Rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of Rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries. Find out more on the homepage of the project, where you can also sign up for the mailing list.
Extended documentation:
If you are new to Rasa NLU and want to create a bot, you should start with the tutorial.
Contents:
From pypi:
pip install rasa_nlu
From github:
git clone [email protected]:RasaHQ/rasa_nlu.git
cd rasa_nlu
pip install -r requirements.txt
pip install -e .
To test the installation use (this will run a very stupid default model. you need to train your own model to do something useful!):
python -m rasa_nlu.server &
curl 'http://localhost:5000/parse?q=hello'
Before you start, ensure you have the latest version of docker engine on your machine. You can check if you have docker installed by typing docker -v in your terminal.
docker build -t rasa_nlu .
docker run -p 5000:5000 rasa_nlu start
Caveat for Docker for Windows users: please share your C: in docker settings, and add -v C:\path\to\rasa_nlu:/app to your docker run commands for download and training to work correctly.
curl 'http://localhost:5000/parse?q=hello'
The intended audience is mainly people developing bots, starting from scratch or looking to find a a drop-in replacement for wit, LUIS, or api.ai. The setup process is designed to be as simple as possible. Rasa NLU is written in Python, but you can use it from any language through a HTTP API. If your project is written in Python you can simply import the relevant classes. If you're currently using wit/LUIS/api.ai, you just:
- Download your app data from wit, LUIS, or api.ai and feed it into Rasa NLU
- Run Rasa NLU on your machine and switch the URL of your wit/LUIS api calls to
localhost:5000/parse.
- You don't have to hand over your data to FB/MSFT/GOOG
- You don't have to make a
httpscall to parse every message. - You can tune models to work well on your particular use case.
These points are laid out in more detail in a blog post. Rasa is a set of tools for building more advanced bots, developed by the company Rasa. Rasa NLU is the natural language understanding module, and the first component to be open sourced.
Short answer: English, German, and Spanish currently. Longer answer: If you want to add a new language, the key things you need are a tokenizer and a set of word vectors. More information can be found in the language documentation.
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.
In general the process is rather simple:
- create an issue describing the feature you want to work on (or have a look at issues with the label help wanted)
- write your code, tests and documentation
- 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.
Releasing a new version is quite simple, as the packages are build and distributed by travis. The following things need to be done to release a new version
- update rasa_nlu/version.py to reflect the correct version number
- edit the CHANGELOG.rst, create a new section for the release (eg by moving the items from the collected master section) and create a new master logging section
- edit the migration guide to provide assistance for users updating to the new version
- commit all the above changes and tag a new release, e.g. using
travis will build this tag and push a package to pypi
git tag -f 0.7.0 -m "Some helpful line describing the release" git push origin master --tags - only if it is a major release, a new branch should be created pointing to the same commit as the tag to allow for future minor patches, e.g.
git checkout -b 0.7.x git push origin 0.7.x
Licensed under the Apache License, Version 2.0. Copyright 2016 LastMile Technologies Ltd. Copy of the license.
As a reference, the following contains a listing of the licenses of the different dependencies as of this writing. Licenses of minimal dependencies:
| required package | License |
|---|---|
| gevent | MIT |
| flask | BSD 3-clause |
| boto3 | Apache License 2.0 |
| typing | PSF |
| future | MIT |
| six | MIT |
| jsonschema | MIT |
Licenses of optional dependencies (only required for certain components of Rasa NLU. Hence, they are optional):
| optional package | License |
|---|---|
| MITIE | Boost Software License 1.0 |
| spacy | MIT |
| scikit-learn | BSD 3-clause |
| scipy | BSD 3-clause |
| numpy | BSD 3-clause |
| duckling | Apache License 2.0 |
| sklearn-crfsuite | MIT |
| cloudpickle | BSD 3-clause |
| google-cloud-storage | Apache License 2.0 |