GoldenMatch

v0.7.1 Feature

This release adds 1 notable feature for engineering teams evaluating rollout.

Published 4mo 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

New goldenmatch label command builds ground truth CSV interactively

Full changelog

Ground Truth Builder

New goldenmatch label command builds ground truth CSV by showing pairs interactively:

goldenmatch label data.csv -c config.yaml -o ground_truth.csv -n 50

Type y (match), n (no match), s (skip), q (quit). Three strategies: borderline (most ambiguous), random, hardest. Supports --append. Output feeds directly into goldenmatch evaluate.

Documentation

All docs updated with Ray backend and label command. 911 tests, 21 CLI commands.

pip install --upgrade 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.