The If Response Contains node allows you to create dynamic paths in your workflow by checking if LLM responses contain specific text patterns or match regex expressions.

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Functionality

The If Response Contains node acts as a content filter, analyzing LLM response text and directing the flow based on pattern matching. This can be useful for:

  • Response Validation: Ensure AI responses meet specific criteria
  • Content Classification: Route responses based on detected themes or content
  • Quality Control: Identify and handle responses with particular characteristics
  • Response Filtering: Filter out unwanted content patterns
  • Automated Response Handling: Route different types of responses to appropriate processing steps

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Node Properties

For each condition you add, you can configure:

  • Text to Match: The pattern or text to search for in the LLM response
  • Use Regex: Toggle to enable regular expression pattern matching
  • Case Sensitive: Toggle to make the pattern matching case sensitive

Multiple conditions are evaluated using OR logic — if any condition is true, the workflow will proceed down the true path. To use AND logic, simply chain multiple If Response Contains nodes together.

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Usage Examples

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Scenario: Response Quality Control

Let’s say you want to handle AI responses differently based on their content characteristics:

  1. Add an If Response Contains node after your LLM node
  2. Add conditions for quality checks:
    • Text: “I apologize” (to catch uncertainty or inability to answer)
    • Text: “I don’t know” (to identify knowledge gaps)
  3. Connect the “True” path to fallback handling
  4. Connect the “False” path to normal processing

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Scenario: Content Classification with Regex

To categorize responses based on specific patterns:

  1. Add an If Response Contains node
  2. Enable “Use regex”
  3. Add a condition with pattern: \$\d+(\.\d{2})? (to identify responses containing dollar amounts)
  4. Route matching responses to financial processing nodes

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Tips and Best Practices

  • Place this node immediately after LLM nodes to analyze their outputs
  • Use simple text matches for basic content detection
  • Implement regex for more complex pattern recognition
  • Consider case sensitivity based on your content requirements
  • Chain multiple nodes for sophisticated response analysis
  • Test conditions with various LLM responses to ensure reliable routing
  • Consider checking the Execution Logs to track which conditions are being triggered