The Image Search Engine is a modern AI-powered web application leveraging Retrieval-Augmented Generation (RAG) to combine large language models (LLMs) with advanced image retrieval techniques. Designed for seamless user experience, this tool allows users to upload and search through images in a highly accurate, visually engaging way. Utilizing vector embeddings through OpenAI’s models, the engine achieves context-aware search results that surpass traditional keyword-based searches—ideal for image-heavy applications like e-commerce.

• Image upload with drag-and-drop support
• Image search functionality using vector embeddings
• Responsive grid layout for displaying search results
• Modern UI with smooth animations and transitions
• Integration with Google Drive for image storage
• Utilization of OpenAI’s embeddings models for advanced search capabilities

• Frontend: React.js with Tailwind CSS for styling
• Backend: FastAPI with PostgreSQL and pgvector
• Image Storage: Google Drive
• Vector Embeddings: OpenAI embeddings models
• API: RESTful API for image upload and search

• Node.js and npm
• Python 3.7+
• PostgreSQL with pgvector extension
• Google Drive API credentials

1. Clone the repository:

   git clone https://github.com/TNAHOM/Image-Search-Engine.git
   cd Image-Search-Engine/frontend
   

2. Install dependencies:

   npm install
   

3. Create a .env file in the frontend directory and add necessary environment variables:

   REACT_APP_API_URL=http://localhost:8000
   

4. Start the development server:

   npm start
   

1. Navigate to the backend directory:

   cd ../backend
   

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install required packages:

   pip install -r requirements.txt
   

4. Create a .env file in the backend directory and add necessary environment variables:

   DATABASE_URL=postgresql://username:password@localhost/dbname
   OPENAI_API_KEY=your_openai_api_key
   GOOGLE_DRIVE_CREDENTIALS_FILE=path/to/your/credentials.json
   

5. Set up the database:

   alembic upgrade head
   

6. Start the FastAPI server:

   uvicorn main:app --reload
   

1. Uploading Images:

   • Drag and drop an image into the upload area, or click to select a file.
   • Click the “Upload Image” button to submit the image.

2. Searching Images:

   • Use the search bar at the top of the page to enter your search query.
   • Press Enter or click the search button to view results.

3. Viewing Results:

   • Scroll through the responsive grid of image results.
   • Click on an image to view it in full size (if implemented).

• /upload/: POST request to upload an image
• /search/: GET request to search for images using vector embeddings

For detailed API documentation, run the backend server and visit http://localhost:8000/docs.

Contributions to the Image Search Engine project are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch: git checkout -b feature-branch-name
3. Make your changes and commit them: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature-branch-name
5. Submit a pull request

For any inquiries or issues, please open an issue on the GitHub repository.