Building AI Agents with LLMs, RAG, and Knowledge Graphs

Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomouslyKey FeaturesImplement RAG and knowledge graphs for advanced problem-solvingLeverage innovative approaches like LangChain to create real-world intelligent systemsIntegrate 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. 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.What you will learnLearn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external dataBuild and query knowledge graphs for structured context and factual groundingDevelop AI agents that plan, reason, and use tools to complete tasksIntegrate LLMs with external APIs and databases to incorporate live dataApply techniques to minimize hallucinations and ensure accurate outputsOrchestrate multiple agents to solve complex, multi-step problemsOptimize prompts, memory, and context handling for long-running tasksDeploy and monitor AI agents in production environmentsWho this book is forIf 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.]]>