Update product naming

docs/docs/extraction/audio.md

@@ -9,7 +9,7 @@ to extract speech from audio files.
 
 !!! note
 
-    NVIDIA Ingest and nv-ingest is now known as the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 Currently, you can extract speech from the following file types:
 

docs/docs/extraction/content-metadata.md

@@ -10,7 +10,7 @@ Metadata can be extracted from a source or content, or generated by using models
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 

docs/docs/extraction/contributing.md

@@ -1,4 +1,4 @@
-# Contributing to NeMo Retriever
+# Contributing to NeMo Retriever Library
 
-External contributions to NeMo Retriever will be welcome soon, and they are greatly appreciated! 
-For more information, refer to [Contributing to NeMo Retriever](https://github.com/NVIDIA/NeMo-Retriever/blob/main/CONTRIBUTING.md).
+External contributions to NeMo Retriever Library will be welcome soon, and they are greatly appreciated! 
+For more information, refer to [Contributing to NeMo Retriever Library](https://github.com/NVIDIA/NeMo-Retriever/blob/main/CONTRIBUTING.md).

docs/docs/extraction/custom-metadata.md

@@ -56,7 +56,7 @@ meta_df.to_csv(file_path)
 ### Example: Add Custom Metadata During Ingestion
 
 The following example adds custom metadata during ingestion. 
-For more information about the `Ingestor` class, see [Use the NeMo Retriever Python API](python-api-reference.md).
+For more information about the `Ingestor` class, see [Use the NeMo Retriever Library Python API](python-api-reference.md).
 For more information about the `vdb_upload` method, see [Upload Data](data-store.md).
 
 ```python

docs/docs/extraction/data-store.md

@@ -4,7 +4,7 @@ Use this documentation to learn how [NeMo Retriever Library](overview.md) handle
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 ## Overview

docs/docs/extraction/environment-config.md

@@ -5,7 +5,7 @@ You can specify these in your .env file or directly in your environment.
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 ## General Environment Variables

docs/docs/extraction/faq.md

@@ -4,7 +4,7 @@ This documentation contains the Frequently Asked Questions (FAQ) for [NeMo Retri
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 
@@ -57,18 +57,12 @@ You can set those directly in `docker-compose.yaml`, or in an [environment varia
 
 ### Library Mode
 
-For production environments, you should use the provided Helm charts. For [library mode](quickstart-library-mode.md), you should set the environment variable `NVIDIA_API_KEY`. This is because the NeMo Retriever containers and the NeMo Retriever services running inside them do not have access to the environment variables on the host machine where you run the `docker compose` command. Setting the variables in the `.env` file ensures that they are passed into the containers and available to the services that need them.
+For production environments, you should use the provided Helm charts. For [library mode](quickstart-library-mode.md), you should set the environment variable `NVIDIA_API_KEY`. This happens because the NeMo Retriever Library containers—and the services running inside them—don’t have access to the environment variables of the host machine where the `docker compose` command is executed. Setting the variables in the `.env` file ensures that they are passed into the containers and available to the services that need them.
 
 For advanced scenarios, you might want to use library mode with self-hosted NIM instances. 
 You can set custom endpoints for each NIM. 
 For examples of `*_ENDPOINT` variables, refer to [NeMo-Retriever/docker-compose.yaml](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docker-compose.yaml).
 
-
-
-
-
-
-
 ## What parameters or settings can I adjust to optimize extraction from my documents or data? 
 
 See the [Profile Information](quickstart-guide.md#profile-information) section 

docs/docs/extraction/nemoretriever-parse.md

@@ -13,7 +13,7 @@ to run [NeMo Retriever Library](overview.md) with nemotron-parse.
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 ## Limitations
@@ -32,19 +32,19 @@ Use the following procedure to run the NIM locally.
     Due to limitations in available VRAM controls in the current release of nemotron-parse, it must run on a [dedicated additional GPU](support-matrix.md). Edit docker-compose.yaml to set nemotron-parse's device_id to a dedicated GPU: device_ids: ["1"] or higher.
 
 
-1. Start the NeMo Retriever services with the `nemotron-parse` profile. This profile includes the necessary components for extracting text and metadata from images. Use the following command.
+1. Start the NeMo Retriever Library services with the `nemotron-parse` profile. This profile includes the necessary components for extracting text and metadata from images. Use the following command.
 
     - The --profile nemotron-parse flag ensures that vision-language retrieval services are launched.  For more information, refer to [Profile Information](quickstart-guide.md#profile-information).
 
     ```shell
     docker compose --profile nemotron-parse up
     ```
 
-2. After the services are running, you can interact with NeMo Retriever by using Python.
+2. After the services are running, you can interact with NeMo Retriever Library by using Python.
 
     - The `Ingestor` object initializes the ingestion process.
     - The `files` method specifies the input files to process.
-    - The `extract` method tells NeMo Retriever to use `nemotron-parse` for extracting text and metadata from images.
+    - The `extract` method tells NeMo Retriever Library to use `nemotron-parse` for extracting text and metadata from images.
     - The `document_type` parameter is optional, because `Ingestor` should detect the file type automatically.
 
     ```python
@@ -60,12 +60,12 @@ Use the following procedure to run the NIM locally.
 
     !!! tip
 
