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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
NVIDIA AI Blueprint for video search and summarization (VSS) is a GPU-accelerated reference architecture for building video analytics agents with real-time verified alerts, visual Q&A, and automated reporting. The VSS Blueprint uses vision language models (VLMs) such as NVIDIA Cosmos, LLMs such as NVIDIA Nemotron, RAG, and NVIDIA NIMs.
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Today
+1 stars today
This week
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This month
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Momentum
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growth rate 0.51%/day
<h2>NVIDIA AI Blueprint: Video Search and Summarization (VSS)</h2> **Build GPU-accelerated video AI agents that search, analyze, summarize, and reason over live or recorded video using natural language.** NVIDIA AI Blueprint for Video Search and Summarization (VSS) combines vision-language models, RAG, and NVIDIA NIM microservices to deliver real-time video analytics, visual Q&A, alert verification, clip retrieval, and long-video summarization. - Search video streams or archives using natural language queries - Summarize hours of video - Ask visual questions and automatically generate reports - Detect and verify real-time alerts with VLMs **[🚀 Try the Demo](https://build.nvidia.com/nvidia/video-search-and-summarization)** · **[⚡ Quickstart](#quickstart-guide)** · **[📚 Documentation](https://docs.nvidia.com/vss/latest/index.html)** · **[🏗️ Architecture](#software-components)** · **[📦 Latest Release](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/releases/latest)** ### Table of Contents - [Overview](#overview) - [Use Case / Problem Description](#use-case--problem-description) - [Agent Workflows](#agent-workflows) - [Software Components](#software-components) - [Target Audience](#target-audience) - [Repository Structure Overview](#repository-structure-overview) - [Documentation](#documentation) - [Prerequisites](#prerequisites) - [Hardware Requirements](#hardware-requirements) - [Quickstart Guide](#quickstart-guide) - [Contributing](#contributing) - [License](#license) ## Overview The [NVIDIA Blueprint for Video Search and Summarization (VSS)](https://docs.nvidia.com/vss/latest/index.html) provides a suite of reference architectures for building vision agents and AI-powered video analytics applications. Those architectures bring together accelerated vision microservices, vision language models (VLMs), and large language models (LLMs) so you can use them in existing applications, as standalone microservices, or as part of a larger vision agent. VSS is organized into three areas of processing and analysis: **real-time video intelligence** (feature extraction, embeddings, and stream understanding with results published to a message broker), **downstream analytics** (enrichment of metadata into trajectories, incidents, and verified alerts), and **agentic and offline processing** (orchestrated tools for search, Q&A, summarization, and clip retrieval, including via the Model Context Protocol). This repository implements the blueprint and powers the [NVIDIA build experience](https://build.nvidia.com/nvidia/video-search-and-summarization) for natural-language video agents—search, summarization, visual Q&A, and related workflows—backed by generative AI, VLMs and LLMs, and [NVIDIA NIM](https://build.nvidia.com/) microservices as configured in the stacks below. ## Use Case / Problem Description The NVIDIA AI Blueprint for Video Search and Summarization addresses the challenge of deploying visual agents capable of interacting with large volumes of video data, both stored and streamed. This can be used to create vision AI agents, that can be applied to a multitude of use cases such as monitoring smart spaces, warehouse automation, and SOP validation. This is important where quick and accurate video analysis can lead to better decision-making and enhanced operational efficiency. ## Agent Workflows We provide multiple reference [Agent Workflows](https://docs.nvidia.com/vss/latest/agent-workflows.html) which demonstrate how the individual components can be leveraged by an agent: | Workflow | Description | |----------|-------------| | [Q&A and Report Generation (Quickstart)](https://docs.nvidia.com/vss/latest/quickstart.html) | Video retrieval, VLM-based Q&A, and report generation on short video clips | | [Alert Verification](https://docs.nvidia.com/vss/latest/agent-workflow-alert-verification.html) | Realtime processing of videos using perception (object detection, tracking) and behavior analytics to gene
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:462098897bc79d8d, topic:vision-language-model, topic:vlm, desc:vision-language
matched fp:462098897bc79d8d, topic:computer-vision, readme:object detection
matched fp:462098897bc79d8d, topic:rag, topic:retrieval-augmented-generation
matched fp:462098897bc79d8d, topic:model-context-protocol, readme:model context protocol