ResearchBridge

ResearchBridge is an innovative research platform developed for the Hypermode Knowledge Graph + AI Challenge. It empowers researchers with real-time collaboration tools, advanced citation management, and AI-powered research assistance.

Project Goals

• Streamline academic research workflow through AI-assisted paper analysis
• Enable real-time collaboration between researchers
• Provide intelligent citation network visualization and management
• Offer contextual research suggestions and gap analysis
• Create a knowledge graph of research papers and their relationships

Youtube Link - https://www.youtube.com/watch?v=FGaYIt-YvlQ

Devpost Link - https://devpost.com/software/researchbridge

Screenshots

Main dashboard showing the project interface

Workflow diagram explaining the system architecture

Real-time collaborative text editor interface

AI-powered research assistant integrated with Modus

User profile and research management page

Implementation

Architecture

The project is built using a modern tech stack:

• Frontend: React with TypeScript
• Backend: Go with WebSocket support
• AI Integration: Google's Gemini Pro API via Modus Framework
• Knowledge Graph: Neo4j for citation network analysis
• Real-time Collaboration: WebSocket-based communication

AI Models & Integration

1. Gemini Pro Integration

   • Implemented through Modus Framework for research analysis
   • Provides paper summaries, methodology analysis, and research gaps identification
   • Generates contextual research suggestions and related topics
   • Analyzes research impact and limitations

2. Citation Analysis

   • Uses Google Scholar API integration for citation data
   • Tracks citation metrics and impact factors
   • Generates citation networks and relationship graphs

Knowledge Graph Implementation

The project utilizes Neo4j as the primary knowledge graph database to: - Store and analyze paper relationships - Track citation networks and academic influence - Identify research clusters and emerging topics - Enable graph-based paper recommendations

Modus Framework Integration

The Modus Framework is used to: - Manage AI service connections - Handle authentication and API key management - Provide structured endpoints for AI interactions - Enable seamless integration with Gemini Pro API

Key Features

• Real-time collaborative text editor with multi-user support
• Advanced citation network visualization
• AI-powered research assistant integration
• WebSocket-based real-time communication
• Modern, intuitive user interface with exceptional UX design
• Seamless document management and version control

Technical Requirements

Before running the project, ensure you have: - Node.js (v14 or higher) - Go (v1.16 or higher) - npm or yarn package manager - Neo4j Database - Google Cloud API credentials for Gemini Pro - SERP API key for Google Scholar integration

Setup Instructions

Frontend Setup

bash cd frontend npm install

Backend Setup

bash cd backend npm install

Citation Network Setup

bash cd citation-network go mod tidy

Environment Configuration

Create a .env file in the citation-network directory with: MODUS_GEMINI_API_KEY=your_gemini_api_key SERP_API_KEY=your_serp_api_key NEO4J_URI=your_neo4j_uri NEO4J_USER=your_neo4j_user NEO4J_PASSWORD=your_neo4j_password

Development Server

1. Start Neo4j database

2. Run the citation network service: bash cd citation-network go run .

3. WebSocket Server bash cd backend node websocket-server.js WebSocket connections at ws://localhost:8080

4. Start the backend server: bash cd backend npm run start

5. Launch the frontend: bash cd frontend npm run dev

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests for any enhancements.

License

This project is licensed under the MIT License - see the LICENSE file for details.