  In this work, we propose the use of "aligned visual captions" as a mechanism
for integrating information contained within videos into retrieval augmented
generation (RAG) based chat assistant systems. These captions are able to
describe the visual and audio content of videos in a large corpus while having
the advantage of being in a textual format that is both easy to reason about &
incorporate into large language model (LLM) prompts, but also typically require
less multimedia content to be inserted into the multimodal LLM context window,
where typical configurations can aggressively fill up the context window by
sampling video frames from the source video. Furthermore, visual captions can
be adapted to specific use cases by prompting the original foundational model /
captioner for particular visual details or fine tuning. In hopes of helping
advancing progress in this area, we curate a dataset and describe automatic
evaluation procedures on common RAG tasks.
