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GraphRAG for Developers: Knowledge Graphs & RAG
New
102 students

GraphRAG for Developers: Knowledge Graphs & RAG

Build GraphRAG Applications with Neo4j, LLMs, Embeddings, Vector Search & Hybrid Retrieval
Created byExcel Mojo
Last updated 11/2026
English

What you'll learn

  • Understand traditional RAG, GraphRAG, vector retrieval, and graph retrieval
  • Identify when relationship-based retrieval can add value
  • Design knowledge graphs using entities, relationships, and properties
  • Build and query connected business data using Neo4j and Cypher
  • Preserve source information and provenance for extracted knowledge
  • Extract entities and relationships from documents using an LLM
  • Validate extracted graph information before loading it into Neo4j
  • Create embeddings for document chunks using Python
  • Perform semantic search using Neo4j vector indexes
  • Connect semantic search results to knowledge graph entities
  • Identify entities from natural-language user questions
  • Combine keyword and vector search through
  • Generate grounded answers using retrieved context
  • Build a practical supply-chain GraphRAG application
  • Evaluate retrieval quality and control irrelevant results
  • Understand GraphRAG performance, retrieval depth, and cost trade-offs
  • Apply a basic production checklist to a GraphRAG application

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Course content

9 sections • 40 lectures • 3h 26m total length

  • Course Introduction4:45

    Discover how GraphRAG helps AI applications understand connections between information, not just retrieve relevant documents. Get an overview of knowledge graphs, semantic search, embeddings, vector indexes, and the complete supply-chain GraphRAG project.

  • Welcome & What You Will Build4:51

    Explore the learning journey ahead, from understanding traditional RAG limitations to building a practical GraphRAG application. See how knowledge graphs, Neo4j, embeddings, semantic retrieval, and graph-based context come together.

  • The Problem with Traditional RAG9:59

    Understand why traditional similarity-based retrieval may struggle with questions involving multiple entities and relationships. Explore how information scattered across documents and data sources can make relationship-based questions difficult to answer.

  • Vector RAG vs GraphRAG7:45

    Compare vector-based retrieval with graph-based retrieval. Understand how vector search identifies relevant information while knowledge graphs help reveal relationships, dependencies, and connections between entities.

  • When Should You Use GraphRAG.3:13

    Learn when traditional RAG, vector retrieval, or GraphRAG is appropriate. Understand the trade-offs involved and why hybrid retrieval can be useful when applications need both semantic search and relationship-aware context.

Requirements

  • Basic familiarity with Python is helpful
  • Basic familiarity with AI and Large Language Models is helpful
  • Familiarity with documents and structured business data is useful
  • Basic database concepts can be helpful
  • No prior GraphRAG experience is required
  • No prior knowledge graph experience is required

Description

**This course contains the use of artificial intelligence.**

Build AI applications that understand connections, not just information.

Traditional Retrieval-Augmented Generation can help an AI application find relevant information from documents. But some questions require more than finding a relevant paragraph.

Consider this question: Which products could be affected if a supplier stops supplying a particular component?

Answering it may require connecting a supplier to a component, the component to several products, and the products to their manufacturers. The information may be distributed across different documents and data sources. This is where GraphRAG becomes useful.

In this course, you'll learn how to combine Knowledge Graphs, Neo4j, Python, LLMs, embeddings, semantic search, vector indexes, graph retrieval, and hybrid retrieval to build a practical GraphRAG workflow.

You'll begin by understanding traditional RAG and why similarity-based retrieval can struggle with relationship-based and multi-hop questions. You'll then learn the fundamentals of knowledge graphs, including nodes, labels, properties, relationships, graph paths, entities, and graph schemas.

Next, you'll work with Neo4j and Cypher to create connected business information. Also, you'll learn how structured data can be transformed into a knowledge graph and queried through relationships.

The course then moves from structured data to unstructured documents. You'll learn how documents can be cleaned and chunked, how an LLM can extract candidate entities and relationships, and how Python can validate the extracted information before it is loaded into Neo4j. You'll also learn why source information and provenance matter when building knowledge from LLM extraction.

You'll then build the semantic-search side of the system. You'll learn what embeddings are, how document chunks can be converted into vectors, and how Neo4j vector indexes can be used to find semantically relevant content.

From there, you'll connect vector search with graph retrieval. You'll learn how retrieved document chunks can act as a bridge into the knowledge graph. Moreover, you will understand how to identify entities from natural-language questions and how keyword search and vector search can be combined through hybrid retrieval.

The final part brings everything together through a practical supply-chain GraphRAG project. You’ll prepare the data, build a knowledge graph, and implement semantic retrieval. Then, you’ll combine graph and vector retrieval to construct focused context and generate answers from retrieved evidence.

You'll also explore how to reduce hallucinations, improve retrieval quality, and balance retrieval depth with processing time and cost. Also, you will learn to use a production checklist covering data quality, retrieval quality, hallucination control, performance, and monitoring.

By the end, you'll have seen the complete journey from documents and structured data to knowledge graphs, embeddings, retrieval, graph context, and grounded AI answers.

Through This Course, You'll Be Able to:

  • Explain how GraphRAG differs from traditional RAG and vector retrieval

  • Identify when relationship-based retrieval can add value

  • Design knowledge graphs using entities, relationships, and properties

  • Build and query connected business data using Neo4j and Cypher

  • Extract entities and relationships from documents using an LLM

  • Validate extracted graph information before loading it into Neo4j

  • Preserve source information and provenance for extracted knowledge

  • Create embeddings for document chunks using Python

  • Perform semantic search using Neo4j vector indexes

  • Connect semantic search results to knowledge graph entities

  • Identify entities from natural-language user questions

  • Combine keyword and vector search through hybrid retrieval

  • Construct focused graph context for an LLM

  • Generate grounded answers using retrieved context

  • Build a practical supply-chain GraphRAG application

  • Evaluate retrieval quality and control irrelevant results

  • Understand GraphRAG performance, retrieval depth, and cost trade-offs

  • Apply a basic production checklist to a GraphRAG application

What Makes the Course Different

1. The Course Is Built Around the Complete GraphRAG Workflow

Instead of teaching isolated GraphRAG concepts, the course connects the components from RAG and knowledge graphs through retrieval and final answer generation.

2. Learn Why GraphRAG Is Needed Before Building It

The course first explains where traditional similarity-based retrieval can struggle with multi-hop and relationship-based questions before introducing the graph-based solution.

3. From Structured Data and Documents to a Knowledge Graph

You'll see both structured business data and unstructured documents transformed into connected knowledge, including LLM-based extraction and validation.

4. Semantic Search and Graph Retrieval Work Together

The course demonstrates that vector search and graph retrieval serve different purposes: semantic search helps find relevant information, while the graph helps expose relationships around that information.

5. Practical Hybrid Retrieval

You'll go beyond basic vector search by combining keyword-based and semantic retrieval before using the selected entities for graph traversal.

6. The Course Goes Beyond Getting an Answer

The later modules address hallucination control, retrieval quality, performance trade-offs, monitoring, evaluation, and a production checklist rather than stopping at a basic working demonstration.

About the Course Director

Dheeraj Vaidya is a CFA and FRM professional, an IIT and IIM alumnus, and a former analyst at JPMorgan and CLSA.

As Course Director, Dheeraj oversees the development and academic direction of courses designed to combine practical applications with structured learning.

For this course, the learning journey follows a practical progression. You’ll start with GraphRAG fundamentals and knowledge graphs, then move into Neo4j, LLM-based extraction, embeddings, and semantic search. From there, you’ll explore hybrid retrieval and graph context before bringing everything together in a complete supply-chain GraphRAG application.


Who this course is for:

  • Python developers interested in building GraphRAG applications
  • AI developers working with RAG and Large Language Models
  • Developers exploring knowledge graphs and Neo4j
  • Data professionals working with connected business information
  • Learners interested in semantic and graph-based retrieval
  • AI practitioners wanting to combine vector and graph retrieval

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