by NVIDIA-AI-Blueprints · MCP Server · ★ 1.8k
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NVIDIA AI Blueprint: Video Search and Summarization (VSS) Table of Contents Overview Use Case / Problem Description Agent Workflows Software Components Target Audience Repository Structure Overview Documentation Prerequisites Hardware Requirements Quickstart Guide Contributing License Overview The NVIDIA Blueprint for Video Search and Summarization (VSS) 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 Mode
| Stars | 1,789 |
| Forks | 370 |
| Language | C++ |
| Category | MCP Server |
| Quality Score | 56.1682996156658/100 |
| Open Issues | 135 |
| Last Updated | 2026-08-09 |
| Created | 2024-10-22 |
| Platforms | mcp |
| Est. Tokens | ~19k |
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video-search-and-summarization is 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 r. It is categorized as a MCP Server with 1.8k GitHub stars.
video-search-and-summarization is primarily written in C++. It covers topics such as computer-vision, generative-ai, long-video-understanding.
You can find installation instructions and usage details in the video-search-and-summarization GitHub repository at github.com/NVIDIA-AI-Blueprints/video-search-and-summarization. The project has 1.8k stars and 370 forks, indicating an active community.
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