6/15/2023 0 Comments Whatsapp chatbot builderfastapi: A package for FastAPI, a modern web framework for building APIs with Python 3.7+ based on standard Python type hints.Here is a breakdown of these dependencies: Start with creating a new virtual environment: If you are not familiar with ORM, we recommend you to read this wiki page to get an idea of what it is and how it works.īefore building the chatbot, you need to set up our development environment. A basic understanding of what an ORM is.A basic understanding of FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.6+.A smartphone with WhatsApp installed to test your AI chatbot.If you don't have one, you can create a free account here. To follow this tutorial, you will need the following prerequisites: The chatbot knows implicitly that it's a business coach. Also, the user is not supposed to tell the chatbot its role. Here is a sample conversation this AI chatbot can create:Īs you can see, this AI chatbot is focused on business questions, so it can't teach you how to cook or participate in a non-relevant topic. So you'll get a richer response than Davinci's chatbot.Īt the end of this tutorial, you will be able to build a customized chatbot on WhatsApp. This is 10x cheaper than the Davinci model. 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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