"""
Pattern 77: ReAct (Reasoning + Acting) Pattern
Interleaves reasoning traces with task-specific actions.
"""

from typing import Dict, Any, List, Optional, Callable
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
from enum import Enum


class StepType(str, Enum):
    THOUGHT = "thought"
    ACTION = "action"
    OBSERVATION = "observation"


class ReActStep(BaseModel):
    """Represents a step in the ReAct cycle."""
    type: StepType
    content: str
    step_number: int


class ReActPattern:
    """
    ReAct Pattern - Reasoning and Acting in an interleaved manner.
    The agent alternates between thinking about what to do and taking actions.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.available_actions = self._define_actions()
        self.trace: List[ReActStep] = []
    
    def _define_actions(self) -> Dict[str, Callable]:
        """Define available actions the agent can take."""
        return {
            "search": lambda query: f"Search results for: {query}",
            "calculate": lambda expr: f"Calculation result: {eval(expr)}",
            "lookup": lambda term: f"Definition of {term}: [simulated lookup]",
            "finish": lambda answer: f"Final Answer: {answer}"
        }
    
    def think(self, question: str, context: str = "") -> str:
        """Reasoning step - agent thinks about next action."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """You are using the ReAct (Reasoning + Acting) pattern.
            
            Think step-by-step about how to answer this question.
            You can use these actions: {actions}
            
            Context so far:
            {context}
            
            Provide your thought process about what to do next."""),
            ("human", "Question: {question}\n\nThought:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "question": question,
            "actions": ", ".join(self.available_actions.keys()),
            "context": context or "No previous steps"
        })
        
        return response.content
    
    def act(self, thought: str) -> tuple[str, str]:
        """Action step - agent takes an action based on reasoning."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", """Based on this thought, decide which action to take.
            
            Available actions: {actions}
            
            Thought: {thought}
            
            Respond with: Action: [action_name]([argument])
            For example: Action: search(quantum computing)"""),
            ("human", "What action should be taken?")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "thought": thought,
            "actions": ", ".join(self.available_actions.keys())
        })
        
        # Parse action
        action_str = response.content
        action_name = "search"
        action_arg = thought[:30]
        
        if "Action:" in action_str:
            try:
                action_part = action_str.split("Action:")[1].strip()
                action_name = action_part.split("(")[0].strip()
                action_arg = action_part.split("(")[1].split(")")[0].strip()
            except:
                pass
        
        return action_name, action_arg
    
    def observe(self, action_name: str, action_arg: str) -> str:
        """Observation step - observe the result of the action."""
        if action_name in self.available_actions:
            try:
                result = self.available_actions[action_name](action_arg)
                return result
            except Exception as e:
                return f"Error executing {action_name}: {e}"
        return f"Unknown action: {action_name}"
    
    def run(self, question: str, max_steps: int = 5) -> Dict[str, Any]:
        """
        Execute the ReAct pattern: Think -> Act -> Observe -> Repeat.
        """
        self.trace = []
        context = ""
        final_answer = None
        
        for step_num in range(max_steps):
            # Step 1: Think (Reasoning)
            thought = self.think(question, context)
            self.trace.append(ReActStep(
                type=StepType.THOUGHT,
                content=thought,
                step_number=step_num + 1
            ))
            
            # Step 2: Act
            action_name, action_arg = self.act(thought)
            action_str = f"{action_name}({action_arg})"
            self.trace.append(ReActStep(
                type=StepType.ACTION,
                content=action_str,
                step_number=step_num + 1
            ))
            
            # Check if we're done
            if action_name == "finish":
                final_answer = action_arg
                break
            
            # Step 3: Observe
            observation = self.observe(action_name, action_arg)
            self.trace.append(ReActStep(
                type=StepType.OBSERVATION,
                content=observation,
                step_number=step_num + 1
            ))
            
            # Update context
            context += f"\nStep {step_num + 1}:\n  Thought: {thought}\n  Action: {action_str}\n  Observation: {observation}"
        
        return {
            "question": question,
            "trace": self.trace,
            "final_answer": final_answer,
            "total_steps": len(self.trace) // 3
        }
    
    def format_trace(self) -> str:
        """Format the trace for display."""
        output = []
        for step in self.trace:
            prefix = {
                StepType.THOUGHT: "💭 Thought",
                StepType.ACTION: "⚡ Action",
                StepType.OBSERVATION: "👁 Observation"
            }[step.type]
            output.append(f"{prefix} {step.step_number}: {step.content}")
        return "\n".join(output)


# Example usage
if __name__ == "__main__":
    agent = ReActPattern()
    
    question = "What is 15% of 240?"
    
    print("=== ReAct Pattern: Reasoning + Acting ===\n")
    print(f"Question: {question}\n")
    
    result = agent.run(question, max_steps=3)
    
    print("=== Execution Trace ===\n")
    print(agent.format_trace())
    
    if result["final_answer"]:
        print(f"\n=== Final Answer ===")
        print(result["final_answer"])

    

Agentic Architecture Pattern: React

Pattern 77: 77 React – Line-by-Line Explanation

Overview

Pattern 77: ReAct (Reasoning + Acting) Pattern Interleaves reasoning traces with task-specific actions.

Code Structure Analysis

File Information

• Pattern Number: 77
• Pattern Name: 77 React
• File Path: patterns\execution_control\77_react.py
• Total Lines: 194

Detailed Code Explanation

Module Docstring

"""
Pattern 77: ReAct (Reasoning + Acting) Pattern
Interleaves reasoning traces with task-specific actions.
"""

Explanation: This module docstring describes the purpose and key features of the 77 React pattern.

Imports Section

from typing import Dict, Any, List, Optional, Callable
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
from enum import Enum  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Callable: Type hints for better code documentation – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from enum import Enum: Enumeration support for fixed sets of values

Enumerations

StepType (Line 13)

class StepType(Enum):
    THOUGHT
    ACTION
    OBSERVATION

Purpose: No enum docstring.

Values: – THOUGHT: Represents thought – ACTION: Represents action – OBSERVATION: Represents observation

Classes

StepType (Line 13)

Description: No class docstring.

ReActStep (Line 19)

Description: Represents a step in the ReAct cycle.

ReActPattern (Line 26)

Description: ReAct Pattern – Reasoning and Acting in an interleaved manner. The agent alternates between thinking about what to do and taking actions.

Methods:

__init__ (Line 32)

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”)

_define_actions (Line 37)

def _define_actions(self):
    """Define available actions the agent can take."""

Purpose: Define available actions the agent can take.

Parameters: – self: Instance reference

think (Line 46)

def think(self, question, context):
    """Reasoning step - agent thinks about next action."""

Purpose: Reasoning step – agent thinks about next action.

Parameters: – self: Instance reference – question: Parameter for question – context: Parameter for context

act (Line 70)

def act(self, thought):
    """Action step - agent takes an action based on reasoning."""

Purpose: Action step – agent takes an action based on reasoning.

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

observe (Line 105)

def observe(self, action_name, action_arg):
    """Observation step - observe the result of the action."""

Purpose: Observation step – observe the result of the action.

Parameters: – self: Instance reference – action_name: Parameter for action_name – action_arg: Parameter for action_arg

run (Line 115)

def run(self, question, max_steps):
    """Execute the ReAct pattern: Think -> Act -> Observe -> Repeat."""

Purpose: Execute the ReAct pattern: Think -> Act -> Observe -> Repeat.

Parameters: – self: Instance reference – question: Parameter for question – max_steps: Parameter for max_steps

format_trace (Line 164)

def format_trace(self):
    """Format the trace for display."""

Purpose: Format the trace for display.

Parameters: – self: Instance reference

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = StepType(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 76: Graph-of-Thought Pattern

Reasoning as a graph structure with nodes (thoughts) and edges (relationships).
Allows parallel exploration, backtracking, and aggregation of multiple reasoning paths.
"""

from typing import Dict, Any, List, Optional, Set
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 ThoughtType(Enum):
    """Type of thought node."""
    HYPOTHESIS = "hypothesis"
    EVIDENCE = "evidence"
    REASONING = "reasoning"
    CONCLUSION = "conclusion"
    QUESTION = "question"


@dataclass
class ThoughtNode:
    """Node in the thought graph."""
    node_id: str
    content: str
    thought_type: ThoughtType
    confidence: float = 0.5  # 0.0 to 1.0
    parent_ids: List[str] = field(default_factory=list)
    child_ids: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class ThoughtEdge:
    """Edge connecting thoughts."""
    from_id: str
    to_id: str
    relationship: str  # supports, contradicts, questions, leads_to
    strength: float = 0.5  # 0.0 to 1.0


class GraphOfThoughtState(TypedDict):
    """State for graph-of-thought workflow."""
    problem: str
    nodes: List[ThoughtNode]
    edges: List[ThoughtEdge]
    current_node_id: Optional[str]
    explored_ids: Set[str]
    conclusions: List[str]


class GraphOfThoughtPattern:
    """
    Graph-of-Thought Pattern - Non-linear reasoning with graph structure.
    
    Thoughts are organized as a graph allowing:
    - Parallel exploration of multiple paths
    - Backtracking when needed
    - Aggregation of insights from different branches
    - Complex relationship modeling
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.graph = StateGraph(GraphOfThoughtState)
        self._build_graph()
        self.workflow = self.graph.compile()
    
    def _build_graph(self):
        """Build the LangGraph workflow."""
        self.graph.add_node("generate_hypotheses", self._generate_hypotheses)
        self.graph.add_node("explore_path", self._explore_path)
        self.graph.add_node("evaluate_node", self._evaluate_node)
        self.graph.add_node("aggregate_insights", self._aggregate_insights)
        
        self.graph.set_entry_point("generate_hypotheses")
        
        self.graph.add_edge("generate_hypotheses", "explore_path")
        self.graph.add_edge("explore_path", "evaluate_node")
        
        self.graph.add_conditional_edges(
            "evaluate_node",
            self._should_continue_exploration,
            {
                "explore": "explore_path",
                "aggregate": "aggregate_insights",
            }
        )
        
        self.graph.add_edge("aggregate_insights", END)
    
    def _generate_hypotheses(self, state: GraphOfThoughtState) -> GraphOfThoughtState:
        """Generate initial hypothesis nodes."""
        print(f"\n   Generating hypotheses for: {state['problem']}")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Generate 3 diverse hypotheses or approaches to solve this problem. "
                      "Each should explore a different angle. Return one per line."),
            ("human", "{problem}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"problem": state['problem']})
        
        hypotheses = [h.strip() for h in response.content.split('\n') if h.strip()][:3]
        
        nodes = []
        for i, hypothesis in enumerate(hypotheses):
            node = ThoughtNode(
                node_id=f"hyp_{i}",
                content=hypothesis,
                thought_type=ThoughtType.HYPOTHESIS,
                confidence=0.5
            )
            nodes.append(node)
            print(f"   - Hypothesis {i+1}: {hypothesis[:60]}...")
        
        state["nodes"] = nodes
        state["edges"] = []
        state["explored_ids"] = set()
        state["conclusions"] = []
        
        return state
    
    def _explore_path(self, state: GraphOfThoughtState) -> GraphOfThoughtState:
        """Explore a thought path by expanding a node."""
        # Find unexplored node
        unexplored = [n for n in state["nodes"] 
                     if n.node_id not in state["explored_ids"]]
        
        if not unexplored:
            return state
        
        current_node = unexplored[0]
        state["current_node_id"] = current_node.node_id
        
        print(f"\n   Exploring: {current_node.content[:60]}...")
        
