Purpose

Stand up the RT-VLM dense-captioning microservice on its own and exercise every endpoint it exposes (file upload, generate_captions, stream add/delete, chat-completions, Kafka topics).

Prerequisites

For standalone RT-VLM deployment:

• Docker, Docker Compose, NVIDIA Container Toolkit, and a visible GPU.
• NGC registry credentials in $NGC_CLI_API_KEY for docker login nvcr.io, image pulls, and local NGC model/artifact downloads.
• curl, jq, and any writable working directory for the standalone compose copy.

For API calls against an existing service:

• Running RT-VLM service reachable at $BASE_URL.
• Bearer token in $RTVI_VLM_API_KEY or $NGC_CLI_API_KEY, depending on how the service was configured.

For full VSS profile deployment:

• Use ../vss-deploy-profile/SKILL.md; this skill does not deploy full VSS profiles.

Instructions

Follow the routing tables and step-by-step workflows below. Each section that ends in workflow, quick start, or flow is intended to be executed top-to-bottom. Detailed reference material lives in references/; execute the documented workflows directly unless a future revision names a concrete helper.

Examples

Worked end-to-end examples are kept under evals/ (each *.json manifest contains a runnable scenario) and inline in the per-workflow curl blocks below. Run a Tier-3 evaluation with nv-base validate <this-skill-dir> --agent-eval to replay them.

Limitations

• Requires either a standalone RT-VLM service deployed via this skill or an existing RT-VLM service reachable from the caller.
• NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and license restrictions.
• Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.
• Keep NGC_CLI_API_KEY, RTVI_VLM_API_KEY, and rtvi-vlm.env files out of git and out of logs; do not echo credential values or include them in final responses.
• Docker group access and sudo are effectively root-level privileges. Use the non-interactive sudo -n guard in the deploy reference and stop for host-owner action when passwordless sudo is unavailable.

Troubleshooting

• Error: REST call returns connection refused. Cause: target microservice not running. Solution: probe /docs or /health; redeploy via vss-deploy-profile or the matching vss-deploy-* skill.
• Error: HTTP 401/403 from NGC pulls. Cause: missing/expired NGC_CLI_API_KEY. Solution: docker login nvcr.io and re-export the key before retrying.
• Error: container OOM or model fails to load. Cause: insufficient GPU memory for the selected profile. Solution: switch to a smaller variant or free GPUs via docker compose down.

Deploy and Use RT-VLM Dense Captioning (VSS 3.2)

RT-VLM is NVIDIA's real-time vision-language microservice: decode video (file or RTSP), segment it into chunks, run a VLM (cosmos-reason1, cosmos-reason2, cosmos-reason3, or any OpenAI-compatible model), stream dense captions back over SSE/HTTP, and publish captions, incident alerts, and errors to Kafka. Use this skill to deploy the standalone RT-VLM service when a full VSS profile is not already running, then call its /v1/... API for caption generation, file upload, live-stream management, health checks, NIM-compatible chat completions, or Prometheus metrics. API reference: https://docs.nvidia.com/vss/latest/real-time-vlm-api.html.

Deployment Routing

If the user asks to deploy a full VSS profile, use ../vss-deploy-profile/SKILL.md. That skill owns profile routing, generated.env, resolved.yml, multi-service sizing, and full-stack deploy/teardown.

If the user asks for standalone RT-VLM dense captioning, or no VSS profile is already running, use the standalone RT-VLM flow in references/deploy-rt-vlm-service.md before calling the API. This follows the same compose-centric pattern as vss-deploy-profile: gather context, run preflights, work from a local copy, dry-run with docker compose config, review, deploy, then wait for health.

Standalone Deployment Flow

Always follow this sequence. Never skip the dry-run.

# 1. Copy deploy/docker/services/rtvi/rtvi-vlm/rtvi-vlm-docker-compose.yml
#    into any writable standalone working directory.
# 2. Derive RTVI_VLM_IMAGE_TAG from that compose copy.
# 3. Strip the standalone-only dangling depends_on block from the copy.
# 4. Create a gitignored rtvi-vlm.env with the required RT-VLM values.
# 5. Prepare host bind paths such as $VSS_DATA_DIR/data_log/vst/clip_storage.
#    Use `sudo -n` for ownership fixes; if passwordless sudo is unavailable,
#    stop and ask the host owner to run the printed command manually.
# 6. docker compose --env-file rtvi-vlm.env -f rtvi-vlm-docker-compose.yml config --quiet
# 7. docker pull the exact RT-VLM image tag.
# 8. docker compose ... up -d rtvi-vlm, wait for ready, then smoke test.

