Personal Knowledge Assistant

# Intelligence and Analytics Features This document describes the comprehensive intelligent data processing and analysis features implemented in the Personal Knowledge Assistant MCP Server. ## Overview The IPA-MCP now includes sophisticated AI-powered features for: - Cross-platform search and content discovery - Communication pattern analysis and insights - Social media intelligence and optimization - Intelligent task management and project analysis - Advanced NLP processing and analytics ## Core Components ### 1. Universal Search Engine (`src/tools/search_tools.py`) **Capabilities:** - **Cross-Platform Search**: Search across Gmail, Google Drive, Twitter, LinkedIn simultaneously - **Contextual Search**: Find related content and map relationships between items - **Smart Filtering**: Advanced filtering with relevance scoring and faceted search - **Privacy-Aware**: Respects privacy settings and anonymizes sensitive data **Key Features:** ```python # Universal search across all platforms results = await search_engine.universal_search( query="project alpha", scope=SearchScope.ALL, sort_by=SortOrder.RELEVANCE ) # Contextual relationship mapping contextual_results = await search_engine.contextual_search( query="meeting notes", context_items=["project alpha", "Q4 planning"] ) ``` ### 2. Communication Analysis Engine (`src/tools/analysis_tools.py`) **Capabilities:** - **Communication Pattern Analysis**: Analyze email frequency, response times, peak hours - **Network Analysis**: Map relationships and identify key connectors - **Sentiment Analysis**: Track communication sentiment trends over time - **Productivity Insights**: Detect patterns and provide actionable recommendations **Key Features:** ```python # Analyze communication patterns patterns = await analyzer.analyze_communication_patterns( time_range=TimeRange(start=start_date, end=end_date), include_sentiment=True ) # Relationship analysis relationship = await analyzer.analyze_contact_relationship( contact_identifier="john@example.com" ) # Network analysis network = await analyzer.analyze_communication_network( time_range=time_range, min_interactions=3 ) ``` ### 3. Social Media Intelligence (`src/tools/social_tools.py`) **Capabilities:** - **Content Performance Analysis**: Track engagement across different content types - **Audience Insights**: Understand follower behavior and preferences - **Hashtag Intelligence**: Analyze hashtag performance and trends - **Posting Optimization**: Find optimal posting times and strategies - **Content Suggestions**: AI-generated content recommendations - **Competitor Analysis**: Benchmark against competitors **Key Features:** ```python # Content performance analysis performance = await social_analyzer.analyze_content_performance( platform=SocialPlatform.TWITTER, time_range=time_range ) # Posting schedule optimization schedule = await social_analyzer.optimize_posting_schedule( platform=SocialPlatform.LINKEDIN, time_range=time_range ) # AI content suggestions suggestions = await social_analyzer.generate_content_suggestions( platform=SocialPlatform.TWITTER, content_themes=["AI", "productivity"], count=5 ) ``` ### 4. Intelligent Task Management (`src/tools/task_tools.py`) **Capabilities:** - **Task Extraction**: Automatically extract tasks from emails and documents - **Follow-up Detection**: Identify items requiring follow-up action - **Project Context Aggregation**: Gather all project-related information - **Priority Scoring**: Intelligent priority and urgency classification - **Collaboration Analysis**: Detect collaboration patterns and effectiveness - **Productivity Insights**: Analyze work patterns and provide recommendations **Key Features:** ```python # Extract tasks from email content tasks = await task_engine.extract_tasks_from_email( email_content="Please review the proposal by Friday", email_metadata={"from": "john@example.com", "id": "email_123"} ) # Detect follow-up items follow_ups = await task_engine.detect_follow_ups( time_range=time_range, include_overdue=True ) # Project context analysis project_context = await task_engine.aggregate_project_context( project_name="Project Alpha" ) ``` ### 5. NLP Processing Engine (`src/utils/nlp_processor.py`) **Capabilities:** - **Text Classification**: Categorize content by type and urgency - **Entity Extraction**: Identify people, organizations, locations - **Sentiment Analysis**: Multi-model sentiment analysis with confidence scores - **Topic Modeling**: Extract themes and topics from document collections - **Text Summarization**: Generate extractive summaries - **Privacy Protection**: Anonymize sensitive information **Key Features:** ```python # Comprehensive text analysis analysis = await nlp_processor.analyze_text( text="Your text here", include_entities=True, include_topics=True, anonymize=True ) # Batch processing results = await nlp_processor.batch_analyze_texts( texts=["text1", "text2", "text3"] ) # Topic modeling topic_model = await nlp_processor.train_topic_model( texts=document_collection, n_topics=10 ) ``` ### 6. Advanced Analytics Engine (`src/utils/analytics_engine.py`) **Capabilities:** - **Time Series Analysis**: Trend detection, seasonality analysis, anomaly detection - **Statistical Analysis**: Correlation analysis, significance testing - **Clustering Analysis**: Automatic data segmentation and pattern discovery - **Recommendation Engine**: Personalized recommendations based on behavior - **Privacy-Preserving Analytics**: Differential privacy implementation **Key Features:** ```python # Time series analysis analysis = await analytics_engine.analyze_time_series( data=time_series_points, detect_trends=True, detect_seasonality=True, detect_anomalies=True ) # Correlation analysis correlations = await analytics_engine.analyze_correlations( data={"metric1": [1,2,3], "metric2": [4,5,6]} ) # Generate recommendations recommendations = await analytics_engine.generate_recommendations( user_data=user_behavior_data ) ``` ## Privacy and Security All intelligence features are built with privacy-first principles: - **Data Anonymization**: Sensitive information is automatically anonymized - **Differential Privacy**: Statistical noise added to protect individual privacy - **Encrypted Storage**: All cached data is encrypted at rest - **Minimal Data Retention**: Data is only kept as long as necessary - **User Control**: Users can control what data is analyzed and how ## Integration Examples ### Complete Workflow Example ```python async def analyze_productivity_and_optimize(): # 1. Extract tasks from recent emails time_range = TimeRange( start=datetime.now() - timedelta(days=30), end=datetime.now() ) task_engine = await get_task_engine() communication_analyzer = await get_communication_analyzer() social_analyzer = await get_social_analyzer() # 2. Analyze communication patterns comm_patterns = await communication_analyzer.analyze_communication_patterns( time_range=time_range ) # 3. Get productivity insights productivity_insights = await task_engine.analyze_productivity_patterns( time_range=time_range ) # 4. Optimize social media posting social_optimization = await social_analyzer.optimize_posting_schedule( platform=SocialPlatform.LINKEDIN, time_range=time_range ) # 5. Generate comprehensive report return { "communication_analysis": comm_patterns, "productivity_insights": productivity_insights, "social_optimization": social_optimization, "recommendations": generate_combined_recommendations( comm_patterns, productivity_insights, social_optimization ) } ``` ## Configuration The intelligence features can be configured through the settings: ```python # In your settings intelligence_settings = { "nlp": { "enable_transformers": True, "anonymize_entities": True, "sentiment_threshold": 0.1 }, "analytics": { "enable_differential_privacy": True, "noise_scale": 0.1, "min_data_points": 10 }, "search": { "max_results_per_source": 100, "enable_caching": True, "cache_ttl_hours": 24 }, "social": { "min_posts_for_analysis": 10, "engagement_rate_threshold": 0.02 } } ``` ## Performance Considerations - **Async Processing**: All operations are asynchronous for better performance - **Caching**: Intelligent caching reduces redundant processing - **Batch Processing**: Efficient batch operations for large datasets - **Resource Management**: Automatic cleanup and resource management - **Scalable Architecture**: Designed to handle growing data volumes ## Testing Comprehensive test suite included: ```bash # Run intelligence feature tests python -m pytest tests/test_intelligence_integration.py -v # Run specific component tests python -m pytest tests/test_nlp_processor.py -v python -m pytest tests/test_analytics_engine.py -v ``` ## Error Handling All components include robust error handling: - **Graceful Degradation**: System continues to work even if some features fail - **Retry Logic**: Automatic retries for transient failures - **Fallback Mechanisms**: Alternative approaches when primary methods fail - **Comprehensive Logging**: Detailed logging for troubleshooting ## Future Enhancements Planned improvements: - Real-time processing capabilities - Advanced machine learning model training - Multi-language support - Integration with more platforms - Enhanced visualization features - Mobile-optimized analytics ## Getting Started 1. **Install Dependencies**: ```bash pip install -r requirements.txt ``` 2. **Initialize Components**: ```python from src.tools import search_tools, analysis_tools, social_tools, task_tools # Initialize all intelligence components search_engine = await search_tools.get_search_engine() analyzer = await analysis_tools.get_communication_analyzer() social_analyzer = await social_tools.get_social_analyzer() task_engine = await task_tools.get_task_engine() ``` 3. **Start Analyzing**: ```python # Begin with a simple search results = await search_engine.universal_search("project status") # Analyze your communication patterns patterns = await analyzer.analyze_communication_patterns(time_range) # Get task insights tasks = await task_engine.extract_tasks_from_content(email_content) ``` For more detailed examples and API documentation, see the individual module docstrings and test files.