Building Knowledge Graphs with LLMs
Learn how to use Generative AI and LLMs to convert unstructured data into knowledge graphs.
In this 1-hour course, you will learn
In this hands-on course, you will learn how to create and query knowledge graphs using Large Language Models (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.).
You will use the Neo4j LLM Graph Builder to build knowledge graphs from unstructured data.
Finally, you will learn how to customize the Full definition for data model (opens in a new tab)The labels, relationship types and properties chosen to represent a domain. and query the Full definition for knowledge graph (opens in a new tab)A representation of real-world entities and their relationships, stored according to organizing principles, typically in a graph database. using 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..
Prerequisites
You should:
- Understand graph and Neo4 fundamental concepts - Neo4j and Graph Fundamentals.
- Have an understanding of GenerativeAI, LLMs, and vector indexes - Neo4j & GenerativeAI Fundamentals.
- Be able to read and write simple Cypher queries - Cypher Fundamentals.
Knowledge Graphs
What a knowledge graph is and how it can support the operation of GenAI applications.
Unstructured Data
How a knowledge graph can be created from unstructured data using an LLM.
LLM Graph Builder
How to use the LLM Graph Builder to create a prototype knowledge graph and explore your data.
Query Knowledge Graphs
How to query a knowledge graph using Cypher.