demo_ai_framework.py

#!/usr/bin/env python3
"""
AI Framework Demonstration Script

This script demonstrates the complete AI integration framework for 3D agents,
including Tasks 8-10 that were completed:

Task 8: AI Integration Framework (Core Components)
Task 9: LangChain Adapter Integration  
Task 10: AI Provider Interface

Features demonstrated:
- Multiple AI providers with fallback mechanisms
- Conversation handling and context management
- Decision making engine
- World state integration
- Action system integration
- LangChain tools integration
"""

import time
import sys
from pathlib import Path

# Add src to path
sys.path.insert(0, str(Path(__file__).parent / "src"))

def main():
    print("🤖 AI Framework Demonstration")
    print("=" * 50)
    
    # Import core components
    from core import WorldState, ActionSystem, Player, WorldObject
    from ai import (
        AI_AVAILABLE, AIManager, ConversationHandler, DecisionEngine,
        ProviderManager, MockProvider, LangChainAdapter, create_world_state_tools,
        create_action_tools
    )
    
    if not AI_AVAILABLE:
        print("❌ AI components not available")
        return
        
    print("✅ AI Framework Available")
    print()
    
    # 1. Setup World State
    print("🌍 Setting up World State...")
    world_state = WorldState()
    world_state.set_world_info("demo_world", "AI Framework Demo World")
    
    # Add some players and objects
    alice = Player("alice", "Alice", [0, 0, 0])
    bob = Player("bob", "Bob", [5, 0, 5])
    world_state.add_player(alice)
    world_state.add_player(bob)
    
    tree = WorldObject("tree1", "tree", [10, 0, 10])
    rock = WorldObject("rock1", "rock", [3, 0, 3])
    world_state.add_object(tree)
    world_state.add_object(rock)
    
    print(f"   Added {len(world_state.get_all_players())} players")
    print(f"   Added {len([obj for obj in world_state.objects.values() if not isinstance(obj, Player)])} objects")
    print()
    
    # 2. Setup Action System
    print("⚡ Setting up Action System...")
    action_system = ActionSystem()
    print("   Action system initialized")
    print()
    
    # 3. Setup AI Provider Manager
    print("🧠 Setting up AI Provider Manager...")
    provider_manager = ProviderManager()
    
    # Register multiple providers with different priorities
    primary_provider = MockProvider({
        "model": "mock-gpt-4",
        "available": True,
        "response_delay": 0.1
    })
    
    backup_provider = MockProvider({
        "model": "mock-gpt-3.5", 
        "available": True,
        "response_delay": 0.2,
        "failure_rate": 0.3  # 30% failure rate
    })
    
    # Register providers (higher priority number = higher priority)
    provider_manager.register_provider("primary", primary_provider, priority=2)
    provider_manager.register_provider("backup", backup_provider, priority=1)
    
    print(f"   Registered {len(provider_manager.providers)} providers")
    print(f"   Available providers: {provider_manager.get_available_providers()}")
    print()
    
    # 4. Setup AI Manager
    print("🎯 Setting up AI Manager...")
    ai_manager = AIManager({"ai_provider": "mock"})
    print(f"   AI Manager using provider: {ai_manager.name}")
    print()
    
    # 5. Setup Conversation Handler
    print("💬 Setting up Conversation Handler...")
    conversation = ConversationHandler("demo_agent", max_history=20)
    print("   Conversation handler initialized")
    print()
    
    # 6. Setup Decision Engine
    print("🎲 Setting up Decision Engine...")
    decision_engine = DecisionEngine("demo_agent", ai_manager)
    print("   Decision engine initialized")
    print()
    
    # 7. Setup LangChain Adapter (if available)
    print("🔗 Setting up LangChain Integration...")
    langchain_adapter = LangChainAdapter("demo_agent")
    
    if langchain_adapter.is_available:
        # Integrate with world state and action system
        langchain_adapter.integrate_world_state(world_state)
        langchain_adapter.integrate_action_system(action_system, alice)
        langchain_adapter.integrate_conversation_handler(conversation)
        
        print("   LangChain adapter configured")
        print(f"   Available tools: {langchain_adapter.get_available_tools()}")
    else:
        print("   LangChain not available - using mock mode")
    print()
    
    # 8. Demonstrate Provider Manager Fallback
    print("🔄 Testing Provider Fallback Mechanism...")
    
    # Test normal operation
    response1 = provider_manager.generate_response("Hello, how are you?")
    if response1:
        print(f"   Response 1: {response1.content[:50]}...")
        print(f"   Provider used: {response1.provider}")
    
