Warning: This document is for an old version of Rasa. The latest version is 1.3.9.

Jupyter Notebooks

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:

pip install nest_asyncio

Then in the first cell of your notebook, include:

import nest_asyncio

nest_asyncio.apply()
print("Event loop ready.")

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:

from rasa.cli.scaffold import create_initial_project
import os

project = "test-project"
create_initial_project(project)

# move into project directory and show files
os.chdir(project)
print(os.listdir("."))

To train a model, you will have to tell the train function where to find the relevant files. To define variables that contain these paths, run:

config = "config.yml"
training_files = "data/"
domain = "domain.yml"
output = "models/"
print(config, training_files, domain, output)

Train a Model

Now we can train a model by passing in the paths to the rasa.train function. 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.

import rasa

model_path = rasa.train(domain, config, [training_files], output)
print(model_path)

Chat with your assistant

To start chatting to an assistant, call the chat function, passing in the path to your saved model:

from rasa.jupyter import chat
chat(model_path)

Evaluate your model against test data

Rasa has a convenience function for getting your training data. Rasa’s 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.

import rasa.data as data
stories_directory, nlu_data_directory = data.get_core_nlu_directories(training_files)
print(stories_directory, nlu_data_directory)

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.

rasa.test(model_path, stories_directory, nlu_data_directory)
print("Done testing.")

The results of the core evaluation will be written to a file called results. NLU errors will be reported to errors.json. Together, they contain information about the accuracy of your model’s predictions and other metrics.

if os.path.isfile("errors.json"):
    print("NLU Errors:")
    print(open("errors.json").read())
else:
    print("No NLU errors.")

if os.path.isdir("results"):
      print("\n")
      print("Core Errors:")
      print(open("results/failed_stories.md").read())