- Change WeChat, Browser, and Email RAG examples to use all-MiniLM-L6-v2
- Previous Qwen/Qwen3-Embedding-0.6B was too slow for these use cases
- all-MiniLM-L6-v2 is a fast 384-dim model, ideal for large-scale personal data
- Add --chunk-size and --chunk-overlap parameters to all RAG examples
- Preserve original default values for each data source:
- Document: 256/128 (optimized for general documents)
- Email: 256/25 (smaller overlap for email threads)
- Browser: 256/128 (standard for web content)
- WeChat: 192/64 (smaller chunks for chat messages)
- Make --file-types optional filter instead of restriction in document_rag
- Update README to clarify interactive mode and parameter usage
- Fix LLM default model documentation (gpt-4o, not gpt-4o-mini)
- All examples now have --embedding-mode parameter (unified interface benefit)
- Default is 'sentence-transformers' (consistent with original behavior)
- Users can now use OpenAI or MLX embeddings with any data source
- Maintains functional equivalence with original scripts
- Fix import sorting and unused imports
- Update type annotations to use built-in types (list, dict) instead of typing.List/Dict
- Fix trailing whitespace and end-of-file issues
- Fix Chinese fullwidth comma to regular comma
- Update test_main_cli.py to test_document_rag.py
- Add backward compatibility test for main_cli_example.py
- Pass all pre-commit hooks (ruff, ruff-format, etc.)
- Create BaseRAGExample base class for all RAG examples
- Refactor 4 examples to use unified interface:
- document_rag.py (replaces main_cli_example.py)
- email_rag.py (replaces mail_reader_leann.py)
- browser_rag.py (replaces google_history_reader_leann.py)
- wechat_rag.py (replaces wechat_history_reader_leann.py)
- Maintain 100% parameter compatibility with original files
- Add interactive mode support for all examples
- Unify parameter names (--max-items replaces --max-emails/--max-entries)
- Update README.md with new examples usage
- Add PARAMETER_CONSISTENCY.md documenting all parameter mappings
- Keep main_cli_example.py for backward compatibility with migration notice
All default values, LeannBuilder parameters, and chunking settings
remain identical to ensure full compatibility with existing indexes.
