docs(26.05): reconcile release docs from main

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/audio-video.md

@@ -100,6 +100,8 @@ Instead of running the pipeline locally, you can call Parakeet through [build.nv
 
 2. Run inference from Python with the hosted gRPC endpoint and credentials from that page (the example below uses the default hosted gRPC hostname; confirm values in the **Get API Key** flow for your deployment). Pass hosted endpoint, function ID, and API key through `ASRParams` (`audio_endpoints`, `function_id`, `auth_token`).
 
+    For parameter details, refer to the [Python API guide](nemo-retriever-api-reference.md).
+
     ```python
     from nemo_retriever import create_ingestor
     from nemo_retriever.params.models import ASRParams

docs/docs/extraction/concepts.md

@@ -2,40 +2,40 @@
 
 These terms appear throughout NeMo Retriever Library documentation.
 
-## Job
+## Job { #job }
 
-An **ingestion job** is a unit of work you run on input content (documents, audio, video, and other supported types). You submit jobs through the **ingestor Python API** (for example `Ingestor` task chains such as `.extract(...)`) or the **`retriever ingest` CLI**—not by posting a standalone JSON job document. Default tasks target strong recall; customize behavior with task keyword arguments (including chunking and splitting on `.extract()`) or custom UDF-style operations ([NeMo Retriever graph](https://github.com/NVIDIA/NeMo-Retriever/tree/main/nemo_retriever/src/nemo_retriever/graph#nemo-retriever-graph)). Results are structured metadata and annotations (Ray Dataset, pandas `DataFrame`, or similar).
+An **ingestion job** is a unit of work you run on input content (documents, audio, video, and other supported types). You submit jobs through the **ingestor Python API** (for example `Ingestor` task chains such as `.extract(...)`) or the **`retriever ingest` CLI**—not by posting a standalone JSON job document. Default tasks target strong recall; customize behavior with task keyword arguments (including chunking and splitting on `.extract()`) or custom UDF-style operations. For UDFs and other extension paths, refer to [Customize & extend](customize-extend.md). Results are structured metadata and annotations (Ray Dataset, pandas `DataFrame`, or similar).
 
-## Pipeline and tasks
+## Pipeline and tasks { #pipeline-and-tasks }
 
-NeMo Retriever Library does **not** run one static pipeline on every document. You configure **tasks** such as parsing, chunking, embedding, storage, and filtering per job. Related topics: [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).
+NeMo Retriever Library does **not** run one static pipeline on every document. You configure **tasks** such as parsing, chunking, embedding, storage, and filtering per job. For UDFs, custom graph stages, and other extension paths, refer to [Customize & extend](customize-extend.md).
 
-## Extraction metadata
+## Extraction metadata { #extraction-metadata }
 
 Output is a **Ray Dataset** (Ray Data) or **pandas** `DataFrame` listing extracted objects (text regions, tables, images, and so on), processing notes, and timing or trace data. Field-level detail is in the [metadata reference](content-metadata.md).
 
-## Embeddings and retrieval
+## Embeddings and retrieval { #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).
+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. For parameter details, refer to the [Python API guide](nemo-retriever-api-reference.md).
 
-## Deployment modes
+## Deployment modes { #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.
-- **Notebooks** — [Jupyter examples](notebooks/index.md) for experimentation and RAG demos.
+- **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).