Improved bot capabilities

server/api/monitoring.py

@@ -14,9 +14,9 @@ Endpoints:
 - /api/system/info - System information
 """
 
-from typing import Dict, Any, Optional
+from typing import Optional
 from fastapi import APIRouter, HTTPException, Query
-from datetime import datetime, timedelta
+from datetime import datetime
 
 from logger import logger
 

server/core/cache.py

@@ -14,14 +14,11 @@ Features:
 """
 
 import asyncio
-import time
-import json
 import hashlib
 from datetime import datetime, timedelta
-from typing import Any, Dict, Optional, List, Union, Callable, TypeVar
+from typing import Any, Dict, Optional, Callable, TypeVar
 from collections import OrderedDict
 from dataclasses import dataclass
-import weakref
 
 from logger import logger
 

server/core/error_handling.py

@@ -10,7 +10,7 @@ import asyncio
 import time
 import traceback
 from enum import Enum
-from typing import Any, Callable, Dict, List, Optional, TypeVar, Generic
+from typing import Any, Callable, Dict, List, Optional, TypeVar
 from functools import wraps
 from dataclasses import dataclass
 from fastapi import WebSocket

server/core/health.py

@@ -15,9 +15,8 @@ Features:
 import asyncio
 import time
 from datetime import datetime, timedelta
-from typing import Dict, Any, List, Optional, Callable, NamedTuple
+from typing import Dict, Any, List, Optional, NamedTuple
 from enum import Enum
-import json
 
 from logger import logger
 

server/core/performance.py

@@ -21,7 +21,6 @@ from typing import Dict, Any, List, Optional, Callable
 from dataclasses import dataclass, field
 from collections import defaultdict, deque
 from contextlib import asynccontextmanager
-import weakref
 
 from logger import logger
 

server/websocket/message_handlers.py

@@ -12,11 +12,10 @@ from fastapi import WebSocket
 from logger import logger
 from .webrtc_signaling import WebRTCSignalingHandlers
 from core.error_handling import (
-    error_handler, 
-    WebSocketError, 
+    error_handler,
+    WebSocketError,
     ValidationError,
-    with_websocket_error_handling,
-    ErrorSeverity
+    ErrorSeverity,
 )
 
 if TYPE_CHECKING:

server/websocket/webrtc_signaling.py

@@ -9,12 +9,7 @@ from typing import Any, Dict, TYPE_CHECKING
 from fastapi import WebSocket
 
 from logger import logger
-from core.error_handling import (
-    with_webrtc_error_handling,
-    WebRTCError,
-    ErrorSeverity,
-    error_handler
-)
+from core.error_handling import with_webrtc_error_handling
 
 if TYPE_CHECKING:
     from core.session_manager import Session

voicebot/ai_providers.py

@@ -0,0 +1,514 @@
+"""
+AI Provider Integration for Advanced Bot Management.
+
+This module provides support for multiple AI providers including OpenAI, Anthropic,
+and local models for enhanced bot capabilities.
+"""
+
+import os
+import time
+import asyncio
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Any, AsyncIterator
+from enum import Enum
+from dataclasses import dataclass
+from pydantic import BaseModel, Field
+
+from logger import logger
+
+
+class AIProviderType(str, Enum):
+    """Supported AI provider types."""
+    OPENAI = "openai"
+    ANTHROPIC = "anthropic"
+    LOCAL = "local"
+    CUSTOM = "custom"
+
+
+class MessageRole(str, Enum):
+    """Message roles in conversation."""
+    SYSTEM = "system"
+    USER = "user"
+    ASSISTANT = "assistant"
+
+
+@dataclass
+class ConversationMessage:
+    """Individual message in a conversation."""
+    role: MessageRole
+    content: str
+    timestamp: float = None
+    metadata: Dict[str, Any] = None
+    
+    def __post_init__(self):
+        if self.timestamp is None:
+            self.timestamp = time.time()
+        if self.metadata is None:
+            self.metadata = {}
+
+
+class AIProviderConfig(BaseModel):
+    """Configuration for AI providers."""
+    provider_type: AIProviderType
+    api_key: Optional[str] = None
+    base_url: Optional[str] = None
+    model: str = "gpt-3.5-turbo"
+    max_tokens: int = 1000
+    temperature: float = 0.7
+    timeout: float = 30.0
+    retry_attempts: int = 3
+    retry_delay: float = 1.0
+    
+    # Advanced settings
+    top_p: Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+    stop_sequences: List[str] = Field(default_factory=list)
+    
+    class Config:
+        extra = "allow"  # Allow additional provider-specific configs
+
+
+class ConversationContext(BaseModel):
+    """Conversation context and memory management."""
+    session_id: str
+    bot_name: str
+    messages: List[ConversationMessage] = Field(default_factory=list)
+    created_at: float = Field(default_factory=time.time)
+    last_updated: float = Field(default_factory=time.time)
+    
+    # Context management
+    max_history: int = 50
+    context_window: int = 4000  # Token limit for context
+    personality_prompt: Optional[str] = None
+    
+    # Metadata
+    user_preferences: Dict[str, Any] = Field(default_factory=dict)
+    conversation_state: Dict[str, Any] = Field(default_factory=dict)
+    
+    def add_message(self, role: MessageRole, content: str, metadata: Dict[str, Any] = None):
+        """Add a message to the conversation."""
+        message = ConversationMessage(role=role, content=content, metadata=metadata or {})
+        self.messages.append(message)
+        self.last_updated = time.time()
+        
+        # Trim history if needed
+        if len(self.messages) > self.max_history:
+            # Keep system messages and recent messages
+            system_messages = [m for m in self.messages if m.role == MessageRole.SYSTEM]
+            recent_messages = [m for m in self.messages if m.role != MessageRole.SYSTEM][-self.max_history:]
+            self.messages = system_messages + recent_messages
+    
+    def get_context_messages(self) -> List[Dict[str, str]]:
+        """Get messages formatted for AI provider APIs."""
+        messages = []
+        for msg in self.messages:
+            messages.append({
+                "role": msg.role.value,
+                "content": msg.content
+            })
+        return messages
+
+
+class AIProvider(ABC):
+    """Abstract base class for AI providers."""
+    
+    def __init__(self, config: AIProviderConfig):
+        self.config = config
+        self.provider_type = config.provider_type
+    
+    @abstractmethod
+    async def generate_response(
+        self, 
+        context: ConversationContext, 
+        message: str
+    ) -> str:
+        """Generate a response to a message."""
+        pass
+    
+    @abstractmethod
+    async def stream_response(
+        self, 
+        context: ConversationContext, 
+        message: str
+    ) -> AsyncIterator[str]:
+        """Stream a response to a message."""
+        pass
+    
+    @abstractmethod
+    async def health_check(self) -> bool:
+        """Check if the provider is healthy and available."""
+        pass
+
+
+class OpenAIProvider(AIProvider):
+    """OpenAI provider implementation."""
+    
+    def __init__(self, config: AIProviderConfig):
+        super().__init__(config)
+        self._client = None
+    
+    def _get_client(self):
+        """Lazy initialization of OpenAI client."""
+        if self._client is None:
+            try:
+                import openai
+                self._client = openai.AsyncOpenAI(
+                    api_key=self.config.api_key or os.getenv("OPENAI_API_KEY"),
+                    base_url=self.config.base_url,
+                    timeout=self.config.timeout
+                )
+            except ImportError:
+                raise ImportError("OpenAI package not installed. Install with: pip install openai")
+        return self._client
+    
+    async def generate_response(self, context: ConversationContext, message: str) -> str:
+        """Generate response using OpenAI API."""
+        client = self._get_client()
+        
+        # Add user message to context
+        context.add_message(MessageRole.USER, message)
+        
+        messages = context.get_context_messages()
+        
+        for attempt in range(self.config.retry_attempts):
+            try:
+                response = await client.chat.completions.create(
+                    model=self.config.model,
+                    messages=messages,
+                    max_tokens=self.config.max_tokens,
+                    temperature=self.config.temperature,
+                    top_p=self.config.top_p,
+                    frequency_penalty=self.config.frequency_penalty,
+                    presence_penalty=self.config.presence_penalty,
+                    stop=self.config.stop_sequences or None
+                )
+                
+                response_text = response.choices[0].message.content
+                context.add_message(MessageRole.ASSISTANT, response_text)
+                return response_text
+                
+            except Exception as e:
+                logger.warning(f"OpenAI API attempt {attempt + 1} failed: {e}")
+                if attempt < self.config.retry_attempts - 1:
+                    await asyncio.sleep(self.config.retry_delay * (2 ** attempt))
+                else:
+                    raise
+    
+    async def stream_response(self, context: ConversationContext, message: str) -> AsyncIterator[str]:
+        """Stream response using OpenAI API."""
+        client = self._get_client()
+        
+        context.add_message(MessageRole.USER, message)
+        messages = context.get_context_messages()
+        
+        try:
+            stream = await client.chat.completions.create(
+                model=self.config.model,
+                messages=messages,
+                max_tokens=self.config.max_tokens,
+                temperature=self.config.temperature,
+                stream=True
+            )
+            
+            full_response = ""
+            async for chunk in stream:
+                if chunk.choices[0].delta.content:
+                    content = chunk.choices[0].delta.content
+                    full_response += content
+                    yield content
+            
+            # Add complete response to context
+            context.add_message(MessageRole.ASSISTANT, full_response)
+            
+        except Exception as e:
+            logger.error(f"OpenAI streaming failed: {e}")
+            raise
+    
+    async def health_check(self) -> bool:
+        """Check OpenAI API health."""
+        try:
+            client = self._get_client()
+            await client.models.list()
+            return True
+        except Exception as e:
+            logger.warning(f"OpenAI health check failed: {e}")
+            return False
+
+
+class AnthropicProvider(AIProvider):
+    """Anthropic Claude provider implementation."""
+    
+    def __init__(self, config: AIProviderConfig):
+        super().__init__(config)
+        self._client = None
+    
+    def _get_client(self):
+        """Lazy initialization of Anthropic client."""
+        if self._client is None:
+            try:
+                import anthropic
+                self._client = anthropic.AsyncAnthropic(
+                    api_key=self.config.api_key or os.getenv("ANTHROPIC_API_KEY"),
+                    timeout=self.config.timeout
+                )
+            except ImportError:
+                raise ImportError("Anthropic package not installed. Install with: pip install anthropic")
+        return self._client
+    
+    async def generate_response(self, context: ConversationContext, message: str) -> str:
+        """Generate response using Anthropic API."""
+        client = self._get_client()
+        
+        context.add_message(MessageRole.USER, message)
+        
+        # Convert messages for Anthropic format
+        messages = []
+        system_prompt = None
+        
+        for msg in context.messages:
+            if msg.role == MessageRole.SYSTEM:
+                system_prompt = msg.content
+            else:
+                messages.append({
+                    "role": msg.role.value,
+                    "content": msg.content
+                })
+        
+        for attempt in range(self.config.retry_attempts):
+            try:
+                kwargs = {
+                    "model": self.config.model,
+                    "messages": messages,
+                    "max_tokens": self.config.max_tokens,
+                    "temperature": self.config.temperature,
+                }
+                
+                if system_prompt:
+                    kwargs["system"] = system_prompt
+                
+                response = await client.messages.create(**kwargs)
+                response_text = response.content[0].text
+                context.add_message(MessageRole.ASSISTANT, response_text)
+                return response_text
+                
+            except Exception as e:
+                logger.warning(f"Anthropic API attempt {attempt + 1} failed: {e}")
+                if attempt < self.config.retry_attempts - 1:
+                    await asyncio.sleep(self.config.retry_delay * (2 ** attempt))
+                else:
+                    raise
+    
+    async def stream_response(self, context: ConversationContext, message: str) -> AsyncIterator[str]:
+        """Stream response using Anthropic API."""
+        client = self._get_client()
+        
+        context.add_message(MessageRole.USER, message)
+        messages = context.get_context_messages()
+        
+        try:
+            async with client.messages.stream(
+                model=self.config.model,
+                messages=messages,
+                max_tokens=self.config.max_tokens,
+                temperature=self.config.temperature
+            ) as stream:
+                full_response = ""
+                async for text in stream.text_stream:
+                    full_response += text
+                    yield text
+                
+                context.add_message(MessageRole.ASSISTANT, full_response)
+                
+        except Exception as e:
+            logger.error(f"Anthropic streaming failed: {e}")
+            raise
+    
+    async def health_check(self) -> bool:
+        """Check Anthropic API health."""
+        try:
+            client = self._get_client()
+            # Simple test to verify API connectivity
+            await client.messages.create(
+                model=self.config.model,
+                messages=[{"role": "user", "content": "test"}],
+                max_tokens=1
+            )
+            return True
+        except Exception as e:
+            logger.warning(f"Anthropic health check failed: {e}")
+            return False
+
+
+class LocalProvider(AIProvider):
+    """Local model provider (e.g., Ollama, llama.cpp)."""
+    
+    def __init__(self, config: AIProviderConfig):
+        super().__init__(config)
+        self.base_url = config.base_url or "http://localhost:11434"
+    
+    async def generate_response(self, context: ConversationContext, message: str) -> str:
+        """Generate response using local model API."""
+        context.add_message(MessageRole.USER, message)
+        
+        import aiohttp
+        
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": self.config.model,
+                "messages": context.get_context_messages(),
+                "stream": False,
+                "options": {
+                    "temperature": self.config.temperature,
+                    "num_predict": self.config.max_tokens
+                }
+            }
+            
+            try:
+                async with session.post(
+                    f"{self.base_url}/api/chat",
+                    json=payload,
+                    timeout=aiohttp.ClientTimeout(total=self.config.timeout)
+                ) as resp:
+                    if resp.status == 200:
+                        result = await resp.json()
+                        response_text = result["message"]["content"]
+                        context.add_message(MessageRole.ASSISTANT, response_text)
+                        return response_text
+                    else:
+                        raise Exception(f"Local API returned status {resp.status}")
+                        
+            except Exception as e:
+                logger.error(f"Local provider failed: {e}")
+                raise
+    
+    async def stream_response(self, context: ConversationContext, message: str) -> AsyncIterator[str]:
+        """Stream response using local model API."""
+        context.add_message(MessageRole.USER, message)
+        
+        import aiohttp
+        
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": self.config.model,
+                "messages": context.get_context_messages(),
+                "stream": True
+            }
+            
+            try:
+                async with session.post(
+                    f"{self.base_url}/api/chat",
+                    json=payload,
+                    timeout=aiohttp.ClientTimeout(total=self.config.timeout)
+                ) as resp:
+                    if resp.status == 200:
+                        full_response = ""
+                        async for line in resp.content:
+                            if line:
+                                import json
+                                try:
+                                    data = json.loads(line.decode())
+                                    if "message" in data and "content" in data["message"]:
+                                        content = data["message"]["content"]
+                                        full_response += content
+                                        yield content
+                                except json.JSONDecodeError:
+                                    continue
+                        
+                        context.add_message(MessageRole.ASSISTANT, full_response)
+                    else:
+                        raise Exception(f"Local API returned status {resp.status}")
+                        
+            except Exception as e:
+                logger.error(f"Local provider streaming failed: {e}")
+                raise
+    
+    async def health_check(self) -> bool:
+        """Check local model health."""
+        try:
+            import aiohttp
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    f"{self.base_url}/api/tags",
+                    timeout=aiohttp.ClientTimeout(total=5)
+                ) as resp:
+                    return resp.status == 200
+        except Exception as e:
+            logger.warning(f"Local provider health check failed: {e}")
+            return False
+
+
+class AIProviderManager:
+    """Manager for AI providers and configurations."""
+    
+    def __init__(self):
+        self.providers: Dict[str, AIProvider] = {}
+        self.default_configs = self._load_default_configs()
+    
+    def _load_default_configs(self) -> Dict[AIProviderType, AIProviderConfig]:
+        """Load default configurations for providers."""
+        return {
+            AIProviderType.OPENAI: AIProviderConfig(
+                provider_type=AIProviderType.OPENAI,
+                model=os.getenv("OPENAI_MODEL", "gpt-3.5-turbo"),
+                max_tokens=int(os.getenv("OPENAI_MAX_TOKENS", "1000")),
+                temperature=float(os.getenv("OPENAI_TEMPERATURE", "0.7"))
+            ),
+            AIProviderType.ANTHROPIC: AIProviderConfig(
+                provider_type=AIProviderType.ANTHROPIC,
+                model=os.getenv("ANTHROPIC_MODEL", "claude-3-sonnet-20240229"),
+                max_tokens=int(os.getenv("ANTHROPIC_MAX_TOKENS", "1000")),
+                temperature=float(os.getenv("ANTHROPIC_TEMPERATURE", "0.7"))
+            ),
+            AIProviderType.LOCAL: AIProviderConfig(
+                provider_type=AIProviderType.LOCAL,
+                base_url=os.getenv("LOCAL_MODEL_URL", "http://localhost:11434"),
+                model=os.getenv("LOCAL_MODEL_NAME", "llama2"),
+                max_tokens=int(os.getenv("LOCAL_MAX_TOKENS", "1000")),
+                temperature=float(os.getenv("LOCAL_TEMPERATURE", "0.7"))
+            )
+        }
+    
+    def create_provider(self, provider_type: AIProviderType, config: Optional[AIProviderConfig] = None) -> AIProvider:
+        """Create an AI provider instance."""
+        if config is None:
+            config = self.default_configs.get(provider_type)
+            if config is None:
+                raise ValueError(f"No default config for provider type: {provider_type}")
+        
+        if provider_type == AIProviderType.OPENAI:
+            return OpenAIProvider(config)
+        elif provider_type == AIProviderType.ANTHROPIC:
+            return AnthropicProvider(config)
+        elif provider_type == AIProviderType.LOCAL:
+            return LocalProvider(config)
+        else:
+            raise ValueError(f"Unsupported provider type: {provider_type}")
+    
+    def register_provider(self, name: str, provider: AIProvider):
+        """Register a provider instance."""
+        self.providers[name] = provider
+        logger.info(f"Registered AI provider: {name} ({provider.provider_type})")
+    
+    def get_provider(self, name: str) -> Optional[AIProvider]:
+        """Get a registered provider."""
+        return self.providers.get(name)
+    
+    def list_providers(self) -> List[str]:
+        """List all registered provider names."""
+        return list(self.providers.keys())
+    
+    async def health_check_all(self) -> Dict[str, bool]:
+        """Health check all registered providers."""
+        results = {}
+        for name, provider in self.providers.items():
+            try:
+                results[name] = await provider.health_check()
+            except Exception as e:
+                logger.error(f"Health check failed for provider {name}: {e}")
+                results[name] = False
+        return results
+
+
+# Global provider manager instance
+ai_provider_manager = AIProviderManager()

voicebot/bots/ai_chatbot.py

@@ -0,0 +1,359 @@
+"""Enhanced AI Chatbot with Multi-Provider Support and Personality System.
+
+This bot demonstrates the advanced capabilities including:
+- Multi-provider AI integration (OpenAI, Anthropic, Local models)
+- Personality system with configurable traits
+- Conversation context and memory management
+- Enhanced response generation with streaming support
+"""
+
+import os
+import time
+import uuid
+from typing import Dict, Optional, Callable, Awaitable, Any
+from aiortc import MediaStreamTrack
+
+# Import system modules
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from logger import logger
+from shared.models import ChatMessageModel
+
+# Import advanced bot management modules
+try:
+    from voicebot.ai_providers import (
+        AIProviderType, AIProviderConfig, ai_provider_manager,
+        ConversationContext, MessageRole
+    )
+    from voicebot.personality_system import personality_manager, PersonalityTrait, CommunicationStyle
+    from voicebot.conversation_context import context_manager
+    AI_PROVIDERS_AVAILABLE = True
+except ImportError as e:
+    logger.warning(f"Advanced AI features not available: {e}")
+    AI_PROVIDERS_AVAILABLE = False
+
+
+AGENT_NAME = "ai_chatbot"
+AGENT_DESCRIPTION = "Advanced AI chatbot with multi-provider support, personality system, and conversation memory"
+
+# Bot configuration from environment
+BOT_PERSONALITY = os.getenv("AI_CHATBOT_PERSONALITY", "helpful_assistant")
+BOT_AI_PROVIDER = os.getenv("AI_CHATBOT_PROVIDER", "openai")
+BOT_STREAMING = os.getenv("AI_CHATBOT_STREAMING", "false").lower() == "true"
+BOT_MEMORY_ENABLED = os.getenv("AI_CHATBOT_MEMORY", "true").lower() == "true"
+
+# Fallback responses when AI providers are not available
+FALLBACK_RESPONSES = {
+    "greeting": [
+        "Hello! I'm an AI chatbot ready to help you.",
+        "Hi there! How can I assist you today?",
+        "Greetings! I'm here to chat and help with any questions you have."
+    ],
+    "help": [
+        "I'm an advanced AI chatbot that can help with various topics. Just ask me anything!",
+        "I can assist with questions, have conversations, and provide information on many subjects.",
+        "Feel free to ask me questions or just chat - I'm here to help!"
+    ],
+    "capabilities": [
+        "I support multiple AI providers, have configurable personalities, and maintain conversation context.",
+        "My capabilities include natural conversation, information retrieval, and adaptive personality responses.",
+        "I can remember our conversation context and adapt my responses based on configured personality traits."
+    ],
+    "default": [
+        "That's interesting! Tell me more about that.",
+        "I understand. What would you like to discuss next?",
+        "Thanks for sharing! How can I help you further?",
+        "I see. Is there anything specific you'd like to know about?"
+    ],
+    "error": [
+        "I apologize, but I'm having trouble processing that right now. Could you try rephrasing?",
+        "Something went wrong on my end. Could you ask that again?",
+        "I encountered an issue. Please try your question again."
+    ]
+}
+
+
+class EnhancedAIChatbot:
+    """Enhanced AI chatbot with advanced features."""
+    
+    def __init__(self, session_name: str):
+        self.session_name = session_name
+        self.session_id = str(uuid.uuid4())
+        self.ai_provider = None
+        self.personality = None
+        self.conversation_context = None
+        self.initialized = False
+        
+        # Initialize advanced features if available
+        if AI_PROVIDERS_AVAILABLE:
+            self._initialize_ai_features()
+        else:
+            logger.warning("Running in fallback mode - advanced AI features disabled")
+    
+    def _initialize_ai_features(self):
+        """Initialize AI provider, personality, and context management."""
+        try:
+            # Initialize personality
+            self.personality = personality_manager.create_personality_from_template(BOT_PERSONALITY)
+            if not self.personality:
+                logger.warning(f"Personality template '{BOT_PERSONALITY}' not found, using default")
+                self.personality = personality_manager.create_personality_from_template("helpful_assistant")
+            
+            # Initialize AI provider
+            provider_type = AIProviderType(BOT_AI_PROVIDER)
+            self.ai_provider = ai_provider_manager.create_provider(provider_type)
+            ai_provider_manager.register_provider(f"{AGENT_NAME}_{self.session_id}", self.ai_provider)
+            
+            # Initialize conversation context if memory is enabled
+            if BOT_MEMORY_ENABLED:
+                self.conversation_context = context_manager.get_or_create_context(
+                    session_id=self.session_id,
+                    bot_name=AGENT_NAME,
+                    conversation_id=f"{AGENT_NAME}_{self.session_id}_{int(time.time())}"
+                )
+            
+            self.initialized = True
+            logger.info(f"Enhanced AI chatbot initialized: provider={BOT_AI_PROVIDER}, personality={BOT_PERSONALITY}, memory={BOT_MEMORY_ENABLED}")
+            
+        except Exception as e:
+            logger.error(f"Failed to initialize AI features: {e}")
+            self.initialized = False
+    
+    async def generate_response(self, message: str) -> str:
+        """Generate a response using AI provider with personality and context."""
+        if not self.initialized or not self.ai_provider:
+            return self._get_fallback_response(message)
+        
+        try:
+            # Prepare conversation context
+            if self.conversation_context:
+                # Create a new AI conversation context with personality
+                ai_context = ConversationContext(
+                    session_id=self.session_id,
+                    bot_name=AGENT_NAME,
+                    personality_prompt=self.personality.generate_system_prompt() if self.personality else None
+                )
+                
+                # Add personality system message
+                if self.personality:
+                    ai_context.add_message(MessageRole.SYSTEM, self.personality.generate_system_prompt())
+                
+                # Add conversation history context
+                context_summary = context_manager.get_context_for_response(self.conversation_context.conversation_id)
+                if context_summary:
+                    ai_context.add_message(MessageRole.SYSTEM, f"Conversation context: {context_summary}")
+            else:
+                # Simple context without memory
+                ai_context = ConversationContext(
+                    session_id=self.session_id,
+                    bot_name=AGENT_NAME
+                )
+                if self.personality:
+                    ai_context.add_message(MessageRole.SYSTEM, self.personality.generate_system_prompt())
+            
+            # Generate response
+            if BOT_STREAMING:
+                # For streaming, collect the full response
+                response_parts = []
+                async for chunk in self.ai_provider.stream_response(ai_context, message):
+                    response_parts.append(chunk)
+                response = "".join(response_parts)
+            else:
+                response = await self.ai_provider.generate_response(ai_context, message)
+            
+            # Store conversation turn in context manager
+            if self.conversation_context:
+                context_manager.add_conversation_turn(
+                    conversation_id=self.conversation_context.conversation_id,
+                    user_message=message,
+                    bot_response=response,
+                    context_used={"ai_provider": BOT_AI_PROVIDER, "personality": BOT_PERSONALITY},
+                    metadata={"timestamp": time.time(), "streaming": BOT_STREAMING}
+                )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"AI response generation failed: {e}")
+            return self._get_fallback_response(message, error=True)
+    
+    def _get_fallback_response(self, message: str, error: bool = False) -> str:
+        """Get fallback response when AI providers are unavailable."""
+        if error:
+            return FALLBACK_RESPONSES["error"][hash(message) % len(FALLBACK_RESPONSES["error"])]
+        
+        message_lower = message.lower()
+        
+        # Simple keyword matching for fallback responses
+        if any(word in message_lower for word in ["hello", "hi", "hey", "greetings"]):
+            return FALLBACK_RESPONSES["greeting"][hash(message) % len(FALLBACK_RESPONSES["greeting"])]
+        elif any(word in message_lower for word in ["help", "what can you do", "capabilities"]):
+            return FALLBACK_RESPONSES["help"][hash(message) % len(FALLBACK_RESPONSES["help"])]
+        elif any(word in message_lower for word in ["features", "abilities", "advanced"]):
+            return FALLBACK_RESPONSES["capabilities"][hash(message) % len(FALLBACK_RESPONSES["capabilities"])]
+        else:
+            return FALLBACK_RESPONSES["default"][hash(message) % len(FALLBACK_RESPONSES["default"])]
+    
+    async def health_check(self) -> Dict[str, Any]:
+        """Perform health check on bot components."""
+        health = {
+            "bot_name": AGENT_NAME,
+            "session_id": self.session_id,
+            "initialized": self.initialized,
+            "ai_providers_available": AI_PROVIDERS_AVAILABLE,
+            "configuration": {
+                "personality": BOT_PERSONALITY,
+                "ai_provider": BOT_AI_PROVIDER,
+                "streaming": BOT_STREAMING,
+                "memory_enabled": BOT_MEMORY_ENABLED
+            }
+        }
+        
+        if self.initialized and self.ai_provider:
+            try:
+                provider_healthy = await self.ai_provider.health_check()
+                health["ai_provider_status"] = "healthy" if provider_healthy else "unhealthy"
+            except Exception as e:
+                health["ai_provider_status"] = f"error: {e}"
+        
+        if self.personality:
+            health["personality_loaded"] = True
+            health["personality_traits"] = [trait.value for trait in self.personality.traits]
+        
+        if self.conversation_context:
+            health["conversation_turns"] = len(self.conversation_context.turns)
+            health["context_summary"] = self.conversation_context.get_conversation_summary()
+        
+        return health
+
+
+# Global bot instance
+_bot_instance: Optional[EnhancedAIChatbot] = None
+
+
+def agent_info() -> Dict[str, str]:
+    """Return agent information."""
+    return {
+        "name": AGENT_NAME,
+        "description": AGENT_DESCRIPTION,
+        "has_media": "false",
+        "features": [
+            "multi_provider_ai",
+            "personality_system", 
+            "conversation_memory",
+            "streaming_responses",
+            "health_monitoring"
+        ]
+    }
+
+
+def create_agent_tracks(session_name: str) -> Dict[str, MediaStreamTrack]:
+    """AI chatbot doesn't provide media tracks - it's chat-only."""
+    return {}
+
+
+async def handle_chat_message(
+    chat_message: ChatMessageModel, 
+    send_message_func: Callable[[str], Awaitable[None]]
+) -> Optional[str]:
+    """Handle incoming chat messages and provide AI-powered responses."""
+    global _bot_instance
+    
+    try:
+        # Initialize bot instance if needed
+        if _bot_instance is None:
+            _bot_instance = EnhancedAIChatbot(chat_message.nick)
+            logger.info(f"Initialized enhanced AI chatbot for session: {chat_message.nick}")
+        
+        # Generate response
+        response = await _bot_instance.generate_response(chat_message.message)
+        
+        # Send response
+        if response:
+            await send_message_func(response)
+            logger.info(f"AI Chatbot responded to {chat_message.nick}: {response[:100]}...")
+        
+        return response
+        
+    except Exception as e:
+        logger.error(f"Error in AI chatbot: {e}")
+        error_response = "I apologize, but I encountered an error. Please try again."
+        await send_message_func(error_response)
+        return error_response
+
+
+async def get_bot_status() -> Dict[str, Any]:
+    """Get detailed bot status and health information."""
+    global _bot_instance
+    
+    status = {
+        "agent_name": AGENT_NAME,
+        "agent_description": AGENT_DESCRIPTION,
+        "features_available": AI_PROVIDERS_AVAILABLE,
+        "configuration": {
+            "personality_template": BOT_PERSONALITY,
+            "ai_provider": BOT_AI_PROVIDER,
+            "streaming_enabled": BOT_STREAMING,
+            "memory_enabled": BOT_MEMORY_ENABLED
+        }
+    }
+    
+    if _bot_instance:
+        health_info = await _bot_instance.health_check()
+        status.update(health_info)
+    else:
+        status["instance_status"] = "not_initialized"
+    
+    # Add system-level information
+    if AI_PROVIDERS_AVAILABLE:
+        status["available_personalities"] = [
+            template.id for template in personality_manager.list_templates()
+        ]
+        status["available_providers"] = ai_provider_manager.list_providers()
+        
+        # Get context manager statistics
+        if BOT_MEMORY_ENABLED:
+            context_stats = context_manager.get_statistics()
+            status["conversation_statistics"] = context_stats
+    
+    return status
+
+
+# Additional helper functions for advanced features
+async def switch_personality(personality_id: str) -> bool:
+    """Switch bot personality at runtime."""
+    global _bot_instance
+    
+    if not AI_PROVIDERS_AVAILABLE or not _bot_instance:
+        return False
+    
+    try:
+        new_personality = personality_manager.create_personality_from_template(personality_id)
+        if new_personality:
+            _bot_instance.personality = new_personality
+            logger.info(f"Switched to personality: {personality_id}")
+            return True
+    except Exception as e:
+        logger.error(f"Failed to switch personality: {e}")
+    
+    return False
+
+
+async def switch_ai_provider(provider_type: str) -> bool:
+    """Switch AI provider at runtime."""
+    global _bot_instance
+    
+    if not AI_PROVIDERS_AVAILABLE or not _bot_instance:
+        return False
+    
+    try:
+        provider_enum = AIProviderType(provider_type)
+        new_provider = ai_provider_manager.create_provider(provider_enum)
+        _bot_instance.ai_provider = new_provider
+        logger.info(f"Switched to AI provider: {provider_type}")
+        return True
+    except Exception as e:
+        logger.error(f"Failed to switch AI provider: {e}")
+    
+    return False

voicebot/conversation_context.py

@@ -0,0 +1,385 @@
+"""
+Conversation Context Management for Advanced Bot Management.
+
+This module manages conversation context, memory, and state for enhanced
+bot interactions with persistent conversation awareness.
+"""
+
+import json
+import time
+import os
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass, field
+from pydantic import BaseModel, Field
+from collections import defaultdict
+
+from logger import logger
+
+
+@dataclass
+class ConversationTurn:
+    """Individual turn in a conversation."""
+    turn_id: str
+    timestamp: float
+    user_message: str
+    bot_response: str
+    context_used: Dict[str, Any] = field(default_factory=dict)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization."""
+        return {
+            "turn_id": self.turn_id,
+            "timestamp": self.timestamp,
+            "user_message": self.user_message,
+            "bot_response": self.bot_response,
+            "context_used": self.context_used,
+            "metadata": self.metadata
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'ConversationTurn':
+        """Create from dictionary."""
+        return cls(**data)
+
+
+class ConversationMemory(BaseModel):
+    """Memory system for conversation context."""
+    
+    # Core conversation data
+    session_id: str
+    bot_name: str
+    user_name: Optional[str] = None
+    conversation_id: str
+    
+    # Conversation history
+    turns: List[ConversationTurn] = Field(default_factory=list)
+    created_at: float = Field(default_factory=time.time)
+    last_updated: float = Field(default_factory=time.time)
+    
+    # Memory components
+    facts_learned: Dict[str, Any] = Field(default_factory=dict)  # Facts about user/context
+    preferences: Dict[str, Any] = Field(default_factory=dict)    # User preferences
+    topics_discussed: List[str] = Field(default_factory=list)   # Topics covered
+    emotional_context: Dict[str, Any] = Field(default_factory=dict)  # Emotional state
+    
+    # Conversation state
+    current_topic: Optional[str] = None
+    conversation_stage: str = "greeting"  # greeting, discussion, conclusion
+    user_intent: Optional[str] = None
+    bot_goals: List[str] = Field(default_factory=list)
+    
+    # Memory management
+    max_turns: int = 100
+    max_facts: int = 50
+    memory_decay_factor: float = 0.95  # How quickly old memories fade
+    
+    class Config:
+        arbitrary_types_allowed = True
+    
+    def add_turn(self, turn: ConversationTurn):
+        """Add a conversation turn to memory."""
+        self.turns.append(turn)
+        self.last_updated = time.time()
+        
+        # Extract facts and context from the turn
+        self._extract_context_from_turn(turn)
+        
+        # Trim history if needed
+        if len(self.turns) > self.max_turns:
+            self.turns = self.turns[-self.max_turns:]
+    
+    def _extract_context_from_turn(self, turn: ConversationTurn):
+        """Extract contextual information from a conversation turn."""
+        # Simple keyword-based fact extraction (can be enhanced with NLP)
+        user_message = turn.user_message.lower()
+        
+        # Extract preferences
+        if "i like" in user_message or "i love" in user_message:
+            # Simple preference extraction
+            preference_start = max(user_message.find("i like"), user_message.find("i love"))
+            preference_text = user_message[preference_start:].split('.')[0]
+            self.preferences[f"preference_{len(self.preferences)}"] = preference_text
+        
+        # Extract facts
+        if "my name is" in user_message:
+            name_start = user_message.find("my name is") + len("my name is")
+            name = user_message[name_start:].split()[0].strip()
+            if name:
+                self.facts_learned["user_name"] = name
+                self.user_name = name
+        
+        # Topic tracking
+        if turn.metadata.get("detected_topics"):
+            for topic in turn.metadata["detected_topics"]:
+                if topic not in self.topics_discussed:
+                    self.topics_discussed.append(topic)
+        
+        # Emotional context (simple sentiment analysis)
+        emotional_indicators = {
+            "happy": ["happy", "great", "wonderful", "excited", "joy"],
+            "sad": ["sad", "unhappy", "disappointed", "depressed"],
+            "frustrated": ["frustrated", "annoyed", "angry", "upset"],
+            "confused": ["confused", "don't understand", "unclear", "puzzled"],
+            "satisfied": ["good", "thanks", "helpful", "satisfied"]
+        }
+        
+        for emotion, indicators in emotional_indicators.items():
+            if any(indicator in user_message for indicator in indicators):
+                self.emotional_context["current_emotion"] = emotion
+                self.emotional_context["last_emotion_update"] = time.time()
+                break
+    
+    def get_recent_context(self, turns: int = 5) -> List[ConversationTurn]:
+        """Get recent conversation turns for context."""
+        return self.turns[-turns:] if self.turns else []
+    
+    def get_relevant_facts(self, query: str) -> Dict[str, Any]:
+        """Get facts relevant to a query."""
+        relevant_facts = {}
+        query_lower = query.lower()
+        
+        for key, value in self.facts_learned.items():
+            if isinstance(value, str) and any(word in value.lower() for word in query_lower.split()):
+                relevant_facts[key] = value
+        
+        return relevant_facts
+    
+    def get_conversation_summary(self) -> str:
+        """Generate a summary of the conversation."""
+        if not self.turns:
+            return "No conversation history."
+        
+        summary_parts = []
+        
+        if self.user_name:
+            summary_parts.append(f"User: {self.user_name}")
+        
+        if self.topics_discussed:
+            topics_str = ", ".join(self.topics_discussed[:5])
+            summary_parts.append(f"Topics discussed: {topics_str}")
+        
+        if self.preferences:
+            prefs = list(self.preferences.values())[:3]
+            summary_parts.append(f"User preferences: {'; '.join(prefs)}")
+        
+        if self.emotional_context.get("current_emotion"):
+            summary_parts.append(f"Current mood: {self.emotional_context['current_emotion']}")
+        
+        summary_parts.append(f"Conversation turns: {len(self.turns)}")
+        
+        return " | ".join(summary_parts)
+
+
+class ConversationContextManager:
+    """Manager for conversation contexts and memory."""
+    
+    def __init__(self, storage_path: Optional[str] = None):
+        self.storage_path = storage_path or "./conversation_contexts"
+        self.active_contexts: Dict[str, ConversationMemory] = {}
+        self.context_index: Dict[str, List[str]] = defaultdict(list)  # bot_name -> conversation_ids
+        
+        # Ensure storage directory exists
+        os.makedirs(self.storage_path, exist_ok=True)
+        
+        # Load existing contexts
+        self._load_existing_contexts()
+    
+    def _load_existing_contexts(self):
+        """Load existing conversation contexts from storage."""
+        try:
+            context_files = [f for f in os.listdir(self.storage_path) if f.endswith('.json')]
+            for file in context_files:
+                try:
+                    file_path = os.path.join(self.storage_path, file)
+                    with open(file_path, 'r') as f:
+                        data = json.load(f)
+                    
+                    # Convert turn data back to ConversationTurn objects
+                    turns = [ConversationTurn.from_dict(turn_data) for turn_data in data.get('turns', [])]
+                    data['turns'] = turns
+                    
+                    context = ConversationMemory(**data)
+                    conversation_id = context.conversation_id
+                    
+                    self.active_contexts[conversation_id] = context
+                    self.context_index[context.bot_name].append(conversation_id)
+                    
+                except Exception as e:
+                    logger.warning(f"Failed to load context from {file}: {e}")
+            
+            logger.info(f"Loaded {len(self.active_contexts)} conversation contexts")
+            
+        except Exception as e:
+            logger.error(f"Failed to load conversation contexts: {e}")
+    
+    def get_or_create_context(
+        self, 
+        session_id: str, 
+        bot_name: str, 
+        conversation_id: Optional[str] = None
+    ) -> ConversationMemory:
+        """Get existing context or create a new one."""
+        
+        if conversation_id and conversation_id in self.active_contexts:
+            return self.active_contexts[conversation_id]
+        
+        # Create new conversation ID if not provided
+        if not conversation_id:
+            conversation_id = f"{session_id}_{bot_name}_{int(time.time())}"
+        
+        # Create new context
+        context = ConversationMemory(
+            session_id=session_id,
+            bot_name=bot_name,
+            conversation_id=conversation_id
+        )
+        
+        self.active_contexts[conversation_id] = context
+        self.context_index[bot_name].append(conversation_id)
+        
+        logger.info(f"Created new conversation context: {conversation_id}")
+        return context
+    
+    def save_context(self, conversation_id: str):
+        """Save a conversation context to storage."""
+        if conversation_id not in self.active_contexts:
+            logger.warning(f"Context {conversation_id} not found for saving")
+            return
+        
+        context = self.active_contexts[conversation_id]
+        
+        try:
+            # Convert to dict for serialization
+            data = context.model_dump()
+            # Convert ConversationTurn objects to dicts
+            data['turns'] = [turn.to_dict() for turn in context.turns]
+            
+            file_path = os.path.join(self.storage_path, f"{conversation_id}.json")
+            with open(file_path, 'w') as f:
+                json.dump(data, f, indent=2)
+            
+            logger.debug(f"Saved context: {conversation_id}")
+            
+        except Exception as e:
+            logger.error(f"Failed to save context {conversation_id}: {e}")
+    
+    def add_conversation_turn(
+        self, 
+        conversation_id: str, 
+        user_message: str, 
+        bot_response: str,
+        context_used: Optional[Dict[str, Any]] = None,
+        metadata: Optional[Dict[str, Any]] = None
+    ):
+        """Add a conversation turn to the specified context."""
+        if conversation_id not in self.active_contexts:
+            logger.warning(f"Context {conversation_id} not found")
+            return
+        
+        turn = ConversationTurn(
+            turn_id=f"{conversation_id}_{len(self.active_contexts[conversation_id].turns)}",
+            timestamp=time.time(),
+            user_message=user_message,
+            bot_response=bot_response,
+            context_used=context_used or {},
+            metadata=metadata or {}
+        )
+        
+        self.active_contexts[conversation_id].add_turn(turn)
+        
+        # Auto-save after each turn
+        self.save_context(conversation_id)
+    
+    def get_context_for_response(self, conversation_id: str) -> Optional[str]:
+        """Get formatted context for generating bot responses."""
+        if conversation_id not in self.active_contexts:
+            return None
+        
+        context = self.active_contexts[conversation_id]
+        context_parts = []
+        
+        # Add conversation summary
+        summary = context.get_conversation_summary()
+        if summary != "No conversation history.":
+            context_parts.append(f"Conversation context: {summary}")
+        
+        # Add recent turns for immediate context
+        recent_turns = context.get_recent_context(3)
+        if recent_turns:
+            context_parts.append("Recent conversation:")
+            for turn in recent_turns:
+                context_parts.append(f"User: {turn.user_message}")
+                context_parts.append(f"Bot: {turn.bot_response}")
+        
+        # Add relevant facts
+        if context.facts_learned:
+            facts_str = "; ".join([f"{k}: {v}" for k, v in list(context.facts_learned.items())[:3]])
+            context_parts.append(f"Known facts: {facts_str}")
+        
+        # Add emotional context
+        if context.emotional_context.get("current_emotion"):
+            context_parts.append(f"User's current mood: {context.emotional_context['current_emotion']}")
+        
+        return "\n".join(context_parts) if context_parts else None
+    
+    def get_contexts_for_bot(self, bot_name: str) -> List[ConversationMemory]:
+        """Get all contexts for a specific bot."""
+        conversation_ids = self.context_index.get(bot_name, [])
+        return [self.active_contexts[cid] for cid in conversation_ids if cid in self.active_contexts]
+    
+    def cleanup_old_contexts(self, max_age_days: int = 30):
+        """Clean up old conversation contexts."""
+        current_time = time.time()
+        max_age_seconds = max_age_days * 24 * 60 * 60
+        
+        contexts_to_remove = []
+        for conversation_id, context in self.active_contexts.items():
+            if current_time - context.last_updated > max_age_seconds:
+                contexts_to_remove.append(conversation_id)
+        
+        for conversation_id in contexts_to_remove:
+            context = self.active_contexts[conversation_id]
+            
+            # Remove from index
+            if context.bot_name in self.context_index:
+                if conversation_id in self.context_index[context.bot_name]:
+                    self.context_index[context.bot_name].remove(conversation_id)
+            
+            # Remove context file
+            try:
+                file_path = os.path.join(self.storage_path, f"{conversation_id}.json")
+                if os.path.exists(file_path):
+                    os.remove(file_path)
+            except Exception as e:
+                logger.warning(f"Failed to remove context file {conversation_id}: {e}")
+            
+            # Remove from active contexts
+            del self.active_contexts[conversation_id]
+        
+        if contexts_to_remove:
+            logger.info(f"Cleaned up {len(contexts_to_remove)} old conversation contexts")
+    
+    def get_statistics(self) -> Dict[str, Any]:
+        """Get statistics about conversation contexts."""
+        total_contexts = len(self.active_contexts)
+        total_turns = sum(len(context.turns) for context in self.active_contexts.values())
+        
+        bot_stats = {}
+        for bot_name, conversation_ids in self.context_index.items():
+            active_conversations = [cid for cid in conversation_ids if cid in self.active_contexts]
+            bot_stats[bot_name] = {
+                "active_conversations": len(active_conversations),
+                "total_turns": sum(len(self.active_contexts[cid].turns) for cid in active_conversations)
+            }
+        
+        return {
+            "total_contexts": total_contexts,
+            "total_turns": total_turns,
+            "average_turns_per_context": total_turns / total_contexts if total_contexts > 0 else 0,
+            "bot_statistics": bot_stats
+        }
+
+
+# Global context manager instance
+context_manager = ConversationContextManager()

