nemo-retriever Helm chart

A Kubernetes Helm chart for running the service mode of\nnemo-retriever: a FastAPI document ingestion server that\nstreams uploads through a set of NVIDIA NIM microservices\n(page-elements, table-structure, OCR, VLM embed by default) and exposes\nresult + status APIs over HTTP / SSE.

Unsupported developer path: ad-hoc Docker Compose workflows (not\nchart-managed) are documented separately in ../docker.md.\nUse Helm (this chart and/or the additional Library charts documented in the\nNeMo Retriever Library)\nfor supported NIM and service deployment.

The chart ships two deployable layers behind feature flags:

the service — always on; one Deployment (standalone) or three\nDeployments (split topology: gateway / realtime / batch), built from\nDockerfile --target service.
the NIMs — optional, GPU-backed NIMCache + NIMService custom\nresources (apiVersion: apps.nvidia.com/v1alpha1) reconciled by the\nNVIDIA NIM Operator. The chart auto-wires the operator-managed\nService URLs into the retriever-service config when the operator CRDs\nare present in the cluster.

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NIM Operator prerequisite. The NIM templates are gated on the\napps.nvidia.com/v1alpha1 API group. Install the NIM Operator before\nrunning helm install:\nhttps://docs.nvidia.com/nim-operator/
\n
Without the operator the chart still installs cleanly — every NIMCache /\nNIMService template short-circuits and the service falls back to\nexternal NIM URLs supplied via serviceConfig.nimEndpoints.*.
\n

\n
Persistence today is SQLite on a single ReadWriteOnce PVC, which caps\nthe service at one replica. The chart already exposes the HPA scaffolding\nso it's a one-line change once the planned PostgreSQL backend lands.
\n

\n
For behavioral consistency between local HuggingFace deployments and Helm service deployments:\n`results = ingestor.ingest(...return_results=True)\nreturn_results defaults to True. This incurs a significant performance and system memory usage cost.\nUnless you know explicitly you need to fetch extraction results to the client, you should use:\nreturn_results=False\nIf you must return results, you may need to increase pod memory specs to support the increased pod memory usage.
\n

Layout

nemo_retriever/helm/\n├── Chart.yaml\n├── values.yaml\n├── README.md            <-- this file\n├── .helmignore\n└── templates/\n    ├── _helpers.tpl\n    ├── NOTES.txt\n    ├── configmap.yaml                         # renders retriever-service.yaml\n    ├── deployment.yaml                        # the service Deployment(s)\n    ├── service.yaml                           # ClusterIP/NodePort for the service\n    ├── ingress.yaml                           # optional Ingress\n    ├── hpa.yaml                               # optional HorizontalPodAutoscaler\n    ├── servicemonitor.yaml                    # optional Prometheus ServiceMonitor\n    ├── serviceaccount.yaml\n    ├── pvc.yaml                               # SQLite database PVC\n    ├── secrets.yaml                           # ngc-secret + ngc-api\n    └── nims/\n        ├── nemotron-page-elements-v3.yaml     # NIMCache + NIMService\n        ├── nemotron-table-structure-v1.yaml   # NIMCache + NIMService\n        ├── nemotron-ocr-v1.yaml               # NIMCache + NIMService (OCR)\n        ├── llama-nemotron-embed-vl-1b-v2.yaml           # NIMCache + NIMService (VLM embed)\n        ├── llama-nemotron-rerank-vl-1b-v2.yaml  # NIMCache + NIMService (optional; not auto-wired)\n        ├── nemotron-parse.yaml                # NIMCache + NIMService (optional; not auto-wired)\n        ├── nemotron-3-nano-omni-30b-a3b-reasoning.yaml  # NIMCache + NIMService (optional; not auto-wired)\n        └── audio.yaml                         # NIMCache + NIMService (optional; not auto-wired)\n

Quick start

1. Service image

The chart defaults to the staging image published to NGC:

nvcr.io/nvstaging/nim/nemo-retriever-service:043020205-001\n

Pulling from nvcr.io/nvstaging requires an NGC pull secret — either set\nngcImagePullSecret.create=true (see below) or pre-create one in the\nnamespace named ngc-secret.

To run a locally built image instead, build and push it from the repo root,\nthen override service.image.repository / service.image.tag:

/nemo-retriever-service: .\ndocker push /nemo-retriever-service:\n\">

# from the repo root:\ndocker build \\\n    --target service \\\n    -t <YOUR_REGISTRY>/nemo-retriever-service:<TAG> .\ndocker push <YOUR_REGISTRY>/nemo-retriever-service:<TAG>\n

Audio and video extraction require the ffmpeg and ffprobe system\nbinaries inside the service container. The bundled service image can install\nthem at container startup when you set service.installFfmpeg=true, which\nsets INSTALL_FFMPEG=true for the image entrypoint:

/nemo-retriever-service \\\n --set service.image.tag= \\\n --set service.installFfmpeg=true\n\">

helm upgrade --install retriever ./nemo_retriever/helm \\\n  --set service.image.repository=<YOUR_REGISTRY>/nemo-retriever-service \\\n  --set service.image.tag=<TAG> \\\n  --set service.installFfmpeg=true\n

Do not also set INSTALL_FFMPEG in service.env; the chart fails rendering\nwhen both are configured so the rendered Pod does not contain duplicate\nenvironment variables.

When service.installFfmpeg=false (the default), the service still starts\nnormally and processes PDF, image, text and HTML uploads. Audio / video\nuploads are rejected up-front with HTTP 501:

Audio and video ingestion require FFmpeg in the retriever service\ncontainer, but the following dependencies are missing: ffmpeg, ffprobe.\nRe-deploy the Helm chart with `--set service.installFfmpeg=true` …\n

The retriever-service container also logs a WARNING at startup when\nFFmpeg is missing so cluster operators can fix the deployment before\nthe first media upload arrives, instead of debugging a Ray worker\ntraceback (RuntimeError: MediaChunkActor requires media dependencies; missing: ffmpeg, ffprobe) after the fact. The same WARNING is emitted\non every pod (gateway, realtime, batch) because all roles classify\nuploads — flipping service.installFfmpeg=true updates them all.

Runtime installation uses passwordless sudo scoped to installing the\nffmpeg package in the service image. The pod must have network egress to the\nUbuntu package repositories, a writable root filesystem, and a security policy\nthat allows sudo/setuid behavior. Do not set\nservice.securityContext.allowPrivilegeEscalation: false or\nservice.securityContext.readOnlyRootFilesystem: true for this path.

For air-gapped or locked-down clusters, see\nDeployment options — Air-gapped and disconnected deployment.\nOn a connected staging host you can extend the service image, for example:

/nemo-retriever-service:\nUSER root\nRUN apt-get update && apt-get install -y --no-install-recommends ffmpeg \\\n && rm -rf /var/lib/apt/lists/*\nUSER nemo\n\">

FROM <YOUR_REGISTRY>/nemo-retriever-service:<BASE_TAG>\nUSER root\nRUN apt-get update && apt-get install -y --no-install-recommends ffmpeg \\\n    && rm -rf /var/lib/apt/lists/*\nUSER nemo\n

2. Install with external NIM endpoints (operator not required)

If you already have NIM endpoints reachable from the cluster (e.g. another\nnamespace, or NVIDIA Build), turn the master switch off and supply the\nURLs directly:

helm install retriever ./nemo_retriever/helm \\\n  --set nims.enabled=false \\\n  --set ngcImagePullSecret.create=true \\\n  --set ngcImagePullSecret.password=$NGC_API_KEY \\\n  --set ngcApiSecret.create=true \\\n  --set ngcApiSecret.password=$NGC_API_KEY \\\n  --set serviceConfig.nimEndpoints.pageElementsInvokeUrl=http://page-elements.svc:8000/v1/infer \\\n  --set serviceConfig.nimEndpoints.tableStructureInvokeUrl=http://table-structure.svc:8000/v1/infer \\\n  --set serviceConfig.nimEndpoints.ocrInvokeUrl=http://ocr.svc:8000/v1/infer \\\n  --set serviceConfig.nimEndpoints.embedInvokeUrl=http://embed.svc:8000/v1/embeddings\n

ngcApiSecret materialises an ngc-api Secret containing both\nNGC_API_KEY and NGC_CLI_API_KEY keys; the service container reads it\nvia optional: true secretKeyRef, so the install still succeeds when\nthe secret is absent (useful for fully local NIM endpoints).

3. Install with the NIM Operator (in-cluster NIMs)

Install the NIM Operator first so\nthe NIMCache / NIMService CRDs (apps.nvidia.com/v1alpha1) are\nregistered. A plain helm install reconciles the four core NIMs\n(page_elements, table_structure, ocr, vlm_embed) — every other\nNIM (the VL reranker rerankqa, Nemotron Parse, Omni 30B, and the\nParakeet audio ASR NIM) is disabled by default to honor the\n\"optional and disabled by default\" contract in\ndeployment-options.md;\nsee Recommended minimal install (26.05)\nfor the opt-in --set flags that turn any of them on.

helm install retriever ./nemo_retriever/helm \\\n  --set ngcImagePullSecret.create=true \\\n  --set ngcImagePullSecret.password=$NGC_API_KEY \\\n  --set ngcApiSecret.create=true \\\n  --set ngcApiSecret.password=$NGC_API_KEY\n

Recommended minimal install (26.05) { #recommended-minimal-install-2605 }

Deploy only the four core NIMs that the retriever service auto-wires (page_elements, table_structure, ocr, vlm_embed):

helm install retriever ./nemo_retriever/helm \\\n  --set ngcImagePullSecret.create=true \\\n  --set ngcImagePullSecret.password=$NGC_API_KEY \\\n  --set ngcApiSecret.create=true \\\n  --set ngcApiSecret.password=$NGC_API_KEY\n

\n
The VL reranker (rerankqa), Nemotron Parse, the Nemotron 3 Nano Omni 30B caption NIM, and the Parakeet audio ASR NIM are all off by default in 26.05 — they only reconcile when you explicitly opt in. Opt-in flags:
\n
\n
VL reranker — --set nimOperator.rerankqa.enabled=true
\n
Nemotron Parse — --set nimOperator.nemotron_parse.enabled=true
\n
Omni 30B captioner — --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true
\n
Parakeet ASR — --set nimOperator.audio.enabled=true (also set serviceConfig.nimEndpoints.audioGrpcEndpoint=audio:50051 to wire ASR into the service, plus service.installFfmpeg=true if your image does not bundle ffmpeg)
\n
\n
This matches the \"optional and disabled by default\" contract in deployment-options.md and avoids silently pulling ≈ 62 GiB of Omni weights or claiming a second dedicated GPU on a \"default\" install. See the model hardware requirements table for per-NIM GPU and disk costs.
\n

The chart auto-wires the operator-managed in-cluster URLs of the four\n\"core\" NIMs into the service's nim_endpoints block:

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key	operator-managed Service	invoke path
`nimOperator.page_elements`	`nemotron-page-elements-v3`	`/v1/infer`
`nimOperator.table_structure`	`nemotron-table-structure-v1`	`/v1/infer`
`nimOperator.ocr`	`nemotron-ocr-v1`	`/v1/infer`
`nimOperator.vlm_embed`	`llama-nemotron-embed-vl-1b-v2`	`/v1/embeddings`

Track operator reconciliation with:

\nkubectl describe nimservice nemotron-page-elements-v3 -n \n\">

kubectl get nimcache,nimservice -n <namespace>\nkubectl describe nimservice nemotron-page-elements-v3 -n <namespace>\n

First-time NIMCache reconciliation downloads model weights to a PVC. By\ndefault (nimOperator.nimCache.keepOnUninstall: true) every NIMCache\ncarries helm.sh/resource-policy: keep so those downloads survive\nhelm uninstall. NIMService CRs do not use keep and are removed by\nHelm on uninstall.

Why NIM resources still exist after `helm uninstall`

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What you see	Typical cause
`NIMCache` + PVC remain	Expected when `keepOnUninstall` is true (default). Helm intentionally skips deleting caches so you do not re-pull multi‑GiB weights.
`NIMService` CR remains	Not expected on a normal uninstall. Usually an orphan from a failed install/upgrade (release never recorded the resource, or the chart renamed a NIM).
Deployments / GPU pods still running	Often the operator workload for a kept `NIMCache`, or a stale `NIMService` that Helm did not own. Check `kubectl get nimservice,nimcache -n <ns>`.
`nemotron--job-` pods in `Error`	The NIM Operator's model-download Job for a `NIMCache` (not the retriever service). Failed cache pulls retry and leave Error pods until the Job or `NIMCache` is deleted. Common after a failed `helm install` when the release is rolled back but `keep` retains the cache CR.
`helm uninstall` appears to do nothing	Release may be missing or failed (`helm list -n <ns> -a`). CRs created before a failed install can be left without a release to clean them up.

Full teardown (dev cluster — deletes caches and PVCs Helm kept):

/dev/null || true\n\n# Orphans and kept NIMCaches (Helm keep does not block kubectl delete):\nkubectl delete nimservice,nimcache -n "${NS}" --all\n# Optional: drop model PVCs if you will re-pull from NGC\nkubectl delete pvc -n "${NS}" -l 'app.kubernetes.io/managed-by=nvidia-nim-operator' 2>/dev/null || true\n\">

NS=retriever\nREL=nemo-retriever\n\nhelm uninstall \"${REL}\" -n \"${NS}\" 2>/dev/null || true\n\n# Orphans and kept NIMCaches (Helm keep does not block kubectl delete):\nkubectl delete nimservice,nimcache -n \"${NS}\" --all\n# Optional: drop model PVCs if you will re-pull from NGC\nkubectl delete pvc -n \"${NS}\" -l 'app.kubernetes.io/managed-by=nvidia-nim-operator' 2>/dev/null || true\n

Dev installs that should not retain caches on uninstall:

helm upgrade --install \"${REL}\" ./nemo_retriever/helm -n \"${NS}\" \\\n  --set nimOperator.nimCache.keepOnUninstall=false \\\n  ...\n

Values reference (highlights)

The full schema lives in values.yaml. Below is the\nshort list of knobs you'll touch first.

