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

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781836200918

Length 334 pages

Edition 1st Edition

Languages

Python

Tools

Docker

Concepts

GPT/LLMs

Author (1):

Denis Rothman

View More author details

Table of Contents (14) Chapters

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

What is RAG?

When a generative AI model doesn’t know how to answer accurately, some say it is hallucinating or producing bias. Simply said, it just produces nonsense. However, it all boils down to the impossibility of providing an adequate response when the model’s training didn’t include the information requested beyond the classical model configuration issues. This confusion often leads to random sequences of the most probable outputs, not the most accurate ones.

RAG begins where generative AI ends by providing the information an LLM model lacks to answer accurately. RAG was designed (Lewis et al., 2020) for LLMs. The RAG framework will perform optimized information retrieval tasks, and the generation ecosystem will add this information to the input (user query or automated prompt) to produce improved output. The RAG framework can be summed up at a high level in the following figure:

A diagram of a library

Description automatically generated

Figure 1.1: The two main components of RAG-driven generative AI

Think of yourself as a student in a library. You have an essay to write on RAG. Like ChatGPT, for example, or any other AI copilot, you have learned how to read and write. As with any Large Language Model (LLM), you are sufficiently trained to read advanced information, summarize it, and write content. However, like any superhuman AI you will find from Hugging Face, Vertex AI, or OpenAI, there are many things you don’t know.

In the retrieval phase, you search the library for books on the topic you need (the left side of Figure 1.1). Then, you go back to your seat, perform a retrieval task by yourself or a co-student, and extract the information you need from those books. In the generation phase (the right side of Figure 1.1), you begin to write your essay. You are a RAG-driven generative human agent, much like a RAG-driven generative AI framework.

As you continue to write your essay on RAG, you stumble across some tough topics. You don’t have the time to go through all the information available physically! You, as a generative human agent, are stuck, just as a generative AI model would be. You may try to write something, just as a generative AI model does when its output makes little sense. But you, like the generative AI agent, will not realize whether the content is accurate or not until somebody corrects your essay and you get a grade that will rank your essay.

At this point, you have reached your limit and decide to turn to a RAG generative AI copilot to ensure you get the correct answers. However, you are puzzled by the number of LLM models and RAG configurations available. You need first to understand the resources available and how RAG is organized. Let’s go through the main RAG configurations.