# Vss Setup Video Analytics API

Use to deploy the vss-video-analytics-api REST service standalone with its...

- Repository: https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization (1959 stars)
- Category: DevOps
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-setup-video-analytics-api -g -a claude-code -y`
Codex: `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-setup-video-analytics-api -g -a codex -y`
Cursor: `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-setup-video-analytics-api -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-setup-video-analytics-api -g -a gemini-cli -y`

## Description

Use to deploy the vss-video-analytics-api REST service standalone with its Elasticsearch ingest-pipeline, selectable Kafka/Redis stream type, and Kafka-topic readiness gates when Kafka is selected. Not for full warehouse deploy.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Purpose

Deploy the video-analytics-api REST service standalone with the user's chosen config and data-log bind. The service exposes port 8081 and `/livez` only after Elasticsearch, the `insertion-timestamp-pipeline`, and, when `STREAM_TYPE=kafka`, the configured Kafka topic requirements are ready.

## 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/`.

## Examples

Worked end-to-end examples are kept under `evals/` (each `*.json` manifest
contains a runnable scenario). Run a Tier-3 evaluation to replay them:

```bash
nv-base validate skills/deployment/vss-setup-video-analytics-api --agent-eval
```

A minimal standalone bring-up looks like:

```bash
cd $REPO/deploy/docker
export VSS_APPS_DIR=$(pwd)
export VSS_DATA_DIR=${VSS_DATA_DIR:-/tmp/vss-data}
mkdir -p "$VSS_DATA_DIR/data_log/vss_video_analytics_api"
docker compose -f services/analytics/video-analytics-api/compose.yml up -d vss-video-analytics-api
curl -sf http://localhost:8081/livez
```

Follow [`references/deploy-video-analytics-api-service.md`](references/deploy-video-analytics-api-service.md) for the full
workflow (config source, data-log bind, infrastructure dependencies, REST endpoints).
For the field-by-field JSON config reference, see [`references/configuration.md`](references/configuration.md).

## Limitations

- Requires the matching VSS profile / microservice to be deployed and 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.

## Troubleshooting

The rest: https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/blob/HEAD/skills/deployment/vss-setup-video-analytics-api/SKILL.md