Service

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Path	Default	Notes
`service.image.repository`	`localhost:32000/nemo-retriever-service`	Override to a published image.
`service.image.tag`	`latest`
`service.replicas`	`1`	Hard cap = 1 while SQLite is the backend.
`service.installFfmpeg`	`false`	Install `ffmpeg`/`ffprobe` at container startup by setting `INSTALL_FFMPEG=true`. Requires network egress, writable root filesystem, and sudo/setuid allowed. Not for air-gapped clusters — use a custom image instead.
`service.resources.requests`	`16 / 16Gi`	Tune in tandem with `serviceConfig.pipeline.*Workers`.
`service.resources.limits`	`96 / 96Gi`
`service.gpu.enabled`	`false`	The service does not need a GPU.

For audio and video extraction, set service.installFfmpeg=true when your\ncluster allows runtime package installation. For air-gapped clusters, see\nDeployment options — Air-gapped and disconnected deployment.

Audio and video (Parakeet ASR) { #audio-video-parakeet }

To run self-hosted Parakeet for audio and video extraction:

Set nimOperator.audio.enabled=true (it is on by default; disable other optional NIMs you do not need per Recommended minimal install (26.05)).
Pin the ASR NIMService to a dedicated GPU with nimOperator.audio.resources, nodeSelector, or tolerations (see NIM Operator).
Confirm the GPU SKU in Model hardware requirements (footnote ⁴ lists Blackwell limitations).
Set service.installFfmpeg=true when the retriever service will process audio or video (see service.installFfmpeg above).

The retriever service picks up the in-cluster ASR endpoint when nimOperator.audio is enabled; see NIM Operator sub-stack.

Service configuration (rendered into `retriever-service.yaml`)

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Path	Default	Notes
`serviceConfig.server.port`	`7670`	Container + Service port.
`serviceConfig.pipeline.realtimeWorkers`	`24`	Per-pod realtime worker count.
`serviceConfig.pipeline.batchWorkers`	`48`	Per-pod batch worker count. See Timeouts and alleviating ingest failures if embed or pool errors appear under load.
`serviceConfig.nimEndpoints.*InvokeUrl`	`\"\"`	Override the auto-resolved NIM Operator URL. Available knobs: `pageElementsInvokeUrl`, `tableStructureInvokeUrl`, `ocrInvokeUrl`, `embedInvokeUrl`, and `captionInvokeUrl` (see Image captioning (Omni 30B)).
`serviceConfig.nimEndpoints.captionModelName`	`\"\"`	Model id sent to the remote VLM. Auto-set to `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning` whenever a caption URL is resolved.
`serviceConfig.vectordb.enabled`	`true`	Deploy the LanceDB vectordb Pod. When `true` the chart requires a resolvable embed endpoint (see VectorDB and the embed endpoint); `helm install` / `helm upgrade` fails fast otherwise.
`serviceConfig.vectordb.lancedbUri`	`/data/vectordb`	LanceDB on the vectordb Pod's PVC.
`serviceConfig.vectordb.embedModel`	`nvidia/llama-nemotron-embed-vl-1b-v2`	Passed to vectordb + worker `embed_model_name`.

VectorDB and the embed endpoint { #vectordb-and-the-embed-endpoint }

The vectordb Pod's /v1/query handler embeds the incoming query text\nbefore searching LanceDB. It needs a NIM embedding endpoint to do that,\nand rendering the Deployment with an empty --embed-endpoint produces a\nPod that passes its /v1/health probe but answers every /v1/query\nrequest with HTTP 501 No embedding endpoint configured. — a healthy\ndeployment that silently breaks retrieval.

To prevent this, the chart now refuses to render\ndeployment-vectordb.yaml when no embed endpoint can be resolved.\nhelm install / helm upgrade --install fails with a message listing\nthe three supported escape valves:

:/v1/embeddings\n 2. --set nimOperator.vlm_embed.enabled=true # requires apps.nvidia.com/v1alpha1 CRDs\n 3. --set serviceConfig.vectordb.enabled=false\n\">

serviceConfig.vectordb.enabled=true but the embed endpoint could not be\nresolved.  Pick one of:\n\n  1. --set serviceConfig.nimEndpoints.embedInvokeUrl=http://<host>:<port>/v1/embeddings\n  2. --set nimOperator.vlm_embed.enabled=true   # requires apps.nvidia.com/v1alpha1 CRDs\n  3. --set serviceConfig.vectordb.enabled=false\n

Resolution order matches the rest of the chart (see Mix and match NIM\nsources):

Explicit serviceConfig.nimEndpoints.embedInvokeUrl always wins.
Otherwise the operator-managed URL of\nnimOperator.vlm_embed.nimServiceName is used, provided\nnimOperator.vlm_embed.enabled=true and the\napps.nvidia.com/v1alpha1 CRDs are installed in the cluster.
Otherwise the chart fails the install.

NIM Operator sub-stack

Each NIM block under nimOperator.<key> renders a NIMCache + NIMService\npair gated on three conditions ALL holding:

The apps.nvidia.com/v1alpha1 CRDs are installed in the cluster.
The master switch nims.enabled is true.
The per-NIM nimOperator.<key>.enabled is true.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Path	Default	Notes
`nims.enabled`	`true`	Master switch. Set false to render no NIM resources.
`nimOperator.page_elements.enabled`	`true`	Page-elements detector NIM.
`nimOperator.table_structure.enabled`	`true`	Table-structure detector NIM.
`nimOperator.ocr.enabled`	`true`	OCR NIM.
`nimOperator.ocr.image`	`nvcr.io/nim/nvidia/nemotron-ocr-v1:1.3.0`	Default OCR NIM image.
`nimOperator.vlm_embed.enabled`	`true`	Multimodal embedding NIM (also used by the vectordb Pod).
`nimOperator.vlm_embed.nimServiceName`	`llama-nemotron-embed-vl-1b-v2`	NIMService / in-cluster DNS name.
`nimOperator.vlm_embed.image`	`nvcr.io/nim/nvidia/llama-nemotron-embed-vl-1b-v2:1.12.0`	Default VLM embed NIM image.
`nimOperator.rerankqa.enabled`	`false`	VL reranker NIM (optional; not auto-wired). Set `true` to opt in. Default `false` so 26.05 installs honor the \"optional and disabled by default\" contract in deployment-options.md and do not silently provision an extra ≈ 3.1 GiB GPU NIM. The image points at the VL SKU (`llama-nemotron-rerank-vl-1b-v2`) per prerequisites-support-matrix.md — the text-only `llama-nemotron-rerank-1b-v2` silently degrades multimodal reranking and is not the documented POR.
`nimOperator.nemotron_parse.enabled`	`false`	Structured-parse NIM (optional). Set `true` when using `extract_method=\"nemotron_parse\"`. Default `false` so 26.05 installs honor the \"optional and disabled by default\" contract in deployment-options.md. Image tag follows the image tag conventions.
`nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled`	`false`	Omni 30B caption NIM (optional). Set `true` to enable image captioning — see Image captioning (Omni 30B). Default `false` so 26.05 installs do not silently pull ≈ 62 GiB of BF16 weights or claim a second dedicated GPU. Image tag follows the image tag conventions.
`nimOperator.audio.enabled`	`false`	Parakeet ASR NIM (optional). Set `true` for audio/video transcription; pair with `serviceConfig.nimEndpoints.audioGrpcEndpoint=audio:50051` so the retriever-service can reach it.
`nimOperator.<key>.image.repository`	`nvcr.io/nim/nvidia/...`	Per-NIM image.
`nimOperator.<key>.image.pullSecrets`	`[ngc-secret]`	Referenced by the NIMService CR.
`nimOperator.<key>.authSecret`	`ngc-api`	NIM auth Secret name.
`nimOperator.<key>.storage.pvc.size`	`25Gi` (50Gi for vlm_embed/rerankqa, 100Gi parse, 300Gi VL)	NIMCache PVC size.
`nimOperator.<key>.replicas`	`1`	Per-NIMService replica count.
`nimOperator.nimServiceGpuLimit`	`1`	Default `nvidia.com/gpu` limit on every NIMService when per-NIM `resources` is `{}`. Set to `null` for operator-only reconciliation (not reliable on all NIM Operator versions — see GPU limits and `helm upgrade`).
`nimOperator.<key>.resources`	`{}`	Per-NIM override of the whole `resources` block. Empty uses `nimServiceGpuLimit`; non-empty replaces the chart default (may require `--force-conflicts` on later `helm upgrade`).
`nimOperator.modelProfile`	`{}`	Chart-wide NIMCache GPU/profile filter. Applied to every NIMCache that does not have its own override. See Filtering cached GPU profiles.
`nimOperator.<key>.modelProfile`	`{}`	Per-NIM NIMCache GPU/profile filter. Non-empty values REPLACE the chart-wide default (no merge). See Filtering cached GPU profiles.
`nimOperator.<key>.expose.service.port`	`8000` (9000 for audio)	HTTP port.
`nimOperator.<key>.expose.service.grpcPort`	`8001` (50051 for audio)	gRPC port.

\n
Only the four \"core\" NIMs (page_elements, table_structure, ocr, vlm_embed)\nare auto-wired into the retriever-service config. Optional NIMs may reconcile\nwhen nimOperator.<key>.enabled is true in values.yaml, but the\nretriever-service won't call them unless you wire your pipeline to use them.\nFor 26.05, prefer the minimal install overrides.
\n

Filtering cached GPU profiles { #filtering-cached-gpu-profiles }

Every NIMCache the chart renders supports the NIM Operator's\nspec.source.ngc.model block, which restricts which model profiles the\ncache job downloads. The chart exposes this through two values:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Path	Scope	Behaviour
`nimOperator.modelProfile`	Chart-wide	Applied to every NIMCache that doesn't carry its own override.
`nimOperator.<key>.modelProfile`	Per-NIM	When non-empty, REPLACES the chart-wide default (no merge).

Both default to {}. With both empty the chart emits no model:\nblock and the NIM Operator falls back to its \"cache every profile\napplicable to the detected GPUs\" default — fine on a single-GPU\nlaptop, but on heterogeneous clusters (or any cluster with ≥ 3 NIMs)\nthis wastes tens of GiB of PVC storage, NGC bandwidth, and cache-job\nruntime.

The mapping is rendered verbatim under spec.source.ngc.model, so the\nshape lines up 1:1 with the NIMCache CRD.\nTwo filter dimensions are supported (use whichever fits your cluster;\ngpus is the common case):

/:`.\n # - "11111111-2222-3333-4444-555555555555"\n\">

nimOperator:\n  modelProfile:\n    gpus:\n      # NIMCache only downloads profiles compatible with at least one\n      # of these GPU selectors. Each selector is {ids: [...], product: ...}.\n      - ids: [\"26B5\"]                       # PCI device ID(s)\n        product: \"NVIDIA-H100-80GB-HBM3\"    # NVIDIA marketing name\n    # profiles:\n    #   # Alternative: list of exact profile UUIDs from `ngc registry\n    #   # model list-profiles <repo>/<image>:<tag>`.\n    #   - \"11111111-2222-3333-4444-555555555555\"\n

Equivalent overrides via --set:

# Homogeneous H100 80 GB cluster — every NIMCache only pulls the H100 profile:\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n  --set 'nimOperator.modelProfile.gpus[0].ids[0]=26B5' \\\n  --set 'nimOperator.modelProfile.gpus[0].product=NVIDIA-H100-80GB-HBM3'\n\n# Restrict only the page_elements NIMCache to a specific profile UUID, leave the rest alone:\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n  --set 'nimOperator.page_elements.modelProfile.profiles[0]=11111111-2222-3333-4444-555555555555'\n\n# Chart-wide H100 default plus a per-NIM override (the override REPLACES the global; it does NOT merge):\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n  --set 'nimOperator.modelProfile.gpus[0].product=NVIDIA-H100-80GB-HBM3' \\\n  --set 'nimOperator.vlm_embed.modelProfile.profiles[0]=22222222-3333-4444-5555-666666666666'\n

Tips:

Run ngc registry model list-profiles nvcr.io/nim/nvidia/<image>:<tag> to enumerate the available profiles for any chart-pinned NIM image and pick the smallest profile that matches your GPU.
Filter mismatches surface as NIMCache events such as NoCompatibleProfile; check with kubectl describe nimcache <name>.
The chart's defaults ({}) preserve operator behaviour, so adding modelProfile is a strict opt-in — existing releases keep working unchanged.

Image tag conventions { #image-tag-conventions }

Every NIM in this chart pins an exact NGC image tag in values.yaml\n— there is no :latest floating reference. Two tag families show up:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Family	Example	Meaning
Plain semver	`nemotron-page-elements-v3:1.8.0`	A standard NIM release, identical bytes on every pull. Used by the four core NIMs and the reranker / ASR NIMs.
`<semver>-variant`	`nemotron-parse-v1.2:1.7.0-variant`, `nemotron-3-nano-omni-30b-a3b-reasoning:1.7.0-variant`	The Nemotron Parse and Nemotron 3 Nano Omni 30B builds that ship per-GPU TensorRT engine variants the NIM Operator selects from at reconciliation time (see the Omni and Parse rows in the model hardware requirements table). The `-variant` suffix is the NGC tag that ships alongside the 26.05 chart and matches footnote ³ of the support matrix.

