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Machine learning based dialogue engine for conversational software.

Project description

# Rasa Core

[![Join the chat on Rasa Community Forum](](
[![PyPI version](](
[![Supported Python Versions](](
[![Build Status](](
[![Coverage Status](](
[![Documentation Status](](

- **What do Rasa Core & NLU do? 🤔**
[Read About the Rasa Stack](

- **I'd like to read the detailed docs 🤓**
[Read The Docs](

- **I'm ready to install Rasa Core! 🚀**

- **I have a question ❓**
[Rasa Community Forum](

- **I would like to contribute 🤗**
[How to contribute](#how-to-contribute)

## Introduction

Rasa Core is a framework for building conversational software, which includes
Chat Bots on :
- Facebook Messenger
- Slack
- Microsoft Bot Framework
- Rocket.Chat
- Mattermost
- Telegram
- Twilio

But you can also build assistants as
- Alexa Skills
- Google Home Actions

Rasa Core's primary purpose is to help you build contextual, layered
conversations with lots of back-and-forth. To have a real conversation,
you need to have some memory and build on things that were said earlier.
Rasa Core lets you do that in a scalable way.

There's a lot more background information in this
[blog post](

## Where to get help

There is extensive documentation:

- [master]( 
(if you install from **github**) or
- [stable](  
(if you install from **pypi**)

Please use [Rasa Community Forum]( for quick answers to

### README Contents:
- [How to contribute](#how-to-contribute)
- [Development Internals](#development-internals)
- [License](#license)

### How to contribute
We are very happy to receive and merge your contributions. There is
some more information about the style of the code and docs in the

In general the process is rather simple:
1. create an issue describing the feature you want to work on (or
have a look at issues with the label
[help wanted](
2. write your code, tests and documentation
3. create a pull request describing your changes

You pull request will be reviewed by a maintainer, who might get
back to you about any necessary changes or questions. You will
also be asked to sign a
[Contributor License Agreement](

## Development Internals
### Running and changing the documentation
To build & edit the docs, first install all necessary dependencies:

brew install sphinx
pip install -r dev-requirements.txt

After the installation has finished, you can run and view the documentation
locally using
make livedocs

Visit the local version of the docs at http://localhost:8000 in your browser.
You can now change the docs locally and the web page will automatically reload
and apply your changes.

### Running and changing the unit test
To build & edit the docs, first install all necessary dependencies:

docker build -f . -t kidx_core_dev

docker run --name kidx_core_dev -v "$PWD":/app -it kidx_core_dev bash

After the docker container start run command in docker
pip install -e . --no-cache-dir -i
make lint
make test

Look the coverage should be no failure and pass 100%

## License
Licensed under the Apache License, Version 2.0.
Copyright 2018 Kidx AI Technologies Education. [Copy of the license](LICENSE.txt).

A list of the Licenses of the dependencies of the project can be found at
the bottom of the
[Libraries Summary](

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