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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Building AI Agents with LLMs, RAG, and Knowledge Graphs

Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents

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Profile Icon Salvatore Raieli Profile Icon Gabriele Iuculano
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$35.98 $44.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (2 Ratings)
Paperback Jul 2025 560 pages 1st Edition
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Arrow left icon
Profile Icon Salvatore Raieli Profile Icon Gabriele Iuculano
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$35.98 $44.99
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Paperback Jul 2025 560 pages 1st Edition
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$24.99 $35.99
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Building AI Agents with LLMs, RAG, and Knowledge Graphs

Analyzing Text Data with Deep Learning

Language is one of the most amazing abilities of human beings; it evolves during the individual’s lifetime and is capable of conveying a message with complex meaning. Language in its natural form is not understandable to machines, and it is extremely challenging to develop an algorithm that can pick up the different nuances. Therefore, in this chapter, we will discuss how to represent text in a form that is digestible by machines.

In natural form, text cannot be directly fed to a deep learning model. In this chapter, we will discuss how text can be represented in a form that can be used by machine learning models. Starting with natural text, we will transform the text into numerical vectors that are increasingly sophisticated (one-hot encoding, bag of words (BoW), term frequency-inverse document frequency (TF-IDF)) until we create vectors of real numbers that represent the meaning of a word (or document) and allow us to conduct operations...

Technical requirements

In this chapter, we will use standard libraries for Python. The necessary libraries can be found within each of the Jupyter notebooks that are in the GitHub repository for this chapter: https://github.com/PacktPublishing/Modern-AI-Agents/tree/main/chr1. The code can be executed on a CPU, but a GPU is advised.

Representing text for AI

Compared to other types of data (such as images or tables), it is much more challenging to represent text in a digestible representation for computers, especially because there is no unique relationship between the meaning of a word (signified) and the symbol that represents it (signifier). In fact, the meaning of a word changes from the context and the author’s intentions in using it in a sentence. In addition, native text has to be transformed into a numerical representation to be ingested by an algorithm, which is not a trivial task. Nevertheless, several approaches were initially developed to be able to find a vector representation of a text. These vector representations have the advantage that they can then be used as input to a computer.

First, a collection of texts (corpus) should be divided into fundamental units (words). This process requires making certain decisions and process operations that collectively are called text normalization....

Embedding, application, and representation

In the previous section, we discussed how to use vectors to represent text. These vectors are digestible for a computer, but they still suffer from some problems (sparsity, high dimensionality, etc.). According to the distributional hypothesis, words with a similar meaning frequently appear close together (or words that appear often in the same context have the same meaning). Similarly, a word can have a different meaning depending on its context: “I went to deposit money in the bank” or “We went to do a picnic on the river bank.” In the following diagram, we have a high-level representation of the embedding process. So, we want a process that allows us to start from text to obtain an array of vectors, where each vector corresponds to the representation of a word. In this case, we want a model that will then allow us to map each word to a vector representation. In the next section, we will describe the process in...

RNNs, LSTMs, GRUs, and CNNs for text

So far, we have discussed how to represent text in a way that is digestible for the model; in this section, we will discuss how to analyze the text once a representation has been obtained. Traditionally, once we obtained a representation of the text, it was fed to models such as naïve Bayes or even algorithms such as logistic regression. The success of neural networks has made these machine learning algorithms outdated. In this section, we will discuss deep learning models that can be used for various tasks.

RNNs

The problem with classical neural networks is that they have no memory. This is especially problematic for time series and text inputs. In a sequence of words t, the word w at time t depends on the w at time t-1. In fact, in a sentence, the last word is often dependent on several words in the sentence. Therefore, we want an NN model that maintains a memory of previous inputs. An RNN maintains an internal state that maintains...

Performing sentiment analysis with embedding and deep learning

In this section, we will train a model for conducting sentiment analysis on movie reviews. The model we will train will be able to classify reviews as positive or negative. To build and train the model, we will exploit the elements we have encountered so far. In brief, we’re doing the following:

  • We are preprocessing the dataset, transforming in numerical vectors, and harmonizing the vectors
  • We are defining a neural network with an embedding and training it

The dataset consists of 50,000 positive and negative reviews. We can see that it contains a heterogeneous length for reviews and that on average, there are 230 words:

Figure 1.16 – Graphs showing the distribution of the length of the review in the text; the left plot is for positive reviews, while the right plot is for negative reviews

Figure 1.16 – Graphs showing the distribution of the length of the review in the text; the left plot is for positive reviews, while the right plot is for negative reviews

In addition, the most prevalent words are, obviously, “movie”...

Summary

In this chapter, we saw how to transform text to an increasingly complex vector representation. This numerical representation of text allowed us to be able to use machine learning models. We saw how to preserve the contextual information (word embedding) of a text and how this can then be used for later analysis (for example, searching synonyms or clustering words). In addition, we saw how neural networks (RNNs, LSTM, GRUs) can be used to analyze text and perform tasks (for example, sentiment analysis).

In the next chapter, we will see how to solve some of the remaining unsolved challenges and see how this will lead to the natural evolution of the models seen here.

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Key benefits

  • Implement RAG and knowledge graphs for advanced problem-solving
  • Leverage innovative approaches like LangChain to create real-world intelligent systems
  • Integrate large language models, graph databases, and tool use for next-gen AI solutions
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

This AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.

Who is this book for?

If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.

What you will learn

  • Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data
  • Build and query knowledge graphs for structured context and factual grounding
  • Develop AI agents that plan, reason, and use tools to complete tasks
  • Integrate LLMs with external APIs and databases to incorporate live data
  • Apply techniques to minimize hallucinations and ensure accurate outputs
  • Orchestrate multiple agents to solve complex, multi-step problems
  • Optimize prompts, memory, and context handling for long-running tasks
  • Deploy and monitor AI agents in production environments
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Length: 560 pages
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Table of Contents

16 Chapters
Part 1: The AI Agent Engine: From Text to Large Language Models Chevron down icon Chevron up icon
Chapter 1: Analyzing Text Data with Deep Learning Chevron down icon Chevron up icon
Chapter 2: The Transformer: The Model Behind the Modern AI Revolution Chevron down icon Chevron up icon
Chapter 3: Exploring LLMs as a Powerful AI Engine Chevron down icon Chevron up icon
Part 2: AI Agents and Retrieval of Knowledge Chevron down icon Chevron up icon
Chapter 4: Building a Web Scraping Agent with an LLM Chevron down icon Chevron up icon
Chapter 5: Extending Your Agent with RAG to Prevent Hallucinations Chevron down icon Chevron up icon
Chapter 6: Advanced RAG Techniques for Information Retrieval and Augmentation Chevron down icon Chevron up icon
Chapter 7: Creating and Connecting a Knowledge Graph to an AI Agent Chevron down icon Chevron up icon
Chapter 8: Reinforcement Learning and AI Agents Chevron down icon Chevron up icon
Part 3: Creating Sophisticated AI to Solve Complex Scenarios Chevron down icon Chevron up icon
Chapter 9: Creating Single- and Multi-Agent Systems Chevron down icon Chevron up icon
Chapter 10: Building an AI Agent Application Chevron down icon Chevron up icon
Chapter 11: The Future Ahead Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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