Master Knowledge Graph Retrieval-Augmented Generation with Neo4j
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.
4.4
9 Lessons
3h
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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.
Knowledge graphs are powerful tools that structure information into entities and relationships, making data more accessible and ...Show More
WHAT YOU'LL LEARN
An understanding of fundamental concepts of generative AI, large language models (LLMS), and retrieval-augmented generation (RAG)
Familiarity with differences between RAG with knowledge graphs and RAG with vector databases
Hands-on experience implementing knowledge graph RAG with Neo4j and OpenAI GPT-4
The ability to build knowledge graphs for text documents from scratch using OpenAI GPT-4 for named entity recognition and relationship extraction
The ability to store entities and relationships in the Neo4j graph database using Cypher query language
Hands-on experience using Cypher to retrieve relevant information from the knowledge graph to include it in the prompt to LLM
The ability to build a personalized question-answering chatbot with graph RAG
An understanding of fundamental concepts of generative AI, large language models (LLMS), and retrieval-augmented generation (RAG)
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Learning Roadmap
Certificate of Completion
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