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In this GraphRAG course, you will learn how to use a knowledge graph to implement an RAG application with Neo4j and OpenAI ChatGPT, enhancing response accuracy and reducing hallucinations.

Generative AI with LLMs and Knowledge Graphs using Neo4j.png

neo4jllmkgstreamlit.tar.gz

runPythonCode

runningneo4j-live

runningneo4j-streamlit

runPythonCode-Live

kg_generation_app

Graph_RAG_app

Knowledge graphs are powerful tools that structure information into entities and relationships, making data more accessible and meaningful for AI applications. They are essential for enhancing the performance of LLMs by providing structured context, improving response accuracy and cohesiveness, and reducing hallucinations on datasets outside the LLM’s training data.

In this course, you’ll explore knowledge graphs for retrieval-augmented generation (RAG) and dive deep into traditional to advanced NER and relationship extraction techniques. You’ll learn to construct and refine knowledge graphs from raw text, store and query them effectively with Neo4j, and integrate them with LLMs to boost their performance and build personalized chatbots using custom datasets.

After completing this course, you’ll gain expertise in implementing graph RAG for complex scenarios, advancing your skills in building generative AI applications.

Master Knowledge Graph Retrieval-Augmented Generation with Neo4j

Review what we have learned in this course.

Conclusion

Introduction to the Course

Constructing Knowledge Graphs

Create a Knowledge Graph from Text

Neo4j: Storing Entities and Relationships to Graph Database

Integrating Knowledge Graphs with LLMs

Conclusion

What we have achieved