3 Commits

Author	SHA1	Message	Date
BobAi	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
BobAi	55500a2977	Integrate LLM synthesis across all interfaces and update demo ... 🔧 Integration Updates - Added --synthesize flag to main rag-mini CLI - Updated README with synthesis examples and 10 result default - Enhanced demo script with 8 complete results (was cutting off at 5) - Updated rag-tui default from 5 to 10 results - Updated rag-mini-enhanced script defaults 📈 User Experience Improvements - All components now consistently default to 10 results - Demo shows complete 8-result workflow with multi-line previews - Documentation reflects new AI analysis capabilities - Seamless integration preserves existing workflows Users get more comprehensive results by default and can optionally add intelligent AI analysis with a simple --synthesize flag!	2025-08-12 17:13:21 +10:00
BobAi	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