Run preflights before any pull or up; stop and fix failures here before debugging RT-VLM itself:

nvidia-smi --query-gpu=index,name --format=csv,noheader
nvidia-container-cli info
docker compose version
docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi

For standalone single-file deployments, do not run the raw deploy/docker/services/rtvi/rtvi-vlm/rtvi-vlm-docker-compose.yml directly: it contains depends_on references to sibling VLM/NIM services that are only defined in the full VSS/met-blueprints compose project. The standalone reference shows how to copy the compose file, derive the current image tag from it, strip the depends_on block, and validate the result before up.

For agent-driven validation, never let sudo prompt interactively. Before any privileged ownership or Docker operation, use the non-interactive guard in references/deploy-rt-vlm-service.md: prefer plain docker; otherwise use sudo -n docker; if sudo -n fails, stop with the exact manual command for the host owner instead of retrying with interactive sudo or weakening permissions.

If docker pull fails with a containerd snapshotter/unpack error on Docker 28+, apply the /etc/docker/daemon.json containerd-snapshotter=false fix in the standalone reference before retrying.

Minimum standalone rtvi-vlm.env values:

Host env var	Required when	Purpose
NGC_CLI_API_KEY	Standalone deploy path	NGC registry image pull and NGC model/artifact download
RTVI_VLM_API_KEY or NGC_CLI_API_KEY	Authenticated API calls	RT-VLM bearer auth after the service is running
RTVI_VLM_PORT	Always	Host API port mapped to container 8000
HOST_IP	Always	Kafka bootstrap host (${HOST_IP}:9092)
VSS_DATA_DIR	Always	Required clip-storage bind mount
RTVI_VLM_MODEL_TO_USE	Always for standalone	Backend selector; use cosmos-reason3 for the default local model or openai-compat for a remote/sibling endpoint
RTVI_VLM_MODEL_PATH	Local self-hosted model	Source-backed Cosmos Reason3 Nano BF16 path: ngc:nim/nvidia/cosmos3-nano-reasoner:bf16-final
RTVI_VLM_ENDPOINT	RTVI_VLM_MODEL_TO_USE=openai-compat	Remote/sibling OpenAI-compatible VLM endpoint
VLM_NAME	RTVI_VLM_MODEL_TO_USE=openai-compat	Model/deployment name exposed by that endpoint

Setup

export BASE_URL="http://localhost:${RTVI_VLM_PORT:-8018}"  # host-side RT-VLM port
export API_KEY="${NGC_CLI_API_KEY:-${RTVI_VLM_API_KEY:-}}" # bearer token used by host-side curl commands
: "${API_KEY:?Set NGC_CLI_API_KEY or RTVI_VLM_API_KEY before calling authenticated endpoints}"

Every request below uses Authorization: Bearer $API_KEY. Health endpoints (/v1/health/*, /v1/ready, /v1/live, /v1/startup) typically work without auth.

Smoke test before use:

curl -fsS "$BASE_URL/v1/health/ready"
MODEL_ID="$(curl -fsS "$BASE_URL/v1/models" -H "Authorization: Bearer $API_KEY" | jq -r '.data[0].id // .id')"
curl -fsS "$BASE_URL/openapi.json" | jq -r '.paths | keys[]' | sort

RTSP Sample Stream Guard

When a task or eval names RTSP_SAMPLE_URL, treat that exact environment variable as a required input. Verify it is set and non-empty before probing or registering any stream; if it is missing, stop with a clear failure message. Do not derive a substitute from NvStreamer, VIOS, sample-data bundles, or any other fallback, because that validates a different stream than the caller requested.

