Theory alone doesn't build production AI systems-practical implementation does.
Graph RAG in Practice is a project-driven guide that demonstrates how to build, deploy, and optimize real-world Graph RAG applications using today's leading technologies, including Neo4j, LangGraph, the Model Context Protocol (MCP), and agentic AI workflows.
Working through complete, end-to-end projects, you'll learn how to design scalable knowledge graph pipelines, orchestrate intelligent retrieval workflows, integrate graph databases with Large Language Models, and deploy enterprise-ready AI applications. Along the way, you'll explore performance optimization, monitoring, testing, security, and deployment best practices for modern Graph RAG systems.
Inside you'll learn how to:
- Build complete Graph RAG applications from start to finish.
- Integrate Neo4j with LLM-powered retrieval pipelines.
- Create agentic workflows using LangGraph and MCP.
- Develop scalable knowledge graph ingestion pipelines.
- Deploy production-ready Graph RAG services.
- Optimize performance, reliability, and cost.
- Apply Graph RAG to enterprise search, AI assistants, research systems, and domain-specific applications.
Whether you're developing intelligent assistants, enterprise search platforms, or advanced AI agents, Graph RAG in Practice equips you with the practical skills and implementation patterns needed to deliver robust, production-ready Graph RAG solutions.
