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* 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>
88 lines
2.1 KiB
Markdown
88 lines
2.1 KiB
Markdown
# LEANN Tests
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This directory contains automated tests for the LEANN project using pytest.
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## Test Files
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### `test_readme_examples.py`
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Tests the examples shown in README.md:
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- The basic example code that users see first
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- Import statements work correctly
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- Different backend options (HNSW, DiskANN)
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- Different LLM configuration options
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### `test_basic.py`
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Basic functionality tests that verify:
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- All packages can be imported correctly
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- C++ extensions (FAISS, DiskANN) load properly
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- Basic index building and searching works for both HNSW and DiskANN backends
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- Uses parametrized tests to test both backends
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### `test_document_rag.py`
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Tests the document RAG example functionality:
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- Tests with facebook/contriever embeddings
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- Tests with OpenAI embeddings (if API key is available)
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- Tests error handling with invalid parameters
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- Verifies that normalized embeddings are detected and cosine distance is used
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## Running Tests
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### Install test dependencies:
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```bash
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# Using extras
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uv pip install -e ".[test]"
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```
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### Run all tests:
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```bash
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pytest tests/
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# Or with coverage
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pytest tests/ --cov=leann --cov-report=html
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# Run in parallel (faster)
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pytest tests/ -n auto
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```
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### Run specific tests:
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```bash
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# Only basic tests
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pytest tests/test_basic.py
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# Only tests that don't require OpenAI
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pytest tests/ -m "not openai"
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# Skip slow tests
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pytest tests/ -m "not slow"
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```
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### Run with specific backend:
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```bash
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# Test only HNSW backend
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pytest tests/test_basic.py::test_backend_basic[hnsw]
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# Test only DiskANN backend
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pytest tests/test_basic.py::test_backend_basic[diskann]
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```
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## CI/CD Integration
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Tests are automatically run in GitHub Actions:
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1. After building wheel packages
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2. On multiple Python versions (3.9 - 3.13)
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3. On both Ubuntu and macOS
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4. Using pytest with appropriate markers and flags
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### pytest.ini Configuration
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The `pytest.ini` file configures:
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- Test discovery paths
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- Default timeout (600 seconds)
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- Environment variables (HF_HUB_DISABLE_SYMLINKS, TOKENIZERS_PARALLELISM)
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- Custom markers for slow and OpenAI tests
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- Verbose output with short tracebacks
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### Known Issues
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- OpenAI tests are automatically skipped if no API key is provided
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