Building Knowledge Graphs with LLMs

Course Duration: 2 hours
Categories: Generative AI

Learn how to use Generative AI, LLMs and Python to convert unstructured data into graphs.

Course Description

In this hands-on course, you will learn how to create and query knowledge graphs using Large Language Models (LLMs).

You will use the Neo4j LLM Graph Builder and Python to build knowledge graphs from unstructured data.

You will learn how to query the knowledge graph using Cypher and LLMs to generate Cypher queries.

Finally, you will explore how to integrate into knowledge graphs into GenAI applications.

Prerequisites

You should:

Understand graph and Neo4 fundamental concepts - Neo4j and Graph Fundamentals.
Have an understanding of LLMs, LangChain, and vector indexes - Neo4j & LLM Fundamentals.
Be able to read and write simple Cypher queries - Cypher Fundamentals.
Be able to read and understand simple Python programs.
You may also find it useful to complete the following courses:
- Introduction to Vector Indexes and Unstructured data.
- Building Neo4j Applications with Python.

Duration

2 hours

What you will learn

What you will learn:

What a knowledge graph is and how it can support the operation of GenAI applications.
How a knowledge graph can be created from unstructured data using an LLM.
How to use the LLM Graph Builder to create a prototype knowledge graph and explore your data.
How to use Python, LangChain, and an LLM to create and query a knowledge graph.

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.