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

## User Guide

- [Installation](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/installation/)
- [Tutorial: Rasa Basics](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/rasa-tutorial/)
- [Tutorial: Building Assistants](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/building-assistants/)
- [Command Line Interface](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/command-line-interface/)
- [Architecture](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/architecture/)
- [Messaging and Voice Channels](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/messaging-and-voice-channels/)
- [Testing Your Assistant](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/testing-your-assistant/)
- [Setting up CI/CD](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/setting-up-ci-cd/)
- [Validate Data](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/validate-files/)
- [Configuring the HTTP API](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/configuring-http-api/)
- [Deploying Your Rasa Assistant](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/how-to-deploy/)
- [Cloud Storage](https://legacy-docs-v1.rasa.com/1.10.5/user-guide/cloud-storage/)

## NLU

- [About](https://legacy-docs-v1.rasa.com/1.10.5/nlu/about/)
- [Using NLU Only](https://legacy-docs-v1.rasa.com/1.10.5/nlu/using-nlu-only/)
- [Training Data Format](https://legacy-docs-v1.rasa.com/1.10.5/nlu/training-data-format/)
- [Language Support](https://legacy-docs-v1.rasa.com/1.10.5/nlu/language-support/)
- [Choosing a Pipeline](https://legacy-docs-v1.rasa.com/1.10.5/nlu/choosing-a-pipeline/)
- [Components](https://legacy-docs-v1.rasa.com/1.10.5/nlu/components/)
- [Entity Extraction](https://legacy-docs-v1.rasa.com/1.10.5/nlu/entity-extraction/)

## Core

- [About](https://legacy-docs-v1.rasa.com/1.10.5/core/about/)
- [Stories](https://legacy-docs-v1.rasa.com/1.10.5/core/stories/)
- [Domains](https://legacy-docs-v1.rasa.com/1.10.5/core/domains/)
- [Responses](https://legacy-docs-v1.rasa.com/1.10.5/core/responses/)
- [Actions](https://legacy-docs-v1.rasa.com/1.10.5/core/actions/)
- [Reminders and External Events](https://legacy-docs-v1.rasa.com/1.10.5/core/reminders-and-external-events/)
- [Policies](https://legacy-docs-v1.rasa.com/1.10.5/core/policies/)
- [Slots](https://legacy-docs-v1.rasa.com/1.10.5/core/slots/)
- [Forms](https://legacy-docs-v1.rasa.com/1.10.5/core/forms/)
- [Retrieval Actions](https://legacy-docs-v1.rasa.com/1.10.5/core/retrieval-actions/)
- [Interactive Learning](https://legacy-docs-v1.rasa.com/1.10.5/core/interactive-learning/)
- [Fallback Actions](https://legacy-docs-v1.rasa.com/1.10.5/core/fallback-actions/)
- [Knowledge Base Actions](https://legacy-docs-v1.rasa.com/1.10.5/core/knowledge-bases/)

## Conversation Design

- [Dialogue Elements](https://legacy-docs-v1.rasa.com/1.10.5/dialogue-elements/dialogue-elements/)
- [Small Talk](https://legacy-docs-v1.rasa.com/1.10.5/dialogue-elements/small-talk/)
- [Completing Tasks](https://legacy-docs-v1.rasa.com/1.10.5/dialogue-elements/completing-tasks/)
- [Guiding Users](https://legacy-docs-v1.rasa.com/1.10.5/dialogue-elements/guiding-users/#)

## API Reference

- [Action Server](https://legacy-docs-v1.rasa.com/1.10.5/api/action-server/)
- [HTTP API](https://legacy-docs-v1.rasa.com/1.10.5/api/http-api/)
- [Jupyter Notebooks](https://legacy-docs-v1.rasa.com/1.10.5/api/jupyter-notebooks/)
- [Agent](https://legacy-docs-v1.rasa.com/1.10.5/api/agent/)
- [Custom NLU Components](https://legacy-docs-v1.rasa.com/1.10.5/api/custom-nlu-components/)
- [Rasa SDK](https://legacy-docs-v1.rasa.com/1.10.5/api/rasa-sdk/)
- [Events](https://legacy-docs-v1.rasa.com/1.10.5/api/events/)
- [Tracker](https://legacy-docs-v1.rasa.com/1.10.5/api/tracker/)
- [Tracker Stores](https://legacy-docs-v1.rasa.com/1.10.5/api/tracker-stores/)
- [Event Brokers](https://legacy-docs-v1.rasa.com/1.10.5/api/event-brokers/)
- [Lock Stores](https://legacy-docs-v1.rasa.com/1.10.5/api/lock-stores/)
- [Training Data Importers](https://legacy-docs-v1.rasa.com/1.10.5/api/training-data-importers/)
- [Featurization of Conversations](https://legacy-docs-v1.rasa.com/1.10.5/api/core-featurization/)
- [TensorFlow Configuration](https://legacy-docs-v1.rasa.com/1.10.5/api/tensorflow_usage/)
- [Migration Guide](https://legacy-docs-v1.rasa.com/1.10.5/migration-guide/)
- [Rasa Open Source Change Log](https://legacy-docs-v1.rasa.com/1.10.5/changelog/)

