دانلود کتاب ساخت ایجنت‌های LLM با RAG، گراف‌های دانش و بازتاب

Artificial intelligence has moved beyond generating text — it is beginning to reason, retrieve, and act. This transformation marks the shift from language models as conversation engines to autonomous systems that plan, learn, and improve. Building LLM Agents with RAG, Knowledge Graphs & Reflection was written to make this evolution accessible. It is designed for those who want to understand how today’s intelligent systems work beneath the surface — and, more importantly, how to build them from first principles. This book begins at the foundations: how transformers think and why LLMs are limited by static training data. It then progresses through retrieval-augmented generation (RAG), knowledge graphs, and reflective reasoning — culminating in the architecture of multi-agent collaboration. Each chapter blends theory with application, guiding you through concepts and code patterns that translate directly into working systems. Every “Agent in Action” section illustrates how a concept becomes an implementation. This book does not aim to be another collection of tutorials. Instead, it offers a blueprint for engineering cognitive behavior — a method for creating AI agents that are factual, contextual, and self-correcting. The work you are reading is the first in a two-volume series. While this volume focuses on designing and building individual and small-team agents, the second will explore how these intelligent systems scale into distributed, collaborative networks capable of tackling enterprise- and ecosystem-level challenges.