25ed48e9e3
* fix(python): recover only missing immutable portfolio release artifacts * docs(python): record tested public tutorial gist and immutable revision * fix: unblock Python recovery with targeted dependency and CI repairs * fix: patch Quinn advisories and preserve frozen inventory on Windows * ci: verify release inventory checkout policy changes * fix: replace unmaintained YAML parser and stabilize serving acceptance
2.8 KiB
Native attention quickstart
Use published ruvector-attention==0.2.0 to blend synthetic embeddings with scaled dot product attention. This example calls the RuVector Rust runtime and checks its output against an independent NumPy reference.
Install and run
Python 3.10 or later; the native ruvector==0.1.3 wheel must support your platform. Save this README and attention_quickstart.py together.
uv venv .venv
uv pip install --python .venv/bin/python ruvector-attention==0.2.0 ruvector==0.1.3 numpy==2.2.6
.venv/bin/python attention_quickstart.py
On Windows use .venv/Scripts/python.exe in the two commands above. The exact script is attention_quickstart.py.
Expected output:
ruvector-attention=0.2.0 ruvector=0.1.3
weights=0.575975,0.283995,0.140029
blended=0.435946,0.283995
PASS: native attention matches reference; invalid inputs rejected
What the example proves
The three candidates act as both keys and values. The runtime computes softmax(query @ candidates.T / sqrt(dimension)) and their weighted sum. The first candidate receives the greatest weight. The example also verifies rejection of a dimension mismatch and a nonfinite query.
Fresh PyPI installation and the exact script passed on October 12, 2026, using CPython 3.10.22, Linux x86_64, glibc 2.39 and NumPy 2.2.6. This is a numerical correctness smoke test, with no speed or quality benchmark. This run does not establish fresh macOS or Windows execution.
Scope and limitations
There is no model download, text generation, trained cross encoder or model training. Attention ordering is dot product ordering. The package exposes this attention operation, not full npm feature parity. The wrapper converts inputs to contiguous float32; conversion can allocate before shape validation. Use external resource limits for untrusted workloads.
The published 0.2.0 wheel requires ruvector==0.1.3. Current repository main changes that dependency to 0.1.4. This tutorial deliberately reproduces the immutable published artifact and does not install unreleased source.
Sources and release recovery
PyPI package · Native runtime · Immutable source · Portfolio PR #1188 · Recovery ADR
The portfolio release is partially delivered. Recovery checks every existing filename and checksum against the original artifact ledger, then permits only missing original files through protected publishing. That does not change the successfully published attention wheel used here.