* docs: config guidance
* feat: add comprehensive configuration guide and update README
- Create docs/configuration-guide.md with detailed guidance on:
- Embedding model selection (small/medium/large)
- Index selection (HNSW vs DiskANN)
- LLM engine and model comparison
- Parameter tuning (build/search complexity, top-k)
- Performance optimization tips
- Deep dive into LEANN's recomputation feature
- Update README.md to link to the configuration guide
- Include latest 2025 model recommendations (Qwen3, DeepSeek-R1, O3-mini)
* chore: move evaluation data .gitattributes to correct location
* docs: Weaken DiskANN emphasis in README
- Change backend description to emphasize HNSW as default
- DiskANN positioned as optional for billion-scale datasets
- Simplify evaluation commands to be more generic
* docs: Adjust DiskANN positioning in features and roadmap
- features.md: Put HNSW/FAISS first as default, DiskANN as optional
- roadmap.md: Reorder to show HNSW integration before DiskANN
- Consistent with positioning DiskANN as advanced option for large-scale use
* docs: Improve configuration guide based on feedback
- List specific files in default data/ directory (2 AI papers, literature, tech report)
- Update examples to use English and better RAG-suitable queries
- Change full dataset reference to use --max-items -1
- Adjust small model guidance about upgrading to larger models when time allows
- Update top-k defaults to reflect actual default of 20
- Ensure consistent use of full model name Qwen/Qwen3-Embedding-0.6B
- Reorder optimization steps, move MLX to third position
- Remove incorrect chunk size tuning guidance
- Change README from 'Having trouble' to 'Need best practices'
* docs: Address all configuration guide feedback
- Fix grammar: 'If time is not a constraint' instead of 'time expense is not large'
- Highlight Qwen3-Embedding-0.6B performance (nearly OpenAI API level)
- Add OpenAI quick start section with configuration example
- Fold Cloud vs Local trade-offs into collapsible section
- Update HNSW as 'default and recommended for extreme low storage'
- Add DiskANN beta warning and explain PQ+rerank architecture
- Expand Ollama models: add qwen3:0.6b, 4b, 7b variants
- Note OpenAI as current default but recommend Ollama switch
- Add 'need to install extra software' warning for Ollama
- Remove incorrect latency numbers from search-complexity recommendations
* docs: add a link
* refactor: Unify examples interface with BaseRAGExample
- 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.
* fix: Update CI tests for new unified examples interface
- Rename test_main_cli.py to test_document_rag.py
- Update all references from main_cli_example.py to document_rag.py
- Update tests/README.md documentation
The tests now properly test the new unified interface while maintaining
the same test coverage and functionality.
* fix: Fix pre-commit issues and update tests
- 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.)
* refactor: Remove old example scripts and migration references
- Delete old example scripts (mail_reader_leann.py, google_history_reader_leann.py, etc.)
- Remove migration hints and backward compatibility
- Update tests to use new unified examples directly
- Clean up all references to old script names
- Users now only see the new unified interface
* fix: Restore embedding-mode parameter to all examples
- 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
* docs: Improve parameter categorization in README
- Clearly separate core (shared) vs specific parameters
- Move LLM and embedding examples to 'Example Commands' section
- Add descriptive comments for all specific parameters
- Keep only truly data-source-specific parameters in specific sections
* docs: Make example commands more representative
- Add default values to parameter descriptions
- Replace generic examples with real-world use cases
- Focus on data-source-specific features in examples
- Remove redundant demonstrations of common parameters
* docs: Reorganize parameter documentation structure
- Move common parameters to a dedicated section before all examples
- Rename sections to 'X-Specific Arguments' for clarity
- Remove duplicate common parameters from individual examples
- Better information architecture for users
* docs: polish applications
* docs: Add CLI installation instructions
- Add two installation options: venv and global uv tool
- Clearly explain when to use each option
- Make CLI more accessible for daily use
* docs: Clarify CLI global installation process
- Explain the transition from venv to global installation
- Add upgrade command for global installation
- Make it clear that global install allows usage without venv activation
* docs: Add collapsible section for CLI installation
- Wrap CLI installation instructions in details/summary tags
- Keep consistent with other collapsible sections in README
- Improve document readability and navigation
* style: format
* docs: Fix collapsible sections
- Make Common Parameters collapsible (as it's lengthy reference material)
- Keep CLI Installation visible (important for users to see immediately)
- Better information hierarchy
* docs: Add introduction for Common Parameters section
- Add 'Flexible Configuration' heading with descriptive sentence
- Create parallel structure with 'Generation Model Setup' section
- Improve document flow and readability
* docs: nit
* fix: Fix issues in unified examples
- Add smart path detection for data directory
- Fix add_texts -> add_text method call
- Handle both running from project root and examples directory
* fix: Fix async/await and add_text issues in unified examples
- Remove incorrect await from chat.ask() calls (not async)
- Fix add_texts -> add_text method calls
- Verify search-complexity correctly maps to efSearch parameter
- All examples now run successfully
* feat: Address review comments
- Add complexity parameter to LeannChat initialization (default: search_complexity)
- Fix chunk-size default in README documentation (256, not 2048)
- Add more index building parameters as CLI arguments:
- --backend-name (hnsw/diskann)
- --graph-degree (default: 32)
- --build-complexity (default: 64)
- --no-compact (disable compact storage)
- --no-recompute (disable embedding recomputation)
- Update README to document all new parameters
* feat: Add chunk-size parameters and improve file type filtering
- 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)
* feat: Update documentation based on review feedback
- Add MLX embedding example to README
- Clarify examples/data content description (two papers, Pride and Prejudice, Chinese README)
- Move chunk parameters to common parameters section
- Remove duplicate chunk parameters from document-specific section
* docs: Emphasize diverse data sources in examples/data description
* fix: update default embedding models for better performance
- 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 response highlight
* change rebuild logic
* fix some example
* feat: check if k is larger than #docs
* fix: WeChat history reader bugs and refactor wechat_rag to use unified architecture
* fix email wrong -1 to process all file
* refactor: reorgnize all examples/ and test/
* refactor: reorganize examples and add link checker
* fix: add init.py
* fix: handle certificate errors in link checker
* fix wechat
* merge
* docs: update README to use proper module imports for apps
- Change from 'python apps/xxx.py' to 'python -m apps.xxx'
- More professional and pythonic module calling
- Ensures proper module resolution and imports
- Better separation between apps/ (production tools) and examples/ (demos)
---------
Co-authored-by: yichuan520030910320 <yichuan_wang@berkeley.edu>
* fix: auto-detect normalized embeddings and use cosine distance
- Add automatic detection for normalized embedding models (OpenAI, Voyage AI, Cohere)
- Automatically set distance_metric='cosine' for normalized embeddings
- Add warnings when using non-optimal distance metrics
- Implement manual L2 normalization in HNSW backend (custom Faiss build lacks normalize_L2)
- Fix DiskANN zmq_port compatibility with lazy loading strategy
- Add documentation for normalized embeddings feature
This fixes the low accuracy issue when using OpenAI text-embedding-3-small model with default MIPS metric.
* style: format
- Manual Release workflow now directly publishes to PyPI after downloading CI artifacts
- No more duplicate builds - reuses artifacts from CI
- build-and-publish.yml renamed to 'CI - Build Multi-Platform Packages'
- Publishing in CI workflow only for emergency manual triggers
- Updated RELEASE.md to reflect the new streamlined process
This fixes the issue where releases would trigger redundant builds.
- Add continue-on-error to TestPyPI step
- Check if TEST_PYPI_API_TOKEN exists before attempting upload
- Add graceful failure handling with clear messages
- Update docs to explain TestPyPI token configuration
- Clarify that TestPyPI testing is optional
Now the release won't fail if TestPyPI is not configured or upload fails
- Download pre-built wheels from successful CI runs
- Avoids duplicate builds and ensures consistency
- CI artifacts are already tested across all platforms
- Faster release process (no build time)
- Updates release documentation to reflect new flow
This ensures the released packages are exactly what was tested in CI.
- Add ci.yml for continuous integration on every commit
- Test builds on Ubuntu/macOS with Python 3.9/3.10/3.11
- Ensure code quality before any release
- Add release-manual.yml for controlled releases
- Manual trigger prevents accidental releases
- Version validation and tag creation
- Optional TestPyPI testing before production
- Only creates tag after validation passes
- Keep build-and-publish.yml for automated PyPI deployment
- Triggered by new tags (separation of concerns)
- Handles multi-platform wheel building
- Allows retry if PyPI upload fails
- Update RELEASE.md with clear prerequisites and workflow
This setup ensures:
1. Every commit is tested (CI)
2. Releases are deliberate (manual trigger)
3. Failed CI won't create broken tags
4. PyPI publish can be retried independently
