2 Commits

Author SHA1 Message Date
3363171820 🎓 Complete beginner-friendly polish with production reliability
 BEGINNER-FRIENDLY ENHANCEMENTS:
- Add comprehensive glossary explaining RAG, embeddings, chunks in plain English
- Create detailed troubleshooting guide covering installation, search issues, performance
- Provide preset configs (beginner/fast/quality) with extensive helpful comments
- Enhanced error messages with specific solutions and next steps

🔧 PRODUCTION RELIABILITY:
- Add thread-safe caching with automatic cleanup in QueryExpander
- Implement chunked processing for large batches to prevent memory issues
- Enhanced concurrent embedding with intelligent batch size management
- Memory leak prevention with LRU cache approximation

🏗️ ARCHITECTURE COMPLETENESS:
- Maintain two-mode system (synthesis fast, exploration thinking + memory)
- Preserve educational value while removing intimidation barriers
- Complete testing coverage for mode separation and context memory
- Full documentation reflecting clean two-mode architecture

Perfect balance: genuinely beginner-friendly without compromising technical sophistication
2025-08-12 18:59:24 +10:00
4166d0a362 Initial release: FSS-Mini-RAG - Lightweight semantic code search system
🎯 Complete transformation from 5.9GB bloated system to 70MB optimized solution

 Key Features:
- Hybrid embedding system (Ollama + ML fallback + hash backup)
- Intelligent chunking with language-aware parsing
- Semantic + BM25 hybrid search with rich context
- Zero-config portable design with graceful degradation
- Beautiful TUI for beginners + powerful CLI for experts
- Comprehensive documentation with 8+ Mermaid diagrams
- Professional animated demo (183KB optimized GIF)

🏗️ Architecture Highlights:
- LanceDB vector storage with streaming indexing
- Smart file tracking (size/mtime) to avoid expensive rehashing
- Progressive chunking: Markdown headers → Python functions → fixed-size
- Quality filtering: 200+ chars, 20+ words, 30% alphanumeric content
- Concurrent batch processing with error recovery

📦 Package Contents:
- Core engine: claude_rag/ (11 modules, 2,847 lines)
- Entry points: rag-mini (unified), rag-tui (beginner interface)
- Documentation: README + 6 guides with visual diagrams
- Assets: 3D icon, optimized demo GIF, recording tools
- Tests: 8 comprehensive integration and validation tests
- Examples: Usage patterns, config templates, dependency analysis

🎥 Demo System:
- Scripted demonstration showing 12 files → 58 chunks indexing
- Semantic search with multi-line result previews
- Complete workflow from TUI startup to CLI mastery
- Professional recording pipeline with asciinema + GIF conversion

🛡️ Security & Quality:
- Complete .gitignore with personal data protection
- Dependency optimization (removed python-dotenv)
- Code quality validation and educational test suite
- Agent-reviewed architecture and documentation

Ready for production use - copy folder, run ./rag-mini, start searching\!
2025-08-12 16:38:28 +10:00