URL.create creates the URL object which is used as the argument for the create_engine function.Here's a breakdown of what each part does: This code sets up a connection to a PostgreSQL database using SQLAlchemy and creates a table named conversations. You'll use it to test your Twilio webhook while you send WhatsApp messages. pyngrok: A Python wrapper for ngrok, a tool that allows you to expose a web server running on your local machine to the internet.In the case of this tutorial, it will be used to handle form data from the user's input through the WhatsApp chatbot. python-multipart: A library that allows you to parse multipart form data in Python, which is commonly used to handle form submissions that contain files such as images or videos. psycopg2-binary: A Python package that provides a PostgreSQL database adapter for the Python programming language.It provides a set of high-level APIs for connecting to relational databases, executing SQL queries, and mapping database tables to Python classes. sqlalchemy: A package for SQLAlchemy, a Python SQL toolkit and Object-Relational Mapping (ORM) library.It allows you to store your settings in an environment file, instead of hardcoding them into your code. python-decouple: A library for separating the settings of your Python application from the source code.OpenAI offers various AI models, including the text-davinci-002 model, which is used in this tutorial to power the chatbot. openai: A Python client for OpenAI, a research company that focuses on developing and advancing artificial intelligence in a way that is safe and beneficial for humanity.twilio: A Python helper library for Twilio, a cloud communications platform that allows software developers to programmatically make and receive phone calls, send and receive text messages, and perform other communication functions using its web service APIs.uvicorn: A package for Uvicorn, a fast ASGI server implementation, using the websockets library for long-polling connections, and based on uvloop and httptools.It's designed to be easy to use, fast, and to provide automatic validation of request and response data. fastapi: 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 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 your 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: 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 models in the GPT-3.5 series. 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. This AI chatbot is a ChatGPT-like interface but on WhatsApp. With this chatbot, you'll be able to provide customers with intelligent responses to their inquiries. In this tutorial, you'll learn how to build an AI chatbot with the OpenAI API that can engage with customers on WhatsApp. Its ability to understand natural language and generate human-like responses has made it a popular tool for businesses looking to improve their customer service through AI chatbots. ChatGPT has been making headlines lately as one of the most advanced and widely-used language models.
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