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.

通过使用大型语言模型（LLM）生成搜索查询，检索由语音和视觉元数据索引的相关视频片段，并将用户查询与此元数据集成以生成具有特定视频时间戳的响应，我们提出了一种在视频库中应用检索增强生成（RAG）的可互操作体系结构，该方法公有多媒体内容检索和人工智能辅助视频内容创建中潜在应用。