Warning: This document is for an old version of Rasa Core.

But I don’t code in python!

While python is the lingua franca of machine learning, we’re aware that most chatbots are built in javascript, and that many enterprises are more comfortable building & shipping applications in java, C#, etc.

We’ve made every effort to make sure that you can still use Rasa Core even if you don’t want to use python. However, do consider that Rasa Core is a framework, and doesn’t fit into a REST API as easily as Rasa NLU does.

Rasa Core with minimal Python

You can build a chatbot with Rasa Core by:

The only part where you need to write python is when you want to define custom actions. There’s an excellent python library called requests, which makes HTTP programming painless. If Rasa just needs to interact with your other services over HTTP, your actions will all look something like this:

from rasa_core.actions import Action
import requests

class ApiAction(Action):
    def name(self):
        return "my_api_action"

    def run(self, dispatcher, tracker, domain):
        data = requests.get(url).json
        return [SlotSet("api_result", data)]

Rasa Core with Docker

We provide a Dockerfile which allows you to build an image of Rasa Core with a simple command: docker build -t rasa_core .

The default command of the resulting container starts the Rasa Core server with the --core and --nlu options. At this stage the container does not yet contain any models, so you have to mount them from a local folder into the container’s /app/model/dialogue and app/model/nlu directories. The full run command looks like this:

docker run \
   --mount type=bind,source=<PATH_TO_DIALOGUE_MODEL_DIR>,target=/app/dialogue \
   --mount type=bind,source=<PATH_TO_NLU_MODEL_DIR>,target=/app/nlu \

You also have the option to use the container to train a model with

docker run \
   --mount type=bind,source=<PATH_TO_STORIES_FILE>/stories.md,target=/app/stories.md \
   --mount type=bind,source=<PATH_TO_DOMAIN_FILE>/domain.yml,target=/app/domain.yml \
   --mount type=bind,source=<OUT_PATH>,target=/app/out \
   rasa_core train

You may in addition run any Rasa Core command inside the container with docker run rasa_core run [COMMAND].

Rasa Core with ZERO Python

If you are really constrained to not use any python, you can also use Rasa Core through a HTTP API.