Introduction to AI Product Development

Picking the stack - Navigating JavaScript and Python

Designing a Hybrid Web Application Architecture with JavaScript and Python

Streaming events with Server-Sent Events and WebSockets

System Design for AI applications

Discovering the OpenAI Completion API

Handling Server-Sent Events with JavaScript

Building the useCompletion hook

Rendering Completion Output

Mocking Streams

Testing the useCompletion hook

In this module, we're diving into the creation of an AI product with a frontend focus.

Building the Front-End

Creating a FastAPI server

Exploring asynchronous programming in Python

Integrating Langchain with FastAPI for Asynchronous Streaming

Testing with PyTest and LangChain

In this module, we're diving into the creation of an AI product with a Backend focus.

Building the Backend

Building the useChat hook

Building the User Interface

In this module, we learn how to build a chat.

Building a Chat

Discovering Retrieval Augmented Generation

Building a Semantic Search Engine with Chroma

Adding Retrieval-Augmented Generation to the chat

In this module, we implement Retrieval Augmented Generation"

Implementing Retrieval Augmented Generation

Final words

Conclusion

Exploring AI Agents - Toward Autonomous Systems

Building an Asynchronous Event-Driven Service Using Context Variables

Adding Tools To The Chat

Building a Generative UI

In this lessons we will explore how AI agents function with a focus on autonomy, tool usage, and self-correction. They will also be introduced to the potential future developments in AI agent technology.

Building an Agent

Join us as we dive into the world of AI-powered streaming text and chat UIs using TypeScript, React, and Python. 

Large Language Models (LLMs) are revolutionizing various industries, but creating simple demos in notebooks or with tools like Gradio is not enough. To truly harness the potential of these models, we need to build sophisticated, real-world applications.

This comprehensive course is structured into six detailed modules where you will:
- Develop a dynamic front-end with React.
- Integrate FastAPI with LangChain to create a robust backend.
- Utilize Server-Sent Events (SSE) for seamless real-time data streaming.

This comprehensive course comes packed with valuable extras:
- The complete source code under MIT licence
- Exclusive access to our community forum
- Detailed exploration of Retrieval-Augmented Generation (RAG) for enhancing model responses
- Hands-on experience with developing intelligent agents capable of maintaining context in conversations

Taught by [Louis Sanna](https://www.linkedin.com/in/louis-sanna-0168363a/), a seasoned Tech Lead with expertise in GenAI applications and digital transformation projects for major organizations like UNESCO and Renault. His deep industry insights and practical experience will guide you from basics to advanced implementations.

Taking this course will spare you countless days of troubleshooting and challenges, offering a shortcut through the learning curve. Moreover, it presents a unique opportunity to engage with one of the most significant technological trends of our era. This is your chance to become a part of the groundbreaking wave of LLM advancements, positioning you at the forefront of innovation.

### Problem this Course Solves

This course is designed to tackle specific problems that AI engineers face when integrating LLM capabilities into web applications. Streaming text presents a unique challenge for engineers, requiring the integration of multiple technologies and concepts. This course addresses this by:

- Exploring the use of Langchain for seamless LLM provider switching. Given Langchain's rapid development, understanding its streaming, asynchronous, and callback functionalities can be daunting.
- Demystifying asynchronous programming in Python, which can be more complex than in JavaScript. We'll cover the differences between asynchronous and synchronous programming, the benefits of asynchronous operations, and how to implement and integrate asynchronous function calls within a streaming context.
- Selecting and implementing a protocol for streaming text from the backend to the frontend. We will explain why Server-Sent Events (SSE) is an ideal standard, despite being relatively new and unfamiliar to many.
- Creating an SSE endpoint using a Python server framework, such as FastAPI, to facilitate real-time data streaming.
- Reading the stream in the front-end using the ReadableStream interface of the Fetch API, ensuring efficient data handling.
- Updating the React state of prompts with consideration for React's batching behavior to prevent unexpected issues during rapid updates.
- Integrating custom components within text responses to enable dynamic, on-the-fly generative UIs.

By the end of this course, you will have a comprehensive understanding of how to effectively integrate and manage LLM capabilities within web applications, overcoming the technical hurdles associated with real-time text streaming.

### Preview the Application

Experience the power of what you'll build firsthand. Try out the demo app we will develop during the course:
[The Demo App](https://build-ai-driven-web-applications.vercel.app/)

Enroll now and start building transformative AI-powered applications that streamline communication and enhance user engagement!

### Course Content Overview

##  Basic Package
- Total Modules: **6**
- Total Lessons: **20**
- Total Video Runtime: **1 hour, 10 minutes**

##  Advanced Package
- Total Modules: **7**
- Total Lessons: **24**
- Total Lines of Code: **3706**
- Total Video Runtime: **1 hour, 34 minutes**

Dive into Retrieval Augmented Generation and Autonomous Agents with LangChain, Chroma and FastAPI

Large Language Models are reshaping industries, yet integrating them into real-time streaming UIs presents unique challenges. In this course we will learn how to seamlessly integrate LLM APIs into applications and build AI-powered streaming text and chat UIs with TypeScript, React, and Python. Step-by-step, we will build a full-stack AI application with quality code and very flexible implementation.

The LLM application in this course includes:
- Completion Use-Case (english to emojis)
- Chat
- Retrieval Augmented Generation use-case
- AI Agent Use-Cases (code execution, data-Analyste agent)

This app can be used as a starting point in most projects, saving a huge amount of time, and its flexibilty allows new tools to be added as needed.

At the end of this course, you will have mastered end-to-end implementation of a flexible and high-quality LLM application. This course will also equip you with the knowledge and skills necessary to create sophisticated LLM solutions of your own.

Responsive LLM Applications with Server-Sent Events

How to design systems for AI applications

How to stream the answer of a Large Language Model

Differences between Server-Sent Events and WebSockets

How asynchronous programming in Python works

What problems Retrieval Augmented Generation can solve

In this lesson, we'll walk through the creation of a simple chat user interface (UI) using React.

. This component initializes with optional starting messages.

export function Chat({ initMessages = defaultInitMessages }) {
  const { messages, submit, input, setInput, isLoading } = useChat({
    initMessages,
  });

  const { messagesEndRef } = useScrollToMessage({ messages });

  return (
    <div className="relative flex h-full min-h-[50vh] flex-col overflow-hidden rounded-xl bg-muted/50 lg:col-span-2">
      <MessageList messages={messages} isLoading={isLoading} />
      <div ref={messagesEndRef} className="h-2" />
      {/* Input and button components here */}
    </div>
  );
}

 hook to automatically scroll to the latest message. This functionality enhances user experience, especially in dynamic chat interactions.

function useScrollToMessage({ messages }) {
  const messagesEndRef = useRef(null);
  useEffect(() => {
    messagesEndRef.current?.scrollIntoView();
  }, [messages]);

  return { messagesEndRef };
}

---
title: Building the User Interface
isPublicLesson: true
privateVideoUrl: https://stream.mux.com/1gtLshXP00O6Kz7lxCue7w00PF9S9aKliIxWQ3sUEW0002E.m3u8
code: https://gitlab.com/fullstackio/books/newline-course-apps/louis-sanna-real-time-streaming-for-generative-ui-app/-/tree/main/module-4
---

# Building the User Interface

In this lesson, we'll walk through the creation of a simple chat user interface (UI) using React.

#### The Chat Component

Our main component is `Chat`. This component initializes with optional starting messages.

```jsx
export function Chat({ initMessages = defaultInitMessages }) {
  const { messages, submit, input, setInput, isLoading } = useChat({
    initMessages,
  });

  const { messagesEndRef } = useScrollToMessage({ messages });

  return (
    <div className="relative flex h-full min-h-[50vh] flex-col overflow-hidden rounded-xl bg-muted/50 lg:col-span-2">
      <MessageList messages={messages} isLoading={isLoading} />
      <div ref={messagesEndRef} className="h-2" />
      {/* Input and button components here */}
    </div>
  );
}
```

:::tip Automatic Scrolling

We build a `useScrollToMessage` hook to automatically scroll to the latest message. This functionality enhances user experience, especially in dynamic chat interactions.

```jsx
function useScrollToMessage({ messages }) {
  const messagesEndRef = useRef(null);
  useEffect(() => {
    messagesEndRef.current?.scrollIntoView();
  }, [messages]);

Louis Sanna

Welcome back in this lesson. We will be the UI for our chart. So here we are in

We can see the UI we can see the list of messages below an input where we use

our code in putting your message and send the message

It's a real component and we are already been the who concapsulate team the

So we simply need to call our hook and we'll get restate and actions

Farest head forward now. We just need to build

The UI so it's HTML says says you are well retain wine and

You get a UI as you will do for any use case now there are two good shares. I

The first is that you need to under markdown because

The completion of your model is in markdown. So for proper formatting you must

We've already done it in a previous module

We can reuse this component stream text to make sure our model

That's the first workshop versus call on stream

Let me show you it's a feature you may want to implement on earth when the

We automatically scroll so the user gets to see

You may do want to do it on your own chat if

Model is a bit slow, but if it's fast you can just keep it

Targeting a div at the bottom of the message list and then anytime the messages

We scroll to the bottom and that's all that's all for the UI and in next lesson

use the Rekleval Augmented Generation. See you soon!

Building the User Interface

The Chat Component#