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

Standardized evaluation frameworks

Key technical components of your RAG system include the embedding model that makes your embeddings, the vector store, the vector search, and the LLM. When you look at the different options for each technical component, there are a number of standardized metrics that are available for each that help you compare them against each other. Here are some common metrics for each category.

Embedding model benchmarks

The Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard evaluates the performance of embedding models on various retrieval tasks across different datasets. The MTEB leaderboard ranks models based on their average performance across many embedding and retrieval-related tasks. You can visit the leaderboard using this link: https://huggingface.co/spaces/mteb/leaderboard

When visiting this web page, click on the Retrieval and Retrieval w/Instructions tabs for retrieval-specific embedding ratings. To evaluate each of the models on...

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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

Standardized evaluation frameworks

Embedding model benchmarks

Authors (1)

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Table of Contents (20) Chapters

Standardized evaluation frameworks

Embedding model benchmarks

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