These docs are for version 1.x of Rasa Open Source.

## User Guide

- [Installation](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/installation/)
- [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/rasa-tutorial/)
- [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/building-assistants/)
- [Command Line Interface](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/command-line-interface/)
- [Architecture](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/architecture/)
- [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/messaging-and-voice-channels/)
- [Evaluating Models](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/evaluating-models/)
- [Validate Data](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/validate-files/)
- [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/configuring-http-api/)
- [Deploying your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/how-to-deploy/)
- [Cloud Storage](https://legacy-docs-v1.rasa.com/1.7.4/user-guide/cloud-storage/)

## NLU

- [About](https://legacy-docs-v1.rasa.com/1.7.4/nlu/about/)
- [Using NLU Only](https://legacy-docs-v1.rasa.com/1.7.4/nlu/using-nlu-only/)
- [Training Data Format](https://legacy-docs-v1.rasa.com/1.7.4/nlu/training-data-format/)
- [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.7.4/nlu/choosing-a-pipeline/)
- [Language Support](https://legacy-docs-v1.rasa.com/1.7.4/nlu/language-support/)
- [Entity Extraction](https://legacy-docs-v1.rasa.com/1.7.4/nlu/entity-extraction/)
- [Components](https://legacy-docs-v1.rasa.com/1.7.4/nlu/components/)

## Core

- [About](https://legacy-docs-v1.rasa.com/1.7.4/core/about/)
- [Stories](https://legacy-docs-v1.rasa.com/1.7.4/core/stories/)
- [Domains](https://legacy-docs-v1.rasa.com/1.7.4/core/domains/)
- [Responses](https://legacy-docs-v1.rasa.com/1.7.4/core/responses/)
- [Actions](https://legacy-docs-v1.rasa.com/1.7.4/core/actions/)
- [Policies](https://legacy-docs-v1.rasa.com/1.7.4/core/policies/)
- [Slots](https://legacy-docs-v1.rasa.com/1.7.4/core/slots/)
- [Forms](https://legacy-docs-v1.rasa.com/1.7.4/core/forms/)
- [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.7.4/core/retrieval-actions/)
- [Interactive Learning](https://legacy-docs-v1.rasa.com/1.7.4/core/interactive-learning/#)
- [Fallback Actions](https://legacy-docs-v1.rasa.com/1.7.4/core/fallback-actions/)
- [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.7.4/core/knowledge-bases/)

## Conversation Design

- [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.7.4/dialogue-elements/dialogue-elements/)
- [Small Talk](https://legacy-docs-v1.rasa.com/1.7.4/dialogue-elements/small-talk/)
- [Completing Tasks](https://legacy-docs-v1.rasa.com/1.7.4/dialogue-elements/completing-tasks/)
- [Guiding Users](https://legacy-docs-v1.rasa.com/1.7.4/dialogue-elements/guiding-users/)

## API Reference

- [Action Server](https://legacy-docs-v1.rasa.com/1.7.4/api/action-server/)
- [HTTP API](https://legacy-docs-v1.rasa.com/1.7.4/api/http-api/)
- [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.7.4/api/jupyter-notebooks/)
- [Agent](https://legacy-docs-v1.rasa.com/1.7.4/api/agent/)
- [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.7.4/api/custom-nlu-components/)
- [Rasa SDK](https://legacy-docs-v1.rasa.com/1.7.4/api/rasa-sdk/)
- [Events](https://legacy-docs-v1.rasa.com/1.7.4/api/events/)
- [Tracker](https://legacy-docs-v1.rasa.com/1.7.4/api/tracker/)
- [Tracker Stores](https://legacy-docs-v1.rasa.com/1.7.4/api/tracker-stores/)
- [Event Brokers](https://legacy-docs-v1.rasa.com/1.7.4/api/event-brokers/)
- [Lock Stores](https://legacy-docs-v1.rasa.com/1.7.4/api/lock-stores/)
- [Training Data Importers](https://legacy-docs-v1.rasa.com/1.7.4/api/training-data-importers/)
- [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.7.4/api/core-featurization/)
- [Migration Guide](https://legacy-docs-v1.rasa.com/1.7.4/migration-guide/)
- [Rasa OSS Change Log](https://legacy-docs-v1.rasa.com/1.7.4/changelog/)

## Migrate from (beta)

- [Dialogflow](https://legacy-docs-v1.rasa.com/1.7.4/migrate-from/google-dialogflow-to-rasa/)
- [Wit.ai](https://legacy-docs-v1.rasa.com/1.7.4/migrate-from/facebook-wit-ai-to-rasa/)
- [LUIS](https://legacy-docs-v1.rasa.com/1.7.4/migrate-from/microsoft-luis-to-rasa/)
- [IBM Watson](https://legacy-docs-v1.rasa.com/1.7.4/migrate-from/ibm-watson-to-rasa/)

## Reference

- [Glossary](https://legacy-docs-v1.rasa.com/1.7.4/glossary/)

### Interactive Learning

This page shows how to use interactive learning on the command line.

In interactive learning mode, you provide feedback to your bot while you talk to it. This is a powerful way to explore what your bot can do, and the easiest way to fix any mistakes it makes. One advantage of machine learning-based dialogue is that when your bot doesn’t know how to do something yet, you can just teach it! Some people call this [Software 2.0](https://medium.com/@karpathy/software-2-0-a64152b37c35).

#### Running Interactive Learning

Run the following command to start interactive learning:

```
rasa run actions --actions actions&

rasa interactive \
  -m models/20190515-135859.tar.gz \
  --endpoints endpoints.yml
```

The first command starts the action server.

The second command starts interactive learning mode.

In interactive mode, Rasa will ask you to confirm every prediction made by NLU and Core before proceeding.

#### Providing feedback on errors

For this example we are going to use the `concertbot` example, so make sure you have the domain & data for it. You can download the data from our [github repo](https://github.com/RasaHQ/rasa/tree/master/examples/concertbot).

#### Visualization of conversations

During the interactive learning, Rasa will plot the current conversation and a few similar conversations from the training data to help you keep track of where you are.

You can view the visualization at [http://localhost:5005/visualization.html](http://localhost:5005/visualization.html) as soon as you’ve started interactive learning.

#### Interactive Learning with Forms

If you’re using a FormAction, there are some additional things to keep in mind when using interactive learning.

##### The `form:` prefix

The form logic is described by your `FormAction` class, and not by the stories. The machine learning policies should not have to learn this behavior...

```plaintext
* request_restaurant
    - restaurant_form
    - form{"name": "restaurant_form"}
    - slot{"requested_slot": "cuisine"}
* form: inform{"cuisine": "mexican"}
    - slot{"cuisine": "mexican"}
    - form: restaurant_form
    - slot{"cuisine": "mexican"}
    - slot{"requested_slot": "num_people"}
* form: inform{"number": "2"}
    - form: restaurant_form
    - slot{"num_people": "2"}
    - form{"name": null}
    - slot{"requested_slot": null}
    - utter_slots_values
```

### Input validation

...

**WARNING: FormPolicy predicted no form validation based on previous training stories. Make sure to remove contradictory stories from training data**

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