# Python
pip install goldenmatch && goldenmatch dedupe customers.csv

# TypeScript / Node.js
npm install goldenmatch

🆕 v1.5.0 (Python) · v0.3.1 (npm) — Auto-config now runs a preflight + postflight verification layer. Biblio / domain-extracted schemas no longer crash under zero-config, remote-asset scorers are demoted by default (offline-safe CI), strict mode for deterministic parity runs, and every DedupeResult carries an inspectable postflight_report. See Auto-Config Verification. Built by Ben Severn.

Why GoldenMatch?

• Zero-config — auto-detects columns, picks scorers, and runs. No training data needed
• 97.2% F1 on DBLP-ACM out of the box. DQBench ER score: 95.30
• Privacy-preserving — match across organizations without sharing raw data (PPRL, 92.4% F1)
• 30 MCP tools — use from Claude Desktop, Claude Code, or any AI assistant (Smithery)
• Production-ready — Postgres sync, daemon mode, lineage tracking, review queues

Choose your path

TypeScript / Node.js

GoldenMatch ships an npm package with full feature parity — same scorers, clustering, golden records, and YAML configs.

npm install goldenmatch

import { dedupe } from "goldenmatch";

const rows = [
  { id: 1, name: "John Smith", email: "john@example.com", zip: "12345" },
  { id: 2, name: "Jon Smith",  email: "john@example.com", zip: "12345" },
  { id: 3, name: "Jane Doe",   email: "jane@example.com", zip: "54321" },
];

const result = dedupe(rows, {
  fuzzy: { name: 0.85 },
  blocking: ["zip"],
  threshold: 0.85,
});

console.log(result.stats);  // { totalRecords: 3, totalClusters: 2, ... }

• Edge-safe core — runs in browsers, Vercel Edge Runtime, Cloudflare Workers, Deno
• Feature parity with Python: fuzzy scorers, probabilistic Fellegi-Sunter, PPRL, graph ER, LLM reranking, MCP/REST/A2A servers, 11+ CLI commands, interactive TUI
• 478 tests, strict TypeScript (noUncheckedIndexedAccess, exactOptionalPropertyTypes)
• Zero-dep install works — optional peer deps unlock native paths (hnswlib-node, @huggingface/transformers for ONNX cross-encoder, piscina for worker threads, pg/duckdb/snowflake for data connectors)

Full docs: benzsevern.github.io/goldenmatch/typescript See packages/goldenmatch-js/examples/ for 10+ usage examples.

Installation

pip install goldenmatch                    # core (files only)
pip install goldenmatch[embeddings]        # + sentence-transformers, FAISS
pip install goldenmatch[llm]               # + Claude/OpenAI for LLM boost
pip install goldenmatch[postgres]          # + Postgres database sync
pip install goldenmatch[snowflake]        # + Snowflake connector
pip install goldenmatch[bigquery]         # + BigQuery connector
pip install goldenmatch[databricks]       # + Databricks connector
pip install goldenmatch[salesforce]       # + Salesforce connector
pip install goldenmatch[duckdb]           # + DuckDB backend
pip install goldenmatch[quality]          # + GoldenCheck data quality scanning

# Run the setup wizard to configure GPU, API keys, and database:
goldenmatch setup

Python API

GoldenMatch exposes 95 functions and classes from a single import. See examples/ for complete runnable scripts.

import goldenmatch as gm

Quick Start

import goldenmatch as gm

# Deduplicate a CSV (zero-config)
result = gm.dedupe("customers.csv")

# Exact + fuzzy matching
result = gm.dedupe("customers.csv", exact=["email"], fuzzy={"name": 0.85, "zip": 0.95})
result.golden.write_csv("deduped.csv")
print(result)  # DedupeResult(records=5000, clusters=847, match_rate=12.0%)

# Match across files
result = gm.match("new_customers.csv", "master.csv", fuzzy={"name": 0.85})
result.to_csv("matches.csv")

# With YAML config
result = gm.dedupe("data.csv", config="config.yaml")

# With LLM scorer for product matching
result = gm.dedupe("products.csv", fuzzy={"title": 0.80}, llm_scorer=True)

# With Ray backend for large datasets
result = gm.dedupe("huge.parquet", exact=["email"], backend="ray")

Auto-Config Verification (v1.5.0)

Zero-config used to crash on bibliographic and domain-extracted schemas — auto-config would emit a matchkey referencing __title_key__ without enabling config.domain, and the pipeline would raise ValueError: Missing required columns. v1.5.0 closes the gap with a preflight + postflight verification layer that runs automatically around auto_configure_df.

