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Building AI Agents with LLMs, RAG, and Knowledge Graphs

You're reading from Building AI Agents with LLMs, RAG, and Knowledge Graphs A practical guide to autonomous and modern AI agents

Product type Paperback

Published in Jul 2025

Publisher Packt

ISBN-13 9781835087060

Length 560 pages

Edition 1st Edition

Concepts

GPT/LLMs

Authors (2):

Salvatore Raieli

Gabriele Iuculano

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: The AI Agent Engine: From Text to Large Language Models

2. Chapter 1: Analyzing Text Data with Deep Learning FREE CHAPTER

3. Chapter 2: The Transformer: The Model Behind the Modern AI Revolution

4. Chapter 3: Exploring LLMs as a Powerful AI Engine

5. Part 2: AI Agents and Retrieval of Knowledge

6. Chapter 4: Building a Web Scraping Agent with an LLM

7. Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations

8. Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation

9. Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent

10. Chapter 8: Reinforcement Learning and AI Agents

11. Part 3: Creating Sophisticated AI to Solve Complex Scenarios

12. Chapter 9: Creating Single- and Multi-Agent Systems

13. Chapter 10: Building an AI Agent Application

14. Chapter 11: The Future Ahead

15. Index

Why subscribe?

16. Other Books You May Enjoy

Introducing the transformer model

Despite this decisive advance though, several problems remain in machine translation:

The model fails to capture the meaning of the sentence and is still error-prone
In addition, we have problems with words that are not in the initial vocabulary
Errors in pronouns and other grammatical forms
The model fails to maintain context for long texts
It is not adaptable if the domain in the training set and test data is different (for example, if it is trained on literary texts and the test set is finance texts)
RNNs are not parallelizable, and you have to compute sequentially

Considering these points, Google researchers in 2016 came up with the idea of eliminating RNNs altogether rather than improving them. According to the authors of the Attention is All You Need seminal article; all you need is a model that is based on multi-head self-attention. Before going into detail, the transformer consists entirely of stacked layers...

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Authors (2)

Salvatore Raieli

Salvatore Raieli

Salvatore Raieli is a senior data scientist in a pharmaceutical company with a focus on using AI for drug discovery against cancer. He has led different multidisciplinary projects with LLMs, agents, NLP, and other AI techniques. He has an MSc in AI and a PhD in immunology and has experience in building neural networks to solve complex problems with large datasets. He enjoys building AI applications for concrete challenges that can lead to societal benefits. In his spare time, he writes on his popularization blog on AI (on Medium).

See other products by Salvatore Raieli

Gabriele Iuculano

Gabriele Iuculano

Gabriele Iuculano boasts extensive expertise in embedded systems and AI. Leading a team as the test platform architect, Gabriele has been instrumental in architecting a sophisticated simulation system that underpins a cutting-edge test automation platform. He is committed to integrating AI-driven solutions, focusing on predictive maintenance systems to anticipate needs and prevent downtimes. He obtained his MSc in AI from the University of Leeds, demonstrating expertise in leveraging AI for system efficiencies. Gabriele aims to revolutionize current business through the power of new disruptive technologies such as AI.

See other products by Gabriele Iuculano

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