- Add Windows installer (install_windows.bat) and launcher (rag.bat)
- Enhance both Linux and Windows installers with intelligent Qwen3 model detection and setup
- Fix installation script continuation issues and improve user guidance
- Update README with side-by-side Linux/Windows commands
- Auto-save model preferences to config.yaml for consistent experience
Makes FSS-Mini-RAG fully cross-platform with zero-friction Windows adoption 🚀
Major fixes:
- Fix model selection to prioritize qwen3:1.7b instead of qwen3:4b for testing
- Correct context length from 80,000 to 32,000 tokens (proper Qwen3 limit)
- Implement content-preserving safeguards instead of dropping responses
- Fix all test imports from claude_rag to mini_rag module naming
- Add virtual environment warnings to all test entry points
- Fix TUI EOF crash handling with proper error handling
- Remove warmup delays that were causing startup lag and unwanted model calls
- Fix command mappings between bash wrapper and Python script
- Update documentation to reflect qwen3:1.7b as primary recommendation
- Improve TUI box alignment and formatting
- Make language generic for any documents, not just codebases
- Add proper folder names in user feedback instead of generic terms
Technical improvements:
- Unified model rankings across all components
- Better error handling for missing dependencies
- Comprehensive testing and validation of all fixes
- All tests now pass and system is deployment-ready
All major crashes and deployment issues resolved.
🔧 Script Handling Improvements:
- Fix infinite recursion in bash wrapper for index/search commands
- Improve embedding system diagnostics with intelligent detection
- Add timeout protection and progress indicators to installer test
- Enhance interactive input handling with graceful fallbacks
🎯 User Experience Enhancements:
- Replace confusing error messages with educational diagnostics
- Add RAG performance tips about model sizing (4B optimal, 8B+ overkill)
- Correct model recommendations (qwen3:4b not qwen3:3b)
- Smart Ollama model detection shows available models
- Clear guidance for next steps after installation
🛠 Technical Fixes:
- Add get_embedding_info() method to CodeEmbedder class
- Robust test prompt handling with /dev/tty input
- Path validation and permission fixing in test scripts
- Comprehensive error diagnostics with actionable solutions
Installation now completes reliably with clear feedback and guidance.
✨ Features:
- One-click Ollama installation using official script
- Educational LLM model recommendations after successful install
- Smart 3-option menu: auto-install, manual, or skip
- Clear performance vs quality guidance for model selection
🛡 Safety & UX:
- Uses official ollama.com/install.sh script
- Shows exact commands before execution
- Graceful fallback to manual installation
- Auto-starts Ollama server and verifies health
- Educational approach with size/performance trade-offs
🎯 Model Recommendations:
- qwen3:0.6b (lightweight, 400MB)
- qwen3:1.7b (balanced, 1GB)
- qwen3:3b (excellent for this project, 2GB)
- qwen3:8b (premium results, 5GB)
- Creative suggestions: mistral for storytelling, qwen3-coder for development
Transforms installation from multi-step manual process to guided automation.
Based on feedback in PR comment, implemented:
Installer improvements:
- Added choice between code/docs sample testing
- Created FSS-Mini-RAG specific sample files (chunker.py, ollama_integration.py, etc.)
- Timing-based estimation for full project indexing
- Better sample content that actually relates to this project
TUI enhancements:
- Replaced generic searches with FSS-Mini-RAG relevant questions:
* "chunking strategy"
* "ollama integration"
* "indexing performance"
* "why does indexing take long"
- Added search count tracking and sample limitation reminder
- Intelligent transition to full project after 2 sample searches
- FSS-Mini-RAG specific follow-up question patterns
Key fixes:
- No more dead search results (removed auth/API queries that don't exist)
- Sample questions now match actual content that will be found
- User gets timing estimate for full indexing based on sample performance
- Clear transition path from sample to full project exploration
This prevents the "installed malware" feeling when searches return no results.
- Replace slow full-project test with fast 3-file sample
- Add beginner guidance and welcome messages
- Add sample questions to combat prompt paralysis
- Add intelligent follow-up question suggestions
- Improve TUI with contextual next steps
Installer improvements:
- Create minimal sample project (3 files) for testing
- Add helpful tips and guidance for new users
- Better error messaging and progress indicators
TUI enhancements:
- Welcome message for first-time users
- Sample search questions (authentication, error handling, etc.)
- Pattern-based follow-up question generation
- Contextual suggestions based on search results
These changes address user feedback about installation taking too long
and beginners not knowing what to search for.
Complete rebrand to eliminate any Claude/Anthropic references:
Directory Changes:
- claude_rag/ → mini_rag/ (preserving git history)
Content Changes:
- Replaced 930+ Claude references across 40+ files
- Updated all imports: from claude_rag → from mini_rag
- Updated all file paths: .claude-rag → .mini-rag
- Updated documentation and comments
- Updated configuration files and examples
Testing Changes:
- All tests updated to use mini_rag imports
- Integration tests verify new module structure
This ensures complete independence from Claude/Anthropic
branding while maintaining all functionality and git history.
