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Machine learning based dialogue engine for conversational software.

Project description

# Rasa Core


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- **What do Rasa Core & NLU do? 🤔**
[Read About the Rasa Stack](https://rasa.com/products/rasa-stack/)

- **I'd like to read the detailed docs 🤓**
[Read The Docs](https://rasa.com/docs/core)

- **I'm ready to install Rasa Core! 🚀**
[Installation](https://rasa.com/docs/core/installation.html)

- **I have a question ❓**
[Rasa Community Forum](https://forum.rasa.com)

- **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](https://medium.com/rasa-blog/a-new-approach-to-conversational-software-2e64a5d05f2a)

## Where to get help

There is extensive documentation:

- [master](https://rasa.com/docs/core/master/) 
(if you install from **github**) or
- [stable](https://rasa.com/docs/core)  
(if you install from **pypi**)


Please use [Rasa Community Forum](https://forum.rasa.com) for quick answers to
questions.



### 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
[documentation](https://nlu.rasa.com/contribute.html).

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](https://github.com/RasaHQ/rasa_core/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22))
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](https://cla-assistant.io/RasaHQ/rasa_core)


## 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 Dockerfile.dev . -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 https://mirrors.aliyun.com/pypi/simple/
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](https://libraries.io/github/RasaHQ/rasa_core).

Project details


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