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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Retrieval and generation

In our RAG application code, we have combined the Retrieval and Generation stages. From a diagram standpoint, this looks like what’s shown in Figure 4.6:

Figure 4.6 – Vectors during the Indexing stage of the RAG process

While retrieval and generation are two separate stages serving two important functions of the RAG application, they are combined in our code. When we invoke rag_chain as the last step, it is stepping through both of these stages, making them difficult to separate when talking about the code. But conceptually, we will separate them here, and then show how they pull them together to process the user query and provide an intelligent generative AI response. Let’s start with the retrieval step.

Retrieval focused steps

In the complete code (which can be found in Chapter 2), there are only two areas in this code where actual retrieval takes place or is processed. This is the first:

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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

Retrieval and generation

Retrieval focused steps

Authors (1)

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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

Retrieval and generation

Retrieval focused steps

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Authors (1)

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