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

Rasa NLU: Language Understanding for chatbots and AI assistants

Note

This is the documentation for version 0.14.0 of Rasa NLU. Please make sure you are reading the documentation that matches the version you have installed.

Rasa NLU is an open-source natural language processing tool for intent classification and entity extraction in chatbots. 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 target audience is developers building chatbots and voice apps.

The main reasons for using open source NLU are that:

• you don’t have to hand over all your training data to Google, Microsoft, Amazon, or Facebook.
• Machine Learning is not one-size-fits all. You can tweak and customize models for your training data.
• Rasa NLU runs wherever you want, so you don’t have to make an extra network request for every message that comes in.

You can read about the advantages of using open source NLU in this blog post . You can see an independent benchmark comparing Rasa NLU to closed source alternatives here.

Have questions or feedback?

We have a very active support community on Rasa Community Forum that is happy to help you with your questions. If you have any feedback for us or a specific suggestion for improving the docs, feel free to share it by creating an issue on Rasa NLU GitHub repository.