backend/integrations/atom_ai_integration.py

"""
ATOM AI Integration Module
Seamless AI integration within unified communication ecosystem with cross-platform intelligence
"""

import asyncio
from collections import Counter, defaultdict
from dataclasses import asdict, dataclass
from datetime import datetime, timedelta, timezone
from enum import Enum
import json
import logging
import os
from typing import Any, Dict, List, Optional, Union
import aiohttp
import httpx

# Import existing ATOM services
try:
    from core.llm_service import LLMService
    from atom_discord_integration import atom_discord_integration
    from atom_google_chat_integration import atom_google_chat_integration
    from atom_ingestion_pipeline import AtomIngestionPipeline
    from atom_memory_service import AtomMemoryService
    from atom_search_service import AtomSearchService
    from atom_slack_integration import atom_slack_integration
    from atom_teams_integration import atom_teams_integration
    from atom_workflow_service import AtomWorkflowService
except ImportError as e:
    logging.warning(f"AI integration services not available: {e}")

# Configure logging
logger = logging.getLogger(__name__)

@dataclass
class AIConversationContext:
    """Context for AI conversation (Modernized)"""
    conversation_id: str
    user_id: str
    platform: str
    messages: List[Dict[str, Any]]
    metadata: Dict[str, Any]
    last_updated: datetime = datetime.utcnow()

class AtomAIIntegration:
    """Main AI integration class for unified communication ecosystem"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.atom_memory = config.get('atom_memory_service')
        self.atom_search = config.get('atom_search_service')
        self.atom_workflow = config.get('atom_workflow_service')
        self.atom_ingestion = config.get('atom_ingestion_pipeline')
        
        # Platform integrations
        self.platform_integrations = {
            'slack': atom_slack_integration,
            'teams': atom_teams_integration,
            'google_chat': atom_google_chat_integration,
            'discord': atom_discord_integration
        }
        
        # AI service (Modernized)
        self.llm_service = config.get('llm_service') or LLMService(workspace_id=config.get('workspace_id', 'default'))
        
        # Integration state
        self.is_initialized = False
        self.active_ai_features = []
        self.intelligent_workspaces = []
        self.ai_analytics = []
        
        # AI conversation management
        self.conversation_manager = AIConversationManager(self.llm_service)
        
        # AI-powered search
        self.search_manager = IntelligentSearchManager(self.llm_service, self.atom_search, self.atom_ingestion)
        
        # AI workflow automation
        self.workflow_intelligence = WorkflowIntelligenceManager(self.llm_service, self.atom_workflow)
        
        # Cross-platform AI features
        self.cross_platform_ai = CrossPlatformAIManager(self.llm_service, self.platform_integrations)
        
        logger.info("ATOM AI Integration initialized")
    
    async def initialize(self) -> bool:
        """Initialize AI integration with ATOM services"""
        try:
            if not all([self.llm_service, self.atom_memory, self.atom_search]):
                logger.error("Required services not available for AI integration")
                return False
            
            # Start AI integration workers
            await self._start_ai_integration_workers()
            
            # Initialize AI features
            await self._initialize_ai_features()
            
            # Setup intelligent search
            await self.search_manager.initialize()
            
            # Setup workflow intelligence
            await self._setup_workflow_intelligence()
            
            # Setup cross-platform AI
            await self._setup_cross_platform_ai()
            
            self.is_initialized = True
            logger.info("AI integration with ATOM ecosystem initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"Error initializing AI integration: {e}")
            return False
    
    async def get_intelligent_workspaces(self, user_id: str = None) -> List[Dict[str, Any]]:
        """Get workspaces with AI-enhanced features"""
        try:
            intelligent_workspaces = []
            
            # Get all platform workspaces
            for platform, integration in self.platform_integrations.items():
                if not integration:
                    continue
                
                workspaces = await integration.get_unified_workspaces(user_id)
                
                for workspace in workspaces:
                    # Add AI-enhanced features
                    intelligent_workspace = {
                        'id': workspace['id'],
                        'name': workspace['name'],
                        'platform': workspace['platform'],
                        'type': workspace['type'],
                        'status': workspace['status'],
                        'member_count': workspace['member_count'],
                        'channel_count': workspace['channel_count'],
                        'icon_url': workspace['icon_url'],
                        'description': workspace['description'],
                        'capabilities': workspace['capabilities'],
                        'integration_data': workspace['integration_data'],
                        # AI-enhanced features
                        'ai_features': {
                            'intelligent_search': True,
                            'message_summarization': True,
                            'sentiment_analysis': True,
                            'topic_extraction': True,
                            'workflow_recommendations': True,
                            'conversation_analysis': True,
                            'predictive_analytics': True,
                            'natural_language_commands': True,
                            'content_generation': True,
                            'voice_analysis': workspace['capabilities'].get('voice_chat', False)
                        },
                        'ai_insights': {
                            'engagement_level': await self._calculate_engagement_level(workspace),
                            'activity_trends': await self._get_activity_trends(workspace),
                            'communication_patterns': await self._get_communication_patterns(workspace),
                            'predicted_activity': await self._predict_activity(workspace),
                            'recommended_actions': await self._get_recommended_actions(workspace)
                        },
                        'ai_settings': {
                            'ai_enabled': True,
                            'analysis_level': 'comprehensive',
                            'prediction_horizon': '7_days',
                            'sentiment_tracking': True,
                            'topic_detection': True,
                            'workflow_suggestions': True,
                            'content_recommendations': True
                        }
                    }
                    intelligent_workspaces.append(intelligent_workspace)
            
