> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bondata.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Regex Pattern

> Classify, extract, detect, or multi-label records using regular expressions

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.

## Configuration

| Setting                 | Description                                                               |
| ----------------------- | ------------------------------------------------------------------------- |
| **Mode**                | Operation mode: **Classify**, **Extract**, **Detect**, or **Multi-Label** |
| **Input Field**         | The field to apply regex patterns to                                      |
| **Case Insensitive**    | Ignore case when matching (default: enabled)                              |
| **Virtual Object Name** | Namespace prefix for output fields (default: `regex_pattern`)             |

### Mode-specific settings

**Classify** - first matching pattern assigns a label:

| Setting               | Description                                         |
| --------------------- | --------------------------------------------------- |
| **Rules**             | Ordered list of `{label, pattern, exclude_pattern}` |
| **Output Field Name** | Name for the label column                           |
| **Default Value**     | Label when no pattern matches                       |

**Extract** - capture groups pull out structured data:

| Setting             | Description                                                |
| ------------------- | ---------------------------------------------------------- |
| **Extract Pattern** | Regex with capture groups                                  |
| **Extract Groups**  | Map each group to `{group_index, output_field, cast_type}` |

**Detect** - single pattern produces a boolean:

| Setting               | Description                 |
| --------------------- | --------------------------- |
| **Rules**             | Single pattern rule         |
| **Output Field Name** | Name for the boolean column |

**Multi-Label** - each pattern produces an independent boolean:

| Setting   | Description                                                         |
| --------- | ------------------------------------------------------------------- |
| **Rules** | List of `{label, pattern}` - each creates a separate boolean column |

## How It Works

<Steps>
  <Step title="Choose a mode">
    Select the operation that fits your use case - classification, extraction, detection, or multi-labeling.
  </Step>

  <Step title="Select the input field">
    Choose which field to apply patterns to.
  </Step>

  <Step title="Define patterns">
    Write regex patterns. For Classify and Multi-Label, add multiple rules with labels.
  </Step>
</Steps>

## 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

## Examples

### Classify products by name

* Mode: **Classify**
* Input: Product Name
* Rules:
  * Label "Electronics" → pattern `phone|laptop|tablet`
  * Label "Clothing" → pattern `shirt|pants|jacket`
* Default: "Other"

### 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)

### Detect email addresses

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

## 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

## Related Nodes

* **[Transform](/guide/nodes/transform/transform-node)** - rule-based field computation without regex
* **[Data Normalization](/guide/nodes/transform/data-normalization-node)** - LLM-powered text cleaning when regex is too rigid
* **[AI Enrichment](/guide/nodes/enrichment/ai-enrichment-node)** - LLM-based classification when pattern matching isn't sufficient