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

Retrieval and generation

In the code, the retrieval and generation stages are combined within the chain we set up to represent the entire RAG process. This leverages pre-built components from the LangChain Hub, such as prompt templates, and integrates them with a selected LLM. We will also utilize the LangChain Expression Language (LCEL) to define a chain of operations that retrieves relevant documents based on an input question, formats the retrieved content, and feeds it into the LLM to generate a response. Overall, the steps we take in retrieval and generation are as follows:

Take in a user query.
Vectorize that user query.
Perform a similarity search of the vector store to find the closest vectors to the user query vector, as well as their associated content.
Pass the retrieved content into a prompt template, a process known as hydrating.
Pass that hydrated prompt to the LLM.
Once you receive a response from the LLM, present it to the user.

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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

Retrieval and generation

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Table of Contents (20) Chapters

Retrieval and generation

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