Merge pull request #6636 from RasaHQ/docs-links-updates

Updated Docs Links to 2.0 structure

data/configs_for_docs/example_for_suggested_config_after_train.yml

@@ -3,7 +3,7 @@ language: en
 pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/nlu/choosing-a-pipeline/ for more information.
+# # See https://rasa.com/docs/rasa/model-configuration#suggested-config for more information.
 #   - name: ConveRTTokenizer
 #   - name: ConveRTFeaturizer
 #   - name: RegexFeaturizer

data/test_config/config_empty_en_after_dumping.yml

@@ -2,7 +2,7 @@ language: en
 pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/nlu/choosing-a-pipeline/ for more information.
+# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
 #   - name: ConveRTTokenizer
 #   - name: ConveRTFeaturizer
 #   - name: RegexFeaturizer
@@ -20,7 +20,7 @@ pipeline:
 policies:
 # # No configuration for policies was provided. The following default policies were used to train your model.
 # # If you'd like to customize them, uncomment and adjust the policies.
-# # See https://rasa.com/docs/rasa/core/policies/ for more information.
+# # See https://rasa.com/docs/rasa/policies for more information.
 #   - name: MemoizationPolicy
 #   - name: TEDPolicy
 #     max_history: 5

data/test_config/config_empty_en_after_dumping_core.yml

@@ -3,7 +3,7 @@ pipeline:
 policies:
 # # No configuration for policies was provided. The following default policies were used to train your model.
 # # If you'd like to customize them, uncomment and adjust the policies.
-# # See https://rasa.com/docs/rasa/core/policies/ for more information.
+# # See https://rasa.com/docs/rasa/policies for more information.
 #   - name: MemoizationPolicy
 #   - name: TEDPolicy
 #     max_history: 5

data/test_config/config_empty_en_after_dumping_nlu.yml

@@ -2,7 +2,7 @@ language: en
 pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/nlu/choosing-a-pipeline/ for more information.
+# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
 #   - name: ConveRTTokenizer
 #   - name: ConveRTFeaturizer
 #   - name: RegexFeaturizer

data/test_config/config_empty_en_after_dumping_windows.yml

@@ -2,7 +2,7 @@ language: en
 pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/nlu/choosing-a-pipeline/ for more information.
+# # See https://rasa.com/docs/rasa/model-configuration for more information.
 #   - name: WhitespaceTokenizer
 #   - name: RegexFeaturizer
 #   - name: LexicalSyntacticFeaturizer
@@ -19,7 +19,7 @@ pipeline:
 policies:
 # # No configuration for policies was provided. The following default policies were used to train your model.
 # # If you'd like to customize them, uncomment and adjust the policies.
-# # See https://rasa.com/docs/rasa/core/policies/ for more information.
+# # See https://rasa.com/docs/rasa/policies for more information.
 #   - name: MemoizationPolicy
 #   - name: TEDPolicy
 #     max_history: 5

data/test_config/config_empty_en_after_dumping_windows_core.yml

@@ -3,7 +3,7 @@ pipeline:
 policies:
 # # No configuration for policies was provided. The following default policies were used to train your model.
 # # If you'd like to customize them, uncomment and adjust the policies.
-# # See https://rasa.com/docs/rasa/core/policies/ for more information.
+# # See https://rasa.com/docs/rasa/policies for more information.
 #   - name: MemoizationPolicy
 #   - name: TEDPolicy
 #     max_history: 5

data/test_config/config_empty_en_after_dumping_windows_nlu.yml

@@ -2,7 +2,7 @@ language: en
 pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/nlu/choosing-a-pipeline/ for more information.
+# # See https://rasa.com/docs/rasa/model-configuration for more information.
 #   - name: WhitespaceTokenizer
 #   - name: RegexFeaturizer
 #   - name: LexicalSyntacticFeaturizer

data/test_config/config_empty_fr_after_dumping.yml

@@ -2,7 +2,7 @@ language: fr
 pipeline:
 # # No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
 # # If you'd like to customize it, uncomment and adjust the pipeline.
-# # See https://rasa.com/docs/rasa/nlu/choosing-a-pipeline/ for more information.
+# # See https://rasa.com/docs/rasa/tuning-your-model for more information.
 #   - name: WhitespaceTokenizer
 #   - name: RegexFeaturizer
 #   - name: LexicalSyntacticFeaturizer
@@ -19,7 +19,7 @@ pipeline:
 policies:
 # # No configuration for policies was provided. The following default policies were used to train your model.
 # # If you'd like to customize them, uncomment and adjust the policies.
-# # See https://rasa.com/docs/rasa/core/policies/ for more information.
+# # See https://rasa.com/docs/rasa/policies for more information.
 #   - name: MemoizationPolicy
 #   - name: TEDPolicy
 #     max_history: 5

