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

Code lab 10.2 – LangChain Retrievers

In this code lab, we will cover a few examples of the most important component in the retrieval process: the LangChain retriever. Like the LangChain vector store, there are too many options for LangChain retrievers to list here. We will focus on a few popular choices that are particularly applicable to RAG applications, and we encourage you to look at all the others to see if there are better options for your specific situation. Just like we discussed with the vector stores, there is ample documentation on the LangChain website that will help you find your best solution: https://python.langchain.com/v0.2/docs/integrations/retrievers/

The documentation for the retriever package can be found here: https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.retrievers

Now, let’s get started with coding for retrievers!

Retrievers, LangChain, and RAG

Retrievers are responsible for querying the vector...

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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 10.2 – LangChain Retrievers

Retrievers, 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 10.2 – LangChain Retrievers

Retrievers, LangChain, and RAG

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Authors (1)

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