fix more links

docs/docs/concepts/components/custom-graph-components.mdx

@@ -7,8 +7,8 @@ abstract: You can extend Rasa with custom NLU components and policies. This page
 
 import useBaseUrl from "@docusaurus/useBaseUrl";
 
-Rasa provides a variety of [NLU components](../building-classic-assistants/components.mdx) and
-[policies](policies.mdx) out of the box. You can customize them or create your
+Rasa provides a variety of [NLU components](../../building-classic-assistants/components.mdx) and
+[policies](../policies.mdx) out of the box. You can customize them or create your
 own components from scratch by using custom graph components.
 
 To use your custom graph component with Rasa it has to fulfill the following
@@ -27,7 +27,7 @@ requirements:
 
 ## Graph Components
 
-Rasa uses the passed in [model configuration](../building-classic-assistants/model-configuration.mdx) to build a
+Rasa uses the passed in [model configuration](../../building-classic-assistants/model-configuration.mdx) to build a
 [directed acyclic graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph).
 This graph describes the dependencies between the items in your model configuration and
 how data flows between them. This has two major benefits:
@@ -41,12 +41,12 @@ how data flows between them. This has two major benefits:
   software architecture to the used model architecture.
 
 When translating the model configuration to the computational graph
-[policies](policies.mdx) and [NLU components](../building-classic-assistants/components.mdx) become nodes within this graph.
+[policies](../policies.mdx) and [NLU components](../../building-classic-assistants/components.mdx) become nodes within this graph.
 While there is a distinction between policies and NLU components in your model
 configuration, the distinction is abstracted away when they are placed within the graph.
 At this point policies and NLU components become abstract _graph components_.
 In practice this is represented by the
-[`GraphComponent`](../concepts/custom-graph-components.mdx#the-graphcomponent-interface)
+[`GraphComponent`](#the-graphcomponent-interface)
 interface: Both policies and NLU components have to inherit from this interface to
 become compatible and executable for Rasa's graph.
 
@@ -61,7 +61,7 @@ become compatible and executable for Rasa's graph.
 ## Getting Started
 
 Before you get started, you have to decide whether you want to implement a custom
-[NLU component](../building-classic-assistants/components.mdx) or a [policy](policies.mdx). If you are implementing
+[NLU component](../../building-classic-assistants/components.mdx) or a [policy](../policies.mdx). If you are implementing
 a custom policy, then we recommend extending the existing
 `rasa.core.policies.policy.Policy` class which already implements the `GraphComponent`
 interface.
@@ -122,7 +122,7 @@ overridden. Rasa passes the following parameters when calling the method:
   - `should_add_diagnostic_data`: If `True` then additional diagnostic metadata
     should be added to your graph component's predictions on top of the actual prediction.
   - `is_finetuning`: If `True` then the graph component can be trained using
-    [finetuning](command-line-interface.mdx#incremental-training).
+    [finetuning](../../command-line-interface.mdx#incremental-training).
   - `graph_schema`: The `graph_schema` describes the computational graph which is used
     to train your assistant or to make predictions with it.
   - `node_name`: The `node_name` is a unique identifier for the step in the graph
@@ -346,7 +346,7 @@ class MyComponent(GraphComponent):
 To make your graph component available to Rasa you may have to register your
 graph component with a recipe. Rasa uses recipes to translate the content
 of your model configuration to executable
-[graphs](../concepts/custom-graph-components.mdx#graph-components).
+[graphs](#graph-components).
 Currently, Rasa supports the `default.v1` and the experimental `graph.v1` recipes.
 For `default.v1` recipe, you need to register your graph component by using the `DefaultV1Recipe.register`
 decorator:
@@ -365,29 +365,29 @@ to specify the following details:
   intent classifier and entity extractor):
 
   - `ComponentType.MODEL_LOADER`: Component type for
-    [language models](../building-classic-assistants/components.mdx#language-models). Graph components of this type provide
+    [language models](../../building-classic-assistants/components.mdx#language-models). Graph components of this type provide
     pretrained models to other graph components' `train`, `process_training_data` and
     `process` methods if they have specified `model_from=<model loader name>`.
     This graph component is run during training and
     inference. Rasa will use the graph component's `provide` method to
     retrieve the model which should be provided to dependent graph components.
 
   - `ComponentType.MESSAGE_TOKENIZER`: Component type for
-    [tokenizers](../building-classic-assistants/components.mdx#tokenizers). This graph component is run during training and
+    [tokenizers](../../building-classic-assistants/components.mdx#tokenizers). This graph component is run during training and
     inference. Rasa will use the graph component's `train` method if
     `is_trainable=True` is specified. Rasa will use
     `process_training_data` for tokenizing training data examples and `process`
     to tokenize messages during inference.
 
   - `ComponentType.MESSAGE_FEATURIZER`: Component type for
-    [featurizers](../building-classic-assistants/components.mdx#featurizers). This graph component is run during training and
+    [featurizers](../../building-classic-assistants/components.mdx#featurizers). This graph component is run during training and
     inference. Rasa will use the graph component's `train` method if
     `is_trainable=True` is specified. Rasa will use
     `process_training_data` for featurizing training data examples and `process`
     to featurize messages during inference.
 
   - `ComponentType.INTENT_CLASSIFIER`: Component type for
-    [intent classifiers](../building-classic-assistants/components.mdx#intent-classifiers). This graph component is run only
+    [intent classifiers](../../building-classic-assistants/components.mdx#intent-classifiers). This graph component is run only
     during training if `is_trainable=True`. The graph component is always run during
     inference.
     Rasa will use the graph component's `train` method if
@@ -396,16 +396,16 @@ to specify the following details:
     inference.
 
   - `ComponentType.ENTITY_EXTRACTOR`: Component type for
-    [entity extractors](../building-classic-assistants/components.mdx#entity-extractors). This graph component is run only
+    [entity extractors](../../building-classic-assistants/components.mdx#entity-extractors). This graph component is run only
     during training if `is_trainable=True`. The graph component is always run during
     inference.
     Rasa will use the graph component's `train` method if
     `is_trainable=True` is specified. Rasa will use
     the graph component's `process` method to extract entities during inference.
 
   - `ComponentType.POLICY_WITHOUT_END_TO_END_SUPPORT`: Component type for
-    [policies](policies.mdx) which don't require additional end-to-end features
-    (see [end-to-end training](../building-classic-assistants/stories.mdx#end-to-end-training) for more information).
+    [policies](../policies.mdx) which don't require additional end-to-end features
+    (see [end-to-end training](../../building-classic-assistants/stories.mdx#end-to-end-training) for more information).
     This graph component is run only during training if `is_trainable=True`.
     The graph component is always run during inference.
     Rasa will use the graph component's `train` method if
@@ -414,8 +414,8 @@ to specify the following details:
     next action which should be run within a conversation.
 
   - `ComponentType.POLICY_WITH_END_TO_END_SUPPORT`: Component type for
-    [policies](policies.mdx) which require additional end-to-end features
-    (see [end-to-end training](../building-classic-assistants/stories.mdx#end-to-end-training) for more information).
+    [policies](../policies.mdx) which require additional end-to-end features
+    (see [end-to-end training](../../building-classic-assistants/stories.mdx#end-to-end-training) for more information).
     The end-to-end features are passed into the graph component's `train` and
     `predict_action_probabilities` as `precomputations` parameter.
     This graph component is run only during training if `is_trainable=True`.
@@ -430,17 +430,17 @@ to specify the following details:
   predictions
 
 - `model_from`: Specifies if a pretrained
-  [language model](../building-classic-assistants/components.mdx#language-models) needs to be provided to the `train`,
+  [language model](../../building-classic-assistants/components.mdx#language-models) needs to be provided to the `train`,
   `process_training_data` and `process` methods of the graph component. These methods
   have to support the parameter `model` to receive the language model. Note that you
   still need to make sure that the graph component which provides this model is part
   of your model configuration. A common use case for this is if you want to expose the
-  [SpacyNLP](../building-classic-assistants/components.mdx#spacynlp) language model to your other NLU components.
+  [SpacyNLP](../../building-classic-assistants/components.mdx#spacynlp) language model to your other NLU components.
 
 ## Using Custom Components in your Model Configuration
 
 You can use custom graph components like any other NLU component or policy within your
-[model configuration](../building-classic-assistants/model-configuration.mdx). The only change is that you have to specify
+[model configuration](../../building-classic-assistants/model-configuration.mdx). The only change is that you have to specify
 the full module name instead of the class name only. The full module name depends on
 your module's location in relation to the specified
 [PYTHONPATH](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONPATH).
@@ -468,7 +468,7 @@ policies:
 
 ### Message Metadata
 
-When you [define metadata for your intent examples in your training data](../building-classic-assistants/training-data-format.mdx#training-examples),
+When you [define metadata for your intent examples in your training data](../../building-classic-assistants/training-data-format.mdx#training-examples),
 your NLU component can access both the intent metadata and the intent example metadata during processing:
 
 ```python
@@ -493,7 +493,7 @@ The sentence features are represented by a matrix of size `(1 x feature-dimensio
 
 ### Dense Message Featurizer
 
-The following is the example of a dense [message featurizer](../building-classic-assistants/components.mdx#featurizers)
+The following is the example of a dense [message featurizer](../../building-classic-assistants/components.mdx#featurizers)
 which uses a pretrained model:
 
 ```python (docs/sources/data/test_classes/custom_graph_components/nlu_dense.py)
@@ -502,7 +502,7 @@ which uses a pretrained model:
 
