[DOCS] Update refs to release branch - Metro part 2

Author: wiwaszko-intel

education-ai-suite/smart-classroom/docs/user-guide/get-started.md

@@ -14,7 +14,7 @@ Download from [https://ffmpeg.org/download.html](https://ffmpeg.org/download.htm
 
 Download the archive from [DL Streamer assets on GitHub](https://github.com/open-edge-platform/edge-ai-libraries/releases) Extract to a new folder, for example `C:\\dlstreamer_dlls`.
 
-For details, refer to [Install Guide](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dl-streamer/get_started/install/install_guide_windows.html).
+For details, refer to [Install Guide](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dl-streamer/get_started/install/install_guide_windows.html).
 
 **Run your shell with admin privileges before starting the application**
 
@@ -57,7 +57,7 @@ asr:
   temperature: 0.0
 
 summarizer:
-  provider: openvino          
+  provider: openvino
   name: Qwen/Qwen2-7B-Instruct # Examples: Qwen/Qwen1.5-7B-Chat, Qwen/Qwen2-7B-Instruct, Qwen/Qwen2.5-7B-Instruct
   device: GPU                 # Options: GPU or CPU
   weight_format: int8         # Supported: fp16, fp32, int4, int8
@@ -81,7 +81,7 @@ asr:
 Activate the environment before running the application:
 
 ```bash
-smartclassroom\Scripts\activate  
+smartclassroom\Scripts\activate
 ```
 
 Run the backend:

metro-ai-suite/live-video-analysis/live-video-captioning/docs/user-guide/get-started.md

@@ -12,7 +12,7 @@ By following this guide, you will learn how to:
 - Verify that your system meets the minimum requirements. See [System Requirements](./get-started/system-requirements.md) for details.
 - Install Docker: [Installation Guide](https://docs.docker.com/get-docker/).
 - Install Docker Compose: [Installation Guide](https://docs.docker.com/compose/install/).
-- RTSP stream source (live camera or test feed). Please refer to this [guide](https://github.com/open-edge-platform/scenescape/tree/main/tools/streamer) to create simulated RTSP test feed stram using exisiting video files.
+- RTSP stream source (live camera or test feed). Please refer to this [guide](https://github.com/open-edge-platform/scenescape/tree/release-2026.0/tools/streamer) to create simulated RTSP test feed stram using exisiting video files.
 - OpenVINO-compatible VLM in `ov_models/`. User may use the [script](../../download_models.sh) provided to prepare the model.
 - OpenVINO-compatible Object Detection Models in `ov_detection_models/`. This is only required
 when object detection in the pipeline is enabled. Please refer to the [Object Detection Pipeline configuration](./object-detection-pipeline.md) guide for information on how to enable it.

metro-ai-suite/metro-sdk-manager/docs/user-guide/metro-vision-ai-sdk/get-started.md

@@ -156,17 +156,17 @@ Profiling and monitoring performance of Metro Vision AI workloads using command-
 
 ### Technical Documentation
 
-- [DL Streamer](http://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dl-streamer/index.html)
+- [DL Streamer](http://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dl-streamer/index.html)
   \- Comprehensive documentation for Intel's GStreamer-based video analytics framework
-- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
+- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
   \- RESTful microservice architecture documentation for scalable video analytics deployment
 - [OpenVINO](https://docs.openvino.ai/2025/get-started.html)
   \- Complete reference for Intel's cross-platform inference optimization toolkit
 - [OpenVINO Model Server](https://docs.openvino.ai/2025/model-server/ovms_what_is_openvino_model_server.html)
   \- Model serving infrastructure documentation for production deployments
-- [Edge AI Libraries](https://docs.openedgeplatform.intel.com/dev/ai-libraries.html)
+- [Edge AI Libraries](https://docs.openedgeplatform.intel.com/2026.0/ai-libraries.html)
   \- Comprehensive development toolkit documentation and API references
-- [Edge AI Suites](https://docs.openedgeplatform.intel.com/dev/ai-suite-metro.html)
+- [Edge AI Suites](https://docs.openedgeplatform.intel.com/2026.0/ai-suite-metro.html)
   \- Complete application suite documentation with implementation examples
 
 ### Support Channels

metro-ai-suite/metro-sdk-manager/docs/user-guide/visual-ai-demo-kit/get-started.md

@@ -175,17 +175,17 @@ Create compelling visualization experiences for your AI applications. This tutor
 
 ### Technical Documentation
 
-- [DL Streamer](http://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dl-streamer/index.html)
+- [DL Streamer](http://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dl-streamer/index.html)
   \- Comprehensive documentation for Intel's GStreamer-based video analytics framework
-- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
+- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
   \- RESTful microservice architecture documentation for scalable video analytics deployment
 - [OpenVINO](https://docs.openvino.ai/2025/get-started.html)
   \- Complete reference for Intel's cross-platform inference optimization toolkit
 - [OpenVINO Model Server](https://docs.openvino.ai/2025/model-server/ovms_what_is_openvino_model_server.html)
   \- Model serving infrastructure documentation for production deployments
-- [Edge AI Libraries](https://docs.openedgeplatform.intel.com/dev/ai-libraries.html)
+- [Edge AI Libraries](https://docs.openedgeplatform.intel.com/2026.0/ai-libraries.html)
   \- Comprehensive development toolkit documentation and API references
-- [Edge AI Suites](https://docs.openedgeplatform.intel.com/dev/ai-suite-metro.html)
+- [Edge AI Suites](https://docs.openedgeplatform.intel.com/2026.0/ai-suite-metro.html)
   \- Complete application suite documentation with implementation examples
 
