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. 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.

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Spis treści

Building AI Agents with LLMs, RAG, and Knowledge Graphs. A Practical Guide to Autonomous and Modern AI Agents eBook -- spis treści

• 1. How to analyze text data with deep learning
• 2. The transformer: The model behind the modern AI revolution
• 3. The engine behind an AI agent: Large Language model
• 4. Building a Web Scraping Agent with an LLM
• 5. Extend your agent with RAG (Retrieval Augmented Generation) to prevent hallucinations
• 6. Advanced RAG Techniques for Information Retrieval and Augmentation
• 7. Create and connect a Knowledge Graph to an AI agent
• 8. Reinforcement Learning and AI agent
• 9. Creating Single and Multi-Agent Systems
• 10. Create your AI agent and connect to web API
• 11. Build and deploy an AI agent application