updating files based on sphinx warning during documentation build (NVIDIA-AI-Blueprints#459)

* updating files based on sphinx warning during documentation build

* Update docs/deploy-helm.md

Co-authored-by: nkmcalli <nkmcalli@yahoo.com>

---------

Co-authored-by: nkmcalli <nkmcalli@yahoo.com>

Author: kheiss-uwzoo

docs/api-rag.md

@@ -10,8 +10,6 @@ This documentation contains the OpenAPI reference for the RAG server.
 :::{tip}
 To view this documentation on docs.nvidia.com, browse to [https://docs.nvidia.com/rag/latest/api-rag](https://docs.nvidia.com/rag/latest/api-rag.html).
 :::
-=======
-To view this documentation on docs.nvidia.com, browse to [https://docs.nvidia.com/rag/latest/api-rag](https://docs.nvidia.com/rag/latest/api-rag.html).
 
 
 :::{swagger-plugin} ../docs/api_reference/openapi_schema_rag_server.json

docs/conf.py

@@ -74,7 +74,9 @@
             "icon": "fa-brands fa-github",
         }
     ],
-    "switcher": {"json_url": "../versions1.json", "version_match": release},
+    # Path is resolved from the Sphinx conf directory (docs/). ../versions1.json
+    # points at the repo root and breaks local builds; versions1.json lives in docs/.
+    "switcher": {"json_url": "versions1.json", "version_match": release},
     "extra_head": {
         """
     <script src="https://assets.adobedtm.com/5d4962a43b79/c1061d2c5e7b/launch-191c2462b890.min.js" ></script>

docs/deploy-helm.md

@@ -125,7 +125,7 @@ To deploy End-to-End RAG Server and Ingestor Server, use the following procedure
    Refer to [NIM Model Profile Configuration](model-profiles.md) for using non-default NIM LLM profile.
    :::
 
-   For **Nemotron 3 Super** on Helm, see the [Nemotron 3 Super deployment guide](nemotron3-super-deployment.md#helm-deployment-nemotron-3-super).
+   For **Nemotron 3 Super** on Helm, see the [Nemotron 3 Super deployment guide](helm-deployment-nemotron-3-super).
 
 
 ## Verify a Deployment

docs/index.md

@@ -49,6 +49,7 @@ For detailed requirements, refer to [Support Matrix](support-matrix.md).
 
 - [Deploy with Docker (Self-Hosted Models)](deploy-docker-self-hosted.md)
 - [Deploy with Docker (NVIDIA-Hosted Models)](deploy-docker-nvidia-hosted.md)
+- [Nemotron 3 Super (120B) deployment](nemotron3-super-deployment.md)
 - [Deploy on Kubernetes with Helm](deploy-helm.md)
 - [Deploy on Kubernetes with Helm from the repository](deploy-helm-from-repo.md)
 - [Deploy on Kubernetes with Helm and MIG Support](mig-deployment.md)
@@ -167,7 +168,6 @@ After you deploy the RAG blueprint, you can customize it for your use cases.
    :hidden:
 
    Get an API Key <api-key.md>
-   Get Started with the RAG Blueprint <deploy-docker-self-hosted.md>
    Web User Interface <user-interface.md>
    Use the RAG Python Package <python-client.md>
    Notebooks <notebooks.md>
@@ -182,6 +182,7 @@ After you deploy the RAG blueprint, you can customize it for your use cases.
 
    Deploy with Docker (Self-Hosted Models) <deploy-docker-self-hosted.md>
    Deploy with Docker (NVIDIA-Hosted Models) <deploy-docker-nvidia-hosted.md>
+   Nemotron 3 Super deployment <nemotron3-super-deployment.md>
    Deploy on Kubernetes with Helm <deploy-helm.md>
    Deploy on Kubernetes with Helm from the repository <deploy-helm-from-repo.md>
    Deploy on Kubernetes with Helm and MIG Support <mig-deployment.md>

docs/nemotron3-super-deployment.md

@@ -78,7 +78,7 @@ export PROMPT_CONFIG_FILE=$(pwd)/deploy/compose/nemotron3-super-prompt.yaml
 
    Check the [model page](https://build.nvidia.com/nvidia/nemotron-3-super-120b-a12b/modelcard) for more details.
 
