videorag

We have hosted the application videorag in order to run this application in our online workstations with Wine or directly.

Quick description about videorag:

VideoRAG is a retrieval-augmented generation (RAG) framework tailored for Video content that enables AI systems to answer questions, summarize, and reason over long videos by combining visual embeddings with contextual search. The system works by first breaking Video into clips, extracting visual and audio-textual features, and indexing them into embeddings, then using an LLM with a retriever to pull relevant segments on demand. When a user query is received, VideoRAG locates semantically relevant moments in the Video using the embedding index, retrieves associated clips or transcripts, and feeds them to a generative model to produce accurate, grounded answers or summaries. This approach allows it to handle videos of arbitrary length without requiring the entire content to be passed into the model at once, overcoming token limits and enabling detailed, context-aware interaction.

Features:

• Multi-modal Video embedding and indexing
• Retriever that scales to long videos
• LLM-powered question answering on Video content
• Summarization and relevance scoring
• Support for both visual features and speech transcripts
• Searchable semantic index of Video clips

Programming Language: Python.
Categories: