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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

By : Keith Bourne
5 (2)
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Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

5 (2)
By: Keith Bourne

Overview of this book

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. 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.
Table of Contents (20 chapters)
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Free Chapter
1
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
7
Part 2 – Components of RAG
14
Part 3 – Implementing Advanced RAG

UI

At some point, to make this application more professional and usable, you must add a way for regular users who do not have your code to enter their queries directly and see the results. The UI serves as the primary point of interaction between the user and the system and therefore is a critical component when building a RAG application. Advanced interfaces might include natural language understanding (NLU) capabilities to interpret the user’s intent more accurately, a form of natural language processing (NLP) that focuses on the understanding part of natural language. This component is crucial for ensuring that users can easily and effectively communicate their needs to the system.

This begins with replacing this last line with a UI:

rag_chain.invoke("What are the Advantages of using RAG?")

This line would be replaced with an entry field for the user to submit a text question, rather than a set string that we pass it in, as shown here.

This also includes...

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