## Migrate from (beta)

- [Dialogflow](https://legacy-docs-v1.rasa.com/1.10.5/migrate-from/google-dialogflow-to-rasa/)
- [Wit.ai](https://legacy-docs-v1.rasa.com/1.10.5/migrate-from/facebook-wit-ai-to-rasa/)
- [LUIS](https://legacy-docs-v1.rasa.com/1.10.5/migrate-from/microsoft-luis-to-rasa/)
- [IBM Watson](https://legacy-docs-v1.rasa.com/1.10.5/migrate-from/ibm-watson-to-rasa/)

## Reference

- [Glossary](https://legacy-docs-v1.rasa.com/1.10.5/glossary/)

# Guiding Users

## Implicit Confirmation  
Implicit confirmation involves repeating details back to the user to reassure them that they were understood correctly. This also gives the user a chance to intervene if your assistant misunderstood.

### Example:
Can I get a large hawaiian and bbq pizza

Sure, that's one large hawaiian and one regular bbq pizza.

Anything else?

No they should both be large!

## Explicit Confirmation  
Explicit confirmation means asking the user to clarify how you should help them. An important thing to remember about AI assistants is that **the user is never wrong**. When a user tells you something like I just moved, they are being perfectly clear, even if your assistant is not sure how to help them. If the user goal is ambiguous to your assistant, ask for clarification.

### Example:
I just moved

I'm not sure I understood you correctly. Do you mean ...

I want to cancel my contract

I want to update my personal details

You can configure the `TwoStageFallbackPolicy` to ask your user to clarify, and present them with quick replies for the most likely intents.

```yaml
policies:
- name: TwoStageFallbackPolicy
  nlu_threshold: 0.3
  core_threshold: 0.3
  fallback_core_action_name: "action_default_fallback"
  fallback_nlu_action_name: "action_default_fallback"
  deny_suggestion_intent_name: "out_of_scope"
...
```

## Explaining Possibilities  
AI assistants are always limited to helping users with a specific set of tasks, and should be able to explain to a user what they can do. That includes coherently responding to requests that are out of scope.

### Example:
What can you do?

I can help you update your personal details, change your plan, and answer any questions you have about our products.

Can you get me a pizza?

I'm afraid I can't help with that.

## Collecting User Feedback  
Asking for feedback is one of the best tools you have to understand your users and determine whether you solved their problem! Storing this feedback is a powerful way to figure out how you can improve your assistant.

### Example:
Was that helpful?

no.

Thanks. Why wasn't I able to help?

you didn't understand me correctly

you understood me, but your answers weren't very helpful.

Use a form to collect user feedback. To do this, define a custom form action (see [Forms](https://legacy-docs-v1.rasa.com/1.10.5/core/forms/#forms) for more details about forms).

```python
from rasa_sdk.action import FormAction

class FeedbackForm(FormAction):

def name(self):
        return "feedback_form"

@staticmethod
    def required_slots(tracker):
        return ["feedback", "negative_feedback_reason"]
```

Add the form and slots to your domain:

```yaml
forms:
  - feedback_form
slots:
  feedback:
    type: bool
  feedback_reason:
    type: text
  requested_slot:
    type: text
```

And make sure the `FormPolicy` is present in your configuration file:

```yaml
policies:
  - FormPolicy
  ...
```

## Handing off to a Human  
Users will be very frustrated if your assistant cannot help them and there is no way to reroute the conversation to a human agent. There should always be a way to break out of a conversation! There are multiple reasons why you might trigger a human handoff:

- the user asks to speak to a human
- the assistant is struggling to understand the user
- the assistant understands what the user wants, but a human is required to resolve the issue.

### Example:
let me speak to a human

let me put you in touch with someone.

I want to cancel

I'm afraid I can't help you with that.

let me put you in touch with someone.

The direct request to speak with a human can be handled using the mapping policy:

```yaml
intents:
  - request_human: {"triggers": "action_human_handoff"}
```

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