            # Store in intelligent workspaces
            self.intelligent_workspaces = intelligent_workspaces
            
            return intelligent_workspaces
            
        except Exception as e:
            logger.error(f"Error getting intelligent workspaces: {e}")
            return []
    
    async def get_intelligent_channels(self, workspace_id: str, user_id: str = None) -> List[Dict[str, Any]]:
        """Get channels with AI-enhanced features"""
        try:
            intelligent_channels = []
            
            # Determine platform from workspace ID
            platform = self._get_platform_from_workspace(workspace_id)
            integration = self.platform_integrations.get(platform)
            
            if not integration:
                return []
            
            # Get channels
            channels = await integration.get_unified_channels(workspace_id, user_id)
            
            for channel in channels:
                # Add AI-enhanced features
                intelligent_channel = {
                    'id': channel['id'],
                    'name': channel['name'],
                    'display_name': channel['display_name'],
                    'type': channel['type'],
                    'platform': channel['platform'],
                    'workspace_id': channel['workspace_id'],
                    'workspace_name': channel['workspace_name'],
                    'status': channel['status'],
                    'member_count': channel['member_count'],
                    'message_count': channel['message_count'],
                    'unread_count': channel['unread_count'],
                    'is_private': channel['is_private'],
                    'is_text': channel['is_text'],
                    'is_voice': channel['is_voice'],
                    'capabilities': channel['capabilities'],
                    'integration_data': channel['integration_data'],
                    # AI-enhanced features
                    'ai_features': {
                        'intelligent_search': True,
                        'message_summarization': True,
                        'sentiment_analysis': True,
                        'topic_extraction': True,
                        'trend_analysis': True,
                        'engagement_prediction': True,
                        'content_recommendations': True,
                        'natural_language_commands': True,
                        'voice_analysis': channel['is_voice']
                    },
                    'ai_insights': {
                        'engagement_level': await self._calculate_channel_engagement(channel),
                        'topic_trends': await self._get_channel_topic_trends(channel),
                        'sentiment_evolution': await self._get_sentiment_evolution(channel),
                        'peak_activity_times': await self._get_peak_activity_times(channel),
                        'predicted_messages': await self._predict_message_volume(channel),
                        'suggested_actions': await self._get_channel_suggestions(channel)
                    },
                    'ai_settings': {
                        'ai_enabled': True,
                        'analysis_frequency': 'real_time',
                        'sentiment_tracking': True,
                        'topic_detection': True,
                        'engagement_prediction': True,
                        'auto_summarization': True
                    }
                }
                intelligent_channels.append(intelligent_channel)
            
            return intelligent_channels
            
        except Exception as e:
            logger.error(f"Error getting intelligent channels: {e}")
            return []
    
    async def get_intelligent_messages(self, workspace_id: str, channel_id: str,
                                  limit: int = 100, user_id: str = None,
                                  options: Dict[str, Any] = None) -> List[Dict[str, Any]]:
        """Get messages with AI-enhanced analysis"""
        try:
            options = options or {}
            intelligent_messages = []
            
            # Determine platform from channel ID
            platform = self._get_platform_from_channel(channel_id)
            integration = self.platform_integrations.get(platform)
            
            if not integration:
                return []
            
            # Get messages
            messages = await integration.get_unified_messages(
                workspace_id, channel_id, limit, options
            )
            
            # Process messages with AI
            for message in messages:
                # Get AI analysis for message
                ai_analysis = await self._get_message_ai_analysis(message)
                
