docs(extraction): link fixes and remove stale release version tags (NVBug 6179241)

docs/docs/extraction/concepts.md

@@ -16,26 +16,26 @@ Output is a **Ray Dataset** (Ray Data) or **pandas** `DataFrame` listing extract
 
 ## Embeddings and retrieval
 
-Optionally, the library can compute **embeddings** for extracted content and store vectors in [LanceDB](https://lancedb.com/) for downstream semantic search in your application. For upload and retrieval APIs, see [Vector databases](vdbs.md). For multimodal (VLM) embedding options, see [Multimodal embeddings (VLM)](embedding.md).
+Optionally, the library can compute **embeddings** for extracted content and store vectors in [LanceDB](https://lancedb.com/) for downstream semantic search in your application. For upload and retrieval APIs, refer to [Vector databases](vdbs.md). For multimodal (VLM) embedding options, refer to [Multimodal embeddings (VLM)](embedding.md).
 
 ## Chunking { #chunking }
 
 Chunking is built into the `.extract()` task and depends on **content type**:
 
 - **PDF, DOCX, and PPTX** — Text is grouped using built-in **page** boundaries (one chunk per page where the format has pages).
-- **Plain text (`.txt`) and HTML** — Formats without natural page breaks are split into segments of **1024 tokens** by default, using the [Llama 3.2 1B tokenizer](https://huggingface.co/meta-llama/Llama-3.2-1B) so chunk boundaries stay aligned with the default embedding tokenizer. The NeMo Retriever container image bundles this tokenizer, so default text chunking does not require a Hugging Face access token. See [Token-based splitting](#token-based-splitting) and [Environment variables](environment-config.md) for overrides and other runtimes.
-- **Audio and video** — Media is split into **segments** for decoding and ASR using ffmpeg-based rules (configurable **size**, **time**, or **frame** split modes in the media chunking stage). With the Parakeet ASR path, you can optionally emit **sentence-like segments** using `extract_audio_params={"segment_audio": True}`; see [Speech and audio extraction](audio-video.md#speech-and-audio-extraction).
+- **Plain text (`.txt`) and HTML** — Formats without natural page breaks are split into segments of **1024 tokens** by default, using the [Llama 3.2 1B tokenizer](https://huggingface.co/meta-llama/Llama-3.2-1B) so chunk boundaries stay aligned with the default embedding tokenizer. The NeMo Retriever container image bundles this tokenizer, so default text chunking does not require a Hugging Face access token. Refer to [Token-based splitting](#token-based-splitting) and [Environment variables](environment-config.md) for overrides and other runtimes.
+- **Audio and video** — Media is split into **segments** for decoding and ASR using ffmpeg-based rules (configurable **size**, **time**, or **frame** split modes in the media chunking stage). With the Parakeet ASR path, you can optionally emit **sentence-like segments** using `extract_audio_params={"segment_audio": True}`; refer to [Speech and audio extraction](audio-video.md#speech-and-audio-extraction).
 
-For PDF parallelism before Ray processing (large files), see [PDF pre-splitting for parallel ingest](nemo-retriever-api-reference.md#pdf-pre-splitting-for-parallel-ingest).
+For PDF parallelism before Ray processing (large files), refer to [PDF pre-splitting for parallel ingest](nemo-retriever-api-reference.md#pdf-pre-splitting-for-parallel-ingest).
 
 ### Token-based splitting { #token-based-splitting }
 
 Token-based splitting uses the Llama 3.2 1B tokenizer (default `meta-llama/Llama-3.2-1B`) with configurable `max_tokens` and `overlap_tokens` when you add an explicit `.split(...)` stage or when the pipeline applies the default text segmentation for unstructured text. In the shipped NeMo Retriever container, tokenizer assets are included locally, so you do not need `HF_ACCESS_TOKEN` for this default path. If your runtime loads the tokenizer from the Hugging Face Hub instead (for example, some library-only installs), set `HF_ACCESS_TOKEN` or pass `hf_access_token` in task params when the Hub requires it. Details appear in the [Python API guide](nemo-retriever-api-reference.md).
 
