Fixed the actual root cause instead of just masking it in tests:
1. Root Problem:
- C++ side's ZmqDistanceComputer creates ZMQ connections but doesn't clean them
- Python 3.9/3.13 are more sensitive to cleanup timing during shutdown
2. Core Fixes in SearcherBase and LeannSearcher:
- Added cleanup() method to BaseSearcher that cleans ZMQ and embedding server
- LeannSearcher.cleanup() now also handles ZMQ context cleanup
- Both HNSW and DiskANN searchers now properly delete C++ index objects
3. Backend-Specific Cleanup:
- HNSWSearcher.cleanup(): Deletes self.index to trigger C++ destructors
- DiskannSearcher.cleanup(): Deletes self._index and resets state
- Both force garbage collection after deletion
4. Test Infrastructure:
- Added auto_cleanup_searcher fixture for explicit resource management
- Global cleanup now more aggressive with ZMQ context destruction
This is the proper fix - cleaning up resources at the source, not just
working around the issue in tests. The hanging was caused by C++ side
ZMQ connections not being properly terminated when is_recompute=True.
Based on excellent analysis from user, implemented comprehensive fixes:
1. ZMQ Socket Cleanup:
- Set LINGER=0 on all ZMQ sockets (client and server)
- Use try-finally blocks to ensure socket.close() and context.term()
- Prevents blocking on exit when ZMQ contexts have pending operations
2. Global Test Cleanup:
- Added tests/conftest.py with session-scoped cleanup fixture
- Cleans up leftover ZMQ contexts and child processes after all tests
- Lists remaining threads for debugging
3. CI Improvements:
- Apply timeout to ALL Python versions on Linux (not just 3.13)
- Increased timeout to 180s for better reliability
- Added process cleanup (pkill) on timeout
4. Dependencies:
- Added psutil>=5.9.0 to test dependencies for process management
Root cause: Python 3.9/3.13 are more sensitive to cleanup timing during
interpreter shutdown. ZMQ's default LINGER=-1 was blocking exit, and
atexit handlers were unreliable for cleanup.
This should resolve the 'all tests pass but CI hangs' issue.
- Add flush=True to all print statements in convert_to_csr.py to prevent buffer deadlock
- Redirect embedding server stdout/stderr to DEVNULL in CI environment (CI=true)
- Fix timeout in embedding_server_manager.stop_server() final wait call
- Replace 'int | None' with 'Optional[int]' everywhere
- Replace 'subprocess.Popen | None' with 'Optional[subprocess.Popen]'
- Add Optional import to all affected files
- Update ruff target-version from py310 to py39
- The '|' syntax for Union types was introduced in Python 3.10 (PEP 604)
Fixes TypeError: unsupported operand type(s) for |: 'type' and 'NoneType'
* 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
* feat: add OpenAI embeddings support to google_history_reader_leann.py
- Add --embedding-model and --embedding-mode arguments
- Support automatic detection of normalized embeddings
- Works correctly with cosine distance for OpenAI embeddings
* feat: add --use-existing-index option to google_history_reader_leann.py
- Allow using existing index without rebuilding
- Useful for testing pre-built indices
* fix: Improve OpenAI embeddings handling in HNSW backend
* fix: improve macOS C++ compatibility and add CI tests
* refactor: improve test structure and fix main_cli example
- Move pytest configuration from pytest.ini to pyproject.toml
- Remove unnecessary run_tests.py script (use test extras instead)
- Fix main_cli_example.py to properly use command line arguments for LLM config
- Add test_readme_examples.py to test code examples from README
- Refactor tests to use pytest fixtures and parametrization
- Update test documentation to reflect new structure
- Set proper environment variables in CI for test execution
* fix: add --distance-metric support to DiskANN embedding server and remove obsolete macOS ABI test markers
- Add --distance-metric parameter to diskann_embedding_server.py for consistency with other backends
- Remove pytest.skip and pytest.xfail markers for macOS C++ ABI issues as they have been fixed
- Fix test assertions to handle SearchResult objects correctly
