Agent-Skills.md

Agent Skills: RTVI VLM Usage API (VSS 3.1)

'Use this skill when working with the RTVI VLM or RT-VLM microservice

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UncategorizedID: autohandai/community-skills/nvidia-video-search-and-summarization-rt-vlm

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autohandai/community-skills
autohandaiLicense: Apache-2.0
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Skill Metadata

Name
nvidia-video-search-and-summarization-rt-vlm
Description
'Use this skill when working with the RTVI VLM or RT-VLM microservice

RTVI VLM Usage API (VSS 3.1)

RTVI VLM is NVIDIA's real-time vision-language microservice: decode video (file or RTSP) → segment into chunks → run a VLM (cosmos-reason1, cosmos-reason2, or any OpenAI-compatible model) → stream dense captions back over SSE/HTTP and publish captions + incident alerts + errors to Kafka. Use this skill whenever you need to hit any /v1/... endpoint on the VSS 3.1 rtvi-vlm microservice: caption generation, file upload, live-stream management, health checks, NIM-compatible chat completions, Prometheus metrics. API reference: https://docs.nvidia.com/vss/latest/real-time-vlm-api.html.

Setup

export BASE_URL="http://localhost:8000"     # RTVI VLM host:port — matches $RTVI_VLM_PORT in compose
export API_KEY="$NGC_API_KEY"               # Bearer token (NGC key works if the service was deployed with NGC auth)

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 "$BASE_URL/v1/health/ready" && curl -fsS "$BASE_URL/v1/models" | jq

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_alerts" \
  -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\": \"cosmos-reason1\",
    \"chunk_duration\": 10,
    \"stream\": true
  }"

Endpoints

Captions

Generate VLM captions and alerts for videos and live streams.

POST /v1/generate_captions_alerts — Generate VLM captions (and alerts) for video/stream

Required: | Field | Type | Description | |-------|------|-------------| | id | string | array | UUID of a previously-uploaded file, or id of an active live stream. Accepts a list of ids for batch | | prompt | string | User prompt to the VLM (e.g. dense-caption instruction) | | model | string | Model name — see GET /v1/models |

Key optional fields: | Field | Type | Default | Description | |-------|------|---------|-------------| | system_prompt | string | — | System prompt; use <think></think><answer></answer> tags to enable reasoning on Cosmos Reason | | enable_reasoning | boolean | false | Turn on reasoning for Cosmos Reason models | | enable_audio | boolean | false | Transcribe audio (via Riva) and fold into captions | | chunk_duration | integer | — | Segment video into N-second chunks (0 = no chunking) | | chunk_overlap_duration | integer | 0 | Overlap between consecutive chunks | | num_frames_per_second_or_fixed_frames_chunk | number | — | FPS (if use_fps_for_chunking=true) or fixed frames per chunk | | use_fps_for_chunking | boolean | false | Interpret above as FPS vs. fixed-frame count | | vlm_input_width / vlm_input_height | int | — | Resize frames before inference (0 = native) | | media_info | object | — | {"start_offset_ms": ..., "end_offset_ms": ...} to process a slice of a file (not live streams) | | stream | boolean | false | SSE: emit per-chunk caption deltas as data: events (recommended for long videos) | | max_tokens / temperature / top_p / top_k / seed / ignore_eos | | | Standard sampling controls | | response_format | object | — | Query response format object | | mm_processor_kwargs | object | — | Extra kwargs for the multimodal processor (e.g. size, shortest/longest edge) |

curl -N -X POST "$BASE_URL/v1/generate_captions_alerts" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "id": "123e4567-e89b-12d3-a456-426614174000",
    "prompt": "Dense-caption this warehouse video, one sentence per 10s chunk.",
    "model": "cosmos-reason1",
    "chunk_duration": 10,
    "stream": true
  }'

Response (200, SSE when stream=true): each event payload has start_ts, end_ts, content, and a terminal {"status": "completed"} event. Response (200, non-stream): { "id", "object": "caption", "choices": [{...}], "usage": {...} }.