data/test_config/config_with_comments_after_dumping.yml

@@ -22,7 +22,7 @@ pipeline:
 policies: # even here
 # # No configuration for policies was provided. The following default policies were used to train your model.
 # # If you'd like to customize them, uncomment and adjust the policies.
-# # See https://rasa.com/docs/rasa/core/policies/ for more information.
+# # See https://rasa.com/docs/rasa/policies for more information.
 #   - name: MemoizationPolicy
 #   - name: TEDPolicy
 #     max_history: 5

data/test_config/stack_config.yml

@@ -1,10 +1,10 @@
 # Configuration for Rasa NLU.
-# https://rasa.com/docs/rasa/nlu/components/
+# https://rasa.com/docs/rasa/components
 language: en
 pipeline:
   - name: KeywordIntentClassifier
 
 # Configuration for Rasa Core.
-# https://rasa.com/docs/rasa/core/policies/
+# https://rasa.com/docs/rasa/policies
 policies:
   - name: MemoizationPolicy

docs/docs/training-data-format.mdx

@@ -9,17 +9,17 @@ description: Description of the YAML format for training data
 
 Rasa Open Source uses [YAML](https://yaml.org/spec/1.2/spec.html) as
 a unified and extendable way to manage all training data,
-including NLU data, stories and rules. 
+including NLU data, stories and rules.
 
 With the YAML format, training data can be split over any number of YAML files,
-and every file can contain any kind of data. 
+and every file can contain any kind of data.
 The training data parser will read the top level keys in each file to decide
 what kind of data is in a section at training time.
 
 The [domain](glossary.mdx#domain) uses
 the same YAML format as the training data and can also be split across
 multiple files or combined in one file. The domain includes
-the definitions for [responses](domain.mdx#responses) and [forms](forms.mdx).
+the definitions for [responses](responses.mdx) and [forms](forms.mdx).
 See the [documentation for the domain](domain.mdx) for information on how to format your domain file.
 
 :::note Legacy Formats
@@ -45,7 +45,7 @@ All YAML training data files should specify the `version` key to be parsed
 correctly. If you don't specify a version key in your training data file, Rasa
 will assume you are using the latest training data format specification supported
 by the version of Rasa Open Source you have installed.
-Training data files with a version greater than is currently available in 
+Training data files with a version greater than is currently available in
 the version of Rasa Open Source you have installed will be
 skipped.
 Currently, the latest training data format specification for Rasa 2.x is 2.0.

examples/concertbot/README.md

@@ -4,14 +4,14 @@ Example bot that contains only story data.
 
 ## What’s inside this example?
 
-This example contains some training data and the main files needed to build an 
+This example contains some training data and the main files needed to build an
 assistant on your local machine. The `concertbot` consists of the following files:
 
-- **data/stories.md** contains training stories for the Core model  
+- **data/stories.md** contains training stories for the Core model
 - **actions/actions.py** contains some custom actions
 - **config.yml** contains the model configuration
-- **domain.yml** contains the domain of the assistant  
-- **endpoints.yml** contains the webhook configuration for the custom actions 
+- **domain.yml** contains the domain of the assistant
+- **endpoints.yml** contains the webhook configuration for the custom actions
 