    # Test with provider failure
    response2 = provider_manager.generate_response("Tell me about the weather")
    if response2:
        print(f"   Response 2: {response2.content[:50]}...")
        print(f"   Provider used: {response2.provider}")
    
    # Show provider stats
    stats = provider_manager.get_provider_stats()
    print("   Provider Statistics:")
    for provider_name, stat in stats.items():
        print(f"     {provider_name}: {stat['successes']} successes, {stat['failures']} failures")
    print()
    
    # 9. Demonstrate Conversation Flow
    print("💭 Demonstrating Conversation Flow...")
    
    # Simulate user messages
    conversation.add_user_message("alice", "Hello agent! What can you see around here?")
    conversation.add_user_message("bob", "Hey, I'm new here. Can you help me?")
    
    # Create decision context
    context = decision_engine.create_context(
        world_state=world_state,
        conversation_context=conversation.get_conversation_context("alice"),
        agent_state={"position": [0, 0, 0]}
    )
    
    # Make decision
    decision = decision_engine.make_decision(context)
    if decision:
        print(f"   Decision made: {decision.action}")
        print(f"   Confidence: {decision.confidence:.2f}")
        print(f"   Reasoning: {decision.reasoning}")
        
        # Generate AI response based on decision
        if decision.decision_type == "conversation_response":
            ai_response = ai_manager.generate_response(
                f"User asked: {conversation.messages[-2].content}. "
                f"Respond helpfully about the world around us. "
                f"I can see {len([obj for obj in world_state.objects.values() if not isinstance(obj, Player)])} objects and "
                f"{len(world_state.get_all_players())} players."
            )
            
            conversation.add_agent_message(ai_response)
            print(f"   AI Response: {ai_response}")
    else:
        print("   No decision made (cooldown or no applicable rules)")
    print()
    
    # 10. Demonstrate World State Integration
    print("🌐 Demonstrating World State Integration...")
    
    # Get world stats
    stats = world_state.get_stats()
    print("   World Statistics:")
    for key, value in stats.items():
        print(f"     {key}: {value}")
    
    # Test spatial queries
    nearby_objects = world_state.get_objects_in_radius([0, 0, 0], 5.0)
    print(f"   Objects within 5 units of origin: {len(nearby_objects)}")
    for obj in nearby_objects:
        print(f"     - {obj.object_id} ({obj.object_type}) at {obj.position.to_list()}")
    print()
    
    # 11. Demonstrate Action System Integration
    print("⚙️ Demonstrating Action System Integration...")
    
    # Queue some actions using DelayAction for demonstration
    from core.action_system import DelayAction
    
    action1 = DelayAction(duration=0.1, priority=1)
    action2 = DelayAction(duration=0.2, priority=2)
    
    action_system.queue_action(action1)
    action_system.queue_action(action2)
    
    print(f"   Queued {action_system.get_stats()['queue_size']} actions")
    
    # Process actions briefly
    start_time = time.time()
    while action_system.get_stats()['queue_size'] > 0 and time.time() - start_time < 1.0:
        action_system.update(0.1)  # 0.1 second delta time
        time.sleep(0.1)
    
    action_stats = action_system.get_stats()
    print(f"   Completed {action_stats['total_completed']} actions")
    print()
    
    # 12. Final Summary
    print("📊 Final Summary:")
    print(f"   World Objects: {len([obj for obj in world_state.objects.values() if not isinstance(obj, Player)])}")
    print(f"   Players: {len(world_state.get_all_players())}")
    print(f"   Conversation Messages: {len(conversation.messages)}")
    print(f"   Decisions Made: {len(decision_engine.get_decision_history())}")
    print(f"   Actions Completed: {action_system.get_stats()['total_completed']}")
    print(f"   AI Provider: {ai_manager.name}")
    print(f"   Provider Manager Strategy: {provider_manager.fallback_strategy.name}")
    
    if langchain_adapter.is_available:
        print(f"   LangChain Tools: {len(langchain_adapter.tools)}")
    print()
    
    print("✨ AI Framework Demonstration Complete!")
    print("\nKey Features Demonstrated:")
    print("• Multiple AI provider management with fallback")
    print("• Conversation flow and context tracking")
    print("• AI decision making based on world state")
    print("• World state management and spatial queries") 
    print("• Action system integration")
    print("• LangChain tools integration")
    print("• Provider statistics and monitoring")


if __name__ == "__main__":
    main()