For air-gapped mirror pipelines: mirror the exact tag — both the\nplain semver and the -variant form — and do not substitute :latest.\nSubstituting :latest would pin to a moving target that may not match\nthe engine plans the NIM Operator profile expects for a given GPU.

If you want a different NIM build, override the tag explicitly:

\n\">

helm upgrade --install retriever ./nemo_retriever/helm \\\n  --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true \\\n  --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.image.tag=<your-tag>\n

and validate against the same release of the retriever service before\nproduction rollout.

Charts and captioning (26.05). Charts and infographics use page_elements\nand ocr (no graphic_elements operator NIM in this chart). For image\ncaptioning, set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true — see\nImage captioning (Omni 30B) for the\nchart-side wiring and\nImage captioning (26.05)\nfor the product matrix.

Image captioning (Omni 30B) { #image-captioning-omni-30b }

The Nemotron 3 Nano Omni VLM is the canonical image-caption NIM for\n26.05. When you enable it,

helm upgrade --install retriever ./nemo_retriever/helm \\\n  --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true \\\n  ...\n

the chart now auto-wires two fields into the rendered\nretriever-service.yaml ConfigMap:

nim_endpoints:\n  caption_invoke_url: \"http://nemotron-3-nano-omni-30b-a3b-reasoning:8000/v1/chat/completions\"\n  caption_model_name: \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\"\n

The service derives caption_enabled=true from a non-null\ncaption_invoke_url, so the ingestion pipeline routes caption work to\nthe in-cluster Omni Pod with no manual ConfigMap edits.

Resolution order mirrors every other NIM endpoint (see the\nNIM Operator sub-stack section):

Explicit serviceConfig.nimEndpoints.captionInvokeUrl always wins\n(use this to point at a hosted endpoint, e.g.\nhttps://integrate.api.nvidia.com/v1/chat/completions).
Otherwise the operator-managed URL of\nnemotron-3-nano-omni-30b-a3b-reasoning is used, provided\nnimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true\nand the apps.nvidia.com/v1alpha1 CRDs are installed.
Otherwise caption_invoke_url stays null and the caption stage\nis disabled.

serviceConfig.nimEndpoints.captionModelName follows the same order —\nit defaults to the canonical Omni remote model id\n(nvidia/nemotron-3-nano-omni-30b-a3b-reasoning, matching\nnemo_retriever.caption.model_profiles.OMNI_REMOTE_MODEL_ID) whenever\nthe chart resolves any caption URL. Override only when pointing at a\ndifferent VLM SKU.

GPU limits and `helm upgrade` { #gpu-limits-and-helm-upgrade }

The chart defaults to nimOperator.nimServiceGpuLimit: 1, which\nrenders spec.resources.limits.nvidia.com/gpu: 1 on every NIMService\nunless a per-NIM resources map overrides it. This is required on\nNIM Operator v3.1.1 (and other versions tested on A100/H100): when\nthe chart omits the resources block entirely, the operator often\ndoes not populate GPU limits from the model profile, and NIM pods\nstart without GPU access (The NVIDIA Driver was not detected).

Trade-off: Helm and the NIM Operator may both server-side-apply\nspec.resources.limits.nvidia.com/gpu. A later helm upgrade --install\ncan then fail with:

/ apps.nvidia.com/v1alpha1, Kind=NIMService:\n Apply failed with 1 conflict:\n conflict with "manager" using apps.nvidia.com/v1alpha1:\n .spec.resources.limits.nvidia.com/gpu\n\">

Error: UPGRADE FAILED: conflict occurred while applying object\n  <ns>/<nim> apps.nvidia.com/v1alpha1, Kind=NIMService:\n  Apply failed with 1 conflict:\n  conflict with \"manager\" using apps.nvidia.com/v1alpha1:\n    .spec.resources.limits.nvidia.com/gpu\n

Operator-only mode (omit GPU limits from Helm — only if your NIM\nOperator version reliably reconciles them):

nimOperator:\n  nimServiceGpuLimit: null\n

If upgrades hit SSA conflicts after the operator has reconciled GPU\nlimits, use one of:

helm upgrade --install … --force-conflicts --server-side
kubectl -n <ns> edit nimservice <name> to set GPU limits outside Helm

To pin a non-default GPU count chart-wide, set nimServiceGpuLimit: 2\n(or set per-NIM resources.limits.nvidia.com/gpu).

OCR NIM configuration { #ocr-nim-configuration }

The core OCR NIM is configured under nimOperator.ocr (the ocr:\nblock). Confirm image.repository and image.tag before you upgrade.

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Path	Role
`nimOperator.nimCache.keepOnUninstall`	When `true`, NIMCache CRs survive `helm uninstall` (`helm.sh/resource-policy: keep`). NIMService CRs are always removed. Set `false` for dev clusters that should fully tear down on uninstall.
`nimOperator.ocr.enabled`	Reconcile the OCR `NIMService`
`nimOperator.ocr.image.repository`	NIM image (default `nvcr.io/nim/nvidia/nemotron-ocr-v1`)
`nimOperator.ocr.image.tag`	Pin the image tag for reproducible upgrades

Override the auto-wired in-cluster URL with serviceConfig.nimEndpoints.ocrInvokeUrl\nwhen the OCR service runs outside the operator sub-stack.

Persistence

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Path	Default	Notes
`persistence.enabled`	`true`
`persistence.size`	`50Gi`
`persistence.accessModes`	`[ReadWriteOnce]`	Required by SQLite.
`persistence.storageClass`	`\"\"`	Use cluster default unless set. Use `\"-\"` to disable a `storageClassName`.
`persistence.mountPath`	`/var/lib/nemo-retriever`	Both DB and log file are written here.

Secrets

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Path	Default	Notes
`ngcImagePullSecret.create`	`false`	Chart-managed dockerconfigjson Secret.
`ngcImagePullSecret.name`	`ngc-secret`	Name referenced by every Pod and every NIMService.
`ngcImagePullSecret.password`	`\"\"`	NGC API key.
`ngcApiSecret.create`	`false`	Chart-managed Opaque Secret.
`ngcApiSecret.name`	`ngc-api`	Name referenced by NIMCache/NIMService `authSecret`.
`ngcApiSecret.password`	`\"\"`	NGC API key (populates `NGC_API_KEY` + `NGC_CLI_API_KEY`).
`imagePullSecrets`	`[]`	Extra pre-existing pull secrets appended to every Pod.

Optional features

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Feature	Toggle	Default
Ingress	`ingress.enabled`	`true`
Autoscaling (HPA)	`autoscaling.enabled`	`false` (max=1 anyway)
ServiceMonitor	`serviceMonitor.enabled`	`false` (auto-enabled in split mode)

Configuration recipes

Mount a custom retriever-service.yaml verbatim

The chart renders retriever-service.yaml from structured values so you\nshouldn't normally need to ship a verbatim file. If you really want to,\nmount one via service.extraVolumes + service.extraVolumeMounts at\n/etc/nemo-retriever/retriever-service.yaml (which silently overrides the\nchart-managed ConfigMap because subPath mounts win).

Use externally managed Secrets

ngcImagePullSecret:\n  create: false        # don't render; reference an existing Secret\n  name: my-org-ngc-pull\nngcApiSecret:\n  create: false\n  name: my-org-ngc-api\n

The chart will skip Secret creation. Make sure my-org-ngc-pull exists\nas kubernetes.io/dockerconfigjson and my-org-ngc-api as Opaque with\nan NGC_API_KEY key, in the release namespace.

Disable one NIM and supply an external URL for it

nimOperator:\n  vlm_embed:\n    enabled: false   # don't deploy the embed NIM in-cluster\n\nserviceConfig:\n  nimEndpoints:\n    embedInvokeUrl: https://integrate.api.nvidia.com/v1/embeddings\n

The chart's resolution order is explicit URL → operator-managed URL →\nempty, so per-endpoint overrides Just Work.

Roll the service after editing values

The Deployment carries a checksum/config annotation derived from the\nConfigMap, so helm upgrade automatically rolls the pod when any\nserviceConfig.* value changes.

Timeouts and alleviating ingest failures

Batch ingest fans out extract and embed work to remote NIM HTTP endpoints.\nUnder heavy parallelism a single slow or overloaded NIM can cause timeouts,\nand a worker process crash can surface as many simultaneous failed\ndocument callbacks even though only one root cause occurred.

What the chart configures

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Layer	Default	Where it is set
Remote embed HTTP calls	600 s (10 min)	Service image (`EmbedParams.request_timeout_s`); not a Helm value today.
Gateway → realtime/batch proxy	300 s	Rendered `gateway.timeout_s` in `retriever-service.yaml` (split topology).
VLM embed model name	`serviceConfig.vectordb.embedModel`	Also copied into worker `nim_endpoints.embed_model_name` in the ConfigMap.

Symptoms to look for in pod logs:

Embedding error occurred: timed out or httpx.ReadTimeout on the batch pod.
Batch process pool broken (worker crash) followed by many\nBrokenProcessPool failures on other in-flight documents.
Embed NIM pod messages such as failed to allocate pinned system memory\n(GPU pressure from too many concurrent /v1/embeddings requests).

The gateway pod usually only logs status=failed callbacks; diagnose on\nbatch (and realtime for page-sized uploads), plus the embed NIM pod.

Recommended mitigations

1. Lower batch worker concurrency (first step).

The default serviceConfig.pipeline.batchWorkers is 48, which can saturate\na single in-cluster VLM embed NIM. If you see embed timeouts or pool crashes,\nreduce batch parallelism to 16 and redeploy:

helm upgrade retriever ./nemo_retriever/helm \\\n  --reuse-values \\\n  --set serviceConfig.pipeline.batchWorkers=16\n

You can tune further (for example 8 on small GPU nodes), but 16 is a\nreasonable starting point when moving off the default. Realtime workers\n(realtimeWorkers, default 24) are less likely to overload embed NIMs\nbecause they handle smaller units of work; adjust them only if realtime\ningest shows the same timeout pattern.

2. Confirm embed wiring.

Ensure nim_endpoints.embed_model_name in the mounted config matches the\nVLM embed NIM SKU (serviceConfig.vectordb.embedModel, default\nnvidia/llama-nemotron-embed-vl-1b-v2). A model mismatch produces\nHTTP 404 on /v1/embeddings, not a timeout, but is worth ruling out when\ndebugging failed ingests.

3. Retry failed documents.

Failures caused by a one-time pool restart are often transient. After lowering\nbatchWorkers and rolling the batch Deployment, resubmit documents that\nfailed with rows=0.

4. Scale or isolate the embed NIM.

If timeouts persist at batchWorkers: 16, add embed NIM replicas (when your\ncluster has GPU capacity), point serviceConfig.nimEndpoints.embedInvokeUrl\nat an external embed endpoint, or temporarily disable optional NIMs on\ndev clusters to free GPU memory for vlm_embed.

5. Client and ingress timeouts.

Long batch jobs may exceed the gateway proxy timeout (300 s) or an Ingress\nproxy-read-timeout. Increase ingress annotations if clients disconnect\nwhile workers are still processing; see the commented example on\ningress.annotations in values.yaml.

Queue-depth autoscaling (split mode)

In topology.mode: split deployments the realtime and batch worker\npods scale horizontally based on queue fill ratio and\n95th-percentile processing latency. Both signals come straight out\nof the pods' /metrics endpoint — the publisher is always on (see\nnemo_retriever_pool_queue_depth_ratio in\nprometheus.py).\nThe only choice you have to make is how the metrics get from\nPrometheus into the Kubernetes HPA.

Why queue depth (and not CPU)

CPU-based HPA reacts to the pod that has already saturated its work.\nFor an ingest pipeline that fans out to remote NIM endpoints, the work\nspends most of its time blocked on HTTP — CPU stays low even when the\nqueue is full. Queue depth measures demand to be served, which is\nwhat we actually want to scale on. A 95th-percentile-latency signal\nrides alongside to catch the inverse case (a single hot pod whose\nqueue is shallow but whose per-item processing has stalled).

Backend choices

The chart's autoscaling.queueDepth.backend controls which path is\nwired up. All three options leave the metrics publisher untouched:

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backend	When to pick it	Cluster prerequisite
`prometheus-adapter` (default)	Production. One adapter feeds HPA + Grafana + future autoscalers.	Prometheus Operator + `prometheus-community/prometheus-adapter`.
`cpu`	Bootstrap / dev cluster without Prometheus.	None — built-in.
`keda`	Already standardised on KEDA org-wide.	KEDA operator (you install + apply your own `ScaledObject`).

The chart-recommended path is prometheus-adapter. The reasoning is\ndocumented in values.yaml; in short, it keeps a single Prometheus as\nthe source of truth, supports HPA's multi-metric arithmetic-mean\nevaluation out of the box, and doesn't force the chart to bundle new\nCRDs.

Wiring up prometheus-adapter (recommended)

The chart renders a ConfigMap named\n<release>-nemo-retriever-prom-adapter-rules containing PromQL rules\nfor the External Metrics API. You point your existing\nprometheus-adapter at it:

-nemo-retriever-prom-adapter-rules\n\">

helm upgrade prometheus-adapter prometheus-community/prometheus-adapter \\\n  --namespace monitoring \\\n  --reuse-values \\\n  --set rules.existing=<release>-nemo-retriever-prom-adapter-rules\n

Then verify both metrics show up in the External Metrics API:

kubectl get --raw \\\n  \"/apis/external.metrics.k8s.io/v1beta1/namespaces/$NS/nemo_retriever_pool_queue_depth_ratio_avg?labelSelector=pool%3Drealtime\" \\\n  | jq .\n

Once that returns a non-empty items array, the HPAs rendered by this\nchart will start consuming them. The HPA annotation\nnemo-retriever.nvidia.com/hpa-signals documents the active set per\nHPA, e.g. queueRatio=true latencyP95=true cpu=false.