voicebot/enhanced_bot_configs.json

@@ -0,0 +1,85 @@
+{
+  "ai_chatbot": {
+    "personality": "technical_expert",
+    "ai_provider": "openai",
+    "streaming": false,
+    "memory_enabled": true,
+    "advanced_features": true,
+    "description": "Advanced AI chatbot with OpenAI integration and helpful personality",
+    "features": [
+      "multi_provider_ai",
+      "personality_system",
+      "conversation_memory",
+      "streaming_responses"
+    ]
+  },
+  "technical_expert": {
+    "personality": "technical_expert",
+    "ai_provider": "anthropic",
+    "streaming": false,
+    "memory_enabled": true,
+    "advanced_features": true,
+    "description": "Technical expert bot with detailed explanations and Anthropic Claude integration",
+    "features": [
+      "technical_expertise",
+      "detailed_explanations",
+      "conversation_memory"
+    ]
+  },
+  "creative_companion": {
+    "personality": "creative_companion",
+    "ai_provider": "local",
+    "streaming": true,
+    "memory_enabled": true,
+    "advanced_features": true,
+    "description": "Creative writing and brainstorming companion with local AI model",
+    "features": [
+      "creative_writing",
+      "brainstorming",
+      "streaming_responses",
+      "conversation_memory"
+    ]
+  },
+  "business_advisor": {
+    "personality": "business_advisor",
+    "ai_provider": "openai",
+    "streaming": false,
+    "memory_enabled": true,
+    "advanced_features": true,
+    "description": "Business and strategic advisor with professional communication style",
+    "features": [
+      "business_analysis",
+      "strategic_planning",
+      "professional_communication",
+      "conversation_memory"
+    ]
+  },
+  "comedy_bot": {
+    "personality": "comedy_bot",
+    "ai_provider": "local",
+    "streaming": true,
+    "memory_enabled": false,
+    "advanced_features": true,
+    "description": "Entertainment-focused bot with humor and casual conversation",
+    "features": [
+      "humor_generation",
+      "casual_conversation",
+      "entertainment",
+      "streaming_responses"
+    ]
+  },
+  "wise_mentor": {
+    "personality": "wise_mentor",
+    "ai_provider": "anthropic",
+    "streaming": false,
+    "memory_enabled": true,
+    "advanced_features": true,
+    "description": "Wise mentor providing thoughtful guidance and life advice",
+    "features": [
+      "life_advice",
+      "thoughtful_guidance",
+      "wisdom_sharing",
+      "conversation_memory"
+    ]
+  }
+}

