[DOCS] Fixing toctrees in documentation

Author: sgolebiewski-intel

microservices/audio-analyzer/docs/toc.rst

@@ -1,5 +1,5 @@
 # Audio Analyzer
-Audio Analyzer microservice is used to generate transcription of audio from video files. 
+Audio Analyzer microservice is used to generate transcription of audio from video files.
 
 ## Overview
 The Audio Analyzer microservice provides an automated solution for extracting and transcribing audio from video files. Designed for seamless integration into modern AI pipelines this microservice enables converting spoken content within videos into accurate, searchable text. By leveraging state-of-the-art speech-to-text models, the service supports a wide range of audio formats and languages, making it suitable for diverse applications such as video summary, media analysis, compliance monitoring, and content indexing.
@@ -60,7 +60,7 @@ The following Whisper model variants are supported by the service (for both GGML
 | tiny.en  | Tiny model  | ~75M | English-only |
 | base     | Base model  | ~150M | Multilingual |
 | base.en  | Base model  | ~150M | English-only |
-| small    | Small model | ~450M | Multilingual | 
+| small    | Small model | ~450M | Multilingual |
 | small.en | Small model | ~450M | English-only |
 | medium   | Medium model | ~1.5GB | Multilingual |
 | medium.en | Medium model | ~1.5GB | English-only |
@@ -70,6 +70,17 @@ The following Whisper model variants are supported by the service (for both GGML
 
 ## Supporting Resources
 
-* [Get Started Guide](get-started.md)
-* [API Reference](api-reference.md)
-* [System Requirements](system-requirements.md)
+* [Get Started Guide](./get-started.md)
+* [API Reference](./api-reference.md)
+* [System Requirements](./system-requirements.md)
+
+:::{toctree}
+:hidden:
+
+overview-architecture
+system-requirements
+get-started
+how-to-build-from-source
+api-reference
+release-notes
+:::

microservices/audio-analyzer/docs/user-guide/Overview.md

@@ -1,24 +1,4 @@
 .. toctree::
    :hidden:
-   
-   Overview
-
-.. toctree::
-   
-   overview-architecture
-   system-requirements
-   get-started
-
-.. toctree::
-   :caption: How to
 
-   how-to-build-from-source
-
-.. toctree::
-   :caption: References
-   
-   api-reference
-
-.. toctree::
-   
-   release-notes
+   Overview

microservices/audio-analyzer/docs/user-guide/index.rst

@@ -14,25 +14,53 @@ Video analytics involves the conversion of video streams into valuable insights
 Here is the high level description of functionality of DL Streamer Pipeline Server module:
 
    - **RESTful Interface**</br>
-      Exposes RESTful endpoints to discover, start, stop and customize pipelines in JSON format. 
-   
+      Exposes RESTful endpoints to discover, start, stop and customize pipelines in JSON format.
+
    - **DL Streamer Pipeline Server Core**</br>
       Manages and processes the REST requests interfacing with the core DL Streamer Pipeline Server components and Pipeline Server Library.
-   
+
    - **DL Streamer Pipeline Server Configuration handler**</br>
       Reads the contents of config file and accordingly constructs/starts pipelines. Dynamic configuration change is supported via REST API.
-   
+
    - **GST UDF Loader**</br>
       DL Streamer Pipeline Server provides a [GStreamer plugin](https://gstreamer.freedesktop.org/documentation/plugins_doc.html?gi-language=c) - `udfloader` using which users can configure and load arbitrary UDFs. With `udfloader`, DL Streamer Pipeline Server provides an easy way to bring user developed programs and run them as a part of GStreamer pipelines. A User Defined Function (UDF) is a chunk of user code that can transform video frames and/or manipulate metadata. For example, a UDF can act as filter, preprocessor, classifier or a detector. These User Defined Functions can be developed in Python.
-   
+
    - **DL Streamer Pipeline Server Publisher**</br>
       Supports publishing metadata to file, MQTT/Kafka message brokers and frame along with metadata to MQTT message broker. We also support publishing metadata and frame over OPCUA. The frames can also be saved on S3 compliant storage.
-   
+
    - **DL Streamer Pipeline Server Model Update**</br>
       Supports integration with Model Registry service [Model Registry](https://docs.openedgeplatform.intel.com/edge-ai-libraries/model-registry/1.2.0/user-guide/Overview.html) for model download, deployment and management.
 
    - **Open Telemetry**</br>
-      Supports gathering metrics over Open Telemetry for seamless visualization and analysis. 
-
+      Supports gathering metrics over Open Telemetry for seamless visualization and analysis.
 
