rasa NLU

Find the extended documentation here, or find out more on the homepage of the project, where you can also sign up for the mailing list.

FAQ

What is this?

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.

Who is it for?

The intended audience is mainly people developing bots, 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.

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 LASTMILE. rasa NLU is the natural language understanding module, and the first component to be open sourced.

Why don't you have feature X?

Check the issues here on GitHub, there might be someone else talking about the same thing. If there isn't, please create a new issue, describe your use case, and the community can discuss how it could be implemented.

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.

Use

A. Install Locally

python setup.py install
python -m rasa_nlu.server -e wit &
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

3. Test it!

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

C. (Experimental) Deploying to Docker Cloud

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

sheldon/rasa_nlu