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Home > Data > GPT/LLMs > Unlocking Data with Generative AI and RAG
Unlocking Data with Generative AI and RAG
Unlocking Data with Generative AI and RAG

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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Unlocking Data with Generative AI and RAG

What Is Retrieval-Augmented Generation (RAG)

The field of artificial intelligence (AI) is rapidly evolving. At the center of it all is generative AI. At the center of generative AI is retrieval-augmented generation (RAG). RAG is emerging as a significant addition to the generative AI toolkit, harnessing the intelligence and text generation capabilities of large language models (LLMs) and integrating them with a company’s internal data. This offers a method to enhance organizational operations significantly. This book focuses on numerous aspects of RAG, examining its role in augmenting the capabilities of LLMs and leveraging internal corporate data for strategic advantage.

As this book progresses, we will outline the potential of RAG in the enterprise, suggesting how it can make AI applications more responsive and smarter, aligning them with your organizational objectives. RAG is well-positioned to become a key facilitator of customized, efficient, and insightful AI solutions...

Understanding RAG – Basics and principles

Modern-day LLMs are impressive, but they have never seen your company’s private data (hopefully!). This means the ability of an LLM to help your company fully utilize its data is very limited. This very large barrier has given rise to the concept of RAG, where you are using the power and capabilities of the LLM but combining it with the knowledge and data contained within your company’s internal data repositories. This is the primary motivation for using RAG: to make new data available to the LLM and significantly increase the value you can extract from that data.

Beyond internal data, RAG is also useful in cases where the LLM has not been trained on the data, even if it is public, such as the most recent research papers or articles about a topic that is strategic to your company. In both cases, we are talking about data that was not present during the training of the LLM. You can have the latest LLM trained on the most...

RAG vocabulary

Now is as good a time as any to review some vocabulary that should help you become familiar with the various concepts in RAG. In the following subsections, we will familiarize ourselves with some of this vocabulary, including LLMs, prompting concepts, inference, context windows, fine-tuning approaches, vector databases, and vectors/embeddings. This is not an exhaustive list, but understanding these core concepts should help you understand everything else we will teach you about RAG in a more effective way.

LLM

Most of this book will deal with LLMs. LLMs are generative AI technologies that focus on generating text. We will keep things simple by concentrating on the type of model that most RAG pipelines use, the LLM. However, we would like to clarify that while we will focus primarily on LLMs, RAG can also be applied to other types of generative models, such as those for images, audio, and videos. We will focus on these other types of models and how they are used...

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...

Implementing RAG in AI applications

RAG is rapidly becoming a cornerstone of generative AI platforms in the corporate world. RAG combines the power of retrieving internal or new data with generative language models to enhance the quality and relevance of the generated text. This technique can be particularly useful for companies across various industries to improve their products, services, and operational efficiencies. The following are some examples of how RAG can be used:

  • Customer support and chatbots: These can exist without RAG, but when integrated with RAG, it can connect those chatbots with past customer interactions, FAQs, support documents, and anything else that was specific to that customer.
  • Technical support: With better access to customer history and information, RAG-enhanced chatbots can provide a significant improvement to current technical support chatbots.
  • Automated reporting: RAG can assist in creating initial drafts or summarizing existing articles...

Comparing RAG with conventional generative AI

Conventional generative AI has already shown to be a revolutionary change for companies, helping their employees reach new levels of productivity. LLMs such as ChatGPT are assisting users with a rapidly growing list of applications that include writing business plans, writing and improving code, writing marketing copy, and even providing healthier recipes for a specific diet. Ultimately, much of what users are doing is getting done faster.

However, conventional generative AI does not know what it does not know. And that includes most of the internal data in your company. Can you imagine what you could do with all the benefits mentioned previously, but combined with all the data within your company – about everything your company has ever done, about your customers and all their interactions, or about all your products and services combined with a knowledge of what a specific customer’s needs are? You do not have to imagine...

Comparing RAG with model fine-tuning

LLMs can be adapted to your data in two ways:

  • Fine-tuning: With fine-tuning, you are adjusting the weights and/or biases that define the model’s intelligence based on new training data. This directly impacts the model, permanently changing how it will interact with new inputs.
  • Input/prompts: This is where you use the model, using the prompt/input to introduce new knowledge that the LLM can act upon.

