From ad1626fc9f9507284744352e53497f6cbbb5cf4b Mon Sep 17 00:00:00 2001 From: Alan Nichol Date: Wed, 20 Sep 2023 10:50:15 +0200 Subject: [PATCH] fix more links --- .../components/custom-graph-components.mdx | 46 +++++++++---------- .../docs/concepts/components/graph-recipe.mdx | 2 +- docs/docs/concepts/components/overview.mdx | 8 ++-- docs/docs/docker/building-in-docker.mdx | 2 +- .../installation/rasa-pro/installation.mdx | 8 ++-- .../getting-started-with-analytics.mdx | 2 +- .../operating/analytics/realtime-markers.mdx | 4 +- 7 files changed, 36 insertions(+), 36 deletions(-) diff --git a/docs/docs/concepts/components/custom-graph-components.mdx b/docs/docs/concepts/components/custom-graph-components.mdx index f71b77b9e473..38f12acfed17 100644 --- a/docs/docs/concepts/components/custom-graph-components.mdx +++ b/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: @@ -365,7 +365,7 @@ 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=`. This graph component is run during training and @@ -373,21 +373,21 @@ to specify the following details: 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 @@ -396,7 +396,7 @@ 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 @@ -404,8 +404,8 @@ to specify the following details: 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) diff --git a/docs/docs/concepts/components/graph-recipe.mdx b/docs/docs/concepts/components/graph-recipe.mdx index dc267ec8fa44..3558a5edcca6 100644 --- a/docs/docs/concepts/components/graph-recipe.mdx +++ b/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: diff --git a/docs/docs/concepts/components/overview.mdx b/docs/docs/concepts/components/overview.mdx index ac56dec57bd6..b7024ff09986 100644 --- a/docs/docs/concepts/components/overview.mdx +++ b/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. diff --git a/docs/docs/docker/building-in-docker.mdx b/docs/docs/docker/building-in-docker.mdx index 002e7cd2072f..6fba53caac3a 100644 --- a/docs/docs/docker/building-in-docker.mdx +++ b/docs/docs/docker/building-in-docker.mdx @@ -117,7 +117,7 @@ Here's what's happening in that command:
  • train: Execute the rasa train command within the container. For more - information see Command Line Interface.
    • + information see Command Line Interface. In this case, we've also passed values for the location of the domain file, training diff --git a/docs/docs/installation/rasa-pro/installation.mdx b/docs/docs/installation/rasa-pro/installation.mdx index 34df14d6a966..3b1babc885d9 100644 --- a/docs/docs/installation/rasa-pro/installation.mdx +++ b/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) diff --git a/docs/docs/operating/analytics/getting-started-with-analytics.mdx b/docs/docs/operating/analytics/getting-started-with-analytics.mdx index 6e952dcb0109..439bd557ee02 100644 --- a/docs/docs/operating/analytics/getting-started-with-analytics.mdx +++ b/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 diff --git a/docs/docs/operating/analytics/realtime-markers.mdx b/docs/docs/operating/analytics/realtime-markers.mdx index c08d0717c405..6a7dbd6f445a 100644 --- a/docs/docs/operating/analytics/realtime-markers.mdx +++ b/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?