Warning: This document is for an old version of Rasa. The latest version is 1.4.0.

Rasa as open source alternative to Microsoft LUIS - Migration Guide

This guide shows you how to migrate your application built with Microsoft LUIS to Rasa. Here are a few reasons why we see developers switching:

  • Faster: Runs locally - no http requests and server round trips required
  • Customizable: Tune models and get higher accuracy with your data set
  • Open source: No risk of vendor lock-in - Rasa is under the Apache 2.0 licence and you can use it in commercial projects
In addition, our open source tools allow developers to build contextual AI assistants and manage dialogues with machine learning instead of rules - learn more in this blog post.

Let’s get started with migrating your application from LUIS to Rasa:

Step 1: Export your Training Data from LUIS

Go to your list of LUIS applications and click on the three dots menu next to the app you want to export.

LUIS Export

Select ‘Export App’. This will download a file with a .json extension that can be imported directly into Rasa.

Step 2: Create a Rasa Project

To create a Rasa project, run:

rasa init

This will create a directory called data. Remove the files in this directory, and move your json file into this directory.

rm -r data/*
mv /path/to/file.json data/

Step 3: Train your NLU model

To train a model using your LUIS data, run:

rasa train nlu

Step 4: Test your NLU model

Let’s see how your NLU model will interpret some test messages. To start a testing session, run:

rasa shell nlu

This will prompt your for input. Type a test message and press ‘Enter’. The output of your NLU model will be printed to the screen. You can keep entering messages and test as many as you like. Press ‘control + C’ to quit.

Step 5: Start a Server with your NLU Model

To start a server with your NLU model, run:

rasa run nlu

This will start a server listening on port 5005.

To send a request to the server, run:

    
curl 'localhost:5005/model/parse?emulation_mode=luis' -d '{"text": "hello"}'
copied!

The emulation_mode parameter tells Rasa that you want your json response to have the same format as you would get from LUIS. You can also leave it out to get the result in the usual Rasa format.

Terminology:

The words intent, entity, and utterance have the same meaning in Rasa as they do in LUIS. LUIS’s patterns feature is very similar to Rasa NLU’s regex features LUIS’s phrase lists feature does not currently have an equivalent in Rasa NLU.

Join the Rasa Community Forum and let us know how your migration went!