Build AI data analyst with sandboxed code execution using TS, and GPT-4o

This text explains how to use the E2B Code Interpreter SDK with OpenAI's GPT-4o model to execute LLM-generated code tasks in a secure cloud environment. It describes setting up a TypeScript project that utilizes the SDK to create a cloud sandbox where a Jupyter server runs, allowing for custom code interpreters that can access the internet, filesystem, and install packages. The guide outlines the initialization process, including the storage of API keys, necessary imports, and configuration, and defines a system prompt to guide the AI's behavior as a Python data scientist. It details the creation of tools like "execute_python" for running Python code in Jupyter notebook cells, and a chat function that facilitates interaction with the model, which uses the CodeInterpreter to execute code and generate outputs such as charts. The example program demonstrates how to generate and plot a differential equation's solution, subsequently interpreting the plot with the model to create a new chart with altered initial conditions, highlighting the model's capability to analyze visual outputs and adjust tasks accordingly.