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SageSearch Logo

SageSearch

Intelligent web search and information synthesis platform

Features • Demo • Installation • Usage • Architecture • Contributing • License

GitHub stars GitHub forks GitHub issues License

📋 Overview

SageSearch is a sophisticated search platform built with Flutter and FastAPI that transforms how users discover information online. It intelligently retrieves, analyzes, and synthesizes content from multiple web sources, delivering comprehensive AI-enhanced responses to user queries while maintaining source attribution.

SageSearch in action - Intelligent search results with source attribution

✨ Features

Frontend

  • 🎯 Intuitive Interface - Clean, modern UI with focus on content and readability
  • 📱 Fully Responsive - Seamless experience across desktop, tablet, and mobile devices
  • 💡 Smart Suggestions - Contextual search suggestions and history tracking
  • 🎨 Customizable Theme - Dark mode support with elegant color scheme
  • ⚡ Performance Optimized - Fast loading times and smooth animations

Backend

  • 🔍 Intelligent Web Search - Leverages Tavily API for accurate and relevant search results
  • 🧠 Smart Source Ranking - Uses sentence transformers to prioritize the most relevant sources
  • 🤖 AI-Powered Responses - Generates comprehensive answers using Google Gemini
  • 🔄 Real-time Communication - WebSocket support for streaming responses
  • 🌐 RESTful API - Clean and well-documented endpoints for easy integration

🎬 Demo

Home Page

SageSearch Home Page

Loading Page

SageSearch Loading Page

Result Page

SageSearch Result Page

🚀 Getting Started

Prerequisites

Frontend

  • Flutter SDK (latest version)
  • Dart SDK (latest version)
  • A code editor (VS Code, Android Studio, or IntelliJ IDEA)

Backend

Installation

Frontend

  1. Clone the repository

    git clone https://github.com/AyushKr2003/sage_search.git
cd sage_search

  2. Install dependencies

    cd frontend
flutter pub get

  3. Environment Setup

    Create a .env file in the frontend directory based on the provided example:

    copy .env.example .env

    Then update the values in .env with your configuration:

    BACKEND_URL='ws://localhost:8000/ws/chats'

  4. Run the application

    flutter run

Backend

  1. Navigate to the backend directory

    cd backend

  2. Set up the environment

    # Create and activate virtual environment
python -m venv venv
.\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
copy .env.example .env

    Edit the .env file with your API keys:

    TAVILY_API_KEY=your_tavily_api_key
GEMINI_API_KEY=your_gemini_api_key

  3. Run the server

    # Development mode with auto-reload
uvicorn main:app --reload

# Production mode
uvicorn main:app --host 0.0.0.0 --port 8000

    The server will be available at http://localhost:8000

📖 Usage

SageSearch provides an intuitive interface for searching and discovering information:

  1. Enter your query in the search bar
  2. Review the AI-synthesized answer with highlighted sources
  3. Explore related topics through suggested searches
  4. Toggle between light and dark themes based on your preference

🏗️ Architecture

Project Structure

sage_search/

├── lib/
│   ├── constant/
│   │   └── const.dart               # Application constants and configuration
│   ├── pages/ 
│   │   └── home_page.dart           # Main application interface
│   ├── theme/ 
│   │   └── colors.dart              # Theme definitions and styling
│   ├── widgets/     
│   │   ├── custome_list.dart        # Reusable list component
│   │   ├── search_section.dart      # Search interface component
│   │   ├── side_bar.dart            # Navigation sidebar
│   │   ├── side_bar_button.dart     # Interactive sidebar elements
│   │   └── top_bar.dart             # Application header component
│   ├── services/                    
│   │   └── web_socket_service.dart  # API and data services
│   └──  main.dart                   # Application entry point
│
├── server/
│   ├── main.py                      # FastAPI application and endpoints
│   ├── config.py                    # Configuration settings
│   ├── services/
│   │   ├── search_service.py        # Tavily API integration
│   │   ├── sort_sorce_service.py    # Source ranking
│   │   └── llm_service.py           # Gemini AI integration
│   ├── pydantic_model/
│   │   └── chat_body.py             # Request/Response models
│   ├── tests/                       # Unit and integration tests
│   └── requirements.txt             # Project dependencies

API Reference

REST Endpoints

POST /chats

Request body:

{
  "query": "What are the latest developments in quantum computing?"
}

WebSocket

Connect to /ws/chats to receive streaming responses.

Example client usage:

const ws = new WebSocket('ws://localhost:8000/ws/chats');
ws.onopen = () => {
  ws.send(JSON.stringify({ query: "What are the latest developments in quantum computing?" }));
};
ws.onmessage = (event) => {
  const data = JSON.parse(event.data);
  console.log(data);
};

🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/amazing-feature)
  3. Commit your Changes (git commit -m 'Add some amazing feature')
  4. Push to the Branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

See the open issues for a list of proposed features and known issues.

📄 License

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

👏 Acknowledgments

Frontend

Backend

Team

Built with ❤️ by Ayush Kumar Singh