dominicbreuker/rasa_nlu repository overview

⁠Rasa NLU

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:

• latest⁠  (if you install from github) or
• stable⁠ (if you install from pypi)

If you are new to Rasa NLU and want to create a bot, you should start with the tutorial⁠.

Contents:

• Setup⁠
• FAQ⁠
• How to contribute⁠
• Development Internals⁠
• License⁠

⁠Setup

⁠A. Install Locally

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'

⁠B. Install with Docker

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.

⁠1. Build the image:

docker build -t rasa_nlu .

⁠2. Start the web server:

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.

⁠3. Test it!

curl 'http://localhost:5000/parse?q=hello'

⁠C. (Experimental) Deploying to Docker Cloud

⁠FAQ

⁠Who is it for?

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:

1. Download your app data from wit, LUIS, or api.ai and feed it into Rasa NLU
2. Run Rasa NLU on your machine and switch the URL of your wit/LUIS api calls to localhost:5000/parse.

⁠Why should I use Rasa NLU?

• You don't have to hand over your data to FB/MSFT/GOOG
• You don't have to make a https call 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.

⁠What languages does it support?

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.

⁠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.

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.

⁠Development Internals

⁠Steps to release a new version

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

1. update rasa_nlu/version.py⁠ to reflect the correct version number
2. 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
3. edit the migration guide⁠ to provide assistance for users updating to the new version
4. commit all the above changes and tag a new release, e.g. using

   git tag -f 0.7.0 -m "Some helpful line describing the release"
   git push origin master --tags
   

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   travis will build this tag and push a package to pypi⁠
5. 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
   

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⁠License

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:

Licenses of optional dependencies (only required for certain components of Rasa NLU. Hence, they are optional):