Fix links

Author: aversey

docs/setup_installation/common/arrow_flight_duckdb.md

@@ -12,8 +12,8 @@ When the service is enabled, clients will automatically use it for the following
 - [reading Feature Groups][hsfs.feature_group.FeatureGroup.read]
 - [reading Queries][hsfs.constructor.query.Query.read]
 - [reading Training Datasets][hsfs.feature_view.FeatureView.get_training_data]
-- [creating In-Memory Training Datasets](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#training_data)
-- [reading Batch Inference Data](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#get_batch_data)
+- [creating In-Memory Training Datasets][hsfs.feature_view.FeatureView.training_data]
+- [reading Batch Inference Data][hsfs.feature_view.FeatureView.get_batch_data]
 For larger datasets, clients can still make use of the Spark/Hive backend by explicitly setting
 `read_options={"use_hive": True}`.
 

docs/user_guides/fs/compute_engines.md

@@ -21,14 +21,14 @@ Hopsworks is aiming to provide functional parity between the computational engin
 
 | Functionality | Method | Spark | Python | Flink | Beam | Java | Comment |
 | --- | --- | --- | --- | --- | --- | --- | --- |
-| Feature Group Creation from dataframes | [`FeatureGroup.create_feature_group()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#create_feature_group) | :white_check_mark: | :white_check_mark: | - | - | - | Currently Flink/Beam/Java doesn't support registering feature group metadata. Thus it needs to be pre-registered before you can write real time features computed by Flink/Beam. |
-| Training Dataset Creation from dataframes | [`TrainingDataset.save()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/training_dataset_api/#save) | :white_check_mark: | - | - | - | - | Functionality was deprecated in version 3.0 |
-| Data validation using Great Expectations for streaming dataframes | [`FeatureGroup.validate()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#validate) <br/> [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#insert_stream) | - | - | - | - | - | `insert_stream` does not perform any data validation even when a expectation suite is attached. |
-| Stream ingestion | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#insert_stream) | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
-| Reading from Streaming Storage Connectors | [`KafkaConnector.read_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/storage_connector_api/#read_stream) | :white_check_mark: | - | - | - | - | Python/Pandas/Polars has currently no notion of streaming. For Flink/Beam/Java only write operations are supported |
-| Reading training data from external storage other than S3 | [`FeatureView.get_training_data()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#get_training_data) | :white_check_mark: | - | - | - | - | Reading training data that was written to external storage using a Storage Connector other than S3 can currently not be read using HSFS APIs, instead you will have to use the storage's native client. |
-| Reading External Feature Groups into Dataframe | [`ExternalFeatureGroup.read()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/external_feature_group_api/#read) | :white_check_mark: | - | - | - | - | Reading an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the [Query API](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/query_api/) to create Feature Views/Training Data containing External Feature Groups. |
-| Read Queries containing External Feature Groups into Dataframe | [`Query.read()`](https://docs.hopsworks.ai/feature-store-api/{{{hopsworks_version}}}/generated/api/query_api/#read) | :white_check_mark: | - | - | - | - | Reading a Query containing an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the Query to create Feature Views/Training Data and write the data to a Storage Connector, from where you can read up the data into a Pandas/Polars Dataframe. |
+| Feature Group Creation from dataframes | [`FeatureStore.create_feature_group`][hsfs.feature_store.FeatureStore.create_feature_group] | :white_check_mark: | :white_check_mark: | - | - | - | Currently Flink/Beam/Java doesn't support registering feature group metadata. Thus it needs to be pre-registered before you can write real time features computed by Flink/Beam. |
+| Training Dataset Creation from dataframes | [`TrainingDataset.save`][hsfs.training_dataset.TrainingDataset.save] | :white_check_mark: | - | - | - | - | Functionality was deprecated in version 3.0 |
+| Data validation using Great Expectations for streaming dataframes | [`FeatureGroup.validate`][hsfs.feature_group.FeatureGroup.validate] <br/> [`FeatureGroup.insert_stream`][hsfs.feature_group.FeatureGroup.insert_stream] | - | - | - | - | - | `insert_stream` does not perform any data validation even when a expectation suite is attached. |
+| Stream ingestion | [`FeatureGroup.insert_stream`][hsfs.feature_group.FeatureGroup.insert_stream] | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
+| Reading from Streaming Storage Connectors | [`KafkaConnector.read_stream`][hsfs.storage_connector.KafkaConnector.read_stream] | :white_check_mark: | - | - | - | - | Python/Pandas/Polars has currently no notion of streaming. For Flink/Beam/Java only write operations are supported |
+| Reading training data from external storage other than S3 | [`FeatureView.get_training_data`][hsfs.feature_view.FeatureView.get_training_data] | :white_check_mark: | - | - | - | - | Reading training data that was written to external storage using a Storage Connector other than S3 can currently not be read using HSFS APIs, instead you will have to use the storage's native client. |
+| Reading External Feature Groups into Dataframe | [`ExternalFeatureGroup.read`][hsfs.feature_group.ExternalFeatureGroup.read] | :white_check_mark: | - | - | - | - | Reading an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the [Query API][hsfs.constructor.query.Query] to create Feature Views/Training Data containing External Feature Groups. |
+| Read Queries containing External Feature Groups into Dataframe | [`Query.read`][hsfs.constructor.query.Query.read] | :white_check_mark: | - | - | - | - | Reading a Query containing an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the Query to create Feature Views/Training Data and write the data to a Storage Connector, from where you can read up the data into a Pandas/Polars Dataframe. |
 
 ## Python
 

docs/user_guides/fs/data_source/usage.md

@@ -160,4 +160,4 @@ For a detailed walkthrough on managing and utilizing training data, refer to the
 ## Next Steps
 
 We have gone through the basic use cases of a data source.
-For more details about the API functionality for any specific connector type, checkout the [API section](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/storage_connector_api/#storage-connector).
+For more details about the API functionality for any specific connector type, checkout the [API section][hsfs.storage_connector.StorageConnector].

docs/user_guides/fs/feature_group/create.md

@@ -39,7 +39,7 @@ Using the HSFS API you can execute:
     )
     ```
 
-You can read [the full method documentation](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#featuregroup) to get more details.
+You can read the full [`FeatureStore.create_feature_group`][hsfs.feature_store.FeatureStore.create_feature_group] documentation to get more details.
 If you need to create a feature group with vector similarity search supported, refer to [the vector similarity guide](../vector_similarity_search.md#extending-feature-groups-with-similarity-search).
 `name` is the only mandatory parameter of the `create_feature_group` and represents the name of the feature group.
 
@@ -353,7 +353,7 @@ If a feature group is online enabled, the `insert` method will store the feature
 
 ### API Reference
 
-[FeatureGroup](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#featuregroup)
+[`FeatureGroup`][hsfs.feature_group.FeatureGroup]
 
 ## Create using the UI
 

docs/user_guides/fs/feature_group/create_external.md

@@ -77,7 +77,7 @@ Once you have defined the metadata, you can
     fg.save()
     ```
 
-You can read [the full method documentation](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/external_feature_group_api/#externalfeaturegroup) for more details.
+You can read the full [`FeatureStore.create_external_feature_group`][hsfs.feature_store.FeatureStore.create_external_feature_group] documentation for more details.
 `name` is a mandatory parameter of the `create_external_feature_group` and represents the name of the feature group.
 
 The version number is optional, if you don't specify the version number the APIs will create a new version by default with a version number equals to the highest existing version number plus one.
@@ -126,18 +126,18 @@ For an external feature group to be available online, during the creation of the
     ```
 
 The `insert()` method takes a DataFrame as parameter and writes it _only_ to the online feature store.
-Users can select which subset of the feature group data they want to make available on the online feature store by using the [query APIs](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/query_api/).
+Users can select which subset of the feature group data they want to make available on the online feature store by using the [query APIs][hsfs.constructor.query.Query].
 
 ### Limitations
 
 Hopsworks Feature Store does not support time-travel queries on external feature groups.
 
 Additionally, support for `.read()` and `.show()` methods when using by the Python engine is limited to external feature groups defined on BigQuery and Snowflake and only when using the [Feature Query Service](../../../setup_installation/common/arrow_flight_duckdb.md).
-Nevertheless, external feature groups defined top of any data source can be used to create a training dataset from a Python environment invoking one of the following methods: [create_training_data](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#create_training_data), [create_train_test_split](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#create_train_test_split) or the [create_train_validation_test_split](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#create_train_validation_test_split)
+Nevertheless, external feature groups defined top of any data source can be used to create a training dataset from a Python environment invoking one of the following methods: [`FeatureView.create_training_data`][hsfs.feature_view.FeatureView.create_training_data], [`FeatureView.create_train_test_split`][hsfs.feature_view.FeatureView.create_train_test_split] or [`FeatureView.create_train_validation_test_split`][hsfs.feature_view.FeatureView.create_train_validation_test_split].
 
 ### API Reference
 
-[External FeatureGroup](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/external_feature_group_api/#externalfeaturegroup)
+[`ExternalFeatureGroup`][hsfs.feature_group.ExternalFeatureGroup].
 
 ## Create using the UI
 

docs/user_guides/fs/feature_group/create_spine.md

@@ -60,4 +60,4 @@ You just need to make sure it has the same schema.
 
 ### API Reference
 
-[SpineGroup](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/spine_group_api/#spinegroup)
+[`SpineGroup`][hsfs.feature_group.SpineGroup].

docs/user_guides/fs/feature_group/data_types.md

@@ -105,9 +105,9 @@ When in doubt, use the TEXT type instead, but note that it comes with a potentia
 
 Hopsworks allows users to store complex types (e.g. *ARRAY<INT>*) in the online feature store.
 Hopsworks serializes the complex features transparently and stores them as VARBINARY in the online feature store.
-The serialization happens when calling the [save()](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#save),
-[insert()](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#insert) or [insert_stream()](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#insert_stream) methods.
-The deserialization will be executed when calling the [get_serving_vector()](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/training_dataset_api/#get_serving_vector) method to retrieve data from the online feature store.
+The serialization happens when calling the [`FeatureGroup.save`][hsfs.feature_group.FeatureGroup.save],
+[`FeatureGroup.insert`][hsfs.feature_group.FeatureGroup.insert] or [`FeatureGroup.insert_stream`][hsfs.feature_group.FeatureGroup.insert_stream] methods.
+The deserialization will be executed when calling the [`TrainingDataset.get_serving_vector`][hsfs.training_dataset.TrainingDataset.get_serving_vector] method to retrieve data from the online feature store.
 If users query directly the online feature store, for instance using the `fs.sql("SELECT ...", online=True)` statement, it will return a binary blob.
 
 On the feature store UI, the online feature type for complex features will be reported as *VARBINARY*.
@@ -221,7 +221,7 @@ The most important validation checks or error messages are mentioned below along
           df['text_column'] = df['text_column'].str.slice(0, max_length)
           ```
 
-      - Another option is to simply [create new version of the feature group](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#get_or_create_feature_group) and insert the dataframe.
+      - Another option is to simply [create new version of the feature group][hsfs.feature_store.FeatureStore.get_or_create_feature_group] and insert the dataframe.
 
       !!!note
           The total row size limit should be less than 30kb as per [row size restrictions](#online-restrictions-for-row-size).
@@ -252,7 +252,7 @@ The most important validation checks or error messages are mentioned below along
 All timestamp features are stored in Hopsworks in UTC time.
 Also, all timestamp-based functions (such as [point-in-time joins](../../../concepts/fs/feature_view/offline_api.md#point-in-time-correct-training-data)) use UTC time.
 This ensures consistency of timestamp features across different client timezones and simplifies working with timestamp-based functions in general.
-When ingesting timestamp features, the [Feature Store Write API](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#insert) will automatically handle the conversion to UTC, if necessary.
+When ingesting timestamp features, the [`FeatureGroup.insert`][hsfs.feature_group.FeatureGroup.insert] will automatically handle the conversion to UTC, if necessary.
 The following table summarizes how different timestamp types are handled:
 
 | Data Frame (Data Type) | Environment | Handling |
@@ -261,7 +261,7 @@ The following table summarizes how different timestamp types are handled:
 | Pandas DataFrame (datetime64[ns, tz]) | Python-only and PySpark | timezone-sensitive conversion from 'tz' to UTC |
 | Spark (TimestampType) | PySpark and Spark | interpreted as UTC, independent of the client's timezone |
 
-Timestamp features retrieved from the Feature Store, e.g., using the [Feature Store Read API](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_group_api/#read), use a timezone-unaware format:
+Timestamp features retrieved from the Feature Store, e.g., using the [Feature Store Read API][hsfs.feature_group.FeatureGroup.read], use a timezone-unaware format:
 
 | Data Frame (Data Type)                | Environment             | Timezone               |
 |---------------------------------------|-------------------------|------------------------|

docs/user_guides/fs/feature_view/feature-vectors.md

@@ -15,7 +15,7 @@ If you need to get more familiar with the concept of feature vectors, you can re
 
 You can get back feature vectors from either python or java client by providing the primary key value(s) for the feature view.
 Note that filters defined in feature view and training data will not be applied when feature vectors are returned.
-If you need to retrieve a complete value of feature vectors without missing values, the required `entry` are [feature_view.primary_keys](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#primary_keys).
+If you need to retrieve a complete value of feature vectors without missing values, the required `entry` are [FeatureView.primary_keys][hsfs.feature_view.FeatureView.primary_keys].
 Alternative, you can provide the primary key of the feature groups as the key of the entry.
 It is also possible to provide a subset of the entry, which will be discussed [below](#partial-feature-retrieval).
 
@@ -55,7 +55,7 @@ It is also possible to provide a subset of the entry, which will be discussed [b
 Starting from python client v3.4, you can specify different values for the primary key of the same name which exists in multiple feature groups but are not joint by the same name.
 The table below summarises the value of `primary_keys` in different settings.
 Considering that you are joining 2 feature groups, namely, `left_fg` and `right_fg`, the feature groups have different primary keys, and features (`feature_*`) in each setting.
-Also, the 2 feature groups are [joint](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/query_api/#join) on different *join conditions* and *prefix* as `left_fg.join(right_fg, <join conditions>, prefix=<prefix>)`.
+Also, the 2 feature groups are [joint][hsfs.constructor.query.Query.join] on different *join conditions* and *prefix* as `left_fg.join(right_fg, <join conditions>, prefix=<prefix>)`.
 
 For java client, and python client before v3.4, the `primary_keys` are the set of primary key of all the feature groups in the query.
 Python client is backward compatible.
@@ -285,7 +285,7 @@ This implementation is available as of Hopsworks 3.7.
 Initialise the client by calling the `init_serving` method on the Feature View object before starting to fetch feature vectors.
 This will initialise the chosen client, test the connection, and initialise the transformation functions registered with the Feature View.
 Note to use the REST client in the Hopsworks Cluster python environment you will need to provide an API key explicitly as JWT authentication is not yet supported.
-More configuration options can be found in the [API documentation](https://docs.hopsworks.ai/hopsworks-api/{{{hopsworks_version}}}/generated/api/feature_view_api/#init_serving).
+More configuration options can be found in the [API documentation][hsfs.feature_view.FeatureView.init_serving].
 
 === "Python"