You're reading from Unlocking Data with Generative AI and RAG Enhance generative AI systems by integrating internal data with large language models using RAG

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781835887905

Length 346 pages

Edition 1st Edition

Concepts

GPT/LLMs

Author (1):

Keith Bourne

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Table of Contents (20) Chapters

Preface

1. Part 1 – Introduction to Retrieval-Augmented Generation (RAG) FREE CHAPTER

2. Chapter 1: What Is Retrieval-Augmented Generation (RAG)

3. Chapter 2: Code Lab – An Entire RAG Pipeline

4. Chapter 3: Practical Applications of RAG

5. Chapter 4: Components of a RAG System

6. Chapter 5: Managing Security in RAG Applications

7. Part 2 – Components of RAG

8. Chapter 6: Interfacing with RAG and Gradio

9. Chapter 7: The Key Role Vectors and Vector Stores Play in RAG

10. Chapter 8: Similarity Searching with Vectors

11. Chapter 9: Evaluating RAG Quantitatively and with Visualizations

12. Chapter 10: Key RAG Components in LangChain

13. Chapter 11: Using LangChain to Get More from RAG

14. Part 3 – Implementing Advanced RAG

15. Chapter 12: Combining RAG with the Power of AI Agents and LangGraph

16. Chapter 13: Using Prompt Engineering to Improve RAG Efforts

17. Chapter 14: Advanced RAG-Related Techniques for Improving Results

18. Index

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Summary

This chapter provided a comprehensive code lab that walked through the implementation of a complete RAG pipeline. We began by installing the necessary Python packages, including LangChain, Chroma DB, and various LangChain extensions. Then, we learned how to set up an OpenAI API key, load documents from a web page using WebBaseLoader, and preprocess the HTML content with BeautifulSoup to extract relevant sections.

Next, the loaded documents were split into manageable chunks using SemanticChunker from LangChain’s experimental module. These chunks were then embedded into vector representations using OpenAI’s embedding model and stored in a Chroma DB vector database.

Next, we introduced the concept of a retriever, which is used to perform a vector similarity search on the embedded documents based on a given query. We stepped through the retrieval and generation stages of RAG, which in this case are combined into a LangChain chain using the LCEL. The chain integrates...