Why not use fine-tuning in all situations? Once you have introduced the new knowledge, the LLM will always have it! It is also how the model was created – by being trained with data, right? That sounds right in theory, but in practice, fine-tuning has been more reliable in teaching a model specialized tasks (such as teaching a model how to converse in a certain way), and less reliable for factual recall.

The reason is complicated, but in general, a model’s knowledge of facts is like a human’s long-term...

The architecture of RAG systems

The following are the stages of a RAG process from a user’s perspective:

  1. A user enters a query/question.
  2. The application thinks for a little while before checking the data it has access to so that it can see what is the most relevant.
  3. The application provides a response that focuses on answering the user’s question, but using data that has been provided to it through the RAG pipeline.

From a technical standpoint, this captures two of the stages you will code: the retrieval and generation stages. But there is one other stage, known as indexing, which can be and is often executed before the user enters the query. With indexing, you are turning supporting data into vectors, storing them in a vector database, and likely optimizing the search functionality so that the retrieval step is as fast and effective as possible.

Once the user passes their query into the system, the following steps occur:

  1. The user query...

Summary

In this chapter, we explored RAG and its ability to enhance the capabilities of LLMs by integrating them with an organization’s internal data. We learned how RAG combines the power of LLMs with a company’s private data, enabling the model to utilize information it was not originally trained on, making the LLM’s outputs more relevant and valuable for the specific organization. We also discussed the advantages of RAG, such as improved accuracy and relevance, customization to a company’s domain, flexibility in data sources used, and expansion of the model’s knowledge beyond its original training data. Additionally, we examined the challenges and limitations of RAG, including dependency on data quality, the need for data cleaning, added computational overhead and complexity, and the potential for information overload if not properly filtered.

Midway through this chapter, we defined key vocabulary terms and emphasized the critical importance...

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Key benefits

  • Optimize data retrieval and generation using vector databases
  • Boost decision-making and automate workflows with AI agents
  • Overcome common challenges in implementing real-world RAG systems
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

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.

What you will learn

  • 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
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Table of Contents

