Building an OpenAI-Powered Chatbot using Python and Jupyter Notebooks

Welcome to a step-by-step guide on creating an intelligent chatbot powered by OpenAI using Python and Jupyter Notebooks. In this tutorial, we’ll cover the fundamental concepts and guide you through the process of building a simple yet effective chatbot that leverages the power of OpenAI’s language model.


Before we begin, make sure you have the following installed:

Step 1: Set Up Your Environment

Install OpenAI with pip:

Install JupyterLab with pip:

Now launch JupyterKab with:

Step 2: Set Up OpenAI API

Start by importing the OpenAI library and setting up your API key:

Step 3: Set the initial context for the chatbot

Our chatbot will inspire us for the work week, so we need to inform it about its purpose. That sets the stage for it to respond appropriately with our purpose in mind.

Step 4: Generate Responses

To interact with OpenAI’s language model, use the method. First we need to simulate a user asking a question. That is where the code sets up the user role and related content. In the real world imagine this is the text coming from the user.

Run the code now, and you should see some text such as what i got

Step 5: Ask the next question

Have the user ask the next question

This should be enough to illustrate the chatbot code. Next lets refactor the code to run in a loop so you have your interactive chatbot

Step 6: Build the Chatbot loop

Change the code to this to build the loop. Type exit to get out of the loop


Step 7: A few terms explained

Took the liberty of using  chatgpt to generate some explanation of basic terms that are good to know.

  1. Tokens and Token Limit – The model processes text in chunks called tokens. Both input and output tokens count towards usage. The total number of tokens affects the cost, response time, and whether a request fits within the model’s maximum limit.
  2. max_tokens – ‘Max tokens’ can be used to limit the response length
  3. temperature – The ‘temperature’ parameter controls the randomness of the model’s output. Higher values (e.g., 0.8) make the output more creative, while lower values (e.g., 0.2) make it more focused and deterministic.
  4. model versions – OpenAI may release different versions of language models. The API allows you to specify the model version you want to use. Keep an eye on updates and choose the appropriate version based on your requirements.
  5. Context and Memory – The model has a limited context window, so very long conversations may lead to important context being cut off. Be mindful of the token limit and adjust your conversation accordingly.
  6. System Messages – System level instructions can be provided to guide the model’s behavior throughout the conversation.

Step 8: References

  1. Text generation Models –
  2. Models –