CPU fallback (no Prometheus required)

Set autoscaling.queueDepth.backend: cpu and enable the CPU metric\nunder each role:

autoscaling:\n  queueDepth:\n    backend: cpu\ntopology:\n  realtime:\n    hpa:\n      metrics:\n        queueDepthRatio: { enabled: false }\n        processingLatencyP95: { enabled: false }\n        cpu: { enabled: true, targetUtilizationPercentage: 60 }\n  batch:\n    hpa:\n      metrics:\n        queueDepthRatio: { enabled: false }\n        processingLatencyP95: { enabled: false }\n        cpu: { enabled: true, targetUtilizationPercentage: 80 }\n

The legacy topology.<role>.hpa.targetCPUUtilizationPercentage field\nstill works and behaves as an alias for the metrics.cpu block.

KEDA path

Set autoscaling.queueDepth.backend: keda and disable the chart-managed\nHPAs:

autoscaling:\n  queueDepth: { backend: keda }\ntopology:\n  realtime: { hpa: { enabled: false } }\n  batch:    { hpa: { enabled: false } }\n

Then apply your own ScaledObject — example for the realtime pool:

apiVersion: keda.sh/v1alpha1\nkind: ScaledObject\nmetadata:\n  name: nemo-retriever-realtime\nspec:\n  scaleTargetRef:\n    name: nemo-retriever-realtime\n  minReplicaCount: 2\n  maxReplicaCount: 8\n  cooldownPeriod: 300\n  triggers:\n    - type: prometheus\n      metadata:\n        serverAddress: http://prometheus.monitoring.svc:9090\n        metricName: nemo_retriever_pool_queue_depth_ratio\n        threshold: \"0.5\"\n        query: |\n          avg by (pool) (\n            nemo_retriever_pool_queue_depth{pool=\"realtime\"}\n            /\n            on(pool, instance) group_left()\n            nemo_retriever_pool_max_queue_size{pool=\"realtime\"}\n          )\n    - type: prometheus\n      metadata:\n        serverAddress: http://prometheus.monitoring.svc:9090\n        metricName: nemo_retriever_pool_processing_duration_p95\n        threshold: \"30\"\n        query: |\n          histogram_quantile(\n            0.95,\n            sum by (le, pool) (\n              rate(nemo_retriever_pool_processing_duration_seconds_bucket{pool=\"realtime\"}[2m])\n            )\n          )\n

KEDA's biggest win is scale-from-zero, which we don't use today —\nboth minReplicas defaults are ≥ 1 because the realtime pod is on the\nhot path for SSE consumers. If you do want scale-from-zero (e.g. a\nnightly batch-only job tenant), KEDA is the right tool and this is the\nescape hatch.

Tuning the thresholds

Per-role tuning lives under topology.<role>.hpa.metrics:

topology:\n  realtime:\n    hpa:\n      metrics:\n        queueDepthRatio: { enabled: true, target: \"500m\" }   # 0.5\n        processingLatencyP95: { enabled: true, targetSeconds: \"30\" }\n  batch:\n    hpa:\n      metrics:\n        queueDepthRatio: { enabled: true, target: \"700m\" }   # 0.7 — batch can run hot\n        processingLatencyP95: { enabled: true, targetSeconds: \"120\" }\n

Quantity-string conventions are k8s standard: 500m == 0.5, 2, 2k,\netc. The target is per-replica because the HPA template uses\ntype: AverageValue for both External metrics — that's what makes\n\"scale up when average queue fill across pods exceeds 0.5\" work\nwithout baking the pod count into the publisher.

Verifying it scales

-realtime -o jsonpath='{.metadata.annotations.nemo-retriever\\.nvidia\\.com/hpa-signals}'\n\">

# Cause realtime pressure (anything that submits to /v1/ingest/job/.../page).\n# Then watch the HPA decide:\nkubectl get hpa -w\n\n# And watch the active signals on each HPA:\nkubectl get hpa <release>-realtime -o jsonpath='{.metadata.annotations.nemo-retriever\\.nvidia\\.com/hpa-signals}'\n

The dashboard's Worker Pool Capacity card on the Overview page\nmirrors the same signal Prometheus is seeing, so it's a quick eyeball\nsanity check before opening Grafana.

Air-gapped deployment { #air-gapped-deployment }

See Deployment options — Air-gapped and disconnected deployment for overview and workflow. Chart-specific reference for mirroring:

Container images to mirror (26.05 chart defaults)

Verify tags on the Git branch or tag you ship (for example 26.05 or\n26.05-RC1). Defaults below match\nvalues.yaml on the current chart.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Role	`nimOperator` key	Default image (`repository:tag`)
Retriever service	—	`service.image.repository`:`service.image.tag` (override for production)
Page elements	`page_elements`	`nvcr.io/nim/nvidia/nemotron-page-elements-v3:1.8.0`
Table structure	`table_structure`	`nvcr.io/nim/nvidia/nemotron-table-structure-v1:1.8.0`
OCR	`ocr`	`nvcr.io/nim/nvidia/nemotron-ocr-v1:1.3.0`
VL embed	`vlm_embed`	`nvcr.io/nim/nvidia/llama-nemotron-embed-vl-1b-v2:1.12.0`
VL reranker (optional)	`rerankqa`	`nvcr.io/nim/nvidia/llama-nemotron-rerank-vl-1b-v2:1.10.0`
Nemotron Parse (optional)	`nemotron_parse`	`nvcr.io/nim/nvidia/nemotron-parse-v1.2:1.7.0-variant`
Omni caption (optional)	`nemotron_3_nano_omni_30b_a3b_reasoning`	`nvcr.io/nim/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:1.7.0-variant`
Parakeet ASR (optional)	`audio`	`nvcr.io/nim/nvidia/parakeet-1-1b-ctc-en-us:1.5.0`

GPU SKU support for audio is in Model hardware requirements.

Also mirror images for the vectordb sidecar, Redis, or other subcharts if\nyour values enable them.

Helm values for a private registry

Example overrides (replace placeholders):

helm upgrade --install retriever ./nemo_retriever/helm \\\n  -f my-airgap-values.yaml\n

my-airgap-values.yaml should include at least:

/nemo-retriever-service\n tag: \n pullPolicy: IfNotPresent\n\nimagePullSecrets:\n - name: my-private-registry\n\nngcImagePullSecret:\n create: false # use secrets that authenticate to YOUR mirror\n\nnimOperator:\n page_elements:\n image:\n repository: /nemotron-page-elements-v3\n tag: "1.8.0"\n pullPolicy: IfNotPresent\n # Repeat for table_structure, ocr, vlm_embed, and any optional keys you enable.\n\">

service:\n  image:\n    repository: <PRIVATE_REGISTRY>/nemo-retriever-service\n    tag: <PINNED_TAG>\n    pullPolicy: IfNotPresent\n\nimagePullSecrets:\n  - name: my-private-registry\n\nngcImagePullSecret:\n  create: false   # use secrets that authenticate to YOUR mirror\n\nnimOperator:\n  page_elements:\n    image:\n      repository: <PRIVATE_REGISTRY>/nemotron-page-elements-v3\n      tag: \"1.8.0\"\n      pullPolicy: IfNotPresent\n  # Repeat for table_structure, ocr, vlm_embed, and any optional keys you enable.\n

Set nimOperator.<key>.image.pullSecrets to the Secret name your\nNIMService resources should use (defaults to ngc-secret).
Leave serviceConfig.nimEndpoints.* empty when operator-managed NIMs\nare in-cluster; set explicit URLs only for external or mirrored services\noutside the chart.
For offline captioning, enable\nnimOperator.nemotron_3_nano_omni_30b_a3b_reasoning and point the pipeline\ncaption endpoint at the in-cluster NIM URL (see\nImage captioning (26.05)).

Mirroring pattern

/nemotron-page-elements-v3:1.8.0\ndocker push /nemotron-page-elements-v3:1.8.0\n\">

docker login nvcr.io -u '$oauthtoken' -p \"$NGC_API_KEY\"\ndocker pull nvcr.io/nim/nvidia/nemotron-page-elements-v3:1.8.0\ndocker tag nvcr.io/nim/nvidia/nemotron-page-elements-v3:1.8.0 \\\n  <PRIVATE_REGISTRY>/nemotron-page-elements-v3:1.8.0\ndocker push <PRIVATE_REGISTRY>/nemotron-page-elements-v3:1.8.0\n

For bulk sync, prefer skopeo or\ncrane.\nRecord repository@sha256:... digests for regulated environments.

Roadmap

PostgreSQL backend — replace service.db.engine.DatabaseEngine with\na SQLAlchemy/asyncpg-based engine, then bump the chart to deploy a\nPostgreSQL StatefulSet (or take a sub-chart dependency on Bitnami's\nchart) and lift service.replicas to N.
NetworkPolicies restricting the service Pod to the NIM Pods + DB\nonly.
Gateway autoscaling on inflight-uploads (currently fixed\ntopology.gateway.replicas) — sticky-routing story for SSE\nsubscribers needs to land first.

Validation

The chart is exercised in CI with helm lint and helm template. Run\nlocally:

/tmp/r-op.yaml\n\n# Operator CRDs absent: vectordb has no operator URL to fall back to, so\n# either disable vectordb or supply an explicit embed endpoint.\nhelm template r nemo_retriever/helm \\\n --set serviceConfig.vectordb.enabled=false > /tmp/r.yaml\n# or:\n# helm template r nemo_retriever/helm \\\n# --set serviceConfig.nimEndpoints.embedInvokeUrl=http://embed.svc:8000/v1/embeddings \\\n# > /tmp/r.yaml\n\">

helm lint nemo_retriever/helm\n\n# Operator CRDs present: vectordb resolves vlm_embed via the operator URL.\nhelm template r nemo_retriever/helm \\\n  --api-versions apps.nvidia.com/v1alpha1 > /tmp/r-op.yaml\n\n# Operator CRDs absent: vectordb has no operator URL to fall back to, so\n# either disable vectordb or supply an explicit embed endpoint.\nhelm template r nemo_retriever/helm \\\n  --set serviceConfig.vectordb.enabled=false > /tmp/r.yaml\n#   or:\n# helm template r nemo_retriever/helm \\\n#   --set serviceConfig.nimEndpoints.embedInvokeUrl=http://embed.svc:8000/v1/embeddings \\\n#   > /tmp/r.yaml\n

Both renders should succeed cleanly and parse as valid Kubernetes manifests\n(kubectl apply --dry-run=client -f /tmp/r.yaml). See VectorDB and the\nembed endpoint for why\nhelm template r nemo_retriever/helm without flags is rejected as a\nmisconfiguration.

Governing Terms

The NVIDIA NIM containers that are deployed via this helm chart are governed by NVIDIA Agreements | Enterprise Software | NVIDIA Software License Agreement and NVIDIA Agreements | Enterprise Software | Product Specific Terms for AI Product; and the NeMo Retriever Library container is released under the Apache-2.0 license. Also the use of the models are governed by the NVIDIA Community Model License.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

