Have you ever wondered if you can create your own code interpreter like OpenAI? A code interpreter that can perform multiple tasks like code verification, code generation, and provide feedback on correctness and improvement? Or maybe you want to write natural language and ask the model to generate a chart or perform complex tasks like building a random forest model based on your own data?
In this video, we will explore how you can solve these problems using chatGPT agents. First, we will import the necessary libraries and the OpenAI GPT-3 model. We will then create a schema for the functions we want to use, such as a Python function that executes code and returns the result. We will also create a conversation function that calls the OpenAI functions and iterates until the conversation is complete.
Next, we will demonstrate how to use the code interpreter. We will start with a simple query, like finding a Fibonacci number. The model will recommend calling the Python function and generate the code for us. We can then run the code and get the result.
We can also use the code interpreter to create charts. By specifying the desired chart type and providing the necessary data, the model can generate the code to create the chart and return it as a markdown.
Furthermore, we can extend the code interpreter to handle more complex tasks. For example, we can ingest an Excel or CSV file and perform operations on the data. We can also create machine learning models, such as a random forest model, and evaluate their performance.
The code interpreter allows us to interact with the OpenAI model in a natural language format, making it easier to perform various coding tasks. It provides a powerful tool for developers and data scientists to automate code execution, generate charts, and build machine learning models.
In conclusion, the code interpreter opens up new possibilities for developers and data scientists. It enables us to create our own code interpreters and leverage the power of OpenAI to perform complex coding tasks. With the ability to write natural language queries and receive code suggestions, the code interpreter streamlines the coding process and enhances productivity.
Let us know in the comments how you would utilize this functionality in your use cases!
