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
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
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.
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
NLU errors will be reported to
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())