[DOCS] Fixing broken references

docs/user-guide/architecture/pipelines.md

@@ -24,7 +24,7 @@ Details about the latest version can be found in the [OpenVINO Release Notes](ht
 
 ### DL Streamer Pipeline
 
-Rather than working directly with the OpenVINO APIs our solutions offers more practical ways to interface with OpenVINO. One method is using [Intel DL Streamer](https://github.com/dlstreamer/dlstreamer). This solution provides a no code way based on [GStreamer](https://gstreamer.freedesktop.org/) and [OpenVINO](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/get-started.html) to deploy, process, and output a pipeline.
+Rather than working directly with the OpenVINO APIs our solutions offers more practical ways to interface with OpenVINO. One method is using [Intel DL Streamer](https://github.com/open-edge-platform/dlstreamer). This solution provides a no code way based on [GStreamer](https://gstreamer.freedesktop.org/) and [OpenVINO](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/get-started.html) to deploy, process, and output a pipeline.
 
 The diagrams show how we take advantage of Docker, Docker Compose, and environment variable files to pre-package DLStreamer based pipelines for our use cases. Leveraging Environment Variables allows users to modify properties on the fly when different configurations are required.
 

docs/user-guide/architecture/v2.0.0/multiple-ovms-json-config.md

@@ -116,6 +116,6 @@ For clean-up, we can do deletion of the config json files when `make clean-all`
 
 <!-- [link](requirements-review-process.md) - useful links for the design -->
 
-- <https://docs.openvino.ai/2023.0/ovms_what_is_openvino_model_server.html>
+- <https://docs.openvino.ai/2025/model-server/ovms_what_is_openvino_model_server.html>
 - <https://github.com/openvinotoolkit/model_server>
 - see issue Classification profile crashed when run the 2nd instance switch from CPU to GPU.0 automated-self-checkout#322

docs/user-guide/architecture/v2.0.0/performance_benchmarking.md

@@ -47,19 +47,19 @@ Intel Performance Counter Monitor: System power usage
 
 For performance inputs we support RTSP video streams, USB camera, Intel® RealSense™ Camera, and video files. For longer benchmarking runs its' recommended to use a video loop with an RTSP stream for inference result consistency. As an option an RTSP [Camera Simulator](https://intel-retail.github.io/automated-self-checkout/run_camera_simulator.html) is provided with the performance script.
 
-[Input Source Types](https://intel-retail.github.io/automated-self-checkout/pipelinebenchmarking.html#input-source-type)
+[Input Source Types](https://intel-retail.github.io/automated-self-checkout/OVMS/pipelinebenchmarking.html#input-source-type)
 
 #### Specified Number of Pipelines
 
 If you are looking to test a specific number of pipelines on different hardware SKUs the `--pipelines` parameter can be used. This parameter will start the specified number of pipelines
 
-[Specified Number of Pipelines](https://intel-retail.github.io/automated-self-checkout/pipelinebenchmarking.html#benchmark-specified-number-of-pipelines)
+[Specified Number of Pipelines](https://intel-retail.github.io/automated-self-checkout/OVMS/pipelinebenchmarking.html#benchmark-specified-number-of-pipelines)
 
 [![Specified Number of Pipelines](./images/num-of-pipelines.jpg)](./images/num-of-pipelines.jpg)
 