                intelligent_message = {
                    'id': message['id'],
                    'content': message['content'],
                    'html_content': message['html_content'],
                    'platform': message['platform'],
                    'workspace_id': message['workspace_id'],
                    'channel_id': message['channel_id'],
                    'user_id': message['user_id'],
                    'user_name': message['user_name'],
                    'user_display_name': message['user_display_name'],
                    'user_avatar': message['user_avatar'],
                    'timestamp': message['timestamp'],
                    'thread_id': message['thread_id'],
                    'reply_to_id': message['reply_to_id'],
                    'message_type': message['message_type'],
                    'is_edited': message['is_edited'],
                    'is_pinned': message['is_pinned'],
                    'is_bot': message['is_bot'],
                    'is_webhook': message['is_webhook'],
                    'reactions': message['reactions'],
                    'attachments': message['attachments'],
                    'embeds': message['embeds'],
                    'mentions': message['mentions'],
                    'files': message['files'],
                    'integration_data': message['integration_data'],
                    'metadata': message['metadata'],
                    # AI-enhanced features
                    'ai_analysis': {
                        'sentiment': ai_analysis.get('sentiment'),
                        'sentiment_score': ai_analysis.get('sentiment_score'),
                        'key_topics': ai_analysis.get('key_topics'),
                        'emotions': ai_analysis.get('emotions'),
                        'urgency': ai_analysis.get('urgency'),
                        'importance': ai_analysis.get('importance'),
                        'action_items': ai_analysis.get('action_items'),
                        'category': ai_analysis.get('category'),
                        'language': ai_analysis.get('language'),
                        'confidence': ai_analysis.get('confidence', 0.8)
                    },
                    'ai_features': {
                        'translation_available': True,
                        'sentiment_analysis': True,
                        'topic_extraction': True,
                        'action_item_detection': True,
                        'urgency_detection': True,
                        'translation_target': options.get('translation_language')
                    }
                }
                intelligent_messages.append(intelligent_message)
            
            return intelligent_messages
            
        except Exception as e:
            logger.error(f"Error getting intelligent messages: {e}")
            return []
    
    async def intelligent_search(self, query: str, workspace_id: str = None,
                            channel_id: str = None, user_id: str = None,
                            options: Dict[str, Any] = None) -> List[Dict[str, Any]]:
        """Perform AI-powered search across platforms"""
        try:
            options = options or {}
            
            # Use intelligent search manager
            search_results = await self.search_manager.search(
                query=query,
                workspace_id=workspace_id,
                channel_id=channel_id,
                user_id=user_id,
                options=options
            )
            
            return search_results
            
        except Exception as e:
            logger.error(f"Error in intelligent search: {e}")
            return []
    
    async def send_intelligent_message(self, workspace_id: str, channel_id: str,
                                 content: str, options: Dict[str, Any] = None) -> Dict[str, Any]:
        """Send message with AI enhancement"""
        try:
            options = options or {}
            
            # AI-enhance content
            enhanced_content = await self._enhance_content(content, options)
            
            # Determine platform from channel ID
            platform = self._get_platform_from_channel(channel_id)
            integration = self.platform_integrations.get(platform)
            
            if not integration:
                return {'ok': False, 'error': 'Unsupported platform'}
            
            # Send message
            result = await integration.send_unified_message(
                workspace_id, channel_id, enhanced_content, options
            )
            
            # AI analyze sent message
            if result.get('ok'):
                await self._analyze_message_after_send(result, options)
            
            return result
            
        except Exception as e:
            logger.error(f"Error sending intelligent message: {e}")
            return {'ok': False, 'error': str(e)}
    
    async def create_intelligent_workflow(self, workflow_data: Dict[str, Any]) -> Dict[str, Any]:
        """Create AI-enhanced workflow"""
        try:
            # AI-enhance workflow
            enhanced_workflow = await self.workflow_intelligence.enhance_workflow(workflow_data)
            
            # Create workflow
            if self.atom_workflow:
                result = await self.atom_workflow.create_workflow(enhanced_workflow)
            else:
                result = {'ok': False, 'error': 'Workflow service not available'}
            
            return result
            
        except Exception as e:
            logger.error(f"Error creating intelligent workflow: {e}")
            return {'ok': False, 'error': str(e)}
    
    async def get_intelligent_analytics(self, metric: str, time_range: str,
                                  workspace_id: str = None, options: Dict[str, Any] = None) -> Dict[str, Any]:
        """Get AI-enhanced analytics"""
        try:
            options = options or {}
            
            # Use AI to enhance analytics (Modernized)
            prompt = f"Analyze the following {metric} for the time range {time_range}. " \
                     f"Workspace: {workspace_id}. Options: {json.dumps(options)}. " \
                     "Provide insights and predictions in JSON format."
            
            result = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are a predictive analytics expert for communication platforms."
            )
            
            try:
                # Attempt to parse JSON from AI response
                return json.loads(result)
            except:
                return {'analysis': result}
            
        except Exception as e:
            logger.error(f"Error getting intelligent analytics: {e}")
            return {'ok': False, 'error': str(e)}
    
    async def process_natural_language_command(self, command: str, user_id: str,
                                        workspace_id: str = None, platform: str = None) -> Dict[str, Any]:
        """Process natural language command with AI"""
        try:
            # Use conversation manager for command processing
            result = await self.conversation_manager.process_command(
                command=command,
                user_id=user_id,
                workspace_id=workspace_id,
                platform=platform
            )
            
            return result
            
        except Exception as e:
            logger.error(f"Error processing natural language command: {e}")
            return {'ok': False, 'error': str(e)}
    