Preflight (gm.preflight) runs 6 checks at the end of auto_configure_df:

• column resolution (auto-repairs missing domain-extracted columns by enabling config.domain)
• cardinality bounds on exact matchkeys (drops near-unique and near-constant keys)
• block-size sanity (flags blocks that would stall the scorer)
• remote-asset demotion (any embedding, record_embedding, or cross-encoder rerank is demoted unless you pass allow_remote_assets=True)
• confidence-gated weight capping (low-confidence fields cap at weight 0.3)

Unrepairable issues raise ConfigValidationError with the full PreflightReport attached as err.report. Repaired issues stay on the report as findings with repaired=True.

Postflight (gm.postflight) runs 4 signals after scoring, before clustering:

• score-distribution histogram + bimodality detection (auto-nudges threshold on clear bimodality)
• blocking-recall estimate (gated at 10K+ rows)
• preliminary cluster sizes + oversized-cluster bottleneck pair
• threshold-band overlap percentage (advises --llm-auto when overlap > 20% and LLM is off)

The report attaches to DedupeResult.postflight_report / MatchResult.postflight_report.

import goldenmatch as gm
import polars as pl

df = pl.read_csv("bibliography.csv")

# Zero-config -- preflight + postflight run automatically
result = gm.dedupe_df(df)

# Inspect the preflight report (private-by-convention underscore)
for finding in result.config._preflight_report.findings:
    print(f"[{finding.severity}] {finding.check}: {finding.message}")

# Inspect postflight signals (public)
sig = result.postflight_report.signals
print(f"Scored {sig['total_pairs_scored']} pairs")
print(f"Threshold overlap: {sig['threshold_overlap_pct']:.1%}")
print(f"Oversized clusters: {len(sig['oversized_clusters'])}")

Offline by default. Remote-asset scorers are demoted unless you opt in:

cfg = gm.auto_configure_df(df, allow_remote_assets=True)  # loads cross-encoder etc.

Strict mode for parity runs. strict=True still computes postflight signals and emits advisories, but skips threshold adjustments — use it for DQBench, regression suites, and any reproducible output:

cfg = gm.auto_configure_df(df, strict=True)

New classifier smarts in v1.5.0:

• Columns with cardinality ≥ 0.95 are classified as identifier, not phone / zip / numeric.
• New year col_type routes to blocking, not scoring.
• New multi_name col_type handles comma/semicolon-delimited author-style fields.
• Low-confidence fields (< 0.5) cap at weight 0.3.

See examples/verification_inspection.py and examples/strict_mode_parity.py for runnable walkthroughs.

Privacy-Preserving Linkage

import goldenmatch as gm

# Auto-configured PPRL (picks fields and threshold automatically)
result = gm.pprl_link("hospital_a.csv", "hospital_b.csv")
print(f"Found {result['match_count']} matches across {len(result['clusters'])} clusters")

# Manual field selection
result = gm.pprl_link("party_a.csv", "party_b.csv",
    fields=["first_name", "last_name", "dob", "zip"],
    threshold=0.85, security_level="high")

# Auto-config analysis
config = gm.pprl_auto_config(df)
print(config.recommended_fields)  # ['first_name', 'last_name', 'zip_code', 'birth_year']

Evaluate Accuracy

import goldenmatch as gm

# Measure precision/recall/F1 against ground truth
metrics = gm.evaluate("data.csv", config="config.yaml", ground_truth="gt.csv")
print(f"F1: {metrics['f1']:.1%}, Precision: {metrics['precision']:.1%}")

# Evaluate programmatically
result = gm.evaluate_pairs(predicted_pairs, ground_truth_set)
print(result.f1)

Build Configs Programmatically

import goldenmatch as gm

# Auto-generate config from data
config = gm.auto_configure([("data.csv", "source")])

# Or build manually
config = gm.GoldenMatchConfig(
    matchkeys=[
        gm.MatchkeyConfig(name="exact_email", type="exact",
            fields=[gm.MatchkeyField(field="email", transforms=["lowercase"])]),
        gm.MatchkeyConfig(name="fuzzy_name", type="weighted", threshold=0.85,
            fields=[
                gm.MatchkeyField(field="name", scorer="jaro_winkler", weight=0.7),
                gm.MatchkeyField(field="zip", scorer="exact", weight=0.3),
            ]),
    ],
    blocking=gm.BlockingConfig(strategy="learned"),
    llm_scorer=gm.LLMScorerConfig(enabled=True, mode="cluster"),
    backend="ray",
)

