Merge upstream/main into docs/ocr-v2-defaults

Resolve conflict in multimodal-extraction.md by keeping the PR
branch OCR section with Helm README link and consistent refer-to CTAs.

Author: kheiss-uwzoo

.github/workflows/nrl-docs-nvidia-publish.yml

@@ -134,6 +134,9 @@ jobs:
               EMAILS="${NOTIFY_INPUT}"
             fi
           fi
+          if [[ -z "${EMAILS}" ]]; then
+            EMAILS="kheiss@nvidia.com"
+          fi
           echo "emails_csv=${EMAILS}" >> "$GITHUB_OUTPUT"
 
   build:

docs/docs/extraction/agentic-retrieval-concept.md

@@ -7,4 +7,4 @@ NeMo Retriever Library focuses on document ingestion, embeddings, vector stores,
 **Related**
 
 - [Semantic retrieval](vdbs.md#semantic-retrieval)
-- Framework examples: [LangChain, LlamaIndex, Haystack](integrations-langchain-llamaindex-haystack.md)
+- Framework examples: [Starter kits](starter-kits.md)

docs/docs/extraction/concepts.md

@@ -36,6 +36,6 @@ Token-based splitting uses the Llama 3.2 1B tokenizer (default `meta-llama/Llama
 
 - **Library mode** — Run without the full container stack where appropriate; see [Deployment options](deployment-options.md).
 - **Kubernetes / Helm (self-hosted)** — See [Deploy (Helm chart)](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md) and [deployment options](deployment-options.md) for running the full microservices pipeline on your infrastructure.
-- **Notebooks** — [Jupyter examples](notebooks/index.md) for experimentation and RAG demos.
+- **Notebooks** — [Jupyter examples](starter-kits.md) for experimentation and RAG demos.
 
 For a concise comparison, refer to [Deployment options](deployment-options.md).

docs/docs/extraction/custom-metadata.md

@@ -24,12 +24,11 @@ Build and run the NeMo Retriever service image with the [Docker service image gu
 
 **Core NIMs for the default extraction pipeline** (26.05): `page_elements`, `table_structure`, `ocr`, and `vlm_embed` (`llama-nemotron-embed-vl-1b-v2:1.12.0`). These four are auto-wired into the retriever service. **Nemotron Parse**, **Nemotron 3 Nano Omni**, the **VL reranker**, and **Parakeet ASR** are optional and not auto-wired. For a minimal GPU footprint, disable optional keys you do not need (refer to [Recommended minimal install (26.05)](https://github.com/NVIDIA/NeMo-Retriever/blob/26.05/nemo_retriever/helm/README.md#recommended-minimal-install-2605)). Refer to [Pre-Requisites & Support Matrix — Default Helm NIMs](prerequisites-support-matrix.md#default-helm-nims).
 
-For audio and video extraction in Kubernetes, set `service.installFfmpeg=true` so the service installs `ffmpeg` and `ffprobe` at container startup. This runtime install requires package-repository egress and cluster policy that allows the image's scoped sudo use. For Helm chart procedures, OpenShift-specific configuration, and air-gapped alternatives, refer to [Audio and video (Parakeet ASR)](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md#audio-video-parakeet) and [OpenShift deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/openshift.md) in the Helm chart directory.
+For audio and video extraction in Kubernetes, set `service.installFfmpeg=true` so the service container installs `ffmpeg` and `ffprobe` at startup. This runtime install requires package-repository network egress, a writable root filesystem, and security policy that allows the image's scoped sudo use. If your cluster blocks startup package installation, use a custom service image that already contains `ffmpeg` and `ffprobe`, then set `service.image.repository` and `service.image.tag`. For Parakeet ASR chart values, OpenShift-specific Helm configuration, and air-gapped alternatives, refer to [Audio and video (Parakeet ASR)](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md#audio-video-parakeet) and [OpenShift deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/openshift.md) in the Helm chart directory.
 
 ### I want examples and notebooks
 
-1. [Jupyter Notebooks](notebooks/index.md)
-2. [Integrate with LangChain, LlamaIndex, Haystack](integrations-langchain-llamaindex-haystack.md)
+1. [Jupyter Notebooks](starter-kits.md)
 
 ### I need API details and keys
 

docs/docs/extraction/deployment-options.md

@@ -9,6 +9,6 @@ Typical order:
 3. Deploy using one of:
     - [Deployment options](deployment-options.md) for how to run NeMo Retriever Library
     - **Supported:** [Helm chart](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md) for Kubernetes, plus [NeMo Retriever Library install docs](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) for the published charts
-4. Explore [Jupyter Notebooks](notebooks/index.md) for end-to-end examples.
+4. Explore [Jupyter Notebooks](starter-kits.md) for end-to-end examples.
 
 If you are new to the product, read [What is NeMo Retriever Library?](overview.md) and [Concepts](concepts.md) under **Introduction** first.

docs/docs/extraction/getting-started-about.md

@@ -49,5 +49,5 @@ NeMo Retriever Library supports the following file types:
 - [Deployment options](deployment-options.md) — library, Helm, hosted vs self-hosted NIMs in one place
 - [Deploy on Kubernetes with Helm](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md)
 - [NeMo Retriever Library — prerequisites / deployment](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) (supported Helm charts)
-- [Notebooks](notebooks/index.md)
-- [NVIDIA AI Blueprints catalog](https://build.nvidia.com/explore/discover) — solution cards, enterprise RAG blueprints, and end-to-end patterns (including [Enterprise RAG — multimodal PDF data extraction](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag)); for integration pathways, refer to [Integrations](integrations-langchain-llamaindex-haystack.md).
+- [Notebooks](starter-kits.md)
+- [NVIDIA AI Blueprints catalog](https://build.nvidia.com/explore/discover) — solution cards, enterprise RAG blueprints, and end-to-end patterns (including [Enterprise RAG — multimodal PDF data extraction](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag)); for integration pathways, refer to [Starter kits](starter-kits.md).

docs/docs/extraction/integrations-langchain-llamaindex-haystack.md

@@ -1,6 +1,6 @@
-# Notebooks for NeMo Retriever Library
+# Starter Kits for NeMo Retriever Library
 
-To get started using [NeMo Retriever Library](../overview.md), you can try one of the ready-made notebooks that are available.
+To get started using [NeMo Retriever Library](overview.md), you can try one of the ready-made notebooks that are available.
 
 ## Dataset Downloads for Benchmarking
 
@@ -10,9 +10,9 @@ If you plan to run benchmarking or evaluation tests, you must download the [Benc
 
 To get started with the basics, try one of the following guides or notebooks:
 
-- [Quickstart: retriever CLI](../../reference/retriever-cli-quickstart.md)
-- [Workflow: Ingest documents](../workflow-document-ingestion.md)
-- [How to add metadata to your documents and filter searches](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/nemo_retriever_retriever_query_metadata_filter.ipynb)
+- [Quickstart: retriever CLI](../reference/retriever-cli-quickstart.md)
+- [Workflow: Ingest documents](workflow-document-ingestion.md)
+- [Adding Custom Metadata for Filtered Search/Retrieval](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/nemo_retriever_retriever_query_metadata_filter.ipynb) — also summarized on [Vector databases — Metadata and filtering](vdbs.md#metadata-and-filtering)
 
 
 For more advanced scenarios, try one of the following notebooks: