This commit provides a minimal, focused fix for CI hanging issues by addressing the root causes:
**Key Changes:**
1. **ZMQ Resource Management:**
- Remove `context.term()` calls that were causing hangs
- Add `socket.setsockopt(zmq.LINGER, 0)` to prevent blocking on close
- Keep socket operations simple with default timeouts (no artificial limits)
2. **Process Cleanup:**
- Add timeout (1s) to final `process.wait()` in embedding server manager
- Prevent infinite waiting that was causing CI hangs
3. **Resource Cleanup Methods:**
- Add simple `cleanup()` methods to searchers and API classes
- Focus on C++ object destruction for DiskANN backend
- Avoid complex cleanup logic that could introduce new issues
4. **Basic Test Safety:**
- Simple pytest-timeout configuration (300s)
- Basic test session cleanup using psutil
- Minimal conftest.py without complex logic
**Philosophy:**
This solution avoids the complex multi-layered fixes from the previous PR chain.
Instead, it targets the specific root causes:
- ZMQ context termination blocking
- Process wait() without timeout
- C++ resource leaks in backends
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* feat: Add Ollama embedding support for local embedding models
* docs: Add clear documentation for Ollama embedding usage
* feat: Enhance Ollama embedding with better error handling and concurrent processing
- Add intelligent model validation and suggestions (inspired by OllamaChat)
- Implement concurrent processing for better performance
- Add retry mechanism with timeout handling
- Provide user-friendly error messages with emojis
- Auto-detect and recommend embedding models
- Add text truncation for long texts
- Improve progress bar display logic
* docs: don't mention it in README
- Improve grammar and sentence structure in MCP section
- Add proper markdown image formatting with relative paths
- Optimize mcp_leann.png size (1.3MB -> 224KB)
- Update data description to be more specific about Chinese content
- Remove --no-index so numpy/scipy/etc can be resolved on Python 3.13
- Keep --find-links to force our packages from local dist
Fixes: dependency resolution failure on Ubuntu Python 3.13 (numpy missing)
- Build leann-core and leann on macOS too
- Install all packages via --find-links and --no-index across platforms
- Lower macOS MACOSX_DEPLOYMENT_TARGET to 12.0 for wider compatibility
This ensures consistency and avoids PyPI drift while improving macOS compatibility.
- 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'
- Ubuntu: Install all packages from local builds with --no-index
- macOS: Install core packages from PyPI, backends from local builds
- Remove --no-index for macOS backend installation to allow dependency resolution
- Pin versions when installing from PyPI to ensure consistency
Fixes error: 'leann-core was not found in the provided package locations'
- Explicitly specify Python version when creating venv with uv
- Prevents mismatch between build Python (e.g., 3.10) and test Python
- Fixes: _diskannpy.cpython-310-x86_64-linux-gnu.so in Python 3.11 error
The issue: uv venv was defaulting to Python 3.11 regardless of matrix version
- Use --find-links with --no-index to let uv select correct wheel
- Prevents installing wrong Python version wheel (e.g., cp310 for Python 3.11)
- Fixes ImportError: _diskannpy.cpython-310-x86_64-linux-gnu.so in Python 3.11
The issue was that *.whl glob matched all Python versions, causing
uv to potentially install a cp310 wheel in a Python 3.11 environment.
- Remove '--plat linux_x86_64' which is not a valid platform tag
- Let auditwheel automatically determine the correct platform
- Based on CI output, it will use manylinux_2_35_x86_64
This was causing auditwheel repair to fail, preventing proper wheel repair
- Check wheel contents before and after auditwheel repair
- Verify _diskannpy module installation after pip install
- List installed package directory structure
- Add explicit platform tag for auditwheel repair
This helps diagnose why ImportError: cannot import name '_diskannpy' occurs
- Change from --find-links to direct wheel installation with --force-reinstall
- This ensures CI uses locally built packages with latest source code
- Prevents uv from using PyPI packages with same version number but old code
- Fixes CI test failures where old code (without metadata_file_path) was used
Root cause: CI was installing leann-backend-diskann v0.2.1 from PyPI
instead of the locally built wheel with same version number.
- Add logging in DiskANN embedding server to show metadata_file_path
- Add debug logging in PassageManager to trace path resolution
- This will help identify why CI fails to find passage files
- Pin ruff==0.12.7 in pyproject.toml dev dependencies
- Update CI to use exact ruff version instead of latest
- Add comments explaining version pinning rationale
- Ensures consistent formatting across local, CI, and pre-commit
- Add GraphPartitioner class for advanced graph partitioning
- Add partition_graph_simple function for easy-to-use partitioning
- Add pybind11 dependency for C++ executable building
- Update __init__.py to export partition functions
- Include test scripts for partition functionality
The partition functionality allows optimizing disk-based indices
for better search performance and memory efficiency.
* docs: Update co-contributors with GitHub usernames
* docs: Use GitHub links for co-contributors and improve order
* docs: Change to Contributors and use personal homepage
* docs: Specify core contributors and welcome new contributors
* 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
