🚀 Search Engine

The matching search engine to my web crawler.

✨ Features

• 🌐 Modern 3D Interactive Landing Page with full-screen visualizations that dynamically adapt to different screen sizes
• 🤖 AI-Powered Answers providing instant responses with verified sources
• 📚 Knowledge Panels displaying relevant information from Wikipedia and trusted sources
• ⚡ Smart Search with real-time search speed display
• 📝 Website Summaries generated by AI to save reading time
• 🔍 Google Integration for comprehensive search results
• 🌙 Dark Mode Support for comfortable viewing in all lighting conditions
• 📱 Responsive Design optimized for all devices
• 🗄️ MongoDB Backend for efficient data operations

📄 Detailed Feature Overview

🌐 Modern 3D Landing Page

The search engine features a stunning 3D visualization on the landing page created with Three.js. The visualization creates an immersive experience with:

• 🖱️ Dynamic node connections that respond to mouse movements
• 📱 Responsive design that works across devices

🤖 AI-Powered Features

The search engine leverages AI for multiple features:

• 💡 Smart Answers: Get direct answers to questions without needing to visit multiple websites
• 📝 Content Summaries: AI-generated summaries of web pages to quickly understand their content
• 🔍 Query Understanding: Intelligent parsing of search queries to deliver more relevant results

📚 Knowledge Panels

Knowledge panels provide quick access to key information about searched topics:

• 🌐 Information from Wikipedia and other trusted sources
• 📸 Quick facts, images, and related links
• 🔍 Context-aware information based on search query

⚡ Search Interface

The search interface is designed for maximum usability:

• 🧼 Clean, distraction-free design
• 💬 Real-time search suggestions
• ⏱️ Search speed display
• 🔍 Filter options for refined results

🛠️ Technical Specifications

• 🖥️ Built with Flask and modern frontend technologies
• 🗄️ MongoDB for efficient data storage and retrieval
• 📱 Responsive Bootstrap-based UI with custom enhancements
• 🌐 Three.js for 3D visualizations
• 🌙 Dark mode implementation with CSS variables and prefers-color-scheme media queries

🐳 Docker Instructions

🛠️ Building the Docker Image

To build the Docker image, run the following command in the root directory of the repository:

docker build -t ghcr.io/schbenedikt/search-engine:latest .

🚀 Running the Docker Container

To run the Docker container, use the following command:

docker run -p 5560:5560 ghcr.io/schbenedikt/search-engine:latest

This will start the Flask application using Gunicorn as the WSGI server, and it will be accessible at http://localhost:.

📥 Pulling the Docker Image

The Docker image is publicly accessible. To pull the Docker image from GitHub Container Registry, use the following command:

docker pull ghcr.io/schbenedikt/search-engine:latest

📝 Note

Ensure that the tags field in the GitHub Actions workflow is correctly set to ghcr.io/schbenedikt/search-engine:latest to avoid multiple packages.

🐳 Running with Docker Compose

To run both the search engine and MongoDB containers using Docker Compose, use the following command:

docker-compose up

This will start both containers and the Flask application will be accessible at http://localhost:.

📄 Docker Compose File

The docker-compose.yml file is used to manage both the search engine and MongoDB containers. Here is an example of the docker-compose.yml file:

version: '3.8'

services:
  search-engine:
    image: ghcr.io/schbenedikt/search-engine:latest
    depends_on:
      - mongodb
    ports:
      - "5560:5560"

  mongodb:
    image: mongo:latest
    ports:
      - "27017:27017"
    volumes:
      - mongo-data:/data/db

volumes:
  mongo-data:

🗄️ Database Configuration

The db_config.json file is used to store the database configuration. Here is an example of the db_config.json file:

[
  {
    "url": "mongodb://localhost:27017",
    "name": "search_engine",
    "username": "your_username",
    "password": "your_password"
  }
]

⚙️ Settings Page

The settings.html file has been updated to include fields for username and password. You can access the settings page at http://localhost:5560/settings to update the database configuration.

🛠️ Installation and Setup

📋 Requirements

• Python 3.10+
• MongoDB (local installation or remote access)
• Modern web browser with JavaScript enabled

🚀 Installation Steps

1. Clone the repository

   git clone https://github.com/SchBenedikt/search-engine.git
   cd search-engine

2. Create and activate a virtual environment (optional but recommended)

   python -m venv venv
   # On Windows
   venv\Scripts\activate
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure your database

   • Update the db_config.json with your MongoDB credentials
   • Ensure MongoDB is running and accessible

5. Start the application

   python3 app.py

6. Open your browser and navigate to http://localhost:5560

📖 Usage Guide

🔍 Basic Search

1. Enter your query in the search box on the homepage
2. Press Enter or click the search button
3. View the search results, including AI-powered answers and knowledge panels

⚙️ Advanced Features

• AI Assistance: Prefix your query with "Ask AI:" to get more detailed AI-generated answers
• Filter Results: Use the filter options on the search results page to refine your search
• Dark Mode: Toggle dark mode in the settings or let it automatically adjust based on your system preferences
• Settings Customization: Visit the settings page to customize your search experience

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.