GraphRAG Implementation for Healthcare Product Catalog

This repository contains a GraphRAG (Graph-enhanced Retrieval-Augmented Generation) implementation for a healthcare company's product catalog using the official Microsoft GraphRAG package. The implementation automatically extracts entities and relationships from unstructured text documents to build a knowledge graph, which is then used to enhance retrieval and answer questions.

What is GraphRAG?

GraphRAG is an approach that combines the strengths of knowledge graphs with retrieval-augmented generation. It addresses limitations of traditional RAG systems by:

1. Automatically extracting entities and relationships from documents
2. Building a knowledge graph to represent structured information
3. Using the graph structure to enhance retrieval beyond simple vector similarity
4. Integrating graph-based and vector-based retrieval for more comprehensive answers

How It Works

This implementation uses the official Microsoft GraphRAG CLI to:

1. Index documents: The system processes text files in the input directory, extracting entities and relationships to build a knowledge graph.
2. Query the graph: The system supports both global and local search methods to answer questions about the healthcare products.
3. Create statistics: The system generates statistics about the knowledge graph, such as the number of entities and relationships.
4. Visualize the knowledge graph: The system generates a visual representation of the entities and relationships in the knowledge graph.

Project Structure

• pipeline.py: The main implementation file that contains the GraphRAGPipeline class for indexing and querying
• analyzer.py: Contains the GraphRAGAnalyzer class for analyzing and visualizing the knowledge graph
• main.py: Example script demonstrating the GraphRAG functionality
• input/: Directory containing the input text files
• output/: Directory where GraphRAG stores its output files (entities, relationships, etc., will be created by GraphRAG)
• logs/: Directory for log files (will be created by GraphRAG)
• cache/: Directory for cached data (will be created by GraphRAG)

Requirements

• Python 3.10 or higher
• GraphRAG CLI installed and configured
• Dependencies listed in the Pipfile

Installation

1. Install pipenv if you don't have it

pip install pipenv

2. Install dependencies from Pipfile

pipenv install

Project Setup

1. Initialize GraphRAG project

pipenv run graphrag init --root ./

2. Add API key (here OpenAI key)

GRAPHRAG_API_KEY=<API_KEY>

3. Approve environment settings

direnv allow

Usage

Run the example script:

pipenv run python main.py

The script will:

• Run the indexing process
• Execute example search queries
• Generate statistics and visualization of the knowledge graph

Features

• CLI-based interaction: Uses the GraphRAG CLI for indexing and querying
• Graph analysis: Provides statistics and insights about the knowledge graph
• Knowledge graph visualization: Creates visual representations of entities and relationships

References

• Microsoft GraphRAG GitHub Repository
• Microsoft Research Blog Post
• Blog Post