Visualize

Introduction

The visualize function illustrates PromptQL's ability to transform data into meaningful visual insights without requiring technical visualization expertise. While you can provide styling preferences, PromptQL automatically determines the most effective way to represent your data - choosing appropriate chart types, scales, and interactive features based on the data's structure and the insights you're seeking.

Example

Here's an example showing how PromptQL creates an interactive visualization of restaurant categories:

"Can you help me make sense of all this restaurant data?"

viz_result = executor.visualize(
    instructions="""
    Create an interactive bar chart showing the distribution of restaurant categories.
    Requirements:
    - Use a horizontal bar chart for better readability of category names
    - Sort bars by count in descending order
    - Use a pleasant color scheme (preferably blues or teals)
    - Include hover tooltips showing exact count
    - Add a clear title 'Restaurant Categories Distribution'
    - Make the chart responsive to container width
    - Include total number of restaurants in each category
    """,
    data=viz_data
)

The instructions focus on the desired outcome rather than technical specifications. PromptQL automatically handles the complexity of creating responsive, interactive visualizations - managing details like proper data scaling, axis formatting, and mobile responsiveness while ensuring the resulting visualization effectively communicates your data's story.

Next steps

If you've explored all the built-in primitives which PromptQL can access, it's time to check out how to add your own custom business logic and extend what PromptQL can do!