Newline – Responsive LLM Applications with Server-Sent Events 2024-10

Responsive LLM Applications with Server-Sent Events. This course explores the challenges of integrating large language models (LLMs) into real-time streaming user interfaces (UIs). In this course, you will learn how to seamlessly integrate LLM APIs into applications and build AI-powered text and chat UIs using TypeScript, React, and Python. Step by step, we will build a full-stack AI application with high-quality code and highly flexible implementation.

This application can be used as a starting point for most projects, saving significant time, and its flexibility allows for the addition of new tools as needed. By the end of this course, you will have mastered the complete implementation of a flexible, high-quality LLM application. This course will also equip you with the knowledge and skills to create your own complex LLM solutions.

What you will learn:

• How to design systems for AI applications
• How to stream the response of a large language model
• Differences between Server-Sent Events and WebSockets
• The importance of real-time for the GenAI user interface
• How asynchronous programming works in Python
• How to integrate LangChain with FastAPI
• What problems can enhanced recovery solve?
• How to create an AI agent

This course is suitable for people who:

• This course is designed to address specific challenges that AI engineers face when integrating LLM capabilities into web applications. Text streaming presents a unique challenge for engineers and requires the integration of multiple technologies and concepts. This course addresses this topic by:
• Exploring the use of Langchain for easy LLM provider switching. Given the rapid development of Langchain, its streaming, asynchronous, and callback functions can be difficult to understand.
• Decoding asynchronous programming in Python, which can be more complex than JavaScript. We’ll cover the difference between asynchronous and synchronous programming, the benefits of asynchronous operations, and how to implement and integrate asynchronous function calls into a streaming context.
• Choosing 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, even though it is relatively new and unfamiliar to many.
• Create an SSE endpoint using a Python server framework like FastAPI to facilitate real-time data streaming.
• Reading streams on the frontend using the ReadableStream API Fetch interface, ensuring efficient data handling.
• React status updates from notifications taking into account React batch behavior to avoid unexpected issues during rapid updates.
• Integrate custom components into text responses to enable dynamic and real-time user interfaces.

Responsive LLM Applications with Server-Sent Events Course Details

• Publisher: Newline
• Instructor: Louis Sanna
• Training level: Beginner to advanced
• Training duration: 1 hour and 18 minutes
• Number of lessons: 20

Course headings

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Sample course video

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Download file – 376 MB

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376 MB