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Applies regex patterns to field values for classification, extraction, detection, or multi-labeling. Use it to categorize records, pull out structured data from text, or flag records matching specific patterns.

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Configuration

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Mode-specific settings

Classify - first matching pattern assigns a label: Extract - capture groups pull out structured data: Detect - single pattern produces a boolean: Multi-Label - each pattern produces an independent boolean:

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How It Works

1

Choose a mode

Select the operation that fits your use case - classification, extraction, detection, or multi-labeling.
2

Select the input field

Choose which field to apply patterns to.
3

Define patterns

Write regex patterns. For Classify and Multi-Label, add multiple rules with labels.

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Output

Depends on the mode:
  • Classify: a single label column with the first matching category
  • Extract: one column per capture group, optionally cast to specific types
  • Detect: a single boolean column
  • Multi-Label: one boolean column per rule

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Examples

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Classify products by name

  • Mode: Classify
  • Input: Product Name
  • Rules:
    • Label “Electronics” → pattern phone|laptop|tablet
    • Label “Clothing” → pattern shirt|pants|jacket
  • Default: “Other”

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Extract price and currency

  • Mode: Extract
  • Input: Price Text (e.g., “USD 149.99”)
  • Pattern: ([A-Z]{3})\s+(\d+\.\d+)
  • Groups: group 1 → currency (str), group 2 → amount (float)

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Detect email addresses

  • Mode: Detect
  • Input: Notes field
  • Pattern: [\w.-]+@[\w.-]+\.\w+
  • Output: has_email (boolean)

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Best Practices

  • Use Classify for first-match-wins categorization (order rules from most specific to most general)
  • Use Extract when you need to pull structured data out of text
  • Use Detect for simple yes/no pattern presence checks
  • Use Multi-Label when a record can belong to multiple categories simultaneously
  • Test patterns on sample data before running on the full dataset
  • Transform - rule-based field computation without regex
  • Data Normalization - LLM-powered text cleaning when regex is too rigid
  • AI Enrichment - LLM-based classification when pattern matching isn’t sufficient