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

Code lab 10.1 – LangChain vector store

The goal for all these code labs is to help you become more familiar with how the options for each key component offered within the LangChain platform can enhance your RAG system. We will dive deep into what each component does, available functions, parameters that make a difference, and ultimately, all of the options you can take advantage of for a better RAG implementation. Starting with Code lab 8.3, (skipping Chapter 9’s evaluation code), we will step through these elements in order of how they appear in code, starting with the vector stores. You can find this code in its entirety in the Chapter 10 code folder on GitHub also labeled as 10.1.

Vector stores, LangChain, and RAG

Vector stores play a crucial role in RAG systems by efficiently storing and indexing vector representations of the knowledge base documents. LangChain provides seamless integration with various vector store implementations, such as Chroma, Weaviate...

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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

Code lab 10.1 – LangChain vector store

Vector stores, LangChain, and RAG

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

Code lab 10.1 – LangChain vector store

Vector stores, LangChain, and RAG

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Authors (1)

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