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)

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

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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19. Other Books You May Enjoy

Summary

In this chapter, we learned about various components in LangChain that can enhance a RAG application. Code lab 11.1 focused on document loaders, which are used to load and process documents from various sources such as text files, PDFs, web pages, or databases. The chapter covered examples of loading documents from HTML, PDF, Microsoft Word, and JSON formats using different LangChain document loaders, noting that some document loaders add metadata which may require adjustments in the code.

Code lab 11.2 discussed text splitters, which divide documents into chunks suitable for retrieval, addressing issues with large documents and context representation in vector embeddings. The chapter covered CharacterTextSplitter, which splits text into arbitrary N-character-sized chunks, and RecursiveCharacterTextSplitter, which recursively splits text while trying to keep related pieces together. SemanticChunker was introduced as an experimental splitter that combines semantically similar...

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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

Table of Contents (20) Chapters

Summary

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Table of Contents (20) Chapters

Summary

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