Merge pull request #663 from roboflow/fix/seo_issues_with_docs

Fix docs links

docs/enterprise/active-learning/active_learning.md

@@ -41,7 +41,7 @@ Active Learning data collection may be combined with different components of the
 - self-hosted `inference` server - where data is collected while processing requests
 - Roboflow hosted `inference` - where you let us make sure you get your predictions and data registered. No 
 infrastructure needs to run on your end, we take care of everything
-  - [Roboflow `workflows`](../../workflows/about.md) - our newest feature - supports [`ActiveLearningDataCollectionBlock`](../../workflows/active_learning.md)
+  - [Roboflow `workflows`](../../workflows/about.md) - our newest feature - supports [`Roboflow Dataset Upload block`](/workflows/blocks/roboflow_dataset_upload/)
 
 
 ## Sampling Strategies

docs/enterprise/active-learning/classes_based.md

@@ -37,4 +37,3 @@ Here is an example of a configuration manifest for the close to threshold sampli
 }
 ```
 
-Learn how to [configure active learning](../active_learning.md#configuration) for your model.

docs/enterprise/active-learning/close_to_threshold_sampling.md

@@ -55,4 +55,3 @@ Here is an example of a configuration manifest for the close to threshold sampli
 }
 ```
 
-Learn how to [configure active learning](../active_learning.md#configuration) for your model.

docs/enterprise/active-learning/detection_number.md

@@ -42,5 +42,3 @@ This strategy is available for the following model types:
   ]
 }
 ```
-
-Learn how to [configure active learning](../active_learning.md#configuration) for your model.

docs/enterprise/active-learning/random_sampling.md

@@ -38,5 +38,3 @@ Here is an example of a configuration manifest for random sampling strategy:
   ]
 }
 ```
-
-Learn how to [configure active learning](../active_learning.md#configuration) for your model.

docs/foundation/clip.md

@@ -19,7 +19,7 @@ In this guide, we will show:
 
 - directly from `inference[clip]` package, integrating the model directly into your code
 - using `inference` HTTP API (hosted locally, or on the Roboflow platform), integrating via HTTP protocol
-  - using `inference-sdk` package (`pip install inference-sdk`) and [`InferenceHTTPClient`](/docs/inference_sdk/http_client.md)
+  - using `inference-sdk` package (`pip install inference-sdk`) and [`InferenceHTTPClient`](/inference_helpers/inference_sdk/)
   - creating custom code to make HTTP requests (see [API Reference](/api/))
 
 ## Supported CLIP versions

docs/index.md

@@ -239,7 +239,7 @@ Below you can find list of extras available for `inference` and `inference-gpu`
 </tr>
 <tr>
   <td><code>yolo-world</code></td>
-  <td><a href="/foundation/yolo-world">Yolo-World model</a></td>
+  <td><a href="/foundation/yolo_world/">Yolo-World model</a></td>
   <td><code>N/A</code></td>
 </tr>
 </table>

docs/inference_helpers/inference_sdk.md

@@ -451,7 +451,7 @@ CLIENT.unload_model(model_id="some/1")
 ```
 
 Sometimes (to avoid OOM at server side) - unloading model will be required.
-[test_postprocessing.py](..%2F..%2Ftests%2Finference_client%2Funit_tests%2Fhttp%2Futils%2Ftest_postprocessing.py)
+
 
 !!! tip
 

docs/models/from_local_weights.md

@@ -1,4 +1,4 @@
-You can upload [supported weights](/models/supported_models/) to Roboflow and deploy them to your device.
+You can upload [supported weights](#supported_models) to Roboflow and deploy them to your device.
 
 This is ideal if you have already trained a model outside of Roboflow that you want to deploy with Inference.
 
@@ -33,7 +33,7 @@ version = project.version(1)
 version.deploy("model-type", "path/to/training/results/")
 ```
 
-The following model types are supported:
+<a name="supported_models">The following model types are supported:</a>
 
 |Model Architecture|Task      |Model Type ID                 |
 |------------------|----------------|-------------------|
@@ -56,7 +56,7 @@ The following model types are supported:
 
 In the code above, replace:
 
-1. `your-project-id` with the ID of your project. [Learn how to retrieve your Roboflow project ID](/docs/projects/where_is_my_project_id/).
+1. `your-project-id` with the ID of your project. [Learn how to retrieve your Roboflow project ID](https://docs.roboflow.com/api-reference/workspace-and-project-ids).
 2. `1` with the version number of your project.
 3. `model-type` with the model type you want to deploy.
 4. `path/to/training/results/` with the path to the weights you want to upload. This path will vary depending on what model architecture you are using.

docs/server_configuration/environmental_variables.md

@@ -1,6 +1,6 @@
 # Environmental vaiables
 
-`Inference` behavior can be controlled by set of environmental variables. All environmental variables are listed in [inference/core/env.py](inference/core/env.py)
+`Inference` behavior can be controlled by set of environmental variables. All environmental variables are listed in [inference/core/env.py](https://github.com/roboflow/inference/blob/main/inference/core/env.py)
 
 Below is a list of some environmental values that require more in-depth explanation.
 

docs/using_inference/inference_pipeline.md

@@ -319,7 +319,7 @@ of frames in tiles mosaic.
 
