Multi-modal RAG on slide decks

Retrieval augmented generation (RAG) in large language model (LLM) app development has expanded to include visual content from slide decks, enabled by multi-modal LLMs like GPT-4V. This advancement allows for interactive chat and Q&A by retrieving and synthesizing information from visual data. Two main approaches to multi-modal RAG in slides are multi-modal embeddings, which involve embedding slide images to retrieve relevant content, and multi-vector retrievers, which summarize images before retrieval. While multi-modal embeddings offer simplicity, they face challenges in distinguishing visually similar slides, whereas image summarization, though more complex and costly, enhances retrieval accuracy. A public benchmark evaluation using a Datadog presentation demonstrated that multi-modal methods significantly outperform text-only RAG, achieving accuracy scores of 60% and 90% compared to 20% for text-only approaches. The study also highlighted the effectiveness of GPT-4V in extracting structured data from images and emphasized the importance of accurate image retrieval for successful question answering. To support further exploration and deployment, a template leveraging Chroma and OpenCLIP multi-modal embeddings has been released.

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