"""
Pattern 062: World Model Learning

Description:
    World Model Learning involves an agent building an internal model of its
    environment's dynamics. The agent learns to predict future states given
    current states and actions, enabling model-based planning, simulation,
    and what-if analysis without direct environment interaction.

Components:
    1. State Representation: How the world is encoded
    2. Dynamics Model: Predicts next state from current state and action
    3. Reward Predictor: Estimates rewards for state-action pairs
    4. Experience Collection: Gathers real-world data
    5. Model Training: Updates predictions based on experience
    6. Planning Module: Uses model for decision-making

Use Cases:
    - Robotics simulation and planning
    - Game AI with environment modeling
    - Strategic decision-making
    - Risk assessment and scenario planning
    - Sample-efficient reinforcement learning
    - Counterfactual reasoning

LangChain Implementation:
    Implements world modeling using LLM-based state prediction, transition
    dynamics, and outcome forecasting for planning and decision-making.
"""

import os
import time
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import random

from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

load_dotenv()


class StateType(Enum):
    """Types of state representations"""
    DISCRETE = "discrete"  # Finite states
    CONTINUOUS = "continuous"  # Continuous values
    HYBRID = "hybrid"  # Mix of both
    SYMBOLIC = "symbolic"  # Symbolic/linguistic


@dataclass
class State:
    """World state representation"""
    state_id: str
    description: str
    variables: Dict[str, Any] = field(default_factory=dict)
    timestamp: float = field(default_factory=time.time)
    
    def to_text(self) -> str:
        """Convert state to text"""
        text = f"State: {self.description}\n"
        if self.variables:
            text += "Variables:\n"
            for key, value in self.variables.items():
                text += f"  - {key}: {value}\n"
        return text


@dataclass
class Action:
    """Action that can be taken"""
    action_id: str
    name: str
    parameters: Dict[str, Any] = field(default_factory=dict)
    
    def to_text(self) -> str:
        """Convert action to text"""
        text = f"Action: {self.name}"
        if self.parameters:
            params_str = ", ".join(f"{k}={v}" for k, v in self.parameters.items())
            text += f" ({params_str})"
        return text


@dataclass
class Transition:
    """State transition experience"""
    state: State
    action: Action
    next_state: State
    reward: float
    info: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Prediction:
    """Model prediction"""
    predicted_state: State
    confidence: float
    predicted_reward: float
    alternative_outcomes: List[Tuple[State, float]] = field(default_factory=list)


@dataclass
class SimulationResult:
    """Result from simulation"""
    initial_state: State
    action_sequence: List[Action]
    predicted_trajectory: List[State]
    cumulative_reward: float
    success_probability: float


class WorldModel:
    """
    World model for environment dynamics.
    
    Features:
    1. State transition prediction
    2. Reward prediction
    3. Experience-based learning
    4. Multi-step simulation
    5. Uncertainty estimation
    """
    
    def __init__(self, domain: str = "general"):
        self.domain = domain
        self.experiences: List[Transition] = []
        
        # Model for dynamics prediction
        self.dynamics_model = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            temperature=0.3
        )
        
        # Model for reward prediction
        self.reward_model = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            temperature=0.2
        )
        
        # Model for uncertainty estimation
        self.uncertainty_model = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            temperature=0.4
        )
    
    def add_experience(self, transition: Transition):
        """Add experience to model's memory"""
        self.experiences.append(transition)
    
    def _get_relevant_experiences(
        self,
        state: State,
        action: Action,
        k: int = 3
    ) -> List[Transition]:
        """Retrieve relevant past experiences"""
        
        if not self.experiences:
            return []
        
        # Simple relevance: recent experiences are more relevant
        # In production, would use embedding similarity
        return self.experiences[-k:] if len(self.experiences) >= k else self.experiences
    
    def predict_next_state(
        self,
        current_state: State,
        action: Action
    ) -> Prediction:
        """Predict next state given current state and action"""
        
        # Retrieve relevant experiences
        relevant = self._get_relevant_experiences(current_state, action)
        
        # Build context from experiences
        context = ""
        if relevant:
            context = "Past Similar Experiences:\n"
            for i, exp in enumerate(relevant, 1):
                context += f"{i}. State: {exp.state.description}\n"
                context += f"   Action: {exp.action.name}\n"
                context += f"   Result: {exp.next_state.description}\n"
                context += f"   Reward: {exp.reward}\n\n"
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"""You are a world model for {self.domain}.

Predict what happens when an action is taken in a given state.

{context}

Provide:
1. Description of the resulting state
2. Key variables that changed
3. Confidence (0.0-1.0) in prediction"""),
            ("user", """Current State:
{state}

Action Taken:
{action}

Predicted Next State:""")
        ])
        
        chain = prompt | self.dynamics_model | StrOutputParser()
        prediction_text = chain.invoke({
            "state": current_state.to_text(),
            "action": action.to_text()
        })
        
        # Parse prediction
        lines = prediction_text.strip().split('\n')
        description = lines[0] if lines else "Unknown state"
        
        # Extract confidence if mentioned
        confidence = 0.7  # Default
        for line in lines:
            if 'confidence' in line.lower():
                try:
                    confidence = float(''.join(c for c in line if c.isdigit() or c == '.'))
                    if confidence > 1.0:
                        confidence = confidence / 100.0
                    confidence = max(0.0, min(1.0, confidence))
                except:
                    pass
        
        # Predict reward
        predicted_reward = self._predict_reward(current_state, action, description)
        
        predicted_state = State(
            state_id=f"predicted_{int(time.time())}",
            description=description,
            variables={}
        )
        
        return Prediction(
            predicted_state=predicted_state,
            confidence=confidence,
            predicted_reward=predicted_reward
        )
    
    def _predict_reward(
        self,
        state: State,
        action: Action,
        outcome: str
    ) -> float:
        """Predict reward for state-action-outcome"""
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"""Estimate the reward/value for an outcome in {self.domain}.

Provide a number between -1.0 (very bad) and +1.0 (very good).
Consider: goal achievement, efficiency, safety, side effects.

Respond with just a number."""),
            ("user", """State: {state}
Action: {action}
Outcome: {outcome}

Reward:""")
        ])
        
        chain = prompt | self.reward_model | StrOutputParser()
        
        try:
            reward_str = chain.invoke({
                "state": state.description,
                "action": action.name,
                "outcome": outcome
            })
            reward = float(''.join(c for c in reward_str if c.isdigit() or c == '.' or c == '-'))
            return max(-1.0, min(1.0, reward))
        except:
            return 0.0
    
    def simulate_trajectory(
        self,
        initial_state: State,
        action_sequence: List[Action],
        max_steps: int = 10
    ) -> SimulationResult:
        """Simulate a trajectory through the world"""
        
        current_state = initial_state
        trajectory = [initial_state]
        cumulative_reward = 0.0
        confidences = []
        
        for i, action in enumerate(action_sequence[:max_steps]):
            # Predict next state
            prediction = self.predict_next_state(current_state, action)
            
            # Update trajectory
            trajectory.append(prediction.predicted_state)
            cumulative_reward += prediction.predicted_reward
            confidences.append(prediction.confidence)
            
            # Move to next state
            current_state = prediction.predicted_state
        
        # Success probability = average confidence
        success_prob = sum(confidences) / len(confidences) if confidences else 0.5
        
        return SimulationResult(
            initial_state=initial_state,
            action_sequence=action_sequence,
            predicted_trajectory=trajectory,
            cumulative_reward=cumulative_reward,
            success_probability=success_prob
        )
    
    def plan_with_model(
        self,
        current_state: State,
        goal: str,
        num_alternatives: int = 3
    ) -> List[SimulationResult]:
        """Use model for planning"""
        
        # Generate alternative action sequences
        planning_prompt = ChatPromptTemplate.from_messages([
            ("system", f"""You are planning in {self.domain}.

Generate {num_alternatives} different action sequences to achieve the goal.

For each plan, list 3-5 actions.

Format:
Plan 1:
1. [action]
2. [action]
...

Plan 2:
1. [action]
..."""),
            ("user", """Current State: {state}

Goal: {goal}

Action Plans:""")
        ])
        
        chain = planning_prompt | self.dynamics_model | StrOutputParser()
        plans_text = chain.invoke({
            "state": current_state.to_text(),
            "goal": goal
        })
        
        # Parse action sequences
        action_sequences = []
        current_sequence = []
        
        for line in plans_text.split('\n'):
            line = line.strip()
            
            if line.startswith('Plan'):
                if current_sequence:
                    action_sequences.append(current_sequence)
                    current_sequence = []
            elif line and (line[0].isdigit() or line.startswith('-')):
                action_name = line.lstrip('0123456789.-) ').strip()
                if action_name:
                    action = Action(
                        action_id=f"action_{len(current_sequence)}",
                        name=action_name
                    )
                    current_sequence.append(action)
        
        if current_sequence:
            action_sequences.append(current_sequence)
        
