Research Assistant 🔍

A powerful AI-powered research assistant that combines hybrid search with Retrieval-Augmented Generation (RAG) to help you find, understand, and synthesize academic content.

🌐 Live Demo

Try it now: Research Assistant

Experience the full functionality through our interactive web interface:

• 📚 Research paper search and analysis
• 🔍 Hybrid search capabilities (semantic + keyword)
• 🤖 Real-time RAG-powered question answering
• 📊 Document processing and knowledge base management

✨ Features

🎯 Intelligent Search

• Hybrid Retrieval: Combines semantic (vector) and keyword (BM25) search
• Multi-Source: arXiv papers, web articles, custom documents
• Smart Ranking: Advanced fusion algorithms for relevance

🧠 AI-Powered Analysis

• RAG Integration: Context-aware answers using retrieved documents
• Multiple LLM Support: Google Gemini, OpenAI, and more
• Flexible Modes: Strict RAG (document-only) or hybrid (document + general knowledge)

📊 Data Processing

• Smart Chunking: Sentence-boundary aware document segmentation
• High-Quality Embeddings: Google Gemini text embeddings
• Efficient Indexing: Optimized storage and retrieval

🖥️ Multiple Interfaces

• Web App: Beautiful Streamlit interface
• CLI Tool: Powerful command-line interface
• Python API: Programmatic access for integration

🚀 Quick Start

Prerequisites

• Python 3.9+
• Google Gemini API key (free tier available)

Installation

1. Clone the repository

   git clone https://github.com/Fane-Nathan/Research-Assistant.git
   cd Research-Assistant

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment

   cp .env.example .env
   # Edit .env with your API keys

4. Run the web interface

   streamlit run app.py

📖 Documentation

• 📘 Complete Documentation
• 🚀 Quick Start Guide
• 🏗️ Architecture Overview
• 🔧 Configuration Guide

🛠️ Usage Examples

Web Interface

Visit our live demo or run locally:

streamlit run app.py

Command Line

# Search arXiv papers
python scripts/cli.py search "machine learning transformers"

# Process custom documents
python scripts/cli.py process --url "https://example.com/paper.pdf"

# Ask questions about your documents
python scripts/cli.py query "What are the main contributions of this paper?"

Python API

from hybrid_search_rag import ResearchAssistant

# Initialize
assistant = ResearchAssistant()

# Search and get answers
results = assistant.search_and_answer(
    query="What are recent advances in neural networks?",
    mode="hybrid_rag"
)

🏗️ Architecture

Research Assistant is built with a modular, scalable architecture:

┌─────────────────┐    ┌─────────────────┐     ┌─────────────────┐
│   Data Sources  │    │   Processing    │     │   Retrieval     │
│                 │    │                 │     │                 │
│ • arXiv API     │───▶│ • Text Chunking │───▶│ • Vector Search │
│ • Web Crawling  │    │ • Embeddings    │     │ • BM25 Search   │
│ • Custom Docs   │    │ • Indexing      │     │ • Hybrid Fusion │
└─────────────────┘    └─────────────────┘     └─────────────────┘
                                                         │
┌─────────────────┐    ┌─────────────────┐               │
│  User Interface │    │   Generation    │               │
│                 │    │                 │               │
│ • Web App       │◀───│ • LLM Reasoning │◀─────────────┘
│ • CLI Tool      │    │ • RAG Synthesis │
│ • Python API    │    │ • Answer Format │
└─────────────────┘    └─────────────────┘

🧪 Testing

Run the test suite:

# Unit tests
python -m pytest tests/unit/

# Integration tests  
python -m pytest tests/integration/

# Deployment verification
python deployment_verification.py

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for your changes
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📝 License

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

🔗 Links

• Documentation: https://fane-nathan.github.io/Research-Assistant/
• Live Demo: https://research-assistant-flx.streamlit.app/
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🙏 Acknowledgments

• Google Gemini for embeddings and LLM capabilities
• The open-source community for amazing libraries
• arXiv for providing free access to research papers

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Built with ❤️ for the research community

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