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The chart auto-wires the operator-managed\n Service URLs into the retriever-service config when the operator CRDs\n are present in the cluster.\n\n> **NIM Operator prerequisite.** The NIM templates are gated on the\n> `apps.nvidia.com/v1alpha1` API group. Install the NIM Operator before\n> running `helm install`:\n> https://docs.nvidia.com/nim-operator/\n>\n> Without the operator the chart still installs cleanly — every NIMCache /\n> NIMService template short-circuits and the service falls back to\n> external NIM URLs supplied via `serviceConfig.nimEndpoints.*`.\n\n> **Persistence today is SQLite on a single ReadWriteOnce PVC**, which caps\n> the service at one replica. The chart already exposes the HPA scaffolding\n> so it's a one-line change once the planned PostgreSQL backend lands.\n\n> For behavioral consistency between local HuggingFace deployments and Helm service deployments: \n> `results = ingestor.ingest(...return_results=True)\n> return_results defaults to True. This incurs a significant performance and system memory usage cost. \n> Unless you know explicitly you need to fetch extraction results to the client, you should use:\n> return_results=False\n> If you must return results, you may need to increase pod memory specs to support the increased pod memory usage.\n\n---\n\n## Layout\n\n```\nnemo_retriever/helm/\n├── Chart.yaml\n├── values.yaml\n├── README.md <-- this file\n├── .helmignore\n└── templates/\n ├── _helpers.tpl\n ├── NOTES.txt\n ├── configmap.yaml # renders retriever-service.yaml\n ├── deployment.yaml # the service Deployment(s)\n ├── service.yaml # ClusterIP/NodePort for the service\n ├── ingress.yaml # optional Ingress\n ├── hpa.yaml # optional HorizontalPodAutoscaler\n ├── servicemonitor.yaml # optional Prometheus ServiceMonitor\n ├── serviceaccount.yaml\n ├── pvc.yaml # SQLite database PVC\n ├── secrets.yaml # ngc-secret + ngc-api\n └── nims/\n ├── nemotron-page-elements-v3.yaml # NIMCache + NIMService\n ├── nemotron-table-structure-v1.yaml # NIMCache + NIMService\n ├── nemotron-ocr-v1.yaml # NIMCache + NIMService (OCR)\n ├── llama-nemotron-embed-vl-1b-v2.yaml # NIMCache + NIMService (VLM embed)\n ├── llama-nemotron-rerank-vl-1b-v2.yaml # NIMCache + NIMService (optional; not auto-wired)\n ├── nemotron-parse.yaml # NIMCache + NIMService (optional; not auto-wired)\n ├── nemotron-3-nano-omni-30b-a3b-reasoning.yaml # NIMCache + NIMService (optional; not auto-wired)\n └── audio.yaml # NIMCache + NIMService (optional; not auto-wired)\n```\n\n---\n\n## Quick start\n\n### 1. Service image\n\nThe chart defaults to the staging image published to NGC:\n\n```\nnvcr.io/nvstaging/nim/nemo-retriever-service:043020205-001\n```\n\nPulling from `nvcr.io/nvstaging` requires an NGC pull secret — either set\n`ngcImagePullSecret.create=true` (see below) or pre-create one in the\nnamespace named `ngc-secret`.\n\nTo run a locally built image instead, build and push it from the repo root,\nthen override `service.image.repository` / `service.image.tag`:\n\n```bash\n# from the repo root:\ndocker build \\\n --target service \\\n -t /nemo-retriever-service: .\ndocker push /nemo-retriever-service:\n```\n\nAudio and video extraction require the `ffmpeg` and `ffprobe` system\nbinaries inside the service container. The bundled service image can install\nthem at container startup when you set `service.installFfmpeg=true`, which\nsets `INSTALL_FFMPEG=true` for the image entrypoint:\n\n```bash\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n --set service.image.repository=/nemo-retriever-service \\\n --set service.image.tag= \\\n --set service.installFfmpeg=true\n```\n\nDo not also set `INSTALL_FFMPEG` in `service.env`; the chart fails rendering\nwhen both are configured so the rendered Pod does not contain duplicate\nenvironment variables.\n\nWhen `service.installFfmpeg=false` (the default), the service still starts\nnormally and processes PDF, image, text and HTML uploads. Audio / video\nuploads are rejected up-front with **HTTP 501**:\n\n```text\nAudio and video ingestion require FFmpeg in the retriever service\ncontainer, but the following dependencies are missing: ffmpeg, ffprobe.\nRe-deploy the Helm chart with `--set service.installFfmpeg=true` …\n```\n\nThe retriever-service container also logs a `WARNING` at startup when\nFFmpeg is missing so cluster operators can fix the deployment before\nthe first media upload arrives, instead of debugging a Ray worker\ntraceback (`RuntimeError: MediaChunkActor requires media dependencies;\nmissing: ffmpeg, ffprobe`) after the fact. The same WARNING is emitted\non every pod (gateway, realtime, batch) because all roles classify\nuploads — flipping `service.installFfmpeg=true` updates them all.\n\nRuntime installation uses passwordless `sudo` scoped to installing the\n`ffmpeg` package in the service image. The pod must have network egress to the\nUbuntu package repositories, a writable root filesystem, and a security policy\nthat allows sudo/setuid behavior. Do not set\n`service.securityContext.allowPrivilegeEscalation: false` or\n`service.securityContext.readOnlyRootFilesystem: true` for this path.\n\nFor air-gapped or locked-down clusters, see\n[Deployment options — Air-gapped and disconnected deployment](https://docs.nvidia.com/nemo/retriever/latest/extraction/deployment-options/#air-gapped-deployment).\nOn a connected staging host you can extend the service image, for example:\n\n```dockerfile\nFROM /nemo-retriever-service:\nUSER root\nRUN apt-get update && apt-get install -y --no-install-recommends ffmpeg \\\n && rm -rf /var/lib/apt/lists/*\nUSER nemo\n```\n\n### 2. Install with external NIM endpoints (operator not required)\n\nIf you already have NIM endpoints reachable from the cluster (e.g. another\nnamespace, or NVIDIA Build), turn the master switch off and supply the\nURLs directly:\n\n```bash\nhelm install retriever ./nemo_retriever/helm \\\n --set nims.enabled=false \\\n --set ngcImagePullSecret.create=true \\\n --set ngcImagePullSecret.password=$NGC_API_KEY \\\n --set ngcApiSecret.create=true \\\n --set ngcApiSecret.password=$NGC_API_KEY \\\n --set serviceConfig.nimEndpoints.pageElementsInvokeUrl=http://page-elements.svc:8000/v1/infer \\\n --set serviceConfig.nimEndpoints.tableStructureInvokeUrl=http://table-structure.svc:8000/v1/infer \\\n --set serviceConfig.nimEndpoints.ocrInvokeUrl=http://ocr.svc:8000/v1/infer \\\n --set serviceConfig.nimEndpoints.embedInvokeUrl=http://embed.svc:8000/v1/embeddings\n```\n\n`ngcApiSecret` materialises an `ngc-api` Secret containing both\n`NGC_API_KEY` and `NGC_CLI_API_KEY` keys; the service container reads it\nvia `optional: true` `secretKeyRef`, so the install still succeeds when\nthe secret is absent (useful for fully local NIM endpoints).\n\n### 3. Install with the NIM Operator (in-cluster NIMs)\n\nInstall the [NIM Operator](https://docs.nvidia.com/nim-operator/) first so\nthe `NIMCache` / `NIMService` CRDs (`apps.nvidia.com/v1alpha1`) are\nregistered. A plain `helm install` reconciles the four core NIMs\n(`page_elements`, `table_structure`, `ocr`, `vlm_embed`) — every other\nNIM (the VL reranker `rerankqa`, Nemotron Parse, Omni 30B, and the\nParakeet `audio` ASR NIM) is **disabled by default** to honor the\n\"optional and disabled by default\" contract in\n[deployment-options.md](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/deployment-options.md);\nsee [Recommended minimal install (26.05)](#recommended-minimal-install-2605)\nfor the opt-in `--set` flags that turn any of them on.\n\n```bash\nhelm install retriever ./nemo_retriever/helm \\\n --set ngcImagePullSecret.create=true \\\n --set ngcImagePullSecret.password=$NGC_API_KEY \\\n --set ngcApiSecret.create=true \\\n --set ngcApiSecret.password=$NGC_API_KEY\n```\n\n### Recommended minimal install (26.05) { #recommended-minimal-install-2605 }\n\nDeploy only the four core NIMs that the retriever service auto-wires (`page_elements`, `table_structure`, `ocr`, `vlm_embed`):\n\n```bash\nhelm install retriever ./nemo_retriever/helm \\\n --set ngcImagePullSecret.create=true \\\n --set ngcImagePullSecret.password=$NGC_API_KEY \\\n --set ngcApiSecret.create=true \\\n --set ngcApiSecret.password=$NGC_API_KEY\n```\n\n> The VL reranker (`rerankqa`), Nemotron Parse, the Nemotron 3 Nano Omni 30B caption NIM, and the Parakeet `audio` ASR NIM are **all off by default** in 26.05 — they only reconcile when you explicitly opt in. Opt-in flags:\n>\n> * VL reranker — `--set nimOperator.rerankqa.enabled=true`\n> * Nemotron Parse — `--set nimOperator.nemotron_parse.enabled=true`\n> * Omni 30B captioner — `--set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true`\n> * Parakeet ASR — `--set nimOperator.audio.enabled=true` (also set `serviceConfig.nimEndpoints.audioGrpcEndpoint=audio:50051` to wire ASR into the service, plus `service.installFfmpeg=true` if your image does not bundle ffmpeg)\n>\n> This matches the \"optional and disabled by default\" contract in [deployment-options.md](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/deployment-options.md) and avoids silently pulling ≈ 62 GiB of Omni weights or claiming a second dedicated GPU on a \"default\" install. See the [model hardware requirements](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/prerequisites-support-matrix.md#model-hardware-requirements) table for per-NIM GPU and disk costs.\n\nThe chart auto-wires the operator-managed in-cluster URLs of the four\n\"core\" NIMs into the service's `nim_endpoints` block:\n\n| key | operator-managed Service | invoke path |\n| --- | ------------------------ | ----------- |\n| `nimOperator.page_elements` | `nemotron-page-elements-v3` | `/v1/infer` |\n| `nimOperator.table_structure` | `nemotron-table-structure-v1` | `/v1/infer` |\n| `nimOperator.ocr` | `nemotron-ocr-v1` | `/v1/infer` |\n| `nimOperator.vlm_embed` | `llama-nemotron-embed-vl-1b-v2` | `/v1/embeddings` |\n\nTrack operator reconciliation with:\n\n```bash\nkubectl get nimcache,nimservice -n \nkubectl describe nimservice nemotron-page-elements-v3 -n \n```\n\nFirst-time NIMCache reconciliation downloads model weights to a PVC. By\ndefault (`nimOperator.nimCache.keepOnUninstall: true`) every **NIMCache**\ncarries `helm.sh/resource-policy: keep` so those downloads survive\n`helm uninstall`. **NIMService** CRs do not use `keep` and are removed by\nHelm on uninstall.\n\n### Why NIM resources still exist after `helm uninstall`\n\n| What you see | Typical cause |\n|--------------|----------------|\n| `NIMCache` + PVC remain | **Expected** when `keepOnUninstall` is true (default). Helm intentionally skips deleting caches so you do not re-pull multi‑GiB weights. |\n| `NIMService` CR remains | **Not expected** on a normal uninstall. Usually an **orphan** from a failed install/upgrade (release never recorded the resource, or the chart renamed a NIM). |\n| Deployments / GPU pods still running | Often the operator workload for a **kept** `NIMCache`, or a stale `NIMService` that Helm did not own. Check `kubectl get nimservice,nimcache -n `. |\n| `nemotron-*-job-*` pods in `Error` | The NIM Operator's **model-download Job** for a `NIMCache` (not the retriever service). Failed cache pulls retry and leave Error pods until the Job or `NIMCache` is deleted. Common after a failed `helm install` when the release is rolled back but `keep` retains the cache CR. |\n| `helm uninstall` appears to do nothing | Release may be missing or failed (`helm list -n -a`). CRs created before a failed install can be left without a release to clean them up. |\n\n**Full teardown** (dev cluster — deletes caches and PVCs Helm kept):\n\n```bash\nNS=retriever\nREL=nemo-retriever\n\nhelm uninstall \"${REL}\" -n \"${NS}\" 2>/dev/null || true\n\n# Orphans and kept NIMCaches (Helm keep does not block kubectl delete):\nkubectl delete nimservice,nimcache -n \"${NS}\" --all\n# Optional: drop model PVCs if you will re-pull from NGC\nkubectl delete pvc -n \"${NS}\" -l 'app.kubernetes.io/managed-by=nvidia-nim-operator' 2>/dev/null || true\n```\n\n**Dev installs** that should not retain caches on uninstall:\n\n```bash\nhelm upgrade --install \"${REL}\" ./nemo_retriever/helm -n \"${NS}\" \\\n --set nimOperator.nimCache.keepOnUninstall=false \\\n ...\n```\n\n---\n\n## Values reference (highlights)\n\nThe full schema lives in [`values.yaml`](./values.yaml). Below is the\nshort list of knobs you'll touch first.\n\n### Service\n\n| Path | Default | Notes |\n|-------------------------------|------------------------------------|-------|\n| `service.image.repository` | `localhost:32000/nemo-retriever-service` | Override to a published image. |\n| `service.image.tag` | `latest` | |\n| `service.replicas` | `1` | Hard cap = 1 while SQLite is the backend. |\n| `service.installFfmpeg` | `false` | Install `ffmpeg`/`ffprobe` at container startup by setting `INSTALL_FFMPEG=true`. Requires network egress, writable root filesystem, and sudo/setuid allowed. Not for air-gapped clusters — use a custom image instead. |\n| `service.resources.requests` | `16 / 16Gi` | Tune in tandem with `serviceConfig.pipeline.*Workers`. |\n| `service.resources.limits` | `96 / 96Gi` | |\n| `service.gpu.enabled` | `false` | The service does **not** need a GPU. |\n\nFor audio and video extraction, set `service.installFfmpeg=true` when your\ncluster allows runtime package installation. For air-gapped clusters, see\n[Deployment options — Air-gapped and disconnected deployment](https://docs.nvidia.com/nemo/retriever/latest/extraction/deployment-options/#air-gapped-deployment).\n\n### Audio and video (Parakeet ASR) { #audio-video-parakeet }\n\nTo run self-hosted Parakeet for [audio and video extraction](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/audio-video.md):\n\n1. Set `nimOperator.audio.enabled=true` (it is on by default; disable other optional NIMs you do not need per [Recommended minimal install (26.05)](#recommended-minimal-install-2605)).\n2. Pin the ASR `NIMService` to a **dedicated GPU** with `nimOperator.audio.resources`, `nodeSelector`, or `tolerations` (see [NIM Operator](https://docs.nvidia.com/nim-operator/latest/index.html)).\n3. Confirm the GPU SKU in [Model hardware requirements](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/prerequisites-support-matrix.md#model-hardware-requirements) (footnote ⁴ lists Blackwell limitations).\n4. Set `service.installFfmpeg=true` when the retriever service will process audio or video (see `service.installFfmpeg` above).\n\nThe retriever service picks up the in-cluster ASR endpoint when `nimOperator.audio` is enabled; see [NIM Operator sub-stack](#nim-operator-sub-stack).\n\n### Service configuration (rendered into `retriever-service.yaml`)\n\n| Path | Default | Notes |\n|---------------------------------------------------|---------|-------|\n| `serviceConfig.server.port` | `7670` | Container + Service port. |\n| `serviceConfig.pipeline.realtimeWorkers` | `24` | Per-pod realtime worker count. |\n| `serviceConfig.pipeline.batchWorkers` | `48` | Per-pod batch worker count. See [Timeouts and alleviating ingest failures](#timeouts-and-alleviating-ingest-failures) if embed or pool errors appear under load. |\n| `serviceConfig.nimEndpoints.*InvokeUrl` | `\"\"` | Override the auto-resolved NIM Operator URL. Available knobs: `pageElementsInvokeUrl`, `tableStructureInvokeUrl`, `ocrInvokeUrl`, `embedInvokeUrl`, and `captionInvokeUrl` (see [Image captioning (Omni 30B)](#image-captioning-omni-30b)). |\n| `serviceConfig.nimEndpoints.captionModelName` | `\"\"` | Model id sent to the remote VLM. Auto-set to `nvidia/nemotron-3-nano-omni-30b-a3b-reasoning` whenever a caption URL is resolved. |\n| `serviceConfig.vectordb.enabled` | `true` | Deploy the LanceDB vectordb Pod. When `true` the chart **requires** a resolvable embed endpoint (see [VectorDB and the embed endpoint](#vectordb-and-the-embed-endpoint)); `helm install` / `helm upgrade` fails fast otherwise. |\n| `serviceConfig.vectordb.lancedbUri` | `/data/vectordb` | LanceDB on the vectordb Pod's PVC. |\n| `serviceConfig.vectordb.embedModel` | `nvidia/llama-nemotron-embed-vl-1b-v2` | Passed to vectordb + worker `embed_model_name`. |\n\n#### VectorDB and the embed endpoint { #vectordb-and-the-embed-endpoint }\n\nThe vectordb Pod's `/v1/query` handler embeds the incoming query text\nbefore searching LanceDB. It needs a NIM embedding endpoint to do that,\nand rendering the Deployment with an empty `--embed-endpoint` produces a\nPod that passes its `/v1/health` probe but answers every `/v1/query`\nrequest with `HTTP 501 No embedding endpoint configured.` — a healthy\ndeployment that silently breaks retrieval.\n\nTo prevent this, the chart now refuses to render\n`deployment-vectordb.yaml` when no embed endpoint can be resolved.\n`helm install` / `helm upgrade --install` fails with a message listing\nthe three supported escape valves:\n\n```\nserviceConfig.vectordb.enabled=true but the embed endpoint could not be\nresolved. Pick one of:\n\n 1. --set serviceConfig.nimEndpoints.embedInvokeUrl=http://:/v1/embeddings\n 2. --set nimOperator.vlm_embed.enabled=true # requires apps.nvidia.com/v1alpha1 CRDs\n 3. --set serviceConfig.vectordb.enabled=false\n```\n\nResolution order matches the rest of the chart (see [Mix and match NIM\nsources](#3-install-with-the-nim-operator-in-cluster-nims)):\n\n1. Explicit `serviceConfig.nimEndpoints.embedInvokeUrl` always wins.