 ## Deployment modes
 
-- **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.
+- **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.
 
 For a concise comparison, refer to [Deployment options](deployment-options.md).

docs/docs/extraction/deployment-options.md

@@ -22,7 +22,7 @@ Build and run the NeMo Retriever service image with the [Docker service image gu
 3. **Published Library Helm charts (supported):** cluster install and upgrade procedures are covered in the [NeMo Retriever Library](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) — use alongside the NeMo Retriever chart README for your release
 4. [Environment variables](environment-config.md) and [Troubleshoot](troubleshoot.md) as needed
 
-**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).
+**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.
 
@@ -69,17 +69,17 @@ Consider self-hosting when:
 
 The **default document extraction pipeline** (page elements, table structure, OCR, and VL embed) runs disconnected when you mirror images and models into a private registry and configure the [NIM Operator for air-gapped environments](https://docs.nvidia.com/nim-operator/latest/air-gap.html).
 
-On a staging host with internet access, pull from NGC, retag to your private registry, stage chart archives, then install in the enclave with registry overrides. Procedures, the 26.05 image inventory, and Helm value patterns are in [Helm — Air-gapped deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/26.05/nemo_retriever/helm/README.md#air-gapped-deployment).
+On a staging host with internet access, pull from NGC, retag to your private registry, stage chart archives, then install in the enclave with registry overrides. Procedures, the chart image inventory, and Helm value patterns are in [Helm — Air-gapped deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md#air-gapped-deployment).
 
 !!! 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.
 
-For offline image captioning, deploy the in-cluster [Nemotron 3 Nano Omni](prerequisites-support-matrix.md#image-captioning-2605) NIM and point your pipeline caption endpoint at the in-cluster HTTP URL instead of `integrate.api.nvidia.com` or other hosted APIs.
+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.
 
 **Related**
 
-- [Deploy (Helm chart)](https://github.com/NVIDIA/NeMo-Retriever/blob/26.05/nemo_retriever/helm/README.md) ([`nemo_retriever/helm`](https://github.com/NVIDIA/NeMo-Retriever/tree/26.05/nemo_retriever/helm) on GitHub) — [air-gapped deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/26.05/nemo_retriever/helm/README.md#air-gapped-deployment)
+- [Deploy (Helm chart)](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md) ([`nemo_retriever/helm`](https://github.com/NVIDIA/NeMo-Retriever/tree/main/nemo_retriever/helm) on GitHub) — [air-gapped deployment](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md#air-gapped-deployment)
 - [NeMo Retriever Library — prerequisites / deployment](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/) (supported **Helm** handoff)
 - [Pre-Requisites & Support Matrix](prerequisites-support-matrix.md)
 - [Audio and video](audio-video.md)

docs/docs/extraction/prerequisites-support-matrix.md

@@ -93,18 +93,18 @@ Default VL embedder container and model for release deployments:
 
 ### Optional Helm NIMs (not auto-wired) { #optional-helm-nims-not-auto-wired-by-default }
 
-These NIM microservices are **optional** for the default extraction pipeline. The retriever service does **not** call them until you enable the matching pipeline stage (reranker, Nemotron Parse, caption, or audio). For **26.05 production**, disable 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)). Set `nimOperator.<key>.enabled=true` when you want that NIM reconciled. Chart keys are in the [NeMo Retriever Helm chart README](https://github.com/NVIDIA/NeMo-Retriever/blob/26.05/nemo_retriever/helm/README.md#nim-operator-sub-stack).
+These NIM microservices are **optional** for the default extraction pipeline. The retriever service does **not** call them until you enable the matching pipeline stage (reranker, Nemotron Parse, caption, or audio). In production, disable 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)). Set `nimOperator.<key>.enabled=true` when you want that NIM reconciled. Chart keys are in the [NeMo Retriever Helm chart README](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/helm/README.md#nim-operator-sub-stack).
 
