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

View More author details

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

Why subscribe?

19. Other Books You May Enjoy

Code lab 10.3 – LangChain LLMs

We now turn our attention to the last key component for RAG: the LLM. Just like the retriever in the retrieval stage, without the LLM for the generation stage, there is no RAG. The retrieval stage simply retrieves data from our data source, typically data the LLM does not know about. However, that does not mean that the LLM does not play a vital role in our RAG implementation. By providing the retrieved data to the LLM, we quickly catch that LLM up with what we want it to talk about, and this allows the LLM to do what it is really good at, providing a response based on that data to answer the original question posed by the user.

The synergy between LLMs and RAG systems stems from the complementary strengths of these two technologies. RAG systems enhance the capabilities of LLMs by incorporating external knowledge sources, enabling the generation of responses that are not only contextually relevant but also factually accurate and up to date....

The rest of the chapter is locked

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

Code lab 10.3 – LangChain LLMs

Authors (1)

Personalised recommendations for you

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

Code lab 10.3 – LangChain LLMs

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you