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Full Description
From LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs, master the skills to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously
Key Features
Implementing Retrieval-Augmented Generation and Knowledge Graphs for Advanced Problem-Solving
Harness RAG, Knowledge Graphs, and LangChain to Create Real-World Intelligent Systems
Integrating 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
Book DescriptionThis 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. It focuses on Retrieval-Augmented Generation (RAG), knowledge graphs, and agent-based architectures, teaching you to harness these techniques for truly intelligent behavior. By blending large language models 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. Discover how to connect language models with external data via RAG pipelines for factual accuracy, and how to incorporate knowledge graphs for context-rich reasoning. You'll build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. With concrete Python examples using popular libraries, along with real-world case studies, reinforce each concept and show how these techniques come together.
You will use hands-on techniques and industrial applications. By the end, this book will equip you to build intelligent AI agents that reason, retrieve, and interact dynamically empowering you to deploy powerful AI solutions across industries.What you will learn
Design RAG pipelines to connect LLMs with 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.
Who this book is forIf you are a data scientist or researcher who wants to learn how you can 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 the state-of-the-art for LLM and LLM-based applications.
Contents
Table of Contents
How to analyze text data with deep learning
The transformer: The model behind the modern AI revolution
The engine behind an AI agent: Large Language model
Building a Web Scraping Agent with an LLM
Extend your agent with RAG (Retrieval Augmented Generation) to prevent hallucinations
Advanced RAG Techniques for Information Retrieval and Augmentation
Create and connect a Knowledge Graph to an AI agent
Reinforcement Learning and AI agent
Creating Single and Multi-Agent Systems
Create your AI agent and connect to web API
Build and deploy an AI agent application
