Neo4j and Generative AI Workshop
Learn how to build GraphRAG workflows with Neo4j, Python, and LLM-powered retrieval techniques.
In this 2-hour workshop, you will learn
Welcome to GraphAcademy and the Neo4j and 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. workshop.
In this workshop you will:
- Learn about Generative AI, 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., and 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..
- Build a Full definition for knowledge graph (opens in a new tab)A graph of entities and the relationships between them, used as a source of facts. from unstructured and structured data.
- Use Vector indexes and Full definition for embedding (opens in a new tab)Information represented as a numerical vector, positioned so that similar information sits close together. in Neo4j to perform similarity search.
- Create vector, vector + Full definition for Cypher (opens in a new tab)Neo4j's implementation of GQL, the ISO standard query language for graph databases. It is declarative: you describe the pattern to find, and the database decides how to find it., and text to Cypher Full definition for retriever (opens in a new tab)A component that searches a data source and returns the information relevant to a query. Often used to provide context for a language model..
- Build a conversational agent using Neo4j, Python, and LangChain
Prerequisites
Before taking this workshop, you should have:
- A basic understanding of Graph Databases and Neo4j
- Able to read and understand basic Cypher queries
- Knowledge of Python and capable of reading and executing simple programs
To take this course we recommend that you have taken these beginner courses in GraphAcademy:
The fundamentals of Generative AI and Large Language Models (LLMs)
What Retrieval-Augmented Generation (RAG) is and why it is important
How GraphRAG can improve the quality of LLM-generated content
How to build knowledge graphs from unstructured PDF documents using entity extraction and relationship mapping
How to enrich knowledge graphs with structured data
How to use Vectors in Neo4j for similarity search
To build different types of retrievers using the neo4j-graphrag for Python package.
To build a conversational agent using Neo4j, Python, and LangChain.
What you need to take part.
A GraphAcademy account
The workshop is delivered here on GraphAcademy, so you need to be signed in to work through the lessons and keep your progress. Creating an account is free.
A GitHub account, or Git on your own machine
You write code against the workshop-genai repository. The quickest route is a GitHub Codespace, an online editor that clones the code and installs everything for you — that needs a GitHub account.
If you would rather work locally, clone the repository with Git and run it in your own editor instead. No GitHub account is needed for that.