voicebot/personality_system.py

@@ -0,0 +1,449 @@
+"""
+Bot Personality System for Advanced Bot Management.
+
+This module provides personality templates, configuration, and behavior
+management for creating diverse and engaging bot characters.
+"""
+
+import json
+import os
+from typing import Dict, List, Optional, Any
+from enum import Enum
+from pydantic import BaseModel, Field
+
+from logger import logger
+
+
+class PersonalityTrait(str, Enum):
+    """Personality traits that can be assigned to bots."""
+    FRIENDLY = "friendly"
+    PROFESSIONAL = "professional"
+    HUMOROUS = "humorous"
+    SARCASTIC = "sarcastic"
+    EMPATHETIC = "empathetic"
+    ANALYTICAL = "analytical"
+    CREATIVE = "creative"
+    ASSERTIVE = "assertive"
+    PATIENT = "patient"
+    ENTHUSIASTIC = "enthusiastic"
+    MYSTERIOUS = "mysterious"
+    WISE = "wise"
+    PLAYFUL = "playful"
+    FORMAL = "formal"
+    CASUAL = "casual"
+
+
+class CommunicationStyle(str, Enum):
+    """Communication styles for bot responses."""
+    CONCISE = "concise"
+    DETAILED = "detailed"
+    CONVERSATIONAL = "conversational"
+    TECHNICAL = "technical"
+    STORYTELLING = "storytelling"
+    QUESTION_BASED = "question_based"
+    SUPPORTIVE = "supportive"
+    DIRECT = "direct"
+    DIPLOMATIC = "diplomatic"
+
+
+class ExpertiseDomain(str, Enum):
+    """Domains of expertise for specialized bots."""
+    GENERAL = "general"
+    TECHNOLOGY = "technology"
+    SCIENCE = "science"
+    ARTS = "arts"
+    BUSINESS = "business"
+    EDUCATION = "education"
+    HEALTH = "health"
+    ENTERTAINMENT = "entertainment"
+    SPORTS = "sports"
+    COOKING = "cooking"
+    TRAVEL = "travel"
+    FINANCE = "finance"
+    LEGAL = "legal"
+    PSYCHOLOGY = "psychology"
+    PHILOSOPHY = "philosophy"
+
+
+class BotPersonality(BaseModel):
+    """Complete personality configuration for a bot."""
+    
+    # Identity
+    name: str = "Assistant"
+    description: str = "A helpful AI assistant"
+    backstory: Optional[str] = None
+    
+    # Core personality
+    traits: List[PersonalityTrait] = Field(default_factory=lambda: [PersonalityTrait.FRIENDLY])
+    communication_style: CommunicationStyle = CommunicationStyle.CONVERSATIONAL
+    expertise_domains: List[ExpertiseDomain] = Field(default_factory=lambda: [ExpertiseDomain.GENERAL])
+    
+    # Behavior settings
+    response_length_preference: str = "medium"  # short, medium, long
+    emoji_usage: bool = True
+    formality_level: float = 0.5  # 0.0 = very casual, 1.0 = very formal
+    humor_level: float = 0.3  # 0.0 = no humor, 1.0 = very humorous
+    empathy_level: float = 0.7  # 0.0 = analytical only, 1.0 = highly empathetic
+    
+    # Language preferences
+    preferred_language: str = "en"
+    technical_complexity: float = 0.5  # 0.0 = simple terms, 1.0 = technical jargon
+    
+    # Conversation patterns
+    greeting_style: str = "warm"  # formal, warm, casual, quirky
+    farewell_style: str = "friendly"  # formal, friendly, casual, memorable
+    
+    # Custom prompts and examples
+    system_prompt: Optional[str] = None
+    example_responses: List[Dict[str, str]] = Field(default_factory=list)
+    custom_instructions: List[str] = Field(default_factory=list)
+    
+    # Behavioral boundaries
+    topics_to_avoid: List[str] = Field(default_factory=list)
+    preferred_topics: List[str] = Field(default_factory=list)
+    conversation_limits: Dict[str, Any] = Field(default_factory=dict)
+    
+    def generate_system_prompt(self) -> str:
+        """Generate a comprehensive system prompt based on personality."""
+        if self.system_prompt:
+            return self.system_prompt
+        
+        prompt_parts = []
+        
+        # Identity
+        prompt_parts.append(f"You are {self.name}, {self.description}")
+        if self.backstory:
+            prompt_parts.append(f"Background: {self.backstory}")
+        
+        # Personality traits
+        if self.traits:
+            traits_str = ", ".join([trait.value for trait in self.traits])
+            prompt_parts.append(f"Your personality is: {traits_str}")
+        
+        # Communication style
+        style_desc = self._get_style_description()
+        prompt_parts.append(f"Communication style: {style_desc}")
+        
+        # Expertise
+        if self.expertise_domains and self.expertise_domains != [ExpertiseDomain.GENERAL]:
+            domains_str = ", ".join([domain.value for domain in self.expertise_domains])
+            prompt_parts.append(f"Areas of expertise: {domains_str}")
+        
+        # Behavior guidelines
+        behavior_guidelines = self._generate_behavior_guidelines()
+        if behavior_guidelines:
+            prompt_parts.append(f"Behavior guidelines: {behavior_guidelines}")
+        
+        # Custom instructions
+        if self.custom_instructions:
+            prompt_parts.append("Additional instructions:")
+            prompt_parts.extend([f"- {instruction}" for instruction in self.custom_instructions])
+        
+        # Topic boundaries
+        if self.topics_to_avoid:
+            avoid_str = ", ".join(self.topics_to_avoid)
+            prompt_parts.append(f"Avoid discussing: {avoid_str}")
+        
+        if self.preferred_topics:
+            prefer_str = ", ".join(self.preferred_topics)
+            prompt_parts.append(f"Preferred discussion topics: {prefer_str}")
+        
+        return "\n\n".join(prompt_parts)
+    
+    def _get_style_description(self) -> str:
+        """Get communication style description."""
+        style_map = {
+            CommunicationStyle.CONCISE: "Keep responses brief and to the point",
+            CommunicationStyle.DETAILED: "Provide comprehensive, detailed explanations",
+            CommunicationStyle.CONVERSATIONAL: "Use natural, conversational language",
+            CommunicationStyle.TECHNICAL: "Use precise, technical terminology when appropriate",
+            CommunicationStyle.STORYTELLING: "Frame responses as engaging narratives",
+            CommunicationStyle.QUESTION_BASED: "Ask follow-up questions to better understand needs",
+            CommunicationStyle.SUPPORTIVE: "Provide encouraging and supportive responses",
+            CommunicationStyle.DIRECT: "Be straightforward and direct in communication",
+            CommunicationStyle.DIPLOMATIC: "Use diplomatic and tactful language"
+        }
+        return style_map.get(self.communication_style, "Communicate naturally")
+    
+    def _generate_behavior_guidelines(self) -> str:
+        """Generate behavior guidelines based on settings."""
+        guidelines = []
+        
+        if self.formality_level > 0.7:
+            guidelines.append("maintain a formal and professional tone")
+        elif self.formality_level < 0.3:
+            guidelines.append("use casual and relaxed language")
+        
+        if self.humor_level > 0.5:
+            guidelines.append("incorporate appropriate humor when suitable")
+        elif self.humor_level < 0.2:
+            guidelines.append("maintain a serious and professional demeanor")
+        
+        if self.empathy_level > 0.7:
+            guidelines.append("show high emotional intelligence and empathy")
+        elif self.empathy_level < 0.3:
+            guidelines.append("focus on logical and analytical responses")
+        
+        if self.emoji_usage:
+            guidelines.append("use emojis appropriately to enhance communication")
+        
+        if self.response_length_preference == "short":
+            guidelines.append("keep responses concise")
+        elif self.response_length_preference == "long":
+            guidelines.append("provide detailed explanations")
+        
+        return "; ".join(guidelines) if guidelines else ""
+
+
+class PersonalityTemplate(BaseModel):
+    """Pre-defined personality templates for common bot types."""
+    
+    id: str
+    name: str
+    description: str
+    personality: BotPersonality
+    category: str = "general"
+    tags: List[str] = Field(default_factory=list)
+    
+    class Config:
+        extra = "allow"
+
+
+class PersonalityManager:
+    """Manager for bot personalities and templates."""
+    
+    def __init__(self):
+        self.templates: Dict[str, PersonalityTemplate] = {}
+        self.custom_personalities: Dict[str, BotPersonality] = {}
+        self._load_default_templates()
+    
+    def _load_default_templates(self):
+        """Load default personality templates."""
+        default_templates = [
+            PersonalityTemplate(
+                id="helpful_assistant",
+                name="Helpful Assistant",
+                description="A friendly and helpful general-purpose assistant",
+                personality=BotPersonality(
+                    name="Assistant",
+                    description="a helpful and friendly AI assistant",
+                    traits=[PersonalityTrait.FRIENDLY, PersonalityTrait.EMPATHETIC, PersonalityTrait.PATIENT],
+                    communication_style=CommunicationStyle.CONVERSATIONAL,
+                    expertise_domains=[ExpertiseDomain.GENERAL],
+                    formality_level=0.4,
+                    humor_level=0.3,
+                    empathy_level=0.8
+                ),
+                category="general",
+                tags=["helpful", "friendly", "general"]
+            ),
+            
+            PersonalityTemplate(
+                id="technical_expert",
+                name="Technical Expert",
+                description="A knowledgeable technical specialist",
+                personality=BotPersonality(
+                    name="TechBot",
+                    description="a knowledgeable technical expert and problem solver",
+                    traits=[PersonalityTrait.ANALYTICAL, PersonalityTrait.PROFESSIONAL, PersonalityTrait.PATIENT],
+                    communication_style=CommunicationStyle.TECHNICAL,
+                    expertise_domains=[ExpertiseDomain.TECHNOLOGY, ExpertiseDomain.SCIENCE],
+                    formality_level=0.7,
+                    humor_level=0.1,
+                    empathy_level=0.4,
+                    technical_complexity=0.8,
+                    emoji_usage=False
+                ),
+                category="technical",
+                tags=["technical", "expert", "analytical"]
+            ),
+            
+            PersonalityTemplate(
+                id="creative_companion",
+                name="Creative Companion",
+                description="An imaginative and inspiring creative partner",
+                personality=BotPersonality(
+                    name="CreativeBot",
+                    description="an imaginative and inspiring creative companion",
+                    traits=[PersonalityTrait.CREATIVE, PersonalityTrait.ENTHUSIASTIC, PersonalityTrait.PLAYFUL],
+                    communication_style=CommunicationStyle.STORYTELLING,
+                    expertise_domains=[ExpertiseDomain.ARTS, ExpertiseDomain.ENTERTAINMENT],
+                    formality_level=0.2,
+                    humor_level=0.7,
+                    empathy_level=0.6,
+                    emoji_usage=True,
+                    custom_instructions=[
+                        "Encourage creative thinking and exploration",
+                        "Offer multiple perspectives and ideas",
+                        "Use vivid and imaginative language"
+                    ]
+                ),
+                category="creative",
+                tags=["creative", "artistic", "inspiring"]
+            ),
+            
+            PersonalityTemplate(
+                id="business_advisor",
+                name="Business Advisor",
+                description="A professional business consultant and strategist",
+                personality=BotPersonality(
+                    name="BusinessBot",
+                    description="a professional business advisor and strategic consultant",
+                    traits=[PersonalityTrait.PROFESSIONAL, PersonalityTrait.ANALYTICAL, PersonalityTrait.ASSERTIVE],
+                    communication_style=CommunicationStyle.DIRECT,
+                    expertise_domains=[ExpertiseDomain.BUSINESS, ExpertiseDomain.FINANCE],
+                    formality_level=0.8,
+                    humor_level=0.2,
+                    empathy_level=0.5,
+                    emoji_usage=False,
+                    response_length_preference="detailed",
+                    custom_instructions=[
+                        "Provide actionable business insights",
+                        "Focus on practical solutions and ROI",
+                        "Use business terminology appropriately"
+                    ]
+                ),
+                category="business",
+                tags=["business", "professional", "strategic"]
+            ),
+            
+            PersonalityTemplate(
+                id="comedy_bot",
+                name="Comedy Bot",
+                description="A humorous entertainer that loves jokes and wordplay",
+                personality=BotPersonality(
+                    name="ComedyBot",
+                    description="a humorous entertainer who loves jokes, puns, and making people laugh",
+                    traits=[PersonalityTrait.HUMOROUS, PersonalityTrait.PLAYFUL, PersonalityTrait.ENTHUSIASTIC],
+                    communication_style=CommunicationStyle.CONVERSATIONAL,
+                    expertise_domains=[ExpertiseDomain.ENTERTAINMENT],
+                    formality_level=0.1,
+                    humor_level=0.9,
+                    empathy_level=0.6,
+                    emoji_usage=True,
+                    greeting_style="quirky",
+                    farewell_style="memorable",
+                    custom_instructions=[
+                        "Look for opportunities to make appropriate jokes",
+                        "Use wordplay and puns when fitting",
+                        "Keep humor light and positive"
+                    ]
+                ),
+                category="entertainment",
+                tags=["funny", "jokes", "entertainment"]
+            ),
+            
+            PersonalityTemplate(
+                id="wise_mentor",
+                name="Wise Mentor",
+                description="A thoughtful mentor who provides wisdom and guidance",
+                personality=BotPersonality(
+                    name="Mentor",
+                    description="a wise and thoughtful mentor who provides guidance and wisdom",
+                    traits=[PersonalityTrait.WISE, PersonalityTrait.EMPATHETIC, PersonalityTrait.PATIENT],
+                    communication_style=CommunicationStyle.SUPPORTIVE,
+                    expertise_domains=[ExpertiseDomain.PSYCHOLOGY, ExpertiseDomain.PHILOSOPHY, ExpertiseDomain.EDUCATION],
+                    formality_level=0.5,
+                    humor_level=0.3,
+                    empathy_level=0.9,
+                    response_length_preference="detailed",
+                    custom_instructions=[
+                        "Ask thoughtful questions to understand deeper needs",
+                        "Provide perspective and context for challenges",
+                        "Encourage personal growth and reflection"
+                    ]
+                ),
+                category="guidance",
+                tags=["wise", "mentor", "supportive"]
+            )
+        ]
+        
+        for template in default_templates:
+            self.templates[template.id] = template
+        
+        logger.info(f"Loaded {len(default_templates)} default personality templates")
+    
+    def get_template(self, template_id: str) -> Optional[PersonalityTemplate]:
+        """Get a personality template by ID."""
+        return self.templates.get(template_id)
+    
+    def list_templates(self, category: Optional[str] = None) -> List[PersonalityTemplate]:
+        """List all templates, optionally filtered by category."""
+        templates = list(self.templates.values())
+        if category:
+            templates = [t for t in templates if t.category == category]
+        return templates
+    
+    def get_categories(self) -> List[str]:
+        """Get all available template categories."""
+        categories = set(t.category for t in self.templates.values())
+        return sorted(list(categories))
+    
+    def create_personality_from_template(self, template_id: str, customizations: Optional[Dict[str, Any]] = None) -> Optional[BotPersonality]:
+        """Create a personality instance from a template with optional customizations."""
+        template = self.get_template(template_id)
+        if not template:
+            return None
+        
+        # Start with template personality
+        personality_dict = template.personality.model_dump()
+        
+        # Apply customizations
+        if customizations:
+            for key, value in customizations.items():
+                if key in personality_dict:
+                    personality_dict[key] = value
+        
+        return BotPersonality(**personality_dict)
+    
+    def save_custom_personality(self, personality_id: str, personality: BotPersonality):
+        """Save a custom personality."""
+        self.custom_personalities[personality_id] = personality
+        logger.info(f"Saved custom personality: {personality_id}")
+    
+    def get_custom_personality(self, personality_id: str) -> Optional[BotPersonality]:
+        """Get a custom personality by ID."""
+        return self.custom_personalities.get(personality_id)
+    
+    def list_custom_personalities(self) -> List[str]:
+        """List all custom personality IDs."""
+        return list(self.custom_personalities.keys())
+    
+    def export_personality(self, personality: BotPersonality) -> str:
+        """Export a personality to JSON string."""
+        return personality.model_dump_json(indent=2)
+    
+    def import_personality(self, json_str: str) -> BotPersonality:
+        """Import a personality from JSON string."""
+        return BotPersonality.model_validate_json(json_str)
+    
+    def load_personalities_from_file(self, file_path: str):
+        """Load custom personalities from a JSON file."""
+        try:
+            if os.path.exists(file_path):
+                with open(file_path, 'r') as f:
+                    data = json.load(f)
+                    for personality_id, personality_data in data.items():
+                        personality = BotPersonality(**personality_data)
+                        self.custom_personalities[personality_id] = personality
+                logger.info(f"Loaded {len(data)} custom personalities from {file_path}")
+        except Exception as e:
+            logger.error(f"Failed to load personalities from {file_path}: {e}")
+    
+    def save_personalities_to_file(self, file_path: str):
+        """Save custom personalities to a JSON file."""
+        try:
+            data = {}
+            for personality_id, personality in self.custom_personalities.items():
+                data[personality_id] = personality.model_dump()
+            
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            with open(file_path, 'w') as f:
+                json.dump(data, f, indent=2)
+            logger.info(f"Saved {len(data)} custom personalities to {file_path}")
+        except Exception as e:
+            logger.error(f"Failed to save personalities to {file_path}: {e}")
+
+
+# Global personality manager instance
+personality_manager = PersonalityManager()

voicebot/step_5b_integration_demo.py

@@ -0,0 +1,477 @@
+"""Step 5B Integration: Enhanced Bot Orchestrator with Advanced Bot Management.
+
+This module demonstrates how the new advanced bot management features integrate
+with the existing bot orchestrator to provide:
+
+1. AI Provider-powered bots with multiple backend support
+2. Personality-driven bot behavior and responses  
+3. Conversation context and memory management
+4. Dynamic bot configuration and health monitoring
+
+This integration enhances the existing bot discovery and management system
+without breaking compatibility with existing bot implementations.
+"""
+
+import os
+import json
+import time
+import asyncio
+from typing import Dict, Optional, Any
+from pathlib import Path
+
+# Import existing bot orchestrator functionality
+from bot_orchestrator import discover_bots
+
+# Import advanced bot management modules
+try:
+    from voicebot.ai_providers import ai_provider_manager, AIProviderType
+    from voicebot.personality_system import personality_manager
+    from voicebot.conversation_context import context_manager
+    AI_FEATURES_AVAILABLE = True
+except ImportError as e:
+    print(f"Warning: Advanced AI features not available: {e}")
+    AI_FEATURES_AVAILABLE = False
+
+from logger import logger
+
+
+class EnhancedBotOrchestrator:
+    """Enhanced bot orchestrator with Step 5B advanced management features."""
+    
+    def __init__(self):
+        self.enhanced_bots = {}  # Enhanced bots with AI features
+        self.bot_configurations = {}  # Bot-specific configurations
+        self.health_stats = {}  # Health monitoring data
+        
+        # Load configurations
+        self._load_bot_configurations()
+        
+        # Initialize AI systems if available
+        if AI_FEATURES_AVAILABLE:
+            self._initialize_ai_systems()
+    
+    def _load_bot_configurations(self):
+        """Load bot configurations from JSON file."""
+        config_path = Path(__file__).parent / "enhanced_bot_configs.json"
+        
+        default_configs = {
+            "ai_chatbot": {
+                "personality": "helpful_assistant",
+                "ai_provider": "openai",
+                "streaming": True,
+                "memory_enabled": True,
+                "advanced_features": True
+            },
+            "technical_expert": {
+                "personality": "technical_expert", 
+                "ai_provider": "anthropic",
+                "streaming": False,
+                "memory_enabled": True,
+                "advanced_features": True
+            },
+            "creative_companion": {
+                "personality": "creative_companion",
+                "ai_provider": "local",
+                "streaming": True, 
+                "memory_enabled": True,
+                "advanced_features": True
+            }
+        }
+        
+        try:
+            if config_path.exists():
+                with open(config_path, 'r') as f:
+                    self.bot_configurations = json.load(f)
+            else:
+                self.bot_configurations = default_configs
+                self._save_bot_configurations()
+        except Exception as e:
+            logger.error(f"Failed to load bot configurations: {e}")
+            self.bot_configurations = default_configs
+    
+    def _save_bot_configurations(self):
+        """Save bot configurations to JSON file."""
+        config_path = Path(__file__).parent / "enhanced_bot_configs.json"
+        try:
+            with open(config_path, 'w') as f:
+                json.dump(self.bot_configurations, f, indent=2)
+        except Exception as e:
+            logger.error(f"Failed to save bot configurations: {e}")
+    
+    def _initialize_ai_systems(self):
+        """Initialize AI provider and personality systems."""
+        try:
+            # Ensure default personality templates are loaded
+            personality_manager.ensure_default_templates()
+            
+            # Register available AI providers based on environment
+            providers_to_init = []
+            
+            if os.getenv("OPENAI_API_KEY"):
+                providers_to_init.append(AIProviderType.OPENAI)
+            
+            if os.getenv("ANTHROPIC_API_KEY"):
+                providers_to_init.append(AIProviderType.ANTHROPIC)
+            
+            # Local provider is always available
+            providers_to_init.append(AIProviderType.LOCAL)
+            
+            for provider_type in providers_to_init:
+                try:
+                    provider = ai_provider_manager.create_provider(provider_type)
+                    ai_provider_manager.register_provider(f"system_{provider_type.value}", provider)
+                    logger.info(f"Initialized AI provider: {provider_type.value}")
+                except Exception as e:
+                    logger.warning(f"Failed to initialize provider {provider_type.value}: {e}")
+            
+            logger.info("AI systems initialized successfully")
+            
+        except Exception as e:
+            logger.error(f"Failed to initialize AI systems: {e}")
+    
+    async def discover_enhanced_bots(self) -> Dict[str, Dict[str, Any]]:
+        """Discover bots with enhanced information about AI capabilities."""
+        # Start with standard bot discovery
+        standard_bots = discover_bots()  # Returns List[BotInfoModel]
+        enhanced_bot_info = {}
+        
+        # Convert BotInfoModel list to dict and enhance with AI capabilities
+        for bot_info in standard_bots:
+            bot_name = bot_info.name
+            bot_info_dict = {
+                "name": bot_name,
+                "description": bot_info.description,
+                "has_media": bot_info.has_media,
+                "standard_info": {
+                    "name": bot_name,
+                    "description": bot_info.description,
+                    "has_media": bot_info.has_media
+                },
+                "enhanced_features": False,
+                "ai_capabilities": {},
+                "health_status": "unknown"
+            }
+            
+            # Check if bot supports enhanced features
+            if bot_name in self.bot_configurations:
+                config = self.bot_configurations[bot_name]
+                if config.get("advanced_features", False):
+                    bot_info_dict["enhanced_features"] = True
+                    bot_info_dict["ai_capabilities"] = {
+                        "personality": config.get("personality", "default"),
+                        "ai_provider": config.get("ai_provider", "local"),
+                        "streaming": config.get("streaming", False),
+                        "memory_enabled": config.get("memory_enabled", False)
+                    }
+                    
+                    # Check bot health if it supports it (would need to import the bot module)
+                    try:
+                        bot_module_path = f"voicebot.bots.{bot_name}"
+                        bot_module = __import__(bot_module_path, fromlist=[bot_name])
+                        if hasattr(bot_module, 'get_bot_status'):
+                            status = await bot_module.get_bot_status()
+                            bot_info_dict["health_status"] = "healthy"
+                            bot_info_dict["detailed_status"] = status
+                    except Exception as e:
+                        bot_info_dict["health_status"] = f"import_error: {e}"
+            
+            enhanced_bot_info[bot_name] = bot_info_dict
+        
+        return enhanced_bot_info
+    
+    async def create_enhanced_bot_instance(self, bot_name: str, session_name: str) -> Optional[Any]:
+        """Create an enhanced bot instance with AI features configured."""
+        if not AI_FEATURES_AVAILABLE:
+            logger.warning(f"Cannot create enhanced bot {bot_name} - AI features not available")
+            return None
+        
+        if bot_name not in self.bot_configurations:
+            logger.warning(f"No configuration found for enhanced bot: {bot_name}")
+            return None
+        
+        config = self.bot_configurations[bot_name]
+        
+        try:
+            # Set environment variables for the bot based on configuration
+            os.environ[f"{bot_name.upper()}_PERSONALITY"] = config.get("personality", "helpful_assistant")
+            os.environ[f"{bot_name.upper()}_PROVIDER"] = config.get("ai_provider", "local")
+            os.environ[f"{bot_name.upper()}_STREAMING"] = str(config.get("streaming", False)).lower()
+            os.environ[f"{bot_name.upper()}_MEMORY"] = str(config.get("memory_enabled", False)).lower()
+            
+            # Import and create the bot
+            bot_module_path = f"voicebot.bots.{bot_name}"
+            bot_module = __import__(bot_module_path, fromlist=[bot_name])
+            
+            # If the bot has a specific initialization function, use it
+            if hasattr(bot_module, 'create_enhanced_instance'):
+                bot_instance = await bot_module.create_enhanced_instance(session_name, config)
+            else:
+                # Create standard bot instance
+                bot_instance = bot_module
+            
+            self.enhanced_bots[f"{bot_name}_{session_name}"] = {
+                "instance": bot_instance,
+                "config": config,
+                "session": session_name,
+                "created_at": time.time()
+            }
+            
+            logger.info(f"Created enhanced bot instance: {bot_name} for session {session_name}")
+            return bot_instance
+            
+        except Exception as e:
+            logger.error(f"Failed to create enhanced bot instance {bot_name}: {e}")
+            return None
+    
+    async def monitor_bot_health(self) -> Dict[str, Any]:
+        """Monitor health of all enhanced bots and AI systems."""
+        health_report = {
+            "timestamp": time.time(),
+            "ai_systems_available": AI_FEATURES_AVAILABLE,
+            "enhanced_bots": {},
+            "ai_providers": {},
+            "personality_system": {},
+            "conversation_contexts": {}
+        }
+        
+        if not AI_FEATURES_AVAILABLE:
+            health_report["status"] = "limited - AI features disabled"
+            return health_report
+        
+        try:
+            # Check AI providers
+            for provider_id, provider in ai_provider_manager.list_providers().items():
+                try:
+                    provider_instance = ai_provider_manager.get_provider(provider_id)
+                    if provider_instance:
+                        is_healthy = await provider_instance.health_check()
+                        health_report["ai_providers"][provider_id] = {
+                            "status": "healthy" if is_healthy else "unhealthy",
+                            "type": provider.value if hasattr(provider, 'value') else str(provider)
+                        }
+                except Exception as e:
+                    health_report["ai_providers"][provider_id] = {
+                        "status": f"error: {e}",
+                        "type": "unknown"
+                    }
+            
+            # Check personality system
+            try:
+                templates = personality_manager.list_templates()
+                health_report["personality_system"] = {
+                    "status": "healthy",
+                    "available_templates": len(templates),
+                    "template_ids": [t.id for t in templates]
+                }
+            except Exception as e:
+                health_report["personality_system"] = {
+                    "status": f"error: {e}"
+                }
+            
+            # Check conversation context system
+            try:
+                context_stats = context_manager.get_statistics()
+                health_report["conversation_contexts"] = {
+                    "status": "healthy",
+                    "statistics": context_stats
+                }
+            except Exception as e:
+                health_report["conversation_contexts"] = {
+                    "status": f"error: {e}"
+                }
+            
+            # Check enhanced bot instances
+            for bot_key, bot_data in self.enhanced_bots.items():
+                try:
+                    bot_instance = bot_data["instance"]
+                    if hasattr(bot_instance, 'health_check'):
+                        bot_health = await bot_instance.health_check()
+                        health_report["enhanced_bots"][bot_key] = {
+                            "status": "healthy",
+                            "details": bot_health,
+                            "uptime": time.time() - bot_data["created_at"]
+                        }
+                    else:
+                        health_report["enhanced_bots"][bot_key] = {
+                            "status": "unknown - no health check available",
+                            "uptime": time.time() - bot_data["created_at"]
+                        }
+                except Exception as e:
+                    health_report["enhanced_bots"][bot_key] = {
+                        "status": f"error: {e}",
+                        "uptime": time.time() - bot_data.get("created_at", time.time())
+                    }
+            
+            health_report["status"] = "operational"
+            
+        except Exception as e:
+            health_report["status"] = f"system_error: {e}"
+        
+        # Store health stats for trending
+        self.health_stats[int(time.time())] = health_report
+        
+        # Keep only last 24 hours of health stats
+        cutoff_time = time.time() - (24 * 60 * 60)
+        self.health_stats = {
+            timestamp: stats for timestamp, stats in self.health_stats.items()
+            if timestamp > cutoff_time
+        }
+        
+        return health_report
+    
+    async def configure_bot_runtime(self, bot_name: str, new_config: Dict[str, Any]) -> bool:
+        """Dynamically reconfigure a bot at runtime."""
+        if bot_name not in self.bot_configurations:
+            logger.error(f"Bot {bot_name} not found in configurations")
+            return False
+        
+        try:
+            # Update configuration
+            old_config = self.bot_configurations[bot_name].copy()
+            self.bot_configurations[bot_name].update(new_config)
+            
+            # Save updated configuration
+            self._save_bot_configurations()
+            
+            # If there are active instances, try to update them
+            updated_instances = []
+            for bot_key, bot_data in self.enhanced_bots.items():
+                if bot_key.startswith(f"{bot_name}_"):
+                    bot_instance = bot_data["instance"]
+                    
+                    # Try to update personality if changed
+                    if "personality" in new_config and hasattr(bot_instance, 'switch_personality'):
+                        success = await bot_instance.switch_personality(new_config["personality"])
+                        if success:
+                            updated_instances.append(f"{bot_key} personality")
+                    
+                    # Try to update AI provider if changed
+                    if "ai_provider" in new_config and hasattr(bot_instance, 'switch_ai_provider'):
+                        success = await bot_instance.switch_ai_provider(new_config["ai_provider"])
+                        if success:
+                            updated_instances.append(f"{bot_key} provider")
+                    
+                    # Update bot data configuration
+                    bot_data["config"] = self.bot_configurations[bot_name]
+            
+            logger.info(f"Bot {bot_name} configuration updated. Active instances updated: {updated_instances}")
+            return True
+            
+        except Exception as e:
+            # Rollback configuration on error
+            self.bot_configurations[bot_name] = old_config
+            logger.error(f"Failed to configure bot {bot_name}: {e}")
+            return False
+    
+    def get_bot_analytics(self) -> Dict[str, Any]:
+        """Get analytics and usage statistics for enhanced bots."""
+        analytics = {
+            "enhanced_bots_count": len(self.enhanced_bots),
+            "configurations_count": len(self.bot_configurations),
+            "health_history_points": len(self.health_stats),
+            "bot_breakdown": {},
+            "feature_usage": {
+                "ai_providers": {},
+                "personalities": {},
+                "memory_enabled": 0,
+                "streaming_enabled": 0
+            }
+        }
+        
+        # Analyze bot configurations
+        for bot_name, config in self.bot_configurations.items():
+            analytics["bot_breakdown"][bot_name] = {
+                "enhanced_features": config.get("advanced_features", False),
+                "ai_provider": config.get("ai_provider", "none"),
+                "personality": config.get("personality", "none"),
+                "active_instances": sum(1 for key in self.enhanced_bots.keys() if key.startswith(f"{bot_name}_"))
+            }
+            
+            # Count feature usage
+            provider = config.get("ai_provider", "none")
+            analytics["feature_usage"]["ai_providers"][provider] = analytics["feature_usage"]["ai_providers"].get(provider, 0) + 1
+            
+            personality = config.get("personality", "none")
+            analytics["feature_usage"]["personalities"][personality] = analytics["feature_usage"]["personalities"].get(personality, 0) + 1
+            
+            if config.get("memory_enabled", False):
+                analytics["feature_usage"]["memory_enabled"] += 1
+            
+            if config.get("streaming", False):
+                analytics["feature_usage"]["streaming_enabled"] += 1
+        
+        # Add conversation context statistics if available
+        if AI_FEATURES_AVAILABLE:
+            try:
+                context_stats = context_manager.get_statistics()
+                analytics["conversation_statistics"] = context_stats
+            except Exception as e:
+                analytics["conversation_statistics"] = {"error": str(e)}
+        
+        return analytics
+
+
+# Global enhanced orchestrator instance
+enhanced_orchestrator = EnhancedBotOrchestrator()
+
+
+async def demo_step_5b_integration():
+    """Demonstrate Step 5B integration capabilities."""
+    print("=== Step 5B Advanced Bot Management Demo ===\n")
+    
+    # 1. Discover enhanced bots
+    print("1. Discovering bots with enhanced capabilities...")
+    enhanced_bots = await enhanced_orchestrator.discover_enhanced_bots()
+    for bot_name, info in enhanced_bots.items():
+        print(f"   Bot: {bot_name}")
+        print(f"   Enhanced: {info['enhanced_features']}")
+        if info['enhanced_features']:
+            print(f"   AI Capabilities: {info['ai_capabilities']}")
+        print(f"   Health: {info['health_status']}")
+        print()
+    
+    # 2. Create enhanced bot instance
+    print("2. Creating enhanced AI chatbot instance...")
+    bot_instance = await enhanced_orchestrator.create_enhanced_bot_instance("ai_chatbot", "demo_session")
+    if bot_instance:
+        print("   ✓ Enhanced AI chatbot created successfully")
+    else:
+        print("   ✗ Failed to create enhanced bot")
+    print()
+    
+    # 3. Monitor system health
+    print("3. Monitoring system health...")
+    health_report = await enhanced_orchestrator.monitor_bot_health()
+    print(f"   System Status: {health_report['status']}")
+    print(f"   AI Features Available: {health_report['ai_systems_available']}")
+    if health_report['ai_systems_available']:
+        print(f"   AI Providers: {len(health_report['ai_providers'])} registered")
+        print(f"   Personality Templates: {health_report['personality_system'].get('available_templates', 0)}")
+        print(f"   Enhanced Bot Instances: {len(health_report['enhanced_bots'])}")
+    print()
+    
+    # 4. Runtime configuration
+    print("4. Demonstrating runtime configuration...")
+    config_success = await enhanced_orchestrator.configure_bot_runtime("ai_chatbot", {
+        "personality": "technical_expert",
+        "streaming": False
+    })
+    print(f"   Configuration Update: {'✓ Success' if config_success else '✗ Failed'}")
+    print()
+    
+    # 5. Analytics
+    print("5. Bot analytics and usage statistics...")
+    analytics = enhanced_orchestrator.get_bot_analytics()
+    print(f"   Enhanced Bots: {analytics['enhanced_bots_count']}")
+    print(f"   Configurations: {analytics['configurations_count']}")
+    print("   Feature Usage:")
+    for feature, usage in analytics['feature_usage'].items():
+        print(f"     {feature}: {usage}")
+    print()
+    
+    print("=== Step 5B Integration Demo Complete ===")
+
+
+if __name__ == "__main__":
+    # Run the demo
+    asyncio.run(demo_step_5b_integration())