 | Helm flag | NIM | Role |
 |-----------|-----|------|
 | `rerankqa` | [llama-nemotron-rerank-vl-1b-v2](https://huggingface.co/nvidia/llama-nemotron-rerank-vl-1b-v2) | Reranking for improved retrieval accuracy |
 | `nemotron_parse` | [nemotron-parse](https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.2) | Optional PDF `extract_method="nemotron_parse"` (default PDF extraction uses **pdfium**) |
-| `nemotron_3_nano_omni_30b_a3b_reasoning` | [nemotron-3-nano-omni-30b-a3b-reasoning](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) | Supported image captioning for 26.05 when you enable the caption stage |
+| `nemotron_3_nano_omni_30b_a3b_reasoning` | [nemotron-3-nano-omni-30b-a3b-reasoning](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) | Supported image captioning when you enable the caption stage |
 | `audio` | [parakeet-1-1b-ctc-en-us](https://huggingface.co/nvidia/parakeet-ctc-1.1b) | [Audio and video](audio-video.md) transcription |
 
-### Image captioning (26.05) { #image-captioning-2605 }
+### Image captioning { #image-captioning }
 
-For 26.05, use **`nemotron_3_nano_omni_30b_a3b_reasoning`** when you enable the caption stage (hosted model ID `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning`). The Helm key is in the [optional NIMs](#optional-helm-nims-not-auto-wired-by-default) table above.
+Use **`nemotron_3_nano_omni_30b_a3b_reasoning`** when you enable the caption stage (hosted model ID `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning`). The Helm key is in the [optional NIMs](#optional-helm-nims-not-auto-wired-by-default) table above.
 
 Optional features listed in the table above require additional GPU support, disk space, and feature-specific system dependencies beyond the four default NIMs.
 

docs/docs/extraction/workflow-document-ingestion.md

@@ -14,7 +14,7 @@ Follow these steps:
 
 Pipeline concepts and stage overview appear in [Key concepts](concepts.md). Default chunking behavior is summarized under [Chunking](concepts.md#chunking).
 
-`create_ingestor(...)` returns a `GraphIngestor`, which chains `.extract()`, `.embed()`, and `.vdb_upload()` into one graph. The Python example below stops after `.embed()` so you can inspect chunks first; append `.vdb_upload(vdb_op="lancedb", vdb_kwargs={...})` before `.ingest()` to write directly to LanceDB (refer to [Vector databases](vdbs.md)). For directory-scale corpus ingest, the `graph_pipeline` CLI below is the canonical path used in evaluation workflows.
+`create_ingestor(...)` returns a `GraphIngestor`, which chains `.extract()`, `.embed()`, and `.vdb_upload()` into one graph. The Python example below stops after `.embed()` so you can inspect chunks first; append `.vdb_upload(vdb_op="lancedb", vdb_kwargs={...})` before `.ingest()` to write directly to LanceDB (refer to [Vector databases](vdbs.md)).
 
 ## Choose how you call the library
 
@@ -47,20 +47,4 @@ result = ingestor.ingest()  # ``pandas.DataFrame`` (``batch`` and ``inprocess``)
 
 Run the above with your working directory at the repository root (so `data/multimodal_test.pdf` resolves), or adjust `documents` to the absolute path of the test PDF.
 
-### Ingest a test corpus (CLI)
-
-`graph_pipeline` is the canonical ingestion script used throughout the [QA evaluation guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/src/nemo_retriever/evaluation/README.md#step-1-ingest-and-embed-pdfs-nemo-retriever). Point it at a **directory** of PDFs to produce a ready-to-query LanceDB table.
-
-!!! note "Corpus size and LanceDB indexing"
-
-    LanceDB's default IVF index needs enough chunks to train its partitions (often on the order of tens of chunks). A single small PDF can be insufficient; use a directory with enough documents for your index settings. Replace `/your-example-dir` with your corpus path.
-
-```bash
-python -m nemo_retriever.examples.graph_pipeline \
-  /your-example-dir \
-  --vdb-kwargs-json '{"uri":"lancedb","table_name":"nemo-retriever"}'
-```
-
-For build.nvidia.com hosted inference, set [`NVIDIA_API_KEY`](api-keys.md#nvidia-api-key) and pass the `--*-invoke-url` / `--embed-invoke-url` options shown in the [README remote inference section](https://github.com/NVIDIA/NeMo-Retriever/blob/main/nemo_retriever/README.md#ingest-a-test-corpus-cli).
-
 **Next:** [Semantic retrieval](vdbs.md#semantic-retrieval) when serving queries (also refer to [Evaluate on your data](evaluate-on-your-data.md) for reranking and quality checks).

