dfee / rasa_cli

This is a template integrating the click command line suite and the rasa chat agent.

rasa_cli

This is a template integrating the click command line suite and the rasa chat agent.

Quickstart

1. Train the interpreter (rasa_nlu)

   ./agent.py train_nlu -r data/nlu.md
   

2. Train the policy (keras)

   ./agent.py train_policy -d data/domain.yaml -s data/stories.md
   

3. Chat with the agent

   ./agent.py chat
   

Visualization

After training the interpreter and training the policy, you can visualize the graph by running (note, this requires installing graphviz as descriped in Setup below):

./agent.py visualize -s data/stories.md story-graph.png

Setup

These setup instructions assume a modern version of Python 3.5+, but it should work on Python 2.7+ or Python 3.3+.

1. clone the repository, and enter the directory:

   git clone https://github.com/dfee/rasa_nlu_cli.git && cd rasa_nlu_cli
   

2. create a virtualenv (and we'll activate it too):

   python3 -m venv env && source env/bin/activate
   

3. install the dependencies:

   pip install -r requirements.txt
   

4. install the spacy language files:

   python -m spacy download en
   

5. (optional) install graphviz to enable visualization

   For MacOS (assuming homebrew is installed):

   brew install graphviz && pip install pygraphviz
   

   For Ubuntu (or anything in the Debian family using apt):

   apt-get -qq install -y graphviz libgraphviz-dev pkg-config && pip install pygraphviz