Key Characteristics of Dynamic Columns
- Automated Value Generation: Values are computed dynamically rather than manually entered.
- Regenerable Data: Dynamic columns can refresh their values when the underlying logic or data changes.
How Dynamic Columns Work
- User selects a dynamic column type and configures its settings.
- The system uses parallel execution to compute values efficiently for large datasets.
- Each row in the column receives a computed value based on the logic defined.
Why Use Dynamic Columns?
- Efficiency: Reduces manual data entry and updates values automatically.
- Scalability: Works efficiently on large datasets with thousands of rows.
- Flexibility: Supports various logic types, from simple conditions to external data integrations.
- Data Consistency: Ensures uniform value generation across the dataset.
Supported Methods:
- Run Prompt: Run a LLM prompt which can also utilize data from static columns to get the desired output
- Vector Retrieval: Connect to a Vector Database, and retrieve the top-k chunks for a particular query.
- Entity Extraction: Automatically extract named entities like people, organizations, or locations from the static columns using a specified model.
- JSON Key Extraction: Parse a JSON field to extract specific keys or values.
- Custom Code Execution: Write and execute Python code for transformations or complex operations.
- Text Classification: Assign categories or labels using a specified models.
- API Calls: Generate a column and new entries for every row by making API calls to a specified endpoint.
- Conditional Logic: Apply different actions to your data based on specified conditions, allowing for branching logic.