You can install Rasa Open Source using pip (requires Python 3.6 or 3.7).
$ pip3 install rasa
Having trouble installing? Read our step-by-step installation guide.
You can also build Rasa Open Source 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.
Note that pip in this refers to pip3 as Rasa Open Source requires python3. To see which version the pip command on your machine calls use pip –version.
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.
3. Install Rasa Open Source¶
Congratulations! You have successfully installed Rasa Open Source!
You can now head over to the tutorial.
Building from Source¶
If you want to use the development version of Rasa Open Source, 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 Open Source, 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 Open Source 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
en_core_web_sm model. Small models require less
memory to run, but will somewhat reduce intent classification performance.
Dependencies for MITIE¶
$ pip install git+https://github.com/mit-nlp/MITIE.git $ pip install rasa[mitie]
and then download the
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).
Mitie support is likely to be deprecated in a future release.