        # Simulate each sequence
        simulations = []
        for seq in action_sequences[:num_alternatives]:
            if seq:  # Only simulate non-empty sequences
                sim = self.simulate_trajectory(current_state, seq)
                simulations.append(sim)
        
        # Sort by expected reward
        simulations.sort(key=lambda s: s.cumulative_reward, reverse=True)
        
        return simulations


def demonstrate_world_model():
    """Demonstrate World Model Learning pattern"""
    
    print("=" * 80)
    print("PATTERN 062: WORLD MODEL LEARNING DEMONSTRATION")
    print("=" * 80)
    print("\nLearning environment dynamics for model-based planning\n")
    
    # Test 1: Building a world model from experience
    print("\n" + "=" * 80)
    print("TEST 1: Learning from Experience")
    print("=" * 80)
    
    world = WorldModel(domain="business strategy")
    
    # Add some experiences
    print("\n📚 Adding experiences to world model...")
    
    experiences = [
        Transition(
            state=State("s1", "Low market share, limited brand awareness"),
            action=Action("a1", "Launch aggressive marketing campaign"),
            next_state=State("s2", "Increased brand visibility, higher costs"),
            reward=0.3
        ),
        Transition(
            state=State("s2", "Increased brand visibility, higher costs"),
            action=Action("a2", "Optimize pricing strategy"),
            next_state=State("s3", "Improved market share, balanced budget"),
            reward=0.7
        ),
        Transition(
            state=State("s3", "Improved market share, balanced budget"),
            action=Action("a3", "Expand product line"),
            next_state=State("s4", "Diversified revenue, market leadership"),
            reward=0.9
        )
    ]
    
    for exp in experiences:
        world.add_experience(exp)
        print(f"   ✓ {exp.state.description} --[{exp.action.name}]--> {exp.next_state.description}")
    
    print(f"\n   Total experiences: {len(world.experiences)}")
    
    # Test 2: Predicting next state
    print("\n" + "=" * 80)
    print("TEST 2: State Prediction")
    print("=" * 80)
    
    current_state = State(
        "s_test",
        "New competitor entering market"
    )
    
    test_action = Action(
        "a_test",
        "Form strategic partnership"
    )
    
    print(f"\n🔮 Making prediction...")
    print(f"   Current: {current_state.description}")
    print(f"   Action: {test_action.name}")
    
    prediction = world.predict_next_state(current_state, test_action)
    
    print(f"\n✨ Predicted outcome:")
    print(f"   State: {prediction.predicted_state.description}")
    print(f"   Confidence: {prediction.confidence:.2f}")
    print(f"   Expected Reward: {prediction.predicted_reward:.2f}")
    
    # Test 3: Multi-step simulation
    print("\n" + "=" * 80)
    print("TEST 3: Trajectory Simulation")
    print("=" * 80)
    
    initial = State("s_start", "Startup with innovative product")
    
    actions = [
        Action("a1", "Secure seed funding"),
        Action("a2", "Build MVP"),
        Action("a3", "Beta testing with early adopters"),
        Action("a4", "Launch marketing campaign")
    ]
    
    print(f"\n🎬 Simulating trajectory...")
    print(f"   Initial State: {initial.description}")
    print(f"   Actions: {len(actions)}")
    
    simulation = world.simulate_trajectory(initial, actions)
    
    print(f"\n📊 Simulation Results:")
    print(f"   Success Probability: {simulation.success_probability:.2%}")
    print(f"   Cumulative Reward: {simulation.cumulative_reward:.2f}")
    print(f"\n   Trajectory:")
    for i, state in enumerate(simulation.predicted_trajectory):
        if i == 0:
            print(f"   0. [START] {state.description}")
        else:
            action = simulation.action_sequence[i-1]
            print(f"   {i}. --[{action.name}]--> {state.description}")
    
    # Test 4: Model-based planning
    print("\n" + "=" * 80)
    print("TEST 4: Model-Based Planning")
    print("=" * 80)
    
    planning_state = State(
        "s_plan",
        "Established company facing digital transformation"
    )
    
    goal = "Successfully transition to cloud-based operations"
    
    print(f"\n🎯 Planning Goal: {goal}")
    print(f"   Current State: {planning_state.description}")
    print(f"\n   Generating alternative plans...")
    
    plans = world.plan_with_model(planning_state, goal, num_alternatives=3)
    
    print(f"\n📋 Generated {len(plans)} alternative plans:\n")
    
    for i, plan in enumerate(plans, 1):
        print(f"   Plan {i}:")
        print(f"   Expected Reward: {plan.cumulative_reward:.2f}")
        print(f"   Success Probability: {plan.success_probability:.2%}")
        print(f"   Actions:")
        for j, action in enumerate(plan.action_sequence, 1):
            print(f"      {j}. {action.name}")
        print()
    
    if plans:
        print(f"   🏆 Recommended: Plan 1 (highest expected reward)")
    
    # Test 5: What-if analysis
    print("\n" + "=" * 80)
    print("TEST 5: What-If Analysis")
    print("=" * 80)
    
    world2 = WorldModel(domain="project management")
    
    project_state = State("proj", "Project behind schedule, team morale low")
    
    scenarios = [
        ("Hire additional developers", Action("a1", "Hire additional developers")),
        ("Reduce scope", Action("a2", "Reduce project scope")),
        ("Extend deadline", Action("a3", "Negotiate deadline extension"))
    ]
    
    print(f"\n🔍 Analyzing scenarios for: {project_state.description}\n")
    
    for scenario_name, action in scenarios:
        prediction = world2.predict_next_state(project_state, action)
        
        print(f"   Scenario: {scenario_name}")
        print(f"      Predicted outcome: {prediction.predicted_state.description}")
        print(f"      Expected reward: {prediction.predicted_reward:+.2f}")
        print(f"      Confidence: {prediction.confidence:.2%}")
        print()
    
    # Summary
    print("\n" + "=" * 80)
    print("WORLD MODEL LEARNING PATTERN SUMMARY")
    print("=" * 80)
    print("""
Key Benefits:
1. Sample Efficiency: Learn from limited experience
2. Planning Capability: Simulate before acting
3. Risk Assessment: Evaluate outcomes without execution
4. What-If Analysis: Compare alternative scenarios
5. Transfer Learning: Generalize across similar situations

World Model Components:
1. State Representation: How world is encoded
   - Discrete states (finite set)
   - Continuous states (real values)
   - Symbolic states (descriptions)
   - Hybrid (combination)

2. Dynamics Model: Predicts transitions
   - Input: Current state + Action
   - Output: Next state prediction
   - Confidence estimation

3. Reward Model: Estimates outcomes
   - Input: State + Action + Result
   - Output: Expected reward/value
   - Risk assessment

4. Experience Buffer: Stores transitions
   - Real experiences
   - Simulated experiences
   - Relevance-based retrieval

5. Planning Module: Uses model
   - Forward simulation
   - Trajectory optimization
   - Alternative comparison

Learning Process:
1. Collect Experience: Interact with environment
2. Update Model: Learn from transitions
3. Validate Predictions: Test accuracy
4. Refine Model: Improve based on errors
5. Plan Actions: Use model for decisions

Model Types:
1. Forward Model: State → Next State
   - Most common
   - Direct prediction

2. Inverse Model: Current State + Next State → Action
   - Action discovery
   - Imitation learning

3. Reward Model: State + Action → Reward
   - Value estimation
   - Policy evaluation

4. Termination Model: State → Done?
   - Episode completion
   - Goal achievement

Prediction Strategies:
- Deterministic: Single prediction
- Stochastic: Sample from distribution
- Ensemble: Multiple models
- Uncertainty-aware: Confidence bounds

Use Cases:
- Robotics: Simulate movements before execution
- Game AI: Plan strategies by modeling game
- Business: Scenario planning and forecasting
- Healthcare: Treatment outcome prediction
- Finance: Market behavior modeling
- Education: Learning path optimization

Planning with World Models:
1. Model-Predictive Control (MPC)
   - Rolling horizon planning
   - Re-plan at each step

2. Tree Search
   - Build search tree using model
   - Evaluate trajectories

3. Trajectory Optimization
   - Find optimal action sequence
   - Gradient-based or sampling

4. Policy Learning
   - Use model to improve policy
   - Dyna-style algorithms

Challenges:
1. Model Accuracy: Prediction errors compound
2. Generalization: Unseen states
3. Computational Cost: Simulation overhead
4. Model Bias: Learned from limited data
5. Non-stationarity: Environment changes

Best Practices:
1. Start with simple models
2. Validate predictions frequently
3. Use ensemble of models
4. Estimate uncertainty
5. Combine model-based and model-free
6. Update model continuously
7. Use for planning, not just prediction

