[DOCS] technical adjustments for the build mstr (#26883)

Author: kblaszczak-intel

CONTRIBUTING.md

@@ -52,7 +52,7 @@ product better.
      Since the market of computing devices is constantly evolving, OpenVINO is always open to extending
      its support for new hardware. If you want to run inference on a device that is currently not supported,
      you can see how to develop a new plugin for it in the
-     [Plugin Developer Guide](https://docs.openvino.ai/canonical/openvino_docs_ie_plugin_dg_overview.html).
+     [Plugin Developer Guide](https://docs.openvino.ai/2024/documentation/openvino-extensibility/openvino-plugin-library.html).
 
 
 ### Improve documentation

docs/articles_en/get-started/install-openvino/install-openvino-pip.rst

@@ -15,19 +15,7 @@ Install Intel® Distribution of OpenVINO™ Toolkit from PyPI Repository
    * is dedicated to users of all major OSes: Windows, Linux, and macOS
      (all x86_64 / arm64 architectures)
    * macOS offers support only for CPU inference
-   
-| **Simplified Build and Integration**  
-|   The package includes CMake configurations, precompiled static libraries, and headers, which
-    can be easily accessed through the Python API. You can use the `get_cmake_path()` method to
-    retrieve the paths to the CMake configurations and libraries:
-
-.. code-block:: python
 
-   from openvino import get_cmake_path
-   cmake_path = get_cmake_path()
-   
-For detailed instructions on how to use these configurations in your build setup, check out the
-:ref:`Create a library with extensions <create_a_library_with_extensions>` section.
 
 .. tab-set::
 
@@ -42,10 +30,13 @@ For detailed instructions on how to use these configurations in your build setup
    .. tab-item:: Processor Notes
       :sync: processor-notes
 
-      | To see if your processor includes the integrated graphics technology and supports iGPU inference, refer to:
+      | To see if your processor includes the integrated graphics technology and supports iGPU
+        inference, refer to:
       | `Product Specifications <https://ark.intel.com/>`__
 
 
+
+
 Installing OpenVINO Runtime
 ###########################
 
@@ -137,20 +128,47 @@ to see if your case needs any of them.
 
 
 
+
+
+| **Simplified Build and Integration**
+|   The package includes CMake configurations, precompiled static libraries, and headers, which
+    can be easily accessed through the Python API. You can use the `get_cmake_path()` method to
+    retrieve the paths to the CMake configurations and libraries:
+
+.. code-block:: python
+
+   from openvino import get_cmake_path
+   cmake_path = get_cmake_path()
+
+For detailed instructions on how to use these configurations in your build setup, check out the
+:ref:`Create a library with extensions <create_a_library_with_extensions>` section.
+
+
+
+
+
+
+
 What's Next?
 ####################
 
-Now that you've installed OpenVINO Runtime, you're ready to run your own machine learning applications! Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials.
+Now that you've installed OpenVINO Runtime, you're ready to run your own machine learning
+applications! Learn more about how to integrate a model in OpenVINO applications by trying out
+the following tutorials.
 
 .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
    :width: 400
 
-Try the `Python Quick Start Example <https://docs.openvino.ai/2024/notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+Try the `Python Quick Start Example <https://docs.openvino.ai/2024/notebooks/vision-monodepth-with-output.html>`__
+to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside
+your web browser.
+
 
 Get started with Python
 +++++++++++++++++++++++
 
-Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
+Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more
+Jupyter Notebooks to get you started with OpenVINO, such as:
 
 * `OpenVINO Python API Tutorial <https://docs.openvino.ai/2024/notebooks/openvino-api-with-output.html>`__
 * `Basic image classification program with Hello Image Classification <https://docs.openvino.ai/2024/notebooks/hello-world-with-output.html>`__

docs/sphinx_setup/conf.py

@@ -55,7 +55,9 @@
     '.md': 'markdown',
 }
 
-# html_baseurl = 'https://docs.openvino.ai/canonical/'
+
+# html_baseurl = 'https://docs.openvino.ai/2024/'
+
 
 # -- Sitemap configuration ---------------------------------------------------