Large language models can generate powerful responses, but they do not automatically have access to the knowledge your applications need. Traditional Retrieval-Augmented Generation (RAG) improves this by connecting LLMs to external information, yet many complex questions require more than finding similar text. They require understanding entities, relationships, context, and connected information.

Building Intelligent AI with GraphRAG explores how knowledge graphs and GraphRAG can transform retrieval into a more connected and knowledge-aware process.

Starting with the foundations of knowledge graphs, this book explains entities, relationships, properties, schemas, graph databases, and knowledge representation. It then moves from traditional RAG into graph-based retrieval, showing how knowledge graphs can work alongside documents, embeddings, vector search, and LLMs.

You will learn how to build knowledge graphs from unstructured information, extract entities and relationships with LLMs, resolve duplicate entities, design graph schemas, and build GraphRAG knowledge pipelines.

The book then explores graph retrieval and reasoning, including graph traversal, semantic search, hybrid graph-and-vector retrieval, query expansion, multi-hop retrieval, entity-centric reasoning, and relationship-aware generation.

Building on the agent foundations introduced in Book 1, the book moves into agentic GraphRAG, showing how AI agents can plan retrieval strategies, dynamically explore knowledge graphs, use retrieval tools, and perform multi-hop reasoning across connected information.

You will also learn how knowledge graphs can become persistent agent memory, supporting long-term knowledge, contextual information, knowledge updates, and knowledge-aware agents.

Finally, the book covers advanced GraphRAG architectures, including hierarchical, dynamic, temporal, community-based, and multi-agent GraphRAG, along with evaluation, security, governance, scalability, performance, and production deployment.

Inside this book, you will learn how to:

• Build and model knowledge graphs for AI applications

• Extract entities and relationships from documents using LLMs

• Understand the differences between traditional RAG and GraphRAG

• Combine graph, vector, and semantic retrieval

• Perform multi-hop retrieval and graph-based reasoning

• Build GraphRAG pipelines for LLM applications

• Design agentic GraphRAG architectures

• Use knowledge graphs as persistent agent memory

• Build hybrid, dynamic, temporal, and multi-agent GraphRAG systems

• Evaluate retrieval quality, groundedness, and reasoning

• Secure, monitor, scale, and deploy production GraphRAG applications

From RAG and knowledge graphs to graph reasoning and agentic GraphRAG, this book provides a practical foundation for building AI systems that can retrieve, connect, understand, and reason over knowledge.