\n2. Otherwise the operator-managed URL of\n `nimOperator.vlm_embed.nimServiceName` is used, provided\n `nimOperator.vlm_embed.enabled=true` **and** the\n `apps.nvidia.com/v1alpha1` CRDs are installed in the cluster.\n3. Otherwise the chart fails the install.\n\n### NIM Operator sub-stack\n\nEach NIM block under `nimOperator.` renders a `NIMCache` + `NIMService`\npair gated on three conditions ALL holding:\n\n1. The `apps.nvidia.com/v1alpha1` CRDs are installed in the cluster.\n2. The master switch `nims.enabled` is `true`.\n3. The per-NIM `nimOperator..enabled` is `true`.\n\n| Path | Default | Notes |\n|----------------------------------------|---------|-------|\n| `nims.enabled` | `true` | Master switch. Set false to render no NIM resources. |\n| `nimOperator.page_elements.enabled` | `true` | Page-elements detector NIM. |\n| `nimOperator.table_structure.enabled` | `true` | Table-structure detector NIM. |\n| `nimOperator.ocr.enabled` | `true` | OCR NIM. |\n| `nimOperator.ocr.image` | `nvcr.io/nim/nvidia/nemotron-ocr-v1:1.3.0` | Default OCR NIM image. |\n| `nimOperator.vlm_embed.enabled` | `true` | Multimodal embedding NIM (also used by the vectordb Pod). |\n| `nimOperator.vlm_embed.nimServiceName` | `llama-nemotron-embed-vl-1b-v2` | NIMService / in-cluster DNS name. |\n| `nimOperator.vlm_embed.image` | `nvcr.io/nim/nvidia/llama-nemotron-embed-vl-1b-v2:1.12.0` | Default VLM embed NIM image. |\n| `nimOperator.rerankqa.enabled` | `false` | VL reranker NIM (optional; not auto-wired). Set `true` to opt in. Default `false` so 26.05 installs honor the \"optional and disabled by default\" contract in [deployment-options.md](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/deployment-options.md) and do not silently provision an extra ≈ 3.1 GiB GPU NIM. The image points at the **VL** SKU (`llama-nemotron-rerank-vl-1b-v2`) per [prerequisites-support-matrix.md](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/prerequisites-support-matrix.md#default-helm-nims) — the text-only `llama-nemotron-rerank-1b-v2` silently degrades multimodal reranking and is not the documented POR. |\n| `nimOperator.nemotron_parse.enabled` | `false` | Structured-parse NIM (optional). Set `true` when using `extract_method=\"nemotron_parse\"`. Default `false` so 26.05 installs honor the \"optional and disabled by default\" contract in [deployment-options.md](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/deployment-options.md). Image tag follows the [image tag conventions](#image-tag-conventions). |\n| `nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled` | `false` | Omni 30B caption NIM (optional). Set `true` to enable image captioning — see [Image captioning (Omni 30B)](#image-captioning-omni-30b). Default `false` so 26.05 installs do not silently pull ≈ 62 GiB of BF16 weights or claim a second dedicated GPU. Image tag follows the [image tag conventions](#image-tag-conventions). |\n| `nimOperator.audio.enabled` | `false` | Parakeet ASR NIM (optional). Set `true` for audio/video transcription; pair with `serviceConfig.nimEndpoints.audioGrpcEndpoint=audio:50051` so the retriever-service can reach it. |\n| `nimOperator..image.repository` | `nvcr.io/nim/nvidia/...` | Per-NIM image. |\n| `nimOperator..image.pullSecrets` | `[ngc-secret]` | Referenced by the NIMService CR. |\n| `nimOperator..authSecret` | `ngc-api` | NIM auth Secret name. |\n| `nimOperator..storage.pvc.size` | `25Gi` (50Gi for vlm_embed/rerankqa, 100Gi parse, 300Gi VL) | NIMCache PVC size. |\n| `nimOperator..replicas` | `1` | Per-NIMService replica count. |\n| `nimOperator.nimServiceGpuLimit` | `1` | Default `nvidia.com/gpu` limit on every NIMService when per-NIM `resources` is `{}`. Set to `null` for operator-only reconciliation (not reliable on all NIM Operator versions — see [GPU limits and `helm upgrade`](#gpu-limits-and-helm-upgrade)). |\n| `nimOperator..resources` | `{}` | Per-NIM override of the whole `resources` block. Empty uses `nimServiceGpuLimit`; non-empty replaces the chart default (may require `--force-conflicts` on later `helm upgrade`). |\n| `nimOperator.modelProfile` | `{}` | Chart-wide NIMCache GPU/profile filter. Applied to every NIMCache that does not have its own override. See [Filtering cached GPU profiles](#filtering-cached-gpu-profiles). |\n| `nimOperator..modelProfile` | `{}` | Per-NIM NIMCache GPU/profile filter. Non-empty values REPLACE the chart-wide default (no merge). See [Filtering cached GPU profiles](#filtering-cached-gpu-profiles). |\n| `nimOperator..expose.service.port` | `8000` (9000 for audio) | HTTP port. |\n| `nimOperator..expose.service.grpcPort` | `8001` (50051 for audio) | gRPC port. |\n\n> Only the four \"core\" NIMs (page_elements, table_structure, ocr, vlm_embed)\n> are auto-wired into the retriever-service config. Optional NIMs may reconcile\n> when `nimOperator..enabled` is `true` in `values.yaml`, but the\n> retriever-service won't call them unless you wire your pipeline to use them.\n> For 26.05, prefer the [minimal install](#recommended-minimal-install-2605) overrides.\n\n#### Filtering cached GPU profiles { #filtering-cached-gpu-profiles }\n\nEvery NIMCache the chart renders supports the NIM Operator's\n`spec.source.ngc.model` block, which restricts which model profiles the\ncache job downloads. The chart exposes this through two values:\n\n| Path | Scope | Behaviour |\n| ---- | ----- | --------- |\n| `nimOperator.modelProfile` | Chart-wide | Applied to every NIMCache that doesn't carry its own override. |\n| `nimOperator..modelProfile` | Per-NIM | When non-empty, **REPLACES** the chart-wide default (no merge). |\n\nBoth default to `{}`. With both empty the chart emits no `model:`\nblock and the NIM Operator falls back to its \"cache every profile\napplicable to the detected GPUs\" default — fine on a single-GPU\nlaptop, but on heterogeneous clusters (or any cluster with ≥ 3 NIMs)\nthis wastes tens of GiB of PVC storage, NGC bandwidth, and cache-job\nruntime.\n\nThe mapping is rendered verbatim under `spec.source.ngc.model`, so the\nshape lines up 1:1 with the [NIMCache CRD](https://docs.nvidia.com/nim-operator/latest/reference-nimcache.html).\nTwo filter dimensions are supported (use whichever fits your cluster;\n`gpus` is the common case):\n\n```yaml\nnimOperator:\n modelProfile:\n gpus:\n # NIMCache only downloads profiles compatible with at least one\n # of these GPU selectors. Each selector is {ids: [...], product: ...}.\n - ids: [\"26B5\"] # PCI device ID(s)\n product: \"NVIDIA-H100-80GB-HBM3\" # NVIDIA marketing name\n # profiles:\n # # Alternative: list of exact profile UUIDs from `ngc registry\n # # model list-profiles /:`.\n # - \"11111111-2222-3333-4444-555555555555\"\n```\n\nEquivalent overrides via `--set`:\n\n```bash\n# Homogeneous H100 80 GB cluster — every NIMCache only pulls the H100 profile:\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n --set 'nimOperator.modelProfile.gpus[0].ids[0]=26B5' \\\n --set 'nimOperator.modelProfile.gpus[0].product=NVIDIA-H100-80GB-HBM3'\n\n# Restrict only the page_elements NIMCache to a specific profile UUID, leave the rest alone:\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n --set 'nimOperator.page_elements.modelProfile.profiles[0]=11111111-2222-3333-4444-555555555555'\n\n# Chart-wide H100 default plus a per-NIM override (the override REPLACES the global; it does NOT merge):\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n --set 'nimOperator.modelProfile.gpus[0].product=NVIDIA-H100-80GB-HBM3' \\\n --set 'nimOperator.vlm_embed.modelProfile.profiles[0]=22222222-3333-4444-5555-666666666666'\n```\n\nTips:\n\n- Run `ngc registry model list-profiles nvcr.io/nim/nvidia/:` to enumerate the available profiles for any chart-pinned NIM image and pick the smallest profile that matches your GPU.\n- Filter mismatches surface as `NIMCache` events such as `NoCompatibleProfile`; check with `kubectl describe nimcache `.\n- The chart's defaults (`{}`) preserve operator behaviour, so adding `modelProfile` is a strict opt-in — existing releases keep working unchanged.\n\n#### Image tag conventions { #image-tag-conventions }\n\nEvery NIM in this chart pins an exact NGC image tag in `values.yaml`\n— there is no `:latest` floating reference. Two tag families show up:\n\n| Family | Example | Meaning |\n| ------ | ------- | ------- |\n| Plain semver | `nemotron-page-elements-v3:1.8.0` | A standard NIM release, identical bytes on every pull. Used by the four core NIMs and the reranker / ASR NIMs. |\n| `-variant` | `nemotron-parse-v1.2:1.7.0-variant`, `nemotron-3-nano-omni-30b-a3b-reasoning:1.7.0-variant` | The Nemotron Parse and Nemotron 3 Nano Omni 30B builds that ship per-GPU TensorRT engine variants the NIM Operator selects from at reconciliation time (see the Omni and Parse rows in the [model hardware requirements](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/prerequisites-support-matrix.md#model-hardware-requirements) table). The `-variant` suffix is the NGC tag that ships alongside the 26.05 chart and matches footnote ³ of the support matrix. |\n\nFor air-gapped mirror pipelines: mirror the *exact* tag — both the\nplain semver and the `-variant` form — and do not substitute `:latest`.\nSubstituting `:latest` would pin to a moving target that may not match\nthe engine plans the NIM Operator profile expects for a given GPU.\n\nIf you want a different NIM build, override the tag explicitly:\n\n```bash\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true \\\n --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.image.tag=\n```\n\nand validate against the same release of the retriever service before\nproduction rollout.\n\n**Charts and captioning (26.05).** Charts and infographics use **page_elements**\nand **ocr** (no `graphic_elements` operator NIM in this chart). For image\ncaptioning, set `nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true` — see\n[Image captioning (Omni 30B)](#image-captioning-omni-30b) for the\nchart-side wiring and\n[Image captioning (26.05)](https://docs.nvidia.com/nemo/retriever/latest/extraction/prerequisites-support-matrix/#image-captioning-2605)\nfor the product matrix.\n\n#### Image captioning (Omni 30B) { #image-captioning-omni-30b }\n\nThe Nemotron 3 Nano Omni VLM is the canonical image-caption NIM for\n26.05. When you enable it,\n\n```bash\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n --set nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true \\\n ...\n```\n\nthe chart now auto-wires two fields into the rendered\n`retriever-service.yaml` ConfigMap:\n\n```yaml\nnim_endpoints:\n caption_invoke_url: \"http://nemotron-3-nano-omni-30b-a3b-reasoning:8000/v1/chat/completions\"\n caption_model_name: \"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning\"\n```\n\nThe service derives `caption_enabled=true` from a non-null\n`caption_invoke_url`, so the ingestion pipeline routes caption work to\nthe in-cluster Omni Pod with no manual ConfigMap edits.\n\nResolution order mirrors every other NIM endpoint (see the\n[NIM Operator sub-stack](#nim-operator-sub-stack) section):\n\n1. Explicit `serviceConfig.nimEndpoints.captionInvokeUrl` always wins\n (use this to point at a hosted endpoint, e.g.\n `https://integrate.api.nvidia.com/v1/chat/completions`).\n2. Otherwise the operator-managed URL of\n `nemotron-3-nano-omni-30b-a3b-reasoning` is used, provided\n `nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning.enabled=true`\n **and** the `apps.nvidia.com/v1alpha1` CRDs are installed.\n3. Otherwise `caption_invoke_url` stays `null` and the caption stage\n is disabled.\n\n`serviceConfig.nimEndpoints.captionModelName` follows the same order —\nit defaults to the canonical Omni remote model id\n(`nvidia/nemotron-3-nano-omni-30b-a3b-reasoning`, matching\n`nemo_retriever.caption.model_profiles.OMNI_REMOTE_MODEL_ID`) whenever\nthe chart resolves any caption URL. Override only when pointing at a\ndifferent VLM SKU.\n\n#### GPU limits and `helm upgrade` { #gpu-limits-and-helm-upgrade }\n\nThe chart defaults to **`nimOperator.nimServiceGpuLimit: 1`**, which\nrenders `spec.resources.limits.nvidia.com/gpu: 1` on every NIMService\nunless a per-NIM `resources` map overrides it. This is required on\nNIM Operator **v3.1.1** (and other versions tested on A100/H100): when\nthe chart omits the `resources` block entirely, the operator often\n**does not** populate GPU limits from the model profile, and NIM pods\nstart without GPU access (`The NVIDIA Driver was not detected`).\n\n**Trade-off:** Helm and the NIM Operator may both server-side-apply\n`spec.resources.limits.nvidia.com/gpu`. A later `helm upgrade --install`\ncan then fail with:\n\n```\nError: UPGRADE FAILED: conflict occurred while applying object\n / apps.nvidia.com/v1alpha1, Kind=NIMService:\n Apply failed with 1 conflict:\n conflict with \"manager\" using apps.nvidia.com/v1alpha1:\n .spec.resources.limits.nvidia.com/gpu\n```\n\n**Operator-only mode** (omit GPU limits from Helm — only if your NIM\nOperator version reliably reconciles them):\n\n```yaml\nnimOperator:\n nimServiceGpuLimit: null\n```\n\n**If upgrades hit SSA conflicts** after the operator has reconciled GPU\nlimits, use one of:\n\n1. `helm upgrade --install … --force-conflicts --server-side`\n2. `kubectl -n edit nimservice ` to set GPU limits outside Helm\n\nTo pin a non-default GPU count chart-wide, set `nimServiceGpuLimit: 2`\n(or set per-NIM `resources.limits.nvidia.com/gpu`).\n\n### OCR NIM configuration { #ocr-nim-configuration }\n\nThe core OCR NIM is configured under [`nimOperator.ocr`](./values.yaml) (the `ocr:`\nblock). Confirm `image.repository` and `image.tag` before you upgrade.\n\n| Path | Role |\n|------|------|\n| `nimOperator.nimCache.keepOnUninstall` | When `true`, NIMCache CRs survive `helm uninstall` (`helm.sh/resource-policy: keep`). NIMService CRs are always removed. Set `false` for dev clusters that should fully tear down on uninstall. |\n| `nimOperator.ocr.enabled` | Reconcile the OCR `NIMService` |\n| `nimOperator.ocr.image.repository` | NIM image (default `nvcr.io/nim/nvidia/nemotron-ocr-v1`) |\n| `nimOperator.ocr.image.tag` | Pin the image tag for reproducible upgrades |\n\nOverride the auto-wired in-cluster URL with `serviceConfig.nimEndpoints.ocrInvokeUrl`\nwhen the OCR service runs outside the operator sub-stack.\n\n### Persistence\n\n| Path | Default | Notes |\n|----------------------------|-------------------------------|-------|\n| `persistence.enabled` | `true` | |\n| `persistence.size` | `50Gi` | |\n| `persistence.accessModes` | `[ReadWriteOnce]` | Required by SQLite. |\n| `persistence.storageClass` | `\"\"` | Use cluster default unless set. Use `\"-\"` to disable a `storageClassName`. |\n| `persistence.mountPath` | `/var/lib/nemo-retriever` | Both DB and log file are written here. |\n\n### Secrets\n\n| Path | Default | Notes |\n|-----------------------------------|----------------|-------|\n| `ngcImagePullSecret.create` | `false` | Chart-managed dockerconfigjson Secret. |\n| `ngcImagePullSecret.name` | `ngc-secret` | Name referenced by every Pod and every NIMService. |\n| `ngcImagePullSecret.password` | `\"\"` | NGC API key. |\n| `ngcApiSecret.create` | `false` | Chart-managed Opaque Secret. |\n| `ngcApiSecret.name` | `ngc-api` | Name referenced by NIMCache/NIMService `authSecret`. |\n| `ngcApiSecret.password` | `\"\"` | NGC API key (populates `NGC_API_KEY` + `NGC_CLI_API_KEY`). |\n| `imagePullSecrets` | `[]` | Extra pre-existing pull secrets appended to every Pod. |\n\n### Optional features\n\n| Feature | Toggle | Default |\n|-------------------|---------------------------------|---------|\n| Ingress | `ingress.enabled` | `true` |\n| Autoscaling (HPA) | `autoscaling.enabled` | `false` (max=1 anyway) |\n| ServiceMonitor | `serviceMonitor.enabled` | `false` (auto-enabled in split mode) |\n\n---\n\n## Configuration recipes\n\n### Mount a custom retriever-service.yaml verbatim\n\nThe chart renders `retriever-service.yaml` from structured values so you\nshouldn't normally need to ship a verbatim file. If you really want to,\nmount one via `service.extraVolumes` + `service.extraVolumeMounts` at\n`/etc/nemo-retriever/retriever-service.yaml` (which silently overrides the\nchart-managed ConfigMap because `subPath` mounts win).\n\n### Use externally managed Secrets\n\n```yaml\nngcImagePullSecret:\n create: false # don't render; reference an existing Secret\n name: my-org-ngc-pull\nngcApiSecret:\n create: false\n name: my-org-ngc-api\n```\n\nThe chart will skip Secret creation. Make sure `my-org-ngc-pull` exists\nas `kubernetes.io/dockerconfigjson` and `my-org-ngc-api` as `Opaque` with\nan `NGC_API_KEY` key, in the release namespace.\n\n### Disable one NIM and supply an external URL for it\n\n```yaml\nnimOperator:\n vlm_embed:\n enabled: false # don't deploy the embed NIM in-cluster\n\nserviceConfig:\n nimEndpoints:\n embedInvokeUrl: https://integrate.api.nvidia.com/v1/embeddings\n```\n\nThe chart's resolution order is **explicit URL → operator-managed URL →\nempty**, so per-endpoint overrides Just Work.