Build a GPT-Powered AI Chatbot for Your Django Blog
Integrate JavaScript, HTMX & OpenAI API
If you have a Django blog and want to enhance user experience by integrating a chatbot, you’ve come to the right place. In this article, we’ll show you how to build a GPT-powered AI chatbot using JavaScript, HTMX, and the OpenAI API, and integrate it into your Django blog.
Prerequisites
Before getting started, make sure you have the following:
- A Django blog up and running
- Access to the OpenAI API
- Knowledge of JavaScript and HTMX
Step 1: Set Up the OpenAI API
First, you’ll need to sign up for access to the OpenAI API and obtain an API key. This key will allow you to interact with GPT-3, OpenAI’s powerful language model.
Step 2: Create a Chatbot Interface with JavaScript
Next, you’ll create a chatbot interface using JavaScript. This interface will allow users to interact with the chatbot and receive responses generated by GPT-3.
Step 3: Integrate HTMX for Dynamic Updates
HTMX is a powerful tool for creating dynamic, server-side rendered web applications. By integrating HTMX, you can update the chatbot interface in real-time without needing to reload the page.
Step 4: Connect the Chatbot to Your Django Blog
Finally, you’ll connect the chatbot to your Django blog, allowing users to access the chatbot from any page on your site. This integration will provide users with a seamless and interactive experience.
Conclusion
By following these steps, you can build a GPT-powered AI chatbot for your Django blog and provide an enhanced user experience. The integration of JavaScript, HTMX, and the OpenAI API will allow you to create a powerful and interactive chatbot that can engage users and provide valuable assistance.
A couple times in the video, I mention that we are 'fine tuning' the model. That's actually not true. What we are really doing is 'prompt stuffing' where we use the pandas dataframe to pass relevant context to ChatGPT on a prompt-by-prompt basis.
did you change your api keys lol. nice. htmx ftw.
Hey! I found this video and was hoping to know which vector database you would use, only to find out at the very end that you don't use one 🙂. By the way, I ended up using df.to_pickle() to save my Pandas DataFrame object to a file. This makes it easy to load the df object by using read_pickle().