nemo_retriever/README.md

@@ -309,83 +309,6 @@ Answer:
 Cat is the animal whose activity (jumping onto a laptop) matches the location of the typos, so the cat is responsible for the typos in the documents.
 ```
 
-### Run agentic retrieval
-
-Agentic retrieval runs an LLM-driven ReAct loop over an existing LanceDB index.
-It does not ingest documents; first build the index with one of the ingestion
-flows above, then query the same `lancedb_uri`, `table_name`, and embedding
-model.
-
-For [build.nvidia.com](https://build.nvidia.com/) hosted inference, set
-`NVIDIA_API_KEY`. On CPU-only machines, the CPU embedding actor and agent LLM
-use the hosted NVIDIA endpoints by default:
-
-```bash
-export NVIDIA_API_KEY=nvapi-...
-
-retriever query "Given their activities, which animal is responsible for the typos in my documents?" \
-  --agentic \
-  --agentic-llm-model nvidia/llama-3.3-nemotron-super-49b-v1.5 \
-  --lancedb-uri lancedb \
-  --table-name nemo-retriever \
-  --embed-model-name nvidia/llama-nemotron-embed-1b-v2
-```
-
-The agentic LLM uses the built-in NVIDIA hosted chat-completions endpoint when
-`--agentic-invoke-url` is omitted. On CPU-only machines, embedding actors also
-resolve to CPU/remote implementations and default to hosted endpoints. On
-GPU-capable machines, embedding prefers the local GPU implementation unless an
-endpoint URL, such as `--embed-invoke-url https://integrate.api.nvidia.com/v1/embeddings`,
-is provided.
-
-For a quick smoke test, lower the amount of agent work:
-
-```bash
-retriever query "What is RAG?" \
-  --agentic \
-  --agentic-llm-model nvidia/llama-3.3-nemotron-super-49b-v1.5 \
-  --lancedb-uri lancedb \
-  --table-name nemo-retriever \
-  --embed-model-name nvidia/llama-nemotron-embed-1b-v2 \
-  --top-k 1 \
-  --agentic-react-max-steps 1 \
-  --agentic-backend-top-k 1
-```
-
-The same flow is available from Python. It uses the same `NVIDIA_API_KEY`
-environment variable shown above for hosted embedding and chat-completions
-requests.
-
-```python
-from nemo_retriever.cli.query_workflow import agentic_query_documents
-from nemo_retriever.query.options import (
-    QueryAgenticOptions,
-    QueryEmbedOptions,
-    QueryRequest,
-    QueryRetrievalOptions,
-    QueryStorageOptions,
-)
-
-# Requires NVIDIA_API_KEY=nvapi-... in the environment.
-results = agentic_query_documents(
-    QueryRequest(
-        query="What is RAG?",
-        retrieval=QueryRetrievalOptions(top_k=10),
-        storage=QueryStorageOptions(
-            lancedb_uri="lancedb",
-            table_name="nemo-retriever",
-        ),
-        embed=QueryEmbedOptions(
-            embed_model_name="nvidia/llama-nemotron-embed-1b-v2",
-        ),
-        agentic=QueryAgenticOptions(
-            enabled=True,
-            llm_model="nvidia/llama-3.3-nemotron-super-49b-v1.5",
-        ),
-    )
-)
-```
-
 ### Live RAG SDK (retrieve + answer in one call)
 
 The pattern above -- retrieve hits, build a prompt, call an LLM -- is baked into the SDK as `Retriever.answer()` so live applications can skip the boilerplate. The same `Retriever` instance powers three entry points: