Neo4j & Generative AI Hands-On Workshop

Learn about Generative AI, RAG, and GraphRAG.

Use Vector indexes and embeddings in Neo4j to perform similarity search.

Create vector, vector + cypher, and text to Cypher retrievers.

Build a conversational agent using Neo4j, Python, and LangChain

The basics 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 using Neo4j Data Importer

How to use Vectors in Neo4j for similarity search

To build different types of retrievers using the neo4j-graphrag for Python package.

To create a conversational agent using Neo4j, Python, and LangChain.

A basic understanding of Graph Databases and Neo4j

Able to read and understand basic Cypher queries

Knowledge of Python and capable of reading simple programs