SageSearch

Intelligent web search and information synthesis platform

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

Loading Page

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

• Python 3.8+
• Tavily API key - For web search capabilities
• Google Gemini API key - For AI response generation

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

• Flutter - UI toolkit for building natively compiled applications
• Google Fonts - Beautiful typography
• Material Design - Design system and components

Backend

• Tavily API - For search capabilities
• Google Gemini - For AI response generation
• FastAPI - For the web framework
• Sentence Transformers - For source ranking

Team

• All contributors who have helped shape SageSearch

---

_{Built with ❤️ by Ayush Kumar Singh}