fss-mini-rag-github

BobAi/fss-mini-rag-github

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Author	SHA1	Message	Date
BobAi	2c5eef8596	Complete two-mode architecture documentation and testing - Update README with prominent two-mode explanation (synthesis vs exploration) - Add exploration mode to TUI with full interactive interface - Create comprehensive mode separation tests (test_mode_separation.py) - Update Ollama integration tests to cover both synthesis and exploration modes - Add CLI reference updates showing both modes - Implement complete testing coverage for lazy loading, mode contamination prevention - Add session management tests for exploration mode - Update all examples and help text to reflect clean two-mode architecture	2025-08-12 18:22:19 +10:00
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	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

Author

SHA1

Message

Date

BobAi

2c5eef8596

Complete two-mode architecture documentation and testing

- Update README with prominent two-mode explanation (synthesis vs exploration)
- Add exploration mode to TUI with full interactive interface
- Create comprehensive mode separation tests (test_mode_separation.py)
- Update Ollama integration tests to cover both synthesis and exploration modes
- Add CLI reference updates showing both modes
- Implement complete testing coverage for lazy loading, mode contamination prevention
- Add session management tests for exploration mode
- Update all examples and help text to reflect clean two-mode architecture

2025-08-12 18:22:19 +10:00

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

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

3 Commits