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Fundamentals of Retrieval-Augmented Generation with LangChain

This course covers RAG basics, architecture, and applications and teaches you to build RAG pipelines using LangChain and Streamlit.

Beginner

21 Lessons

Certificate of Completion

This course covers RAG basics, architecture, and applications and teaches you to build RAG pipelines using LangChain and Streamlit.

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Course Overview

What You'll Learn

Course Content

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Course Overview

Retrieval-augmented generation (RAG) is a robust paradigm that makes the most of the best information retrieval and generative model strengths to yield correct and context-relevant results. RAG enhances generative models by integrating external knowledge sources, making them more efficient in various use cases. This course introduces the learners to the basic concepts of RAG, giving them a comprehensive understanding of RAG architecture and applications. You’ll implement RAG using LangChain, gaining pract...Show More

What You'll Learn

An understanding of the basics of retrieval-augmented generation (RAG)

Hands-on experience implementing RAG using LangChain

The ability to create a frontend application for the RAG pipeline using Streamlit

Hands-on experience applying the learned skills to solve a real-world use case

An understanding of the basics of retrieval-augmented generation (RAG)

Course Content

Getting Started

1 Lessons

In this chapter, you will discover what RAG is and why this course might be a good fit for you.

Why Choose RAG and This Course?

The Basics of RAG

5 Lessons

In this chapter, you will explore the essential components of RAG, including indexing techniques and retrieval strategies.

What Are Retrieval and Generative Models?

What Is Retrieval-Augmented Generation?

What Is Indexing?

What Are Retrieval Strategies?

Quiz: The Basics of RAG

RAGs and LangChain

4 Lessons

In this chapter, you will learn how to implement RAG systems using LangChain, covering key topics such as document indexing and retrieval.

How to Index and Retrieve Documents in LangChain for RAG Systems

How to Create the Augmented Query in LangChain

How to Generate Responses in LangChain

Quiz: RAG and LangChains

Build a Frontend for Our RAG System

4 Lessons

In this chapter, you will learn how to build a user-friendly frontend for your RAG system using Streamlit.

What Is Streamlit?

How to Create the Interface for a QA System in Streamlit

How to Add the LangChain Logic in Our Streamlit Application

Quiz: Build a Frontend for Our RAG System

Challenges

6 Lessons

In this chapter, you will tackle practical challenges in enhancing your RAG system.

Challenge: Changing the Vector Store

Solution: Changing the Vector Store

Challenge: Adding Support for PDF Files

Solution: Adding Support for PDF Files

Challenge: QA System on Multiple Files

Solution: QA System on Multiple Files

Conclusion

1 Lessons

In this concluding chapter, you'll review the key concepts covered in the course and explore potential next steps for further learning.

Wrap Up

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