Responsive LLM Applications with Server-Sent Events

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, 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

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

Course Content Overview

• Total Modules: 7
• Total Lessons: 24
• Total Lines of Code: 3706
• Total Video Runtime: 1 hour, 34 minutes