v0.2.0: Early termination + rayon parallelism for high-dim performance#1
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…ormance Spatial index query_radius now uses early termination for Euclidean distance: squared differences are accumulated in chunks of 4 dimensions and bailed out early when the partial sum exceeds eps². For 996-dim embeddings with eps=1.2, most non-neighbor pairs are rejected after ~5-15% of dimensions. Above 1000 points, the brute-force scan is parallelized across CPU cores via rayon. Below that threshold, sequential scan avoids thread pool overhead. Benchmark (10 batches x 1500 points, 996-dim, eps=1.2): v0.1.0: 160.2s total v0.2.0: 24.9s total (6.4x faster) Bump version to 0.2.0 across Cargo.toml, pyproject.toml, and __init__.py. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Build wheels for Linux (x86_64, aarch64), macOS (x86_64, aarch64), and Windows (x86_64) on every push/PR. This catches packaging failures before merge rather than at release time. No publishing — just build verification. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Linux aarch64 cross-compilation container lacks Python interpreters, causing maturin to fail. Specify -i 3.12 explicitly (matching the release workflow pattern). One version is sufficient to verify the build. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
cargo bench --bench batch_scaling): 10 batches x 1,500 points, 996-dim L2-normalized vectors.__init__.py.Benchmark results (10 batches x 1500 points, 996-dim, eps=1.2)
Test plan
test_early_termination_correctness)🤖 Generated with Claude Code