Build RAG Applications: Get Started

Build RAG Applications: Get Started

本课程是多个项目的一部分。

位教师：Wojciech 'Victor' Fulmyk

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3个模块

深入了解一个主题并学习基础知识。

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3个模块

深入了解一个主题并学习基础知识。

116 条评论

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Develop a practical understanding of Retrieval-Augmented Generation (RAG)
Design user-friendly, interactive interfaces for RAG applications using Gradio
Learn about LlamaIndex, its uses in building RAG applications, and how it contrasts with LangChain
Build RAG applications using LangChain and LlamaIndex in Python

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作业

6 项作业

授课语言：英语（English）

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该课程共有3个模块

Data Scientists, AI Researchers, Robotics Engineers, and others who can use Retrieval-Augmented Generation (RAG) can expect to earn entry-level salaries ranging from USD 93,386 to USD 110,720 annually, with highly experienced AI engineers earning as much as USD 172,468 annually (Source: ZipRecruiter).

In this beginner-friendly short course, you’ll begin by exploring RAG fundamentals—learning how RAG enhances information retrieval and user interactions—before building your first RAG pipeline. Next, you’ll discover how to create user-friendly Generative AI applications using Python and Gradio, gaining experience with moving from project planning to constructing a QA bot that can answer questions using information contained in source documents. Finally, you’ll learn about LlamaIndex, a popular framework for building RAG applications. Moreover, you’ll compare LlamaIndex with LangChain and develop a RAG application using LlamaIndex. Throughout this course, you’ll engage in interactive hands-on labs and leverage multiple LLMs, gaining the skills needed to design, implement, and deploy AI-driven solutions that deliver meaningful, context-aware user experiences. Enroll now to gain valuable RAG skills!

This module provides an overview of Retrieval-Augmented Generation (RAG), illustrating how it can enhance information retrieval and summarization for AI applications. The module features a lab designed to introduce the fundamental components of building RAG applications, presented in an easy-to-use Jupyter Notebook format. Through this hands-on project, you’ll learn to split and embed documents and implement retrieval chains using LangChain.

涵盖的内容

4个视频2篇阅读材料2个作业1个应用程序项目1个讨论话题3个插件

4个视频总计22分钟

Course Introduction 3分钟
RAG and Agentic AI Professional Certificate Overview 6分钟
Why RAG? 7分钟
More RAG Details 7分钟

2篇阅读材料总计6分钟

Course Overview 4分钟
Summary and Highlights: Introduction to RAG 2分钟

2个作业总计36分钟

Practice Quiz: What is RAG? 15分钟
Graded Quiz: Introduction to RAG 21分钟

1个应用程序项目总计60分钟

Summarize Private Documents Using RAG, LangChain, and LLMs 60分钟

1个讨论话题总计10分钟

[Optional] Discussion Prompt: Meet and Greet 10分钟

3个插件总计20分钟

Reading: Helpful Tips for Course Completion 5分钟
Reading: What is RAG? 10分钟
Cheat Sheet: Introduction to RAG 5分钟

In this module, you'll learn to build a Retrieval-Augmented Generation (RAG) application using LangChain, gaining hands-on experience in transforming an idea into a fully functional AI solution. You'll also explore Gradio as a user-friendly interface layer for your models, setting up a simple Gradio interface to facilitate real-time interactions. Finally, you'll construct a QA Bot leveraging LangChain and an LLM to answer questions from loaded documents, reinforcing your understanding of end-to-end RAG workflows.

涵盖的内容

1个视频1篇阅读材料2个作业2个应用程序项目2个插件

1个视频总计4分钟

Getting Started with Gradio 4分钟

1篇阅读材料总计2分钟

Summary and Highlights: Building Apps with RAG 2分钟

2个作业总计36分钟

Practice Quiz: Building Apps with RAG 15分钟
Graded Quiz: Building Apps with RAG 21分钟

2个应用程序项目总计60分钟

Lab: Set Up a Simple Gradio Interface to Interact with Your Models 30分钟
Lab: Construct a QA Bot that Leverages the LangChain and LLM to Answer Questions from Loaded Document 30分钟

2个插件总计20分钟

Reading: Introduction to Gradio 15分钟
Cheat Sheet: Building Apps with RAG 5分钟

This module introduces you to LlamaIndex as an alternative to LangChain, helping you understand how to apply your RAG knowledge across different frameworks. You will explore the differences between these frameworks and gain hands-on experience by building a bot with IBM Granite and LlamaIndex that provides individuals with suggestions on engaging in conversations. When completing this project, you will learn about implementing key concepts such as vector databases, embedding models, document chunking, retrievers, and prompt templates to generate high-quality responses. 