If you haven’t installed Rasa NLU and Rasa Core yet, you can do it by navigating to the project directory and running:

pip install -r requirements.txt

You also need to install a spaCy English language model. You can install it by running:

python -m spacy download en

• data/nlu_data.md file contents training data for the NLU model.

• nlu_config.yml file contains the configuration of the Rasa NLU pipeline:

language: "en"

pipeline: spacy_sklearn

• data/stories.md file contains some training stories which represent the conversations between a user and the assistant.
• domain.yml file describes the domain of the assistant which includes intents, entities, slots, templates and actions the assistant should be aware of.
• actions.py file contains the code of a custom action which retrieves results of the latest IPL match by making an external API call.
• endpoints.yml file contains the webhook configuration for custom action.
• policies.yml file contains the configuration of the training policies for Rasa Core model.

Note: If running on Windows, you will either have to install make or copy the following commands from the Makefile

1. You can train the Rasa NLU model by running:
   make train-nlu
   This will train the Rasa NLU model and store it inside the /models/current/nlu folder of your project directory.

2. Train the Rasa Core model by running:
   make train-core
   This will train the Rasa Core model and store it inside the /models/current/dialogue folder of your project directory.

3. In a new terminal start the server for the custom action by running:
   make action-server
   This will start the server for emulating the custom action.

4. Test the assistant by running:
   make cmdline
   This will load the assistant in your terminal for you to chat.

1. Go to your Slack app's settings page and use the Bot User OAuth Access Token: And add this in the slack_credentials.yml file:

slack:
  slack_token: "Bot User OAuth Access Token"
  slack_channel: 

2. Start the action server by typing the following command in terminal:

make action-server

3. Setup ngrok for the port that the action server is using by the following command:

ngrok http 5055

This will give you an output like the following:

4. Copy the highlighted url in the above image into your endpoints.yml file:

action_endpoint: "your_url_here/webhook"
  url: 

5. Start the core server in another terminal window:

python -m rasa_core.run -d models/current/dialogue -u models/current/nlu --port 5002 --connector slack --credentials slack_credentials.yml --endpoints endpoints.yml

This will start the server at port 5002.

6. Now you have to expose this port to the world by using ngrok, open another terminal and type:

ngrok http 5002

7. Take the above url and paste it into the Events Subscription page of your slack app in the following format:

your_url_here/webhooks/slack/webhook

And you should now be able to talk to your chatbot in Slack!