This release fixes issues for SREs watching stability and regressions.
Published 3mo
Data Pipelines & ETL
✓ No known CVEs patched
✓ 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 summaryFixed dedupe_df() zero-config crash and UTF‑8 CSV read errors.
Full changelog
Changes since v1.2.1
- fix: dedupe_df() zero-config auto-detection — calling
dedupe_df(df)with no config now auto-detects column types and builds matchkeys automatically (previously crashed with__placeholder__error) - fix: utf8-lossy encoding for all CSV reads — prevents crashes on files with Latin-1 characters (government data, international names)
- fix: autoconfig classification + blocking safety — dates no longer misclassified as phones, cities no longer misclassified as names, high-null columns skipped for blocking
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About GoldenMatch
All releases →Related context
Related tools
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]