DELETE /v1/generate_captions_alerts/{stream_id} — Stop caption generation for a live stream

Stops inference while leaving the stream registered. Pair with DELETE /v1/streams/delete/{stream_id} to also un-register the RTSP source.

curl -X DELETE "$BASE_URL/v1/generate_captions_alerts/$STREAM_ID" -H "Authorization: Bearer $API_KEY"

Files

Upload and manage media files consumed by /v1/generate_captions_alerts.

POST /v1/files — Upload a media file (multipart)

curl -X POST "$BASE_URL/v1/files" -H "Authorization: Bearer $API_KEY" \
  -F "file=@./video.mp4" -F "purpose=vision" -F "media_type=video"

Response: { "id", "object": "file", "bytes", "created_at", "filename", "purpose" }.

GET /v1/files?purpose=vision — List uploaded files

GET /v1/files/{file_id} — File metadata

GET /v1/files/{file_id}/content — Download original file content

DELETE /v1/files/{file_id} — Delete file (releases asset storage)

Live Stream

RTSP stream lifecycle.

POST /v1/streams/add — Register one or more RTSP streams

Required per stream: liveStreamUrl (must start with rtsp://), description. Optional: username, password, sensor_name, and placement metadata (place_name, place_type, place_lat, place_lon, place_alt, place_coordinate_x, place_coordinate_y).

STREAM_ID=$(curl -fsS -X POST "$BASE_URL/v1/streams/add" \
  -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
  -d '{"streams":[{"liveStreamUrl":"rtsp://cam:8554/live","description":"warehouse cam 1"}]}' \
  | jq -r '.results[0].id')

GET /v1/streams/get-stream-info — List active streams

DELETE /v1/streams/delete/{stream_id} — Remove a single stream

DELETE /v1/streams/delete-batch — Remove many ({"stream_ids":[...]})

NIM Compatible

OpenAI-compatible endpoints for interop with OpenAI/NVIDIA-API clients.

POST /v1/chat/completions — OpenAI-compatible chat (text + multimodal)

Required: messages, model. Text-only requests omit id / video_url / image_url.

curl -X POST "$BASE_URL/v1/chat/completions" -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"cosmos-reason1","messages":[{"role":"user","content":"Summarize this scene."}]}'

POST /v1/completions — OpenAI-compatible legacy completions

GET /v1/version — { "version": "3.1.0-..." }

GET /v1/license — license text

GET /v1/manifest — NIM manifest

GET /v1/health/live · GET /v1/health/ready — NIM-style probes

Models · Metadata · Metrics · Health Check

GET /v1/models — List loaded VLMs: { "data": [{ "id", "object": "model", "owned_by" }] }

GET /v1/metadata — Service metadata (build, release, image tag)

GET /v1/metrics — Prometheus metrics (plain text)

GET /v1/ready · GET /v1/live · GET /v1/startup — Kubernetes-style probes

Common Workflows

The four scenarios from the VSS 3.1 RT-VLM Usage Skill requirements.

1. Dense captions from a stored video file

# Upload → capture file id → generate captions (SSE stream)
FILE_ID=$(curl -fsS -X POST "$BASE_URL/v1/files" \
  -H "Authorization: Bearer $API_KEY" \
  -F "file=@warehouse.mp4" -F "purpose=vision" -F "media_type=video" | jq -r '.id')

curl -N -X POST "$BASE_URL/v1/generate_captions_alerts" \
  -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
  -d "{
    \"id\": \"$FILE_ID\",
    \"prompt\": \"Describe warehouse events in 1 sentence per 10s chunk.\",
    \"model\": \"cosmos-reason1\",
    \"chunk_duration\": 10,
    \"stream\": true
  }"

