EVG Rasa Bot

This is a Q&A bot made with Rasa Stack tools to answer students from EVG(Escola Virtual do Governo). It was built using Rasa Starter Pack.

Setup and installation

It is recommended that you run installation procedures inside a virtual environment. If you already have virtualenv installed just run

$ virtualenv -p python3.6 venv

And activate

$ source venv/bin/activate

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

$ (venv) pip install -r requirements.txt

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

$ (venv) python -m spacy download pt

Files for Rasa NLU model

• data/nlu_data.md file contains training examples of intents. One intent is a set of sentences that the bot expects to receive from the user and means something especific.

• nlu_config.yml file contains the configuration of the Rasa NLU pipeline. The pipeline for this project is tensorflow.

Files for Rasa Core model

• data/stories.md file contains some training stories which represent the conversations between a user and the assistant. In this file you may define bot actions for each intent or group of intents.
• domain.yml file describes the domain of the assistant which includes intents, entities, slots, templates and actions the assistant should be aware of.
• endpoints.yml file contains the webhook configuration for custom action. This project does not have custom actions, so don't worry about this too much if you don't intend to make one.
• policies.yml file contains the configuration of the training policies for Rasa Core model. Here you can set bot precision and trainig configuration.

Runnning the bot

• 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.