GraphAcademy
Course · Part of Generative AI & GraphRAG

Neo4j & GenerativeAI Fundamentals

Learn how Neo4j and GraphRAG can support your Generative AI projects

2 hours15 lessons across 4 modules
Context EngineerDevelopmentIntermediateGenerative AI & GraphRAGBuild a Neo4j-backed ApplicationPath to Neo4j Certification
About this course

In this 2-hour course, you will learn

In this course, you will learn how Neo4j and Knowledge Graphs can help you create Go to glossary for generative AI (opens in a new tab)Models that produce new content rather than classifying or scoring content that already exists. (GenAI) applications.

You will learn why graph databases are a reliable option for grounding GenAI models, using Neo4j to provide factual, reliable information to stop the Go to glossary for large language model (opens in a new tab)A model trained on text to predict the next token, and so to generate language. from giving false information, also known as Go to glossary for hallucination (opens in a new tab)A false statement made confidently by a language model..

You will learn about:

  1. Full definition for embedding (opens in a new tab)Information represented as a numerical vector, positioned so that similar information sits close together. and vector indexes, how they are used in GenAI, and how to use them in Neo4j.
  2. Full definition for retrieval-augmented generation (opens in a new tab)Fetching relevant context from an external data source as additional context to inform a language model's response. (Retrieval Augmented Generation) and how Full definition for GraphRAG (opens in a new tab)Retrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. builds on it to provide a graph-based approach to providing context to Generative AI models.
  3. How to use the Neo4j GraphRAG for Python package to interact with AI models and Neo4j.

This course uses models from OpenAI, although you can use the model and supplier of your choice.

Prerequisites

Before taking this course, you should have:

  • A basic understanding of Graph Databases and Neo4j
  • Knowledge of Python and capable of reading simple programs

We recommend taking the Neo4j Fundamentals course.

To complete the practical tasks within this course, you will need an OpenAI API key.

  • Generative AI Fundamentals

  • Large Language Models

  • RAG

  • GraphRag

  • Integrating Neo4j with Generative AI

Course content

4 modules, 2 hours.

  1. 3 lessons
    1. What is Generative AI5 min
    2. Considerations5 min
    3. Context5 min
  2. 4 lessons
    1. What is RAG?5 min
    2. Vector RAG5 min
    3. Vector Indexes5 min
    4. GraphRAG5 min
  3. 2 lessons
    1. What is a Knowledge Graph5 min
    2. Creating Knowledge Graphs5 min
  4. 6 lessons
    1. GraphRAG for Python5 min
    2. Vector Retriever5 min
    3. RAG Pipeline5 min
    4. Graph-Enhanced Vector Retriever5 min
    5. Text to Cypher Retriever5 min
    6. GenAI Frameworks5 min
Where to next

Keep going on Generative AI & GraphRAG.

View the full path
Up next in path

Building Agents in Neo4j Aura

2 hoursStart
Later in path

Introduction to Vector Indexes and Unstructured Data

1 hourStart
Later in path

Building Knowledge Graphs with LLMs

1 hourStart