Unlock the Power of ChatGPT with Python!

Table of Contents

1. Introduction
2. Installing the Open AI Python Module
3. Creating and Setting APIs
4. List of Available Models
5. Using the Text Generation Model (GPT 3)
6. Accessing Chat GPT
7. Modifying the Number of Tokens
8. Adding Randomness to the Output
9. Pricing and Usage Information
10. Conclusion

Introduction

In this article, we will explore how to access and use Chat GPT in Python Jupyter Notebooks using the Open AI Python module. Chat GPT is a conversational AI model that allows You to generate text Based on a given prompt. We will cover topics such as installing the Open AI Python module, creating and setting APIs, accessing different models, using the text generation model, and more. So, let's get started!

Installing the Open AI Python Module

Before we can access the Open AI API, we need to install the Open AI Python module. This module is designed by the Open AI team and provides an easy way to access the API. To install the Open AI Python module, you can use the following command:

pip install OpenAI

Once the module is installed, we can move on to the next step.

Creating and Setting APIs

To access the Open AI API, we need to Create an API key and set it in our Open AI module. You can create an API key by visiting the Open AI Website and creating an account. Once you have an API key, you can set it in your code using the following steps:

1. Load the API key from a file.
2. Set the organization ID and API key in the Open AI module.

With the API key set, we can now move on to accessing different models.

List of Available Models

The Open AI API provides a list of models that can be accessed through the API. These models include GPT 3.5, GPT 4, DALL-E, Whisper, and more. To retrieve the list of models available, we can use the openai.model.list method. This method returns an Open AI object with information about the available models. We can loop through the models and retrieve their IDs for future use.

Using the Text Generation Model (GPT 3)

One of the models available in the Open AI API is the text generation model (GPT 3). This model allows us to generate text based on a given prompt. To use the text generation model, we first need to create a prompt. We can then use the openai.Completion.create method to generate the text. The response is a dictionary object that contains the generated text. We can extract the text from the response and print it.

Accessing Chat GPT

Chat GPT is a conversational AI model in the Open AI API that allows us to generate text based on a conversation. Unlike the text generation model, which generates text independently of previous messages, Chat GPT takes into consideration the Context of the conversation. To access Chat GPT, we need to use the openai.ChatCompletion.create method and provide a list of messages as input. Each message is a dictionary with a role (user or assistant) and content. The response is a dictionary object that contains the generated text. We can extract the text from the response and print it.

Modifying the Number of Tokens

The Open AI API limits the number of tokens that can be generated in a response. By default, the completions endpoint returns only 16 tokens. However, we can modify this limit using the max_tokens parameter. By setting the value of max_tokens to a higher number, we can retrieve more tokens in the response.

Adding Randomness to the Output

The Open AI models already add some amount of randomness to the generated text. However, we can modify the level of randomness using the temperature parameter. By default, the temperature parameter is set to 1, which adds a moderate level of randomness. We can increase or decrease the value of temperature to adjust the amount of randomness in the output.

Pricing and Usage Information

Using the Open AI API comes at a cost. The pricing is based on the number of tokens generated. The Open AI Website provides a detailed pricing list for each model and the number of tokens consumed. It is important to keep track of your API key usage and stay within the rate limits set by Open AI.

Conclusion

In this article, we covered the basics of accessing and using Chat GPT in Python Jupyter Notebooks using the Open AI Python module. We explored topics such as installing the module, creating and setting APIs, accessing different models, using the text generation model and Chat GPT, modifying the number of tokens and adding randomness to the output, and understanding the pricing and usage information. We hope this article has helped you understand how to use Chat GPT and leverage the power of conversational AI.