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Unlocking Data with Generative AI and RAG

You're reading from   Unlocking Data with Generative AI and RAG Enhance generative AI systems by integrating internal data with large language models using RAG

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835887905
Length 346 pages
Edition 1st Edition
Concepts
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Author (1):
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Keith Bourne Keith Bourne
Author Profile Icon Keith Bourne
Keith Bourne
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Table of Contents (20) Chapters Close

Preface 1. Part 1 – Introduction to Retrieval-Augmented Generation (RAG) FREE CHAPTER
2. Chapter 1: What Is Retrieval-Augmented Generation (RAG) 3. Chapter 2: Code Lab – An Entire RAG Pipeline 4. Chapter 3: Practical Applications of RAG 5. Chapter 4: Components of a RAG System 6. Chapter 5: Managing Security in RAG Applications 7. Part 2 – Components of RAG
8. Chapter 6: Interfacing with RAG and Gradio 9. Chapter 7: The Key Role Vectors and Vector Stores Play in RAG 10. Chapter 8: Similarity Searching with Vectors 11. Chapter 9: Evaluating RAG Quantitatively and with Visualizations 12. Chapter 10: Key RAG Components in LangChain 13. Chapter 11: Using LangChain to Get More from RAG 14. Part 3 – Implementing Advanced RAG
15. Chapter 12: Combining RAG with the Power of AI Agents and LangGraph 16. Chapter 13: Using Prompt Engineering to Improve RAG Efforts 17. Chapter 14: Advanced RAG-Related Techniques for Improving Results 18. Index 19. Other Books You May Enjoy

Vectors

It could be argued that understanding vectors and all the ways they are used in RAG is the most important part of this entire book. As mentioned previously, vectors are simply the mathematical representations of your external data, and they are often referred to as embeddings. These representations capture semantic information in a format that can be processed by algorithms, facilitating tasks such as similarity search, which is a crucial step in the RAG process.

Vectors typically have a specific dimension based on how many numbers are represented by them. For example, this is a four-dimensional vector:

[0.123, 0.321, 0.312, 0.231]

If you didn’t know we were talking about vectors and you saw this in Python code, you might recognize this as a list of four floating points, and you aren’t too far off. However, when working with vectors in Python, you want to recognize them as a NumPy array, rather than lists. NumPy arrays are generally more machine-learning-friendly because they are optimized to be processed much faster and more efficiently than Python lists, and they are more broadly recognized as the de facto representation of embeddings across machine learning packages such as SciPy, pandas, scikit-learn, TensorFlow, Keras, Pytorch, and many others. NumPy also enables you to perform vectorized math directly on the NumPy array, such as performing element-wise operations, without having to code in loops and other approaches you might have to use if you were using a different type of sequence.

When working with vectors for vectorization, there are often hundreds or thousands of dimensions, which refers to the number of floating points present in the vector. Higher dimensionality can capture more detailed semantic information, which is crucial for accurately matching query inputs with relevant documents or data in RAG applications.

In Chapter 7, we will cover the key role vectors and vector databases play in RAG implementation. Then, in Chapter 8, we will dive more into the concept of similarity searches, which utilize vectors to search much faster and more efficiently. These are key concepts that will help you gain a much deeper understanding of how to better implement a RAG pipeline.

Understanding vectors can be a crucial underlying concept to understand how to implement RAG, but how is RAG used in practical applications in the enterprise? We will discuss these practical AI applications of RAG in the next section.

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