That's because the GPT 3.5 turbo model costs $0.002 per 1K tokens. More focused and have role-based responses.In this tutorial, we will focus on solving the above issues to make the new chatbot: ![]() This is cheap, but what if we could build a 10x cheaper chatbot? So if you ask this chatbot about something other than business, you'll get a response like the one to the follow-up clean code question above.Īnother issue is that the chatbot's response didn't contain much more information about each book in its answer for the first question.įurthermore, this API costs $0.02 per 1K tokens, where 1K tokens roughly correspond to 750 words. ![]() This AI chatbot was based on the Davinci model, which basically can talk about anything. Take a look at the following conversation: The AI chatbot mentioned in the previous post has some limitations. With Pyngrok, you'll put the FastAPI localhost on the internet through Python, making it accessible for the Twilio API to communicate with.įinally, the core of this AI chatbot will be built using OpenAI's API and one of the GPT-3.5 series models: the model that powers ChatGPT, the GPT 3.5 turbo model. Then, you'll integrate Twilio's WhatsApp Messaging API, allowing customers to initiate conversations with your WhatsApp chatbot. You'll start by setting up the backend using FastAPI and SQLAlchemy to create a PostgreSQL database to store your customers' conversations. In this article, we'll show you how to build a chatbot powered by OpenAI's ChatGPT API and integrate it with WhatsApp using Python and Twilio. With the rise of artificial intelligence, chatbots have become smarter, more personalized, and more intuitive. ![]() As the world becomes increasingly connected through messaging apps, chatbots have become a crucial tool for businesses to engage with customers on a more personal level.
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