Create Dynamic Column by Conditional Node

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1. Accessing the Column Creation Interface

To create a conditional node column, go to the Data tab in your dataset and click the + Add Columns button. In the Dynamic Columns section, select Conditional Node.

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2. Configuring the Conditional Node

Once selected, configure the following settings:

• Name – Assign a name to this new column.
• Each row in the dataset is processed based on the branching logic defined in the conditional node:
  • If Condition – The first condition to check.
  • Elif Conditions (optional) – Additional conditions checked sequentially if the first condition is false.
  • Else Condition (optional) – The default fallback when none of the conditions match.
• Choosing an Operation Type: The system allows various operations when conditions are met
  • Run Prompt – Generates AI-driven responses using custom LLM prompts.
  • Retrieval – Fetches relevant data from a vector database via similarity search.
  • Extract Entities – Identifies and extracts key information from text columns.
  • Extract JSON Key – Retrieves specific values from JSON-formatted dataset columns.
  • Execute Custom Code – Runs Python scripts for custom row-level transformations.
  • Classification – Assigns labels to dataset rows using a pre-trained AI model.
  • API Calls – Integrates external APIs to fetch and populate dynamic column data.

Once created, the system evaluates each row, applying the conditional logic in sequence:

1. Evaluates Conditions – Checks if, elif, and else in order.
2. Executes Matching Operation – Applies the corresponding transformation.
3. Stores Results – Saves the generated values in the new column.

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Best Practices for Conditional Nodes

• Ensure clear condition hierarchy (if → elif → else) to prevent logical conflicts.
• Match data type with the intended operation to avoid conversion issues.
• Use text transformation for modifying string data dynamically.
• Apply classification logic for structured labelling of dataset rows.
• If integrating API calls, ensure external sources return expected results.

Conditional nodes enable flexible and automated data transformations, allowing datasets to adapt dynamically based on logic-driven workflows.