Production Considerations:
- Model versioning and updates
- Prediction latency requirements
- Memory for experience buffer
- Model accuracy metrics
- Fallback to model-free
- A/B testing model changes
- Continuous validation

Advanced Techniques:
1. Latent World Models: Learn compressed representations
2. Hierarchical Models: Multi-scale predictions
3. Causal Models: Understand mechanisms
4. Meta-Learning Models: Quick adaptation
5. Uncertainty-Aware Models: Calibrated predictions

Comparison with Related Patterns:
- vs. Model-Free RL: More sample efficient
- vs. Imitation Learning: Enables planning
- vs. Heuristic Planning: Data-driven
- vs. Simulation: Learned not programmed

Integration with Other Patterns:
- Curriculum Learning: Progressive model building
- Meta-Learning: Quick model adaptation
- Active Learning: Query informative states
- Memory Patterns: Store experiences

The World Model Learning pattern enables agents to learn
environment dynamics from experience and use these models
for efficient planning, simulation, and decision-making.
""")


if __name__ == "__main__":
    demonstrate_world_model()

    

Agentic Design Pattern: World Model

Pattern 062: World Model – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the World Model pattern implementation.

---

Pattern Description

Pattern 062: World Model Learning

Description: World Model Learning involves an agent building an internal model of its environment’s dynamics. The agent learns to predict future states given current states and actions, enabling model-based planning, simulation, and what-if analysis without direct environment interaction.

Components: 1. State Representation: How the world is encoded 2. Dynamics Model: Predicts next state from current state and action 3. Reward Predictor: Estimates rewards for state-action pairs 4. Experience Collection: Gathers real-world data 5. Model Training: Updates predictions based on experience 6. Planning Module: Uses model for decision-making

Use Cases: – Robotics simulation and planning – Game AI with environment modeling – Strategic decision-making – Risk assessment and scenario planning – Sample-efficient reinforcement learning – Counterfactual reasoning

LangChain Implementation: Implements world modeling using LLM-based state prediction, transition dynamics, and outcome forecasting for planning and decision-making.

---

File Statistics

• Total Lines: 699
• Classes: 7
• Functions: 1
• Constants: 0

---

Module Documentation

"""
Pattern 062: World Model Learning

Description:
    World Model Learning involves an agent building an internal model of its
    environment's dynamics. The agent learns to predict future states given
    current states and actions, enabling model-based planning, simulation,
    and what-if analysis without direct environment interaction.

Components:
    1. State Representation: How the world is encoded
    2. Dynamics Model: Predicts next state from current state and action
    3. Reward Predictor: Estimates rewards for state-action pairs
    4. Experience Collection: Gathers real-world data
    5. Model Training: Updates predictions based on experience
    6. Planning Module: Uses model for decision-making

Use Cases:
    - Robotics simulation and planning
    - Game AI with environment modeling
    - Strategic decision-making
    - Risk assessment and scenario planning
    - Sample-efficient reinforcement learning
    - Counterfactual reasoning

LangChain Implementation:
    Implements world modeling using LLM-based state prediction, transition
    dynamics, and outcome forecasting for planning and decision-making.
"""

Purpose: This module implements the World Model pattern, providing a structured approach to agent design and implementation.

---

Import Statements

Standard Library Imports

import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import random
import time
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Tuple

Purpose: Core Python functionality: – dataclasses.dataclass: Simplified class definitions with @dataclass – dataclasses.field: Simplified class definitions with @dataclass – datetime.datetime: Date and time handling – enum.Enum: Enumeration support – langchain_core.output_parsers.StrOutputParser: Regular expression operations – langchain_core.prompts.ChatPromptTemplate: Regular expression operations – os: Environment variable access and system operations – random: Random number generation – time: Time-related functions – typing.Any: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional) – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Tuple: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows

Other Imports

from dotenv.load_dotenv

Purpose: Additional dependencies for specialized functionality.

---

Classes

Class: StateType

Lines 47-52

Inherits from: Enum

Purpose:

Types of state representations

---

Class: State

Lines 56-70

Purpose:

World state representation

Methods:

to_text(self)

Lines 63-70

Purpose:

Convert state to text

Parameters:

Returns: str

---

Class: Action

Lines 74-86

Purpose:

Action that can be taken

Methods:

to_text(self)

Lines 80-86

Purpose:

Convert action to text

Parameters:

Returns: str

---

Class: Transition

Lines 90-96

Purpose:

State transition experience

---

Class: Prediction

Lines 100-105

Purpose:

Model prediction

---

Class: SimulationResult

Lines 109-115

Purpose:

Result from simulation

---

Class: WorldModel

Lines 118-390

Purpose:

World model for environment dynamics.

