---
title: Leveraging RAG in GenAI to teach new information
linkTitle: Leveraging RAG in GenAI
description:  This guide walks through the process of setting up and utilizing a GenAI stack with Retrieval-Augmented Generation (RAG) systems and graph databases. Learn how to integrate graph databases like Neo4j with AI models for more accurate, contextually-aware responses.
keywords: Docker, GenAI, Retrieval-Augmented Generation, RAG, Graph Databases, Neo4j, AI, LLM
summary: |
  This guide explains setting up a GenAI stack with Retrieval-Augmented Generation (RAG) and Neo4j, covering key concepts, deployment steps, and a case study. It also includes troubleshooting tips for optimizing AI performance with real-time data.
tags: [ai]
params:
  time: 35 minutes
---



## Introduction

Retrieval-Augmented Generation (RAG) is a powerful framework that enhances large language models (LLMs) by integrating information retrieval from external knowledge sources. This guide focuses on a specialized RAG implementation using graph databases like Neo4j, which excel in managing highly connected, relational data. Unlike traditional RAG setups with vector databases, combining RAG with graph databases offers better context-awareness and relationship-driven insights.

In this guide, you will:

* Explore the advantages of integrating graph databases into a RAG framework.
* Configure a GenAI stack with Docker, incorporating Neo4j and an AI model.
* Analyze a real-world case study that highlights the effectiveness of this approach for handling specialized queries.

## Understanding RAG 

RAG is a hybrid framework that enhances the capabilities of large language models by integrating information retrieval. It combines three core components:  

- **Information retrieval** from an external knowledge base  
- **Large Language Model (LLM)** for generating responses  
- **Vector embeddings** to enable semantic search  

In a RAG system, vector embeddings are used to represent the semantic meaning of text in a way that a machine can understand and process. For instance, the words "dog" and "puppy" will have similar embeddings because they share similar meanings. By integrating these embeddings into the RAG framework, the system can combine the generative power of large language models with the ability to pull in highly relevant, contextually-aware data from external sources.

The system operates as follows:  
1. Questions get turned into mathematical patterns that capture their meaning  
2. These patterns help find matching information in a database
3. The found information gets added to the original question before passed to LLM 
4. The LLM generates responses that blend the model's inherent knowledge with the this extra information.  

To hold this vector information in an efficient manner, we need a special type of database.

## Introduction to Graph databases 

Graph databases, such as Neo4j, are specifically designed for managing highly connected data. Unlike traditional relational databases, graph databases prioritize both the entities and the relationships between them, making them ideal for tasks where connections are as important as the data itself.

Graph databases stand out for their unique approach to data storage and querying. They use nodes (or vertices) to represent entities and edges to represent the relationships between these entities. This structure allows for efficient handling of highly connected data and complex queries, which are difficult to manage in traditional database systems.

SQL databases and graph databases differ significantly in their data models. SQL databases use a tabular structure with rows and columns, where relationships between entities are established using foreign keys. This approach works well for structured data and predefined relationships. In contrast, graph databases represent data as nodes (entities) and edges (relationships), making the representation of relationships more intuitive and flexible. This structure is particularly advantageous for complex, interconnected data.