[DOCS] Update refs to release branch - Manuf, Edu, HLS

Author: wiwaszko-intel

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

@@ -61,7 +61,7 @@ pip install --pre --upgrade ipex-llm[xpu_2.6] --extra-index-url https://download
 
 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).
 
 ## Step 2: Configuration
 

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

@@ -2,10 +2,10 @@
 
 <!--hide_directive
 <div class="component_card_widget">
-  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-suites/tree/main/education-ai-suite/smart-classroom">
+  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-suites/tree/release-2026.0.0/education-ai-suite/smart-classroom">
      GitHub project
   </a>
-  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-suites/blob/main/education-ai-suite/smart-classroom/README.md">
+  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/education-ai-suite/smart-classroom/README.md">
      Readme
   </a>
 </div>

education-ai-suite/smart-classroom/docs/user-guide/release-notes.md

@@ -22,4 +22,4 @@ In this release, the **Smart Classroom** application is added. It is an extensib
 
 ### Documentation and Source Code
 
-- <https://github.com/open-edge-platform/edge-ai-suites/tree/main/education-ai-suite>
+- <https://github.com/open-edge-platform/edge-ai-suites/tree/release-2026.0.0/education-ai-suite>

health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/docs/user-guide/index.md

@@ -1,9 +1,9 @@
 <!--hide_directive
 <div class="component_card_widget">
-  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-suites/tree/main/health-and-life-sciences-ai-suite/multi_modal_patient_monitoring">
+  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-suites/tree/release-2026.0.0/health-and-life-sciences-ai-suite/multi_modal_patient_monitoring">
      GitHub project
   </a>
-  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-suites/blob/main/health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/README.md">
+  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/health-and-life-sciences-ai-suite/multi_modal_patient_monitoring/README.md">
      Readme
   </a>
 </div>

manufacturing-ai-suite/README.md

@@ -15,23 +15,23 @@ It includes:
 
 |              |             |            |
 |:-------------|:------------|:-----------|
-| [HMI Augmented worker](./hmi-augmented-worker/)                                           | A RAG-enabled HMI application deployable on type-2 hypervisors.                                 | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/hmi-augmented-worker/index.html)                                                          |
-| [Pallet Defect Detection](./industrial-edge-insights-vision/apps/pallet-defect-detection) | Real-time pallet condition monitoring via multiple AI models.                                   | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/pallet-defect-detection/index.html)                       |
-| [PCB Anomaly Detection](./industrial-edge-insights-vision/apps/pcb-anomaly-detection)     | Real-time anomaly detection in printed circuit boards (PCB) with AI vision systems.             | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/pcb-anomaly-detection/index.html)                         |
-| [Weld Porosity](./industrial-edge-insights-vision/apps/weld-porosity)                     | Real-time detection of welding defects.                                                         | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/weld-porosity/get-started.html)                           |
-| [Worker Safety Gear Detection](./industrial-edge-insights-vision/apps/worker-safety-gear-detection) | Real-time visual analysis of safety gear compliance for workers.                      | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/worker-safety-gear-detection/index.html)                  |
-| [Wind Turbine Anomaly Detection](./industrial-edge-insights-time-series/apps/wind-turbine-anomaly-detection) | A time series use case of detecting anomalous power generation patterns relative to wind speed. | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-time-series/index.html) |
-| [Weld Defect Detection](./industrial-edge-insights-time-series/apps/weld-anomaly-detection) | A time series use case of detecting anomalous weld patterns relative to weld sensor parameters. | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-time-series/index.html) |
-| [Multimodal Weld Defect Detection](./industrial-edge-insights-multimodal/) | A multimodal use case combining vision and sensor data analysis to identify anomalies in welding data. | [Documentation](https://docs.openedgeplatform.intel.com/dev/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-multimodal/index.html) |
+| [HMI Augmented worker](./hmi-augmented-worker/)                                           | A RAG-enabled HMI application deployable on type-2 hypervisors.                                 | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/hmi-augmented-worker/index.html)                                                          |
+| [Pallet Defect Detection](./industrial-edge-insights-vision/apps/pallet-defect-detection) | Real-time pallet condition monitoring via multiple AI models.                                   | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/pallet-defect-detection/index.html)                       |
+| [PCB Anomaly Detection](./industrial-edge-insights-vision/apps/pcb-anomaly-detection)     | Real-time anomaly detection in printed circuit boards (PCB) with AI vision systems.             | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/pcb-anomaly-detection/index.html)                         |
+| [Weld Porosity](./industrial-edge-insights-vision/apps/weld-porosity)                     | Real-time detection of welding defects.                                                         | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/weld-porosity/get-started.html)                           |
+| [Worker Safety Gear Detection](./industrial-edge-insights-vision/apps/worker-safety-gear-detection) | Real-time visual analysis of safety gear compliance for workers.                      | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-vision/worker-safety-gear-detection/index.html)                  |
+| [Wind Turbine Anomaly Detection](./industrial-edge-insights-time-series/apps/wind-turbine-anomaly-detection) | A time series use case of detecting anomalous power generation patterns relative to wind speed. | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-time-series/index.html) |
+| [Weld Defect Detection](./industrial-edge-insights-time-series/apps/weld-anomaly-detection) | A time series use case of detecting anomalous weld patterns relative to weld sensor parameters. | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-time-series/index.html) |
+| [Multimodal Weld Defect Detection](./industrial-edge-insights-multimodal/) | A multimodal use case combining vision and sensor data analysis to identify anomalies in welding data. | [Documentation](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-suites/ai-suite-manufacturing/industrial-edge-insights-multimodal/index.html) |
 
