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

This chapter explored the key technical components of RAG systems in the context of LangChain: vector stores, retrievers, and LLMs. It provided an in-depth look at the various options available for each component and discussed their strengths, weaknesses, and scenarios in which one option might be better than another.

The chapter started by examining vector stores, which play a crucial role in efficiently storing and indexing vector representations of knowledge base documents. LangChain integrates with various vector store implementations, such as Pinecone, Weaviate, FAISS, and PostgreSQL with vector extensions. The choice of vector store depends on factors such as scalability, search performance, and deployment requirements. The chapter then moved on to discuss retrievers, which are responsible for querying the vector store and retrieving the most relevant documents based on the input query. LangChain offers a range of retriever implementations, including dense retrievers...

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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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