Markdown knowledge base desktop management and AI-enhanced search solution design

You are a knowledge management systems architect with expertise in local-first software design. Create a comprehensive plan for a Markdown-based knowledge base desktop application with AI-enhanced retrieval. ## Core Requirements ### 1. Local-First Architecture - All data stored as plain Markdown files on the local filesystem - No cloud dependency for core functionality - Git-compatible for version control and sync - Support for YAML frontmatter metadata ### 2. AI-Enhanced Features **Smart Search & Retrieval:** - Hybrid search: full-text + semantic vector search over local files - Natural language queries ("What did I write about X last month?") - Local embedding model (e.g., all-MiniLM-L6-v2) for privacy - Incremental index updates on file change detection **Auto-Tagging & Classification:** - Automatically suggest tags based on content analysis - Detect duplicate/similar notes and suggest merging - Topic clustering for organizing unstructured notes - Confidence scores for each suggestion **Bidirectional Link Recommendations:** - Analyze note content to suggest relevant connections - Surface "orphan" notes that lack connections - Generate relationship strength scores - Visualize as interactive knowledge graph **AI Writing Assistant:** - Context-aware autocomplete using surrounding notes - Summarize long notes into key points - Expand bullet points into full paragraphs - Generate questions for review/spaced repetition ### 3. Technical Stack Recommendations - Desktop framework (Electron/Tauri/native) - Local vector database (SQLite-vec, LanceDB, or Chroma) - File watcher for real-time index updates - Plugin architecture for extensibility ### 4. Performance Targets - Index 10,000+ notes in <30 seconds initial build - Incremental updates in <100ms per file change - Search results in <200ms - App startup in <2 seconds ## Deliverables 1. System architecture with component diagram 2. Data model (file format + index schema) 3. AI pipeline design (embedding, retrieval, generation) 4. UI/UX wireframes description for key screens 5. Technology selection matrix with trade-offs 6. Development roadmap (MVP → V1 → V2) 7. Privacy & security considerations