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Fundamentals of Retrieval-Augmented Generation with LangChain

Explore this beginner RAG course to learn the basics of retrieval-augmented generation. For hands-on practice, build RAG pipelines using LangChain and create user-friendly applications with Streamlit.

Beginner

21 Lessons

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

27 Playgrounds

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

Retrieval-augmented generation (RAG) is a powerful paradigm that combines the strengths of information retrieval and generative AI models to produce accurate, context-relevant results. This method improves the efficiency of generative models by integrating external knowledge sources for various applications. This beginner RAG course introduces learners to the fundamental concepts of RAG, offering a comprehensive understanding of its architecture and applications. You’ll learn how to implement RAG pipelines...Show More

Retrieval-augmented generation (RAG) is a powerful paradigm that combines the strengths of information retrieval and generative ...Show More

TAKEAWAY SKILLS

Generative AI

Large Language Models (LLMs)

What You'll Learn

A clear understanding of the basics of retrieval-augmented generation (RAG)

Practical experience implementing RAG pipelines using LangChain

The ability to build a frontend application for your RAG pipeline using Streamlit

Real-world application of RAG concepts to solve practical problems

A clear understanding of the basics of retrieval-augmented generation (RAG)

Course Content

Getting Started

1 Lessons

Understand the retrieval-augmented generation (RAG) principles, its architecture, and how it enhances AI accuracy for practical applications.

Why Choose RAG and This Course?

The Basics of RAG

5 Lessons

Learn the logic behind RAG, its essential components, and strategies like indexing and retrieval to build a solid foundation for your RAG systems.

Understanding Retrieval and Generative Models

What Is Retrieval-Augmented Generation (RAG)?

Indexing Essentials: How RAG Organizes Data?

Effective Retrieval Strategies for RAG Pipelines

Quiz: Test Your Knowledge on RAG Basics

RAGs and LangChain

4 Lessons

Explore implementing indexing, querying, and response generation in LangChain to power your RAG systems.

Indexing and Retrieving Documents with LangChain

Creating Augmented Queries Using LangChain

Generating Context-Driven Responses with LangChain

Quiz: Reinforce Your LangChain and RAG Skills

Build a Frontend for Our RAG System

4 Lessons

Use Streamlit and LangChain to build a user-friendly frontend for your RAG system, enabling seamless interaction with your pipeline.

What Is Streamlit?—An Intro for Beginners

Building a QA System Interface in Streamlit

Adding LangChain Logic to Your Streamlit Application

Quiz: Test Your Frontend and Streamlit Knowledge

Challenges

6 Lessons

Tackle advanced challenges to enhance your system, like handling vector store transitions and supporting multiple file formats.

Challenge: Transitioning Between Vector Stores

Solution: Transitioning Between Vector Stores

Challenge: Supporting PDF File Uploads in RAG

Solution: Supporting PDF File Uploads in RAG

Challenge: Building a QA System for Multiple Files

Solution: Building a QA System for Multiple Files

Conclusion

1 Lessons

Tackle practical implementations of RAG systems with LangChain and explore advanced features.

Wrap Up

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