Features:
1. State transition prediction
2. Reward prediction
3. Experience-based learning
4. Multi-step simulation
5. Uncertainty estimation

Methods:

__init__(self, domain)

Lines 130-150

Purpose: Initialize the class instance with required parameters.

Parameters: – domain: Domain

add_experience(self, transition)

Lines 152-154

Purpose:

Add experience to model's memory

Parameters: – transition: Transition

_get_relevant_experiences(self, state, action, k)

Lines 156-169

Purpose:

Retrieve relevant past experiences

Parameters: – state: State – action: Action – k: K

Returns: <ast.Subscript object at 0x000001A06D6601D0>

predict_next_state(self, current_state, action)

Lines 171-246

Purpose:

Predict next state given current state and action

Parameters: – current_state: Current state – action: Action

Returns: Prediction

_predict_reward(self, state, action, outcome)

Lines 248-281

Purpose:

Predict reward for state-action-outcome

Parameters: – state: State – action: Action – outcome: Outcome

Returns: float

simulate_trajectory(self, initial_state, action_sequence, max_steps)

Lines 283-317

Purpose:

Simulate a trajectory through the world

Parameters: – initial_state: Initial state – action_sequence: Action sequence – max_steps: Max steps

Returns: SimulationResult

plan_with_model(self, current_state, goal, num_alternatives)

Lines 319-390

Purpose:

Use model for planning

Parameters: – current_state: Current state – goal: Goal – num_alternatives: Num alternatives

Returns: <ast.Subscript object at 0x000001A06D625C10>

---

Functions

demonstrate_world_model()

Lines 393-694

Purpose:

Demonstrate World Model Learning pattern

---

Key Concepts Summary

Pattern Overview

The World Model pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

1. Agent Class: Main implementation of the pattern logic
2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
3. Tool Integration: Optional tools for extended functionality
4. Memory Management: State handling across interactions
5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

---

Best Practices Demonstrated

1. Environment Configuration

• ✅ Use .env files for API keys
• ✅ Never hardcode credentials
• ✅ Validate environment variables on startup

2. Error Handling

• ✅ Graceful degradation on failures
• ✅ Informative error messages
• ✅ Logging for debugging

3. Prompt Engineering

• ✅ Clear, specific instructions
• ✅ Structured output formats
• ✅ Few-shot examples when beneficial

4. Code Organization

• ✅ Separation of concerns (class-based design)
• ✅ Type hints for clarity
• ✅ Docstrings for documentation

5. Performance Optimization

• ✅ Appropriate temperature settings
• ✅ Token budget management
• ✅ Caching where applicable

---

LangChain Components Used

• ChatOpenAI: OpenAI model interface
• Chains: Sequential component composition

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

---

Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

---

Performance Considerations

Computational Cost

• LLM Calls: Primary cost factor (based on tokens)
• Latency: Depends on model and input size
• Memory: Minimal for most patterns

Optimization Strategies

1. Prompt Optimization: Minimize tokens while maintaining clarity
2. Caching: Cache repeated queries/responses
3. Model Selection: Use smaller models when appropriate
4. Batch Processing: Group similar requests
5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

• GPT-3.5-turbo: ~$0.002 per request
• GPT-4: ~$0.03 per request
• GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

---

Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

---

Further Improvements

Production Enhancements

1. Monitoring: Add logging, metrics, tracing
2. Validation: Input/output schema validation
3. Rate Limiting: Implement request throttling
4. Caching: Redis or in-memory cache for responses
5. Async Support: Non-blocking operations for scalability

Feature Extensions

1. Multi-Model Support: Switch between providers
2. Fallback Models: Graceful degradation strategy
3. Custom Tools: Domain-specific function calling
4. Advanced Memory: Vector stores for long-term memory
5. Evaluation: Automated testing and benchmarking

Code Quality

1. Testing: Unit tests, integration tests
2. Documentation: API docs, usage examples
3. Type Safety: Stricter type hints
4. Configuration: Externalized config files

---

Related Patterns

• Pattern 061
• Pattern 063

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

---

Summary

The World Model pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

---

File: 062_world_model.py
Total Lines: 699
Pattern Category: AI Agent Design Pattern #062

---

Generated from actual implementation code
For educational and reference purposes