-        For more Python examples, refer to [NeMo Retriever: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
+        For more Python examples, refer to [NeMo Retriever Library: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
 
 
 ## Using NVCF Endpoints for Cloud-Based Inference
 
-Instead of running NeMo Retriever locally, you can use NVCF to perform inference by using remote endpoints.
+Instead of running NeMo Retriever Library locally, you can use NVCF to perform inference by using remote endpoints.
 
 1. Set the authentication token in the `.env` file.
 
@@ -85,7 +85,7 @@ Instead of running NeMo Retriever locally, you can use NVCF to perform inference
 
     - The `Ingestor` object initializes the ingestion process.
     - The `files` method specifies the input files to process.
-    - The `extract` method tells NeMo Retriever to use `nemotron-parse` for extracting text and metadata from images.
+    - The `extract` method tells NeMo Retriever Library to use `nemotron-parse` for extracting text and metadata from images.
     - The `document_type` parameter is optional, because `Ingestor` should detect the file type automatically.
 
     ```python
@@ -101,7 +101,7 @@ Instead of running NeMo Retriever locally, you can use NVCF to perform inference
 
     !!! tip
 
-        For more Python examples, refer to [NeMo Retriever: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
+        For more Python examples, refer to [NeMo Retriever Library: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
 
 
 ## Run the Ray batch pipeline with `nemotron-parse`
@@ -130,4 +130,4 @@ Replace `/path/to/pdfs` with the path to your input directory (for example, `/ho
 
 - [Support Matrix](support-matrix.md)
 - [Troubleshoot NeMo Retriever Library](troubleshoot.md)
-- [Use the NeMo Retriever Python API](python-api-reference.md)
+- [Use the NeMo Retriever Library Python API](python-api-reference.md)

docs/docs/extraction/nimclient.md

@@ -5,7 +5,7 @@ This documentation demonstrates how to create custom NIM integrations for use in
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 The NimClient architecture consists of two main components:
 

docs/docs/extraction/notebooks.md

@@ -4,7 +4,7 @@ To get started using [NeMo Retriever Library](overview.md), you can try one of t
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 ## Dataset Downloads for Benchmarking
@@ -15,8 +15,8 @@ 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 notebooks:
 
-- [NeMo Retriever: CLI Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/cli_client_usage.ipynb)
-- [NeMo Retriever: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb)
+- [NeMo Retriever Library: CLI Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/cli_client_usage.ipynb)
+- [NeMo Retriever Library: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb)
 - [How to add metadata to your documents and filter searches](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/metadata_and_filtered_search.ipynb)
 - [How to reindex a collection](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/reindex_example.ipynb)
 
@@ -25,7 +25,7 @@ For more advanced scenarios, try one of the following notebooks:
 
 - [Build a Custom Vector Database Operator](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/building_vdb_operator.ipynb)
 - [Try Enterprise RAG Blueprint](https://github.com/NVIDIA/NeMo-Retriever/blob/main/deploy/pdf-blueprint.ipynb)
-- [Evaluate bo767 retrieval recall accuracy with NeMo Retriever and Milvus](https://github.com/NVIDIA/NeMo-Retriever/blob/main/evaluation/bo767_recall.ipynb)
+- [Evaluate bo767 retrieval recall accuracy with NeMo Retriever Library and Milvus](https://github.com/NVIDIA/NeMo-Retriever/blob/main/evaluation/bo767_recall.ipynb)
 - [Multimodal RAG with LangChain](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/langchain_multimodal_rag.ipynb)
 - [Multimodal RAG with LlamaIndex](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/llama_index_multimodal_rag.ipynb)
 

docs/docs/extraction/overview.md

@@ -6,7 +6,7 @@ to find, contextualize, and extract text, tables, charts and infographics that y
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 NeMo Retriever Library enables parallelization of splitting documents into pages where artifacts are classified (such as text, tables, charts, and infographics), extracted, and further contextualized through optical character recognition (OCR) into a well defined JSON schema. 
 From there, NeMo Retriever Library can optionally manage computation of embeddings for the extracted content, 

docs/docs/extraction/prerequisites.md

@@ -4,7 +4,7 @@ Before you begin using [NeMo Retriever Library](overview.md), ensure the followi
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 
 

docs/docs/extraction/python-api-reference.md

@@ -4,7 +4,7 @@ The [NeMo Retriever Library](overview.md) Python API provides a simple and flexi
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 !!! tip
 

docs/docs/extraction/quickstart-guide.md

@@ -112,7 +112,7 @@ Because many service URIs default to localhost, running inside the `nemo-retriev
 
 ## Step 3: Ingest Documents
 
-You can submit jobs programmatically in Python or using the [NeMo Retriever CLI](cli-reference.md).
+You can submit jobs programmatically in Python or using the [NeMo Retriever Library CLI](cli-reference.md).
 
 The following examples demonstrate how to extract text, charts, tables, and images:
 
@@ -126,7 +126,7 @@ The following examples demonstrate how to extract text, charts, tables, and imag
 
 !!! tip
 
-    For more Python examples, refer to [NeMo Retriever: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
+    For more Python examples, refer to [NeMo Retriever Library: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
 
 <a id="ingest_python_example"></a>
 ```python
@@ -386,7 +386,7 @@ python src/util/image_viewer.py --file_path ./processed_docs/image/multimodal_te
 
