Create Data Visualizations with Azure OpenAI Assistants


In today’s data-driven world, quickly generating and interpreting visual data representations is a game-changer. Azure OpenAI provides powerful tools to create custom AI assistants that make this process effortless. In this article, I will guide you through setting up an Azure OpenAI Assistant to generate data visualizations, manage user interactions, and seamlessly display the results.

What is Azure OpenAI Assistant API?

Azure OpenAI Assistants (Preview) enables you to create AI assistants customized to your requirements using custom instructions and advanced tools like code interpreters and custom functions. The code interpreter provides a sandboxed Python environment for testing and executing code. I found this tool particularly useful for tasks such as math calculations, data analysis, and, most excitingly, data visualization.

You can try it out in the Assistant's playground, which offers a no-code environment to explore the assistants' capabilities. Each assistant can access up to 128 tools, including the code interpreter, and you can also define your own custom tools via functions.


  • Azure Subscription
  • Azure OpenAI Access
  • Azure OpenAI Resource

Creation of Assistants Playground

Step 1. Click Assistants Playground in the Azure OpenAI studio.

 Azure OpenAI

Step 2. Before creating the Assistant name you have to create Deployment first in the Assistant Setup window.

Step 3. Create an Assistant name named as sales-assistant with instructions once you have saved the assistant name in the assistant setup window.

Step 4. You can notice that an Assistant ID has been generated in the Assistant Setup window.

 Assistant ID

Step 5. In the Assistant Tools section enable the Code Interpreter option.

Step 6. Click Add Files and here I have uploaded the sales.csv and you can upload up to 20 files with a max size of 512 MB.

 Add Files

Step 7. In the show panels section enable the Logs option and you can see that whatever the prompts users started typing will be reflected in the Logs section.

 Logs section

Step 8. The users can perform the Mathematical calculations in the Code Interpreter section. Here I have asked the Code Interpreter to perform the arithmetic calculations.

Code Interpreter

Step 9. In the Chat section, I am asking for a prompt like a description of the dataset.

Chat section

Step 10. It displays the result as shown below for the above prompt.


Step 11. I am asking the question as to which region has the highest number of units sold and look at the result shown below.

Highest number

Step 12. The next question I am asking is to summarize the file using a bar chart and the results as shown below.

Bar chart

Step 13. Finally question I am asking to show the code generated for this request.

Code generated


With Azure OpenAI, creating an intelligent assistant for data visualization tasks is both straightforward and powerful. This guide has demonstrated how to set up the environment, create and manage threads, handle user interactions, and enhance visual outputs. Embrace the power of AI to streamline your data analysis and visualization processes. I am highly impressed with the Assistant AI, particularly its ability to utilize tools such as the code interpreter and custom functions. Combined with effective, prompt engineering, I am eager to see the innovative use cases that people will develop on this platform.

Happy Learning!

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