Streaming / Incremental

import goldenmatch as gm

# Match a single new record against existing data
matches = gm.match_one(new_record, existing_df, matchkey)

# Stream processor for continuous matching
processor = gm.StreamProcessor(df, config)
matches = processor.process_record(new_record)

Advanced Features

import goldenmatch as gm

# Domain extraction
rulebooks = gm.discover_rulebooks()  # 7 built-in packs
enhanced_df, low_conf = gm.extract_with_rulebook(df, "title", rulebooks["electronics"])

# Fellegi-Sunter probabilistic
em_result = gm.train_em(df, matchkey, n_sample_pairs=10000)
pairs = gm.score_probabilistic(block_df, matchkey, em_result)

# Explain a match decision
explanation = gm.explain_pair(record_a, record_b, matchkey)

# Cluster operations
gm.unmerge_record(record_id, clusters)  # Remove from cluster
gm.unmerge_cluster(cluster_id, clusters)  # Shatter to singletons

# Data quality
df, fixes = gm.auto_fix_dataframe(df)
anomalies = gm.detect_anomalies(df)
column_map = gm.auto_map_columns(df_a, df_b)  # Schema matching

# Graph ER (multi-table)
clusters = gm.run_graph_er(entities, relationships)

Setup Wizard

Run goldenmatch setup for an interactive walkthrough:

Guides you through GPU mode selection, Vertex AI / Colab / local GPU configuration, LLM boost API keys, and database sync — with copy-paste commands at every step.

Why GoldenMatch?

GoldenMatch is the only tool that combines zero-config operation, probabilistic matching, LLM scoring, privacy-preserving linkage, and golden record synthesis in a single Python package.

Quick Start

Zero-Config (no YAML needed)

goldenmatch dedupe customers.csv

Auto-detects column types (name, email, phone, zip, address, description), assigns appropriate scorers, picks blocking strategy, and launches the TUI for review.

With Config

goldenmatch dedupe customers.csv --config config.yaml --output-all --output-dir results/

Match Mode

goldenmatch match targets.csv --against reference.csv --config config.yaml --output-all

Database Sync

# First run: full scan, create metadata tables
goldenmatch sync --table customers --connection-string "$DATABASE_URL" --config config.yaml

# Subsequent runs: incremental (only new records)
goldenmatch sync --table customers --connection-string "$DATABASE_URL"

How It Works

Files/DB → Ingest → Standardize → Block → Score → Cluster → Golden Records → Output
                                     ↑        ↑
                              SQL blocking   10 scorers
                              ANN blocking   ensemble
                              7 strategies   embeddings
                                             parallel blocks

Pipeline:

1. Ingest — CSV, Excel, Parquet, or Postgres table
2. Standardize — configurable per-column transforms
3. Block — reduce comparison space (multi-pass, ANN, canopy, etc.)
4. Score — compare record pairs with appropriate scorer
5. Cluster — group matches via Union-Find; auto-split oversized clusters via MST; assign quality labels (strong/weak/split)
6. Golden — merge each cluster into one canonical record using quality-weighted survivorship (5 strategies); track field-level provenance
7. Output — files (CSV/Parquet) or database tables + lineage JSON sidecar with provenance

Config Reference

matchkeys:
  - name: exact_email
    type: exact
    fields:
      - field: email
        transforms: [lowercase, strip]

  - name: fuzzy_name_zip
    type: weighted
    threshold: 0.85
    rerank: true             # re-score borderline pairs with cross-encoder
    rerank_band: 0.1         # pairs within threshold +/- 0.1 get reranked
    fields:
      - field: first_name
        scorer: jaro_winkler
        weight: 0.4
        transforms: [lowercase, strip]
      - field: last_name
        scorer: jaro_winkler
        weight: 0.4
        transforms: [lowercase, strip]
      - field: zip
        scorer: exact
        weight: 0.2

