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Unlock the Power of AI: A Comprehensive Guide to Integrating OpenAI’s ChatGPT API in Jupyter Notebook

Transform the way you communicate with AI using OpenAI’s ChatGPT API in Jupyter Notebook.

OpenAI’s ChatGPT is a powerful conversational AI model that can be used to build intelligent chatbots. In this article, I will show you how to use the ChatGPT API in Jupyter notebook to generate responses to your text prompts.

To get started, first and foremost sign up for a free OpenAI account on the OpenAI website and log in to your account. Visit the link here https://openai.com/api/ and signup.

Once your account is created, follow the steps below:

Step 1: Sign Up for OpenAI API Key

To use the ChatGPT API, you need to have an OpenAI API key. Once you have created an account, obtain an API key. Click on your account in the top right corner of your dashboard and go to “View API Keys”

If you don’t already have an API key created, create one and copy the key.

Step 2: Launch your Jupyter notebook and install the OpenAI Library.

To use the ChatGPT API in Jupyter Notebook, you need to install the OpenAI library. You can install the library by running the following command in a code cell in your Jupyter Notebook:

Step 3: Set up the API Key

Once you have installed the OpenAI library, you need to set up your API key so that the library can access the ChatGPT API. You can set up your API key by running the following code

NOTE: Never share your API key for OpenAI publicly because it provides access to your OpenAI account and its resources. Sharing your API key would allow others to use your OpenAI account, potentially incurring charges or affecting your service performance. To maintain the security and integrity of your OpenAI account, it is recommended to keep your API key confidential and not share it with others.

Step 4: Generate Responses

With the OpenAI library and API key set up, you can now use the ChatGPT API to generate responses to text prompts. You can do this by calling the openai.Completion.create() function, and specify the prompt argument to specify the text prompt you want the ChatGPT model to respond to.

Here is an example of how you can achieve that:

result = openai.Completion.create(engine='text-davinci-003', prompt='What is ChatGPT?', max_tokens=1000)

Arguments given:

1. The `engine` argument helps you choose the model. A list of models can be found here: https://platform.openai.com/docs/models/gpt-3
2. The `prompt` argument specifies the text input that is provided to the language model. It can be used to specify the initial text or context for a conversation or to provide a question or statement that the language model should respond to.
3. The `max_tokens` argument in the API determines the maximum number of tokens that should be generated in response to the prompt. In other words, it sets a limit on the maximum number of words that the API should generate in its response.

The OpenAI API returns the prompt response in a JSON format, which stands for JavaScript Object Notation.

• The JSON response from the OpenAI API typically includes information about the generated text, such as the completions generated, the type of API request, tokens, etc.
• total_tokens field in the JSON result is counting more than just the words in the text, such as punctuation marks, symbols, or special characters.

If you just want the text output:

result.choices[0]['text']

Step 6: Use the Response

You can use the generated response in a variety of ways, depending on the requirements of your application. For example, you can display the response in a chat interface, or use it as the input for further processing by your application.

In conclusion, using the ChatGPT API in Jupyter notebook is a simple and straightforward process that allows you to leverage the power of OpenAI’s conversational AI model.

You can find the Jupyter notebook with the code on my GitHub repository.

Thank you for reading. Get in touch via LinkedIn if you have further questions.

Swapnil Kangralkar