    async def start_ai_conversation(self, user_id: str, platform: str,
                                workspace_id: str = None) -> str:
        """Start AI-powered conversation"""
        try:
            conversation_id = await self.conversation_manager.start_conversation(
                user_id=user_id,
                platform=platform,
                workspace_id=workspace_id
            )
            
            return conversation_id
            
        except Exception as e:
            logger.error(f"Error starting AI conversation: {e}")
            return ''
    
    async def continue_ai_conversation(self, conversation_id: str, message: str,
                                   user_id: str) -> Dict[str, Any]:
        """Continue AI-powered conversation"""
        try:
            response = await self.conversation_manager.continue_conversation(
                conversation_id=conversation_id,
                message=message,
                user_id=user_id
            )
            
            return response
            
        except Exception as e:
            logger.error(f"Error continuing AI conversation: {e}")
            return {'ok': False, 'error': str(e)}
    
    # Private helper methods
    async def _start_ai_integration_workers(self):
        """Start background AI integration workers"""
        # Start AI message analysis worker
        asyncio.create_task(self._ai_message_analysis_worker())
        
        # Start intelligent search indexing worker
        asyncio.create_task(self._intelligent_search_indexing_worker())
        
        # Start AI workflow optimization worker
        asyncio.create_task(self._ai_workflow_optimization_worker())
        
        # Start cross-platform AI synchronization worker
        asyncio.create_task(self._cross_platform_ai_worker())
    
    async def _initialize_ai_features(self):
        """Initialize AI features"""
        # Initialize AI features list
        self.active_ai_features = [
            'intelligent_search',
            'message_summarization',
            'sentiment_analysis',
            'topic_extraction',
            'workflow_recommendations',
            'conversation_analysis',
            'predictive_analytics',
            'natural_language_commands',
            'content_generation',
            'voice_analysis',
            'gaming_insights',
            'cross_platform_intelligence'
        ]
    
    async def _setup_intelligent_search(self):
        """Setup intelligent search"""
        await self.search_manager.initialize()
    
    async def _setup_workflow_intelligence(self):
        """Setup workflow intelligence"""
        await self.workflow_intelligence.initialize()
    
    async def _setup_cross_platform_ai(self):
        """Setup cross-platform AI"""
        await self.cross_platform_ai.initialize()
    
    def _get_platform_from_workspace(self, workspace_id: str) -> str:
        """Extract platform from workspace ID"""
        if workspace_id.startswith('slack_'):
            return 'slack'
        elif workspace_id.startswith('teams_'):
            return 'teams'
        elif workspace_id.startswith('google_chat_'):
            return 'google_chat'
        elif workspace_id.startswith('discord_'):
            return 'discord'
        return 'unknown'
    
    def _get_platform_from_channel(self, channel_id: str) -> str:
        """Extract platform from channel ID"""
        if channel_id.startswith('slack_'):
            return 'slack'
        elif channel_id.startswith('teams_'):
            return 'teams'
        elif channel_id.startswith('google_chat_'):
            return 'google_chat'
        elif channel_id.startswith('discord_'):
            return 'discord'
        return 'unknown'
    
    async def _calculate_engagement_level(self, workspace: Dict[str, Any]) -> str:
        """Calculate engagement level for workspace"""
        try:
            # Mock calculation - would use AI analysis
            member_count = workspace.get('member_count', 0)
            channel_count = workspace.get('channel_count', 0)
            
            if member_count > 100 and channel_count > 20:
                return 'high'
            elif member_count > 50 and channel_count > 10:
                return 'medium'
            else:
                return 'low'
        except Exception as e:
            return 'unknown'
    
    async def _get_activity_trends(self, workspace: Dict[str, Any]) -> Dict[str, Any]:
        """Get activity trends for workspace"""
        # Mock trends - would use AI analysis
        return {
            'daily_average': 150,
            'peak_hour': 14,
            'trend': 'increasing',
            'growth_rate': 0.12
        }
    
    async def _get_communication_patterns(self, workspace: Dict[str, Any]) -> Dict[str, Any]:
        """Get communication patterns for workspace"""
        # Mock patterns - would use AI analysis
        return {
            'preferred_channels': ['general', 'random', 'projects'],
            'peak_times': ['09:00', '14:00', '16:00'],
            'response_times': {'average': 5.2, 'median': 3.1},
            'message_types': {'text': 0.85, 'file': 0.15}
        }
    
    async def _predict_activity(self, workspace: Dict[str, Any]) -> Dict[str, Any]:
        """Predict activity for workspace"""
        # Mock prediction - would use AI
        return {
            'next_7_days': {
                'messages': 1200,
                'active_users': 35,
                'confidence': 0.82
            }
        }
    
    async def _get_recommended_actions(self, workspace: Dict[str, Any]) -> List[str]:
        """Get recommended actions for workspace"""
        # Mock recommendations - would use AI
        return [
            'Schedule team sync meeting',
            'Archive inactive channels',
            'Enable automatic summarization',
            'Set up workflow automation'
        ]
    
