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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/business-logic.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Handling Business Logic
abstract: Conversational assistants often need to ask users for information in order to help them. You can use Forms to collect the required user information and fulfill a request.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

Conversational assistants often support user goals that involve collecting required information
from the user before doing something for them. For example, a restaurant search bot would need to gather a few pieces of information
about the user's preferences to find them a suitable restaurant:
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/chitchat-faqs.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Chitchat and FAQs
abstract: FAQ assistants are the simplest assistants to build and typically the first kind of assistant anyone builds. This page is a guide to the concepts and training data you need to handle non-contextual questions like FAQs and chitchat.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

[FAQs](./glossary.mdx#faqs) and [chitchat](./glossary.mdx#chitchat) are two cases where the conversational assistant responds with a fixed set of messages, and the assistant should always answer the same way,
no matter what has happened previously in the conversation.
For example, in the following conversation, every question can be asked at any point in the conversation,
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/components.mdx
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Expand Up @@ -8,6 +8,10 @@ abstract:
pre-processing, and more.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## Language Models

The following components load pre-trained models that are needed if you want to use pre-trained
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Expand Up @@ -5,6 +5,10 @@ title: Contextual Conversations
abstract: Taking context into account is often key to providing a good user experience. This page is a guide to creating contextual conversation patterns.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

In a contextual conversation, something beyond the previous step in the conversation
plays a role in what should happen next. For example, if a user asks "How many?",
it's not clear from the message alone what the user is asking about. In the context of the assistant saying,
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/domain.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Domain
abstract: The domain defines the universe in which your assistant operates. It specifies the intents, entities, slots, responses, forms, and actions your bot should know about. It also defines a configuration for conversation sessions.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

Here is a full example of a domain, taken from the
[concertbot](https://github.com/RasaHQ/rasa/tree/main/examples/concertbot) example:

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3 changes: 3 additions & 0 deletions docs/docs/building-classic-assistants/fallback-handoff.mdx
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Expand Up @@ -6,6 +6,9 @@ abstract: This is a guide on how to handle various failures of your assistant.
---

import useBaseUrl from "@docusaurus/useBaseUrl";
import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

Even if you design your bot perfectly, users will inevitably say things to your
assistant that you did not anticipate. In these cases, your assistant will fail,
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/forms.mdx
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Expand Up @@ -6,6 +6,10 @@ description: Follow a rule-based process of information gathering using forms in
abstract: One of the most common conversation patterns is to collect a few pieces of information from a user in order to do something (book a restaurant, call an API, search a database, etc.). This is also called **slot filling**.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## Usage

To use forms with Rasa you need to make sure that the
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/glossary.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Rasa Glossary
description: Glossary for all Rasa-related terms
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## [Action](../concepts/custom-actions.mdx)

A single step that a bot takes in a conversation (e.g. calling an API or sending a response back to the user).
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/language-support.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Language Support
abstract: You can use Rasa to build assistants in any language you want.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

Your Rasa assistant can be used on training data in **any language**.
If there are no word embeddings for your language, you can train your featurizers
from scratch with the data you provide.
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/model-configuration.mdx
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Expand Up @@ -6,6 +6,10 @@ description: Learn about model configuration for Rasa.
abstract: The configuration file defines the components and policies that your model will use to make predictions based on user input.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

The recipe key allows for different types of config and model architecture.
Currently, "default.v1" and the experimental "graph.v1" recipes are supported.

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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/nlu-only.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Using NLU Only
abstract: Find out how to use only Rasa NLU as a standalone NLU service for your chatbot or virtual assistant.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

If you want to use Rasa only as an NLU component, you can!

## Training NLU-only models
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/nlu-training-data.mdx
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Expand Up @@ -6,6 +6,10 @@ description: Read more about how to format training data with Rasa NLU for open
abstract: NLU training data stores structured information about user messages.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

The goal of NLU (Natural Language Understanding) is to extract structured information from user messages. This usually includes the user's [intent](./glossary.mdx#intent) and any
[entities](./glossary.mdx#entity) their message contains. You can
add extra information such as [regular expressions](#regular-expressions) and [lookup tables](#lookup-tables) to your
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/policies.mdx
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Expand Up @@ -5,6 +5,10 @@ title: Policies
abstract: NLU-based Rasa assistants use a variety of policies to decide which action to take at each step in a conversation. There are machine-learning and rule-based policies that your assistant can use in tandem.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

When building an NLU-based assistant in Rasa, there are a number of dialogue policies available to you.
You can customize the policies your assistant uses by specifying the `policies`
key in your project's `config.yml`.
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Expand Up @@ -5,6 +5,10 @@ title: Reaching Out to the User
abstract: Sometimes you want your assistant to reach out to the user without the user's prompting. For example, you might want the assistant to send a message when the user opens the chat window, or you might want to prompt the user if they haven't sent a message for a while. This page is a guide to enabling your assistant to reach out to the user proactively.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## Reaching out first

