The purpose of this repo is to showcase a contextual AI assistant built with the open source Rasa framework.

Sara is an alpha version and lives in our docs, helping developers getting started with our open source tools. It supports the following user goals:

• Understanding the Rasa framework
• Getting started with Rasa
• Answering some FAQs around Rasa
• Directing technical questions to specific documentation
• Subscribing to the Rasa newsletter
• Requesting a call with Rasa's sales team
• Handling basic chitchat

You can find planned enhancements for Sara in the Project Board

To install Sara, please clone the repo and run:

cd rasa-demo
pip install -r requirements.txt
pip install -e .

This will install the bot and all of its requirements. Note that this bot should be used with python 3.6 or 3.7.

Use rasa train to train a model (this will take a significant amount of memory to train, if you want to train it faster, try the training command with --augmentation 0).

Then, to run, first set up your action server in one terminal window:

rasa run actions --actions actions.actions

There are some custom actions that require connections to external services, specifically SubscribeNewsletterForm and SalesForm. For these to run you would need to have your own MailChimp newsletter and a Google sheet to connect to.

In another window, run the bot:

docker run -p 8000:8000 rasa/duckling
rasa shell --debug

Note that --debug mode will produce a lot of output meant to help you understand how the bot is working under the hood. To simply talk to the bot, you can remove this flag.

If you would like to run Sara on your website, follow the instructions here to place the chat widget on your website.

After doing a rasa train, run the command:

rasa test nlu -u test/test_data.json --model models
rasa test core --stories test/test_stories.md

data/core/ - contains stories

data/nlu - contains NLU training data

actions - contains custom action code

domain.yml - the domain file, including bot response templates

config.yml - training configurations for the NLU pipeline and policy ensemble

To ensure a standardized code style we use the formatter black.

If you want to automatically format your code on every commit, you can use pre-commit. Just install it via pip install pre-commit and execute pre-commit install in the root folder. This will add a hook to the repository, which reformats files on every commit.

If you want to set it up manually, install black via pip install black. To reformat files execute

black .

Licensed under the GNU General Public License v3. Copyright 2018 Rasa Technologies GmbH. Copy of the license. Licensees may convey the work under this license. There is no warranty for the work.