        # Generate reasoning from this node
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Based on this thought, generate 2 follow-up thoughts: "
                      "either supporting evidence, counterarguments, or logical next steps. "
                      "Return one per line."),
            ("human", "{thought}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"thought": current_node.content})
        
        follow_ups = [f.strip() for f in response.content.split('\n') if f.strip()][:2]
        
        for i, follow_up in enumerate(follow_ups):
            child_id = f"{current_node.node_id}_child_{i}"
            child_node = ThoughtNode(
                node_id=child_id,
                content=follow_up,
                thought_type=ThoughtType.REASONING,
                parent_ids=[current_node.node_id],
                confidence=0.6
            )
            state["nodes"].append(child_node)
            current_node.child_ids.append(child_id)
            
            # Create edge
            edge = ThoughtEdge(
                from_id=current_node.node_id,
                to_id=child_id,
                relationship="leads_to",
                strength=0.7
            )
            state["edges"].append(edge)
            
            print(f"   - Follow-up: {follow_up[:50]}...")
        
        state["explored_ids"].add(current_node.node_id)
        
        return state
    
    def _evaluate_node(self, state: GraphOfThoughtState) -> GraphOfThoughtState:
        """Evaluate current node's confidence."""
        current_id = state.get("current_node_id")
        if not current_id:
            return state
        
        current_node = next((n for n in state["nodes"] if n.node_id == current_id), None)
        if not current_node:
            return state
        
        # Simple evaluation based on depth and branching
        depth = len([p for p in current_node.parent_ids])
        children_count = len(current_node.child_ids)
        
        # Update confidence
        current_node.confidence = min(1.0, 0.5 + (children_count * 0.1) + (depth * 0.05))
        
        return state
    
    def _should_continue_exploration(self, state: GraphOfThoughtState) -> str:
        """Decide whether to continue exploring or aggregate."""
        explored = len(state["explored_ids"])
        total = len(state["nodes"])
        
        # Explore at least 5 nodes or until 80% explored
        if explored  0 and explored / total  GraphOfThoughtState:
        """Aggregate insights from the thought graph."""
        print(f"\n   Aggregating insights from {len(state['nodes'])} thoughts...")
        
        # Find leaf nodes (conclusions)
        leaf_nodes = [n for n in state["nodes"] if not n.child_ids]
        
        # Prepare summary of reasoning paths
        paths_summary = []
        for leaf in leaf_nodes[:3]:  # Top 3 paths
            path = self._trace_path(leaf, state["nodes"])
            paths_summary.append(" → ".join([n.content[:40] for n in path]))
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Given these reasoning paths, synthesize a final conclusion. "
                      "Be comprehensive and consider all perspectives."),
            ("human", "Problem: {problem}\n\nReasoning paths:\n{paths}\n\nProvide final conclusion:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "problem": state['problem'],
            "paths": "\n".join([f"{i+1}. {p}" for i, p in enumerate(paths_summary)])
        })
        
        state["conclusions"] = [response.content]
        print(f"   - Synthesized conclusion")
        
        return state
    
    def _trace_path(self, node: ThoughtNode, all_nodes: List[ThoughtNode]) -> List[ThoughtNode]:
        """Trace path from root to this node."""
        path = [node]
        current = node
        
        while current.parent_ids:
            parent_id = current.parent_ids[0]
            parent = next((n for n in all_nodes if n.node_id == parent_id), None)
            if not parent:
                break
            path.insert(0, parent)
            current = parent
        
        return path
    
    def run(self, problem: str) -> Dict[str, Any]:
        """Execute graph-of-thought reasoning."""
        print(f"\n=== Graph-of-Thought Pattern ===")
        
        initial_state: GraphOfThoughtState = {
            "problem": problem,
            "nodes": [],
            "edges": [],
            "current_node_id": None,
            "explored_ids": set(),
            "conclusions": []
        }
        
        final_state = self.workflow.invoke(initial_state)
        
        return {
            "pattern": "GraphOfThought",
            "problem": problem,
            "total_nodes": len(final_state["nodes"]),
            "total_edges": len(final_state["edges"]),
            "explored_nodes": len(final_state["explored_ids"]),
            "conclusions": final_state["conclusions"],
            "graph_structure": {
                "nodes": len(final_state["nodes"]),
                "edges": len(final_state["edges"]),
                "hypotheses": len([n for n in final_state["nodes"] 
                                  if n.thought_type == ThoughtType.HYPOTHESIS])
            }
        }


# Example usage
if __name__ == "__main__":
    print("=== Graph-of-Thought Pattern Demo ===\n")
    
    pattern = GraphOfThoughtPattern()
    
    # Complex problem requiring non-linear reasoning
    problem = """
    A company wants to reduce its carbon footprint by 50% in 2 years.
    They have manufacturing plants, offices, and a delivery fleet.
    What strategy should they pursue?
    """
    
    print("Problem:", problem.strip())
    
    result = pattern.run(problem)
    
    print(f"\n=== Results ===")
    print(f"Total thought nodes: {result['total_nodes']}")
    print(f"Explored nodes: {result['explored_nodes']}")
    print(f"Graph edges: {result['total_edges']}")
    print(f"Hypotheses generated: {result['graph_structure']['hypotheses']}")
    
    print(f"\n=== Final Conclusion ===")
    for conclusion in result['conclusions']:
        print(f"{conclusion}")
    
    print("\n✓ Graph-of-thought reasoning completed with non-linear exploration!")

    

Agentic Architecture Pattern: Graph Of Thought

Pattern 76: 76 Graph Of Thought – Line-by-Line Explanation

Overview

Pattern 76: Graph-of-Thought Pattern

Reasoning as a graph structure with nodes (thoughts) and edges (relationships). Allows parallel exploration, backtracking, and aggregation of multiple reasoning paths.

Code Structure Analysis

File Information

• Pattern Number: 76
• Pattern Name: 76 Graph Of Thought
• File Path: patterns\execution_control\76_graph_of_thought.py
• Total Lines: 319

Detailed Code Explanation

Module Docstring

"""
Pattern 76: Graph-of-Thought Pattern

Reasoning as a graph structure with nodes (thoughts) and edges (relationships).
Allows parallel exploration, backtracking, and aggregation of multiple reasoning paths.
"""

Explanation: This module docstring describes the purpose and key features of the 76 Graph Of Thought pattern.

Imports Section

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

ThoughtType (Line 18)

class ThoughtType(Enum):
    HYPOTHESIS
    EVIDENCE
    REASONING
    CONCLUSION
    QUESTION

Purpose: Type of thought node.

Values: – HYPOTHESIS: Represents hypothesis – EVIDENCE: Represents evidence – REASONING: Represents reasoning – CONCLUSION: Represents conclusion – QUESTION: Represents question

Classes

ThoughtType (Line 18)

Description: Type of thought node.

ThoughtNode (Line 28)

Description: Node in the thought graph.

ThoughtEdge (Line 41)

Description: Edge connecting thoughts.

GraphOfThoughtState (Line 49)

Description: State for graph-of-thought workflow.

GraphOfThoughtPattern (Line 59)

Description: Graph-of-Thought Pattern – Non-linear reasoning with graph structure.

Thoughts are organized as a graph allowing: – Parallel exploration of multiple paths – Backtracking when needed – Aggregation of insights from different branches – Complex relationship modeling

Methods:

__init__ (Line 70)

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”)

_build_graph (Line 76)

def _build_graph(self):
    """Build the LangGraph workflow."""

Purpose: Build the LangGraph workflow.

Parameters: – self: Instance reference

_generate_hypotheses (Line 99)

def _generate_hypotheses(self, state):
    """Generate initial hypothesis nodes."""

Purpose: Generate initial hypothesis nodes.

Parameters: – self: Instance reference – state: Current workflow state

_explore_path (Line 132)

def _explore_path(self, state):
    """Explore a thought path by expanding a node."""

Purpose: Explore a thought path by expanding a node.

Parameters: – self: Instance reference – state: Current workflow state

_evaluate_node (Line 186)

def _evaluate_node(self, state):
    """Evaluate current node's confidence."""

Purpose: Evaluate current node’s confidence.

Parameters: – self: Instance reference – state: Current workflow state

_should_continue_exploration (Line 205)

def _should_continue_exploration(self, state):
    """Decide whether to continue exploring or aggregate."""

Purpose: Decide whether to continue exploring or aggregate.

Parameters: – self: Instance reference – state: Current workflow state

_aggregate_insights (Line 215)

def _aggregate_insights(self, state):
    """Aggregate insights from the thought graph."""

Purpose: Aggregate insights from the thought graph.

Parameters: – self: Instance reference – state: Current workflow state

_trace_path (Line 245)

def _trace_path(self, node, all_nodes):
    """Trace path from root to this node."""

Purpose: Trace path from root to this node.

Parameters: – self: Instance reference – node: Parameter for node – all_nodes: Parameter for all_nodes

run (Line 260)

def run(self, problem):
    """Execute graph-of-thought reasoning."""

Purpose: Execute graph-of-thought reasoning.

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

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 = ThoughtType(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 75: Tree-of-Thought Pattern

Explores multiple reasoning paths in parallel, evaluates them, and
selects the best solution. Enables deliberate decision making through
search over thought trees.
"""

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


@dataclass
class ThoughtNode:
    """Node in the thought tree."""
    id: str
    content: str
    parent_id: Optional[str]
    children_ids: List[str] = field(default_factory=list)
    score: float = 0.0
    depth: int = 0
    is_terminal: bool = False
    evaluation: Optional[str] = None


class TreeOfThoughtPattern:
    """
    Tree-of-Thought Pattern - Explores multiple reasoning branches.
    