In most use cases, when the user opens the chat window with your assistant, you will want the
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/rules.mdx
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Rules describe short pieces of conversations that should always follow the same path.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

**Don't overuse rules**. Rules are great to handle small specific conversation patterns, but
unlike [stories](./stories.mdx), rules don't have the power to generalize to unseen conversation
paths. Combine rules and stories to make your assistant robust and able to handle
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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/stories.mdx
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Expand Up @@ -8,6 +8,10 @@ abstract:
model. Stories can be used to train models that are able to generalize to unseen conversation paths.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## Format

A story is a representation of a conversation between a user and an AI assistant,
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Expand Up @@ -12,8 +12,10 @@ abstract:
import useBaseUrl from "@docusaurus/useBaseUrl";

import RasaProLabel from "@theme/RasaProLabel";

import RasaProBanner from "@theme/RasaProBanner";
import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## Validating Data and Stories

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Expand Up @@ -7,6 +7,10 @@ abstract: This page describes the different types of training data
that go into a Rasa assistant and how this training data is structured.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## Overview

Rasa uses [YAML](https://yaml.org/spec/1.2/spec.html) as
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3 changes: 3 additions & 0 deletions docs/docs/building-classic-assistants/tuning-your-model.mdx
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Expand Up @@ -6,6 +6,9 @@ abstract: Rasa will provide you with a suggested NLU config on initialization of
---

import useBaseUrl from "@docusaurus/useBaseUrl";
import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

## How to Choose a Pipeline

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4 changes: 4 additions & 0 deletions docs/docs/building-classic-assistants/unexpected-input.mdx
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Expand Up @@ -7,6 +7,10 @@ abstract:
will say unexpected things. This page is a guide on handling unexpected input.
---

import RasaNLUBasedBanner from "@theme/RasaNLUBasedBanner";

<RasaNLUBasedBanner />

Unexpected input is a deviation from the [happy path](./glossary.mdx#happy--unhappy-paths)
that you have defined. For example:

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4 changes: 1 addition & 3 deletions docs/themes/theme-custom/theme/RasaDiscoveryBanner/index.jsx
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@@ -1,7 +1,5 @@
import * as React from 'react';
import clsx from 'clsx';

import styles from './styles.module.css';

function RasaDiscoveryBanner({isLoading, ...props}) {
return (
Expand All @@ -19,7 +17,7 @@ function RasaDiscoveryBanner({isLoading, ...props}) {
</div>
<div class="mdx-box admonition-content">
<p>
Discovery features are <strong>highly unstable</strong>. We introduce
Discovery features are <strong>highly unstable</strong>. We introduce
them for product exploration.

These features will change and should only be used for testing purposes.
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This file was deleted.

16 changes: 0 additions & 16 deletions docs/themes/theme-custom/theme/RasaLabsBanner/styles.module.css
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@@ -1,19 +1,3 @@
.label {
background-color: #F6D261;
border: 1px solid transparent;
border-radius: 8px;
padding: 2px 12px;
font-size: 15px !important;
font-weight: 600;

display: inline-block;
}

.label[disabled] {
background-color: var(--ifm-color-gray-500);
cursor: default;
}

.titleExtension {
text-transform: none !important;
}
30 changes: 30 additions & 0 deletions docs/themes/theme-custom/theme/RasaNLUBasedBanner/index.jsx
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@@ -0,0 +1,30 @@
import * as React from 'react';
import useBaseUrl from "@docusaurus/useBaseUrl";

function RasaNLUBasedBanner() {
return (
<>
<div class="mdx-box admonition admonition-tip alert alert--info">
<div class="mdx-box admonition-heading">
<h5>
<span class="admonition-icon">
<svg xmlns="http://www.w3.org/2000/svg" width="12" height="16" viewBox="0 0 12 16">
<path fill-rule="evenodd" d="M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z">
</path>
</svg>
</span>NLU-based assistants
</h5>
</div>
<div class="mdx-box admonition-content">
<p>
This page refers to building NLU-based assistants.
If you are working with <a href={useBaseUrl("/calm")}>Conversational AI with Language Models (CALM)</a>,
this content may not apply to you.
</p>
</div>
</div>
</>
)
}

export default RasaNLUBasedBanner;
3 changes: 0 additions & 3 deletions docs/themes/theme-custom/theme/RasaProBanner/index.jsx
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@@ -1,7 +1,4 @@
import * as React from 'react';
import clsx from 'clsx';

import styles from './styles.module.css';

function RasaProBanner({isLoading, ...props}) {
return (
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18 changes: 0 additions & 18 deletions docs/themes/theme-custom/theme/RasaProBanner/styles.module.css

This file was deleted.

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