- All tests now pass on macOS with the C++ ABI compatibility fixes
* chore: update lock file with test dependencies
* docs: remove obsolete C++ ABI compatibility warnings
- Remove outdated macOS C++ compatibility warnings from README
- Simplify CI workflow by removing macOS-specific failure handling
- All tests now pass consistently on macOS after ABI fixes
* fix: update macOS deployment target for DiskANN to 13.3
- DiskANN uses sgesdd_ LAPACK function which is only available on macOS 13.3+
- Update MACOSX_DEPLOYMENT_TARGET from 11.0 to 13.3 for DiskANN builds
- This fixes the compilation error on GitHub Actions macOS runners
* fix: align Python version requirements to 3.9
- Update root project to support Python 3.9, matching subpackages
- Restore macOS Python 3.9 support in CI
- This fixes the CI failure for Python 3.9 environments
* fix: handle MPS memory issues in CI tests
- Use smaller MiniLM-L6-v2 model (384 dimensions) for README tests in CI
- Skip other memory-intensive tests in CI environment
- Add minimal CI tests that don't require model loading
- Set CI environment variable and disable MPS fallback
- Ensure README examples always run correctly in CI
* fix: remove Python 3.10+ dependencies for compatibility
- Comment out llama-index-readers-docling and llama-index-node-parser-docling
- These packages require Python >= 3.10 and were causing CI failures on Python 3.9
- Regenerate uv.lock file to resolve dependency conflicts
* fix: use virtual environment in CI instead of system packages
- uv-managed Python environments don't allow --system installs
- Create and activate virtual environment before installing packages
- Update all CI steps to use the virtual environment
* add some env in ci
* fix: use --find-links to install platform-specific wheels
- Let uv automatically select the correct wheel for the current platform
- Fixes error when trying to install macOS wheels on Linux
- Simplifies the installation logic
* fix: disable OpenMP parallelism in CI to avoid libomp crashes
- Set OMP_NUM_THREADS=1 to avoid OpenMP thread synchronization issues
- Set MKL_NUM_THREADS=1 for single-threaded MKL operations
- This prevents segfaults in LayerNorm on macOS CI runners
- Addresses the libomp compatibility issues with PyTorch on Apple Silicon
* skip several macos test because strange issue on ci
---------
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
* feat: add OpenAI embeddings support to google_history_reader_leann.py
- Add --embedding-model and --embedding-mode arguments
- Support automatic detection of normalized embeddings
- Works correctly with cosine distance for OpenAI embeddings
* feat: add --use-existing-index option to google_history_reader_leann.py
- Allow using existing index without rebuilding
- Useful for testing pre-built indices
* fix: Improve OpenAI embeddings handling in HNSW backend
* 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
- Fix ambiguous fullwidth characters (commas, parentheses) in strings and comments
- Replace Chinese comments with English equivalents
- Fix unused imports with proper noqa annotations for intentional imports
- Fix bare except clauses with specific exception types
- Fix redefined variables and undefined names
- Add ruff noqa annotations for generated protobuf files
- Add lint and format check to GitHub Actions CI pipeline
- Convert relative paths to absolute paths based on metadata file location
- Fixes FileNotFoundError when starting embedding server
- Resolves issue with passages file not found in different working directories
- Fix version dependencies: update backend packages to depend on leann-core==0.1.1
- Remove duplicate ci.yml workflow (keeping build-and-publish.yml as main CI)
- Update release-manual.yml to reference correct CI workflow name
This fixes the dependency resolution error and eliminates duplicate builds.
* chore: shorter build time
* chore: update faiss
* fix: no longger do embedding server reuse
* fix: do not reuse emb_server and close it properly
* feat: cli tool
* feat: cli more args
* fix: same embedding logic
