Building GraphRAG From Scratch: A Hands-On Walkthrough
Build a complete GraphRAG pipeline step by step, from document chunking and entity-relation extraction to creating a Neo4j knowledge graph. Learn why vanilla RAG struggles with complex, multi-hop questions and how GraphRAG improves retrieval using knowledge graphs. Compare no-retrieval, vanilla RAG, and GraphRAG outputs to understand where graph-based retrieval adds value.
- Learn why vanilla RAG struggles with complex, multi-hop questions and how GraphRAG improves retrieval using knowledge graphs.
- Compare no-retrieval, vanilla RAG, and GraphRAG outputs to understand where graph-based retrieval adds value.
Prabhakaran SPrincipal Data Scientist
Included in All-Access, one membership for every course.
- Lessons
- 12, in 5 chapters
- Video
- 1 h 1 min
- Free to watch
- 2 lessons
- Certificate
- On completion
What you'll be able to do
- How GraphRAG improves retrieval
- Why vanilla RAG falls short
- Document chunking for RAG
- Entity and relation extraction
- Building graphs with Neo4j
- Comparing RAG vs GraphRAG
Skills you will practise
- GraphRAG
- Vanilla RAG
- Knowledge Graphs
- Document Chunking
- Entity Extraction
- Relation Extraction
- Pydantic
- Neo4j
- Graph Visualization
- Vector Indexing
- Hybrid Retrieval
- RAG Comparison
- Graph-based Retrieval
- Multi-hop Question Answering
- LLM-based Extraction
- Answer Comparison
- Retriever Design
- Vector Search
Course content
5 chapters · 12 lessons · 1 h 1 min · 2 free to preview
01Introduction to GraphRAG3 lessons · 13 min
- What you will buildPreview8:48
- Download Resources1:00
- The problem why vanilla RAG isn't enoughPreview2:49
02Project Setup & Data Preparation3 lessons · 9 min
- Setup and environment check5:05
- Inspect the corpus1:05
- Chunking: Making text digestible2:37
03Building the Knowledge Graph3 lessons · 18 min
- Entity & relation extraction with Pydantic14:46
- Build the knowledge graph in Neo4j1:30
- Visualize the graph1:46
04Building the Retrieval System2 lessons · 15 min
- Build the vector index9:01
- Hybrid retriever: The heart of the system5:58
05Final Demo1 lesson · 7 min
- Demo: Same question, three ways7:13
What you need
- Basics of Python
- Foundational knowledge of Data Science
- High school maths
Who this course is for
- Data Science/AI Aspirants
- Data Science/AI Professionals
- AI/ML/Data engineers interested in quantitative analysis
- Professionals working with large datasets
- Data analysts, economists, researchers
Your instructor
Prabhakaran S
Principal Data Scientist
My name is Selva, and I am super excited to mentor you on this course!
I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.
I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!
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- learners
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- courses
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Questions
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