Merge pull request #6660 from RasaHQ/components-page

Components back to one page
RasaHQ · Sep 15, 2020 · 570dea7 · 570dea7
2 parents a34e994 + f2b5321
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diff --git a/.typo-ci.yml b/.typo-ci.yml
@@ -94,6 +94,7 @@ excluded_words:
   - forni
   - gzip
   - gzipped
+  - hftransformersnlp
   - initializer
   - instaclient
   - jwt
@@ -111,6 +112,7 @@ excluded_words:
   - memoization
   - miniconda
   - mitie
+  - mitiefeaturizer
   - mitie's
   - mitienlp
   - dataset
@@ -148,6 +150,7 @@ excluded_words:
   - scipy
   - sklearn
   - spacy
+  - spacyfeaturizer
   - spacynlp
   - ish
   - spaCy

diff --git a/CHANGELOG.mdx b/CHANGELOG.mdx
@@ -186,7 +186,7 @@ https://github.com/RasaHQ/rasa/tree/master/changelog/ . -->
   `from_entity` (see [Forms](./forms.mdx)).
 
   :::note
-  Composite entities are currently just supported by the [DIETClassifier](./components/intent-classifiers.mdx#dietclassifier) and [CRFEntityExtractor](./components/entity-extractors.mdx#crfentityextractor).
+  Composite entities are currently just supported by the [DIETClassifier](./components.mdx#dietclassifier) and [CRFEntityExtractor](./components.mdx#crfentityextractor).
 
   :::
 
@@ -385,7 +385,7 @@ https://github.com/RasaHQ/rasa/tree/master/changelog/ . -->
 
 * [#5006](https://github.com/rasahq/rasa/issues/5006): Channel `hangouts` for Rasa integration with Google Hangouts Chat is now supported out-of-the-box.
 
-* [#5389](https://github.com/rasahq/rasa/issues/5389): Add an optional path to a specific directory to download and cache the pre-trained model weights for [HFTransformersNLP](./components/language-models.mdx#hftransformersnlp).
+* [#5389](https://github.com/rasahq/rasa/issues/5389): Add an optional path to a specific directory to download and cache the pre-trained model weights for [HFTransformersNLP](./components.mdx#hftransformersnlp).
 
 * [#5422](https://github.com/rasahq/rasa/issues/5422): Add options `tensorboard_log_directory` and `tensorboard_log_level` to `EmbeddingIntentClassifier`,
   `DIETClasifier`, `ResponseSelector`, `EmbeddingPolicy` and `TEDPolicy`.
@@ -544,18 +544,18 @@ https://github.com/RasaHQ/rasa/tree/master/changelog/ . -->
 
 * [#4088](https://github.com/rasahq/rasa/issues/4088): Add story structure validation functionality (e.g. rasa data validate stories –max-history 5).
 
-* [#5065](https://github.com/rasahq/rasa/issues/5065): Add [LexicalSyntacticFeaturizer](./components/featurizers.mdx#lexicalsyntacticfeaturizer) to sparse featurizers.
+* [#5065](https://github.com/rasahq/rasa/issues/5065): Add [LexicalSyntacticFeaturizer](./components.mdx#lexicalsyntacticfeaturizer) to sparse featurizers.
 
   `LexicalSyntacticFeaturizer` does the same featurization as the `CRFEntityExtractor`. We extracted the
   featurization into a separate component so that the features can be reused and featurization is independent from the
   entity extraction.
 
 * [#5187](https://github.com/rasahq/rasa/issues/5187): Integrate language models from HuggingFace's [Transformers](https://github.com/huggingface/transformers) Library.
 
-  Add a new NLP component [HFTransformersNLP](./components/language-models.mdx#hftransformersnlp) which tokenizes and featurizes incoming messages using a specified
+  Add a new NLP component [HFTransformersNLP](./components.mdx#hftransformersnlp) which tokenizes and featurizes incoming messages using a specified
   pre-trained model with the Transformers library as the backend.
-  Add [LanguageModelTokenizer](./components/tokenizers.mdx#languagemodeltokenizer) and [LanguageModelFeaturizer](./components/featurizers.mdx#languagemodelfeaturizer) which use the information from
-  [HFTransformersNLP](./components/language-models.mdx#hftransformersnlp) and sets them correctly for message object.
+  Add [LanguageModelTokenizer](./components.mdx#languagemodeltokenizer) and [LanguageModelFeaturizer](./components.mdx#languagemodelfeaturizer) which use the information from
+  [HFTransformersNLP](./components.mdx#hftransformersnlp) and sets them correctly for message object.
   Language models currently supported: BERT, OpenAIGPT, GPT-2, XLNet, DistilBert, RoBERTa.
 
