GoldenMatch

vgoldenmatch-native-v0.1.15 scope: goldenmatch-native Feature

This release adds 2 notable features for engineering teams evaluating rollout.

Published 12d Data Pipelines & ETL

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✓ No known CVEs patched

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✓ No known CVEs patched in this version

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution

+14 more

fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Summary

AI summary

Native FS negative evidence logic added for both kernels with level‑threshold banding.

Full changelog

Native FS negative evidence (FS_SUPPORTS_NE) in both kernels (score_block_pairs_fs + match_fused_fs) and fused custom level_thresholds banding (FUSED_FS_SUPPORTS_LEVEL_THRESHOLDS). NE fires when both values are present post-transform, non-empty, and similarity is strictly below the threshold; contributes the resolved w_fired else exactly 0. Old wheels keep the pure-Python fallback via capability-gated Python callers. See PR #1775.

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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.