 ### Support Channels

metro-ai-suite/metro-vision-ai-app-recipe/README.md

@@ -17,9 +17,9 @@ The key features are:
 
 |              |             |            |
 |:-------------|:------------|:-----------|
-| [Smart Intersection Management (includes optional Scenescape components)](./smart-intersection/)                                           | AI-driven traffic flow optimization and intersection monitoring.                                 | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/smart-intersection/index.html)                                                          |
-| [Loitering Detection](./loitering-detection) | Real-time detection of loitering behavior in transportation hubs                                   | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/pallet-defect-detection/index.html)                       |
-| [Smart Parking](./smart-parking)     | Automated parking space monitoring and management.             | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/loitering-detection/index.html)                         |
+| [Smart Intersection Management (includes optional Scenescape components)](./smart-intersection/)                                           | AI-driven traffic flow optimization and intersection monitoring.                                 | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/smart-intersection/index.html)                                                          |
+| [Loitering Detection](./loitering-detection) | Real-time detection of loitering behavior in transportation hubs                                   | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/pallet-defect-detection/index.html)                       |
+| [Smart Parking](./smart-parking)     | Automated parking space monitoring and management.             | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/loitering-detection/index.html)                         |
 
 
 ## How It Works

metro-ai-suite/metro-vision-ai-app-recipe/loitering-detection/docs/user-guide/get-started.md

@@ -182,7 +182,7 @@ a simplified edge application deployment process.
 
 ## Supporting Resources
 - [Docker Compose Documentation](https://docs.docker.com/compose/)
-- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
+- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer-pipeline-server/index.html)
 
 <!--hide_directive
 :::{toctree}

metro-ai-suite/metro-vision-ai-app-recipe/loitering-detection/docs/user-guide/how-to-guides/customize-application.md

@@ -22,7 +22,7 @@ providing real-time dashboards.
 ## DL Streamer Pipeline Server
 
 > For detailed documentation on Deep Learning Streamer Pipeline Server (DL Streamer Pipeline
-Server), visit the [DL Streamer Pipeline Server Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html).
+Server), visit the [DL Streamer Pipeline Server Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer-pipeline-server/index.html).
 
 ![Pipeline Architecture](../_assets/pipeline.png)
 

metro-ai-suite/metro-vision-ai-app-recipe/loitering-detection/docs/user-guide/how-to-guides/use-gpu-for-inference.md

@@ -36,15 +36,15 @@ Examples of such elements are `vah264dec`, `vah264enc`, `vajpegdec`, `vajpegdec`
 Additionally, one can also enforce zero-copy of buffers using GStreamer capabilities to the
 pipeline by adding `video/x-raw(memory: VAMemory)` for Intel GPUs (integrated and discrete).
 
-Read the DL Streamer [GPU Device Selection](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) document for more details.
+Read the DL Streamer [GPU Device Selection](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) document for more details.
 
 ### GPU specific element properties
 
-DL Streamer inference elements also provides property such as `device=GPU` and `pre-process-backend=va-surface-sharing` to infer and pre-process on GPU. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
+DL Streamer inference elements also provides property such as `device=GPU` and `pre-process-backend=va-surface-sharing` to infer and pre-process on GPU. Read DL Streamer [docs](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) for more.
 
 ### Selecting the GPU render device of your choice if there is more than one GPU device on the system
 
-If you have multiple GPUs (integrated/discrete), please follow the [GPU Device Selection](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html)
+If you have multiple GPUs (integrated/discrete), please follow the [GPU Device Selection](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html)
 DL Streamer document for selecting the GPU render device of your choice for VA codecs plugins.
 
 ## Tutorial on how to use GPU specific pipelines

metro-ai-suite/metro-vision-ai-app-recipe/smart-intersection/docs/user-guide/get-started.md

@@ -158,8 +158,8 @@ Choose one of the following methods to deploy the Smart Intersection Sample Appl
 ## Resources
 
 - [Troubleshooting](./troubleshooting.md): Find detailed steps to resolve common issues during deployments.
-- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer-pipeline-server/index.html): Intel microservice based on Python for video ingestion and deep learning inferencing functions.
-- [Intel® SceneScape](https://docs.openedgeplatform.intel.com/dev/scenescape/index.html): Intel Scene-based AI software framework.
+- [DL Streamer Pipeline Server](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer-pipeline-server/index.html): Intel microservice based on Python for video ingestion and deep learning inferencing functions.
+- [Intel® SceneScape](https://docs.openedgeplatform.intel.com/2026.0/scenescape/index.html): Intel Scene-based AI software framework.
 
 <!--hide_directive
 :::{toctree}

metro-ai-suite/metro-vision-ai-app-recipe/smart-intersection/docs/user-guide/how-to-use-gpu-for-inference.md

@@ -20,4 +20,4 @@ Also, set `"auto_start": false` for the other pipelines in the same configuratio
 Start the application with:
 `docker compose up -d`
 
-> **Note:** If you have multiple GPUs (integrated/discrete), please follow the [GPU Device Selection](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) DL Streamer document for selecting the GPU render device of your choice for VA codecs plugins.
+> **Note:** If you have multiple GPUs (integrated/discrete), please follow the [GPU Device Selection](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/gpu_device_selection.html) DL Streamer document for selecting the GPU render device of your choice for VA codecs plugins.