  - name: semantic
    type: weighted
    threshold: 0.80
    fields:
      - columns: [title, authors, venue]
        scorer: record_embedding
        weight: 1.0
        column_weights: {title: 2.0, authors: 1.0, venue: 0.5}  # bias embedding toward title

llm_scorer:
  enabled: true              # score borderline pairs with GPT/Claude
  auto_threshold: 0.95       # auto-accept pairs above this
  candidate_lo: 0.75         # LLM scores pairs in [0.75, 0.95]
  # provider: openai         # auto-detected from OPENAI_API_KEY
  # model: gpt-4o-mini       # default, cheapest option

blocking:
  strategy: adaptive         # static | adaptive | sorted_neighborhood | multi_pass | ann | ann_pairs | canopy
  auto_select: true          # auto-pick best key by histogram analysis
  keys:
    - fields: [zip]
    - fields: [last_name]
      transforms: [lowercase, soundex]

golden_rules:
  default_strategy: most_complete
  auto_split: true                  # Auto-split oversized clusters via MST
  quality_weighting: true           # Use GoldenCheck quality scores in survivorship
  weak_cluster_threshold: 0.3       # Edge gap threshold for confidence downgrade
  field_rules:
    email: { strategy: majority_vote }
    first_name: { strategy: source_priority, source_priority: [crm, marketing] }

output:
  directory: ./output
  format: csv

Scorers

Blocking Strategies

Database Integration

GoldenMatch can sync against live Postgres databases with incremental matching:

pip install goldenmatch[postgres]

goldenmatch sync \
  --table customers \
  --connection-string "postgresql://user:pass@localhost/mydb" \
  --config config.yaml

Features:

• Incremental sync — only processes records added since last run
• Hybrid blocking — SQL WHERE clauses for exact fields + FAISS ANN for semantic fields, results unioned
• Persistent ANN index — disk cache + DB source of truth, progressive embedding across runs
• Golden record versioning — append-only with is_current flag, full audit trail
• Cluster management — persistent clusters with merge, conflict detection, max size safety cap

Metadata tables (auto-created):

SQL Extensions

Use GoldenMatch directly from PostgreSQL or DuckDB:

-- PostgreSQL
CREATE EXTENSION goldenmatch_pg;
SELECT goldenmatch.goldenmatch_dedupe_table('customers', '{"exact": ["email"]}');
SELECT goldenmatch.goldenmatch_score('John Smith', 'Jon Smyth', 'jaro_winkler');

# DuckDB
pip install goldenmatch-duckdb

import duckdb, goldenmatch_duckdb
con = duckdb.connect()
goldenmatch_duckdb.register(con)
con.sql("SELECT goldenmatch_score('John Smith', 'Jon Smyth', 'jaro_winkler')")

See goldenmatch-extensions for installation and full documentation.

LLM Boost (Optional)

For harder datasets where zero-shot scoring isn't enough:

pip install goldenmatch[llm]

# First run: LLM labels ~300 pairs (~$0.30), fine-tunes embedding model
goldenmatch dedupe products.csv --llm-boost

# Subsequent runs: uses saved model ($0)
goldenmatch dedupe products.csv --llm-boost

Tiered auto-escalation:

• Level 1 — zero-shot (free, instant)
• Level 2 — bi-encoder fine-tuning (~$0.20, ~2 min CPU)
• Level 3 — Ditto-style cross-encoder with data augmentation (~$0.50, ~5 min CPU)

Active sampling selects the most informative pairs for the LLM to label (uncertainty, disagreement, boundary, diversity), reducing label cost by ~45% compared to random sampling.

Iterative calibration: When many borderline pairs exist, iterative calibration samples ~100 pairs per round, learns the optimal threshold via grid search, and applies it to all candidates — typically converging in 2-3 rounds.

Note: LLM boost is most valuable for product matching with local models (MiniLM) where it improved Abt-Buy from 44.5% to 59.5% F1. For structured data (names, addresses, bibliographic), fuzzy matching alone achieves 97%+ F1.

Benchmarks

Leipzig Entity Resolution Benchmarks

Structured data (names, addresses, bibliographic): RapidFuzz multi-pass fuzzy matching at 97.2% — zero cost, zero labels. Product matching: Vertex AI embeddings for candidate generation + GPT-4o-mini scorer for borderline pairs achieves 81.7% at ~$0.74 total cost.

Throughput (Scale Curve)

Measured on a laptop (17GB RAM) with exact + fuzzy matching, blocking, clustering, and golden record generation:

Fuzzy matching speedup: Parallel block scoring + intra-field early termination reduced 100K fuzzy matching from ~100s to ~39s (2.5x) through the pipeline. The 1M exact-only benchmark runs in 7.8s.

Equipment data (401K rows): 27,937 clusters, 384,650 matched, 323s. LLM calibration learned threshold from 200 pairs (~$0.01). ANN fallback created 363 sub-blocks from 15 oversized blocks.