 **Main tools and AI Libraries the Suite uses**
 
 |              |             |
 |:-------------|:------------|
 | [Deep Learning Streamer](https://github.com/open-edge-platform/dlstreamer/tree/main)                                     | A framework for building optimized media analytics pipelines powered by OpenVINO™ toolkit.                                 |
-| [Deep Learning Streamer Pipeline Server](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/dlstreamer-pipeline-server)  | A containerized microservice, built on top of GStreamer, for development and deployment of video analytics pipelines.            |
-| [Model Download](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/model-download)                                      | Providing capabilities to download AI models so that they can be seamlessly used for inferencing with DLStreamer and DLStreamer Pipeline Server.                                                                   |
-| [Time Series Analytics Microservice](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/microservices/time-series-analytics)           | Built on top of **Kapacitor**, a containerized microservice for development and deployment of time series analytics capabilities |
+| [Deep Learning Streamer Pipeline Server](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/microservices/dlstreamer-pipeline-server)  | A containerized microservice, built on top of GStreamer, for development and deployment of video analytics pipelines.            |
+| [Model Download](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/microservices/model-download)                                      | Providing capabilities to download AI models so that they can be seamlessly used for inferencing with DLStreamer and DLStreamer Pipeline Server.                                                                   |
+| [Time Series Analytics Microservice](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/microservices/time-series-analytics)           | Built on top of **Kapacitor**, a containerized microservice for development and deployment of time series analytics capabilities |
 | [Intel® Geti™ SDK](https://github.com/open-edge-platform/geti-sdk)                                                                          | A python package containing tools to interact with a Geti™ server via the REST API, helping you build a full MLOps for vision based use cases. |
 | [OpenVINO™ toolkit](https://github.com/openvinotoolkit/openvino)                                                                                | An open source toolkit for deploying performant AI solutions across Intel hardware for generative and conventional AI models.    |
 | [OpenVINO™ Model Server](https://github.com/openvinotoolkit/model_server)                                                                       | An OpenVINO server solution for enabling remote model inference for AI applications deployed on low-performance devices.         |

manufacturing-ai-suite/hmi-augmented-worker/docs/user-guide/get-started.md

@@ -119,7 +119,7 @@ To use the application effectively, make sure that all the steps mentioned in th
 - [Create Edge Microvisor Toolkit bootable USB drive using source code](https://github.com/open-edge-platform/edge-microvisor-toolkit-standalone-node/blob/main/standalone-node/docs/user-guide/get-started-guide.md#create-a-bootable-usb-drive-using-source-code)
 - [Desktop Virtualization on Edge Microvisor Toolkit](https://github.com/open-edge-platform/edge-microvisor-toolkit-standalone-node/blob/main/standalone-node/docs/user-guide/desktop-virtualization-image-guide.md)
 - [Edge Microvisor Toolkit Documentation](https://github.com/open-edge-platform/edge-microvisor-toolkit/tree/3.0/docs/developer-guide)
-- [Chat Question and Answer Core Main Page](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/chat-question-and-answer-core)
+- [Chat Question and Answer Core Main Page](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/sample-applications/chat-question-and-answer-core)
 
 <!--hide_directive
 :::{toctree}

manufacturing-ai-suite/hmi-augmented-worker/docs/user-guide/get-started/build-from-source.md

@@ -4,11 +4,11 @@ This document provides step-by-step instructions for building the `ChatQnA Core`
 
 ## Build ChatQnA Core from Source
 
-For detailed instructions on building from source, visit the [Build from Source Guide](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/sample-applications/chat-question-and-answer-core/docs/user-guide/build-from-source.md).
+For detailed instructions on building from source, visit the [Build from Source Guide](https://github.com/open-edge-platform/edge-ai-libraries/blob/release-2026.0.0/sample-applications/chat-question-and-answer-core/docs/user-guide/build-from-source.md).
 
 ### ChatQnA Core Docker Compose Deployment
 
-For docker compose deployment instructions, visit the [Running Application Container Guide](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/sample-applications/chat-question-and-answer-core/docs/user-guide/build-from-source.md#running-the-application-container).
+For docker compose deployment instructions, visit the [Running Application Container Guide](https://github.com/open-edge-platform/edge-ai-libraries/blob/release-2026.0.0/sample-applications/chat-question-and-answer-core/docs/user-guide/build-from-source.md#running-the-application-container).
 