 !!! Info
 
-    See our [tutorial on creating a custom Inference Pipeline sink!](/quickstart/create_a_custom_inference_pipeline_sink/)
+    See our [tutorial on creating a custom Inference Pipeline sink!](#custom-sinks)
 
 **prediction**
 

docs/using_inference/native_python_api.md

@@ -20,7 +20,7 @@ The `get_model` method is a utility function which will help us load a computer
 
 !!! Hint
 
-    You can find your models project name and version number <a href="https://docs.roboflow.com/api-reference/workspace-and-project-ids" target="_blank">in the Roboflow App</a>. You can also browse public models that are ready to use on <a href="https://universe.roboflow.com/" target="_blank">Roboflow Universe</a>. In this example, we are using a special model ID that is an alias of <a href="https://universe.roboflow.com/microsoft/coco/model/13" target="_blank">a COCO pretrained model on Roboflow Universe</a>. You can see the list of model aliases [here](../../reference_pages/model_aliases).
+    You can find your models project name and version number <a href="https://docs.roboflow.com/api-reference/workspace-and-project-ids" target="_blank">in the Roboflow App</a>. You can also browse public models that are ready to use on <a href="https://universe.roboflow.com/" target="_blank">Roboflow Universe</a>. In this example, we are using a special model ID that is an alias of <a href="https://universe.roboflow.com/microsoft/coco/model/13" target="_blank">a COCO pretrained model on Roboflow Universe</a>. You can see the list of model aliases [here](/quickstart/aliases/#supported-pre-trained-models).
 
 Next, we can run inference with our model by providing an input image:
 

docs/workflows/create_workflow_block.md

@@ -199,7 +199,7 @@ we will be creating SIMD block.
     If you look deeper into codebase, you will discover those are type aliases - telling `pydantic`
     to expect string matching `$inputs.{name}` and `$steps.{name}.*` patterns respectively, additionally providing 
     extra schema field metadata that tells Workflows ecosystem components that the `kind` of data behind selector is 
-    [image](/workflows/kinds/batch_image/).
+    [image](/workflows/kinds/image/).
 
     * denoting `pydantic` `Field(...)` attribute in the last parts of line `17` is optional, yet appreciated, 
     especially for blocks intended to cooperate with Workflows UI 

docs/workflows/custom_python_code_blocks.md

@@ -4,7 +4,7 @@ When the syntax for Workflow definitions was [outlined](/workflows/definitions/)
 aspect was not covered: the ability to define blocks directly within the Workflow definition itself. This section can
 include the manifest and Python code for blocks defined in-place, which are dynamically interpreted by the 
 Execution Engine. These in-place blocks function similarly to those statically defined in 
-[plugins](/workflows/workflows_bundling/), yet provide much more flexibility.
+[plugins](/workflows/blocks_bundling/), yet provide much more flexibility.
 
 
 !!! Warning

docs/workflows/workflows_execution_engine.md

@@ -77,7 +77,7 @@ As the definition suggests, a SIMD (Single Instruction, Multiple Data) step proc
 same operation is applied to each data point, potentially using non-batch-oriented parameters for configuration. 
 The output from such a step is expected to be a batch of elements, preserving the order of the input batch elements. 
 This applies to both regular processing steps and flow-control steps (see 
-[blocks development guide](/workflows/create_workflow_block/ for more on their nature), where flow-control decisions 
+[blocks development guide](/workflows/create_workflow_block/)), where flow-control decisions 
 affect each batch element individually.
 
 In essence, the type of data fed into the step determines whether it's SIMD or non-SIMD. If a step requests any 

