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

Naïve, advanced, and modular RAG configurations

A RAG framework necessarily contains two main components: a retriever and a generator. The generator can be any LLM or foundation multimodal AI platform or model, such as GPT-4o, Gemini, Llama, or one of the hundreds of variations of the initial architectures. The retriever can be any of the emerging frameworks, methods, and tools such as Activeloop, Pinecone, LlamaIndex, LangChain, Chroma, and many more.

The issue now is to decide which of the three types of RAG frameworks (Gao et al., 2024) will fit the needs of a project. We will illustrate these three approaches in code in the Naïve, advanced, and modular RAG in code section of this chapter:

Naïve RAG: This type of RAG framework doesn’t involve complex data embedding and indexing. It can be efficient to access reasonable amounts of data through keywords, for example, to augment a user’s input and obtain a satisfactory response.
Advanced RAG: This type of RAG involves more complex scenarios, such as with vector search and indexed-base retrieval applied. Advanced RAG can be implemented with a wide range of methods. It can process multiple data types, as well as multimodal data, which can be structured or unstructured.
Modular RAG: Modular RAG broadens the horizon to include any scenario that involves naïve RAG, advanced RAG, machine learning, and any algorithm needed to complete a complex project.

However, before going further, we need to decide if we should implement RAG or fine-tune a model.