 * [#5225](https://github.com/rasahq/rasa/issues/5225): Added a new CLI command `rasa export` to publish tracker events from a persistent
@@ -578,12 +578,12 @@ https://github.com/RasaHQ/rasa/tree/master/changelog/ . -->
   TF_INTRA_OP_PARALLELISM_THREADS="2"
   ```
 
-* [#5266](https://github.com/rasahq/rasa/issues/5266): Added a new NLU component [DIETClassifier](./components/intent-classifiers.mdx#dietclassifier) and a new policy [TEDPolicy](./policies.mdx#ted-policy).
+* [#5266](https://github.com/rasahq/rasa/issues/5266): Added a new NLU component [DIETClassifier](./components.mdx#dietclassifier) and a new policy [TEDPolicy](./policies.mdx#ted-policy).
 
   DIET (Dual Intent and Entity Transformer) is a multi-task architecture for intent classification and entity
-  recognition. You can read more about this component in our [documentation](./components/intent-classifiers.mdx#dietclassifier).
+  recognition. You can read more about this component in our [documentation](./components.mdx#dietclassifier).
   The new component will replace the `EmbeddingIntentClassifier` and the
-  [CRFEntityExtractor](./components/entity-extractors.mdx#crfentityextractor) in the future.
+  [CRFEntityExtractor](./components.mdx#crfentityextractor) in the future.
   Those two components are deprecated from now on.
   See [migration guide](./migration-guide.mdx#migration-to-rasa-1-8) for details on how to
   switch to the new component.
@@ -848,7 +848,7 @@ https://github.com/RasaHQ/rasa/tree/master/changelog/ . -->
     min_df: 5
   ```
 
-* [#4957](https://github.com/rasahq/rasa/issues/4957): To [use custom features in the `CRFEntityExtractor`](./components/entity-extractors.mdx#passing-custom-features-to-crfentityextractor)
+* [#4957](https://github.com/rasahq/rasa/issues/4957): To [use custom features in the `CRFEntityExtractor`](./components.mdx#passing-custom-features-to-crfentityextractor)
   use `text_dense_features` instead of `ner_features`. If
   `text_dense_features` are present in the feature set, the `CRFEntityExtractor` will automatically make use of
   them. Just make sure to add a dense featurizer in front of the `CRFEntityExtractor` in your pipeline and set the
@@ -899,13 +899,13 @@ https://github.com/RasaHQ/rasa/tree/master/changelog/ . -->
 
   Add option `return_sequence` to all featurizers. By default all featurizers return a matrix of size
   (1 x feature-dimension). If the option `return_sequence` is set to `True`, the corresponding featurizer will return
-  a matrix of size (token-length x feature-dimension). See [Text Featurizers](./components/featurizers.mdx).
+  a matrix of size (token-length x feature-dimension). See [Text Featurizers](./components.mdx#featurizers).
   Default value is set to `False`. However, you might want to set it to `True` if you want to use custom features
   in the `CRFEntityExtractor`.
-  See [passing custom features to the `CRFEntityExtractor`](./components/entity-extractors.mdx#passing-custom-features-to-crfentityextractor)
+  See [passing custom features to the `CRFEntityExtractor`](./components.mdx#passing-custom-features-to-crfentityextractor)
 
   Changed some featurizers to use sparse features, which should reduce memory usage with large amounts of training data significantly.
-  Read more: [Text Featurizers](./components/featurizers.mdx) .
+  Read more: [Text Featurizers](./components.mdx#featurizers) .
 
