New GraphAcademy Course: Building Knowledge Graphs With LLMs

There’s a new course on GraphAcademy: opens in new tabBuilding Knowledge Graphs with LLMs. In this new course, you will learn how to create and query knowledge graphs using large language models (LLMs).

Using the text analysis capabilities of LLMs, you can generate a knowledge graph easily, extracting the entities and relationships relevant to your use case.

A knowledge graph is a source of truth and allows you to access and understand the relationships in your unstructured data. Knowledge graphs are an essential tool in grounding GenAI applications using GraphRAG.

You will use the opens in new tabNeo4j LLM Graph Builder and Python to build knowledge graphs from unstructured data.

You will learn the steps required to generate a knowledge graph, how to set a schema, and interpret the results.

This is a hands-on course in which you can upload and process your own unstructured data into a graph data model.

You will develop retrievers and use Cypher generation to get data from the graph.

This is an advanced course and you should have an understanding of Neo4j, integrating LLMs into applications, and Cypher. After completing this course, you will have the knowledge and skills to build a knowledge graph from your unstructured data and use it to ground a GenAI chatbot.

Enroll in the opens in new tabBuilding Knowledge Graphs with LLMs course on opens in new tabNeo4j GraphAcademy.

What is Neo4j GraphAcademy?

At opens in new tabNeo4j GraphAcademy, we offer a wide range of courses completely free of charge, teaching everything from opens in new tabNeo4j Fundamentals to opens in new tabBuilding Neo4j Applications with .NET.

opens in new tabNew GraphAcademy Course: Building Knowledge Graphs With LLMs was originally published in opens in new tabNeo4j Developer Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.