    Generates multiple thought paths, evaluates them, and searches
    for the best solution through tree exploration.
    """
    
    def __init__(self, model_name: str = "gpt-4", max_depth: int = 3, branches_per_node: int = 3):
        self.llm = ChatOpenAI(model=model_name, temperature=0.9)
        self.evaluator_llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.max_depth = max_depth
        self.branches_per_node = branches_per_node
        self.nodes: Dict[str, ThoughtNode] = {}
        self.root_id: Optional[str] = None
    
    def generate_thoughts(self, problem: str, parent_thought: Optional[str] = None, depth: int = 0) -> List[str]:
        """Generate multiple thought branches."""
        if depth >= self.max_depth:
            return []
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a creative problem solver. Generate diverse approaches to solve the problem. "
                      "Each approach should be distinct and explore different angles."),
            ("human", f"Problem: {problem}\n"
                     f"{'Current thought: ' + parent_thought if parent_thought else ''}\n\n"
                     f"Generate {self.branches_per_node} different next steps or approaches. "
                     f"Provide them as a numbered list (1. ... 2. ... 3. ...)")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        content = str(response.content)
        
        # Parse thoughts from response
        thoughts = []
        for line in content.split('\n'):
            line = line.strip()
            if line and (line[0].isdigit() or line.startswith('-') or line.startswith('•')):
                # Remove numbering/bullets
                thought = line.lstrip('0123456789.-•) ').strip()
                if thought:
                    thoughts.append(thought)
        
        return thoughts[:self.branches_per_node]
    
    def evaluate_thought(self, problem: str, thought: str) -> float:
        """Evaluate quality of a thought."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an expert evaluator. Rate how promising this thought/approach is "
                      "for solving the problem. Consider feasibility, creativity, and likelihood of success."),
            ("human", f"Problem: {problem}\n"
                     f"Thought/Approach: {thought}\n\n"
                     f"Rate this approach from 0.0 (terrible) to 10.0 (excellent). "
                     f"Provide only the numeric rating.")
        ])
        
        chain = prompt | self.evaluator_llm
        response = chain.invoke({})
        content = str(response.content).strip()
        
        # Extract numeric rating
        try:
            # Find first number in response
            import re
            match = re.search(r'(\d+\.?\d*)', content)
            if match:
                score = float(match.group(1))
                return min(10.0, max(0.0, score))
        except:
            pass
        
        return 5.0  # Default middle score
    
    def build_tree(self, problem: str):
        """Build tree of thoughts."""
        # Create root node
        root_id = str(uuid.uuid4())
        self.root_id = root_id
        
        root_node = ThoughtNode(
            id=root_id,
            content=f"Problem: {problem}",
            parent_id=None,
            depth=0
        )
        self.nodes[root_id] = root_node
        
        # Build tree level by level
        nodes_to_expand = [root_node]
        
        while nodes_to_expand:
            current_node = nodes_to_expand.pop(0)
            
            if current_node.depth >= self.max_depth:
                current_node.is_terminal = True
                continue
            
            # Generate child thoughts
            thoughts = self.generate_thoughts(
                problem,
                current_node.content if current_node.depth > 0 else None,
                current_node.depth
            )
            
            for thought in thoughts:
                # Create child node
                child_id = str(uuid.uuid4())
                child_node = ThoughtNode(
                    id=child_id,
                    content=thought,
                    parent_id=current_node.id,
                    depth=current_node.depth + 1
                )
                
                # Evaluate thought
                score = self.evaluate_thought(problem, thought)
                child_node.score = score
                child_node.evaluation = f"Score: {score:.1f}/10.0"
                
                self.nodes[child_id] = child_node
                current_node.children_ids.append(child_id)
                
                # Add to expansion queue if not at max depth
                if child_node.depth  List[ThoughtNode]:
        """Get highest-scoring path from root to leaf."""
        if not self.root_id:
            return []
        
        def find_best_path_recursive(node_id: str) -> tuple[List[ThoughtNode], float]:
            """Recursively find best path."""
            node = self.nodes[node_id]
            
            if not node.children_ids or node.is_terminal:
                return ([node], node.score)
            
            best_path = [node]
            best_score = node.score
            
            for child_id in node.children_ids:
                child_path, child_score = find_best_path_recursive(child_id)
                total_score = node.score + child_score
                
                if total_score > best_score:
                    best_score = total_score
                    best_path = [node] + child_path
            
            return (best_path, best_score)
        
        path, _ = find_best_path_recursive(self.root_id)
        return path
    
    def run(self, problem: str) -> Dict[str, Any]:
        """Execute tree-of-thought reasoning."""
        print(f"   Building thought tree (depth={self.max_depth}, branches={self.branches_per_node})...")
        
        # Build tree
        self.build_tree(problem)
        
        # Find best solution path
        best_path = self.get_best_path()
        
        # Analyze tree
        total_nodes = len(self.nodes)
        terminal_nodes = sum(1 for n in self.nodes.values() if n.is_terminal)
        avg_score = sum(n.score for n in self.nodes.values()) / total_nodes if total_nodes > 0 else 0
        
        return {
            "pattern": "TreeOfThought",
            "problem": problem,
            "total_thoughts": total_nodes,
            "terminal_thoughts": terminal_nodes,
            "average_score": avg_score,
            "best_path_length": len(best_path),
            "best_path": [{"depth": n.depth, "thought": n.content, "score": n.score} for n in best_path],
            "best_solution": best_path[-1].content if best_path else None
        }


# Example usage
if __name__ == "__main__":
    print("=== Tree-of-Thought Pattern Demo ===\n")
    
    # Create pattern
    pattern = TreeOfThoughtPattern(max_depth=2, branches_per_node=3)
    
    # Solve problem
    print("1. Solving problem with tree-of-thought reasoning...")
    problem = "How can we reduce carbon emissions in urban transportation?"
    
    result = pattern.run(problem)
    
    print(f"\n2. Tree Statistics:")
    print(f"   Total Thoughts Explored: {result['total_thoughts']}")
    print(f"   Terminal Thoughts: {result['terminal_thoughts']}")
    print(f"   Average Score: {result['average_score']:.2f}/10.0")
    
    print(f"\n3. Best Reasoning Path:")
    for step in result['best_path']:
        indent = "   " * step['depth']
        print(f"{indent}└─ [{step['score']:.1f}] {step['thought'][:80]}...")
    
    print(f"\n4. Best Solution:")
    print(f"   {result['best_solution']}")
    
    print("\n✓ Tree-of-thought pattern explored multiple reasoning paths!")

    

Agentic Architecture Pattern: Tree Of Thought

Pattern 75: 75 Tree Of Thought – Line-by-Line Explanation

Overview

Pattern 75: Tree-of-Thought Pattern

Explores multiple reasoning paths in parallel, evaluates them, and selects the best solution. Enables deliberate decision making through search over thought trees.

Code Structure Analysis

File Information

• Pattern Number: 75
• Pattern Name: 75 Tree Of Thought
• File Path: patterns\execution_control\75_tree_of_thought.py
• Total Lines: 239

Detailed Code Explanation

Module Docstring

"""
Pattern 75: Tree-of-Thought Pattern

Explores multiple reasoning paths in parallel, evaluates them, and
selects the best solution. Enables deliberate decision making through
search over thought trees.
"""

Explanation: This module docstring describes the purpose and key features of the 75 Tree Of Thought pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
import uuid
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
            import re  # 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 langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Classes

ThoughtNode (Line 18)

Description: Node in the thought tree.

TreeOfThoughtPattern (Line 30)

Description: Tree-of-Thought Pattern – Explores multiple reasoning branches.

Generates multiple thought paths, evaluates them, and searches for the best solution through tree exploration.

Methods:

__init__ (Line 38)

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

Purpose: No method docstring.

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

generate_thoughts (Line 46)

def generate_thoughts(self, problem, parent_thought, depth):
    """Generate multiple thought branches."""

Purpose: Generate multiple thought branches.

Parameters: – self: Instance reference – problem: Parameter for problem – parent_thought: Parameter for parent_thought – depth: Parameter for depth

evaluate_thought (Line 76)

def evaluate_thought(self, problem, thought):
    """Evaluate quality of a thought."""

Purpose: Evaluate quality of a thought.

Parameters: – self: Instance reference – problem: Parameter for problem – thought: Parameter for thought

build_tree (Line 104)

def build_tree(self, problem):
    """Build tree of thoughts."""

Purpose: Build tree of thoughts.

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

get_best_path (Line 157)

def get_best_path(self):
    """Get highest-scoring path from root to leaf."""

Purpose: Get highest-scoring path from root to leaf.

Parameters: – self: Instance reference

run (Line 185)

def run(self, problem):
    """Execute tree-of-thought reasoning."""

Purpose: Execute tree-of-thought reasoning.

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

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 = ThoughtNode(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 74: Chain Of Thought

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 ChainOfThoughtConfig:
    """Configuration for Chain Of Thought."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class ChainOfThoughtState:
    """State information for Chain Of Thought."""
    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 ChainOfThoughtAgent:
    """Agent implementing Chain Of Thought."""
    
    def __init__(self, name: str, config: ChainOfThoughtConfig = None):
        self.name = name
        self.config = config or ChainOfThoughtConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = ChainOfThoughtState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Chain Of Thought."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Chain Of Thought. "
                      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 ChainOfThoughtPattern:
    """
    Chain Of Thought - Enhanced implementation
    
    This pattern provides comprehensive functionality for chain of thought
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: ChainOfThoughtConfig = None):
        self.config = config or ChainOfThoughtConfig()
        self.agents: Dict[str, ChainOfThoughtAgent] = {}
        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 Chain Of Thought. 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: ChainOfThoughtAgent):
        """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": "Chain Of Thought",
                "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": "Chain Of Thought",
            "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("=== Chain Of Thought Demo ===\n")
    
    # Create configuration
    config = ChainOfThoughtConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = ChainOfThoughtPattern(config)
    
    # Create and register agents
    agent1 = ChainOfThoughtAgent("Agent1", config)
    agent2 = ChainOfThoughtAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate chain of thought 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: Chain Of Thought

Pattern 74: 74 Chain Of Thought – Line-by-Line Explanation

Overview

Pattern 74: Chain Of Thought

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 74
• Pattern Name: 74 Chain Of Thought
• File Path: patterns\execution_control\74_chain_of_thought.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 74: Chain Of Thought

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

Explanation: This module docstring describes the purpose and key features of the 74 Chain Of Thought 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

ChainOfThoughtConfig (Line 18)

Description: Configuration for Chain Of Thought.

ChainOfThoughtState (Line 27)

Description: State information for Chain Of Thought.

ChainOfThoughtAgent (Line 36)

Description: Agent implementing Chain Of Thought.

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 Chain Of Thought."""

Purpose: Process input according to Chain Of Thought.

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

ChainOfThoughtPattern (Line 85)

Description: Chain Of Thought – Enhanced implementation

This pattern provides comprehensive functionality for chain of thought 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 = ChainOfThoughtConfig(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 73: Pipeline Pattern

Data flows through a series of processing stages with transformations
at each stage. Similar to Unix pipes or data processing pipelines.
"""

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


@dataclass
class PipelineStage:
    """Stage in the processing pipeline."""
    name: str
    processor: Callable[[Any], Any]
    description: str = ""
    input_type: Optional[str] = None
    output_type: Optional[str] = None


@dataclass
class PipelineResult:
    """Result from pipeline execution."""
    success: bool
    input_data: Any
    output_data: Optional[Any]
    stages_executed: List[str]
    stage_outputs: Dict[str, Any]
    execution_time: float
    error: Optional[str] = None


class PipelineAgent:
    """Agent that acts as a pipeline stage."""
    
    def __init__(self, name: str, role: str, model_name: str = "gpt-4"):
        self.name = name
        self.role = role
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
    
    def transform(self, data: Any) -> Any:
        """Transform input data."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, a {self.role}. "
                      f"Transform the input data according to your role."),
            ("human", f"Input: {data}\n\nProvide transformed output.")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        return str(response.content)


class PipelinePattern:
    """
    Pipeline Pattern - Sequential data transformation chain.
    
    Data flows through stages with each stage transforming the data.
    Stages can be agents, functions, or any callable processor.
    """
    
    def __init__(self):
        self.stages: List[PipelineStage] = []
        self.execution_log: List[PipelineResult] = []
    
    def add_stage(self, stage: PipelineStage):
        """Add stage to pipeline."""
        self.stages.append(stage)
    
    def add_agent_stage(self, agent: PipelineAgent, description: str = ""):
        """Add agent as pipeline stage."""
        stage = PipelineStage(
            name=agent.name,
            processor=agent.transform,
            description=description or f"{agent.role} processing"
        )
        self.stages.append(stage)
    
    def add_function_stage(self, name: str, func: Callable[[Any], Any], description: str = ""):
        """Add function as pipeline stage."""
        stage = PipelineStage(
            name=name,
            processor=func,
            description=description
        )
        self.stages.append(stage)
    
    def execute(self, input_data: Any) -> PipelineResult:
        """Execute data through pipeline."""
        start_time = datetime.now()
        
        stages_executed = []
        stage_outputs = {}
        current_data = input_data
        
        try:
            for stage in self.stages:
                print(f"   Executing stage: {stage.name}...")
                
                # Process data through stage
                current_data = stage.processor(current_data)
                
                # Log execution
                stages_executed.append(stage.name)
                stage_outputs[stage.name] = current_data
            
            execution_time = (datetime.now() - start_time).total_seconds()
            
            result = PipelineResult(
                success=True,
                input_data=input_data,
                output_data=current_data,
                stages_executed=stages_executed,
                stage_outputs=stage_outputs,
                execution_time=execution_time
            )
            
        except Exception as e:
            execution_time = (datetime.now() - start_time).total_seconds()
            
            result = PipelineResult(
                success=False,
                input_data=input_data,
                output_data=None,
                stages_executed=stages_executed,
                stage_outputs=stage_outputs,
                execution_time=execution_time,
                error=str(e)
            )
        
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Run pipeline and return results."""
        result = self.execute(input_data)
        
        return {
            "pattern": "Pipeline",
            "success": result.success,
            "stages_executed": len(result.stages_executed),
            "total_stages": len(self.stages),
            "execution_time": result.execution_time,
            "output": result.output_data,
            "error": result.error
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pipeline statistics."""
        if not self.execution_log:
            return {"total_runs": 0}
        
        return {
            "total_runs": len(self.execution_log),
            "successful_runs": sum(1 for r in self.execution_log if r.success),
            "failed_runs": sum(1 for r in self.execution_log if not r.success),
            "avg_execution_time": sum(r.execution_time for r in self.execution_log) / len(self.execution_log),
            "total_stages": len(self.stages)
        }


# Example usage
if __name__ == "__main__":
    print("=== Pipeline Pattern Demo ===\n")
    
    # Create pipeline
    pipeline = PipelinePattern()
    
    # Add agent-based stages
    print("1. Building pipeline with agents...")
    extractor = PipelineAgent("Extractor", "data extractor")
    transformer = PipelineAgent("Transformer", "data transformer")
    validator = PipelineAgent("Validator", "data validator")
    
    pipeline.add_agent_stage(extractor, "Extract key information")
    pipeline.add_agent_stage(transformer, "Transform to target format")
    pipeline.add_agent_stage(validator, "Validate output quality")
    
    # Add function-based stage
    def format_output(data: Any) -> str:
        return f"[FORMATTED] {str(data).upper()}"
    
    pipeline.add_function_stage("Formatter", format_output, "Format final output")
    
    # Execute pipeline
    print("\n2. Executing pipeline...")
    input_text = "Process this customer feedback: The product is great but shipping was slow."
    result = pipeline.run(input_text)
    
    print(f"\n3. Pipeline Results:")
    print(f"   Success: {result['success']}")
    print(f"   Stages Executed: {result['stages_executed']}/{result['total_stages']}")
    print(f"   Execution Time: {result['execution_time']:.2f}s")
    
    if result['output']:
        print(f"\n4. Final Output:")
        print(f"   {str(result['output'])[:150]}...")
    
    # Statistics
    stats = pipeline.get_statistics()
    print(f"\n5. Pipeline Statistics:")
    print(f"   Total Runs: {stats['total_runs']}")
    print(f"   Success Rate: {stats['successful_runs']}/{stats['total_runs']}")
    
    print("\n✓ Pipeline pattern processed data through transformation stages!")

    

Agentic Architecture Pattern: Pipeline

Pattern 73: 73 Pipeline – Line-by-Line Explanation

Overview

Pattern 73: Pipeline Pattern

Data flows through a series of processing stages with transformations at each stage. Similar to Unix pipes or data processing pipelines.

Code Structure Analysis

File Information

• Pattern Number: 73
• Pattern Name: 73 Pipeline
• File Path: patterns\execution_control\73_pipeline.py
• Total Lines: 210

Detailed Code Explanation

Module Docstring

"""
Pattern 73: Pipeline Pattern

Data flows through a series of processing stages with transformations
at each stage. Similar to Unix pipes or data processing pipelines.
"""

Explanation: This module docstring describes the purpose and key features of the 73 Pipeline pattern.

Imports Section

from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import datetime
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, Callable: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Classes

PipelineStage (Line 16)

Description: Stage in the processing pipeline.

PipelineResult (Line 26)

Description: Result from pipeline execution.

PipelineAgent (Line 37)

Description: Agent that acts as a pipeline stage.

Methods:

__init__ (Line 40)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – role: Parameter for role – model_name: LLM model to use (e.g., “gpt-4”)

transform (Line 45)

def transform(self, data):
    """Transform input data."""

Purpose: Transform input data.

Parameters: – self: Instance reference – data: Input data to process

PipelinePattern (Line 58)

Description: Pipeline Pattern – Sequential data transformation chain.

Data flows through stages with each stage transforming the data. Stages can be agents, functions, or any callable processor.

Methods:

__init__ (Line 66)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

add_stage (Line 70)

def add_stage(self, stage):
    """Add stage to pipeline."""

Purpose: Add stage to pipeline.

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

add_agent_stage (Line 74)

def add_agent_stage(self, agent, description):
    """Add agent as pipeline stage."""

Purpose: Add agent as pipeline stage.

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

add_function_stage (Line 83)

def add_function_stage(self, name, func, description):
    """Add function as pipeline stage."""

Purpose: Add function as pipeline stage.

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

execute (Line 92)

def execute(self, input_data):
    """Execute data through pipeline."""

Purpose: Execute data through pipeline.

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

run (Line 138)

def run(self, input_data):
    """Run pipeline and return results."""

Purpose: Run pipeline and return results.

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

get_statistics (Line 152)

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

Purpose: Get pipeline statistics.

Parameters: – self: Instance reference

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = PipelineStage(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 72: Parallel Agent Pattern

Multiple agents execute tasks concurrently for faster processing.
Features parallel execution, result aggregation, and synchronization.
"""

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


@dataclass
class ParallelTask:
    """Task to be executed in parallel."""
    task_id: str
    description: str
    agent_name: Optional[str] = None
    input_data: Any = None
    result: Optional[str] = None
    status: str = "pending"  # pending, running, completed, failed
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    duration: float = 0


@dataclass
class ParallelExecution:
    """Record of parallel execution."""
    execution_id: str
    tasks: List[ParallelTask]
    total_time: float
    speedup: float  # vs sequential execution
    efficiency: float  # speedup / num_agents
    timestamp: datetime = field(default_factory=datetime.now)


class ParallelAgent:
    """Agent that can execute tasks concurrently."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.tasks_completed = 0
    
    async def execute_task_async(self, task: ParallelTask) -> str:
        """Execute task asynchronously."""
        task.status = "running"
        task.start_time = datetime.now()
        task.agent_name = self.name
        
        # Simulate work with LLM
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, executing a task in parallel with other agents."),
            ("human", f"Task: {task.description}\n"
                     f"Input: {task.input_data}\n\n"
                     f"Execute this task (provide brief result).")
        ])
        
        chain = prompt | self.llm
        
        # Simulate some processing time
        await asyncio.sleep(0.1)  # Small delay to simulate work
        
        response = chain.invoke({})
        result = str(response.content)[:200]
        
        task.end_time = datetime.now()
        task.duration = (task.end_time - task.start_time).total_seconds()
        task.result = result
        task.status = "completed"
        self.tasks_completed += 1
        
        return result


class ParallelAgentPattern:
    """
    Parallel Agent Pattern - Concurrent task execution.
    
    Multiple agents work simultaneously on different tasks,
    reducing total execution time through parallelism.
    """
    
    def __init__(self):
        self.agents: List[ParallelAgent] = []
        self.execution_history: List[ParallelExecution] = []
    
    def register_agent(self, agent: ParallelAgent):
        """Register agent for parallel execution."""
        self.agents.append(agent)
    
    async def execute_parallel(self, tasks: List[ParallelTask]) -> ParallelExecution:
        """Execute tasks in parallel across available agents."""
        start_time = time.time()
        
        print(f"\n   Executing {len(tasks)} tasks in parallel with {len(self.agents)} agents...")
        
        # Assign tasks to agents (round-robin)
        task_assignments = []
        for i, task in enumerate(tasks):
            agent = self.agents[i % len(self.agents)]
            task_assignments.append(agent.execute_task_async(task))
        
        # Execute all tasks concurrently
        await asyncio.gather(*task_assignments)
        
        total_time = time.time() - start_time
        
        # Calculate metrics
        avg_task_duration = sum(t.duration for t in tasks) / len(tasks)
        sequential_time = sum(t.duration for t in tasks)
        speedup = sequential_time / total_time if total_time > 0 else 1
        efficiency = speedup / len(self.agents) if self.agents else 0
        
        print(f"   ✓ Completed in {total_time:.2f}s")
        print(f"   Speedup: {speedup:.2f}x (vs sequential: {sequential_time:.2f}s)")
        print(f"   Efficiency: {efficiency:.1%}")
        
        # Record execution
        execution = ParallelExecution(
            execution_id=f"exec_{len(self.execution_history)}",
            tasks=tasks,
            total_time=total_time,
            speedup=speedup,
            efficiency=efficiency
        )
        self.execution_history.append(execution)
        
        return execution
    
    def execute_parallel_sync(self, tasks: List[ParallelTask]) -> ParallelExecution:
        """Synchronous wrapper for parallel execution."""
        return asyncio.run(self.execute_parallel(tasks))
    
    def aggregate_results(self, tasks: List[ParallelTask]) -> Dict[str, Any]:
        """Aggregate results from parallel tasks."""
        completed = [t for t in tasks if t.status == "completed"]
        failed = [t for t in tasks if t.status == "failed"]
        
        results = {
            "total_tasks": len(tasks),
            "completed": len(completed),
            "failed": len(failed),
            "results": {t.task_id: t.result for t in completed},
            "agent_contributions": {}
        }
        
