Kheiss/bugfix reapply

docs/docs/extraction/quickstart-guide.md

@@ -111,7 +111,7 @@ To interact from the host, you'll need a Python environment that has the client
 ```
 uv venv --python 3.12 nv-ingest-dev
 source nv-ingest-dev/bin/activate
-uv pip install nv-ingest==26.1.2 nv-ingest-api==26.1.2 nv-ingest-client==26.1.2
+uv pip install nv-ingest==26.3.0 nv-ingest-api==26.3.0 nv-ingest-client==26.3.0
 ```
 
 !!! tip
@@ -358,7 +358,7 @@ INFO:nv_ingest_client.cli.util.processing:Throughput (Pages/sec): 1.28
 INFO:nv_ingest_client.cli.util.processing:Throughput (Files/sec): 0.43
 ```
 
-## Step 4: Inspecting and Consuming Results
+## Step 3: Inspecting and Consuming Results
 
 After the ingestion steps above have been completed, you should be able to find the `text` and `image` subfolders inside your processed docs folder. Each will contain JSON-formatted extracted content and metadata.
 
@@ -429,6 +429,16 @@ You can specify multiple `--profile` options.
 | `nemotron-parse`      | Advanced | Use [nemotron-parse](https://build.nvidia.com/nvidia/nemotron-parse), which adds state-of-the-art text and table extraction. For more information, refer to [Advanced Visual Parsing](nemoretriever-parse.md). | 
 | `vlm`                 | Advanced | Use [llama 3.1 Nemotron 8B Vision](https://build.nvidia.com/nvidia/llama-3.1-nemotron-nano-vl-8b-v1/modelcard) for image captioning of unstructured images and infographics. This profile enables the `caption` method in the Python API to generate text descriptions of visual content. For more information, refer to [Use Multimodal Embedding](vlm-embed.md) and [Extract Captions from Images](nv-ingest-python-api.md#extract-captions-from-images). | 
 
+## Air-Gapped Deployment (Docker Compose)
+
+When deploying in an air-gapped environment (no internet or NGC registry access), you must pre-stage container images on a machine with network access, then transfer and load them in the isolated environment.
+
+1. On a machine with network access: Clone the repo, authenticate with NGC (`docker login nvcr.io`), and pull all images used by your chosen profile (for example, `docker compose --profile retrieval pull`).
+2. Save images: Export the images to archives (for example, using `docker save` for each image or a script that saves all images referenced by your [docker-compose.yaml](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docker-compose.yaml)).
+3. Transfer the image archives and your `docker-compose.yaml` (and `.env` if used) to the air-gapped system.
+4. On the air-gapped machine: Load the images (`docker load -i <archive>`) and start the stack with the same profile (for example, `docker compose --profile retrieval up`).
+
+Ensure the same image tags and `docker-compose.yaml` version are used in both environments so that service configuration stays consistent.
 
 ## Docker Compose override files
 
@@ -515,6 +525,7 @@ This syntax and structure can be repeated for each NIM model used by CAS, ensuri
 
     Advanced features require additional GPU support and disk space. For more information, refer to [Support Matrix](support-matrix.md).
 
+
 ## Related Topics
 
 - [Troubleshoot](troubleshoot.md)