4 Commits

Author SHA1 Message Date
a7e3e6f474 Add interactive exploration mode with thinking and context memory
- Create separate explore mode with thinking enabled for debugging/learning
- Add lazy loading with LLM warmup using 'testing, just say "hi" <no_think>'
- Implement context-aware conversation memory across questions
- Add interactive CLI with help, summary, and session management
- Enable Qwen3 thinking mode toggle for experimentation
- Support multi-turn conversations for better debugging workflow
- Clean separation between fast synthesis and deep exploration modes
2025-08-12 18:06:08 +10:00
16199375fc Add CPU-only deployment support with qwen3:0.6b model
- Update model rankings to prioritize ultra-efficient CPU models (qwen3:0.6b first)
- Add comprehensive CPU deployment documentation with performance benchmarks
- Configure CPU-optimized settings in default config
- Enable 796MB total model footprint for standard systems
- Support Raspberry Pi, older laptops, and CPU-only environments
- Maintain excellent quality with 522MB qwen3:0.6b model
2025-08-12 17:49:02 +10:00
4925f6d4e4 Add comprehensive testing suite and documentation for new features
📚 DOCUMENTATION
- docs/QUERY_EXPANSION.md: Complete beginner guide with examples and troubleshooting
- Updated config.yaml with proper LLM settings and comments
- Clear explanations of when features are enabled/disabled

🧪 NEW TESTING INFRASTRUCTURE
- test_ollama_integration.py: 6 comprehensive tests with helpful error messages
- test_smart_ranking.py: 6 tests verifying ranking quality improvements
- troubleshoot.py: Interactive tool for diagnosing setup issues
- Enhanced system validation with new features coverage

⚙️ SMART DEFAULTS
- Query expansion disabled by default (CLI speed)
- TUI enables expansion automatically (exploration mode)
- Clear user feedback about which features are active
- Graceful degradation when Ollama unavailable

🎯 BEGINNER-FRIENDLY APPROACH
- Tests explain what they're checking and why
- Clear solutions provided for common problems
- Educational output showing system status
- Offline testing with gentle mocking

Run 'python3 tests/troubleshoot.py' to verify your setup\!
2025-08-12 17:36:32 +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