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

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

Building the Backend

Building the useChat hook

Building the User Interface

Building a Chat

Discovering Retrieval Augmented Generation

Building a Semantic Search Engine with Chroma

Adding Retrieval-Augmented Generation to the chat

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

Building an Agent

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

Our 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.

**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!

Streamline AI Communication with TypeScript and Python

Large Language Models are reshaping industries, yet integrating them into real-time, streaming UIs presents unique challenges. In this course we build AI-powered streaming text and chat UIs with TypeScript and Python. Go step-by-step through building a full-stack AI application with quality code and very flexible implementation.

It includes:
- a completion use-case (english to emojis)
- a Chat
- a Retrieval Augmented Generation use-case
- 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.

Building AI-Powered Streaming Text and Chat UIs

An interactive AI-powered chat application with real-time responses

A Retrieval Augmented Generation (RAG) system to enhance AI responses with external knowledge

An AI agent capable of using tools to solve a tasks

An AI-assisted code generation and explanation system

An assistant capable of performing data analysis tasks

How to stream the answer of a Large Language Model

Differences between Server-Sent Events and WebSockets

What problems Retrieval Augmented Generation can solve

, or RAG. This technique addresses the limitations of Large Language Models (LLMs) that are restricted to their training data, which might be outdated, misleading, or false.

Retrieval Augmented Generation (RAG) is a method where we integrate a retrieval step to pull information from trusted sources to regenerate responses. This approach helps to mitigate the issue of misinformation in the training data of LLMs.

Let's see RAG in action through a demo application on climate question answering use-case:

The model uses reports from reputable sources such as the 

, which is a global scientific organization dedicated to the study of climate change. These reliable sources are what allow our model to generate accurate and informed responses.

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title: Discovering Retrieval Augmented Generation
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privateVideoUrl: https://stream.mux.com/Oya5CVpBOGilIxUbNMEUQS5C7yPZgInOD2Y01tAaFqpg.m3u8
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# Discovering Retrieval Augmented Generation

Welcome to this lesson on [Retrieval Augmented Generation](https://ai.meta.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models/), or RAG. This technique addresses the limitations of Large Language Models (LLMs) that are restricted to their training data, which might be outdated, misleading, or false.

### What is RAG?

Retrieval Augmented Generation (RAG) is a method where we integrate a retrieval step to pull information from trusted sources to regenerate responses. This approach helps to mitigate the issue of misinformation in the training data of LLMs.

Let's see RAG in action through a demo application on climate question answering use-case:

![](https://d2uusema5elisf.cloudfront.net/courses/building-ai-powered-streaming-text-and-chat-uis/module_05/lesson_05.01/public/assets/demo.png)

The model uses reports from reputable sources such as the [IPCC](https://www.ipcc.ch/), which is a global scientific organization dedicated to the study of climate change. These reliable sources are what allow our model to generate accurate and informed responses.

#### App Preview

You can preview the demo app on Replit:

<ReplitEmbed src="//replit.com/@louissanna/module-5"/>

### Semantic Search

Semantic search is a crucial component in RAG. It involves the use of embeddings to transform text into n-dimensional vectors.
An textual embedding is a function that converts text into an n-dimensional vector, which represents the text geometrically as an arrow.

Learn

The newline Guide to Building Your First GraphQL Server with Node and TypeScript

Teach

Amelia Wattenberger

Author of Fullstack D3

Community

Building AI-Powered Streaming Text and Chat UIs

Module 1: System Design for AI applications

Introduction:

Start Here

Introduction: (3:47)

Introduction to AI Product Development

Lesson 1: (6:09)

Picking the stack - Navigating JavaScript and Python

Lesson 2: (5:07)

Designing a Hybrid Web Application Architecture with JavaScript and Python

Lesson 3: (6:30)

Streaming events with Server-Sent Events and WebSockets

Module 2: Building the Front-End

Lesson 1: (6:29)

Discovering the OpenAI Completion API

Lesson 2: (6:13)

Handling Server-Sent Events with JavaScript

Lesson 3: (7:00)

Building the useCompletion hook

Lesson 4: (1:25)

Rendering Completion Output

Lesson 5: (3:28)

Mocking Streams

Lesson 6: (3:10)

Testing the useCompletion hook

Module 3: Building the Backend

Lesson 1: (1:54)

Creating a FastAPI server

Lesson 2: (3:41)

Exploring asynchronous programming in Python

Lesson 3: (4:33)

Integrating Langchain with FastAPI for Asynchronous Streaming

Lesson 4: (1:01)

Testing with PyTest and LangChain

Module 4: Building a Chat

Lesson 1: (5:11)

Building the useChat hook

Lesson 2: (1:52)

Building the User Interface

Module 5: Implementing Retrieval Augmented Generation

Lesson 1: (3:18)

Discovering Retrieval Augmented Generation

Lesson 2: (3:36)

Building a Semantic Search Engine with Chroma

Lesson 3: (2:13)

Adding Retrieval-Augmented Generation to the chat

Module 6: Conclusion

Lesson 1: (1:21)

Final words

Module 7: Building an Agent

Lesson 1: (3:22)

Exploring AI Agents - Toward Autonomous Systems

Lesson 2: (5:24)

Building an Asynchronous Event-Driven Service Using Context Variables

Lesson 3: (5:38)

Adding Tools To The Chat

Lesson 4: (4:04)

Building a Generative UI

Discovering Retrieval Augmented Generation