- Add missing type cast in DistanceFastL2::norm function SSE2 version
- Fixes const float* = const signed char* compilation error
- Ensures consistent type casting across all SIMD code paths
- Resolves template instantiation error for DistanceFastL2<int8_t>
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add missing type casts (float*)a and (float*)b in SSE2 version
- This matches the existing type casts in the AVX version
- Fixes compilation error when instantiating DistanceInnerProduct<int8_t>
- Resolves "cannot initialize const float* with const signed char*" error
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix _mm128_loadu_ps to _mm_loadu_ps (and similar functions)
- This is a known issue in upstream DiskANN code where incorrect function names were used
- Resolves compilation errors on macOS Intel builds
References: Known DiskANN issue with SIMD intrinsics naming
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Auto-detect Homebrew libomp path using OpenMP_ROOT environment variable
- Exclude mkl_set_num_threads on macOS (uses Accelerate framework instead of MKL)
- Fixes compilation on Intel Macs by using correct /usr/local paths
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Auto-detect Homebrew libomp path using OpenMP_ROOT environment variable
- Fallback to CMAKE_PREFIX_PATH/opt/libomp if OpenMP_ROOT not set
- Final fallback to brew --prefix libomp for auto-detection
- Maintains backwards compatibility with old hardcoded path
- Fixes Intel Mac builds that were failing due to hardcoded Apple Silicon paths
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
* 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
- Fix OpenMP library linking in DiskANN CMake configuration
- Add timeout protection for HuggingFace model loading to prevent hangs
- Improve embedding server process termination with better timeouts
- Make DiskANN backend default enabled alongside HNSW
- Update documentation to reflect both backends included by default
- Upgrade all GitHub Actions to v4 (v3 is deprecated)
- Use manual git checkout in manylinux2014 containers to avoid Node.js issues
- Update artifact naming to ensure uniqueness (required by v4)
- Add fail-fast: false to build strategies
- This maintains manylinux2014 compatibility while using latest actions
- 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
* fix: diskann zmq port and passages
* feat: auto discovery of packages and fix passage gen for diskann
* docs: embedding pruning
* refactor: passage structure
* feat: reproducible research datas, rpj_wiki & dpr
* refactor: chat and base searcher
* feat: chat on mps
