دانلود کتاب GraphRAG ضروری – RAG بهبود یافته با گراف دانش

Essential GraphRAG was written to guide readers in enhancing retrieval-augmented generation (RAG) systems by integrating knowledge graphs with large language models (LLMs). The book aims to address the limitations of LLMs, such as outdated knowledge, hallucinations, and a lack of domain-specific data, by combining structured and unstructured data through practical methodologies and hands-on examples. The primary goal of Essential GraphRAG is to demonstrate how knowledge graphs can improve the accuracy, performance, and traceability of RAG systems in generative AI applications. The book explores grounding LLMs with both structured and unstructured data, offering a comprehensive guide to building a GraphRAG system from scratch. It combines years of expertise in graphs, machine learning, and application development to present stable architectural patterns in a rapidly evolving field. Readers will learn to implement GraphRAG without relying on existing frameworks, extract structured knowledge from text, and develop applications that blend vectorbased and graph-based retrieval methods, including Microsoft’s GraphRAG approach. The book encourages active participation through its liveBook discussion forum to refine content and deepen collective understanding.