Docs metro robotics review rel links 26 0 (#2410)

Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>

Author: wiwaszko-intel

manufacturing-ai-suite/hmi-augmented-worker/docs/user-guide/release-notes.md

@@ -15,7 +15,7 @@ Intel® Core™ i7-14700 based systems with Edge Microvisor Toolkit and Wind
 
 **Documentation:**
 
-Documentation is **completed**. [README.md](../../README.md) is updated with installation steps and reference documents.
+Documentation is **completed**. [README.md](https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/manufacturing-ai-suite/hmi-augmented-worker/README.md) is updated with installation steps and reference documents.
 
 **Known Limitations and Issues:**
 

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

@@ -3,6 +3,7 @@
 The Live Video Captioning sample application demonstrates real-time video captioning using Intel® DLStreamer and OpenVINO™. It processes RTSP video stream, applies video analytics pipelines for efficient decoding and inference, and leverages a Vision-Language Model(VLM) to generate live captions for the video content. In addition to captioning, the application provides performance metrics such as throughput and latency, enabling developers to evaluate and optimize end-to-end system performance for real-time scenarios.
 
 By following this guide, you will learn how to:
+
 - **Set up the sample application**: Use Docker Compose to quickly deploy the application in your environment.
 - **Run the application**: Execute the application to see real-time captioning from your video stream.
 - **Modify application parameters**: Customize settings like inference models and VLM parameters to adapt the application to your specific requirements.
@@ -12,14 +13,15 @@ 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) or simulated RTSP stream source using local video files.
-- OpenVINO-compatible VLM in `ov_models/`. User may use the [script](../../download_models.sh) provided to prepare the model.
+- RTSP stream source (live camera or test feed). To create a simulated RTSP test feed stream using existing video files, see the [Streamer readme](https://github.com/open-edge-platform/scenescape/tree/release-2026.0/tools/streamer).
+- OpenVINO-compatible VLM in `ov_models/`. For convenience, use the [download models script](https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/metro-ai-suite/live-video-analysis/live-video-captioning/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.
 
 ## Run the application
 
 1. **Clone the repository**:
+
      ```bash
      # Clone the release branch
      git clone https://github.com/open-edge-platform/edge-ai-suites.git edges-ai-suites -b release-2026.0.0
@@ -28,19 +30,24 @@ when object detection in the pipeline is enabled. Please refer to the [Object De
 > **Note:** Adjust the repo link appropriately in case of forked repo.
 
 2. **Navigate to the Directory**:
+
      ```bash
      cd edge-ai-suites/metro-ai-suite/live-video-analysis/live-video-captioning
      ```
 
 3. **Configure Image Registry and Tag**:
+
      ```bash
         export REGISTRY="intel/"
         export TAG="1.0.0"
      ```
-    Skip this step if you prefer to build the sample applciation from source. For detailed instructions, refer to the [Build from Source](./get-started/build-from-source.md) guide for details.
+
+    Skip this step if you prefer to build the sample application from source. For detailed instructions, refer to the [Build from Source](./get-started/build-from-source.md) guide for details.
 
 4. **Configure Environment**:
-    Create a `.env` file in the repository root:
+
+    Create an `.env` file in the repository root:
+
      ```bash
      WHIP_SERVER_IP=mediamtx
      WHIP_SERVER_PORT=8889
@@ -55,13 +62,16 @@ when object detection in the pipeline is enabled. Please refer to the [Object De
      ALERT_MODE=False
      ENABLE_DETECTION_PIPELINE=False
      ```
+
     Notes:
     - `HOST_IP` must be reachable by the browser client for WebRTC signaling.
     - `PIPELINE_SERVER_URL` defaults to `http://dlstreamer-pipeline-server:8080`.
     - `WEBRTC_BITRATE` controls the video bitrate in kbps for WebRTC streaming (default: 2048).
 