 #### Consolidated Results
 
-To make reading results easier, a consolidation script has been provided. This script will work with a single or multiple runs of the specified number of pipelines. Details about this process are found in [Benchmark Specified Number of Pipelines](https://intel-retail.github.io/automated-self-checkout/pipelinebenchmarking.html#benchmark-specified-number-of-pipelines)
+To make reading results easier, a consolidation script has been provided. This script will work with a single or multiple runs of the specified number of pipelines. Details about this process are found in [Benchmark Specified Number of Pipelines](https://intel-retail.github.io/automated-self-checkout/OVMS/pipelinebenchmarking.html#benchmark-specified-number-of-pipelines)
 
 ```bash
 make consolidate ROOT_DIRECTORY=<output dir>
@@ -69,7 +69,7 @@ make consolidate ROOT_DIRECTORY=<output dir>
 
 The stream density parameter can be used to find the maximum number of pipelines at a target frames per second (FPS) on a specific hardware SKU. By setting the `--stream_density` parameter to the desired FPS the script will continue to create pipelines until the average pipelines FPS falls below the desired FPS. The script will provide a detailed log to show each pipeline FPS during the test run. This option provides a method for testing the top performance when introducing a new pipeline or hardware SKU.
 
-[Stream Density](https://intel-retail.github.io/automated-self-checkout/pipelinebenchmarking.html#benchmark-stream-density)
+[Stream Density](https://intel-retail.github.io/automated-self-checkout/OVMS/pipelinebenchmarking.html#benchmark-stream-density)
 
 [![Stream Density](./images/stream-density.jpg)](./images/stream-density.jpg)
 
@@ -87,4 +87,4 @@ Having a generic and scalable set of performance Docker containers will allow cu
 
 <!-- [link](requirements-review-process.md) - useful links for the design -->
 
-[Pipeline Benchmarking](https://intel-retail.github.io/automated-self-checkout/pipelinebenchmarking.html)
+[Pipeline Benchmarking](https://intel-retail.github.io/automated-self-checkout/OVMS/pipelinebenchmarking.html)

docs/user-guide/architecture/v2.0.0/target-device.md

@@ -44,14 +44,14 @@ Update the platform parameter to match the target_device standard used by OpenVI
 
 | Device                           | Parameter  | Description                                                                                                                                                            | Links                                                                                                |
 | -------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
-| CPU                              | CPU        | Use CPU only                                                                                                                                                           | [OVMS Parameters](https://docs.openvino.ai/2023.1/ovms_docs_parameters.html)                         |
-| GPU                              | GPU        | Use default GPU                                                                                                                                                        | [OVMS Parameters](https://docs.openvino.ai/2023.1/ovms_docs_parameters.html)                         |
+| CPU                              | CPU        | Use CPU only                                                                                                                                                           | [OVMS Parameters](https://docs.openvino.ai/2025/model-server/ovms_docs_parameters.html)                         |
+| GPU                              | GPU        | Use default GPU                                                                                                                                                        | [OVMS Parameters](https://docs.openvino.ai/2025/model-server/ovms_docs_parameters.html)                         |
 | Specified GPU                    | GPU.x      | Use a specific GPU. ex. GPU.0 = integrated GPU, GPU.1 = discrete Arc GPU                                                                                               | [OVMS Parameters](https://docs.openvino.ai/2023.1/ovms_docs_parameters.html)                         |
 | Mixed Contifuration              | MULTI:x,y  | Use a combination of devices for inferencing ex. MULTI:CPU,GPU.1 will use the CPU and discrete Arc GPU for inferencing                                                 | [OVMS Parameters](https://docs.openvino.ai/2023.1/ovms_docs_parameters.html)                         |
 | Automatic Device Selection       | AUTO       | Allow OpenVINO to automatically select the optimal device for inferencing                                                                                              | Possibly depricated?                                                                                 |
 | Automatic Device Selection       | AUTO       | Allow OpenVINO to automatically select the optimal device for inferencing                                                                                              | Possibly depricated?                                                                                 |
-| Heterogeneous Execution          | HETERO     | Allows OpenVINO to execute inference on multiple devices                                                                                                               | [Heterogeneous Execution](https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Hetero_execution.html) |
-| Heterogeneous Execution Priority | HETERO:x,y | Allows OpenVINO to execute inference on multiple devices and set the priority of device. ex. HETERO:CPU,GPU.1 will prioritize CPU and discrete Arc GPU for inferencing | [Heterogeneous Execution](https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Hetero_execution.html) |
+| Heterogeneous Execution          | HETERO     | Allows OpenVINO to execute inference on multiple devices                                                                                                               | [Heterogeneous Execution](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.html) |
+| Heterogeneous Execution Priority | HETERO:x,y | Allows OpenVINO to execute inference on multiple devices and set the priority of device. ex. HETERO:CPU,GPU.1 will prioritize CPU and discrete Arc GPU for inferencing | [Heterogeneous Execution](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.html) |
 
 ## Applicable Repos
 

docs/user-guide/loss-prevention/get-started.md

@@ -9,7 +9,7 @@
 - Intel hardware (CPU, iGPU, dGPU, NPU)
 - Intel drivers:
   - [Intel GPU drivers](https://dgpu-docs.intel.com/driver/client/overview.html)
-  - [NPU](https://dlstreamer.github.io/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#prerequisite-2-install-intel-npu-drivers)
+  - [NPU](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#prerequisite-2-install-intel-npu-drivers)
 - Sufficient disk space for models, videos, and results
 
 > **Note:** First-time setup downloads AI models, sample videos, and Docker images - this may take 5-15 minutes depending on your internet connection.

docs/user-guide/order-accuracy/dine-in/get-started/system-requirements.md

@@ -41,7 +41,7 @@ Ubuntu 22.04 LTS is the validated platform (matches the `python:3.13-slim` base
 
 ### GPU Drivers
 
-Intel GPU drivers must be installed from [packages.intel.com](https://packages.intel.com). Verify the GPU is accessible to Docker:
+Intel GPU drivers must be installed from <https://dgpu-docs.intel.com/driver/installation.html>. Verify the GPU is accessible to Docker:
 
 ```bash
 ls /dev/dri/

docs/user-guide/troubleshooting.md

@@ -32,7 +32,7 @@ eval $gstLaunchCmd
 
 ## Q: How can I use an Intel RealSense Camera as the input?
 
-A: To use a RealSense camera as an input for any of the retail use cases, you need to obtain the serial number first. Follow the instructions in the [RealSense repository](https://github.com/IntelRealSense/librealsense/tree/master/tools/enumerate-devices) to run `rs-enumerate-devices` and obtain the following information:
+A: To use a RealSense camera as an input for any of the retail use cases, you need to obtain the serial number first. Follow the instructions in the [RealSense repository](https://github.com/realsenseai/librealsense/tree/master/tools/enumerate-devices) to run `rs-enumerate-devices` and obtain the following information:
 
 ```
 Device Name                   Serial Number       Firmware Version