GoldenMatch

v1.4.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 3mo 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

Updates Scoring & Survivorship Quality, LLM + Memory Auto-Enablement, and Data-Driven Strategy Selection across a mixed release.

Full changelog

What's New

Scoring & Survivorship Quality (#30)

MST-based cluster auto-splitting — oversized clusters split at weakest edge automatically
Cluster quality labels — strong, weak (confidence downgraded), split (auto-split)
Quality-weighted survivorship — merge strategies use GoldenCheck quality scores
Field-level provenance — tracks source row, strategy, confidence per golden record field

Data-Driven Strategy Selection (#32)

Learned blocking auto-selected for datasets >= 5000 rows (96.9% F1 matching hand-tuned)
Cross-encoder reranking enabled for weighted matchkeys with 3+ fields
Adaptive thresholds from data quality: -0.05 for high null rate, +0.05 for short strings

LLM + Memory Auto-Enablement (#36)

llm_auto flag — dedupe_df(df, llm_auto=True) auto-enables LLM scorer ($0.05 budget cap) and memory store when API key detected
Applied uniformly across all config paths (zero-config, explicit kwargs, YAML config)
Memory store (SQLite, persistent corrections) enabled alongside LLM scorer

Install

pip install --upgrade goldenmatch

Full Changelog: https://github.com/benzsevern/goldenmatch/compare/v1.3.2...v1.4.0

View diff on GitHub

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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.