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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

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781835087060
Length 560 pages
Edition 1st Edition
Concepts
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Authors (2):
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Salvatore Raieli Salvatore Raieli
Author Profile Icon Salvatore Raieli
Salvatore Raieli
Gabriele Iuculano Gabriele Iuculano
Author Profile Icon Gabriele Iuculano
Gabriele Iuculano
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Table of Contents (17) Chapters Close

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 16. Other Books You May Enjoy

Exploring attention and self-attention

In the 1950s, with the beginning of the computer revolution, governments began to become interested in the idea of machine translation, especially for military applications. These attempts failed miserably, for three main reasons: machine translation is more complex than it seems, there was not enough computational power, and there was not enough data. Governments concluded that it was a technically impossible challenge in the 1960s.

By the 1990s, two of the three limitations were beginning to be overcome: the internet finally allowed for abundant text, and the advent of GPUs finally allowed for computational power. The third requirement still had to be met: a model that could harness the newfound computational power to handle the complexity of natural language.

Machine translation captured the interest of researchers because it is a practical problem for which it is easy to evaluate the result (we can easily understand whether a translation...

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