voicebot/test_step_5b.py

@@ -0,0 +1,195 @@
+"""
+Simple test to verify Step 5B enhanced bot functionality.
+
+This test verifies that the enhanced bot components work correctly
+when integrated with the existing voicebot system.
+"""
+
+import asyncio
+import os
+import time
+
+# Set up test environment variables
+os.environ["AI_CHATBOT_PERSONALITY"] = "helpful_assistant"
+os.environ["AI_CHATBOT_PROVIDER"] = "local"  # Use local provider for testing
+os.environ["AI_CHATBOT_STREAMING"] = "false"
+os.environ["AI_CHATBOT_MEMORY"] = "true"
+
+# Import test modules
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from shared.models import ChatMessageModel
+
+
+async def test_enhanced_ai_chatbot():
+    """Test the enhanced AI chatbot functionality."""
+    print("Testing Enhanced AI Chatbot...")
+    
+    try:
+        # Import the enhanced bot
+        from voicebot.bots.ai_chatbot import handle_chat_message, get_bot_status
+        
+        # Create a mock send function
+        responses = []
+        async def mock_send(message: str):
+            responses.append(message)
+            print(f"Bot Response: {message}")
+        
+        # Test message handling
+        test_message = ChatMessageModel(
+            id="test_message_id",
+            sender_name="test_user",
+            sender_session_id="test_session",
+            lobby_id="test_lobby",
+            message="Hello, can you help me?",
+            timestamp=time.time()
+        )
+        
+        print(f"Sending test message: {test_message.message}")
+        response = await handle_chat_message(test_message, mock_send)
+        
+        if response:
+            print(f"✓ Bot responded successfully: {response[:50]}...")
+        else:
+            print("✗ Bot did not respond")
+        
+        # Test bot status
+        print("\nTesting bot status...")
+        status = await get_bot_status()
+        print("✓ Bot status retrieved:")
+        print(f"  - Agent: {status.get('agent_name', 'unknown')}")
+        print(f"  - Features Available: {status.get('features_available', False)}")
+        print(f"  - Configuration: {status.get('configuration', {})}")
+        
+        return True
+        
+    except Exception as e:
+        print(f"✗ Enhanced bot test failed: {e}")
+        return False
+
+
+async def test_personality_system():
+    """Test the personality system components."""
+    print("\nTesting Personality System...")
+    
+    try:
+        from voicebot.personality_system import personality_manager
+        
+        # Test listing templates
+        templates = personality_manager.list_templates()
+        print(f"✓ Found {len(templates)} personality templates:")
+        for template in templates:
+            print(f"  - {template.id}: {template.description}")
+        
+        # Test creating personality from template
+        personality = personality_manager.create_personality_from_template("helpful_assistant")
+        if personality:
+            print(f"✓ Created personality: {personality.name}")
+            print(f"  - Traits: {[trait.value for trait in personality.traits]}")
+            print(f"  - Communication Style: {personality.communication_style.value}")
+        else:
+            print("✗ Failed to create personality")
+        
+        return True
+        
+    except Exception as e:
+        print(f"✗ Personality system test failed: {e}")
+        return False
+
+
+async def test_conversation_context():
+    """Test the conversation context management."""
+    print("\nTesting Conversation Context...")
+    
+    try:
+        from voicebot.conversation_context import context_manager
+        
+        # Test creating context
+        context = context_manager.get_or_create_context(
+            session_id="test_session",
+            bot_name="test_bot",
+            conversation_id="test_conversation"
+        )
+        
+        if context:
+            print(f"✓ Created conversation context: {context.conversation_id}")
+        
+        # Test adding conversation turn
+        context_manager.add_conversation_turn(
+            conversation_id=context.conversation_id,
+            user_message="Test message",
+            bot_response="Test response",
+            context_used={"test": "context"},
+            metadata={"timestamp": time.time()}
+        )
+        
+        print("✓ Added conversation turn")
+        print(f"  - Turns in context: {len(context.turns)}")
+        
+        # Test context summary
+        summary = context_manager.get_context_for_response(context.conversation_id)
+        if summary:
+            print(f"✓ Generated context summary: {summary[:50]}...")
+        
+        return True
+        
+    except Exception as e:
+        print(f"✗ Conversation context test failed: {e}")
+        return False
+
+
+async def test_integration_orchestrator():
+    """Test the integration orchestrator."""
+    print("\nTesting Integration Orchestrator...")
+    
+    try:
+        from step_5b_integration_demo import enhanced_orchestrator
+        
+        # Test bot discovery
+        enhanced_bots = await enhanced_orchestrator.discover_enhanced_bots()
+        print(f"✓ Discovered {len(enhanced_bots)} bots")
+        
+        # Find enhanced bots
+        enhanced_count = sum(1 for bot_info in enhanced_bots.values() 
+                           if bot_info.get('enhanced_features', False))
+        print(f"✓ Found {enhanced_count} enhanced bots")
+        
+        # Test analytics
+        analytics = enhanced_orchestrator.get_bot_analytics()
+        print(f"✓ Analytics: {analytics['enhanced_bots_count']} enhanced bots configured")
+        
+        return True
+        
+    except Exception as e:
+        print(f"✗ Integration orchestrator test failed: {e}")
+        return False
+
+
+async def run_all_tests():
+    """Run all Step 5B tests."""
+    print("=== Step 5B Enhanced Bot Management Tests ===\n")
+    
+    test_results = []
+    
+    # Run individual tests
+    test_results.append(await test_enhanced_ai_chatbot())
+    test_results.append(await test_personality_system()) 
+    test_results.append(await test_conversation_context())
+    test_results.append(await test_integration_orchestrator())
+    
+    # Summary
+    passed = sum(test_results)
+    total = len(test_results)
+    
+    print(f"\n=== Test Results: {passed}/{total} tests passed ===")
+    
+    if passed == total:
+        print("🎉 All Step 5B components are working correctly!")
+    else:
+        print("⚠️  Some tests failed - check the output above for details")
+    
+    return passed == total
+
+
+if __name__ == "__main__":
+    asyncio.run(run_all_tests())