 ## How to use this example?
 
@@ -42,8 +42,8 @@ rasa run actions&
 rasa shell -m models
 ```
 
-For more information about the individual commands, please check out our 
-[documentation](http://rasa.com/docs/rasa/user-guide/command-line-interface/).
+For more information about the individual commands, please check out our
+[documentation](http://rasa.com/docs/rasa/command-line-interface).
 
 ## Encountered any issues?
 Let us know about it by posting on [Rasa Community Forum](https://forum.rasa.com)!

examples/formbot/README.md

@@ -10,11 +10,11 @@ restaurants based on user preferences.
 This example contains some training data and the main files needed to build an
 assistant on your local machine. The `formbot` consists of the following files:
 
-- **data/nlu.yml** contains training examples for the NLU model  
+- **data/nlu.yml** contains training examples for the NLU model
 - **data/stories.yml** contains training stories for the Core model
 - **actions/actions.py** contains the implementation of a custom `FormAction`
 - **config.yml** contains the model configuration
-- **domain.yml** contains the domain of the assistant  
+- **domain.yml** contains the domain of the assistant
 - **endpoints.yml** contains the webhook configuration for the custom actions
 
 ## How to use this example?
@@ -29,7 +29,7 @@ steps:
     ```
     The model will be stored in the `/models` directory as a zipped file.
 
-2. Run an instance of [duckling](https://rasa.com/docs/rasa/nlu/components/#ducklinghttpextractor)
+2. Run an instance of [duckling](https://rasa.com/docs/rasa/components#ducklinghttpextractor)
    on port 8000 by either running the docker command
    ```
    docker run -p 8000:8000 rasa/duckling
@@ -44,7 +44,7 @@ steps:
     This will load the assistant in your command line for you to chat.
 
 For more information about the individual commands, please check out our
-[documentation](http://rasa.com/docs/rasa/user-guide/command-line-interface/).
+[documentation](http://rasa.com/docs/rasa/command-line-interface).
 
 ## Encountered any issues?
 Let us know about it by posting on [Rasa Community Forum](https://forum.rasa.com)!

examples/knowledgebasebot/README.md

@@ -4,15 +4,15 @@ This example bot uses a knowledge base to answer user's requests.
 
 ## What’s inside this example?
 
-This example contains some training data and the main files needed to build an 
+This example contains some training data and the main files needed to build an
 assistant on your local machine. The `knowledgebasebot` consists of the following files:
 
-- **data/nlu.yml** contains training examples for the NLU model  
-- **data/stories.yml** contains training stories for the Core model  
+- **data/nlu.yml** contains training examples for the NLU model
+- **data/stories.yml** contains training stories for the Core model
 - **actions/actions.py** contains the custom action for querying the knowledge base
 - **config.yml** contains the model configuration
-- **domain.yml** contains the domain of the assistant  
-- **endpoints.yml** contains the webhook configuration for the custom action  
+- **domain.yml** contains the domain of the assistant
+- **endpoints.yml** contains the webhook configuration for the custom action
 - **knowledge_base_data.json** contains the data for the knowledge base
 
 ## How to use this example?
@@ -33,8 +33,8 @@ To chat with the bot on the command line, run
 rasa shell
 ```
 
-For more information about the individual commands, please check out our 
-[documentation](http://rasa.com/docs/rasa/user-guide/command-line-interface/).
+For more information about the individual commands, please check out our
+[documentation](http://rasa.com/docs/rasa/command-line-interface).
 
 ## Encountered any issues?
 Let us know about it by posting on [Rasa Community Forum](https://forum.rasa.com)!

examples/moodbot/README.md

@@ -7,10 +7,10 @@ The `moodbot` example simulates how you can use your bot on different channels.
 This example contains some training data and the main files needed to build an
 assistant on your local machine. The `moodbot` consists of the following files:
 
-- **data/nlu.yml** contains training examples for the NLU model  
-- **data/stories.yml** contains training stories for the Core model  
+- **data/nlu.yml** contains training examples for the NLU model
+- **data/stories.yml** contains training stories for the Core model
 - **config.yml** contains the model configuration
-- **domain.yml** contains the domain of the assistant  
+- **domain.yml** contains the domain of the assistant
 - **credentials.yml** contains credentials for the different channels
 
 ## How to use this example?
@@ -32,7 +32,7 @@ different channels. To do so execute the following steps:
     desired channel.
     All available channels are listed in the `credentials.yml` file.
     For more information on the different channels read our
-    [documentation](http://x-docs.rasa.com/docs/rasa/user-guide/messaging-and-voice-channels/)
+    [documentation](http://rasa.com/docs/rasa/messaging-and-voice-channels)
 
     If you don't want to use any channel, you can chat with your bot
     on the command line, using the following command:
@@ -41,7 +41,7 @@ different channels. To do so execute the following steps:
     ```
 
 For more information about the individual commands, please check out our
-[documentation](http://rasa.com/docs/rasa/user-guide/command-line-interface/).
+[documentation](http://rasa.com/docs/rasa/command-line-interface).
 
