Obsidian Elite RAG MCP Server

# Automation Framework for Obsidian-Claude Integration ## System Architecture The automation framework provides intelligent, context-aware automation that bridges Obsidian knowledge management with Claude Code's AI capabilities, creating a self-improving knowledge system. ## Core Automation Components ### 1. Knowledge Processing Pipeline #### Automated Ingestion and Enhancement ```python # automation/knowledge-processor.py class KnowledgeProcessor: def __init__(self, vault_path, claude_client): self.vault_path = vault_path self.claude = claude_client self.rag_engine = RAGEngine(vault_path) async def process_new_note(self, note_path: str): """Process newly created or modified notes""" content = await self.read_note(note_path) # Extract key information entities = await self.extract_entities(content) concepts = await self.extract_concepts(content) relationships = await self.identify_relationships(content) # Enhance with AI analysis enhanced_metadata = await self.enhance_with_ai(content, entities, concepts) # Suggest connections suggested_links = await self.suggest_connections(concepts, enhanced_metadata) # Generate contextual tags smart_tags = await self.generate_smart_tags(content, concepts) # Update note with enhancements await self.update_note_metadata(note_path, { 'entities': entities, 'concepts': concepts, 'relationships': relationships, 'suggested_links': suggested_links, 'tags': smart_tags, 'ai_analysis': enhanced_metadata }) # Trigger related automations await self.trigger_related_automations(note_path, concepts) ``` #### Continuous Knowledge Graph Updates ```javascript // automation/graph-updater.js class KnowledgeGraphUpdater { async updateGraph(vaultPath) { // Scan all notes for updates const updatedNotes = await this.scanForUpdates(vaultPath); for (const note of updatedNotes) { // Extract graph relationships const relationships = await this.extractRelationships(note); // Update centrality scores await this.updateCentralityScores(note, relationships); // Identify clusters and communities await this.updateClusters(note, relationships); // Calculate knowledge freshness await this.updateFreshnessScores(note); } // Generate graph insights const insights = await this.generateGraphInsights(); // Create graph analysis note await this.createGraphAnalysisNote(insights); } } ``` ### 2. Intelligent Context Management #### Predictive Context Loading ```python # automation/context-predictor.py class ContextPredictor: def __init__(self): self.usage_patterns = self.load_usage_patterns() self.context_model = self.load_context_model() async def predict_context(self, current_action: str, user_state: Dict) -> List[str]: # Analyze current action pattern action_pattern = self.analyze_action_pattern(current_action) # Predict likely next actions next_actions = self.context_model.predict_next_actions(action_pattern, user_state) # Pre-load relevant contexts relevant_contexts = [] for action in next_actions: context = await self.get_context_for_action(action) relevant_contexts.extend(context) # Rank and filter contexts return self.rank_contexts(relevant_contexts, current_action) ``` #### Dynamic Context Optimization ```javascript // automation/context-optimizer.js class ContextOptimizer { async optimizeContext(query, currentContext, userState) { // Analyze query complexity and domain const queryAnalysis = await this.analyzeQuery(query); // Determine optimal context size const optimalSize = this.calculateOptimalContextSize(queryAnalysis); // Select most relevant context pieces const selectedContext = await this.selectRelevantContext( currentContext, query, optimalSize ); // Optimize context organization const optimizedContext = await this.optimizeContextOrganization( selectedContext, query ); return optimizedContext; } } ``` ### 3. Automated Learning and Adaptation #### Personalized Learning Automation ```python # automation/learning-automator.py class LearningAutomator: def __init__(self): self.learning_model = self.load_learning_model() self.spaced_repetition = SpacedRepetitionSystem() async def automate_learning(self, user_activity: Dict): # Identify learning opportunities learning_opportunities = await self.identify_learning_opportunities(user_activity) # Generate personalized learning path learning_path = await self.generate_learning_path(learning_opportunities) # Create learning materials for topic in learning_path: await self.create_learning_materials(topic) # Schedule review sessions await self.schedule_review_sessions(learning_path) # Track progress and adapt await self.setup_progress_tracking(learning_path) ``` #### Knowledge Gap Detection and Filling ```javascript // automation/knowledge-gap-filler.js class KnowledgeGapFiller { async detectAndFillGaps(knowledgeBase) { // Analyze knowledge graph for gaps const gaps = await this.identifyKnowledgeGaps(knowledgeBase); for (const gap of gaps) { // Prioritize gaps by importance const priority = await this.calculateGapPriority(gap); if (priority > THRESHOLD) { // Generate research plan const researchPlan = await this.generateResearchPlan(gap); // Execute automated research const researchResults = await self.executeResearch(researchPlan); // Create knowledge from research await this.createKnowledgeFromResearch(gap, researchResults); // Integrate new knowledge await this.integrateNewKnowledge(researchResults); } } } } ``` ## Advanced Automation Features ### 1. Multi-Modal Content Processing ```python # automation/multimodal-processor.py class MultiModalProcessor: async process_multimodal_content(self, content_path: str): # Detect content type content_type = await self.detect_content_type(content_path) # Extract information based on type if content_type == 'document': extracted = await self.process_document(content_path) elif content_type == 'image': extracted = await self.process_image(content_path) elif content_type == 'audio': extracted = await self.process_audio(content_path) elif content_type == 'video': extracted = await self.process_video(content_path) # Generate unified knowledge representation unified_knowledge = await this.create_unified_representation(extracted) # Create structured note await self.create_structured_note(content_path, unified_knowledge) # Identify connections to existing knowledge connections = await self.find_connections(unified_knowledge) # Update knowledge graph await self.update_knowledge_graph(unified_knowledge, connections) ``` ### 2. Workflow Orchestration ```python # automation/workflow-orchestrator.py class WorkflowOrchestrator: def __init__(self): self.active_workflows = {} self.workflow_triggers = self.load_workflow_triggers() async def orchestrate_workflow(self, workflow_type: str, context: Dict): # Create workflow instance workflow = await self.create_workflow(workflow_type, context) # Execute workflow steps results = [] for step in workflow.steps: step_result = await self.execute_workflow_step(step, context) results.append(step_result) # Update context for next step context = await self.update_context(context, step_result) # Check for workflow interruptions if await self.should_interrupt_workflow(step_result): break # Finalize workflow final_result = await self.finalize_workflow(workflow, results) # Create workflow result note await self.create_workflow_note(workflow_type, final_result) return final_result ``` ### 3. Intelligent Assistant Automation ```python # automation/intelligent-assistant.py class IntelligentAssistant: async def provide_intelligent_assistance(self, user_query: str, context: Dict): # Understand user intent intent = await self.understand_intent(user_query, context) # Select appropriate assistance strategy strategy = await self.select_assistance_strategy(intent) # Execute assistance strategy if strategy == 'knowledge_retrieval': assistance = await self.provide_knowledge_assistance(user_query, context) elif strategy == 'workflow_guidance': assistance = await self.provide_workflow_guidance(user_query, context) elif strategy == 'learning_support': assistance = await self.provide_learning_support(user_query, context) elif strategy == 'creative_synthesis': assistance = await self.provide_creative_synthesis(user_query, context) # Personalize assistance personalized_assistance = await self.personalize_assistance( assistance, context ) # Learn from interaction await self.learn_from_interaction(user_query, personalized_assistance, context) return personalized_assistance ``` ## Automation Scheduling and Triggers ### 1. Event-Driven Automation ```python # automation/event-handler.py class AutomationEventHandler: def __init__(self): self.event_handlers = self.load_event_handlers() self.event_queue = asyncio.Queue() async def handle_event(self, event_type: str, event_data: Dict): # Queue event for processing await self.event_queue.put((event_type, event_data)) async def process_events(self): while True: event_type, event_data = await self.event_queue.get() # Find appropriate handlers handlers = self.event_handlers.get(event_type, []) # Execute all handlers for handler in handlers: try: await handler(event_data) except Exception as e: await self.handle_handler_error(handler, event_data, e) ``` ### 2. Scheduled Automation ```python # automation/scheduler.py class AutomationScheduler: def __init__(self): self.scheduled_tasks = self.load_scheduled_tasks() self.scheduler = AsyncIOScheduler() def setup_scheduled_tasks(self): # Daily knowledge synthesis self.scheduler.add_job( self.daily_knowledge_synthesis, 'cron', hour=20, minute=0 ) # Weekly graph analysis self.scheduler.add_job( self.weekly_graph_analysis, 'cron', day_of_week=0, hour=10, minute=0 ) # Monthly learning review self.scheduler.add_job( self.monthly_learning_review, 'cron', day=1, hour=9, minute=0 ) # Start scheduler self.scheduler.start() ``` ## Performance and Optimization ### 1. Caching and Indexing ```python # automation/cache-manager.py class CacheManager: def __init__(self): self.context_cache = LRUCache(maxsize=1000) self.embedding_cache = LRUCache(maxsize=5000) self.graph_cache = LRUCache(maxsize=2000) async def get_cached_context(self, query_hash: str) -> Optional[str]: return self.context_cache.get(query_hash) async def cache_context(self, query_hash: str, context: str): self.context_cache[query_hash] = context ``` ### 2. Performance Monitoring ```python # automation/performance-monitor.py class PerformanceMonitor: def __init__(self): self.metrics = defaultdict(list) async def track_automation_performance(self, automation_type: str, execution_time: float, success: bool): self.metrics[automation_type].append({ 'timestamp': datetime.now(), 'execution_time': execution_time, 'success': success }) # Analyze performance trends await self.analyze_performance_trends(automation_type) ``` ## Configuration and Customization ### 1. Automation Configuration ```yaml # config/automation-config.yaml automation: knowledge_processing: enabled: true interval: 300 # 5 minutes max_batch_size: 10 context_prediction: enabled: true model_path: "models/context_predictor.pkl" max_context_items: 50 learning_automation: enabled: true review_frequency: daily max_new_topics_per_day: 3 performance_monitoring: enabled: true log_level: INFO metrics_retention_days: 30 ``` ### 2. Custom Automation Rules ```python # automation/custom-rules.py class CustomAutomationRules: def __init__(self): self.rules = self.load_custom_rules() async def apply_custom_rules(self, event_data: Dict): for rule in self.rules: if await self.evaluate_rule_condition(rule, event_data): await self.execute_rule_action(rule, event_data) ``` This automation framework creates a self-improving, intelligent knowledge management system that continuously learns from user interactions and optimizes itself for maximum productivity and knowledge retention.