Graph Rag Foundations: Build Production-Ready Knowledge Graph AI Systems with Neo4j, Python, Vector Search, Hybrid Retrieval, and Large Language Model (Paperback) (ISBN-13: 9798189073663)

Master GraphRAG from the ground up by building a production-ready AI knowledge system.

Traditional Retrieval-Augmented Generation (RAG) is excellent at finding relevant documents but it often struggles with questions that require connecting multiple facts across different sources. That's where GraphRAG changes everything.

In Graph RAG Foundations, you'll learn how to design, build, and deploy intelligent AI systems that combine knowledge graphs, vector search, and large language models (LLMs) to deliver more accurate, explainable, and context-aware answers.

Instead of relying on isolated examples, you'll build GraphMind, a complete GraphRAG platform that evolves throughout the book. Beginning with GraphRAG fundamentals, you'll progress through ontology design, document ingestion, entity and relationship extraction, Neo4j graph modeling, hybrid retrieval, LLM integration, evaluation, governance, and production deployment.

Whether you're developing enterprise AI applications, intelligent search systems, compliance platforms, or next-generation AI assistants, this hands-on guide provides the practical skills needed to build scalable GraphRAG solutions.

How GraphRAG differs from traditional RAG and when to use each

Knowledge graph fundamentals and ontology design

Modeling entities, relationships, and graph schemas

Building scalable graph databases with Neo4j

Parsing and processing PDFs, HTML, Markdown, and structured documents

Entity extraction using spaCy and LLM-assisted pipelines

Relationship extraction and ontology validation

Designing hybrid Graph + Vector retrieval systems

Working with embeddings and semantic search

Connecting GraphRAG pipelines to modern Large Language Models

Building explainable AI with provenance and graph traversal

Performance optimization, testing, monitoring, and governance

Deploying production-ready GraphRAG systems using Docker and modern development practices

• AI Engineers
• Machine Learning Engineers
• Python Developers
• Backend Developers
• Data Engineers
• Knowledge Graph Engineers
• Solutions Architects
• Software Engineers building LLM applications
• Students and professionals interested in GraphRAG and enterprise AI

No prior experience with graph databases is required. A basic understanding of Python is recommended.

If you're ready to move beyond basic Retrieval-Augmented Generation and build AI systems capable of connecting, reasoning over, and explaining complex relationships, Graph RAG Foundations is your complete practical guide.