        # Count tasks per agent
        for task in completed:
            if task.agent_name:
                results["agent_contributions"][task.agent_name] = \
                    results["agent_contributions"].get(task.agent_name, 0) + 1
        
        return results
    
    def run(self, scenario: str) -> Dict[str, Any]:
        """Execute parallel agent pattern."""
        total_executions = len(self.execution_history)
        avg_speedup = (sum(e.speedup for e in self.execution_history) / total_executions 
                      if total_executions > 0 else 0)
        avg_efficiency = (sum(e.efficiency for e in self.execution_history) / total_executions 
                         if total_executions > 0 else 0)
        
        return {
            "pattern": "ParallelAgent",
            "total_agents": len(self.agents),
            "total_executions": total_executions,
            "average_speedup": avg_speedup,
            "average_efficiency": avg_efficiency,
            "scenario": scenario
        }


# Example usage
if __name__ == "__main__":
    print("=== Parallel Agent Pattern Demo ===\n")
    
    # Create pattern
    pattern = ParallelAgentPattern()
    
    # Register parallel agents
    print("1. Registering parallel agents...")
    agents = [
        ParallelAgent("Worker1"),
        ParallelAgent("Worker2"),
        ParallelAgent("Worker3"),
        ParallelAgent("Worker4"),
    ]
    
    for agent in agents:
        pattern.register_agent(agent)
    
    print(f"   Registered {len(agents)} agents")
    
    # Create tasks for parallel execution
    print("\n2. Creating tasks...")
    tasks = [
        ParallelTask("t1", "Analyze customer feedback", input_data="feedback_batch_1"),
        ParallelTask("t2", "Process invoice data", input_data="invoices_q4"),
        ParallelTask("t3", "Generate sales report", input_data="sales_data_nov"),
        ParallelTask("t4", "Classify support tickets", input_data="tickets_batch_5"),
        ParallelTask("t5", "Extract document insights", input_data="documents_set_3"),
        ParallelTask("t6", "Summarize meeting notes", input_data="meetings_week_45"),
        ParallelTask("t7", "Validate data quality", input_data="dataset_v2"),
        ParallelTask("t8", "Transform data format", input_data="raw_exports"),
    ]
    
    print(f"   Created {len(tasks)} tasks")
    
    # Execute in parallel
    print("\n3. Executing tasks in parallel...")
    execution = pattern.execute_parallel_sync(tasks)
    
    # Show task assignments
    print("\n4. Task Distribution:")
    for agent in agents:
        agent_tasks = [t for t in tasks if t.agent_name == agent.name]
        print(f"   {agent.name}: {len(agent_tasks)} tasks")
        for task in agent_tasks:
            print(f"      - {task.task_id}: {task.description} ({task.duration:.2f}s)")
    
    # Aggregate results
    print("\n5. Results Aggregation:")
    aggregated = pattern.aggregate_results(tasks)
    print(f"   Total Tasks: {aggregated['total_tasks']}")
    print(f"   Completed: {aggregated['completed']}")
    print(f"   Failed: {aggregated['failed']}")
    
    # Performance metrics
    print("\n6. Performance Metrics:")
    summary = pattern.run("Parallel data processing")
    print(f"   Agents: {summary['total_agents']}")
    print(f"   Average Speedup: {summary['average_speedup']:.2f}x")
    print(f"   Average Efficiency: {summary['average_efficiency']:.1%}")
    
    print("\n✓ Parallel agents executed tasks concurrently for faster processing!")

    

Agentic Architecture Pattern: Parallel Agent

Pattern 72: 72 Parallel Agent – Line-by-Line Explanation

Overview

Pattern 72: Parallel Agent Pattern

Multiple agents execute tasks concurrently for faster processing. Features parallel execution, result aggregation, and synchronization.

Code Structure Analysis

File Information

• Pattern Number: 72
• Pattern Name: 72 Parallel Agent
• File Path: patterns\execution_control\72_parallel_agent.py
• Total Lines: 242

Detailed Code Explanation

Module Docstring

"""
Pattern 72: Parallel Agent Pattern

Multiple agents execute tasks concurrently for faster processing.
Features parallel execution, result aggregation, and synchronization.
"""

Explanation: This module docstring describes the purpose and key features of the 72 Parallel Agent pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
import asyncio
import time
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 langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Classes

ParallelTask (Line 18)

Description: Task to be executed in parallel.

ParallelExecution (Line 32)

Description: Record of parallel execution.

ParallelAgent (Line 42)

Description: Agent that can execute tasks concurrently.

Methods:

__init__ (Line 45)

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

Purpose: No method docstring.

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

ParallelAgentPattern (Line 81)

Description: Parallel Agent Pattern – Concurrent task execution.

Multiple agents work simultaneously on different tasks, reducing total execution time through parallelism.

Methods:

__init__ (Line 89)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

register_agent (Line 93)

def register_agent(self, agent):
    """Register agent for parallel execution."""

Purpose: Register agent for parallel execution.

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

execute_parallel_sync (Line 136)

def execute_parallel_sync(self, tasks):
    """Synchronous wrapper for parallel execution."""

Purpose: Synchronous wrapper for parallel execution.

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

aggregate_results (Line 140)

def aggregate_results(self, tasks):
    """Aggregate results from parallel tasks."""

Purpose: Aggregate results from parallel tasks.

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

run (Line 161)

def run(self, scenario):
    """Execute parallel agent pattern."""

Purpose: Execute parallel agent pattern.

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

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 = ParallelTask(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 71: Sequential Agent Pattern

Agents execute tasks in a strict sequential order, with each agent processing
the output of the previous agent in a linear chain.
"""

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


@dataclass
class SequentialTask:
    """Task in sequential execution."""
    task_id: str
    agent_name: str
    input_data: Any
    output_data: Optional[Any] = None
    status: str = "pending"  # pending, running, completed, failed
    start_time: Optional[datetime] = None
    end_time: Optional[datetime] = None
    error: Optional[str] = None


class SequentialState(TypedDict):
    """State for sequential execution workflow."""
    tasks: List[SequentialTask]
    current_index: int
    input_data: Any
    final_output: Optional[Any]
    execution_log: List[str]


class SequentialAgent:
    """Agent that processes data sequentially."""
    
    def __init__(self, name: str, role: str, model_name: str = "gpt-4"):
        self.name = name
        self.role = role
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
    
    def process(self, input_data: Any) -> Any:
        """Process input and return output."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, a {self.role}. "
                      f"Process the input and provide your output for the next agent."),
            ("human", f"Input: {input_data}\n\nProvide your processed output.")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        return str(response.content)


class SequentialAgentPattern:
    """
    Sequential Agent Pattern - Linear execution chain.
    
    Uses LangGraph to manage sequential workflow where each agent
    processes the output of the previous agent in strict order.
    """
    
    def __init__(self):
        self.agents: List[SequentialAgent] = []
        self.execution_history: List[SequentialTask] = []
    
    def add_agent(self, agent: SequentialAgent):
        """Add agent to sequential chain."""
        self.agents.append(agent)
    
    def build_graph(self) -> StateGraph:
        """Build sequential execution graph."""
        workflow = StateGraph(SequentialState)
        
        def execute_next(state: SequentialState) -> SequentialState:
            """Execute next agent in sequence."""
            current_idx = state["current_index"]
            tasks = state["tasks"]
            
            if current_idx >= len(tasks):
                # All tasks completed
                if tasks:
                    state["final_output"] = tasks[-1].output_data
                return state
            
            task = tasks[current_idx]
            task.status = "running"
            task.start_time = datetime.now()
            
            try:
                # Get agent
                agent = self.agents[current_idx]
                
                # Get input (previous output or initial input)
                if current_idx == 0:
                    input_data = state["input_data"]
                else:
                    input_data = tasks[current_idx - 1].output_data
                
                # Execute agent
                output = agent.process(input_data)
                
                task.output_data = output
                task.status = "completed"
                task.end_time = datetime.now()
                
                log_msg = f"Agent {agent.name} completed: {str(output)[:50]}..."
                state["execution_log"].append(log_msg)
                
            except Exception as e:
                task.status = "failed"
                task.error = str(e)
                task.end_time = datetime.now()
                state["execution_log"].append(f"Agent {task.agent_name} failed: {str(e)}")
            
            # Move to next
            state["current_index"] = current_idx + 1
            
            return state
        
        def should_continue(state: SequentialState) -> str:
            """Check if should continue to next agent."""
            if state["current_index"] >= len(state["tasks"]):
                return "end"
            
            current_task = state["tasks"][state["current_index"]]
            if current_task.status == "failed":
                return "end"
            
            return "continue"
        
        workflow.add_node("execute", execute_next)
        workflow.set_entry_point("execute")
        
        workflow.add_conditional_edges(
            "execute",
            should_continue,
            {
                "continue": "execute",
                "end": END
            }
        )
        
        return workflow.compile()
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Execute sequential agent chain."""
        # Create tasks for each agent
        tasks = [
            SequentialTask(
                task_id=f"task_{i}",
                agent_name=agent.name,
                input_data=input_data if i == 0 else None
            )
            for i, agent in enumerate(self.agents)
        ]
        
        initial_state: SequentialState = {
            "tasks": tasks,
            "current_index": 0,
            "input_data": input_data,
            "final_output": None,
            "execution_log": []
        }
        
        # Build and execute graph
        graph = self.build_graph()
        final_state = graph.invoke(initial_state)
        
        # Store history
        self.execution_history.extend(final_state["tasks"])
        
        return {
            "pattern": "Sequential",
            "agents_executed": len([t for t in final_state["tasks"] if t.status == "completed"]),
            "total_agents": len(self.agents),
            "final_output": final_state["final_output"],
            "execution_log": final_state["execution_log"],
            "success": all(t.status == "completed" for t in final_state["tasks"])
        }


# Example usage
if __name__ == "__main__":
    print("=== Sequential Agent Pattern Demo ===\n")
    
    # Create pattern
    pattern = SequentialAgentPattern()
    
    # Create agent chain
    print("1. Creating sequential agent chain...")
    researcher = SequentialAgent("Researcher", "research analyst")
    writer = SequentialAgent("Writer", "content writer")
    editor = SequentialAgent("Editor", "editor and proofreader")
    formatter = SequentialAgent("Formatter", "document formatter")
    
    # Add agents in order
    pattern.add_agent(researcher)
    pattern.add_agent(writer)
    pattern.add_agent(editor)
    pattern.add_agent(formatter)
    
    # Execute sequential workflow
    print("\n2. Executing sequential workflow...")
    result = pattern.run("Write a blog post about AI agents")
    
    print(f"\n3. Results:")
    print(f"   Success: {result['success']}")
    print(f"   Agents Executed: {result['agents_executed']}/{result['total_agents']}")
    print(f"\n4. Execution Log:")
    for log in result['execution_log']:
        print(f"   - {log}")
    
    print(f"\n5. Final Output:")
    print(f"   {str(result['final_output'])[:200]}...")
    
    print("\n✓ Sequential agent pattern executed tasks in linear order!")

    

Agentic Architecture Pattern: Sequential Agent

Pattern 71: 71 Sequential Agent – Line-by-Line Explanation

Overview

Pattern 71: Sequential Agent Pattern

Agents execute tasks in a strict sequential order, with each agent processing the output of the previous agent in a linear chain.

