This project tests the capabilities of the new Mistral's model on data analysis tasks, using the Code Interpreter SDK by E2B. Codestral doesn't support using tools yet, so in this Python example, we added the code interpreting capabilities.

We are going to build an AI assistant that performs data analysis on a CSV file. It is prompted to plot the average temperature over the years in Algeria,

Ensure all dependencies are installed:

npm install

Create a .env file in the project root directory and add your API keys:

• Copy .env.template to .env
• Get the E2B API KEY
• Get the MISTRAL API KEY

npm run start

The script performs the following steps:

• Loads the API keys from the environment variables.
• Uploads the city_temperature.csv dataset to the E2B sandboxed cloud environment.
• Sends a prompt to the Codestal model to generate Python code for analyzing the dataset.
• Executes the generated Python code using the E2B Code Interpreter SDK.
• Saves any generated visualization as PNG file.

After running the program, you should get the result of the data analysis task saved in a image_1.png file. You should see the plot of average temperature in Algeria evolving in time, something like this:

If you encounter any problems, please let us know at our Discord.

If you want to let the world know about what you're building with E2B, tag @e2b_dev on X (Twitter).

Check the documentation to learn more about how to use E2B.