This is the code repository for Unlocking Data with Generative AI and RAG, published by Packt.
Enhance generative AI systems by integrating internal data with large language models using RAG
Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.
This book covers the following exciting features:
- Understand RAG principles and their significance in generative AI
- Integrate LLMs with internal data for enhanced operations
- Master vectorization, vector databases, and vector search techniques
- Develop skills in prompt engineering specific to RAG and design for precise AI responses
- Familiarize yourself with AI agents’ roles in facilitating sophisticated RAG applications
- Overcome scalability, data quality, and integration issues
- Discover strategies for optimizing data retrieval and AI interpretability
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
os.environ['OPENAI_API_KEY'] = 'sk-###################'
openai.api_key = os.environ['OPENAI_API_KEY']
Following is what you need for this book: This book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
| Chapter | Software required | OS required |
|---|---|---|
| 1-14 | Python 3.x | Windows, Mac OS X, and Linux (Any) |
| 1-14 | LangChain | Windows, Mac OS X, and Linux (Any |
| 1-14 | OpenAI API | Windows, Mac OS X, and Linux (Any) |
| 1-14 | Jupyter notebooks | Windows, Mac OS X, and Linux (Any) |
Keith Bourne is a Senior Generative AI Data Scientist at Johnson & Johnson, leveraging his decade of experience in machine learning. With an MBA from Babson College and a Master of Applied Data Science from the University of Michigan, Keith has made significant contributions to healthcare innovation through his expertise in generative AI, particularly in developing a sophisticated generative AI platform incorporating Retrieval-Augmented Generation (RAG) and other advanced techniques. Keith has worked with a diverse set of clients including University of Michigan Healthcare, NFL, NOAA, Weather Channel, Becton Dickinson, Toyota, and Little Caesars. Originally from Chagrin Falls, OH, Keith resides in Ann Arbor, MI with his wife and three daughters.