Learn how dynamic columns automatically generate data using predefined logic, Python code, API calls, and transformations. Discover the power of automated data processing versus static columns for efficient dataset management.
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.
By leveraging dynamic columns, users can automate data transformation, fetch external insights, and apply complex logic, making their datasets more powerful and adaptive