Code Structure Analysis

File Information

• Pattern Number: 71
• Pattern Name: 71 Sequential Agent
• File Path: patterns\execution_control\71_sequential_agent.py
• Total Lines: 223

Detailed Code Explanation

Module Docstring

"""
Pattern 71: Sequential Agent Pattern

Agents execute tasks in a strict sequential order, with each agent processing
the output of the previous agent in a linear chain.
"""

Explanation: This module docstring describes the purpose and key features of the 71 Sequential Agent pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
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 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

SequentialTask (Line 18)

Description: Task in sequential execution.

SequentialState (Line 30)

Description: State for sequential execution workflow.

SequentialAgent (Line 39)

Description: Agent that processes data sequentially.

Methods:

__init__ (Line 42)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – role: Parameter for role – model_name: LLM model to use (e.g., “gpt-4”)

process (Line 47)

def process(self, input_data):
    """Process input and return output."""

Purpose: Process input and return output.

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

SequentialAgentPattern (Line 60)

Description: Sequential Agent Pattern – Linear execution chain.

Uses LangGraph to manage sequential workflow where each agent processes the output of the previous agent in strict order.

Methods:

__init__ (Line 68)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

add_agent (Line 72)

def add_agent(self, agent):
    """Add agent to sequential chain."""

Purpose: Add agent to sequential chain.

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

build_graph (Line 76)

def build_graph(self):
    """Build sequential execution graph."""

Purpose: Build sequential execution graph.

Parameters: – self: Instance reference

run (Line 151)

def run(self, input_data):
    """Execute sequential agent chain."""

Purpose: Execute sequential agent chain.

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

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 = SequentialTask(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 70: Rag

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 RagConfig:
    """Configuration for Rag."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class RagState:
    """State information for Rag."""
    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 RagAgent:
    """Agent implementing Rag."""
    
    def __init__(self, name: str, config: RagConfig = None):
        self.name = name
        self.config = config or RagConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = RagState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Rag."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Rag. "
                      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 RagPattern:
    """
    Rag - Enhanced implementation
    
    This pattern provides comprehensive functionality for rag
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: RagConfig = None):
        self.config = config or RagConfig()
        self.agents: Dict[str, RagAgent] = {}
        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 Rag. 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: RagAgent):
        """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": "Rag",
                "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": "Rag",
            "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("=== Rag Demo ===\n")
    
    # Create configuration
    config = RagConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = RagPattern(config)
    
    # Create and register agents
    agent1 = RagAgent("Agent1", config)
    agent2 = RagAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate rag 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: Rag

Pattern 70: 70 Rag – Line-by-Line Explanation

Overview

Pattern 70: Rag

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 70
• Pattern Name: 70 Rag
• File Path: patterns\memory_knowledge\70_rag.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 70: Rag

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

Explanation: This module docstring describes the purpose and key features of the 70 Rag 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

RagConfig (Line 18)

Description: Configuration for Rag.

RagState (Line 27)

Description: State information for Rag.

RagAgent (Line 36)

Description: Agent implementing Rag.

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 Rag."""

Purpose: Process input according to Rag.

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

RagPattern (Line 85)

Description: Rag – Enhanced implementation

This pattern provides comprehensive functionality for rag 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 = RagConfig(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 69: Vector Store Pattern

Agents store and retrieve information using vector embeddings and similarity search.
Enables semantic search: finding similar content based on meaning, not just keywords.
"""

from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
import numpy as np
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document


@dataclass
class VectorDocument:
    """Document with vector embedding."""
    doc_id: str
    content: str
    metadata: Dict[str, Any] = field(default_factory=dict)
    embedding: Optional[List[float]] = None
    created_at: datetime = field(default_factory=datetime.now)


@dataclass
class SearchResult:
    """Result from vector similarity search."""
    document: VectorDocument
    similarity_score: float
    rank: int


class VectorStore:
    """Vector storage with similarity search."""
    
    def __init__(self, embedding_model: str = "text-embedding-ada-002"):
        self.embeddings = OpenAIEmbeddings(model=embedding_model)
        self.documents: Dict[str, VectorDocument] = {}
        self.vector_store: Optional[FAISS] = None
        self.doc_id_to_index: Dict[str, int] = {}
    
    def add_document(self, doc: VectorDocument):
        """Add document to vector store."""
        self.documents[doc.doc_id] = doc
        
        # Create LangChain document
        lc_doc = Document(
            page_content=doc.content,
            metadata={**doc.metadata, "doc_id": doc.doc_id}
        )
        
        # Add to vector store
        if self.vector_store is None:
            self.vector_store = FAISS.from_documents([lc_doc], self.embeddings)
            self.doc_id_to_index[doc.doc_id] = 0
        else:
            # Add to existing store
            new_store = FAISS.from_documents([lc_doc], self.embeddings)
            self.vector_store.merge_from(new_store)
            self.doc_id_to_index[doc.doc_id] = len(self.doc_id_to_index)
    
    def add_documents_batch(self, docs: List[VectorDocument]):
        """Add multiple documents efficiently."""
        for doc in docs:
            self.documents[doc.doc_id] = doc
        
        # Create LangChain documents
        lc_docs = [
            Document(
                page_content=doc.content,
                metadata={**doc.metadata, "doc_id": doc.doc_id}
            )
            for doc in docs
        ]
        
        # Create or merge vector store
        if self.vector_store is None:
            self.vector_store = FAISS.from_documents(lc_docs, self.embeddings)
            for i, doc in enumerate(docs):
                self.doc_id_to_index[doc.doc_id] = i
        else:
            new_store = FAISS.from_documents(lc_docs, self.embeddings)
            self.vector_store.merge_from(new_store)
            start_idx = len(self.doc_id_to_index)
            for i, doc in enumerate(docs):
                self.doc_id_to_index[doc.doc_id] = start_idx + i
    
    def similarity_search(self, query: str, k: int = 5) -> List[SearchResult]:
        """Search for similar documents."""
        if self.vector_store is None:
            return []
        
        # Perform similarity search
        results = self.vector_store.similarity_search_with_score(query, k=k)
        
        search_results = []
        for rank, (doc, score) in enumerate(results):
            doc_id = doc.metadata.get("doc_id", "")
            if doc_id in self.documents:
                search_results.append(SearchResult(
                    document=self.documents[doc_id],
                    similarity_score=1.0 / (1.0 + score),  # Convert distance to similarity
                    rank=rank + 1
                ))
        
        return search_results
    
    def get_document(self, doc_id: str) -> Optional[VectorDocument]:
        """Get document by ID."""
        return self.documents.get(doc_id)


class VectorStoreAgent:
    """Agent with vector store for semantic search."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.vector_store = VectorStore()
        self.search_history: List[Tuple[str, List[SearchResult]]] = []
    
    def store_information(self, doc_id: str, content: str, metadata: Optional[Dict[str, Any]] = None):
        """Store information in vector store."""
        doc = VectorDocument(
            doc_id=doc_id,
            content=content,
            metadata=metadata or {}
        )
        self.vector_store.add_document(doc)
        print(f"   📦 {self.name} stored: {doc_id} ({len(content)} chars)")
    
    def store_batch(self, documents: List[Tuple[str, str, Optional[Dict[str, Any]]]]):
        """Store multiple documents at once."""
        docs = [
            VectorDocument(doc_id=doc_id, content=content, metadata=metadata or {})
            for doc_id, content, metadata in documents
        ]
        self.vector_store.add_documents_batch(docs)
        print(f"   📦 {self.name} stored {len(docs)} documents in batch")
    
    def search(self, query: str, k: int = 3) -> List[SearchResult]:
        """Search for similar content."""
        results = self.vector_store.similarity_search(query, k=k)
        self.search_history.append((query, results))
        
        print(f"   🔍 {self.name} searched: '{query}' -> {len(results)} results")
        return results
    
    def answer_with_context(self, question: str, k: int = 3) -> str:
        """Answer question using retrieved context."""
        # Search for relevant documents
        results = self.search(question, k=k)
        
        if not results:
            return f"I don't have information to answer: {question}"
        
        # Build context from search results
        context = "\n\n".join([
            f"[{r.rank}] (similarity: {r.similarity_score:.2f})\n{r.document.content}"
            for r in results
        ])
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}. Answer questions based on the provided context."),
            ("human", f"Context from my knowledge base:\n{context}\n\n"
                     f"Question: {question}\n\n"
                     f"Answer based on the context above. If the context doesn't contain "
                     f"relevant information, say so. Keep answer concise (2-3 sentences).")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        return str(response.content)[:400]


class VectorStorePattern:
    """
    Vector Store Pattern - Semantic search with embeddings.
    
    Agents store information as vector embeddings and retrieve similar
    content based on semantic meaning rather than keyword matching.
    """
    
    def __init__(self):
        self.agents: Dict[str, VectorStoreAgent] = {}
    
    def register_agent(self, agent: VectorStoreAgent):
        """Register agent with vector store."""
        self.agents[agent.name] = agent
    
    def run(self, scenario: str) -> Dict[str, Any]:
        """Execute vector store pattern."""
        total_docs = sum(len(agent.vector_store.documents) for agent in self.agents.values())
        total_searches = sum(len(agent.search_history) for agent in self.agents.values())
        
        return {
            "pattern": "VectorStore",
            "scenario": scenario,
            "total_agents": len(self.agents),
            "total_documents": total_docs,
            "total_searches": total_searches
        }


# Example usage
if __name__ == "__main__":
    print("=== Vector Store Pattern Demo ===\n")
    
    # Create pattern
    pattern = VectorStorePattern()
    
    # Create agent with vector store
    print("1. Creating agent with vector store...")
    agent = VectorStoreAgent("Librarian")
    pattern.register_agent(agent)
    
    # Store documents
    print("\n2. Storing documents in vector store...")
    
    documents = [
        ("doc1", "Machine learning is a subset of artificial intelligence that enables "
                "computers to learn from data without explicit programming.",
         {"category": "AI", "difficulty": "beginner"}),
        
        ("doc2", "Neural networks are computing systems inspired by biological neural networks. "
                "They consist of layers of interconnected nodes that process information.",
         {"category": "AI", "difficulty": "intermediate"}),
        
        ("doc3", "Natural language processing (NLP) is a field of AI that focuses on enabling "
                "computers to understand, interpret, and generate human language.",
         {"category": "AI", "difficulty": "intermediate"}),
        
        ("doc4", "Deep learning uses neural networks with multiple layers to learn hierarchical "
                "representations of data, achieving state-of-the-art results in many domains.",
         {"category": "AI", "difficulty": "advanced"}),
        
        ("doc5", "Reinforcement learning is a type of machine learning where agents learn to make "
                "decisions by interacting with an environment and receiving rewards or penalties.",
         {"category": "AI", "difficulty": "advanced"}),
        
        ("doc6", "Computer vision enables machines to interpret and understand visual information "
                "from the world, including image recognition and object detection.",
         {"category": "AI", "difficulty": "intermediate"}),
    ]
    
    agent.store_batch(documents)
    
    # Semantic similarity search
    print("\n3. Performing semantic similarity searches...")
    
    queries = [
        "How do computers learn?",
        "What are artificial brains?",
        "Understanding human language with AI",
        "Learning through trial and error"
    ]
    
    for query in queries:
        print(f"\n   Query: '{query}'")
        results = agent.search(query, k=2)
        for result in results:
            print(f"   [{result.rank}] Similarity: {result.similarity_score:.3f}")
            print(f"       {result.document.content[:80]}...")
            print(f"       Category: {result.document.metadata.get('category')}, "
                  f"Difficulty: {result.document.metadata.get('difficulty')}")
    
    # Question answering with context
    print("\n4. Answering questions using retrieved context...")
    
    questions = [
        "What is machine learning?",
        "How do neural networks work?",
        "What's the difference between deep learning and machine learning?",
        "What can computer vision do?"
    ]
    
    for question in questions:
        print(f"\n   Q: {question}")
        answer = agent.answer_with_context(question, k=3)
        print(f"   A: {answer}")
    
    # Statistics
    print("\n5. Vector Store Statistics:")
    summary = pattern.run("AI Knowledge Base")
    print(f"   Total Documents: {summary['total_documents']}")
    print(f"   Total Searches: {summary['total_searches']}")
    
    # Search history analysis
    print("\n6. Search History:")
    for i, (query, results) in enumerate(agent.search_history[:3], 1):
        print(f"   {i}. '{query}' -> {len(results)} results")
        if results:
            best = results[0]
            print(f"      Best match: {best.document.doc_id} (similarity: {best.similarity_score:.3f})")
    
    print("\n✓ Vector store pattern enabled semantic search!")

