Random Vectorizer API

The Random Vectorizer API is a production-ready API endpoint that takes a sentence as input and returns a random 500-dimensional array of floats. This project has been developed with considerations for scalability, security, and performance to ensure it meets the requirements of a production environment.

Pre-requisites (for Production deployment):

1. Docker installed in server.

Installation - Development

1. Clone the repository:

   git clone https://github.com/bamwani/random-vectorizer.git
   

2. Navigate to the project directory:

   cd random-vectorizer
   

3. Run the following command to create a virtual environment for the application using Python3-venv:

   python3 -m venv venv
   source venv/bin/activate

4. Update pip and install the reuired libraries using the following command:

   pip3 install--upgrade pip
   pip3 install -r requirements.txt
   

Usage - Development

1. Run the folling command to test and start the server:

   sh ./start_main_app.sh
   
   

This will create a docker image named "random_vectorizer", Run it by using the same name as container name and start the service

Installation - Production (using Docker)

1. Clone the repository:

   git clone https://github.com/bamwani/random-vectorizer.git
   

2. Navigate to the project directory:

   cd random-vectorizer
   

3. Run the following command to install the application using docker:

   sh ./install.sh

This will create a docker image named "random_vectorizer", Run it by using the same name as container name and start the service

Usage - Production (using Docker)

1. Start the docker container:

   sh ./start_docker.sh
   

2. Send a POST request to the API endpoint using your favorite HTTP client or tool (e.g., cURL, Postman).

   POST /vectorize HTTP/1.1
   Host: localhost:5000
   Content-Type: application/json
   
   {
     "sentence": "This is an example sentence"
   }

The API will respond with a JSON object containing the random 500-dimensional array of floats.

Configuration

1. The application uses Gunicorn as the production-ready server. The Gunicorn configuration is defined in the gunicorn_config.py file. You can modify the configuration parameters to customize the server behavior according to your needs.

2. SSL/TLS: By default, the application runs over HTTP. If you want to enable SSL/TLS encryption, you can provide the appropriate SSL/TLS certificate and private key files and configure Gunicorn to use HTTPS. Refer to the Gunicorn documentation for detailed instructions.

Running Tests:

1. To run the unit tests for the application, execute the following command:

   pytest
   

The tests ensure the functionality of the vectorization process and the expected behavior of the API endpoint.

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.