Rasa NLU starter-pack

Looked through the Rasa NLU and Rasa Core documentation and ready to build your first chatbot? We have some resources to help you get started! This repository contains the foundations of you first custom bot, clone it to get started:

git clone https://github.com/RasaHQ/starter-pack-rasa-stack.git

After you clone the repository, you will have a directory called starter-pack-rasa-stack on your local machine containing all the files of this repo. Let's call this directory our project directory.

Setup and installation

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

This will install Rasa NLU and Rasa Core as well as all the dependencies you need to successfully build your first bot.

What’s in this starter-pack?

This starter-pack contains some training data and the main files which you can use as the basis of your first custom bot. It also resembles the usual structure of the project built with Rasa Stack. This starter-pack consists of the following files:

Files for Rasa NLU model

• data/nlu_data.json file contains training examples of six intents:

  • greet
  • goodbye
  • thanks
  • deny
  • joke
  • name (examples of this intent contain an entity called name)

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

language: "en"

pipeline:
- name: "nlp_spacy"                   # loads the spacy language model
- name: "tokenizer_spacy"             # splits the sentence into tokens
- name: "ner_crf"                   # uses the pretrained spacy NER model
- name: "intent_featurizer_spacy"     # transforms the sentence into a vector representation
- name: "intent_classifier_sklearn"   # uses the vector representation to classify using SVM

Files for Rasa Core model

• data/stories.md file contains some training stories representing different conversations between a user and a chatbot.
• domain.yml file describes the universe of the assistant which includes intents, entities, slots, templates and actions an assistant should be aware of.
• actions.py file contains a code of a custom action which retrieves a Chuck Noris joke by making an external api call.

How to use it?

1. You can train the Rasa NLU model by running:
   make train-nlu
   This will train the 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. Test the assistant by running:
   make cmdline
   This will load the assitant in your terminal and you can chat to it.

What's next?

This starter-pack lets you build a simple assistant which can tell Chuck Norris jokes. It's pretty fun, but there is so much more you can do to make a really engaging and cool assistant. Here are some ideas of what you can do to take this assistant to the next level:

• Enrich data/nlu_data.md file with the intents you would like your bot to understand. Retrain the NLU model using the command above and see you assistant improving with every run!
• Enrich data/stories.md file with more training stories with different dialogue turns, intents and actions.
• Implement more custom action inside the actions.py file and add them to stories data as well as the domain file.
• If you need more inspiration we have a really cool Rasa NLU training data file which you can find on Rasa Community Forum. This dataset contains quite a few interesting intents. To use it, append the training examples to data/nlu_data.md file, retrain the NLU model and see how your bot learns new skills.

Let us know who you are getting on with Rasa Stack and what have you built! Join the Rasa Community Forum and share you experience with us!

Make sure to let us know how you are getting on and what have you build. Visit Rasa Community Forum and share your experience.