 5. **Download/Export Models**:
+
     Run the following scripts to download and convert VLM models.
+
      ```bash
      chmod +x download_models.sh
      ./download_models.sh [internvl2_1B|gemma3|internvl2_2B]
@@ -80,7 +90,9 @@ when object detection in the pipeline is enabled. Please refer to the [Object De
      ```
 
 6. **Start the Application**:
+
     Start the application using Docker Compose tool:
+
      ```bash
      docker compose up
      ```
@@ -98,12 +110,15 @@ when object detection in the pipeline is enabled. Please refer to the [Object De
     > **Note:** If running in a proxy network, ensure that your RTSP stream URLs or IPs are added to the `no_proxy` environment variable to allow direct connections to the stream source without going through the proxy.
 
 8. **Stop the Services**:
+
     Stop the sample application services using below:
+
      ```bash
      docker compose down
      ```
 
 ## Advanced Setup Options
+
 For alternative ways to setup the application, see:
 
 - [Build from Source](./get-started/build-from-source.md)

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

@@ -8,6 +8,7 @@ For containerized applications built using the DL Streamer Pipeline Server, firs
 provide GPU device(s) access to the container user.
 
 ### Provide GPU access to the container
+
 This can be done by making the following changes to the docker compose file.
 
 ```yaml
@@ -22,10 +23,12 @@ services:
       # you can add specific devices in case you don't want to provide access to all like below.
       - "/dev:/dev"
 ```
+
 The changes above adds the container user to the `render` group and provides access to the GPU
 devices.
 
 ### Hardware specific encoder/decoders
+
 Unlike the changes done for the container above, the following requires a modification to the
 media pipeline itself.
 
@@ -52,7 +55,7 @@ DL Streamer document for selecting the GPU render device of your choice for VA c
 > **Note:** This sample application already provides a default `compose-without-scenescape.yml`
 > file that includes the necessary GPU access to the containers.
 
-The pipeline `object_tracking_gpu` in [pipeline-server-config](../../../src/dlstreamer-pipeline-server/config.json)
+The pipeline `object_tracking_gpu` in [pipeline-server-config](https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/metro-ai-suite/metro-vision-ai-app-recipe/loitering-detection/src/dlstreamer-pipeline-server/config.json)
 contains GPU specific elements and uses GPU backend for inferencing. We can start the pipeline
 as follows:
 

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

@@ -1,12 +1,14 @@
 # Use GPU for Inference
 
 ## Pre-requisites
+
 In order to benefit from hardware acceleration, pipelines can be constructed in a manner that
 different stages such as decoding, inference etc., can make use of these devices.
 For containerized applications built using the DL Streamer Pipeline Server, first we need to
 provide GPU device(s) access to the container user.
 
 ### Provide GPU access to the container
+
 This can be done by making the following changes to the docker compose file.
 
 ```yaml
@@ -26,6 +28,7 @@ The changes above adds the container user to the `render` group and provides acc
 GPU devices.
 
 ### Hardware specific encoder/decoders
+
 Unlike the changes done for the container above, the following requires a modification to the
 media pipeline itself.
 
@@ -39,6 +42,7 @@ pipeline by adding `video/x-raw(memory: VAMemory)` for Intel GPUs (integrated an
 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 the DL Streamer
 [Model Preparation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/dev_guide/model_preparation.html#model-pre-and-post-processing) documentation for more information.
@@ -53,7 +57,7 @@ DL Streamer document for selecting the GPU render device of your choice for VA c
 > **Note:** This sample application already provides a default `compose-without-scenescape.yml`
 > file that includes the necessary GPU access to the containers.
 
-The pipeline `yolov11s_gpu` in [pipeline-server-config](../../../src/dlstreamer-pipeline-server/config.json)
+The pipeline `yolov11s_gpu` in [pipeline-server-config](https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/metro-ai-suite/metro-vision-ai-app-recipe/smart-parking/src/dlstreamer-pipeline-server/config.json)
 contains GPU specific elements and uses GPU backend for inferencing. We can start the pipeline
 as follows: