1. CI Logging Enhancements: - Added comprehensive diagnostics with process tree, network listeners, file descriptors - Added timestamps at every stage (before/during/after pytest) - Added trap EXIT to always show diagnostics - Added immediate process checks after pytest finishes - Added sub-shell execution with immediate cleanup 2. Fixed Subprocess PIPE Blocking: - Changed Colab mode from PIPE to DEVNULL to prevent blocking - PIPE without reading can cause parent process to wait indefinitely 3. Pytest Session Hooks: - Added pytest_sessionstart to log initial state - Added pytest_sessionfinish for aggressive cleanup before exit - Shows all child processes and their status This should reveal exactly where the hang is happening.
LEANN Tests
This directory contains automated tests for the LEANN project using pytest.
Test Files
test_readme_examples.py
Tests the examples shown in README.md:
- The basic example code that users see first (parametrized for both HNSW and DiskANN backends)
- Import statements work correctly
- Different backend options (HNSW, DiskANN)
- Different LLM configuration options (parametrized for both backends)
- All main README examples are tested with both HNSW and DiskANN backends using pytest parametrization
test_basic.py
Basic functionality tests that verify:
- All packages can be imported correctly
- C++ extensions (FAISS, DiskANN) load properly
- Basic index building and searching works for both HNSW and DiskANN backends
- Uses parametrized tests to test both backends
test_document_rag.py
Tests the document RAG example functionality:
- Tests with facebook/contriever embeddings
- Tests with OpenAI embeddings (if API key is available)
- Tests error handling with invalid parameters
- Verifies that normalized embeddings are detected and cosine distance is used
test_diskann_partition.py
Tests DiskANN graph partitioning functionality:
- Tests DiskANN index building without partitioning (baseline)
- Tests automatic graph partitioning with
is_recompute=True - Verifies that partition files are created and large files are cleaned up for storage saving
- Tests search functionality with partitioned indices
- Validates medoid and max_base_norm file generation and usage
- Includes performance comparison between DiskANN (with partition) and HNSW
- Note: These tests are skipped in CI due to hardware requirements and computation time
Running Tests
Install test dependencies:
# Using extras
uv pip install -e ".[test]"
Run all tests:
pytest tests/
# Or with coverage
pytest tests/ --cov=leann --cov-report=html
# Run in parallel (faster)
pytest tests/ -n auto
Run specific tests:
# Only basic tests
pytest tests/test_basic.py
# Only tests that don't require OpenAI
pytest tests/ -m "not openai"
# Skip slow tests
pytest tests/ -m "not slow"
# Run DiskANN partition tests (requires local machine, not CI)
pytest tests/test_diskann_partition.py
Run with specific backend:
# Test only HNSW backend
pytest tests/test_basic.py::test_backend_basic[hnsw]
pytest tests/test_readme_examples.py::test_readme_basic_example[hnsw]
# Test only DiskANN backend
pytest tests/test_basic.py::test_backend_basic[diskann]
pytest tests/test_readme_examples.py::test_readme_basic_example[diskann]
# All DiskANN tests (parametrized + specialized partition tests)
pytest tests/ -k diskann
CI/CD Integration
Tests are automatically run in GitHub Actions:
- After building wheel packages
- On multiple Python versions (3.9 - 3.13)
- On both Ubuntu and macOS
- Using pytest with appropriate markers and flags
pytest.ini Configuration
The pytest.ini file configures:
- Test discovery paths
- Default timeout (600 seconds)
- Environment variables (HF_HUB_DISABLE_SYMLINKS, TOKENIZERS_PARALLELISM)
- Custom markers for slow and OpenAI tests
- Verbose output with short tracebacks
Known Issues
- OpenAI tests are automatically skipped if no API key is provided