For datasets over 1M records, use goldenmatch sync (database mode) with incremental matching and persistent ANN indexing. See Large Dataset Mode.

How GoldenMatch Compares

GoldenMatch's sweet spot is ease of use + competitive accuracy. On bibliographic matching (DBLP-ACM), GoldenMatch hits 97.2% with zero config. On product matching (Abt-Buy), the LLM scorer reaches 81.7% — within 8pts of Ditto's 89.3%, but with zero training labels and no GPU. Ditto requires 1000+ hand-labeled pairs and a GPU.

Library Comparison (v1.2.7)

Head-to-head against Splink, Dedupe, and RecordLinkage on two datasets. GoldenMatch uses explicit config, zero training data.

Febrl (5,000 synthetic PII records, 6,538 true pairs):

DBLP-ACM (4,910 bibliographic records, 2,224 true matches):

Key takeaway: GoldenMatch is the most consistent performer — top-2 F1 on both datasets with zero training data. Splink dominates structured PII but struggles on non-PII. RecordLinkage wins DBLP-ACM but lags on PII.

config = GoldenMatchConfig(
    blocking=BlockingConfig(
        strategy="multi_pass",
        passes=[
            BlockingKeyConfig(fields=["surname"], transforms=["soundex"]),
            BlockingKeyConfig(fields=["given_name"], transforms=["soundex"]),
            BlockingKeyConfig(fields=["postcode"], transforms=[]),
            BlockingKeyConfig(fields=["date_of_birth"], transforms=[]),
        ],
        max_block_size=500, skip_oversized=True,
    ),
    matchkeys=[MatchkeyConfig(
        name="person", type="weighted", threshold=0.7,
        fields=[
            MatchkeyField(field="given_name", scorer="jaro_winkler", weight=2.0, transforms=["lowercase", "strip"]),
            MatchkeyField(field="surname", scorer="jaro_winkler", weight=2.0, transforms=["lowercase", "strip"]),
            MatchkeyField(field="date_of_birth", scorer="exact", weight=1.5),
            MatchkeyField(field="address_1", scorer="token_sort", weight=1.0, transforms=["lowercase", "strip"]),
            MatchkeyField(field="postcode", scorer="exact", weight=0.5),
        ],
    )],
)
result = goldenmatch.dedupe_df(df, config=config)

Large Dataset Mode

For datasets over 1M records, use database sync mode. GoldenMatch processes records in chunks, maintains a persistent ANN index, and matches incrementally:

# Load into Postgres, then sync
goldenmatch sync --table customers --connection-string "$DATABASE_URL" --config config.yaml

# Watch for new records continuously
goldenmatch watch --table customers --connection-string "$DATABASE_URL" --interval 30

How it works:

• Reads in configurable chunks (default 10K) — never loads entire table into memory
• Hybrid blocking: SQL WHERE for exact fields + persistent FAISS ANN for semantic fields
• Progressive embedding: computes 100K embeddings per run, ANN improves over time
• Persistent clusters with golden record versioning

Scale: Tested to 10M+ records in Postgres. For 100M+, use larger chunk sizes and dedicated Postgres infrastructure.

Interactive TUI

GoldenMatch includes a gold-themed interactive terminal UI:

• Auto-config summary — first screen shows detected columns, scorers, and blocking strategy with Run/Edit/Save options
• Pipeline progress — full-screen progress with stage tracker (✓/●/○) on first run, footer bar on re-runs
• Split-view matches — cluster list on the left, golden record + member details on the right
• Live threshold slider — arrow keys adjust threshold in 0.05 increments with instant cluster count preview
• Keyboard shortcuts — 1-6 jump to tabs (Data, Config, Matches, Golden, Boost, Export), F5 run, ? show all shortcuts, Ctrl+E export

Data profiling:

Match results with cluster detail:

Golden records:

Settings Persistence

GoldenMatch saves preferences across sessions:

• Global: ~/.goldenmatch/settings.yaml — output mode, default model, API keys
• Project: .goldenmatch.yaml — column mappings, thresholds, blocking config

Settings tuned in the TUI can be saved to the project file. Next run picks them up automatically.

CLI Reference

Key dedupe flags:

Remote MCP Server

GoldenMatch is available as a hosted MCP server on Smithery — connect from any MCP client without installing anything.