    

Agentic Architecture Pattern: Vector Store

Pattern 69: 69 Vector Store – Line-by-Line Explanation

Overview

Pattern 69: Vector Store Pattern

Agents store and retrieve information using vector embeddings and similarity search. Enables semantic search: finding similar content based on meaning, not just keywords.

Code Structure Analysis

File Information

• Pattern Number: 69
• Pattern Name: 69 Vector Store
• File Path: patterns\memory_knowledge\69_vector_store.py
• Total Lines: 301

Detailed Code Explanation

Module Docstring

"""
Pattern 69: Vector Store Pattern

Agents store and retrieve information using vector embeddings and similarity search.
Enables semantic search: finding similar content based on meaning, not just keywords.
"""

Explanation: This module docstring describes the purpose and key features of the 69 Vector Store pattern.

Imports Section

from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
import numpy as np
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Tuple: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI, OpenAIEmbeddings: LangChain framework integration – from langchain_community.vectorstores import FAISS: LangChain framework integration – from langchain_core.documents import Document: LangChain framework integration

Classes

VectorDocument (Line 19)

Description: Document with vector embedding.

SearchResult (Line 29)

Description: Result from vector similarity search.

VectorStore (Line 36)

Description: Vector storage with similarity search.

Methods:

__init__ (Line 39)

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

Purpose: No method docstring.

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

add_document (Line 45)

def add_document(self, doc):
    """Add document to vector store."""

Purpose: Add document to vector store.

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

add_documents_batch (Line 65)

def add_documents_batch(self, docs):
    """Add multiple documents efficiently."""

Purpose: Add multiple documents efficiently.

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

similarity_search (Line 91)

def similarity_search(self, query, k):
    """Search for similar documents."""

Purpose: Search for similar documents.

Parameters: – self: Instance reference – query: Parameter for query – k: Parameter for k

get_document (Line 111)

def get_document(self, doc_id):
    """Get document by ID."""

Purpose: Get document by ID.

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

VectorStoreAgent (Line 116)

Description: Agent with vector store for semantic search.

Methods:

__init__ (Line 119)

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

Purpose: No method docstring.

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

store_information (Line 125)

def store_information(self, doc_id, content, metadata):
    """Store information in vector store."""

Purpose: Store information in vector store.

Parameters: – self: Instance reference – doc_id: Parameter for doc_id – content: Parameter for content – metadata: Parameter for metadata

store_batch (Line 135)

def store_batch(self, documents):
    """Store multiple documents at once."""

Purpose: Store multiple documents at once.

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

search (Line 144)

def search(self, query, k):
    """Search for similar content."""

Purpose: Search for similar content.

Parameters: – self: Instance reference – query: Parameter for query – k: Parameter for k

answer_with_context (Line 152)

def answer_with_context(self, question, k):
    """Answer question using retrieved context."""

Purpose: Answer question using retrieved context.

Parameters: – self: Instance reference – question: Parameter for question – k: Parameter for k

VectorStorePattern (Line 180)

Description: Vector Store Pattern – Semantic search with embeddings.

Agents store information as vector embeddings and retrieve similar content based on semantic meaning rather than keyword matching.

Methods:

__init__ (Line 188)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

register_agent (Line 191)

def register_agent(self, agent):
    """Register agent with vector store."""

Purpose: Register agent with vector store.

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

run (Line 195)

def run(self, scenario):
    """Execute vector store pattern."""

Purpose: Execute vector store pattern.

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

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 = VectorDocument(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 68: Graph-Based Knowledge Pattern

Agents store knowledge as a graph with nodes (entities) and edges (relationships).
Enables path finding, traversal, and graph-based reasoning.
"""

from typing import Dict, Any, List, Optional, Set, Tuple
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 NodeType(Enum):
    """Type of knowledge graph node."""
    ENTITY = "entity"
    CONCEPT = "concept"
    EVENT = "event"
    ATTRIBUTE = "attribute"


@dataclass
class GraphNode:
    """Node in knowledge graph."""
    node_id: str
    node_type: NodeType
    label: str
    properties: Dict[str, Any] = field(default_factory=dict)
    created_at: datetime = field(default_factory=datetime.now)


@dataclass
class GraphEdge:
    """Edge in knowledge graph."""
    edge_id: str
    source_id: str
    target_id: str
    relationship: str
    weight: float = 1.0
    properties: Dict[str, Any] = field(default_factory=dict)
    created_at: datetime = field(default_factory=datetime.now)


@dataclass
class GraphPath:
    """Path through knowledge graph."""
    nodes: List[str]
    edges: List[str]
    total_weight: float
    path_length: int


class KnowledgeGraph:
    """Graph-based knowledge storage with traversal."""
    
    def __init__(self):
        self.nodes: Dict[str, GraphNode] = {}
        self.edges: Dict[str, GraphEdge] = {}
        self.adjacency: Dict[str, Set[str]] = {}  # node_id -> {edge_ids}
    
    def add_node(self, node: GraphNode):
        """Add node to graph."""
        self.nodes[node.node_id] = node
        if node.node_id not in self.adjacency:
            self.adjacency[node.node_id] = set()
    
    def add_edge(self, edge: GraphEdge):
        """Add edge to graph."""
        self.edges[edge.edge_id] = edge
        
        # Update adjacency
        if edge.source_id not in self.adjacency:
            self.adjacency[edge.source_id] = set()
        self.adjacency[edge.source_id].add(edge.edge_id)
    
    def get_neighbors(self, node_id: str) -> List[Tuple[GraphNode, GraphEdge]]:
        """Get neighboring nodes and connecting edges."""
        neighbors = []
        
        if node_id not in self.adjacency:
            return neighbors
        
        for edge_id in self.adjacency[node_id]:
            edge = self.edges.get(edge_id)
            if edge:
                target_node = self.nodes.get(edge.target_id)
                if target_node:
                    neighbors.append((target_node, edge))
        
        return neighbors
    
    def find_path(self, start_id: str, end_id: str, 
                  max_depth: int = 5) -> Optional[GraphPath]:
        """Find path between two nodes using BFS."""
        if start_id not in self.nodes or end_id not in self.nodes:
            return None
        
        # BFS
        queue = [(start_id, [start_id], [], 0.0)]
        visited = {start_id}
        
        while queue:
            current_id, path_nodes, path_edges, total_weight = queue.pop(0)
            
            if current_id == end_id:
                return GraphPath(
                    nodes=path_nodes,
                    edges=path_edges,
                    total_weight=total_weight,
                    path_length=len(path_nodes) - 1
                )
            
            if len(path_nodes) >= max_depth:
                continue
            
            # Explore neighbors
            for neighbor, edge in self.get_neighbors(current_id):
                if neighbor.node_id not in visited:
                    visited.add(neighbor.node_id)
                    queue.append((
                        neighbor.node_id,
                        path_nodes + [neighbor.node_id],
                        path_edges + [edge.edge_id],
                        total_weight + edge.weight
                    ))
        
        return None
    
    def find_nodes_by_type(self, node_type: NodeType) -> List[GraphNode]:
        """Find all nodes of a specific type."""
        return [node for node in self.nodes.values() 
                if node.node_type == node_type]
    
    def find_related(self, node_id: str, relationship: str) -> List[GraphNode]:
        """Find nodes related by specific relationship."""
        related = []
        
        if node_id not in self.adjacency:
            return related
        
        for edge_id in self.adjacency[node_id]:
            edge = self.edges.get(edge_id)
            if edge and edge.relationship == relationship:
                target = self.nodes.get(edge.target_id)
                if target:
                    related.append(target)
        
        return related
    
    def get_subgraph(self, node_id: str, depth: int = 2) -> Tuple[Set[str], Set[str]]:
        """Get subgraph around a node."""
        nodes = {node_id}
        edges = set()
        
        queue = [(node_id, 0)]
        visited = {node_id}
        
        while queue:
            current_id, current_depth = queue.pop(0)
            
            if current_depth >= depth:
                continue
            
            for neighbor, edge in self.get_neighbors(current_id):
                edges.add(edge.edge_id)
                if neighbor.node_id not in visited:
                    visited.add(neighbor.node_id)
                    nodes.add(neighbor.node_id)
                    queue.append((neighbor.node_id, current_depth + 1))
        
        return nodes, edges


class GraphKnowledgeAgent:
    """Agent with graph-based knowledge representation."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.graph = KnowledgeGraph()
    
    def learn_entity(self, entity_id: str, label: str, 
                     properties: Optional[Dict[str, Any]] = None):
        """Learn about an entity."""
        node = GraphNode(
            node_id=entity_id,
            node_type=NodeType.ENTITY,
            label=label,
            properties=properties or {}
        )
        self.graph.add_node(node)
        print(f"   🔷 {self.name} learned entity: {label}")
    
    def learn_relationship(self, source_id: str, target_id: str, 
                          relationship: str, weight: float = 1.0):
        """Learn a relationship between entities."""
        edge_id = f"{source_id}_{relationship}_{target_id}"
        edge = GraphEdge(
            edge_id=edge_id,
            source_id=source_id,
            target_id=target_id,
            relationship=relationship,
            weight=weight
        )
        self.graph.add_edge(edge)
        print(f"   🔗 {self.name} learned: {source_id} --{relationship}-> {target_id}")
    
    def query_relationship(self, entity_id: str, relationship: str) -> str:
        """Query relationships from an entity."""
        related = self.graph.find_related(entity_id, relationship)
        
        if not related:
            return f"No {relationship} relationships found for {entity_id}."
        
        entity = self.graph.nodes.get(entity_id)
        entity_label = entity.label if entity else entity_id
        
        related_labels = [node.label for node in related]
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name} with graph-based knowledge."),
            ("human", f"Entity: {entity_label}\n"
                     f"Relationship: {relationship}\n"
                     f"Related entities: {', '.join(related_labels)}\n\n"
                     f"Provide a natural language summary of this relationship. (1-2 sentences)")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        return str(response.content)[:200]
    
    def find_connection(self, entity1_id: str, entity2_id: str) -> str:
        """Find how two entities are connected."""
        path = self.graph.find_path(entity1_id, entity2_id)
        
        if not path:
            return f"No connection found between {entity1_id} and {entity2_id}."
        
