """Extract linguistic features."""

Purpose: Extract linguistic features.

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

VisionLanguageFusion (Line 105)

Description: Fuses vision and language representations.

Methods:

__init__ (Line 108)

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

Purpose: No method docstring.

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

fuse (Line 111)

def fuse(self, vision, language):
    """Create unified representation."""

Purpose: Create unified representation.

Parameters: – self: Instance reference – vision: Parameter for vision – language: Parameter for language

VisionLanguagePattern (Line 139)

Description: Vision-Language Pattern – Processes combined visual and textual inputs.

Integrates computer vision and NLP for multi-modal understanding, enabling agents to reason about images and text together.

Methods:

__init__ (Line 147)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

process (Line 154)

def process(self, input_data):
    """Process multi-modal input."""

Purpose: Process multi-modal input.

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

run (Line 196)

def run(self, image_description, text_query):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – image_description: Parameter for image_description – text_query: Parameter for text_query

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 = ModalityType(name="example", model_name="gpt-4")

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

# Check results
print(result)

Key Concepts

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

Extension Points

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

    """
Pattern 220: Optimization Loop Pattern

Continuous optimization through feedback
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


class FeedbackType(Enum):
    """Types of feedback."""
    POSITIVE = "positive"
    NEGATIVE = "negative"
    CONSTRUCTIVE = "constructive"
    NEUTRAL = "neutral"


@dataclass
class FeedbackItem:
    """Represents a feedback item."""
    source: str
    content: str
    feedback_type: FeedbackType
    score: float = 0.0  # 0.0 to 1.0
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class EvaluationResult:
    """Result of evaluation."""
    score: float
    feedback: List[FeedbackItem]
    improvements: List[str]
    timestamp: datetime = field(default_factory=datetime.now)


class OptimizationloopPattern:
    """
    Optimization Loop Pattern - Continuous optimization through feedback
    
    Provides optimization_loop capabilities for continuous improvement
    and quality enhancement in agentic systems.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.history: List[EvaluationResult] = []
        self.feedback_log: List[FeedbackItem] = []
    
    def evaluate(self, output: str, criteria: List[str]) -> EvaluationResult:
        """Evaluate output against criteria."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are an expert evaluator for Optimization Loop. 
            Evaluate the output against the criteria and provide:
            1. Overall score (0.0 to 1.0)
            2. Specific feedback points
            3. Improvement suggestions"""),
            ("human", """Output to evaluate:
{output}

Criteria:
{criteria}

Provide evaluation.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "output": output,
            "criteria": "\n".join(f"- {c}" for c in criteria)
        })
        
        content = str(response.content)
        
        # Parse response
        score = 0.75  # Default score
        if "score:" in content.lower():
            try:
                score_text = content.lower().split("score:")[1].split("\n")[0].strip()
                score = float(score_text.split()[0].replace("/1.0", "").replace(":", ""))
            except:
                pass
        
        feedback_items = [
            FeedbackItem(
                source=self.__class__.__name__,
                content=content,
                feedback_type=FeedbackType.CONSTRUCTIVE,
                score=score
            )
        ]
        
        improvements = [
            line.strip("- ").strip() 
            for line in content.split("\n") 
            if line.strip().startswith("-") or "improve" in line.lower()
        ][:3]
        
        result = EvaluationResult(
            score=score,
            feedback=feedback_items,
            improvements=improvements
        )
        
        self.history.append(result)
        self.feedback_log.extend(feedback_items)
        
        return result
    
    def provide_feedback(self, output: str) -> Dict[str, Any]:
        """Provide feedback on output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are providing constructive feedback using Optimization Loop pattern."),
            ("human", "{output}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"output": output})
        
        feedback = FeedbackItem(
            source="feedback_provider",
            content=str(response.content),
            feedback_type=FeedbackType.CONSTRUCTIVE,
            score=0.8
        )
        
        self.feedback_log.append(feedback)
        
        return {
            "feedback": feedback.content,
            "score": feedback.score,
            "type": feedback.feedback_type.value
        }
    
    def run(self, output: str, criteria: Optional[List[str]] = None) -> Dict[str, Any]:
        """Main execution method."""
        if criteria is None:
            criteria = ["clarity", "accuracy", "completeness"]
        
        result = self.evaluate(output, criteria)
        
        return {
            "pattern": "Optimization Loop",
            "score": result.score,
            "feedback_count": len(result.feedback),
            "improvements": result.improvements,
            "total_evaluations": len(self.history)
        }


# Example usage
if __name__ == "__main__":
    print("=== Optimization Loop Pattern Demo ===\n")
    
    pattern = OptimizationloopPattern()
    
    # Example evaluation
    test_output = "This is a sample output that demonstrates the optimization_loop functionality."
    result = pattern.run(test_output, ["clarity", "usefulness", "accuracy"])
    
    print(f"Pattern: {result['pattern']}")
    print(f"Score: {result['score']:.2f}")
    print(f"Improvements suggested: {len(result['improvements'])}")
    if result['improvements']:
        print("\nSuggestions:")
        for i, imp in enumerate(result['improvements'][:3], 1):
            print(f"  {i}. {imp}")
    
    print(f"\nTotal evaluations: {result['total_evaluations']}")
    print(f"Total feedback items: {len(pattern.feedback_log)}")

    

Agentic Architecture Pattern: Optimization Loop

Pattern 220: 220 Optimization Loop – Line-by-Line Explanation

Overview

Pattern 220: Optimization Loop Pattern

Continuous optimization through feedback Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

• Pattern Number: 220
• Pattern Name: 220 Optimization Loop
• File Path: patterns\feedback_improvement\220_optimization_loop.py
• Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 220: Optimization Loop Pattern

Continuous optimization through feedback
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 220 Optimization Loop 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
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # 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 – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

Values: – POSITIVE: Represents positive – NEGATIVE: Represents negative – CONSTRUCTIVE: Represents constructive – NEUTRAL: Represents neutral

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

OptimizationloopPattern (Line 45)

Description: Optimization Loop Pattern – Continuous optimization through feedback

Provides optimization_loop capabilities for continuous improvement and quality enhancement in agentic systems.

Methods:

__init__ (Line 53)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

evaluate (Line 58)

def evaluate(self, output, criteria):
    """Evaluate output against criteria."""

Purpose: Evaluate output against criteria.

Parameters: – self: Instance reference – output: Parameter for output – criteria: Parameter for criteria

provide_feedback (Line 118)

def provide_feedback(self, output):
    """Provide feedback on output."""

Purpose: Provide feedback on output.

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

run (Line 143)

def run(self, output, criteria):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – output: Parameter for output – criteria: Parameter for criteria

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 = FeedbackType(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