19 Chapters
Part 1 – Introduction to Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
Part 1 – Introduction to Retrieval-Augmented Generation (RAG)
Chapter 1: What Is Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
Chapter 1: What Is Retrieval-Augmented Generation (RAG)
Understanding RAG – Basics and principles
RAG vocabulary
Vectors
Implementing RAG in AI applications
Comparing RAG with conventional generative AI
Comparing RAG with model fine-tuning
The architecture of RAG systems
Summary
Chapter 2: Code Lab – An Entire RAG Pipeline Chevron down icon Chevron up icon
Chapter 2: Code Lab – An Entire RAG Pipeline
Technical requirements
No interface!
Setting up a large language model (LLM) account
Installing the necessary packages
Indexing
Retrieval and generation
Submitting a question for RAG
Final output
Complete code
Summary
Chapter 3: Practical Applications of RAG Chevron down icon Chevron up icon
Chapter 3: Practical Applications of RAG
Technical requirements
Customer support and chatbots with RAG
RAG for automated reporting
E-commerce support
Utilizing knowledge bases with RAG
Innovation scouting and trend analysis
Leveraging RAG for personalized recommendations in marketing communications
Training and education
Code lab 3.1 – Adding sources to your RAG
Summary
Chapter 4: Components of a RAG System Chevron down icon Chevron up icon
Chapter 4: Components of a RAG System
Technical requirements
Key component overview
Indexing
Retrieval and generation
Prompting
Defining your LLM
UI
Evaluation
Summary
References
Chapter 5: Managing Security in RAG Applications Chevron down icon Chevron up icon
Chapter 5: Managing Security in RAG Applications
Technical requirements
How RAG can be leveraged as a security solution
RAG security challenges
Red teaming
Common areas to target with red teaming
Code lab 5.1 – Securing your keys
Code lab 5.2 – Red team attack!
Code lab 5.3 – Blue team defend!
Summary
Part 2 – Components of RAG Chevron down icon Chevron up icon
Part 2 – Components of RAG
Chapter 6: Interfacing with RAG and Gradio Chevron down icon Chevron up icon
Chapter 6: Interfacing with RAG and Gradio
Technical requirements
Why Gradio?
Benefits of using Gradio
Limitations to using Gradio
Code lab – Adding a Gradio interface
Summary
Chapter 7: The Key Role Vectors and Vector Stores Play in RAG Chevron down icon Chevron up icon
Chapter 7: The Key Role Vectors and Vector Stores Play in RAG
Technical requirements
Fundamentals of vectors in RAG
Where vectors lurk in your code
The amount of text you vectorize matters!
Not all semantics are created equal!
Code lab 7.1 – Common vectorization techniques
Factors in selecting a vectorization option
Getting started with vector stores
Choosing a vector store
Summary
Chapter 8: Similarity Searching with Vectors Chevron down icon Chevron up icon
Chapter 8: Similarity Searching with Vectors
Technical requirements
Distance metrics versus similarity algorithms versus vector search
Vector space
Semantic versus keyword search
Code lab 8.1 – Semantic distance metrics
Different search paradigms – sparse, dense, and hybrid
Code lab 8.2 – Hybrid search with a custom function
Code lab 8.3 – Hybrid search with LangChain’s EnsembleRetriever to replace our custom function
Semantic search algorithms
Enhancing search with indexing techniques
Vector search options
Summary
Chapter 9: Evaluating RAG Quantitatively and with Visualizations Chevron down icon Chevron up icon
Chapter 9: Evaluating RAG Quantitatively and with Visualizations
Technical requirements
Evaluate as you build
Evaluate after you deploy
Evaluation helps you get better
Standardized evaluation frameworks
What is the ground truth?
Code lab 9.1 – ragas
End-to-end evaluation
Other component-wise evaluation
Additional evaluation techniques
Summary
References
Chapter 10: Key RAG Components in LangChain Chevron down icon Chevron up icon
Chapter 10: Key RAG Components in LangChain
Technical requirements
Code lab 10.1 – LangChain vector store
Code lab 10.2 – LangChain Retrievers
Code lab 10.3 – LangChain LLMs
Summary
Chapter 11: Using LangChain to Get More from RAG Chevron down icon Chevron up icon
Chapter 11: Using LangChain to Get More from RAG
Technical requirements
Code lab 11.1 – Document loaders
Code lab 11.2 – Text splitters
Code lab 11.3 – Output parsers
Summary
Part 3 – Implementing Advanced RAG Chevron down icon Chevron up icon
Part 3 – Implementing Advanced RAG
Chapter 12: Combining RAG with the Power of AI Agents and LangGraph Chevron down icon Chevron up icon
Chapter 12: Combining RAG with the Power of AI Agents and LangGraph
Technical requirements
Fundamentals of AI agents and RAG integration
Graphs, AI agents, and LangGraph
Code lab 12.1 – adding a LangGraph agent to RAG
Core concepts of graph theory
Nodes and edges in our agent
Cyclical graph setup
Summary
Chapter 13: Using Prompt Engineering to Improve RAG Efforts Chevron down icon Chevron up icon
Chapter 13: Using Prompt Engineering to Improve RAG Efforts
Technical requirements
Prompt parameters
Take your shot
Prompting, prompt design, and prompt engineering revisited
Prompt design versus engineering approaches
Fundamentals of prompt design
Adapting prompts for different LLMs
Code lab 13.1 – Custom prompt template
Code lab 13.2 – Prompting options
Summary
Chapter 14: Advanced RAG-Related Techniques for Improving Results Chevron down icon Chevron up icon
Chapter 14: Advanced RAG-Related Techniques for Improving Results
Technical requirements
Naïve RAG and its limitations
Hybrid RAG/multi-vector RAG for improved retrieval
Re-ranking in hybrid RAG
Code lab 14.1 – Query expansion
Code lab 14.2 – Query decomposition
Code lab 14.3 – MM-RAG
Other advanced RAG techniques to explore
Summary
Index Chevron down icon Chevron up icon
Index
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About the author

Profile icon Keith Bourne
Keith Bourne
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Keith Bourne is a senior Generative AI data scientist at Johnson & Johnson. He has over a decade of experience in machine learning and AI working across diverse projects in companies that range in size from start-ups to Fortune 500 companies. With an MBA from Babson College and a master's in applied data science from the University of Michigan, he has developed several sophisticated modular Generative AI platforms from the ground up, using numerous advanced techniques, including RAG, AI agents, and foundational model fine-tuning. Keith seeks to share his knowledge with a broader audience, aiming to demystify the complexities of RAG for organizations looking to leverage this promising technology.
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The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela