GraphRAG Example

GraphRAG vs. Baseline RAG: Solving Multi-Hop Reasoning in LLMs

This repository contains the source code for the article "GraphRAG vs. Baseline RAG: Solving Multi-Hop Reasoning in LLMs".

Prerequisites

• Python 3.11+
• An OpenAI API key

Setup Instructions

1. Create and activate a Python virtual environment:

python3 -m venv .venv
source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate

2. Install the required packages:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env

4. Edit .env file and add your API key:

• OPENAI_API_KEY: Your OpenAI API key

Running the Demo

1. Open main.ipynb

2. Run all cells to see the demo in action

The notebook demonstrates:

• Initial setup and utility functions for response handling
• Loading and parsing a corpus of articles
• Construction of a GraphRAG index using LlamaIndex
• Construction of a traditional Vector RAG index for comparison
• Visualization of the knowledge graph (viewable in knowledge_graph.html)
• Comparison of both approaches through multiple test questions
• Performance analysis showing GraphRAG's superior accuracy in multi-hop reasoning tasks