    async def _calculate_channel_engagement(self, channel: Dict[str, Any]) -> str:
        """Calculate engagement level for channel"""
        message_count = channel.get('message_count', 0)
        member_count = channel.get('member_count', 0)
        
        if message_count > 500 and member_count > 20:
            return 'high'
        elif message_count > 200 and member_count > 10:
            return 'medium'
        else:
            return 'low'
    
    async def _get_channel_topic_trends(self, channel: Dict[str, Any]) -> List[str]:
        """Get topic trends for channel"""
        # Mock trends - would use AI
        return ['project updates', 'technical discussions', 'team announcements']
    
    async def _get_sentiment_evolution(self, channel: Dict[str, Any]) -> Dict[str, Any]:
        """Get sentiment evolution for channel"""
        # Mock evolution - would use AI
        return {
            'current': 'positive',
            'trend': 'improving',
            'weekly_scores': [0.65, 0.72, 0.78, 0.82]
        }
    
    async def _get_peak_activity_times(self, channel: Dict[str, Any]) -> List[str]:
        """Get peak activity times for channel"""
        # Mock times - would use AI analysis
        return ['10:00', '14:30', '16:00']
    
    async def _predict_message_volume(self, channel: Dict[str, Any]) -> Dict[str, Any]:
        """Predict message volume for channel"""
        # Mock prediction - would use AI
        return {
            'tomorrow': 45,
            'next_week': 280,
            'confidence': 0.75
        }
    
    async def _get_channel_suggestions(self, channel: Dict[str, Any]) -> List[str]:
        """Get suggestions for channel"""
        # Mock suggestions - would use AI
        return [
            'Enable topic threading',
            'Set up automated moderation',
            'Create channel guidelines',
            'Archive old messages'
        ]
    
    async def _get_message_ai_analysis(self, message: Dict[str, Any]) -> Dict[str, Any]:
        """Get AI analysis for message (Modernized)"""
        try:
            prompt = f"Analyze the following message for sentiment and topics: {message['content']}. " \
                     "Return a JSON object with 'sentiment' (string), 'sentiment_score' (float -1 to 1), and 'key_topics' (list)."
            
            analysis_text = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are a linguistic analysis agent."
            )
            
            try:
                analysis = json.loads(analysis_text)
                return {
                    'sentiment': analysis.get('sentiment', 'neutral'),
                    'sentiment_score': analysis.get('sentiment_score', 0.0),
                    'key_topics': analysis.get('key_topics', []),
                    'emotions': {},
                    'urgency': 'medium',
                    'importance': 'medium',
                    'action_items': [],
                    'category': 'general',
                    'language': 'en',
                    'confidence': 0.8
                }
            except:
                return {
                    'sentiment': 'neutral',
                    'sentiment_score': 0.0,
                    'key_topics': [],
                    'emotions': {},
                    'confidence': 0.5
                }
            
        except Exception as e:
            logger.error(f"Error getting message AI analysis: {e}")
            return {
                'sentiment': 'neutral',
                'sentiment_score': 0.0,
                'key_topics': [],
                'emotions': {},
                'confidence': 0.0
            }
    
    async def _enhance_content(self, content: str, options: Dict[str, Any]) -> str:
        """Enhance content with AI (Modernized)"""
        try:
            if not options.get('enhance_content', True):
                return content
            
            prompt = f"Enhance this content: {content}. Tone: {options.get('tone', 'professional')}. Platform: {options.get('platform')}."
            
            enhanced_content = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are a professional content writer and editor."
            )
            
            return enhanced_content or content
            
        except Exception as e:
            logger.error(f"Error enhancing content: {e}")
            return content
    
    async def _analyze_message_after_send(self, result: Dict[str, Any], options: Dict[str, Any]):
        """Analyze message after sending"""
        try:
            if not options.get('analyze_after_send', True):
                return
            
            # Store analysis in memory
            if self.atom_memory:
                memory_data = {
                    'type': 'sent_message_analysis',
                    'message_id': result.get('message_id'),
                    'channel_id': result.get('channel_id'),
                    'workspace_id': result.get('workspace_id'),
                    'timestamp': datetime.utcnow().isoformat()
                }
                await self.atom_memory.store(memory_data)
            
        except Exception as e:
            logger.error(f"Error analyzing message after send: {e}")
    
    # Background workers
    async def _ai_message_analysis_worker(self):
        """Background worker for AI message analysis"""
        while True:
            try:
                # Process message queue for AI analysis
                await asyncio.sleep(60)  # Process every minute
                
            except Exception as e:
                logger.error(f"Error in AI message analysis worker: {e}")
                await asyncio.sleep(120)  # Wait before retrying
    
    async def _intelligent_search_indexing_worker(self):
        """Background worker for intelligent search indexing"""
        while True:
            try:
                # Index content for intelligent search
                if self.search_manager:
                    await self.search_manager.update_search_index()
                
                await asyncio.sleep(300)  # Process every 5 minutes
                
            except Exception as e:
                logger.error(f"Error in intelligent search indexing worker: {e}")
                await asyncio.sleep(600)  # Wait before retrying
    
    async def _ai_workflow_optimization_worker(self):
        """Background worker for AI workflow optimization"""
        while True:
            try:
                # Optimize workflows with AI
                if self.workflow_intelligence:
                    await self.workflow_intelligence.optimize_workflows()
                
                await asyncio.sleep(1800)  # Process every 30 minutes
                
            except Exception as e:
                logger.error(f"Error in AI workflow optimization worker: {e}")
                await asyncio.sleep(3600)  # Wait before retrying
    
    async def _cross_platform_ai_worker(self):
        """Background worker for cross-platform AI"""
        while True:
            try:
                # Synchronize AI insights across platforms
                if self.cross_platform_ai:
                    await self.cross_platform_ai.synchronize_ai_insights()
                
                await asyncio.sleep(900)  # Process every 15 minutes
                
            except Exception as e:
                logger.error(f"Error in cross-platform AI worker: {e}")
                await asyncio.sleep(1800)  # Wait before retrying

class AIConversationManager:
    """Manages AI-powered conversations (Modernized)"""
    
    def __init__(self, llm_service):
        self.llm_service = llm_service
        self.conversations: Dict[str, AIConversationContext] = {}
    
    async def start_conversation(self, user_id: str, platform: str,
                             workspace_id: str = None) -> str:
        """Start new AI conversation"""
        try:
            conversation_id = f"ai_conv_{user_id}_{platform}_{int(datetime.utcnow().timestamp())}"
            
            context = AIConversationContext(
                conversation_id=conversation_id,
                user_id=user_id,
                platform=platform,
                messages=[],
                metadata={
                    'workspace_id': workspace_id,
                    'created_at': datetime.utcnow().isoformat()
                }
            )
            
            self.conversations[conversation_id] = context
            
            return conversation_id
            
        except Exception as e:
            logger.error(f"Error starting AI conversation: {e}")
            return ''
    
    async def continue_conversation(self, conversation_id: str, message: str,
                               user_id: str) -> Dict[str, Any]:
        """Continue AI conversation"""
        try:
            context = self.conversations.get(conversation_id)
            if not context:
                return {'ok': False, 'error': 'Conversation not found'}
            
            # Add user message
            context.messages.append({
                'role': 'user',
                'content': message,
                'timestamp': datetime.utcnow().isoformat()
            })
            
            # Get AI response using unified LLMService
            messages = []
            for m in context.messages[-10:]:
                messages.append({"role": m['role'], "content": m['content']})
            
            response_text = await self.llm_service.chat_completion(
                messages=messages,
                system_prompt="You are an intelligent assistant for unified communication platforms. Provide helpful, contextually relevant responses."
            )
            
            if response_text:
                # Add AI response
                context.messages.append({
                    'role': 'assistant',
                    'content': response_text,
                    'timestamp': datetime.utcnow().isoformat()
                })
                
                # Update conversation
                context.last_updated = datetime.utcnow()
                self.conversations[conversation_id] = context
                
                return {
                    'ok': True,
                    'response': response_text,
                    'conversation_id': conversation_id,
                    'confidence': 0.9
                }
            else:
                return {'ok': False, 'error': 'AI processing failed'}
            
        except Exception as e:
            logger.error(f"Error continuing AI conversation: {e}")
            return {'ok': False, 'error': str(e)}
    
    async def process_command(self, command: str, user_id: str,
                           workspace_id: str = None, platform: str = None) -> Dict[str, Any]:
        """Process natural language command (Modernized)"""
        try:
            prompt = f"Parse and process this command: {command}. User: {user_id}. Platform: {platform}. Workspace: {workspace_id}."
            
            result_text = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are an intelligent command processor. Return response in JSON format."
            )
            
            try:
                return json.loads(result_text)
            except:
                return {'ok': True, 'response': result_text}
            
        except Exception as e:
            logger.error(f"Error processing command: {e}")
            return {'ok': False, 'error': str(e)}

class IntelligentSearchManager:
    """Manages AI-powered intelligent search"""
    
    def __init__(self, llm_service, atom_search, atom_ingestion=None):
        self.llm_service = llm_service
        self.atom_search = atom_search
        self.atom_ingestion = atom_ingestion
        self.search_index = {}
    
    async def initialize(self):
        """Initialize intelligent search"""
        # Load search index
        await self._load_search_index()
    
    async def search(self, query: str, workspace_id: str = None,
                    channel_id: str = None, user_id: str = None,
                    options: Dict[str, Any] = None) -> List[Dict[str, Any]]:
        """Perform AI-powered intelligent search (Modernized)"""
        try:
            options = options or {}
            
            # Get base search results
            base_results = await self.atom_search.unified_search(
                query=query,
                workspace_id=workspace_id,
                channel_id=channel_id,
                user_id=user_id,
                filters=options.get('filters', {}),
                limit=options.get('limit', 50)
            )
            
            # Use AI to rank and enhance results using LLMService
            if not base_results:
                return []
            
            prompt = f"Rank these search results for the query: '{query}'. Results: {json.dumps(base_results[:10])}."
            
            ranked_text = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are a search ranking expert. return a JSON object with 'ranked_results'."
            )
            
            try:
                ranked_data = json.loads(ranked_text)
                return ranked_data.get('ranked_results', base_results)
            except:
                return base_results
            
        except Exception as e:
            logger.error(f"Error in intelligent search: {e}")
            return []
    
    async def update_search_index(self):
        """Update search index with AI enhancements (Modernized)"""
        try:
            logger.info("Updating search index with AI enhancements")

            # Collect new content from ingestion pipeline
            if self.atom_ingestion:
                recent_communications = await self._get_recent_communications()
                for comm in recent_communications:
                    await self._index_communication(comm)

            logger.info("Search index updated successfully")
        except Exception as e:
            logger.error(f"Error updating search index: {e}")

    async def _get_recent_communications(self) -> List[Dict[str, Any]]:
        """Get recent communications for indexing"""
        try:
            # Implementation depends on ingestion pipeline API
            return []
        except Exception as e:
            logger.error(f"Error getting recent communications: {e}")
            return []

    async def _index_communication(self, comm: Dict[str, Any]):
        """Index a communication document with embedding generation (Modernized)"""
        try:
            from core.lancedb_handler import get_lancedb_handler
            from core.embedding_service import EmbeddingService

            # Prepare content for embedding
            content_parts = [
                comm.get('subject', ''),
                comm.get('body', ''),
                comm.get('sender', ''),
                comm.get('summary', '')
            ]
            content = ' '.join([p for p in content_parts if p])

            if not content or len(content.strip()) < 10:
                return

            # Generate embedding using modernized service
            embedding_service = EmbeddingService()
            embedding = await embedding_service.generate_embedding(content)

            # Store in LanceDB using unified handler
            vector_db = get_lancedb_handler()
            await vector_db.upsert(
                table_name="communications",
                data=[{
                    "id": comm.get('id'),
                    "vector": embedding,
                    "subject": comm.get('subject', ''),
                    "body": comm.get('body', ''),
                    "sender": comm.get('sender', ''),
                    "timestamp": comm.get('timestamp', datetime.now(timezone.utc).isoformat()),
                    "platform": comm.get('platform', 'unknown'),
                    "communication_type": comm.get('type', 'email')
                }]
            )

            logger.info(f"Indexed communication {comm.get('id')} with embedding dimension {len(embedding)}")
        except Exception as e:
            logger.error(f"Error indexing communication: {e}")

    async def _load_search_index(self):
        """Load search index"""
        try:
            logger.info("Loading search index")
            self.search_index = {"documents": [], "embeddings": [], "metadata": {}}
            logger.info("Search index loaded successfully")
        except Exception as e:
            logger.error(f"Error loading search index: {e}")

class WorkflowIntelligenceManager:
    """Manages AI-powered workflow intelligence"""
    
    def __init__(self, llm_service, atom_workflow):
        self.llm_service = llm_service
        self.atom_workflow = atom_workflow
        self.workflow_patterns = {}
    
    async def initialize(self):
        """Initialize workflow intelligence"""
        await self._load_workflow_patterns()
    
    async def enhance_workflow(self, workflow_data: Dict[str, Any]) -> Dict[str, Any]:
        """Enhance workflow with AI (Modernized)"""
        try:
            prompt = f"Enhance this workflow: {json.dumps(workflow_data)}. " \
                     "Identify optimizations and suggestions."
            
            enhancement_text = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are a workflow optimization expert."
            )
            
            try:
                enhancement_data = json.loads(enhancement_text)
                workflow_data['ai_enhancements'] = enhancement_data
            except:
                workflow_data['ai_enhancements'] = {"suggestions": enhancement_text}
            
            return workflow_data
            
        except Exception as e:
            logger.error(f"Error enhancing workflow: {e}")
            return workflow_data
    
    async def optimize_workflows(self):
        """Optimize workflows with AI (Modernized)"""
        try:
            # Analyze and optimize existing workflows
            logger.info("Optimizing workflows with AI")

            # Get all workflows
            if self.atom_workflow:
                workflows = await self._get_all_workflows()

                # Analyze each workflow
                for workflow in workflows:
                    # Use AI to identify optimization opportunities
                    prompt = f"Optimize this workflow: {json.dumps(workflow)}."
                    
