Installation¶

Quick Installation¶

You can install both Rasa and Rasa X using pip (requires Python 3.5.4 or higher).

$ pip install rasa-x --extra-index-url https://pypi.rasa.com/simple

Having trouble installing? Read our step-by-step installation guide.
You can also build Rasa from source.
For advanced installation options such as building from source and installation instructions for custom pipelines, head over here.

When you’re done installing, you can head over to the tutorial!

Step-by-step Installation Guide¶

1. Install the Python development environment¶

Check if your Python environment is already configured:

$ python3 --version
$ pip3 --version

If these packages are already installed, these commands should display version numbers for each step, and you can skip to the next step.

Otherwise, proceed with the instructions below to install them.

Fetch the relevant packages using apt, and install virtualenv using pip.

$ sudo apt update
$ sudo apt install python3-dev python3-pip

Install the Homebrew package manager if you haven’t already.

Once you’re done, you can install Python3.

$ brew update
$ brew install python

Make sure the Microsoft VC++ Compiler is installed, so python can compile any dependencies. You can get the compiler from Visual Studio. Download the installer and select VC++ Build tools in the list.

Install Python 3 (64-bit version) for Windows.

C:\> pip3 install -U pip

2. Create a virtual environment (strongly recommended)¶

Tools like virtualenv and virtualenvwrapper provide isolated Python environments, which are cleaner than installing packages systemwide (as they prevent dependency conflicts). They also let you install packages without root privileges.

Create a new virtual environment by choosing a Python interpreter and making a ./venv directory to hold it:

$ python3 -m venv --system-site-packages ./venv

Activate the virtual environment:

$ source ./venv/bin/activate

Create a new virtual environment by choosing a Python interpreter and making a .\venv directory to hold it:

C:\> python3 -m venv --system-site-packages ./venv

Activate the virtual environment:

C:\> .\venv\Scripts\activate

3. Install Rasa and Rasa X¶

To install both Rasa and Rasa X in one go:

$ pip install rasa-x --extra-index-url https://pypi.rasa.com/simple

If you just want to install Rasa without Rasa X:

$ pip install rasa

Congratulations! You have successfully installed Rasa!

You can now head over to the tutorial.

Building from Source¶

If you want to use the development version of Rasa, you can get it from GitHub:

$ git clone https://github.com/RasaHQ/rasa.git
$ cd rasa
$ pip install -r requirements.txt
$ pip install -e .

NLU Pipeline Dependencies¶

Several NLU components have additional dependencies that need to be installed separately.

Here, you will find installation instructions for each of them below.

How do I choose a pipeline?¶

The page on Choosing a Pipeline will help you pick the right pipeline for your assistant.

I have decided on a pipeline. How do I install the dependencies for it?¶

When you install Rasa, the dependencies for the supervised_embeddings - TensorFlow and sklearn_crfsuite get automatically installed. However, spaCy and MITIE need to be separately installed if you want to use pipelines containing components from those libraries.

Just give me everything!

If you don’t mind the additional dependencies lying around, you can use this to install everything.

You’ll first need to clone the repository and then run the following command to install all the packages:

$ pip install -r alt_requirements/requirements_full.txt

Dependencies for spaCy¶

For more information on spaCy, check out the spaCy docs.

You can install it with the following commands:

$ pip install rasa[spacy]
$ python -m spacy download en_core_web_md
$ python -m spacy link en_core_web_md en

This will install Rasa NLU as well as spacy and its language model for the English language. We recommend using at least the “medium” sized models (_md) instead of the spacy’s default small en_core_web_sm model. Small models require less memory to run, but will somewhat reduce intent classification performance.

Dependencies for MITIE¶

First, run

$ pip install git+https://github.com/mit-nlp/MITIE.git
$ pip install rasa[mitie]

and then download the MITIE models. The file you need is total_word_feature_extractor.dat. Save this anywhere. If you want to use MITIE, you need to tell it where to find this file (in this example it was saved in the data folder of the project directory).

Warning

Mitie support is likely to be deprecated in a future release.