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 338 pages

Edition 1st Edition

Languages

Python

Tools

Docker

Concepts

GPT/LLMs

Author (1):

Denis Rothman

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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

The environment of the video production ecosystem

The Chapter10 directory on GitHub contains the environment for all four notebooks in this chapter:

Videos_dataset_visualization.ipynb
Pipeline_1_The_Generator_and_the_Commentator.ipynb
Pipeline_2_The_Vector_Store_Administrator.ipynb
Pipeline_3_The_Video_Expert.ipynb

Each notebook includes an Installing the environment section, including a set of the following sections that are identical across all notebooks:

Importing modules and libraries
GitHub
Video download and display functions
OpenAI
Pinecone

This chapter aims to establish a common pre-production installation policy that will focus on the pipelines’ content once we dive into the RAG for video production code. This policy is limited to the scenario described in this chapter and will vary depending on the requirements of each real-life production environment.

The notebooks in this...

The rest of the chapter is locked

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

Table of Contents (14) Chapters

The environment of the video production ecosystem

Authors (1)

Personalised recommendations for you

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

Table of Contents (14) Chapters

The environment of the video production ecosystem

Authors (1)

Personalised recommendations for you

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