 ### Sparse Message Featurizer
 
-The following is the example of a dense [message featurizer](../building-classic-assistants/components.mdx#featurizers)
+The following is the example of a dense [message featurizer](../../building-classic-assistants/components.mdx#featurizers)
 which trains a new model:
 
 ```python (docs/sources/data/test_classes/custom_graph_components/nlu_sparse.py)

docs/docs/concepts/components/graph-recipe.mdx

@@ -76,7 +76,7 @@ uses: rasa.graph_components.converters.nlu_message_converter.NLUMessageConverter
 
 You are not required to use Rasa internal graph component classes and you
 can use your own components here. Refer to [custom graph
-components](../concepts/custom-graph-components.mdx) pages to find out how to write your
+components](./custom-graph-components.mdx) pages to find out how to write your
 own graph components.
 
 - `constructor_name`: This is the constructor used to instantiate your component. Example:

docs/docs/concepts/components/overview.mdx

@@ -20,14 +20,14 @@ The assistant identifier will be propagated to each event's metadata, alongside
 Note that if the config file does not include this required key or the placeholder default value is not replaced, a random
 assistant name will be generated and added to the configuration everytime when running `rasa train`.
 
-The language and pipeline keys specify the [components](./components.mdx) used by the  model to make NLU predictions.
-The policies key defines the [policies](./policies.mdx) used by the model to predict the next action.
+The language and pipeline keys specify the components used by the  model to make NLU predictions.
+The policies key defines the policies used by the model to predict the next action.
 
-If you don't know which components or policies to choose, you can use
-the [Suggested Config](./model-configuration.mdx#suggested-config) feature, which will recommend sensible defaults.
 
 ## Suggested Config
 
+TODO: update
+
 You can leave the pipeline and/or policies key out of your configuration file.
 When you run `rasa train`, the Suggested Config feature will select a default configuration
 for the missing key(s) to train the model.

docs/docs/docker/building-in-docker.mdx

@@ -117,7 +117,7 @@ Here's what's happening in that command:
 </li>
 
   <li><inlineCode>train</inlineCode>: Execute the <inlineCode>rasa train</inlineCode> command within the container. For more
-  information see <a href={useBaseUrl("/concepts/command-line-interface")}>Command Line Interface</a>.</li>
+  information see <a href={useBaseUrl("/command-line-interface")}>Command Line Interface</a>.</li>
 </ul>
 
 In this case, we've also passed values for the location of the domain file, training

docs/docs/installation/rasa-pro/installation.mdx

@@ -16,8 +16,8 @@ In this section you will learn how to install **Rasa Pro**, a drop-in replacemen
 
 To learn more about deployments, please visit the following sections:
 
-- [Deploying Rasa Pro in production](../../deploy/deploy-rasa)
-- [Deploying Rasa Pro Services in production](../../deploy/deploy-rasa-pro-services)
+- [Deploying Rasa Pro in production](../../deploy/deploy-rasa.mdx)
+- [Deploying Rasa Pro Services in production](../../deploy/deploy-rasa-pro-services.mdx)
 
 To learn more about compatibility between different versions of Rasa Pro and Rasa Pro Services, please see
 [compatibility matrix](../../compatibility-matrix.mdx).
@@ -210,5 +210,5 @@ You can increase UDP packet size by running `sudo sysctl -w net.inet.udp.maxdgra
 Congratulations! You have now successfully installed Rasa Pro on your development environment.
 To learn more about production deployments, visit:
 
-- [Deploying Rasa Pro in production](../../deploy/deploy-rasa)
-- [Deploying Rasa Pro Services in production](../../deploy/deploy-rasa-pro-services)
+- [Deploying Rasa Pro in production](../../deploy/deploy-rasa.mdx)
+- [Deploying Rasa Pro Services in production](../../deploy/deploy-rasa-pro-services.mdx)

docs/docs/operating/analytics/getting-started-with-analytics.mdx

@@ -122,7 +122,7 @@ to an event broker. The assistant will stream all events to the event broker,
 which will then be consumed by Rasa Pro Services.
 
 The configuration of the assistant is the first step of
-[Installation and Configuration](./deploy/deploy-rasa-pro-services.mdx/#installation-and-configuration).
+[Installation and Configuration](../../deploy/deploy-rasa-pro-services.mdx/#installation-and-configuration).
 No additional configuration is required to connect the assistant to the
 Analytics pipeline. After the assistant is deployed, the Analytics pipeline
 will receive the data from the assistant and persist it to your

docs/docs/operating/analytics/realtime-markers.mdx

@@ -37,7 +37,7 @@ analysis of markers to track solution and abandonment rates in your conversation
 
 ## Defining Markers
 
-Please consult the [Markers](../../production/markers.mdx/#defining-markers) section of Rasa documentation
+Please consult the [Markers](../../production/markers.mdx#defining-markers) section of Rasa documentation
 for details about defining markers.
 
 ## Enable Real-time Processing
@@ -74,7 +74,7 @@ and run this command, then the processing for that marker is stopped.
 
 ### Configuring the CLI command
 
-Visit our [CLI page](../../concepts/command-line-interface.mdx#rasa-marker-upload)
+Visit our [CLI page](../../command-line-interface.mdx#rasa-markers-upload)
 for more information about the command line arguments available.
 
 ## How are Markers processed?