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

View More author details

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

Complete code

Here is the code in its entirety:

%pip install langchain_community
%pip install langchain_experimental
%pip install langchain-openai
%pip install langchainhub
%pip install chromadb
%pip install langchain
%pip install beautifulsoup4

Restart the kernel before running the following code:

import os
from langchain_community.document_loaders import WebBaseLoader
import bs4
import openai
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
import chromadb
from langchain_community.vectorstores import Chroma
from langchain_experimental.text_splitter import SemanticChunker
os.environ['OPENAI_API_KEY'] = 'sk-###################'
openai.api_key = os.environ['OPENAI_API_KEY']
#### INDEXING ####
loader = WebBaseLoader(
    web_paths=("https://kbourne.github.io/chapter1.html&quot...

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Table of Contents (20) Chapters

Complete code

Authors (1)

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Table of Contents (20) Chapters

Complete code

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