Video RAG
byFiloHany
0Enables semantic search through video content, extracting relevant video chunks based on natural language queries.
Acerca de
Video RAG is a powerful video retrieval and analysis system that leverages the Ragie API to process, index, and query video content with natural language. It facilitates semantic search through vast video libraries, allowing users to pinpoint and extract relevant video segments based on text queries. The project demonstrates how to build a video-based Retrieval Augmented Generation (RAG) system powered by the Model Context Protocol (MCP), integrating Ragie's video ingestion and retrieval capabilities as MCP tools within the Cursor IDE for seamless AI assistant interaction.
Características Principales
- Video Processing with audio-video analysis
- Semantic Search for natural language queries
- Video Chunking to extract specific segments
- MCP Integration for AI assistant interaction
- Jupyter Notebook Support for interactive development
- 1 GitHub stars
Casos de Uso
- Retrieve specific video segments by asking natural language questions
- Automate the creation of short video clips from longer content based on query results
- Ingest and index video data for comprehensive search capabilities
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