Neo4j and Generative AI Workshop
Welcome to GraphAcademy and the Neo4j and Generative AI workshop.
Description
In this workshop you will:
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Learn about Generative AI, RAG, and GraphRAG.
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Build a knowledge graph from unstructured and structured data.
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Use Vector indexes and embeddings in Neo4j to perform similarity search.
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Create vector, vector + cypher, and text to Cypher retrievers.
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Build a conversational agent using Neo4j, Python, and LangChain
Prerequisites
Before taking this workshop, you should have:
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A basic understanding of Graph Databases and Neo4j
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Able to read and understand basic Cypher queries
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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:
Duration
3 hours
What you will learn
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The basics of Generative AI and Large Language Models (LLMs)
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What Retrieval-Augmented Generation (RAG) is and why it is important
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How GraphRAG can improve the quality of LLM-generated content
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How to build knowledge graphs from unstructured PDF documents using entity extraction and relationship mapping
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How to enrich knowledge graphs with structured data
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How to use Vectors in Neo4j for similarity search
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To build different types of retrievers using the neo4j-graphrag for Python package.
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To build a conversational agent using Neo4j, Python, and LangChain.
Get Support
If you find yourself stuck at any stage then our friendly community will be happy to help. You can reach out for help on the Neo4j Community Site, or head over to the Neo4j Discord server for real-time discussions.
Feedback
If you have any comments or feedback on this course you can email us on graphacademy@neo4j.com.