 !!! tip
 
-    Beyond inspecting the results, you can read them into things like [llama-index](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/llama_index_multimodal_rag.ipynb) or [langchain](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/langchain_multimodal_rag.ipynb) retrieval pipelines. Also, checkout our [Enterprise RAG Blueprint on build.nvidia.com](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag) to query over document content pre-extracted with NeMo Retriever.
+    Beyond inspecting the results, you can read them into things like [llama-index](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/llama_index_multimodal_rag.ipynb) or [langchain](https://github.com/NVIDIA/NeMo-Retriever/blob/main/examples/langchain_multimodal_rag.ipynb) retrieval pipelines. Also, checkout our [Enterprise RAG Blueprint on build.nvidia.com](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag) to query over document content pre-extracted with NeMo Retriever Library.
 
 
 
@@ -459,7 +459,7 @@ docker compose \
 
 ## Specify MIG slices for NIM models
 
-When you deploy NeMo Retriever with NIM models on MIG‑enabled GPUs, MIG device slices are requested and scheduled through the `values.yaml` file for the corresponding NIM microservice. For IBM Content-Aware Storage (CAS) deployments, this allows NeMo Retriever NIM pods to land only on nodes that expose the desired MIG profiles [raw.githubusercontent](https://raw.githubusercontent.com/NVIDIA/NeMo-Retriever/main/helm/README.md%E2%80%8B).​
+When you deploy NeMo Retriever Library with NIM models on MIG‑enabled GPUs, MIG device slices are requested and scheduled through the `values.yaml` file for the corresponding NIM microservice. For IBM Content-Aware Storage (CAS) deployments, this allows NeMo Retriever Library NIM pods to land only on nodes that expose the desired MIG profiles [raw.githubusercontent](https://raw.githubusercontent.com/NVIDIA/NeMo-Retriever/main/helm/README.md%E2%80%8B).​
 
 To target a specific MIG profile—for example, a 3g.20gb slice on an A100, which is a hardware-partitioned virtual GPU instance that gives your workload a fixed mid-sized share of the A100’s compute plus 20 GB of dedicated GPU memory and behaves like a smaller independent GPU—for a given NIM, configure the `resources` and `nodeSelector` under that NIM’s values path in `values.yaml`.
 
@@ -482,7 +482,7 @@ Key points:
 * Use the appropriate NIM‑specific values path (for example, `nemo_retriever.nvidiaNim.nemoretrieverPageElements.resources`) rather than the generic `nemo_retriever.nim` placeholder.
 * Set `resources.requests` and `resources.limits` to the desired MIG resource name (for example, `nvidia.com/mig-3g.20gb`).
 * Use `nodeSelector` (or tolerations/affinity, if you prefer) to target nodes labeled with the corresponding MIG‑enabled GPU product (for example, `nvidia.com/gpu.product: A100-SXM4-40GB-MIG-3g.20gb`).
-This syntax and structure can be repeated for each NIM model used by CAS, ensuring that each NeMo Retriever NIM pod is mapped to the correct MIG slice type and scheduled onto compatible nodes.
+This syntax and structure can be repeated for each NIM model used by CAS, ensuring that each NeMo Retriever Library NIM pod is mapped to the correct MIG slice type and scheduled onto compatible nodes.
 
 !!! important
 

docs/docs/extraction/quickstart-library-mode.md

@@ -4,7 +4,7 @@
 
 !!! note
 
-    This library is the NeMo Retriever Library.
+    NVIDIA Ingest (nv-ingest) has been renamed to the NeMo Retriever Library.
 
 In addition, you can use library mode, which is intended for the following cases:
 
@@ -65,7 +65,7 @@ You can submit jobs programmatically by using Python.
 
 !!! tip
 
-    For more Python examples, refer to [NeMo Retriever: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
+    For more Python examples, refer to [NeMo Retriever Library: Python Client Quick Start Guide](https://github.com/NVIDIA/NeMo-Retriever/blob/main/client/client_examples/examples/python_client_usage.ipynb).
 
 
 If you have a very high number of CPUs, and see the process hang without progress,