   :::caution
   These changes break model compatibility. You will need to retrain your old models!

diff --git a/changelog/5510.feature.md b/changelog/5510.feature.md
@@ -4,7 +4,7 @@ You can now define what kind of features should be used by what component
 You can set an alias via the option `alias` for every featurizer in your pipeline.
 The `alias` can be anything, by default it is set to the full featurizer class name.
 You can then specify, for example, on the
-[DIETClassifier](./components/intent-classifiers.mdx#diet-classifier) what features from which
+[DIETClassifier](./components.mdx#diet-classifier) what features from which
 featurizers should be used.
 If you don't set the option `featurizers` all available features will be used.
 This is also the default behavior.

diff --git a/changelog/5957.feature.md b/changelog/5957.feature.md
@@ -1,2 +1,2 @@
 Add new entity extractor `RegexEntityExtractor`. The entity extractor extracts entities using the lookup tables
-and regexes defined in the training data. For more information see [RegexEntityExtractor](./components/entity-extractors.mdx#regexentityextractor).
+and regexes defined in the training data. For more information see [RegexEntityExtractor](./components.mdx#regexentityextractor).
diff --git a/changelog/6088.feature.md b/changelog/6088.feature.md
@@ -6,15 +6,15 @@ policies [Mapping Policy](./policies.mdx#mapping-policy),
 deprecated and will be removed in the future. Please see the
 [rules documentation](./rules.mdx) for more information.
 
-Added new NLU component [FallbackClassifier](./components/intent-classifiers.mdx#fallbackclassifier) 
+Added new NLU component [FallbackClassifier](./components.mdx#fallbackclassifier) 
 which predicts an intent `nlu_fallback` in case the confidence was below a given
 threshold. The intent `nlu_fallback` may
 then be used to write stories / rules to handle the fallback in case of low NLU
 confidence.
 
-```python
+```yaml-rasa
 pipeline:
-- ... # Other NLU components
+- # Other NLU components ...
 - name: FallbackClassifier
   # If the highest ranked intent has a confidence lower than the threshold then
   # the NLU pipeline predicts an intent `nlu_fallback` which you can then be used in

diff --git a/changelog/6453.removal.md b/changelog/6453.removal.md
@@ -10,7 +10,7 @@ NLU `Component`:
 
 Removed `_guess_format()` utils method from `rasa.nlu.training_data.loading` (use `guess_format` instead).
 
-Removed several config options for [TED Policy](./policies#ted-policy), [DIETClassifier](./components/intent-classifiers#dietclassifier) and [ResponseSelector](./components/selectors#responseselector):
+Removed several config options for [TED Policy](./policies.mdx#ted-policy), [DIETClassifier](./components.mdx#dietclassifier) and [ResponseSelector](./components.mdx#responseselector):
 - `hidden_layers_sizes_pre_dial`
 - `hidden_layers_sizes_bot`
 - `droprate`

diff --git a/docs/docs/business-logic.mdx b/docs/docs/business-logic.mdx
@@ -188,8 +188,8 @@ data to your NLU file:
 
 :::note
 Entities like `business_email` and `budget` would usually be handled by pretrained entity extractors
-(e.g. [DucklingHTTPExtractor](./components/entity-extractors.mdx#ducklinghttpextractor)
-or [SpacyEntityExtractor](./components/entity-extractors.mdx#spacyentityextractor)), but for this tutorial
+(e.g. [DucklingHTTPExtractor](./components.mdx#ducklinghttpextractor)
+or [SpacyEntityExtractor](./components.mdx#spacyentityextractor)), but for this tutorial
 we want to avoid any additional setup.
 
 :::

diff --git a/docs/docs/chitchat-faqs.mdx b/docs/docs/chitchat-faqs.mdx
@@ -245,7 +245,7 @@ When you need to handle lots of different messages like FAQs or chitchat, the ab
 approach using the `MemoizationPolicy` will become cumbersome. You will need to write
 one story for each of the different intents.
 
-The [ResponseSelector](components/selectors.mdx#responseselector) is designed to
+The [ResponseSelector](components.mdx#responseselector) is designed to
 make it easier to handle conversation patterns like small talk and FAQ messages.
 When you use the `ResponseSelector`, you only need one story to handle all FAQs,
 instead of adding one story for each intent.