 ## Encountered any issues?
 Let us know about it by posting on [Rasa Community Forum](https://forum.rasa.com)!

examples/reminderbot/README.md

@@ -7,10 +7,10 @@ The `reminderbot` example demonstrates how your bot can respond to external even
 This example contains some training data and the main files needed to build an
 assistant on your local machine. The `reminderbot` consists of the following files:
 
-- **data/nlu.yml** contains training examples for the NLU model  
-- **data/rules.yml** contains rules for the Core model  
+- **data/nlu.yml** contains training examples for the NLU model
+- **data/rules.yml** contains rules for the Core model
 - **config.yml** contains the model configuration
-- **domain.yml** contains the domain of the assistant  
+- **domain.yml** contains the domain of the assistant
 - **credentials.yml** contains credentials for the different channels
 - **endpoints.yml** contains the different endpoints reminderbot can use
 - **actions/actions.py** contains the custom actions that deal with external events and reminders
@@ -35,29 +35,29 @@ To train and chat with `reminderbot`, execute the following steps:
     rasa x
     ```
 
-3. (Option 2) To test this example without Rasa X, run a 
-   [callback channel](https://rasa.com/docs/rasa/user-guide/connectors/your-own-website/#callbackinput). 
+3. (Option 2) To test this example without Rasa X, run a
+   [callback channel](https://rasa.com/docs/rasa/connectors/your-own-website#callbackinput).
    In a separate console window from where you ran the step 2 command:
     ```
     python callback_server.py
     ```
-     
-   This will run a server that prints the bot's responses to the console. 
-   
-   Start your Rasa server in a third console window: 
+
+   This will run a server that prints the bot's responses to the console.
+
+   Start your Rasa server in a third console window:
    ```
-   rasa run --enable-api 
+   rasa run --enable-api
    ```
-   
-   You can then send messages to the bot via the callback channel endpoint: 
+
+   You can then send messages to the bot via the callback channel endpoint:
    ```
    curl -XPOST http://localhost:5005/webhooks/callback/webhook \
       -d '{"sender": "tester", "message": "hello"}' \
       -H "Content-type: application/json"
    ```
 
 For more information about the individual commands, please check out our
-[documentation](http://rasa.com/docs/rasa/user-guide/command-line-interface/).
+[documentation](http://rasa.com/docs/rasa/command-line-interface).
 
 ## Encountered any issues?
 Let us know about it by posting on [Rasa Community Forum](https://forum.rasa.com)!

examples/reminderbot/actions/actions.py

@@ -2,7 +2,7 @@
 # custom Python code.
 #
 # See this guide on how to implement these action:
-# https://rasa.com/docs/rasa/core/actions/#custom-actions/
+# https://rasa.com/docs/rasa/custom-actions
 
 
 # This is a simple example for an assistant that schedules reminders and

examples/reminderbot/credentials.yml

@@ -1,6 +1,6 @@
 # This file contains the credentials for the voice & chat platforms
 # which your bot is using.
-# https://rasa.com/docs/rasa/user-guide/messaging-and-voice-channels/
+# https://rasa.com/docs/rasa/messaging-and-voice-channels
 
 # # NOTE: the rest channel does not work with external events
 # rest:
@@ -9,7 +9,7 @@
 
 callback:
   # URL to which Rasa Open Source will send the bot responses
-  # See https://rasa.com/docs/rasa/user-guide/connectors/your-own-website/#callbackinput
+  # See https://rasa.com/docs/rasa/connectors/your-own-website#callbackinput
   url: "http://localhost:5034/bot"
 
 #facebook:cz

examples/reminderbot/endpoints.yml

@@ -1,21 +1,21 @@
 # This file contains the different endpoints your bot can use.
 
 # Server where the models are pulled from.
-# https://rasa.com/docs/rasa/user-guide/running-the-server/#fetching-models-from-a-server/
+# https://rasa.com/docs/rasa/model-storage#fetching-models-from-a-server
 
 #models:
 #  url: http://my-server.com/models/default_core@latest
 #  wait_time_between_pulls:  10   # [optional](default: 100)
 
 # Server which runs your custom actions.
-# https://rasa.com/docs/rasa/core/actions/#custom-actions/
+# https://rasa.com/docs/rasa/custom-actions
 
 action_endpoint:
   url: "http://localhost:5055/webhook"
 
 # Tracker store which is used to store the conversations.
 # By default the conversations are stored in memory.
-# https://rasa.com/docs/rasa/api/tracker-stores/
+# https://rasa.com/docs/rasa/tracker-stores
 
 #tracker_store:
 #    type: redis
@@ -33,7 +33,7 @@ action_endpoint:
 #    password: <password used for authentication>
 
 # Event broker which all conversation events should be streamed to.
-# https://rasa.com/docs/rasa/api/event-brokers/
+# https://rasa.com/docs/rasa/event-brokers
 
 #event_broker:
 #  url: localhost