# When done, free storage:
curl -X DELETE "$BASE_URL/v1/files/$FILE_ID" -H "Authorization: Bearer $API_KEY"

2. Dense captions from an RTSP live stream

# Register the stream
STREAM_ID=$(curl -fsS -X POST "$BASE_URL/v1/streams/add" \
  -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
  -d '{"streams":[{"liveStreamUrl":"rtsp://10.0.0.5:8554/warehouse","description":"warehouse cam"}]}' \
  | jq -r '.results[0].id')

# Start continuous caption generation (runs until stream stops or DELETE)
curl -N -X POST "$BASE_URL/v1/generate_captions_alerts" \
  -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
  -d "{
    \"id\": \"$STREAM_ID\",
    \"prompt\": \"Describe each event; start each sentence with a timestamp.\",
    \"model\": \"cosmos-reason1\",
    \"chunk_duration\": 10,
    \"num_frames_per_second_or_fixed_frames_chunk\": 2,
    \"use_fps_for_chunking\": true,
    \"stream\": true
  }" &

# Tear down when finished:
curl -X DELETE "$BASE_URL/v1/generate_captions_alerts/$STREAM_ID" -H "Authorization: Bearer $API_KEY"
curl -X DELETE "$BASE_URL/v1/streams/delete/$STREAM_ID"  -H "Authorization: Bearer $API_KEY"

Pre-req: the container was started with:

RTVI_VLM_KAFKA_ENABLED=true

RTVI_VLM_KAFKA_TOPIC=vision-llm-messages

RTVI_VLM_KAFKA_INCIDENT_TOPIC=vision-llm-events-incidents

RTVI_VLM_ERROR_MESSAGE_TOPIC=vision-llm-errors

HOST_IP=<kafka-host>

STREAM_ID=$(curl -fsS -X POST "$BASE_URL/v1/streams/add"
-H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json"
-d '{"streams":[{"liveStreamUrl":"rtsp://10.0.0.5:8554/warehouse","description":"warehouse cam"}]}'
| jq -r '.results[0].id')

curl -N -X POST "$BASE_URL/v1/generate_captions_alerts"
-H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json"
-d "{ "id": "$STREAM_ID", "prompt": "You are a warehouse monitoring system. Describe the scene in one sentence, then on a new line output exactly:\nAnomaly Detected: Yes/No\nReason: <one sentence>\nFlag an anomaly if any worker is missing a hard hat or high-vis vest.", "system_prompt": "Answer the user's question correctly in yes or no.", "model": "cosmos-reason2", "chunk_duration": 60, "chunk_overlap_duration": 10, "stream": true }"


