docs(extraction): move Starter kits to examples/README.md (#2266 follow-up)

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: [Starter kits](starter-kits.md)
+- Framework examples: [Starter kits](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.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; refer to [Deployment options](deployment-options.md).
 - **Kubernetes / Helm (self-hosted)** — Refer to [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](starter-kits.md) for experimentation and RAG demos.
+- **Notebooks** — [Jupyter examples](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md) for experimentation and RAG demos.
 
 For a concise comparison, refer to [Deployment options](deployment-options.md).

docs/docs/extraction/deployment-options.md

@@ -24,11 +24,11 @@ Build and run the NeMo Retriever service image with the [Docker service image gu
 
 **Core NIMs for the default extraction pipeline:** `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](https://github.com/NVIDIA/NeMo-Retriever/blob/main/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 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.
+For audio and video extraction in Kubernetes, refer to [Audio and video](audio-video.md).
 
 ### I want examples and notebooks
 
-1. [Jupyter Notebooks](starter-kits.md)
+1. [Jupyter Notebooks](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md)
 
 ### I need API details and keys
 
@@ -73,7 +73,7 @@ On a staging host with internet access, pull from NGC, retag to your private reg
 
 !!! warning "Audio and video extraction"
 
-    [Audio and video](audio-video.md) need **`ffmpeg` and `ffprobe` on `PATH`**. The bundled image omits them. Do **not** use `service.installFfmpeg=true` in an air gap (startup install needs package-repo egress). Build a custom service image on a connected staging host, mirror it, and set `service.image.repository` / `service.image.tag`. Skip this step if you do not use audio/video.
+    Audio and video workflows require `ffmpeg` and `ffprobe` on `PATH`; runtime package installation is not suitable for air-gapped clusters. Refer to [Audio and video](audio-video.md) and the Helm chart [air-gapped deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/26.05/nemo_retriever/helm/README.md#air-gapped-deployment) guide. Skip this if you do not use audio or video.
 
 For offline image captioning, deploy the in-cluster [Nemotron 3 Nano Omni](prerequisites-support-matrix.md#image-captioning) NIM and point your pipeline caption endpoint at the in-cluster HTTP URL instead of `integrate.api.nvidia.com` or other hosted APIs.
 

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

@@ -8,7 +8,7 @@ Typical order:
 2. Confirm the [Pre-Requisites & Support Matrix](prerequisites-support-matrix.md) for your OS, GPU, and software stack.
 3. Deploy using one of:
     - [Deployment options](deployment-options.md) for library, hosted NIMs, and Kubernetes paths
-    - **Supported:** [Helm chart](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md) for Kubernetes cluster install and upgrade
-4. Explore [Jupyter Notebooks](starter-kits.md) for end-to-end examples.
+    - **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](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.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/overview.md

@@ -48,5 +48,6 @@ NeMo Retriever Library supports the following file types:
 - [Pre-Requisites & Support Matrix](prerequisites-support-matrix.md)
 - [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)
-- [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).
+- [NeMo Retriever Library — prerequisites / deployment](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) (supported Helm charts)
+- [Notebooks](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.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](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md).

docs/docs/extraction/workflow-e2e-blueprints.md

@@ -5,4 +5,4 @@ Use these external resources for end-to-end RAG implementations with NeMo Retrie
 - [Enterprise RAG - multimodal PDF data extraction](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag)
 - [NVIDIA AI Blueprints catalog](https://build.nvidia.com/explore/discover)
 
-For framework-specific integration patterns, refer to [Starter kits](starter-kits.md).
+For framework-specific integration patterns, refer to [Starter kits](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md).

docs/mkdocs.yml

@@ -101,7 +101,7 @@ nav:
   - "8. Customize & extend":
       - Extending/Customizing NeMo Retriever Library with custom code: https://github.com/NVIDIA/NeMo-Retriever/tree/main/nemo_retriever/src/nemo_retriever/graph#nemo-retriever-graph
   - "9. Integrations & ecosystem":
-      - "Starter kits": extraction/starter-kits.md
+      - "Starter kits": https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md
   - "10. Evaluation & benchmarks":
       - "Evaluate on your own documents": extraction/evaluate-on-your-data.md
   - "11. Reference":
@@ -156,11 +156,12 @@ plugins:
         extraction/hosted-nims-when-to-use.md: extraction/deployment-options.md
         extraction/releasenotes-nv-ingest.md: extraction/releasenotes.md
         extraction/ngc-api-key.md: extraction/api-keys.md
-        extraction/notebooks/index.md: extraction/starter-kits.md
-        extraction/notebooks.md: extraction/starter-kits.md
+        extraction/notebooks/index.md: https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md
+        extraction/notebooks.md: https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md
+        extraction/starter-kits.md: https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md
         extraction/data-store.md: extraction/vdbs.md
         extraction/custom-metadata.md: extraction/vdbs.md#metadata-and-filtering
-        extraction/integrations-langchain-llamaindex-haystack.md: extraction/starter-kits.md
+        extraction/integrations-langchain-llamaindex-haystack.md: https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/README.md
         extraction/nemoretriever-parse.md: extraction/multimodal-extraction.md#text-and-layout-extraction
         extraction/supported-file-types.md: extraction/multimodal-extraction.md#supported-file-types-and-formats
         extraction/text-layout-extraction.md: extraction/multimodal-extraction.md#text-and-layout-extraction

examples/README.md

@@ -0,0 +1,23 @@
+# Starter Kits for NeMo Retriever Library
+
+Explore ready-made Jupyter notebooks and guides for [NeMo Retriever Library](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/).
+
+## Dataset Downloads for Benchmarking
+
+If you run benchmarking or evaluation tests, download the [Benchmark Datasets (Bo20, Bo767, Bo10k)](https://github.com/NVIDIA/NeMo-Retriever/blob/main/evaluation/digital_corpora_download.ipynb) from Digital Corpora first. This prerequisite applies to all benchmarking operations.
+
+## Getting Started
+
+Start with these guides and notebooks:
+
+- [Quickstart: retriever CLI](https://docs.nvidia.com/nemo/retriever/latest/reference/retriever-cli-quickstart/)
+- [Workflow: Ingest documents](https://docs.nvidia.com/nemo/retriever/latest/extraction/workflow-document-ingestion/)
+- [Adding custom metadata for filtered search and retrieval](nemo_retriever_retriever_query_metadata_filter.ipynb) — also summarized on [Vector databases — Metadata and filtering](https://docs.nvidia.com/nemo/retriever/latest/extraction/vdbs/#metadata-and-filtering)
+
+For advanced scenarios, use these notebooks:
+
+- [Build a custom vector database operator](building_vdb_operator.ipynb)
+- [Try Enterprise RAG Blueprint](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag)
+- [Evaluate bo767 retrieval recall accuracy with NeMo Retriever Library](https://github.com/NVIDIA/NeMo-Retriever/blob/main/evaluation/bo767_recall.ipynb)
+- [Multimodal RAG with LangChain](langchain_multimodal_rag.ipynb)
+- [Multimodal RAG with LlamaIndex](llama_index_multimodal_rag.ipynb)