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Fundamentals of Retrieval-Augmented Generation with LangChain

Gain insights into retrieval-augmented generation by delving into RAG architecture. Learn to implement RAG using LangChain and build user-friendly applications with Streamlit for efficient data retrieval and generation.

Beginner

21 Lessons

Certificate of Completion

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Explanations

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Explanations

This course includes

27 Playgrounds

Course Overview

What You'll Learn

Course Content

Recommendations

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

Get familiar with RAG principles, architecture, and real-world applications for enhanced AI accuracy.

Why Choose RAG and This Course?

The Basics of RAG

5 Lessons

Discover the logic behind Retrieval-Augmented Generation, its components, indexing, 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

Break apart the indexing, querying, and response generation in LangChain for RAG systems.

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

Apply your skills to build an interactive RAG system frontend using Streamlit and LangChain.

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

Map out the steps for overcoming challenges in vector store transitions, and multi-format file uploads.

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

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

Wrap Up

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