In this video, Philipp Brunenberg explains how RAG (retrieval augmented generation) with a Neo4j Knowledge Graph works and how we can set it up on your laptop. He’ll also give a brief introduction on how we can customize the code from the Gen-AI Stack.
We’ll cover:
What is RAG?
How does RAG with a Neo4j Knowledge Graph work?
What are the benefits of using RAG & Neo4j?
How can I set up RAG with Neo4j using a Llama2 model and the GenAI Stack?
Links:
GenAI Walkthrough: https://bit.ly/4cxfPXW
Neo4j & GenAI: https://bit.ly/4cpAY6D
Github Repo: https://github.com/docker/genai-stack
RAG Blogs: https://development.neo4j.dev/developer-blog/tagged/retrieval-augmented-generation/
LLM Blogs: https://development.neo4j.dev/developer-blog/tagged/llm/
@philippbrunenberg https://www.philipp-brunenberg.de/
00:00 Introduction
00:29 What is RAG?
02:09 The Neo4j Vector Store
03:03 Augmenting LLM answers with RAG
04:50 Using the GenAI-Stack
06:37 GenAI-Stack Configuration
08:08 The compose file
09:47 Loading data into Neo4j
11:59 Using the chat frontend
15:30 Conclusion
#neo4j #graphdatabase #knowledgegraph #langchain #genai #rag #ollama