Obsidian Elite RAG MCP Server

# Claude Code Integration Strategies ## Architecture Overview This integration creates a seamless bidirectional flow between Obsidian knowledge management and Claude Code's AI capabilities, enabling elite knowledge workflows with intelligent automation and context-aware AI assistance. ## Core Integration Components ### 1. Knowledge Ingestion Pipeline #### Real-time Note Processing ```bash # Watch Obsidian vault for changes and process for RAG ./scripts/watch-vault.sh /path/to/vault # Automated metadata extraction and enhancement node scripts/enhance-metadata.js --note "path/to/note.md" # Link discovery and suggestion python scripts/discover-links.py --vault-path /path/to/vault ``` #### Smart Content Enhancement ```javascript // scripts/enhance-metadata.js class MetadataEnhancer { async processNote(notePath) { const content = await fs.readFile(notePath, 'utf8'); // Extract key concepts and entities const concepts = await this.extractConcepts(content); // Suggest connections to existing knowledge const connections = await this.suggestConnections(concepts); // Generate contextual tags const tags = await this.generateTags(content, concepts); // Update note with enhanced metadata await this.updateNoteMetadata(notePath, { concepts, connections, tags }); } } ``` ### 2. Context-Aware Claude Integration #### Query Enhancement ```python # integrations/claude-context.py class ClaudeContextProvider: def __init__(self, vault_path): self.vault_path = vault_path self.rag_engine = RAGEngine(vault_path) def get_context_for_query(self, query: str, user_context: Dict) -> str: # Classify query type query_type = self.classify_query(query) # Retrieve relevant knowledge context_docs = self.rag_engine.retrieve( query=query, context_type=user_context.get('domain', 'general'), query_type=query_type ) # Synthesize context for Claude synthesized_context = self.synthesize_context(context_docs, query) # Add active project/workflow context if user_context.get('active_project'): project_context = self.get_project_context(user_context['active_project']) synthesized_context += f"\n\nCurrent Project Context:\n{project_context}" return synthesized_context ``` #### Claude Command Extensions ```bash # Enhanced Claude Code commands alias claude-with-context='/path/to/claude-context.sh' # Usage examples: claude-with-context "How should I implement this feature?" --project "Phoenix" claude-with-context "What are the best practices for X?" --domain "technical" claude-with-context "Help me understand this concept" --learning-mode ``` ### 3. Bidirectional Knowledge Flow #### Claude → Obsidian Integration ```python # integrations/claude-to-obsidian.py class ClaudeToObsidian: def __init__(self, vault_path): self.vault_path = vault_path def create_note_from_claude_response(self, response: str, context: Dict) -> str: # Generate appropriate note structure note_template = self.generate_note_template(context['query_type']) # Extract key insights and connections insights = self.extract_insights(response) connections = self.identify_potential_links(response) # Create well-structured note note_content = note_template.format( title=context['query'], response=response, insights=insights, connections=connections, timestamp=datetime.now().isoformat() ) # Save to appropriate vault location note_path = self.determine_note_location(context) await self.save_note(note_path, note_content) return note_path ``` #### Obsidian → Claude Enhancement ```javascript // integrations/obsidian-to-claude.js class ObsidianToClaude { async prepareClaudeContext(selectedNotes, query) { // Extract structured information from notes const context = await this.extractNoteContext(selectedNotes); // Identify relationships and patterns const relationships = this.identifyRelationships(selectedNotes); // Generate query-specific context const enhancedContext = { query, knowledgeGraph: context, relationships, userIntent: await this.classifyIntent(query), suggestedActions: this.suggestActions(context, query) }; return enhancedContext; } } ``` ## Integration Patterns ### 1. Knowledge-Paired Development #### Contextual Code Generation ```bash # Get context-aware code suggestions claude-code --context-from-vault --project "Current Project" \ "Generate a REST API endpoint for user management" # Include relevant architectural decisions claude-code --include-notes "Architecture Decisions" \ --include-notes "API Standards" \ "Implement the authentication middleware" ``` #### Learning Integration ```python # scripts/learning-integration.py class LearningIntegration: def track_learning_journey(self, concept: str): # Create learning note if doesn't exist learning_note = self.create_or_get_learning_note(concept) # Suggest related concepts to explore related_concepts = self.rag_engine.find_related_concepts(concept) # Generate personalized learning path learning_path = self.generate_learning_path(concept, related_concepts) # Update progress and insights self.update_learning_progress(learning_note, learning_path) ``` ### 2. Workflow Automation #### Smart Task Management ```javascript // integrations/task-automation.js class TaskAutomation { async processTaskCreation(taskDescription, context) { // Extract requirements and dependencies const requirements = await this.extractRequirements(taskDescription); // Suggest relevant knowledge resources const resources = await this.suggestResources(requirements); // Generate subtasks and timeline const subtasks = this.generateSubtasks(requirements, context); // Create structured project note await this.createProjectNote({ task: taskDescription, requirements, resources, subtasks, context }); } } ``` #### Research Assistant ```python # integrations/research-assistant.py class ResearchAssistant: def conduct_research(self, topic: str, depth: int = 3): # Gather initial knowledge base base_knowledge = self.rag_engine.retrieve(topic, limit=20) # Identify research gaps gaps = self.identify_knowledge_gaps(base_knowledge, topic) # Generate research questions questions = self.generate_research_questions(gaps) # Suggest research methodology methodology = self.suggest_research_methodology(topic, questions) # Create research framework note self.create_research_note(topic, { base_knowledge: base_knowledge, gaps: gaps, questions: questions, methodology: methodology, next_steps: self.generate_next_steps(questions) }) ``` ## Advanced Integration Features ### 1. Predictive Context Loading ```python # integrations/predictive-context.py class PredictiveContext: def __init__(self): self.context_model = self.load_context_prediction_model() def predict_relevant_context(self, current_query: str, user_state: Dict) -> List[str]: # Predict likely next queries predicted_queries = self.context_model.predict_next_queries(current_query, user_state) # Pre-load context for predicted queries relevant_notes = [] for query in predicted_queries: notes = self.rag_engine.retrieve(query, limit=5) relevant_notes.extend(notes) return self.deduplicate_and_rank(relevant_notes) ``` ### 2. Knowledge Graph Visualization ```javascript // integrations/graph-visualizer.js class KnowledgeGraphVisualizer { generateGraphData(vaultPath) { // Extract all links and relationships const graphData = this.extractGraphStructure(vaultPath); // Identify clusters and communities const clusters = this.identifyClusters(graphData); // Calculate importance scores const importance = this.calculateNodeImportance(graphData); // Generate visualization-ready data return this.formatForVisualization(graphData, clusters, importance); } } ``` ### 3. Multi-Modal Knowledge Integration ```python # integrations/multimodal-integration.py class MultiModalIntegration: def process_multimodal_content(self, content_path: str): # Extract text from documents text_content = self.extract_text(content_path) # Analyze images and diagrams visual_content = self.analyze_visuals(content_path) # Transcribe audio/video audio_content = self.transcribe_media(content_path) # Create unified knowledge representation unified_content = this.synthesize_multimodal( text_content, visual_content, audio_content ) # Generate cross-references and connections return self.generate_connections(unified_content) ``` ## Implementation Guidelines ### 1. Performance Optimization - Cache frequently accessed contexts - Use incremental indexing for vault changes - Implement lazy loading for large knowledge bases - Optimize vector database queries ### 2. Privacy and Security - Local-only processing for sensitive information - Encrypted storage for personal knowledge - Selective context sharing with Claude - Audit trail for knowledge access ### 3. User Experience - Seamless integration with existing workflows - Minimal context switching between tools - Intelligent suggestion and automation - Progressive disclosure of complex features This integration framework creates a powerful synergy between Obsidian's knowledge management capabilities and Claude Code's AI assistance, enabling elite productivity and continuous learning workflows.