# User Guide

- [Installation](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/installation/)
- [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/rasa-tutorial/)
- [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/building-assistants/)
- [Command Line Interface](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/command-line-interface/)
- [Architecture](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/architecture/)
- [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/messaging-and-voice-channels/)
- [Testing Your Assistant](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/testing-your-assistant/)
- [Setting up CI/CD](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/setting-up-ci-cd/)
- [Validate Data](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/validate-files/)
- [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/configuring-http-api/)
- [Deploying Your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/how-to-deploy/)
- [Cloud Storage](https://legacy-docs-v1.rasa.com/1.10.21/user-guide/cloud-storage/)

# NLU

- [About](https://legacy-docs-v1.rasa.com/1.10.21/nlu/about/)
- [Using NLU Only](https://legacy-docs-v1.rasa.com/1.10.21/nlu/using-nlu-only/)
- [Training Data Format](https://legacy-docs-v1.rasa.com/1.10.21/nlu/training-data-format/)
- [Language Support](https://legacy-docs-v1.rasa.com/1.10.21/nlu/language-support/)
- [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.10.21/nlu/choosing-a-pipeline/)
- [Components](https://legacy-docs-v1.rasa.com/1.10.21/nlu/components/)
- [Entity Extraction](https://legacy-docs-v1.rasa.com/1.10.21/nlu/entity-extraction/)

# Core

- [About](https://legacy-docs-v1.rasa.com/1.10.21/core/about/)
- [Stories](https://legacy-docs-v1.rasa.com/1.10.21/core/stories/)
- [Domains](https://legacy-docs-v1.rasa.com/1.10.21/core/domains/)
- [Responses](https://legacy-docs-v1.rasa.com/1.10.21/core/responses/)
- [Actions](https://legacy-docs-v1.rasa.com/1.10.21/core/actions/)
- [Reminders and External Events](https://legacy-docs-v1.rasa.com/1.10.21/core/reminders-and-external-events/)
- [Policies](https://legacy-docs-v1.rasa.com/1.10.21/core/policies/)
- [Slots](https://legacy-docs-v1.rasa.com/1.10.21/core/slots/)
- [Forms](https://legacy-docs-v1.rasa.com/1.10.21/core/forms/)
- [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.10.21/core/retrieval-actions/)
- [Interactive Learning](https://legacy-docs-v1.rasa.com/1.10.21/core/interactive-learning/)
- [Fallback Actions](https://legacy-docs-v1.rasa.com/1.10.21/core/fallback-actions/)
- [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.10.21/core/knowledge-bases/)

# Conversation Design

- [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.10.21/dialogue-elements/dialogue-elements/)
- [Small Talk](https://legacy-docs-v1.rasa.com/1.10.21/dialogue-elements/small-talk/)
- [Completing Tasks](https://legacy-docs-v1.rasa.com/1.10.21/dialogue-elements/completing-tasks/)
- [Guiding Users](https://legacy-docs-v1.rasa.com/1.10.21/dialogue-elements/guiding-users/)

# API Reference

- [Action Server](https://legacy-docs-v1.rasa.com/1.10.21/api/action-server/)
- [HTTP API](https://legacy-docs-v1.rasa.com/1.10.21/api/http-api/)
- [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.10.21/api/jupyter-notebooks/#)
- [Agent](https://legacy-docs-v1.rasa.com/1.10.21/api/agent/)
- [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.10.21/api/custom-nlu-components/)
- [Rasa SDK](https://legacy-docs-v1.rasa.com/1.10.21/api/rasa-sdk/)
- [Events](https://legacy-docs-v1.rasa.com/1.10.21/api/events/)
- [Tracker](https://legacy-docs-v1.rasa.com/1.10.21/api/tracker/)
- [Tracker Stores](https://legacy-docs-v1.rasa.com/1.10.21/api/tracker-stores/)
- [Event Brokers](https://legacy-docs-v1.rasa.com/1.10.21/api/event-brokers/)
- [Lock Stores](https://legacy-docs-v1.rasa.com/1.10.21/api/lock-stores/)
- [Training Data Importers](https://legacy-docs-v1.rasa.com/1.10.21/api/training-data-importers/)
- [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.10.21/api/core-featurization/)
- [TensorFlow Configuration](https://legacy-docs-v1.rasa.com/1.10.21/api/tensorflow_usage/)
- [Migration Guide](https://legacy-docs-v1.rasa.com/1.10.21/migration-guide/)
- [Rasa Open Source Change Log](https://legacy-docs-v1.rasa.com/1.10.21/changelog/)

# Migrate from (beta)

- [Dialogflow](https://legacy-docs-v1.rasa.com/1.10.21/migrate-from/google-dialogflow-to-rasa/)
- [Wit.ai](https://legacy-docs-v1.rasa.com/1.10.21/migrate-from/facebook-wit-ai-to-rasa/)
- [LUIS](https://legacy-docs-v1.rasa.com/1.10.21/migrate-from/microsoft-luis-to-rasa/)
- [IBM Watson](https://legacy-docs-v1.rasa.com/1.10.21/migrate-from/ibm-watson-to-rasa/)

# Reference

- [Glossary](https://legacy-docs-v1.rasa.com/1.10.21/glossary/)

# Versions

viewing: 1.10.21

# 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.")
```

## Create a Project
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)
```

```
os.chdir(project)
```

```
print(os.listdir("."))
```

## Train a Model
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)
```

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())
```

👋 I can help you get started with Rasa and answer your technical questions.