Domain packs

GoldenMatch includes 7 built-in YAML rulebooks that extract structured fields from unstructured product descriptions and other domain-specific text.

Built-in packs

Pack	Domain	Extracted Fields
`electronics`	Consumer electronics	brand, model, SKU, color, specs
`software`	Software products	name, version, edition, platform
`healthcare`	Medical records	NPI, CPT codes, drug names, dosages
`financial`	Financial instruments	CUSIP, LEI, ticker, account numbers
`real_estate`	Property listings	address, MLS number, lot size, year built
`people`	Person records	name parts, phone, email, SSN pattern
`retail`	General retail	brand, SKU, UPC, size, color

Using domain packs

Auto-detection

import goldenmatch as gm

rulebooks = gm.discover_rulebooks()  # Returns all 7 packs
print(list(rulebooks.keys()))
# ['electronics', 'software', 'healthcare', 'financial', 'real_estate', 'people', 'retail']

Extract fields

import goldenmatch as gm

rulebooks = gm.discover_rulebooks()
enhanced_df, low_confidence = gm.extract_with_rulebook(df, "title", rulebooks["electronics"])

# enhanced_df has new columns: __brand__, __model__, __sku__, etc.
# low_confidence contains records where extraction confidence was low

Auto-detect domain

rulebook = gm.match_domain(df.columns)
# Scores rulebooks by signal words in the column names.
# Returns the best-matching DomainRulebook, or None

YAML config

Enable domain extraction in your config file:

domain:
  enabled: true
  mode: product

Or let GoldenMatch auto-detect:

domain:
  enabled: true

Electronics pack

Extracts brand, model number, SKU, color, and technical specs from product titles.

"Samsung Galaxy S24 Ultra 256GB Titanium Black SM-S928B"
  -> brand: Samsung
  -> model: Galaxy S24 Ultra
  -> sku: SM-S928B
  -> color: Titanium Black
  -> specs: 256GB

Model normalization strips hyphens, region suffixes, and color suffixes for better matching.

Software pack

Extracts name, version, edition, and platform.

"Microsoft Office 365 Professional Plus - Windows"
  -> name: Microsoft Office
  -> version: 365
  -> edition: Professional Plus
  -> platform: Windows

Healthcare pack

Extracts medical identifiers with contextual prefix requirements (e.g., NPI:, CPT:) to avoid false positives on generic numbers.

"Provider NPI:1234567890, CPT:99213 Office Visit"
  -> npi: 1234567890
  -> cpt_code: 99213

Financial pack

Extracts financial identifiers (CUSIP, LEI, ticker). Contextual prefixes required.

"Bond CUSIP:037833AK6, Issuer LEI:HWUPKR0MPOU8FGXBT394"
  -> cusip: 037833AK6
  -> lei: HWUPKR0MPOU8FGXBT394

Custom domain packs

Create your own YAML rulebook and place it in one of the search paths:

Path	Scope
`.goldenmatch/domains/`	Project-local
`~/.goldenmatch/domains/`	Global (user)
`goldenmatch/domains/`	Built-in (read-only)

Rulebook YAML format

# .goldenmatch/domains/my_domain.yaml
name: my_domain
description: Custom domain for matching widgets
signals:
  - pattern: "widget"
    weight: 1.0
  - pattern: "part_?number"
    weight: 0.8
extractors:
  - name: part_number
    pattern: "PN[:-]?\\s*(\\w{6,12})"
    group: 1
  - name: manufacturer
    pattern: "(Acme|Globex|Initech)"
    group: 1
normalizers:
  part_number:
    strip_chars: "-"
    uppercase: true

Create via Python

import goldenmatch as gm

gm.save_rulebook(rulebook, ".goldenmatch/domains/my_domain.yaml")
loaded = gm.load_rulebook(".goldenmatch/domains/my_domain.yaml")

Create via MCP

The MCP server provides tools for domain management:

Tool	Description
`list_domains`	List all available domain packs
`create_domain`	Create a new custom domain pack
`test_domain`	Test a domain pack against sample data

Domain extraction in the pipeline

Domain extraction runs between the standardize and matchkeys steps. It adds extracted fields as new columns (prefixed with __) that can be used in matchkeys:

matchkeys:
  - name: product_match
    type: weighted
    threshold: 0.85
    fields:
      - field: __brand__
        scorer: exact
        weight: 0.3
      - field: __model__
        scorer: jaro_winkler
        weight: 0.5
      - field: title
        scorer: token_sort
        weight: 0.2

Benchmarks

Domain extraction significantly improves product matching:

Dataset	Without Domain	With Domain	Improvement
Abt-Buy (electronics)	44.5% F1	72.2% F1	+27.7pp
Amazon-Google (software)	45.3% F1	42.1% F1	-3.2pp

Domain extraction helps datasets with structured identifiers (brand, model, SKU) but can hurt datasets with unstructured descriptions. For software matching, clean embedding + ANN pipelines perform better.

Get started

Concepts

GoldenMatch

GoldenCheck

GoldenFlow

GoldenPipe

GoldenAnalysis

InferMap

SQL extensions

Reference

Research

Built-in packs

Using domain packs

Auto-detection

Extract fields

Auto-detect domain

YAML config

Electronics pack

Software pack

Healthcare pack

Financial pack

Custom domain packs

Rulebook YAML format

Create via Python

Create via MCP

Domain extraction in the pipeline

Benchmarks

​Built-in packs

​Using domain packs

​Auto-detection

​Extract fields

​Auto-detect domain

​YAML config

​Electronics pack

​Software pack

​Healthcare pack

​Financial pack

​Custom domain packs

​Rulebook YAML format

​Create via Python

​Create via MCP

​Domain extraction in the pipeline

​Benchmarks

Built-in packs

Using domain packs

Auto-detection

Extract fields

Auto-detect domain

YAML config

Electronics pack

Software pack

Healthcare pack

Financial pack

Custom domain packs

Rulebook YAML format

Create via Python

Create via MCP

Domain extraction in the pipeline

Benchmarks