BobAi/fss-mini-rag-github
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3 Commits

Author SHA1 Message Date
BobAi
2f2dd6880b Add comprehensive LLM provider support and educational error handling 
✨ Features:
- Multi-provider LLM support (OpenAI, Claude, OpenRouter, LM Studio)
- Educational config examples with setup guides
- Comprehensive documentation in docs/LLM_PROVIDERS.md
- Config validation testing system

🎯 Beginner Experience:
- Friendly error messages for common mistakes
- Educational explanations for technical concepts
- Step-by-step troubleshooting guidance
- Clear next-steps for every error condition

🛠 Technical:
- Extended LLMConfig dataclass for cloud providers
- Automated config validation script
- Enhanced error handling in core components
- Backward-compatible configuration system

📚 Documentation:
- Provider comparison tables with costs/quality
- Setup instructions for each LLM provider
- Troubleshooting guides and testing procedures
- Environment variable configuration options

All configs pass validation tests. Ready for production use.
 2025-08-14 16:39:12 +10:00
BobAi
a96ddba3c9 MAJOR: Remove all Claude references and rename to Mini-RAG 
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.
 2025-08-12 19:21:30 +10:00
BobAi
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