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RAG-Driven Generative AI

You're reading from   RAG-Driven Generative AI Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836200918
Length 334 pages
Edition 1st Edition
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Toc

Table of Contents (14) Chapters Close

Preface 1. Why Retrieval Augmented Generation? FREE CHAPTER 2. RAG Embedding Vector Stores with Deep Lake and OpenAI 3. Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI 4. Multimodal Modular RAG for Drone Technology 5. Boosting RAG Performance with Expert Human Feedback 6. Scaling RAG Bank Customer Data with Pinecone 7. Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex 8. Dynamic RAG with Chroma and Hugging Face Llama 9. Empowering AI Models: Fine-Tuning RAG Data and Human Feedback 10. RAG for Video Stock Production with Pinecone and OpenAI 11. Other Books You May Enjoy
12. Index
Appendix

Installing the environment

Installing an environment has become complex with the rapid evolution of AI and cross-platform dependency conflicts, as we saw in Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI, in the Setting up the environment section. We will thus freeze the package versions when possible.

For this program, open the Fine_tuning_OpenAI_GPT_4o_mini.ipynb notebook in the Chapter09 directory on GitHub. The program first retrieves the OpenAI API key:

#You can retrieve your API key from a file(1)
# or enter it manually(2)
#Comment this cell if you want to enter your key manually.
#(1)Retrieve the API Key from a file
#Store you key in a file and read it(you can type it directly in the notebook but it will be visible for somebody next to you)
from google.colab import drive
drive.mount('/content/drive')
f = open("drive/MyDrive/files/api_key.txt", "r")
API_KEY=f.readline()
f.close()

We then install openai and set the...

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