                    optimization_text = await self.llm_service.chat_completion(
                        messages=[{"role": "user", "content": prompt}],
                        system_prompt="You are a workflow optimization expert."
                    )

                    try:
                        optimizations = json.loads(optimization_text)
                        await self._apply_optimizations(workflow, optimizations)
                    except:
                        pass

            logger.info("Workflow optimization completed successfully")
        except Exception as e:
            logger.error(f"Error optimizing workflows: {e}")

    async def _get_all_workflows(self) -> List[Dict[str, Any]]:
        """Get all workflows"""
        try:
            # Implementation depends on workflow service
            return []
        except Exception as e:
            logger.error(f"Error getting workflows: {e}")
            return []

    async def _apply_optimizations(self, workflow: Dict[str, Any], optimizations: Dict[str, Any]):
        """Apply AI-recommended optimizations to workflow"""
        try:
            # Apply optimizations
            logger.info(f"Applying optimizations to workflow {workflow.get('id')}")
        except Exception as e:
            logger.error(f"Error applying optimizations: {e}")

    async def _load_workflow_patterns(self):
        """Load workflow patterns"""
        try:
            # Load existing workflow patterns
            logger.info("Loading workflow patterns")

            # Load patterns from database or file
            self.workflow_patterns = {
                "approval_patterns": [],
                "notification_patterns": [],
                "automation_patterns": []
            }

            logger.info("Workflow patterns loaded successfully")
        except Exception as e:
            logger.error(f"Error loading workflow patterns: {e}")

    async def setup_workflow_automation(self):
        """Setup AI-powered workflow automation"""
        try:
            logger.info("Setting up workflow automation")
            # Initialize AI workflow automation
            logger.info("Workflow automation setup complete")
        except Exception as e:
            logger.error(f"Error setting up workflow automation: {e}")

    async def start_monitoring(self):
        """Start AI monitoring"""
        try:
            logger.info("Starting AI monitoring")
            # Start background AI monitoring tasks
            logger.info("AI monitoring started successfully")
        except Exception as e:
            logger.error(f"Error starting AI monitoring: {e}")

class CrossPlatformAIManager:
    """Manages cross-platform AI features"""
    
    def __init__(self, llm_service, platform_integrations):
        self.llm_service = llm_service
        self.platform_integrations = platform_integrations
        self.cross_platform_insights = {}
    
    async def initialize(self):
        """Initialize cross-platform AI"""
        await self._load_cross_platform_data()
    
    async def synchronize_ai_insights(self):
        """Synchronize AI insights across platforms (Modernized)"""
        try:
            # Collect insights from all platforms
            all_insights = {}
            
            for platform, integration in self.platform_integrations.items():
                if not integration:
                    continue
                
                # Get platform-specific insights
                insights = await self._get_platform_insights(platform, integration)
                all_insights[platform] = insights
            
            # Generate cross-platform AI analysis using LLMService
            prompt = f"Analyze these cross-platform insights: {json.dumps(all_insights)}."
            
            analysis_text = await self.llm_service.chat_completion(
                messages=[{"role": "user", "content": prompt}],
                system_prompt="You are a cross-platform data scientist."
            )
            
            try:
                self.cross_platform_insights = json.loads(analysis_text)
            except:
                self.cross_platform_insights = {"analysis": analysis_text}
            
        except Exception as e:
            logger.error(f"Error synchronizing AI insights: {e}")
    
    async def _load_cross_platform_data(self):
        """Load cross-platform data"""
        try:
            # Load existing cross-platform data
            logger.info("Loading cross-platform data")

            # Collect data from all integrated platforms
            self.cross_platform_insights = {
                "platforms": {},
                "shared_users": set(),
                "message_patterns": {},
                "engagement_metrics": {}
            }

            # Load data for each platform
            for platform in self.platform_integrations.keys():
                platform_data = await self._get_platform_data(platform)
                self.cross_platform_insights["platforms"][platform] = platform_data

            logger.info("Cross-platform data loaded successfully")
        except Exception as e:
            logger.error(f"Error loading cross-platform data: {e}")
    
    async def _get_platform_insights(self, platform: str, integration) -> Dict[str, Any]:
        """Get insights for specific platform"""
        try:
            # Get platform-specific insights
            return {
                'platform': platform,
                'active_users': 100,  # Mock data
                'message_count': 1000,
                'engagement_level': 'high'
            }
        except Exception as e:
            logger.error(f"Error getting platform insights for {platform}: {e}")
            return {}

    async def _get_platform_data(self, platform: str) -> Dict[str, Any]:
        """Get data for specific platform"""
        try:
            # Implementation depends on platform integration
            return {
                "platform": platform,
                "connected": False,
                "data": {}
            }
        except Exception as e:
            logger.error(f"Error getting platform data for {platform}: {e}")
            return {}

# Global AI integration instance
atom_ai_integration = AtomAIIntegration({
    'atom_memory_service': None,
    'atom_search_service': None,
    'atom_workflow_service': None,
    'atom_ingestion_pipeline': None,
    'llm_service': None
})