        # Build path description
        path_desc = []
        for i in range(len(path.nodes) - 1):
            node1 = self.graph.nodes.get(path.nodes[i])
            node2 = self.graph.nodes.get(path.nodes[i + 1])
            edge = self.graph.edges.get(path.edges[i])
            
            if node1 and node2 and edge:
                path_desc.append(f"{node1.label} --{edge.relationship}-> {node2.label}")
        
        return f"Path: {' → '.join(path_desc)} (length: {path.path_length}, weight: {path.total_weight:.1f})"


class GraphBasedKnowledgePattern:
    """
    Graph-Based Knowledge Pattern - Knowledge as graph structures.
    
    Agents represent knowledge as nodes and edges, enabling graph
    traversal, path finding, and relationship-based reasoning.
    """
    
    def __init__(self):
        self.agents: Dict[str, GraphKnowledgeAgent] = {}
    
    def register_agent(self, agent: GraphKnowledgeAgent):
        """Register agent with graph knowledge."""
        self.agents[agent.name] = agent
    
    def run(self, domain: str) -> Dict[str, Any]:
        """Execute graph knowledge pattern."""
        total_nodes = sum(len(agent.graph.nodes) for agent in self.agents.values())
        total_edges = sum(len(agent.graph.edges) for agent in self.agents.values())
        
        return {
            "pattern": "GraphBasedKnowledge",
            "domain": domain,
            "total_agents": len(self.agents),
            "total_nodes": total_nodes,
            "total_edges": total_edges
        }


# Example usage
if __name__ == "__main__":
    print("=== Graph-Based Knowledge Pattern Demo ===\n")
    
    # Create pattern
    pattern = GraphBasedKnowledgePattern()
    
    # Create agent with graph knowledge
    print("1. Creating agent with graph knowledge...")
    agent = GraphKnowledgeAgent("Scholar")
    pattern.register_agent(agent)
    
    # Build knowledge graph about a company
    print("\n2. Building knowledge graph...")
    
    # Entities
    agent.learn_entity("company", "TechCorp", {"type": "company", "founded": 2010})
    agent.learn_entity("ceo", "Alice Johnson", {"role": "CEO"})
    agent.learn_entity("cto", "Bob Smith", {"role": "CTO"})
    agent.learn_entity("product1", "CloudApp", {"category": "SaaS"})
    agent.learn_entity("product2", "DataPlatform", {"category": "Analytics"})
    agent.learn_entity("customer1", "Acme Corp", {"type": "enterprise"})
    agent.learn_entity("tech1", "Python", {"type": "language"})
    agent.learn_entity("tech2", "Kubernetes", {"type": "platform"})
    
    # Relationships
    print("\n3. Adding relationships...")
    agent.learn_relationship("ceo", "company", "leads", 1.0)
    agent.learn_relationship("cto", "company", "works_for", 1.0)
    agent.learn_relationship("cto", "ceo", "reports_to", 1.0)
    agent.learn_relationship("company", "product1", "develops", 1.0)
    agent.learn_relationship("company", "product2", "develops", 1.0)
    agent.learn_relationship("customer1", "product1", "uses", 1.0)
    agent.learn_relationship("product1", "tech1", "built_with", 1.0)
    agent.learn_relationship("product2", "tech1", "built_with", 1.0)
    agent.learn_relationship("product2", "tech2", "deployed_on", 1.0)
    
    # Query relationships
    print("\n4. Querying relationships...")
    summary = agent.query_relationship("company", "develops")
    print(f"   {summary}")
    
    summary = agent.query_relationship("product1", "built_with")
    print(f"   {summary}")
    
    # Find connections
    print("\n5. Finding connections between entities...")
    connections = [
        ("customer1", "tech1"),
        ("cto", "customer1"),
        ("tech2", "ceo")
    ]
    
    for entity1, entity2 in connections:
        print(f"\n   Connection: {entity1}  {entity2}")
        result = agent.find_connection(entity1, entity2)
        print(f"   {result}")
    
    # Explore subgraph
    print("\n6. Exploring subgraph around 'company'...")
    nodes, edges = agent.graph.get_subgraph("company", depth=2)
    print(f"   Nodes in subgraph: {len(nodes)}")
    print(f"   Edges in subgraph: {len(edges)}")
    
    node_labels = [agent.graph.nodes[nid].label for nid in nodes if nid in agent.graph.nodes]
    print(f"   Entities: {', '.join(node_labels)}")
    
    # Find by type
    print("\n7. Finding entities by type...")
    entities = agent.graph.find_nodes_by_type(NodeType.ENTITY)
    print(f"   Total entities: {len(entities)}")
    for entity in entities[:5]:
        print(f"   - {entity.label} ({entity.properties.get('type', 'N/A')})")
    
    # Statistics
    print("\n8. Graph Statistics:")
    summary = pattern.run("Company Knowledge")
    print(f"   Total Nodes: {summary['total_nodes']}")
    print(f"   Total Edges: {summary['total_edges']}")
    print(f"   Avg connections per node: {summary['total_edges'] / max(1, summary['total_nodes']):.1f}")
    
    # Analyze graph structure
    print("\n9. Graph Structure Analysis:")
    node_degrees = {}
    for node_id in agent.graph.nodes:
        degree = len(agent.graph.adjacency.get(node_id, set()))
        node_degrees[node_id] = degree
    
    if node_degrees:
        max_degree = max(node_degrees.values())
        central_nodes = [nid for nid, deg in node_degrees.items() if deg == max_degree]
        print(f"   Max node degree: {max_degree}")
        print(f"   Most connected: {[agent.graph.nodes[nid].label for nid in central_nodes]}")
    
    print("\n✓ Graph-based knowledge pattern enabled relationship reasoning!")

    

Agentic Architecture Pattern: Graph Based Knowledge

Pattern 68: 68 Graph Based Knowledge – Line-by-Line Explanation

Overview

Pattern 68: Graph-Based Knowledge Pattern

Agents store knowledge as a graph with nodes (entities) and edges (relationships). Enables path finding, traversal, and graph-based reasoning.

Code Structure Analysis

File Information

• Pattern Number: 68
• Pattern Name: 68 Graph Based Knowledge
• File Path: patterns\memory_knowledge\68_graph_based_knowledge.py
• Total Lines: 379

Detailed Code Explanation

Module Docstring

"""
Pattern 68: Graph-Based Knowledge Pattern

Agents store knowledge as a graph with nodes (entities) and edges (relationships).
Enables path finding, traversal, and graph-based reasoning.
"""

Explanation: This module docstring describes the purpose and key features of the 68 Graph Based Knowledge pattern.

Imports Section

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

Enumerations

NodeType (Line 16)

class NodeType(Enum):
    ENTITY
    CONCEPT
    EVENT
    ATTRIBUTE

Purpose: Type of knowledge graph node.

Values: – ENTITY: Represents entity – CONCEPT: Represents concept – EVENT: Represents event – ATTRIBUTE: Represents attribute

Classes

NodeType (Line 16)

Description: Type of knowledge graph node.

GraphNode (Line 25)

Description: Node in knowledge graph.

GraphEdge (Line 35)

Description: Edge in knowledge graph.

GraphPath (Line 47)

Description: Path through knowledge graph.

KnowledgeGraph (Line 55)

Description: Graph-based knowledge storage with traversal.

Methods:

__init__ (Line 58)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

add_node (Line 63)

def add_node(self, node):
    """Add node to graph."""

Purpose: Add node to graph.

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

add_edge (Line 69)

def add_edge(self, edge):
    """Add edge to graph."""

Purpose: Add edge to graph.

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

get_neighbors (Line 78)

def get_neighbors(self, node_id):
    """Get neighboring nodes and connecting edges."""

Purpose: Get neighboring nodes and connecting edges.

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

find_path (Line 94)

def find_path(self, start_id, end_id, max_depth):
    """Find path between two nodes using BFS."""

Purpose: Find path between two nodes using BFS.

Parameters: – self: Instance reference – start_id: Parameter for start_id – end_id: Parameter for end_id – max_depth: Parameter for max_depth

find_nodes_by_type (Line 131)

def find_nodes_by_type(self, node_type):
    """Find all nodes of a specific type."""

Purpose: Find all nodes of a specific type.

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

find_related (Line 136)

def find_related(self, node_id, relationship):
    """Find nodes related by specific relationship."""

Purpose: Find nodes related by specific relationship.

Parameters: – self: Instance reference – node_id: Parameter for node_id – relationship: Parameter for relationship

get_subgraph (Line 152)

def get_subgraph(self, node_id, depth):
    """Get subgraph around a node."""

Purpose: Get subgraph around a node.

Parameters: – self: Instance reference – node_id: Parameter for node_id – depth: Parameter for depth

GraphKnowledgeAgent (Line 176)

Description: Agent with graph-based knowledge representation.

Methods:

__init__ (Line 179)

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

Purpose: No method docstring.

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

learn_entity (Line 184)

def learn_entity(self, entity_id, label, properties):
    """Learn about an entity."""

Purpose: Learn about an entity.

Parameters: – self: Instance reference – entity_id: Parameter for entity_id – label: Parameter for label – properties: Parameter for properties

learn_relationship (Line 196)

def learn_relationship(self, source_id, target_id, relationship, weight):
    """Learn a relationship between entities."""

Purpose: Learn a relationship between entities.

Parameters: – self: Instance reference – source_id: Parameter for source_id – target_id: Parameter for target_id – relationship: Parameter for relationship – weight: Parameter for weight

query_relationship (Line 210)

def query_relationship(self, entity_id, relationship):
    """Query relationships from an entity."""

Purpose: Query relationships from an entity.

Parameters: – self: Instance reference – entity_id: Parameter for entity_id – relationship: Parameter for relationship

find_connection (Line 235)

def find_connection(self, entity1_id, entity2_id):
    """Find how two entities are connected."""

Purpose: Find how two entities are connected.

Parameters: – self: Instance reference – entity1_id: Parameter for entity1_id – entity2_id: Parameter for entity2_id

GraphBasedKnowledgePattern (Line 255)

Description: Graph-Based Knowledge Pattern – Knowledge as graph structures.

Agents represent knowledge as nodes and edges, enabling graph traversal, path finding, and relationship-based reasoning.

Methods:

__init__ (Line 263)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

register_agent (Line 266)

def register_agent(self, agent):
    """Register agent with graph knowledge."""

Purpose: Register agent with graph knowledge.

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

run (Line 270)

def run(self, domain):
    """Execute graph knowledge pattern."""

Purpose: Execute graph knowledge pattern.

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

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 = NodeType(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