This is documentation for Rasa & Rasa Pro Documentation v2.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (3.x).
This page contains the most important methods for using Rasa in a Jupyter notebook.
Running asynchronous Rasa code in Jupyter Notebooks requires an extra requirement, since Jupyter Notebooks already run on event loops. Install this requirement in the command line before launching jupyter:
Then in the first cell of your notebook, include:
First, you need to create a project if you don't already have one.
To do this, run this cell, which will create the
test-project directory and make it
your working directory:
To train a model, you will have to tell the
where to find the relevant files.
To define variables that contain these paths, run:
Train a Model
Now we can train a model by passing in the paths to the
Note that the training files are passed as a list.
When training has finished,
rasa.train returns the path where the trained model has been saved.
Chat with your assistant
To start chatting to an assistant, call the
chat function, passing
in the path to your saved model. If you do not have custom actions you can set
endpoints = None or omit it:
Evaluate your model against test data
Rasa has a convenience function for getting your training data.
get_core_nlu_directories is a function which
recursively finds all the stories and NLU data files in a directory
and copies them into two temporary directories.
The return values are the paths to these newly created directories.
To test your model, call the
test function, passing in the path
to your saved model and directories containing the stories and nlu data
to evaluate on.
The results of the core evaluation will be written to a file called
NLU errors will be reported to
Together, they contain information about the accuracy of your model's
predictions and other metrics.