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
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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
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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

1
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
2
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
3
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
4
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
5
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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Godfred Afful

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