"""
Pattern 214: Critic Agent Pattern

Specialized agent that provides constructive criticism
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

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


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


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


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


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

Criteria:
{criteria}

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


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

    

Agentic Architecture Pattern: Critic Agent

Pattern 214: 214 Critic Agent – Line-by-Line Explanation

Overview

Pattern 214: Critic Agent Pattern

Specialized agent that provides constructive criticism Implements feedback and improvement mechanisms using LangChain/LangGraph.

Code Structure Analysis

File Information

• Pattern Number: 214
• Pattern Name: 214 Critic Agent
• File Path: patterns\feedback_improvement\214_critic_agent.py
• Total Lines: 179

Detailed Code Explanation

Module Docstring

"""
Pattern 214: Critic Agent Pattern

Specialized agent that provides constructive criticism
Implements feedback and improvement mechanisms using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 214 Critic Agent pattern.

Imports Section

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

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

Enumerations

FeedbackType (Line 18)

class FeedbackType(Enum):
    POSITIVE
    NEGATIVE
    CONSTRUCTIVE
    NEUTRAL

Purpose: Types of feedback.

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

Classes

FeedbackType (Line 18)

Description: Types of feedback.

FeedbackItem (Line 27)

Description: Represents a feedback item.

EvaluationResult (Line 37)

Description: Result of evaluation.

CriticagentPattern (Line 45)

Description: Critic Agent Pattern – Specialized agent that provides constructive criticism

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

Methods:

__init__ (Line 53)

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

Purpose: No method docstring.

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

evaluate (Line 58)

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

Purpose: Evaluate output against criteria.

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

provide_feedback (Line 118)

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

Purpose: Provide feedback on output.

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

run (Line 143)

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

Purpose: Main execution method.

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

Design Patterns Used

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

Usage Example

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

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

# Check results
print(result)

Key Concepts

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

Extension Points

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