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Graph Rag Foundations: Build Production-Ready Knowledge Graph AI Systems with Neo4j, Python, Vector Search, Hybrid Retrieval, and Large Language Model
Paperback
Publisher: Independently Published
Published: Jul 26 2026
Pages: 182
Weight: 0.96
Height: 0.39 Width: 8.50 Depth: 11.00
Language: English
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.
Inside You'll LearnHow 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
Who This Book Is For- 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.
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