Skills

Vss Deploy Video Embedding

Use this skill when deploying, operating, integrating, or customizing the VSS RT-Embed...

Install

npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-deploy-video-embedding -g -a claude-code -y
Category
AI and agents
Updated
4d ago
License
Apache-2.0
Context cost
Mediumabout 2,098 tokens
Runs scripts
No, instructions only

What it does

Use this skill when deploying, operating, integrating, or customizing the VSS RT-Embed Video Embedding microservice. Covers standalone Docker Compose deployment, the `/v1` REST API for text/video embeddings and live streams, Redis/Kafka/OTel integration, troubleshooting, and bring-your-own-model (BYOM) custom embedding backends, with VideoPrism as an example. Do not use for RT-CV, RT-VLM, VSS Agent, or general VSS deployment work that does not include RT-Embed.

VSS Video Embedding (RT-Embed)

Use this skill for the RT-Embed video embedding microservice, including the standard Cosmos-Embed1 deployment path and custom/BYOM embedding model work.

Trigger phrases: vss-deploy-video-embedding, RT-Embed, rtvi-embed, video embedding service, Cosmos-Embed1, embed live stream, embed video file, generate video embeddings, text embedding for video search, RT-Embed BYOM, VideoPrism embed, custom embed model, MODEL_IMPLEMENTATION_PATH, MODEL_REPOSITORY_SCRIPT_PATH, bring your own embedding model.

Do not use this skill for RT-CV, RT-VLM, VSS Agent, or general VSS deployment work unless the request deploys, operates, integrates, or customizes RT-Embed.

When to Use

  • Deploy, size, upgrade, roll back, or tear down standalone RT-Embed (Cosmos-Embed1 or a custom/BYOM model)
  • Call RT-Embed's /v1 API for text/video embeddings, live streams, model listing, health, metrics
  • Wire RT-Embed into another service with Redis, Kafka, OpenTelemetry, auth, or storage
  • Add or validate a custom/BYOM embedding backend
  • Debug RT-Embed readiness, model/cache startup, or Redis/Kafka reachability

Service Snapshot

  • Skill: vss-deploy-video-embedding.
  • Legacy 3.1 name: RT-Embed.
  • Compose service: rtvi-embed.
  • Container name: vss-rtvi-embed.
  • Image: ghcr.io/nvidia-ai-blueprints/vss/vss-rt-embed (override with VSS_RT_EMBED_IMAGE).
  • Default tag: develop-latest (override with VSS_RT_EMBED_TAG; use develop-latest-sbsa for an SBSA/DGX Spark host).
  • Profile: rtvi-embed.
  • Container port: 8000 (host-side ${RTVI_EMBED_PORT}).
  • Default model: cosmos-embed1-448p from nvidia/Cosmos-Embed1-448p.
  • BYOM loader variables: MODEL_PATH, MODEL_IMPLEMENTATION_PATH, MODEL_REPOSITORY_SCRIPT_PATH.
  • Health endpoint: GET /v1/ready.
  • Healthcheck startup grace: 1200s (20 minutes) on first boot.

Route First

Read the whole SKILL.md on GitHub

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