 ## Build File Watcher Service from Source
 
@@ -97,7 +97,7 @@ To build the File Watcher executable binary, follow these steps:
 
 6. Set up Environment Variables using `.bat`.
 
-   To configure the file watcher service, you need to set up the environment variables using the [`set_env_vars.bat`](https://github.com/open-edge-platform/edge-ai-suites/blob/main/manufacturing-ai-suite/hmi-augmented-worker/file_watcher/set_env_vars.bat) file provided. Follow the steps below to ensure proper configuration:
+   To configure the file watcher service, you need to set up the environment variables using the [`set_env_vars.bat`](https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/manufacturing-ai-suite/hmi-augmented-worker/file_watcher/set_env_vars.bat) file provided. Follow the steps below to ensure proper configuration:
 
    - Open and edit the values for the variables with your corresponding setup.
 

manufacturing-ai-suite/hmi-augmented-worker/docs/user-guide/get-started/system-requirements.md

@@ -4,7 +4,7 @@ This page provides detailed hardware, software, and platform requirements to hel
 
 ## Hardware Platforms used for validation
 
-The `Chat Question and Answer Core` sample application system requirements are documented in [this](https://github.com/open-edge-platform/edge-ai-libraries/blob/main/sample-applications/chat-question-and-answer-core/docs/user-guide/get-started/system-requirements.md) page. The Intel® Core™ portfolio mentioned in this page is applicable for `HMI Augmented Worker` sample application too. The delta configurations supported is further described in this page.
+The `Chat Question and Answer Core` sample application system requirements are documented in [this](https://github.com/open-edge-platform/edge-ai-libraries/blob/release-2026.0.0/sample-applications/chat-question-and-answer-core/docs/user-guide/get-started/system-requirements.md) page. The Intel® Core™ portfolio mentioned in this page is applicable for `HMI Augmented Worker` sample application too. The delta configurations supported is further described in this page.
 
 The `HMI Augmented worker` sample application has been validated on Intel® Core™ i7-14700 based systems. The memory configuration used was 32GB, which is the recommended minimum configuration. This machine hosts an Edge Microvisor Toolkit based host together with Windows VM.
 

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

@@ -2,10 +2,10 @@
 
 <!--hide_directive
 <div class="component_card_widget">
-  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-suites/tree/main/manufacturing-ai-suite/hmi-augmented-worker">
+  <a class="icon_github" href="https://github.com/open-edge-platform/edge-ai-suites/tree/release-2026.0.0/manufacturing-ai-suite/hmi-augmented-worker">
      GitHub project
   </a>
-  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-suites/blob/main/manufacturing-ai-suite/hmi-augmented-worker/README.md">
+  <a class="icon_document" href="https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/manufacturing-ai-suite/hmi-augmented-worker/README.md">
      Readme
   </a>
 </div>
@@ -21,7 +21,7 @@ by a Type-2 hypervisor such as
 [Edge Microvisor Toolkit](https://github.com/open-edge-platform/edge-microvisor-toolkit).
 The Retrieval-Augmented Generation (RAG) pipeline and supporting AI services are deployed
 natively on a host system, which is the Edge Microvisor Toolkit in this implementation.
-[Chat Question-and-Answer Core](https://github.com/open-edge-platform/edge-ai-libraries/tree/main/sample-applications/chat-question-and-answer-core)
+[Chat Question-and-Answer Core](https://github.com/open-edge-platform/edge-ai-libraries/tree/release-2026.0.0/sample-applications/chat-question-and-answer-core)
 provides the RAG capability.
 This separation ensures robust isolation between the HMI and AI components, enabling
 independent scaling, maintenance, and updates. The setup leverages the strengths of both
@@ -70,7 +70,7 @@ Developed as a single, monolithic application
 with the complete RAG pipeline integrated into one microservice, it is designed for minimal memory usage.
 
 The `ChatQnA Core` application should be setup on the host system. For further details,
-visit [Chat Question-and-Answer Core Sample Application Overview](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/chat-question-and-answer-core/index.html).
+visit [Chat Question-and-Answer Core Sample Application Overview](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/chat-question-and-answer-core/index.html).
 The application is used as is, without any changes.
 The configurable parameters like the LLM model, Embedding model, Reranker model, or
 Retriever model are setup based on the HMI application requirement.

manufacturing-ai-suite/industrial-edge-insights-multimodal/docs/user-guide/troubleshooting.md

@@ -2,7 +2,7 @@
 
 This article contains troubleshooting steps for known issues. If you encounter any problems
 with the application not addressed here, check the [GitHub Issues](https://github.com/open-edge-platform/edge-ai-suites/issues)
-board. Feel free to file new tickets there (after learning about the guidelines for [Contributing](https://github.com/open-edge-platform/edge-ai-suites/blob/main/CONTRIBUTING.md)).
+board. Feel free to file new tickets there (after learning about the guidelines for [Contributing](https://github.com/open-edge-platform/edge-ai-suites/blob/release-2026.0.0/CONTRIBUTING.md)).
 
 ## 1. Seeing "No Data" in Grafana