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 autonomouslyKey FeaturesImplementing Retrieval-Augmented Generation and Knowledge Graphs for Advanced Problem-SolvingHarness RAG, Knowledge Graphs, and LangChain to Create Real-World Intelligent SystemsIntegrating Large Language Models, Graph Databases, and Tool-Use for Next-Gen AI SolutionsPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnDesign 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.Table of ContentsHow to analyze text data with deep learningThe The model behind the modern AI revolutionThe engine behind an AI Large Language modelBuilding a Web Scraping Agent with an LLMExtend your agent with RAG (Retrieval Augmented Generation) to prevent hallucinationsAdvanced RAG Techniques for Information Retrieval and AugmentationCreate and connect a Knowledge Graph to an AI agentReinforcement Learning and AI agentCreating Single and Mu

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

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