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.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

27 Playgrounds

This course includes

27 Playgrounds

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

TAKEAWAY SKILLS

Generative Ai

Large Language Models (llms)

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

What You'll Learn

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

Course Content

Getting Started

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

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

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

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

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 Lesson

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

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