Claude Desktop / Claude Code:

{
  "mcpServers": {
    "goldenmatch": {
      "url": "https://goldenmatch-mcp-production.up.railway.app/mcp/"
    }
  }
}

Local server (if you prefer to run locally):

pip install goldenmatch[mcp]
goldenmatch mcp-serve data.csv

30 tools available: deduplicate files, match records, explain decisions, review borderline pairs, privacy-preserving linkage, configure rules, scan data quality, run transforms, and synthesize golden records.

Architecture

goldenmatch/
├── cli/            # 21 CLI commands (Typer)
│                   #   Python API: 95 public exports from `import goldenmatch as gm`
│                   #   -- every feature accessible without knowing internal module structure
├── config/         # Pydantic schemas, YAML loader, settings
├── core/           # Pipeline: ingest, block, score, cluster, golden, explainer,
│                   #   report, dashboard, graph, anomaly, diff, rollback,
│                   #   schema_match, chunked, cloud_ingest, api_connector, scheduler,
│                   #   llm_scorer, lineage, match_one, evaluate, gpu, vertex_embedder,
│                   #   probabilistic, learned_blocking, streaming, graph_er, domain
├── domains/        # 7 built-in YAML domain packs (electronics, software, healthcare, ...)
├── plugins/        # Plugin system (scorers, transforms, connectors, golden strategies)
├── connectors/     # Enterprise connectors (Snowflake, Databricks, BigQuery, HubSpot, Salesforce)
├── backends/       # DuckDB backend for out-of-core processing
├── db/             # Postgres: connector, sync, reconcile, clusters, ANN index
├── api/            # REST API server
├── mcp/            # MCP server for Claude Desktop
├── tui/            # Gold-themed Textual TUI + setup wizard
└── utils/          # Transforms, helpers

Run tests: pytest (924 tests)

Part of the Golden Suite

What's New in v1.4.0

• Scoring & survivorship quality -- MST-based cluster auto-splitting at weakest edges, cluster quality labels (strong/weak/split), quality-weighted survivorship strategies using GoldenCheck scores, field-level provenance tracking.
• Smart auto-config -- auto-config now profiles cleaned data (after GoldenCheck/GoldenFlow), detects data domains and extracts identifiers, selects learned blocking for large datasets, enables reranking for multi-field matchkeys, adjusts thresholds from data quality.
• GoldenFlow integration -- optional data transformation step in the pipeline. Phone normalization, date standardization, categorical correction. pip install goldenmatch[transform].
• llm_auto flag -- dedupe_df(df, llm_auto=True) auto-enables LLM scorer ($0.05 budget cap) and memory store when API key detected.

What's New in v1.3.0

• CCMS cluster comparison -- compare two clustering outcomes without ground truth using the Case Count Metric System (Talburt et al.). Classifies each cluster as unchanged, merged, partitioned, or overlapping. Includes Talburt-Wang Index (TWI) for normalized similarity.
• Parameter sensitivity analysis -- sweep threshold, blocking, or matchkey parameters across a range and compare each run against a baseline. stability_report() identifies optimal value ranges. Failed sweep points are logged and skipped, preserving partial results.
• New CLI commands -- goldenmatch compare-clusters for ad-hoc comparison, goldenmatch sensitivity for automated parameter tuning.
• New Python API -- compare_clusters(), CompareResult, run_sensitivity(), SensitivityResult, SweepParam exported from goldenmatch.

What's New in v1.2.7

• Auto-config cardinality guards — three new guards prevent auto-config failures on edge-case data:
  • Blocking: excludes near-unique columns (cardinality_ratio >= 0.95)
  • Matchkeys: skips exact matchkeys for low-cardinality columns (cardinality_ratio < 0.01)
  • Description columns: routes long text to fuzzy matching (token_sort) alongside embedding
• Library comparison benchmarks — head-to-head results against Splink, Dedupe, and RecordLinkage on Febrl (0.971 F1) and DBLP-ACM (0.918 F1). GoldenMatch is the most consistent performer across data types.

What's New in v1.2.6

• Iterative LLM calibration — instead of scoring all candidates, calibrates the decision threshold from 200 sampled pairs. Typically converges in 2-3 rounds at negligible cost ($0.01 on a 401K-row equipment dataset).
• ANN hybrid blocking — oversized blocks that exceed the max block size now fall back to embedding-based ANN sub-blocking automatically, keeping blocks tractable without manual tuning.
• Auto-config classification fixes — improved heuristics for ID and price fields, utility-based field ranking to select better blocking keys, and LLM-assisted classification for ambiguous column names.

Author

Ben Severn

License

MIT