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 11.3 – Output parsers

The file you need to access from the GitHub repository is titled CHAPTER11-3_OUTPUT_PARSERS.ipynb.

The end result of any RAG application is going to be text, along with potentially some formatting, metadata, and some other related data. This output typically comes from the LLM itself. But there are times when you want to get a more structured format than just text. Output parsers are classes that help to structure the responses of the LLM wherever you use it in your RAG application. The output that this provides will then be provided to the next step in the chain, or in the case of all of our code labs, as the final output from the model.

We will cover two different output parsers at the same time, and use them at different times in our RAG pipeline. We start with the parser we know, the string output parser.

Under the relevance_prompt function, add this code to a new cell:

from langchain_core.output_parsers import StrOutputParser
str_output_parser...

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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 11.3 – Output parsers

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Table of Contents (20) Chapters

Code lab 11.3 – Output parsers

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