: "${RTSP_SAMPLE_URL:?Set RTSP_SAMPLE_URL to a reachable RTSP sample stream before RTSP validation}"
case "$RTSP_SAMPLE_URL" in
  rtsp://*) ;;
  *) echo "RTSP_SAMPLE_URL must be an rtsp:// URL, got: $RTSP_SAMPLE_URL" >&2; exit 1 ;;
esac

if command -v ffprobe >/dev/null 2>&1; then
  ffprobe -v error -rtsp_transport tcp \
    -select_streams v:0 -show_entries stream=codec_type \
    -of csv=p=0 "$RTSP_SAMPLE_URL" | grep -qx video
elif command -v gst-discoverer-1.0 >/dev/null 2>&1; then
  gst-discoverer-1.0 "$RTSP_SAMPLE_URL" | grep -qi 'video'
else
  echo "Install ffprobe or gst-discoverer-1.0 before RTSP validation." >&2
  exit 1
fi

Quick Start — dense captions from a local video

# 1. Upload the video, capture its file id
FILE_ID=$(curl -fsS -X POST "$BASE_URL/v1/files" \
  -H "Authorization: Bearer $API_KEY" \
  -F "file=@/path/to/warehouse.mp4" \
  -F "purpose=vision" \
  -F "media_type=video" | jq -r '.id')

# 2. Generate captions + alerts (SSE stream of chunked responses)
curl -N -X POST "$BASE_URL/v1/generate_captions" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d "{
    \"id\": \"$FILE_ID\",
    \"prompt\": \"Write a concise dense caption for each 10-second segment of this warehouse video.\",
    \"model\": \"$MODEL_ID\",
    \"chunk_duration\": 10,
    \"stream\": true
  }"

API Surface

Use the live OpenAPI as the source of truth before calling optional endpoints:

curl -fsS "$BASE_URL/openapi.json" | jq -r '.paths | keys[]' | sort

Core paths for VSS 3.2 are:

• POST /v1/files for multipart media upload; pass the returned file id into caption generation and delete the file when finished.
• POST /v1/generate_captions for file or stream captioning. Use the exact model id returned by GET /v1/models; aliases such as cosmos-reason2 or cosmos-reason3 are backend selectors, not request model ids.
• POST /v1/streams/add, GET /v1/streams/get-stream-info, and DELETE /v1/streams/delete/{stream_id} for RTSP lifecycle. Parse stream ids from results[0].id.
• POST /v1/chat/completions for OpenAI-compatible text and multimodal calls. Current 26.05 builds return HTTP 400 for text-only /v1/completions; treat that as expected when validating legacy behavior.
• GET /v1/health/ready, /v1/models, /v1/assets/stats, and /v1/metrics for service probes. Do not assume /v1/license exists unless OpenAPI lists it.

Detailed endpoint schemas, response shapes, CV-style singular stream endpoints, and 26.05 compatibility notes live in references/api-surface-26.05.md.

Common Workflows

• Stored file captioning: upload with POST /v1/files, call /v1/generate_captions with the returned file id, use stream=true for SSE, then delete the file to release storage.
• RTSP live captioning: when the caller provides RTSP_SAMPLE_URL, use that exact URL and run the RTSP Sample Stream Guard before registration. Do not derive a replacement stream from NvStreamer or VIOS when RTSP_SAMPLE_URL is empty; fail fast instead. Require an actual video stream/caps entry before registration; add the stream, caption it, then unregister it.
• Alert prompts: include a deterministic Anomaly Detected: Yes/No line. Kafka publication is server-side config, additive to HTTP responses, and documented in references/kafka-workflows.md.
• Kafka validation: trust the live vss-rtvi-vlm environment for topic names. In a full VSS alerts real-time profile, use the existing VSS Kafka container mdx-kafka for CLI checks and final incident-consumer commands. For standalone validation, use a broker that advertises ${HOST_IP}:9092; never stop or replace a pre-existing broker without user confirmation.

Error Reference

Common causes: 400 for invalid request shape or model id, 401/403 for missing or wrong bearer token, 404 for deleted files/streams or unsupported endpoints, 413 for oversized uploads, 422 for schema validation, 429 for too much concurrency, 500 for inference/runtime failures, and 503 while startup is still in progress. Inspect docker logs vss-rtvi-vlm for service-side failures.