adding documentation for the parameters transform and on-demand features

Author: manu-sj

docs/user_guides/fs/feature_group/on_demand_transformations.md

@@ -144,18 +144,18 @@ The `get_feature_vectors` function retrieves multiple feature vectors using a li
     )
     ```
 
-#### Retrieving untransformed feature vector
+#### Retrieving feature vector without on-demand features
 
-The `get_feature_vector` and `get_feature_vectors` can also return untransformed features vector without applying model-dependent transformations that contains on-demand features by setting the parameter `transform` to `False`.
+The `get_feature_vector` and `get_feature_vectors` methods can return untransformed feature vectors without on-demand features by disabling model-dependent transformations and excluding on-demand features. To achieve this, set the  parameters `transform` and `on_demand_features` to `False`.
 
 === "Python"    
 !!! example "Returning untransformed feature vectors"
     ```python
     untransformed_feature_vector = feature_view.get_feature_vector(
-        entry={"id": 1}, transform=False
+        entry={"id": 1}, transform=False, on_demand_features=False
     )
     untransformed_feature_vectors = feature_view.get_feature_vectors(
-        entry=[{"id": 1}, {"id": 2}], transform=False
+        entry=[{"id": 1}, {"id": 2}], transform=False, on_demand_features=False
     )
     ```
 
@@ -170,7 +170,7 @@ The `request_parameter` in this case, can be a list of dictionaries that specifi
     ```python
     # Specify request parameters for each serving key.
     untransformed_feature_vector = feature_view.get_feature_vector(
-        entry={"id": 1}, transform=False
+        entry={"id": 1}, transform=False, on_demand_features=False
     )
 
     # re-compute and add on-demand features to the feature vector
@@ -187,7 +187,7 @@ The `request_parameter` in this case, can be a list of dictionaries that specifi
 
     # Specify request parameters for each serving key.
     untransformed_feature_vectors = feature_view.get_feature_vectors(
-        entry=[{"id": 1}, {"id": 2}], transform=False
+        entry=[{"id": 1}, {"id": 2}], transform=False, on_demand_features=False
     )
 
     # re-compute and add on-demand features to the feature vectors - Specify unique request parameter for each feature vector
@@ -228,7 +228,7 @@ On-demand transformation functions can also be accessed and executed as normal f
     ```python
     # Specify request parameters for each serving key.
     feature_vector = feature_view.get_feature_vector(
-        entry={"id": 1}, transform=False, return_type="pandas"
+        entry={"id": 1}, transform=False, on_demand_features=False, return_type="pandas"
     )
 
     # Applying model dependent transformations

docs/user_guides/fs/feature_view/batch-data.md

@@ -55,6 +55,17 @@ feature_view.init_batch_scoring(training_dataset_version=1)
 
 It is important to note that in addition to the filters defined in feature view, [extra filters](./training-data.md#Extra-filters) will be applied if they are defined in the given training dataset version.
 
+## Retrieving untransformed batch data
+
+The  `get_batch_data` function by default returns the batch data with model-dependent transformations applied. However it can also return untransformed batch data without applying model-dependent transformations but contains on-demand features by setting the parameter `transform` to `False`.
+
+=== "Python"    
+!!! example "Returning untransformed batch data"
+    ```python
+    # Fetching untransformed batch data.
+    untransformed_batch_data = feature_view.get_batch_data(transform=False)
+    ```
+
 
 ## Passing Context Variables to Transformation Functions
 After [defining a transformation function using a context variable](../transformation_functions.md#passing-context-variables-to-transformation-function), you can pass the necessary context variables through the `transformation_context` parameter when fetching batch data.

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

@@ -191,6 +191,41 @@ You can also use the parameter to provide values for all the features which are
     )
     ```
 
+## Retrieving untransformed feature vectors
+
+By default, the `get_feature_vector` and `get_feature_vectors` functions return transformed feature vectors, which has model-dependent transformations applied and includes on-demand features.
+
+However, you can retrieve the untransformed feature vectors without applying model-dependent transformations while still including on-demand features by setting the `transform` parameter to False.
+
+=== "Python"    
+!!! example "Returning untransformed feature vectors"
+    ```python
+    # Fetching untransformed feature vector.
+    untransformed_feature_vector = feature_view.get_feature_vector(
+        entry={"id": 1}, transform=False
+    )
+
+    # Fetching untransformed feature vectors.
+    untransformed_feature_vectors = feature_view.get_feature_vectors(
+        entry=[{"id": 1}, {"id": 2}], transform=False
+    )
+    ```
+
+## Retrieving feature vector without on-demand features
+
+The `get_feature_vector` and `get_feature_vectors` methods can also return untransformed feature vectors without on-demand features by disabling model-dependent transformations and excluding on-demand features. To achieve this, set the  parameters `transform` and `on_demand_features` to `False`.
+
+=== "Python"    
+!!! example "Returning untransformed feature vectors"
+    ```python
+    untransformed_feature_vector = feature_view.get_feature_vector(
+        entry={"id": 1}, transform=False, on_demand_features=False
+    )
+    untransformed_feature_vectors = feature_view.get_feature_vectors(
+        entry=[{"id": 1}, {"id": 2}], transform=False, on_demand_features=False
+    )
+    ```
+
 ## Passing Context Variables to Transformation Functions
 After [defining a transformation function using a context variable](../transformation_functions.md#passing-context-variables-to-transformation-function), you can pass the required context variables using the `transformation_context` parameter when fetching the feature vectors.
 

docs/user_guides/fs/feature_view/model-dependent-transformations.md

@@ -129,3 +129,26 @@ Model-dependent transformation functions can also be manually applied to a featu
         encoded_feature_vector = fv.transform(feature_vector)
         ```
 
+#### Retrieving untransformed feature vector and batch inference data
+
+The `get_feature_vector`, `get_feature_vectors`, and `get_batch_data` methods can return untransformed feature vectors and batch data without applying model-dependent transformations while still including on-demand features. To achieve this, set the `transform` parameter to False.
+
+=== "Python"    
+!!! example "Returning untransformed feature vectors and batch data."
+    ```python
+    # Fetching untransformed feature vector.
+    untransformed_feature_vector = feature_view.get_feature_vector(
+        entry={"id": 1}, transform=False
+    )
+
+    # Fetching untransformed feature vectors.
+    untransformed_feature_vectors = feature_view.get_feature_vectors(
+        entry=[{"id": 1}, {"id": 2}], transform=False
+    )
+
+    # Fetching untransformed batch data.
+    untransformed_batch_data = feature_view.get_batch_data(
+        transform=False
+    )
+    ```
+