Warning: This document is for an old version of Rasa NLU. The latest version is 0.15.1.

Language Understanding with Rasa NLU

Note

This is the documentation for version 0.12.0 of Rasa NLU. Make sure you select the appropriate version of the documentation for your local installation!

Rasa NLU is an open-source tool for intent classification and entity extraction. For example, taking a sentence like

"I am looking for a Mexican restaurant in the center of town"

and returning structured data like

{
  "intent": "search_restaurant",
  "entities": {
    "cuisine" : "Mexican",
    "location" : "center"
  }
}

The intended audience is mainly people developing bots. You can use Rasa as a drop-in replacement for wit , LUIS , or Dialogflow, the only change in your code is to send requests to localhost instead (see Migrating an existing app for details).

Why might you use Rasa instead of one of those services?

  • you don’t have to hand over your data to FB/MSFT/GOOG
  • you don’t have to make a https call every time.
  • you can tune models to work well on your particular use case.

These points are laid out in more detail in a blog post .

The quickest quickstart in the west

$ python setup.py install
$ python -m rasa_nlu.server -e wit &
$ curl 'http://localhost:5000/parse?q=hello'
[{"_text": "hello", "confidence": 1.0, "entities": {}, "intent": "greet"}]

There you go! you just parsed some text. Next step, do the Tutorial: A simple restaurant search bot.

Note

This demo uses a very limited ML model. To apply Rasa NLU to your use case, you need to train your own model! Follow the tutorial to get to know how to apply rasa_nlu to your data.

About

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. The setup process is designed to be as simple as possible. If you’re currently using wit, LUIS, or Dialogflow, you just:

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

Rasa NLU is written in Python, but it you can use it from any language through Using Rasa NLU as a HTTP server. If your project is written in Python you can simply import the relevant classes.

Rasa is a set of tools for building more advanced bots, developed by Rasa. This is the natural language understanding module. To build conversational chatbots, you can interface Rasa NLU with libraries that steer the flow of the conversation - more on this in Context-aware Dialogue.

Getting Started

User Documentation

Resources

Developer Documentation