-   > Note: For RTX 6000 Pro GPUs, additional NIM environment variables are required — see [RTX 6000 Pro](#rtx-6000-pro) below.
+   > Note: For RTX 6000 Pro GPUs, additional NIM environment variables are required — see [RTX 6000 Pro](rtx-6000-pro) below.
 
 2. Set nemotron-3-super specific environment variables.
 
@@ -93,7 +93,8 @@ export PROMPT_CONFIG_FILE=$(pwd)/deploy/compose/nemotron3-super-prompt.yaml
 
    Follow [Start services using self-hosted on-premises models](deploy-docker-self-hosted.md#start-services-using-self-hosted-on-premises-models) to start the vectorstore, rag-server, NIMs, and ingestor-server.
 
-**RTX 6000 Pro**
+(rtx-6000-pro)=
+### RTX 6000 Pro
 
 > Note: To deploy TP2 profiles on RTX PRO 6000 Blackwell Server Edition, run the following commands. You don't need to go through these steps if you are using TP4 or TP8 profile.
 
@@ -120,6 +121,7 @@ export PROMPT_CONFIG_FILE=$(pwd)/deploy/compose/nemotron3-super-prompt.yaml
 
 ---
 
+(helm-deployment-nemotron-3-super)=
 ## Helm deployment (`nemotron-3-super-120b-a12b`)
 
 From the repository root, run:
@@ -136,7 +138,7 @@ helm upgrade --install rag -n rag https://helm.ngc.nvidia.com/nvidia/blueprint/c
 
 The prompt file `deploy/compose/nemotron3-super-prompt.yaml` is tuned for `nemotron-3-super-120b-a12b`. To customize it, see [Prompt customization in Helm chart](prompt-customization.md#prompt-customization-in-helm-chart).
 
-**RTX 6000 Pro**
+### RTX 6000 Pro (Helm)
 
 > Note: To deploy TP2 profiles on RTX PRO 6000 Blackwell Server Edition, run the following commands. You don't need to go through these steps if you are using TP4 or TP8 profile.
 

docs/release-notes.md

@@ -30,7 +30,7 @@ This release includes the following key updates:
 - **Added MIG support for RTX 6000.** For details, refer to [MIG Deployment](mig-deployment.md) and use `values-mig-rtx6000.yaml` and `mig-config-rtx6000.yaml`.
 - Added documentation for the experimental Nemotron-parse-only ingestion pipeline. This configuration allows you to perform extraction using only Nemotron Parse through NV-Ingest, without relying on OCR, page-elements, graphic-elements, or table-structure NIMs. For more information, refer to [nemotron-parse-extraction.md](nemotron-parse-extraction.md#experimental-nemotron-parse-only-extraction).
 - Several bug fixes, including frontend CVE resolutions, improved multimodal content concatenation for VLM embeddings, enhanced VDB serialization for high-concurrency parallel ingestion, and updates to observability and NeMo Guardrails configurations.
-- Added agentic skills support: the `rag-blueprint` skill enables AI coding assistants (Claude Code, Cursor, Codex, etc.) to deploy, configure, troubleshoot, and manage the RAG Blueprint autonomously. For details, refer to [RAG Blueprint Agent Skill](../skill-source/README.md).
+- Added agentic skills support: the `rag-blueprint` skill enables AI coding assistants (Claude Code, Cursor, Codex, etc.) to deploy, configure, troubleshoot, and manage the RAG Blueprint autonomously. For details, refer to [RAG Blueprint Agent Skill](https://github.com/NVIDIA-AI-Blueprints/rag/blob/main/skill-source/README.md).
 - Added [accuracy benchmark results](accuracy-benchmarks.md) across seven public datasets (RagBattlepacket, KG-RAG, Financebench, DC767, HotPotQA, Google Frames, and Vidore), comparing LLM and VLM configurations with reasoning on/off. Benchmarks use the NVIDIA Answer Accuracy metric from RAGAS.
 
 ### Fixed Known Issues