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name: nvidia-video-search-and-summarization-video-understanding description: Call the vss agent to run video understanding on video to answer a text question. Use when the user asks about video content, or about visual details that cannot be answered from conversation history, search hits, or metadata alone. license: Apache-2.0 metadata: version: 3.1.0 github-url: https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization tags: nvidia blueprint operational author: NVIDIA

Video QnA using VLM through VSS Agent

Use this skill when you need details about the video which requires VLM to look at the video frames — for example the agent has no usable prior answer and needs a fresh look at the pixels for a specific clip.

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When to Use

• The user asks what happens in the video, what objects / people / actions appear, colors, timing, safety, or other visual facts that require watching the clip.
• The user asks for details that cannot be answered from existing messages, summaries, Elasticsearch/MCP results, or filenames alone—you need model inference on the video.
• Follow-up questions about content details after a coarse summary or after report generation.

Do not use this skill when a database / MCP / prior tool output already answers the question, unless the user explicitly wants verification against the video.

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Deployment prerequisite

This skill requires a VSS profile that serves the video_understanding tool — typically base (recommended) or lvs. Before any request:

1. Probe the VSS agent:

   curl -sf --max-time 5 "http://${HOST_IP}:8000/docs" >/dev/null
   

2. If the probe fails, ask the user:

   "No VSS profile is running on $HOST_IP. Shall I deploy base (recommended for per-clip VLM QnA) using the /deploy skill? If you prefer lvs, say so."

   • If yes → hand off to /deploy -p base (or -p lvs if the user prefers). Return here once it succeeds.
   • If no → stop.

   (If your caller has granted explicit pre-authorization to deploy autonomously — e.g. the request says "pre-authorized to deploy prerequisites", or you are running in a non-interactive evaluation harness with that permission — skip the confirmation and invoke /deploy -p base directly. Prefer base unless the request names another profile.)

3. If the probe passes, proceed.

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Agent workflow

1. Clip — Identify sensor id, filename, or URL for one video segment. If ambiguous, ask the user.
2. Call vss agent with the sensor id and ask for it to call video_understanding tool to answer the user's question. The sensor / file name must be included in the input message to the agent.
3. Return the vss agent's answer back to the user.

Query VSS agent (/generate)

# Set from deployment (compose / .env / host where vss-agent listens)
export VSS_AGENT_BASE_URL="http://localhost:8000"

curl -s -X POST "${VSS_AGENT_BASE_URL}/generate" \
  -H "Content-Type: application/json" \
  -d '{"input_message": "Call video_understanding tool to answer the following question about <sensor-id>: <user query>"}' | jq .

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Cross-Reference

• vios — VST storage/replay URLs so VIDEO_URL is valid for the VLM.
• report — timestamped reports via the VSS agent (/generate); this skill is direct VLM for ad-hoc video Q&A.