inference/core/interfaces/stream/inference_pipeline.py

@@ -157,10 +157,10 @@ def init(
                 without re-raising. Default: None.
             source_buffer_filling_strategy (Optional[BufferFillingStrategy]): Parameter dictating strategy for
                 video stream decoding behaviour. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             source_buffer_consumption_strategy (Optional[BufferConsumptionStrategy]): Parameter dictating strategy for
                 video stream frames consumption. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             class_agnostic_nms (Optional[bool]): Parameter of model post-processing. If not given - value checked in
                 env variable "CLASS_AGNOSTIC_NMS" with default "False"
             confidence (Optional[float]): Parameter of model post-processing. If not given - value checked in
@@ -333,10 +333,10 @@ def init_with_yolo_world(
                 without re-raising. Default: None.
             source_buffer_filling_strategy (Optional[BufferFillingStrategy]): Parameter dictating strategy for
                 video stream decoding behaviour. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             source_buffer_consumption_strategy (Optional[BufferConsumptionStrategy]): Parameter dictating strategy for
                 video stream frames consumption. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             class_agnostic_nms (Optional[bool]): Parameter of model post-processing. If not given - value checked in
                 env variable "CLASS_AGNOSTIC_NMS" with default "False"
             confidence (Optional[float]): Parameter of model post-processing. If not given - value checked in
@@ -483,10 +483,10 @@ def init_with_workflow(
                 without re-raising. Default: None.
             source_buffer_filling_strategy (Optional[BufferFillingStrategy]): Parameter dictating strategy for
                 video stream decoding behaviour. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             source_buffer_consumption_strategy (Optional[BufferConsumptionStrategy]): Parameter dictating strategy for
                 video stream frames consumption. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             video_source_properties (Optional[dict[str, float]]): Optional source properties to set up the video source,
                 corresponding to cv2 VideoCapture properties cv2.CAP_PROP_*. If not given, defaults for the video source
                 will be used.
@@ -651,10 +651,10 @@ def init_with_custom_logic(
                 without re-raising. Default: None.
             source_buffer_filling_strategy (Optional[BufferFillingStrategy]): Parameter dictating strategy for
                 video stream decoding behaviour. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             source_buffer_consumption_strategy (Optional[BufferConsumptionStrategy]): Parameter dictating strategy for
                 video stream frames consumption. By default - tweaked to the type of source given.
-                Please find detailed explanation in docs of [`VideoSource`](../camera/video_source.py)
+                Please find detailed explanation in docs of [`VideoSource`](/docs/reference/inference/core/interfaces/camera/video_source/#inference.core.interfaces.camera.video_source.VideoSource)
             video_source_properties (Optional[Union[Dict[str, float], List[Optional[Dict[str, float]]]]]):
                 Optional source properties to set up the video source, corresponding to cv2 VideoCapture properties
                 cv2.CAP_PROP_*. If not given, defaults for the video source will be used.

tests/workflows/unit_tests/core_steps/classical_cv/test_sift_comparison_v2.py

@@ -3,15 +3,14 @@
 import pytest
 from pydantic import ValidationError
 
-from inference.core.workflows.execution_engine.entities.base import (
-    ImageParentMetadata,
-    WorkflowImageData,
-)
-
 from inference.core.workflows.core_steps.classical_cv.sift_comparison.v2 import (
     SIFTComparisonBlockManifest,
     SIFTComparisonBlockV2,
 )
+from inference.core.workflows.execution_engine.entities.base import (
+    ImageParentMetadata,
+    WorkflowImageData,
+)
 
 
 def test_sift_comparison_validation_when_valid_manifest_is_given() -> None:
@@ -24,7 +23,7 @@ def test_sift_comparison_validation_when_valid_manifest_is_given() -> None:
         "good_matches_threshold": 50,
         "ratio_threshold": 0.7,
         "matcher": "FlannBasedMatcher",
-        "visualize": True
+        "visualize": True,
     }
 
     # when
@@ -39,7 +38,7 @@ def test_sift_comparison_validation_when_valid_manifest_is_given() -> None:
         good_matches_threshold=50,
         ratio_threshold=0.7,
         matcher="FlannBasedMatcher",
-        visualize=True
+        visualize=True,
     )
 
 
@@ -72,7 +71,7 @@ def test_sift_comparison_block_with_descriptors(dogs_image: np.ndarray) -> None:
         input_2=descriptor_2,
         good_matches_threshold=50,
         ratio_threshold=0.7,
-        visualize=False
+        visualize=False,
     )
 
     # then
@@ -82,10 +81,11 @@ def test_sift_comparison_block_with_descriptors(dogs_image: np.ndarray) -> None:
     assert output["visualization_2"] is None
     assert output["visualization_matches"] is None
 
+
 def test_sift_comparison_block_with_images(dogs_image: np.ndarray) -> None:
     # given
     block = SIFTComparisonBlockV2()
-    
+
     # when
     output = block.run(
         input_1=WorkflowImageData(
@@ -98,7 +98,7 @@ def test_sift_comparison_block_with_images(dogs_image: np.ndarray) -> None:
         ),
         good_matches_threshold=50,
         ratio_threshold=0.7,
-        visualize=False
+        visualize=False,
     )
 
     # then
@@ -108,10 +108,11 @@ def test_sift_comparison_block_with_images(dogs_image: np.ndarray) -> None:
     assert output["visualization_2"] is None
     assert output["visualization_matches"] is None
 
+
 def test_sift_comparison_block_with_visualization(dogs_image: np.ndarray) -> None:
     # given
     block = SIFTComparisonBlockV2()
-    
+
     # when
     output = block.run(
         input_1=WorkflowImageData(
@@ -124,7 +125,7 @@ def test_sift_comparison_block_with_visualization(dogs_image: np.ndarray) -> Non
         ),
         good_matches_threshold=50,
         ratio_threshold=0.7,
-        visualize=True
+        visualize=True,
     )
 
     # then
@@ -133,4 +134,3 @@ def test_sift_comparison_block_with_visualization(dogs_image: np.ndarray) -> Non
     assert output["visualization_1"] is not None
     assert output["visualization_2"] is not None
     assert output["visualization_matches"] is not None
-