Awesome

Machine Translation API

An API based on FastAPI for using TartuNLP's public NMT engines. The API is designed to be used together with our translation workers.

The project is developed by the NLP research group at the University of Tartu. Neural machine translation can also be tested in our web demo.

The API is compatible with CAT tool plugins for SDL Trados Studio and MemoQ.

API usage

The API can be used with the following request:

POST /translation/v2

HEADERS (optional):

x-api-key = public

BODY (json):

{
     "text": "Tere",
     "src": "et",
     "tgt": "en",
     "domain": "auto",
     "application": "test-application"
}

Response:

{
    "result": "Hi."
}

The full API documentation is available on the /docs endpoint path, for more info, check out the documentation our public API instance.

RabbitMQ communication

The API forwards requests to various NMT engines using the RabbitMQ message broker. The communication uses a direct exchange named translation and a routing key of the request parameters using the format translation.$src.$tgt.$domain. To ensure compatibility with various CAT tools (and as many low-resource languages don't have 2-letter codes), 3-letter ISO language codes are used. Note that by default, ger is used for German instead on deu.

NMT workers establish queues within the exchange that are bound routing keys that illustrate which requests that the particular model can handle. Therefore, infinite options are available for combining different models to handle requests within the same API.

Setup

The API can be deployed using the docker image published alongside the repository. Each image version correlates to a specific release. The API is designed to work together with our translation worker containers and RabbitMQ.

The service is available on port 8000. By default, logging configuration is loaded from config/logging.prod.ini and service configuration from config/config.yaml files. A default version of the latter is included with comments that explain its format. To modify any config files, they should be mounted at /app/config (the absolute path in the container).

The following environment variables should be specified when running the container:

• MQ_USERNAME - RabbitMQ username
• MQ_PASSWORD - RabbitMQ user password
• MQ_HOST - RabbitMQ host
• MQ_PORT (optional) - RabbitMQ port (5672 by default)
• MQ_TIMEOUT (optional) - Message timeout in seconds (30 by default)
• MQ_EXCHANGE (optional) - RabbitMQ exchange name (translation by default)
• MQ_CONNECTION_NAME (optional) - friendly connection name (Translation API by default)
• API_MAX_INPUT_LENGTH (optional) - maximum input text length in characters (10000 by default)
• API_CONFIG_PATH (optional) - path of the config file used (config/config.yaml)

The entrypoint of the container is ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "80", "--proxy-headers"].

The default CMD is used to define logging configuration ["--log-config", "config/logging.prod.ini"] which can be potentially overridden to define different Uvicorn parameters. For example, ["--log-config", "config/logging.debug.ini", "--root-path", "/translation"] enables debug logging and allows the API to be mounted under to the non-root path /translation when using a proxy server such as Nginx.

The setup can be tested with the following sample docker-compose.yml configuration:

version: '3'
services:
  rabbitmq:
    image: 'rabbitmq'
    environment:
      - RABBITMQ_DEFAULT_USER=${RABBITMQ_USER}
      - RABBITMQ_DEFAULT_PASS=${RABBITMQ_PASS}
  nmt_api:
    image: ghcr.io/tartunlp/translation-api:latest
    environment:
      - MQ_HOST=rabbitmq
      - MQ_PORT=5672
      - MQ_USERNAME=${RABBITMQ_USER}
      - MQ_PASSWORD=${RABBITMQ_PASS}
    ports:
      - '80:8000'
    depends_on:
      - rabbitmq
  nmt_worker:
    image: ghcr.io/tartunlp/translation-worker:latest
    environment:
      - MQ_HOST=rabbitmq
      - MQ_PORT=5672
      - MQ_USERNAME=${RABBITMQ_USER}
      - MQ_PASSWORD=${RABBITMQ_PASS}
      - MKL_NUM_THREADS=8
    volumes:
      - ./models:/app/models
    command: ["--model-name", "septilang"]
    depends_on:
      - rabbitmq

Development setup

The API uses Python 3.10 by default, but is likely to be compatible with earlier versions. All required packages are described in requirements.txt, to install them, use:

pip install --no-cache-dir --upgrade --user -r requirements.txt

Environment variables described in the selection above can be defined in config/.env. The file is ignored by Git and Docker.

To run the API, use the following command. This will start the service on localhost port 8000 and automatically restart the server in case of any code changes.

uvicorn app:app --reload