Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Rasa (formerly Rasa Core + Rasa NLU)
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contexual assistants on:
- Facebook Messenger
- Microsoft Bot Framework
- Your own custom conversational channels
or voice assistants as:
- Alexa Skills
- Google Home Actions
Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a scalable way.
There's a lot more background information in this blog post.
What does Rasa do? 🤔 Check out our Website
I'm new to Rasa 😄 Get Started with Rasa
I'd like to read the detailed docs 🤓 Read The Docs
I'm ready to install Rasa 🚀 Installation
I want to learn how to use Rasa 🚀 Tutorial
I have a question ❓ Rasa Community Forum
I would like to contribute 🤗 How to Contribute
Where to get help
There is extensive documentation in the Rasa Docs. Make sure to select the correct version so you are looking at the docs for the version you installed.
Please use Rasa Community Forum for quick answers to questions.
How to contribute
We are very happy to receive and merge your contributions. You can find more information about how to contribute to Rasa (in lots of different ways!) here.
To contribute via pull request, follow these steps:
- Create an issue describing the feature you want to work on (or have a look at the contributor board)
- Write your code, tests and documentation, and format them with
- Create a pull request describing your changes
Your pull request will be reviewed by a maintainer, who will get back to you about any necessary changes or questions. You will also be asked to sign a Contributor License Agreement.
Running and changing the documentation
To build & edit the docs, first install all necessary dependencies:
pip3 install -r requirements-dev.txt pip3 install -r requirements-docs.txt
After the installation has finished, you can run and view the documentation locally using:
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 the Tests
In order to run the tests, make sure that you have the development requirements installed:
export PIP_USE_PEP517=false pip3 install -r requirements-dev.txt pip3 install -e . make prepare-tests-ubuntu # Only on Ubuntu and Debian based systems make prepare-tests-macos # Only on macOS
Then, run the tests:
They can also be run at multiple jobs to save some time:
JOBS=[n] make test
[n] is the number of jobs desired. If omitted,
[n] will be automatically chosen by pytest.
Steps to release a new version
Releasing a new version is quite simple, as the packages are build and distributed by travis.
- patch release (third version part increases): 1.1.2 -> 1.1.3
- minor release (second version part increases): 1.1.3 -> 1.2.0
- major release (first version part increases): 1.2.0 -> 2.0.0
- Make sure all dependencies are up to date (especially Rasa SDK)
- Switch to the branch you want to cut the release from (
masterin case of a major / minor, the current feature branch for patch releases)
- Create a PR against master or the release branch (e.g.
- Once your PR is merged, tag a new release (this SHOULD always happen on master or release branches), e.g. using
git tag 1.2.0 -m "next release" git push origin 1.2.0 --tagstravis will build this tag and push a package to pypi
- If this is a minor release, a new release branch should be created pointing to the same commit as the tag to allow for future patch releases, e.g.
git checkout -b 1.2.x git push origin 1.2.x
To ensure a standardized code style we use the formatter black. To ensure our type annotations are correct we use the type checker pytype. If your code is not formatted properly or doesn't type check, travis will fail to build.
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 -r requirements-dev.txt.
To reformat files execute
If you want to check types on the codebase, install
pip install -r requirements-dev.txt.
To check the types execute
Deploying documentation updates
sphinx-versioning to build docs for tagged versions and for the master branch.
The static site that gets built is pushed to the
docs branch of this repo, which doesn't contain
any code, only the site.
We host the site on netlify. When there is a reason to update the docs (e.g. master has changed or we have
tagged a new version) we trigger a webhook on netlify (see
Licensed under the Apache License, Version 2.0. Copyright 2019 Rasa Technologies GmbH. Copy of the license.
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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