We will use Redis JSON to store the chat data and also use Redis Streams for handling the real-time communication with the huggingface inference API. When we send prompts to GPT, we need a way to store the prompts and easily retrieve the response. You can read more about GPT-J-6B and Hugging Face Inference API. Huggingface also provides us with an on-demand API to connect with this model pretty much free of charge. I have chosen to use GPT-J-6B because it is an open-source model and doesn’t require paid tokens for simple use cases. GPT-J-6B is a generative language model which was trained with 6 Billion parameters and performs closely with OpenAI's GPT-3 on some tasks.
The Chat UI will communicate with the backend via WebSockets. We will use React version 18 to build the user interface. Let's go over the various parts of the architecture in more detail: Client/User Interface I have drawn up a simple architecture below using draw.io: Fullstack chatbot architecture Sketching out a solution architecture gives you a high-level overview of your application, the tools you intend to use, and how the components will communicate with each other.
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How to Update the Chat Client with the AI Response.Stream Consumer and Real-timeDdata Pull from the Message Queue.How to Simulate Short-term Memory for the AI Model.How to Interact with the Language Model.How to Add Intelligence to Chatbots with AI models.How to Connect to a Redis Cluster in Python with a Redis Client.How to build Real-Time Systems with Redis.How to Generate a Chat Session Token with UUID.How to Build a Chat Server with Python, FastAPI, and WebSockets.How to Set Up the Development Environment.
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You can download the full repository on My Github here. I've carefully divided the project into sections to ensure that you can easily select the phase that is important to you in case you do not wish to code the full application. This is an intermediate full stack software development project that requires some basic Python and JavaScript knowledge. How to build a chat User Interface with React.How to build real-time systems with Redis.How to build APIs with Python, FastAPI, and WebSockets.
Some of the topics we will cover include: So this tutorial will take you through the process of building an AI chatbot to help you learn these concepts in depth. You'll go through designing the architecture, developing the API services, developing the user interface, and finally deploying your application. One of the best ways to learn how to develop full stack applications is to build projects that cover the end-to-end development process. This is why complex large applications require a multifunctional development team collaborating to build the app. In addition to all this, you'll also need to think about the user interface, design and usability of your application, and much more. And you'll need to make many decisions that will be critical to the success of your app.įor example, what language will you use and what platform will you deploy on? Are you going to deploy a containerised software on a server, or make use of serverless functions to handle the backend? Do you plan to use third-party APIs to handle complex parts of your application, like authentication or payments? Where do you store the data? In order to build a working full-stack application, there are so many moving parts to think about.