**Consume alerts from Kafka** (when using the VSS foundational Kafka container).
Kafka values are NvSchema protobuf payloads, so use `print.value=false` for a
clean validation pass that shows timestamp, key, and headers without dumping
binary payload bytes:
```bash
docker exec mdx-kafka kafka-console-consumer \
  --bootstrap-server 127.0.0.1:9092 \
  --topic vision-llm-events-incidents \
  --from-beginning \
  --timeout-ms 5000 \
  --max-messages 10 \
  --property print.timestamp=true \
  --property print.key=true \
  --property print.headers=true \
  --property print.value=false

If Kafka is not running in the VSS mdx-kafka container, use the Kafka CLI from the host running the broker:

kafka-console-consumer \
  --bootstrap-server "$HOST_IP:9092" \
  --topic vision-llm-events-incidents \
  --from-beginning \
  --timeout-ms 5000 \
  --max-messages 10 \
  --property print.timestamp=true \
  --property print.key=true \
  --property print.headers=true \
  --property print.value=false

Incident protobuf (ext.proto :: Incident) key fields: sensorId, timestamp, end, objectIds, frameIds, place, analyticsModule, category, isAnomaly (true for alerts), llm (nested VisionLLM), info map including triggerPhrase, verdict, requestId, chunkIdx, streamId, alertCategory (if the deployment supports the alert_category query field — post-3.1).

3. Kafka workflows (alerts + message bus)

Dense captioning with alerts on an RTSP stream and the HTTP-vs-Kafka response model are documented in references/kafka-workflows.md.

Error Reference

| Code | Meaning | Common Cause | |------|---------|--------------| | 400 | Bad Request | Missing required field (id, prompt, model); unsupported media_type; unknown model name | | 401 | Unauthorized | Missing/invalid Authorization: Bearer $API_KEY — or wrong key format (expect nvapi-...) | | 404 | Not Found | file_id deleted / stream_id not registered / wrong endpoint path (note: {stream_id} is required on DELETE /v1/streams/delete/{stream_id}) | | 413 | Payload Too Large | Uploaded file exceeds server MAX_FILE_SIZE; increase or pre-chunk the video | | 422 | Unprocessable Entity | Pydantic schema violation — e.g. use_fps_for_chunking=true without num_frames_per_second_or_fixed_frames_chunk; stream ids supplied to a file-only field like media_info | | 429 | Rate Limited | Too many concurrent streams — raise VLM_BATCH_SIZE or spread across instances | | 500 | Server Error | VLM inference exception (OOM, model unavailable) — check docker logs rtvi-vlm-* | | 503 | Service Busy | Startup not complete (model still downloading) or upstream NIM dependency unhealthy |

Gotchas

  • 3.1 GA endpoint is /v1/generate_captions_alerts, not /v1/generate_captions. The rename lands in a post-3.1 build. For VSS 3.1 releases (rtvi_vlm/26.01.x–26.02.3), always use the _alerts suffix. https://docs.nvidia.com/vss/latest/real-time-vlm-api.html is the canonical reference.
  • No URL-based input in 3.1 GA — the url/media_type/creation_time fields were added post-3.1. You must upload via POST /v1/files first and then pass the returned id.
  • Alert trigger = the tokens "yes" or "true" in the VLM response (case-insensitive). There is no per-request alert flag. Design prompts with an explicit Anomaly Detected: Yes/No line and set system_prompt to constrain the model to Yes/No answers (per the VSS docs). Every chunk is published to KAFKA_TOPIC; matched chunks additionally go to KAFKA_INCIDENT_TOPIC with isAnomaly=true, info["triggerPhrase"] set to the matched tokens, and info["verdict"]="confirmed".
  • No alert_category query field in the 3.1 OpenAPI spec. The Kafka incident topic defaults incident.category = "vlm-alert" on 3.1. Post-3.1 builds expose an optional alert_category request field to override incident.category.
  • Kafka topics are server-side config, not per-request. The KAFKA_* env vars (via compose RTVI_VLM_KAFKA_* rewrites) are fixed at container start — clients can't override topics on a per-request basis. Kafka publish is additive to the HTTP response, never a replacement.
  • stream=true returns Server-Sent Events, not chunked JSON. Use curl -N (no buffering). Each event is data: {"content": "...", "start_ts": ..., "end_ts": ...}\n\n, terminated by data: {"status":"completed"}\n\n. Without stream=true the server buffers until the full video is processed — fine for short clips (<1 min), avoid for live streams.
  • chunk_duration=0 disables chunking — the entire video is sent to the VLM as one shot. Only meaningful for short clips; long videos will OOM or exceed max_model_len.
  • Default frame budget caps at VLLM_MM_PROCESSOR_VIDEO_NUM_FRAMES (256). Requesting FPS that implies >256 frames per chunk is silently capped; drop FPS or shorten chunk_duration to stay within budget.
  • enable_reasoning requires a Cosmos Reason model. Passing it with Qwen3-VL or other non-reasoning models is a no-op.
  • /v1/metrics requires auth, unlike /v1/health/*. Prometheus scrapers need the Bearer token.
  • File upload is multipart, not JSON. Use -F file=@path -F purpose=vision -F media_type=video; a -d body returns 422.
  • Live-stream lifecycle requires two deletes to fully tear down: DELETE /v1/generate_captions_alerts/{stream_id} stops inference; DELETE /v1/streams/delete/{stream_id} un-registers the stream. Skipping the second leaks RTSP connection resources.