Developing AI agents that remember, adapt, and reason over complex knowledge is no longer a distant goal- it’s now possible with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide expands into the future of agentic systems, showing how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.
You’ll explore the building blocks of agentic memory, including semantic caches, procedural learning via LangMem, and the emerging CoALA framework for cognitive agents. You’ll also learn to integrate GraphRAG with tools like Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.
This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops. With hands-on code and production-ready patterns, you’ll gain the skills to build advanced AI systems that don’t just generate answers- they learn, recall, and evolve.
Written by a seasoned AI educator and engineer, this book blends theoretical clarity with deep practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.

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Spis treści

Unlocking Data with Generative AI and RAG. Learn the fundamentals and build Agents with RAG-powered memory, GraphRAG, and intelligent recall - Second Edition eBook -- spis treści

• 1. What is Retrieval-Augmented Generation (RAG)
• 2. Code Lab - An Entire RAG Pipeline
• 3. Practical Applications of RAG
• 4. Components of a RAG System
• 5. Managing Security in RAG Applications
• 6. Interfacing with RAG and Gradio
• 7. The Key Role Vectors and Vector Stores Play in RAG
• 8. Similarity Searching with Vectors
• 9. Evaluating RAG Quantitatively and with Visualizations
• 10. Key RAG Components in LangChain
• 11. Using LangChain to get More from RAG
• 12. Combining RAG with the Power of AI Agents and LangGraph
• 13. Graph-Based RAG
• 14. Semantic Caches
• 15. Agentic Memory - The Secret Power Behind the Best Agents
• 16. RAG-Based Memory Components in Code
• 17. Prompt Optimization and Self-Improvement with LangMem
• 18. Additional chapter 1
• 19. Additional Chapter 2