Towards Retrieval Augmented Generation over Large Video Libraries
Authors: name: Yannis Tevissen, name: Khalil Guetari, name: Frédéric Petitpont

Video content creators need efficient tools to repurpose content, a task that
often requires complex manual or automated searches. Crafting a new video from
large video libraries remains a challenge. In this paper we introduce the task
of Video Library Question Answering (VLQA) through an interoperable
architecture that applies Retrieval Augmented Generation (RAG) to video
libraries. We propose a system that uses large language models (LLMs) to
generate search queries, retrieving relevant video moments indexed by speech
and visual metadata. An answer generation module then integrates user queries
with this metadata to produce responses with specific video timestamps. This
approach shows promise in multimedia content retrieval, and AI-assisted video
content creation.