+:::{toctree}
+:hidden:
 
+overview-architecture
+system-requirements
+get-started
+troubleshooting-guide
+how-to-change-dlstreamer-pipeline
+how-to-use-gpu-for-decode-and-inference
+how-to-use-cpu-for-decode-and-inference
+how-to-autostart-pipelines
+how-to-launch-configurable-pipelines
+how-to-perform-webrtc-frame-streaming
+how-to-start-dlsps-mqtt-publish
+how-to-store-s3-frame
+how-to-store-metadata-influxdb
+how-to-publish-metadata-over-ros2
+how-to-launch-and-manage-pipeline
+how-to-use-rtsp-camera-as-video-source
+how-to-run-udf-pipelines
+how-to-deploy-with-helm
+how-to-use-image-file-as-source-over-request-payload
+how-to-download-and-run-yolo-models
+how-to-build-from-source
+how-to-add-system-timestamps-to-metadata
+api-reference
+environment-variables
+advanced-guide/Overview
+release_notes/Overview
+:::

microservices/dlstreamer-pipeline-server/docs/toc.rst

@@ -1,56 +1,4 @@
-DL Streamer Pipeline Server
-==============================
-
-
 .. toctree::
    :hidden:
 
    Overview
-
-.. toctree::
-
-   overview-architecture
-   system-requirements
-   get-started
-   troubleshooting-guide
-
-.. toctree::
-   :caption: How to
-
-   how-to-change-dlstreamer-pipeline
-   how-to-use-gpu-for-decode-and-inference
-   how-to-use-cpu-for-decode-and-inference
-   how-to-autostart-pipelines
-   how-to-launch-configurable-pipelines
-   how-to-perform-webrtc-frame-streaming
-   how-to-start-dlsps-mqtt-publish
-   how-to-store-s3-frame
-   how-to-store-metadata-influxdb
-   how-to-publish-metadata-over-ros2
-   how-to-launch-and-manage-pipeline
-   how-to-use-rtsp-camera-as-video-source
-   how-to-run-udf-pipelines
-   how-to-deploy-with-helm
-   how-to-use-image-file-as-source-over-request-payload
-   how-to-download-and-run-yolo-models
-   how-to-build-from-source
-   how-to-add-system-timestamps-to-metadata
-   
-.. toctree::
-   :caption: References
-
-   api-reference
-   environment-variables
-
-
-.. toctree::
-   :hidden:
-
-   advanced-guide/Overview
-
-.. toctree::
-
-   release_notes/Overview
-
-
-

microservices/dlstreamer-pipeline-server/docs/user-guide/Overview.md

@@ -2,27 +2,3 @@
    :hidden:
 
    overview
-
-.. toctree::
-
-   overview-architecture
-   system-requirements
-   get-started
-
-.. toctree::
-   :caption: How to
-
-   build-from-source
-   deploy-with-helm
-   deploy-with-edge-orchestrator
-   how-to-performance
-
-.. toctree::
-   :caption: References
-
-   benchmarks
-   api-reference
-
-.. toctree::
-
-   release-notes

microservices/dlstreamer-pipeline-server/docs/user-guide/index.rst

@@ -33,7 +33,7 @@ The ChatQ&A sample application includes the following components:
 - **ChatQ&A backend microservice**: The Chat&A backend microservice is responsible for responding to the user queries. LangChain framework is used to create the RAG chain and provide the necessary integrations with VectorDB. The backend microservice provides a REST API to accept user queries.
 - **ChatQ&A UI**: A reference UI is provided to enable users quickly try the sample-application. The  UI is not designed to be a comprehensive guide on the design of UI but is more a functional implementation to enable quick demo interaction. It allows users to create the context by uploading the documents and provide weblinks. Users can then interact with the RAG pipeline. It uses the REST API provided by the document ingestion and ChatQ&A backend for its operations.
 
-All the 3rd party components are not built from source but instead the respective pre-built images are pulled from their artefactory (Docker hub for example) and used. The model serving is also reused from their prebuilt versions and used for LLM, Embedding, and Reranker. The Intel microservices can be built from source. Documentation is provided to help users build these microservices from source. 
+All the 3rd party components are not built from source but instead the respective pre-built images are pulled from their artefactory (Docker hub for example) and used. The model serving is also reused from their prebuilt versions and used for LLM, Embedding, and Reranker. The Intel microservices can be built from source. Documentation is provided to help users build these microservices from source.
 
 Further details on the system architecture and customizable options are available [here](./overview-architecture.md).
 
@@ -55,6 +55,21 @@ Detailed hardware and software requirements are available [here](./system-requir
 
 Detailed metrics and analysis can be found in the benchmark report [here](./benchmarks.md).
 
+:::{toctree}
+:hidden:
+
+overview-architecture
+system-requirements
+get-started
+build-from-source
+deploy-with-helm
+deploy-with-edge-orchestrator
+how-to-performance
+benchmarks
+api-reference
+release-notes
+:::
+
 <!--
 ## Support and Community