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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19. Other Books You May Enjoy

Summary

In this chapter, we covered a wide range of topics related to similarity searching with vectors, a crucial component of RAG systems. We explored the concept of a vector space, discussed the differences between semantic and keyword searches, and covered various distance metrics used to compare the similarity between embeddings, providing code examples to demonstrate their calculation.

We reviewed code that implemented hybrid search using the BM25 algorithm for sparse search and a dense retriever for semantic search, showcasing how to combine and rerank the results. We also discussed semantic search algorithms, focusing on k-NN and ANN, and covered indexing techniques that enhance the efficiency of ANN search, such as LSH, tree-based indexing, PQ, and HNSW.

Finally, we provided an overview of several vector search options available in the market, discussing their key features, strengths, and considerations to help you make an informed decision when selecting a vector search...

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Table of Contents (20) Chapters

Summary

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Table of Contents (20) Chapters

Summary

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