\n\n### Roll the service after editing values\n\nThe `Deployment` carries a `checksum/config` annotation derived from the\nConfigMap, so `helm upgrade` automatically rolls the pod when any\n`serviceConfig.*` value changes.\n\n---\n\n## Timeouts and alleviating ingest failures\n\nBatch ingest fans out extract and embed work to remote NIM HTTP endpoints.\nUnder heavy parallelism a single slow or overloaded NIM can cause timeouts,\nand a worker process crash can surface as many simultaneous `failed`\ndocument callbacks even though only one root cause occurred.\n\n### What the chart configures\n\n| Layer | Default | Where it is set |\n|-------|---------|-----------------|\n| Remote embed HTTP calls | **600 s** (10 min) | Service image (`EmbedParams.request_timeout_s`); not a Helm value today. |\n| Gateway → realtime/batch proxy | **300 s** | Rendered `gateway.timeout_s` in `retriever-service.yaml` (split topology). |\n| VLM embed model name | `serviceConfig.vectordb.embedModel` | Also copied into worker `nim_endpoints.embed_model_name` in the ConfigMap. |\n\nSymptoms to look for in pod logs:\n\n- `Embedding error occurred: timed out` or `httpx.ReadTimeout` on the **batch** pod.\n- `Batch process pool broken (worker crash)` followed by many\n `BrokenProcessPool` failures on other in-flight documents.\n- Embed NIM pod messages such as `failed to allocate pinned system memory`\n (GPU pressure from too many concurrent `/v1/embeddings` requests).\n\nThe **gateway** pod usually only logs `status=failed` callbacks; diagnose on\n**batch** (and **realtime** for page-sized uploads), plus the embed NIM pod.\n\n### Recommended mitigations\n\n**1. Lower batch worker concurrency (first step).**\n\nThe default `serviceConfig.pipeline.batchWorkers` is `48`, which can saturate\na single in-cluster VLM embed NIM. If you see embed timeouts or pool crashes,\nreduce batch parallelism to **16** and redeploy:\n\n```bash\nhelm upgrade retriever ./nemo_retriever/helm \\\n --reuse-values \\\n --set serviceConfig.pipeline.batchWorkers=16\n```\n\nYou can tune further (for example `8` on small GPU nodes), but **16** is a\nreasonable starting point when moving off the default. Realtime workers\n(`realtimeWorkers`, default `24`) are less likely to overload embed NIMs\nbecause they handle smaller units of work; adjust them only if realtime\ningest shows the same timeout pattern.\n\n**2. Confirm embed wiring.**\n\nEnsure `nim_endpoints.embed_model_name` in the mounted config matches the\nVLM embed NIM SKU (`serviceConfig.vectordb.embedModel`, default\n`nvidia/llama-nemotron-embed-vl-1b-v2`). A model mismatch produces\nHTTP 404 on `/v1/embeddings`, not a timeout, but is worth ruling out when\ndebugging failed ingests.\n\n**3. Retry failed documents.**\n\nFailures caused by a one-time pool restart are often transient. After lowering\n`batchWorkers` and rolling the batch Deployment, resubmit documents that\nfailed with `rows=0`.\n\n**4. Scale or isolate the embed NIM.**\n\nIf timeouts persist at `batchWorkers: 16`, add embed NIM replicas (when your\ncluster has GPU capacity), point `serviceConfig.nimEndpoints.embedInvokeUrl`\nat an external embed endpoint, or temporarily disable optional NIMs on\ndev clusters to free GPU memory for `vlm_embed`.\n\n**5. Client and ingress timeouts.**\n\nLong batch jobs may exceed the gateway proxy timeout (300 s) or an Ingress\n`proxy-read-timeout`. Increase ingress annotations if clients disconnect\nwhile workers are still processing; see the commented example on\n`ingress.annotations` in `values.yaml`.\n\n---\n\n## Queue-depth autoscaling (split mode)\n\nIn `topology.mode: split` deployments the realtime and batch worker\npods scale horizontally based on **queue fill ratio** and\n**95th-percentile processing latency**. Both signals come straight out\nof the pods' `/metrics` endpoint — the publisher is always on (see\n`nemo_retriever_pool_queue_depth_ratio` in\n[`prometheus.py`](../src/nemo_retriever/service/services/prometheus.py)).\nThe only choice you have to make is **how the metrics get from\nPrometheus into the Kubernetes HPA**.\n\n### Why queue depth (and not CPU)\n\nCPU-based HPA reacts to *the pod that has already saturated its work*.\nFor an ingest pipeline that fans out to remote NIM endpoints, the work\nspends most of its time blocked on HTTP — CPU stays low even when the\nqueue is full. Queue depth measures *demand to be served*, which is\nwhat we actually want to scale on. A 95th-percentile-latency signal\nrides alongside to catch the inverse case (a single hot pod whose\nqueue is shallow but whose per-item processing has stalled).\n\n### Backend choices\n\nThe chart's `autoscaling.queueDepth.backend` controls which path is\nwired up. All three options leave the metrics publisher untouched:\n\n| backend | When to pick it | Cluster prerequisite |\n|------------------------|------------------------------------------------------------------|-----------------------------------|\n| `prometheus-adapter` *(default)* | Production. One adapter feeds HPA + Grafana + future autoscalers. | Prometheus Operator + `prometheus-community/prometheus-adapter`. |\n| `cpu` | Bootstrap / dev cluster without Prometheus. | None — built-in. |\n| `keda` | Already standardised on KEDA org-wide. | KEDA operator (you install + apply your own `ScaledObject`). |\n\nThe chart-recommended path is `prometheus-adapter`. The reasoning is\ndocumented in `values.yaml`; in short, it keeps a single Prometheus as\nthe source of truth, supports HPA's multi-metric arithmetic-mean\nevaluation out of the box, and doesn't force the chart to bundle new\nCRDs.\n\n### Wiring up prometheus-adapter (recommended)\n\nThe chart renders a ConfigMap named\n`-nemo-retriever-prom-adapter-rules` containing PromQL rules\nfor the External Metrics API. You point your existing\nprometheus-adapter at it:\n\n```bash\nhelm upgrade prometheus-adapter prometheus-community/prometheus-adapter \\\n --namespace monitoring \\\n --reuse-values \\\n --set rules.existing=-nemo-retriever-prom-adapter-rules\n```\n\nThen verify both metrics show up in the External Metrics API:\n\n```bash\nkubectl get --raw \\\n \"/apis/external.metrics.k8s.io/v1beta1/namespaces/$NS/nemo_retriever_pool_queue_depth_ratio_avg?labelSelector=pool%3Drealtime\" \\\n | jq .\n```\n\nOnce that returns a non-empty `items` array, the HPAs rendered by this\nchart will start consuming them. The HPA annotation\n`nemo-retriever.nvidia.com/hpa-signals` documents the active set per\nHPA, e.g. `queueRatio=true latencyP95=true cpu=false`.\n\n### CPU fallback (no Prometheus required)\n\nSet `autoscaling.queueDepth.backend: cpu` and enable the CPU metric\nunder each role:\n\n```yaml\nautoscaling:\n queueDepth:\n backend: cpu\ntopology:\n realtime:\n hpa:\n metrics:\n queueDepthRatio: { enabled: false }\n processingLatencyP95: { enabled: false }\n cpu: { enabled: true, targetUtilizationPercentage: 60 }\n batch:\n hpa:\n metrics:\n queueDepthRatio: { enabled: false }\n processingLatencyP95: { enabled: false }\n cpu: { enabled: true, targetUtilizationPercentage: 80 }\n```\n\nThe legacy `topology..hpa.targetCPUUtilizationPercentage` field\nstill works and behaves as an alias for the `metrics.cpu` block.\n\n### KEDA path\n\nSet `autoscaling.queueDepth.backend: keda` and disable the chart-managed\nHPAs:\n\n```yaml\nautoscaling:\n queueDepth: { backend: keda }\ntopology:\n realtime: { hpa: { enabled: false } }\n batch: { hpa: { enabled: false } }\n```\n\nThen apply your own `ScaledObject` — example for the realtime pool:\n\n```yaml\napiVersion: keda.sh/v1alpha1\nkind: ScaledObject\nmetadata:\n name: nemo-retriever-realtime\nspec:\n scaleTargetRef:\n name: nemo-retriever-realtime\n minReplicaCount: 2\n maxReplicaCount: 8\n cooldownPeriod: 300\n triggers:\n - type: prometheus\n metadata:\n serverAddress: http://prometheus.monitoring.svc:9090\n metricName: nemo_retriever_pool_queue_depth_ratio\n threshold: \"0.5\"\n query: |\n avg by (pool) (\n nemo_retriever_pool_queue_depth{pool=\"realtime\"}\n /\n on(pool, instance) group_left()\n nemo_retriever_pool_max_queue_size{pool=\"realtime\"}\n )\n - type: prometheus\n metadata:\n serverAddress: http://prometheus.monitoring.svc:9090\n metricName: nemo_retriever_pool_processing_duration_p95\n threshold: \"30\"\n query: |\n histogram_quantile(\n 0.95,\n sum by (le, pool) (\n rate(nemo_retriever_pool_processing_duration_seconds_bucket{pool=\"realtime\"}[2m])\n )\n )\n```\n\nKEDA's biggest win is **scale-from-zero**, which we don't use today —\nboth `minReplicas` defaults are ≥ 1 because the realtime pod is on the\nhot path for SSE consumers. If you do want scale-from-zero (e.g. a\nnightly batch-only job tenant), KEDA is the right tool and this is the\nescape hatch.\n\n### Tuning the thresholds\n\nPer-role tuning lives under `topology..hpa.metrics`:\n\n```yaml\ntopology:\n realtime:\n hpa:\n metrics:\n queueDepthRatio: { enabled: true, target: \"500m\" } # 0.5\n processingLatencyP95: { enabled: true, targetSeconds: \"30\" }\n batch:\n hpa:\n metrics:\n queueDepthRatio: { enabled: true, target: \"700m\" } # 0.7 — batch can run hot\n processingLatencyP95: { enabled: true, targetSeconds: \"120\" }\n```\n\nQuantity-string conventions are k8s standard: `500m == 0.5`, `2`, `2k`,\netc. The `target` is **per-replica** because the HPA template uses\n`type: AverageValue` for both External metrics — that's what makes\n\"scale up when *average* queue fill across pods exceeds 0.5\" work\nwithout baking the pod count into the publisher.\n\n### Verifying it scales\n\n```bash\n# Cause realtime pressure (anything that submits to /v1/ingest/job/.../page).\n# Then watch the HPA decide:\nkubectl get hpa -w\n\n# And watch the active signals on each HPA:\nkubectl get hpa -realtime -o jsonpath='{.metadata.annotations.nemo-retriever\\.nvidia\\.com/hpa-signals}'\n```\n\nThe dashboard's *Worker Pool Capacity* card on the **Overview** page\nmirrors the same signal Prometheus is seeing, so it's a quick eyeball\nsanity check before opening Grafana.\n\n---\n\n## Air-gapped deployment { #air-gapped-deployment }\n\nSee [Deployment options — Air-gapped and disconnected deployment](https://docs.nvidia.com/nemo/retriever/latest/extraction/deployment-options/#air-gapped-deployment) for overview and workflow. Chart-specific reference for mirroring:\n\n### Container images to mirror (26.05 chart defaults)\n\nVerify tags on the Git branch or tag you ship (for example `26.05` or\n`26.05-RC1`). Defaults below match\n[`values.yaml`](./values.yaml) on the current chart.\n\n| Role | `nimOperator` key | Default image (`repository:tag`) |\n|------|-------------------|----------------------------------|\n| Retriever service | — | `service.image.repository`:`service.image.tag` (override for production) |\n| Page elements | `page_elements` | `nvcr.io/nim/nvidia/nemotron-page-elements-v3:1.8.0` |\n| Table structure | `table_structure` | `nvcr.io/nim/nvidia/nemotron-table-structure-v1:1.8.0` |\n| OCR | `ocr` | `nvcr.io/nim/nvidia/nemotron-ocr-v1:1.3.0` |\n| VL embed | `vlm_embed` | `nvcr.io/nim/nvidia/llama-nemotron-embed-vl-1b-v2:1.12.0` |\n| VL reranker (optional) | `rerankqa` | `nvcr.io/nim/nvidia/llama-nemotron-rerank-vl-1b-v2:1.10.0` |\n| Nemotron Parse (optional) | `nemotron_parse` | `nvcr.io/nim/nvidia/nemotron-parse-v1.2:1.7.0-variant` |\n| Omni caption (optional) | `nemotron_3_nano_omni_30b_a3b_reasoning` | `nvcr.io/nim/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:1.7.0-variant` |\n| Parakeet ASR (optional) | `audio` | `nvcr.io/nim/nvidia/parakeet-1-1b-ctc-en-us:1.5.0` |\n\nGPU SKU support for `audio` is in [Model hardware requirements](https://github.com/NVIDIA/NeMo-Retriever/blob/main/docs/docs/extraction/prerequisites-support-matrix.md#model-hardware-requirements).\n\nAlso mirror images for the vectordb sidecar, Redis, or other subcharts if\nyour values enable them.\n\n### Helm values for a private registry\n\nExample overrides (replace placeholders):\n\n```bash\nhelm upgrade --install retriever ./nemo_retriever/helm \\\n -f my-airgap-values.yaml\n```\n\n`my-airgap-values.yaml` should include at least:\n\n```yaml\nservice:\n image:\n repository: /nemo-retriever-service\n tag: \n pullPolicy: IfNotPresent\n\nimagePullSecrets:\n - name: my-private-registry\n\nngcImagePullSecret:\n create: false # use secrets that authenticate to YOUR mirror\n\nnimOperator:\n page_elements:\n image:\n repository: /nemotron-page-elements-v3\n tag: \"1.8.0\"\n pullPolicy: IfNotPresent\n # Repeat for table_structure, ocr, vlm_embed, and any optional keys you enable.\n```\n\n- Set `nimOperator..image.pullSecrets` to the Secret name your\n `NIMService` resources should use (defaults to `ngc-secret`).\n- Leave `serviceConfig.nimEndpoints.*` empty when operator-managed NIMs\n are in-cluster; set explicit URLs only for external or mirrored services\n outside the chart.\n- For **offline captioning**, enable\n `nimOperator.nemotron_3_nano_omni_30b_a3b_reasoning` and point the pipeline\n caption endpoint at the in-cluster NIM URL (see\n [Image captioning (26.05)](https://docs.nvidia.com/nemo/retriever/latest/extraction/prerequisites-support-matrix/#image-captioning-2605)).\n\n### Mirroring pattern\n\n```bash\ndocker login nvcr.io -u '$oauthtoken' -p \"$NGC_API_KEY\"\ndocker pull nvcr.io/nim/nvidia/nemotron-page-elements-v3:1.8.0\ndocker tag nvcr.io/nim/nvidia/nemotron-page-elements-v3:1.8.0 \\\n /nemotron-page-elements-v3:1.8.0\ndocker push /nemotron-page-elements-v3:1.8.0\n```\n\nFor bulk sync, prefer [skopeo](https://github.com/containers/skopeo) or\n[crane](https://github.com/google/go-containerregistry/blob/main/cmd/crane/README.md).\nRecord `repository@sha256:...` digests for regulated environments.\n\n---\n\n## Roadmap\n\n1. **PostgreSQL backend** — replace `service.db.engine.DatabaseEngine` with\n a SQLAlchemy/asyncpg-based engine, then bump the chart to deploy a\n PostgreSQL StatefulSet (or take a sub-chart dependency on Bitnami's\n chart) and lift `service.replicas` to N.\n2. **NetworkPolicies** restricting the service Pod to the NIM Pods + DB\n only.\n3. **Gateway autoscaling** on inflight-uploads (currently fixed\n `topology.gateway.replicas`) — sticky-routing story for SSE\n subscribers needs to land first.\n\n---\n\n## Validation\n\nThe chart is exercised in CI with `helm lint` and `helm template`. Run\nlocally:\n\n```bash\nhelm lint nemo_retriever/helm\n\n# Operator CRDs present: vectordb resolves vlm_embed via the operator URL.\nhelm template r nemo_retriever/helm \\\n --api-versions apps.nvidia.com/v1alpha1 > /tmp/r-op.yaml\n\n# Operator CRDs absent: vectordb has no operator URL to fall back to, so\n# either disable vectordb or supply an explicit embed endpoint.\nhelm template r nemo_retriever/helm \\\n --set serviceConfig.vectordb.enabled=false > /tmp/r.yaml\n# or:\n# helm template r nemo_retriever/helm \\\n# --set serviceConfig.nimEndpoints.embedInvokeUrl=http://embed.svc:8000/v1/embeddings \\\n# > /tmp/r.yaml\n```\n\nBoth renders should succeed cleanly and parse as valid Kubernetes manifests\n(`kubectl apply --dry-run=client -f /tmp/r.yaml`). See [VectorDB and the\nembed endpoint](#vectordb-and-the-embed-endpoint) for why\n`helm template r nemo_retriever/helm` without flags is rejected as a\nmisconfiguration.\n\n### Governing Terms\n\nThe NVIDIA NIM containers that are deployed via this helm chart are governed by [NVIDIA Agreements | Enterprise Software | NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [NVIDIA Agreements | Enterprise Software | Product Specific Terms for AI Product](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the NeMo Retriever Library container is [released under the Apache-2.0](https://github.com/NVIDIA/nv-ingest/blob/main/LICENSE) license. Also the use of the models are governed by the [NVIDIA Community Model License](https://docs.nvidia.com/ai-foundation-models-community-license.pdf).\n\nYou are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.","displayName","NVIDIA NeMo Retriever Library Helm Chart",["D",1782913539626],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo-microservices/helm-charts/nemo-retriever","rawImageUrl","https://developer-blogs.nvidia.com/wp-content/uploads/2024/03/nemo-retriever-graphic.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":240,"_241":240,"_242":243,"_244":245},"NSPECT-AF3Z-BZUP","value","filter",{"_246":245,"_241":240},"category","label","field",{"_108":1421,"_110":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":258,"_139":282,"_1364":1365,"_1366":56,"_1367":87,"_1368":87,"_1369":1370,"_1371":87,"_1372":1373},"amplitude",{"_1352":1353,"_954":-7,"_1354":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1346":1347,"_1348":1349,"_1350":1351},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1229":362,"_1230":1231,"_1232":1233,"_1234":1235},"announcements",[],"consentState",{"_319":1219,"_1220":87,"_1221":31,"_1222":31,"_1223":1224},"consentConfig",{"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320},"showContactAdmin",1784058153035,[],[],[296,297,298,299,300],{"_45":60,"_47":71,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":103,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":103,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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nemo-retriever Helm chart

