add banners on all DM1 pages

RasaHQ · Sep 20, 2023 · 9ac4746 · 9ac4746
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diff --git a/docs/docs/building-classic-assistants/business-logic.mdx b/docs/docs/building-classic-assistants/business-logic.mdx
@@ -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:

diff --git a/docs/docs/building-classic-assistants/chitchat-faqs.mdx b/docs/docs/building-classic-assistants/chitchat-faqs.mdx
@@ -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](../concepts/glossary.mdx#faqs) and [chitchat](../concepts/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,

diff --git a/docs/docs/building-classic-assistants/components.mdx b/docs/docs/building-classic-assistants/components.mdx
@@ -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

diff --git a/docs/docs/building-classic-assistants/contextual-conversations.mdx b/docs/docs/building-classic-assistants/contextual-conversations.mdx
@@ -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,

diff --git a/docs/docs/building-classic-assistants/fallback-handoff.mdx b/docs/docs/building-classic-assistants/fallback-handoff.mdx
@@ -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,

diff --git a/docs/docs/building-classic-assistants/language-support.mdx b/docs/docs/building-classic-assistants/language-support.mdx
@@ -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.

diff --git a/docs/docs/building-classic-assistants/model-configuration.mdx b/docs/docs/building-classic-assistants/model-configuration.mdx
@@ -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.
 

diff --git a/docs/docs/building-classic-assistants/nlu-only.mdx b/docs/docs/building-classic-assistants/nlu-only.mdx
@@ -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

diff --git a/docs/docs/building-classic-assistants/nlu-training-data.mdx b/docs/docs/building-classic-assistants/nlu-training-data.mdx
@@ -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](../concepts/glossary.mdx#intent) and any
 [entities](../concepts/glossary.mdx#entity) their message contains. You can
 add extra information such as [regular expressions](#regular-expressions) and [lookup tables](#lookup-tables) to your

diff --git a/docs/docs/building-classic-assistants/policies.mdx b/docs/docs/building-classic-assistants/policies.mdx
@@ -5,6 +5,10 @@ title: Policies
 abstract: Your assistant uses 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 />
+
 You can customize the policies your assistant uses by specifying the `policies`
 key in your project's `config.yml`.
 There are different policies to choose from, and you can include

diff --git a/docs/docs/building-classic-assistants/reaching-out-to-user.mdx b/docs/docs/building-classic-assistants/reaching-out-to-user.mdx
@@ -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

diff --git a/docs/docs/building-classic-assistants/rules.mdx b/docs/docs/building-classic-assistants/rules.mdx
@@ -8,6 +8,10 @@ abstract:
   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

diff --git a/docs/docs/building-classic-assistants/stories.mdx b/docs/docs/building-classic-assistants/stories.mdx
@@ -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,

diff --git a/docs/docs/building-classic-assistants/training-data-format.mdx b/docs/docs/building-classic-assistants/training-data-format.mdx
@@ -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

diff --git a/docs/docs/building-classic-assistants/tuning-your-model.mdx b/docs/docs/building-classic-assistants/tuning-your-model.mdx
@@ -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
 

diff --git a/docs/docs/building-classic-assistants/unexpected-input.mdx b/docs/docs/building-classic-assistants/unexpected-input.mdx
@@ -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](../concepts/glossary.mdx#happy--unhappy-paths)
 that you have defined. For example: