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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

By : Keith Bourne
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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

5 (1)
By: Keith Bourne

Overview of this book

Generative AI is helping organizations tap into their data in new ways, with RAG combining the strengths of 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. The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique. *Email sign-up and proof of purchase required
Table of Contents (20 chapters)
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Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
7
Part 2 – Components of RAG
14
Part 3 – Implementing Advanced RAG

Code lab 11.2 – Text splitters

The file you need to access from the GitHub repository is titled CHAPTER11-2_TEXT_SPLITTERS.ipynb.

Text splitters split a document into chunks that can be used for retrieval. Larger documents pose a threat to many parts of our RAG application and the splitter is our first line of defense. If you were able to vectorize a very large document, the larger the document, the more context representation you will lose in the vector embedding. But this assumes you can even vectorize a very large document, which you often can’t! Most embedding models have relatively small limits on the size of documents we can pass to it compared to the large documents many of us work with. For example, the context length for the OpenAI model we are using to generate our embeddings is 8,191 tokens. If we try to pass a document larger than that to the model, it will generate an error. These are the main reasons splitters exist, but these are not the only complexities...

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Unlocking Data with Generative AI and RAG
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