Layout

Quick start

1. Service image

2. Install with external NIM endpoints (operator not required)

3. Install with the NIM Operator (in-cluster NIMs)

Recommended minimal install (26.05) { #recommended-minimal-install-2605 }

Why NIM resources still exist after helm uninstall

Values reference (highlights)

Service

Audio and video (Parakeet ASR) { #audio-video-parakeet }

Service configuration (rendered into retriever-service.yaml)

VectorDB and the embed endpoint { #vectordb-and-the-embed-endpoint }

NIM Operator sub-stack

Filtering cached GPU profiles { #filtering-cached-gpu-profiles }

Image tag conventions { #image-tag-conventions }

Image captioning (Omni 30B) { #image-captioning-omni-30b }

GPU limits and helm upgrade { #gpu-limits-and-helm-upgrade }

OCR NIM configuration { #ocr-nim-configuration }

Persistence

Secrets

Optional features

Configuration recipes

Mount a custom retriever-service.yaml verbatim

Use externally managed Secrets

Disable one NIM and supply an external URL for it

Roll the service after editing values

Timeouts and alleviating ingest failures

What the chart configures

Recommended mitigations

Queue-depth autoscaling (split mode)

Why queue depth (and not CPU)

Backend choices

Wiring up prometheus-adapter (recommended)

CPU fallback (no Prometheus required)

KEDA path

Tuning the thresholds

Verifying it scales

Air-gapped deployment { #air-gapped-deployment }

Container images to mirror (26.05 chart defaults)

Helm values for a private registry

Mirroring pattern

Roadmap

Validation

Governing Terms

Why NIM resources still exist after `helm uninstall`

Service configuration (rendered into `retriever-service.yaml`)

GPU limits and `helm upgrade` { #gpu-limits-and-helm-upgrade }