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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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Table of Contents (14) Chapters Close

Preface 1. Why Retrieval Augmented Generation? 2. RAG Embedding Vector Stores with Deep Lake and OpenAI FREE CHAPTER 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

Prompt and retrieval

This section is the one to use during real-time querying meetings. You can adapt the interface to your needs. We’ll focus on functionality.

Let’s look at the first prompt:

# initial question
prompt = "Millions of years ago, plants used energy from the sun to form what?"
# variant 1 similar
#prompt = "Eons ago, plants used energy from the sun to form what?"
# variant 2 divergent
#prompt = "Eons ago, plants used sun energy to form what?"

You will notice that there are two commented variants under the first prompt. Let’s clarify this:

  • initial question is the exact text that comes from the initial dataset. It isn’t likely that an attendee in the meeting or a user will ask the question that way. But we can use it to verify if the system is working.
  • variant 1 is similar to the initial question and could be asked.
  • variant 2 diverges and may prove challenging.

We will...

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