Module 1 of 2 in Building Integrated AI Services with LangChain & LangGraph

Retrieval-Augmented Generation with LangChain

Nov 12 2024, Python 3.12, LangChain 0.3.x, JupyterLab 4.2.4

This module introduces Retrieval-Augmented Generation (RAG) using LangChain. Students will learn about embeddings, vector databases, and how to enhance LLMs with domain-specific knowledge for apps like intelligent search and chatbots. By Godfred Afful.

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EXPERIENCE

Intermediate level

EST TIME TO COMPLETE

2 hrs, 3 mins

CONTENT

5 lessons

Module outcomes

Explain the concepts of RAG, embeddings, and vector databases in the context of AI apps.
Implement a RAG system using LangChain, including data preparation, embedding extraction, and vector database integration.
Evaluate the accuracy and effectiveness of a RAG system, including the implementation of citation mechanisms.

Covered concepts

RAG
LangChain
Vector Databases

Module content

Introduction to Retrieval-Augmented Generation (RAG) Lesson (21 mins)

This lesson introduces the fundamental concepts of Retrieval Augmented Generation (RAG), its terminologies and how it compares with other LLM systems.

Start

Introduction • Start Reading · 1 min

Introduction to Retrieval-Augmented Generation Reading · 6 mins

Basic RAG Application Demo Video · 3 mins

Introducing Embeddings & Vector Databases Reading · 4 mins

Embeddings & Vector Databases Demo Video · 6 mins

Conclusion Reading · 1 min

Quiz: Review of RAG 6 questions

Working with Embeddings & Vector Databases Lesson (23 mins)

This lesson teaches about embeddings, vector dimensions, vector databases and how to use them with LangChain.

Start

Introduction • Start Reading · 1 min

Vector Databases in RAG Applications Reading · 3 mins

Vector Dimensions & Embeddings Reading · 4 mins

Vector Embeddings Demo Video · 4 mins

Introducing Chroma Database Reading · 6 mins

Chroma Demo Video · 5 mins

Conclusion Reading · 1 min

Quiz: Embeddings & Vector Databases 6 questions

Building a Basic RAG System with LangChain Lesson (25 mins)

Learn how to build a basic RAG system with LangChain and OpenAI.

Start

Introduction • Start Reading · 1 min

Introducing SportsBuddy Reading · 11 mins

Building a Basic RAG App Demo Video · 4 mins

Enhancing a RAG App Reading · 4 mins

Conversational RAG App Demo Video · 4 mins

Conclusion Reading · 1 min

Quiz: Basic RAG System with LangChain 6 questions

Advanced RAG Techniques Lesson (17 mins)

Learn how to enhance a basic RAG application.

Start

Introduction • Start Reading · 1 min

Advanced RAG Techniques Reading · 5 mins

OpenAI & LangChain Demo Video · 4 mins

Enhancing a Basic RAG App Reading · 4 mins

Enhancing a Basic RAG App Demo Video · 3 mins

Conclusion Reading · 1 min

Quiz: Advanced RAG Techniques 6 questions

Evaluating & Optimizing RAG Systems Lesson (36 mins)

Learn how to evaluate and optimize a RAG system.

Start

Introduction • Start Reading · 1 min

Assessing a RAG Pipeline Reading · 12 mins

Assessing a RAG Pipeline Demo Video · 5 mins

Understanding Query Analysis Reading · 7 mins

Understanding Query Analysis Demo Video · 5 mins

Improving Conversational Traits Reading · 5 mins

Conclusion Reading · 1 min

Quiz: Evaluating & Optimizing RAG Systems 6 questions

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Retrieval-Augmented Generation with LangChain

Module outcomes

Covered concepts

Module content

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Check out our On-Demand Bootcamps on iOS Apple Intelligence, visionOS and more!

Module outcomes

Covered concepts

Module content

AI Agents with LangGraph

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