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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

Creating a knowledge graph with your LLM

The construction of a KG is generally a multistep process consisting of the following:

  1. Knowledge creation: The first step, in which we define the purpose of this KG, is to gather the sources from which to extract knowledge. In this step, we have to decide how we build our KG but also where we maintain it. Once built, the KG has to be stored, and we have to have an efficient structure to query it.
  2. Knowledge assessment: In this step, we assess the quality of the KG obtained.
  3. Knowledge cleaning: There are several steps and procedures to make sure there are no errors and then correct them. This step can be conducted at the same time as knowledge assessment, and some pipelines conduct them together.
  4. Knowledge enrichment: This involves a series of steps to identify whether there are gaps in knowledge. We can also integrate additional sources (extract information from other datasets, integrate databases, or merge multiple KGs).
  5. ...
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