Responsive LLM Applications with Server-Sent Events
1h 18m 18s
English
Paid
Course description
Large Language Models (LLM) are transforming entire industries, but integrating them into user interfaces with real-time data streaming comes with unique challenges. In this course, you will learn to seamlessly embed LLM APIs into applications and create AI interfaces for streaming text and chats using TypeScript, React, and Python. We will develop a fully functional AI application step by step with high-quality code and flexible implementation.
Read more about the course
As part of the course, you will create an LLM application that includes:
- autocompletion scenario (translation from English to emoji),
- chat,
- retrieval augmented generation scenario,
- AI agent usage scenarios (code execution, data analysis agent).
This application can become a starting point for most projects, saving a lot of time, and its flexibility allows for the addition of new tools as needed.
By the end of the course, you will have mastered the end-to-end implementation of a flexible and high-quality LLM application. You will also gain the knowledge and skills necessary to create complex solutions based on LLM.
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/ #1: Introduction to AI Product Development
All Course Lessons (20)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction to AI Product Development Demo | 03:48 | |
| 2 | Picking the stack - Navigating JavaScript and Python | 06:10 | |
| 3 | Designing a Hybrid Web Application Architecture with JavaScript and Python | 05:08 | |
| 4 | Streaming events with Server-Sent Events and WebSockets | 06:31 | |
| 5 | Discovering the OpenAI Completion API | 06:30 | |
| 6 | Handling Server-Sent Events with JavaScript | 06:14 | |
| 7 | Building the useCompletion hook | 07:01 | |
| 8 | Rendering Completion Output | 01:26 | |
| 9 | Mocking Streams | 03:29 | |
| 10 | Testing the useCompletion hook | 03:11 | |
| 11 | Creating a FastAPI server | 01:55 | |
| 12 | Exploring asynchronous programming in Python | 03:42 | |
| 13 | Integrating Langchain with FastAPI for Asynchronous Streaming | 04:34 | |
| 14 | Testing with PyTest and LangChain | 01:02 | |
| 15 | Building the useChat hook | 05:12 | |
| 16 | Building the User Interface | 01:53 | |
| 17 | Discovering Retrieval Augmented Generation | 03:19 | |
| 18 | Building a Semantic Search Engine with Chroma | 03:37 | |
| 19 | Adding Retrieval-Augmented Generation to the chat | 02:14 | |
| 20 | Final words | 01:22 |
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