English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 20 Lessons (1h 18m) | 371 MB
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.
Dive into Retrieval Augmented Generation and Autonomous Agents with LangChain, Chroma and FastAPI
What you will learn
- How to design systems for AI applications
- How to stream the answer of a Large Language Model
- Differences between Server-Sent Events and WebSockets
- Importance of real-time for GenAI UI
- How asynchronous programming in Python works
- How to integrate LangChain with FastAPI
- What problems Retrieval Augmented Generation can solve
- How to create an AI agent
Table of Contents
1 Introduction to AI Product Development
2 Picking the stack – Navigating JavaScript and Python
3 Designing a Hybrid Web Application Architecture with JavaScript and Python
4 Streaming events with Server-Sent Events and WebSockets
5 Discovering the OpenAI Completion API
6 Handling Server-Sent Events with JavaScript
7 Building the useCompletion hook
8 Rendering Completion Output
9 Mocking Streams
10 Testing the useCompletion hook
11 Creating a FastAPI server
12 Exploring asynchronous programming in Python
13 Integrating Langchain with FastAPI for Asynchronous Streaming
14 Testing with PyTest and LangChain
15 Building the useChat hook
16 Building the User Interface
17 Discovering Retrieval Augmented Generation
18 Building a Semantic Search Engine with Chroma
19 Adding Retrieval-Augmented Generation to the chat
20 Final words
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