This release adds 2 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
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Summary
AI summaryInitial release of goldenmatch-embed: a pyo3/maturin wheel wrapping the goldenembed-rs ONNX embedding runtime.
Changes in this release
| Type | Severity | Summary | CVE |
|---|---|---|---|
| Feature | Low |
Adds goldenmatch-embed pyo3/maturin wheel over goldenembed-rs ONNX runtime. Adds goldenmatch-embed pyo3/maturin wheel over goldenembed-rs ONNX runtime. Source: llm_adapter@2026-06-05 Confidence: high |
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| Feature | Low |
Integrates goldenmatch_embed_local UDF into DuckDB for local embedding. Integrates goldenmatch_embed_local UDF into DuckDB for local embedding. Source: granite4.1:30b@2026-06-05-audit Confidence: low |
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Full changelog
Initial release of goldenmatch-embed: a thin pyo3/maturin wheel over the pyo3-free goldenembed-rs ONNX embedding runtime.
Used by the DuckDB goldenmatch_embed_local UDF (pip install goldenmatch-duckdb[embed]) so the warehouse embed path runs the same goldenembed-rs kernel as Postgres and DataFusion, with no network and no torch. Part of issue #509.
GoldenEmbed.load(model_dir).embed([text]) -> list of float32 vectors.
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About GoldenMatch
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Earlier breaking changes
- v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
- vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
- vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
- vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
- v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.
Beta — feedback welcome: [email protected]