RAG with Neo4j Knowledge Graph: Setup and Benefits

In this video, Philipp Brunenberg explains how to set up Retrieval Augmented Generation (RAG) with a Neo4j Knowledge Graph using the GenAI Docker stack. He begins by defining RAG and explaining its benefits. RAG allows Large Language Models (LLMs) to retrieve and utilize specific information from a Neo4j Knowledge Graph, which contains unstructured data and relationships that the LLM cannot access directly. By embedding text documents and storing their vector representations in the Neo4j graph, a vector index is created for efficient similarity searches. When a user asks a question, the query is embedded and matched against the vector store to retrieve relevant nodes, which are then used to augment the question before sending it to the LLM for a more accurate response. Philipp provides a step-by-step guide to setting up this system on a local machine. He demonstrates installing and configuring the necessary components, including Olama for LLM serving, and setting up the GenAI stack with Docker Compose. He also shows how to load data from Stack Overflow into Neo4j and use a chat frontend to interact with the system. The video concludes with a demonstration of RAG in action, showing how the augmented responses are more precise than those generated by the LLM alone.