Multimodal RAG for Slide Decks

Unlike traditional text-centric RAG applications, slide decks contain a large amount of visual information, making the use of multimodal LLMs essential. LangChain presented two major approaches for implementing slide deck-based RAG.

The first is the multimodal embedding approach. Slides are extracted as images and vectorized using a multimodal embedding model to retrieve relevant slides. While the structure is simple, there may be limitations in accurately retrieving visually similar charts or tables.

The second is the multi-vector retriever approach. It uses GPT-4V to summarize each slide image into text, then embeds that text for retrieval. This leverages the maturity of text embedding models to enable detailed descriptions, but increases complexity and cost due to the image summarization process.

LangChain provides a public benchmark to compare the pros and cons of these two approaches, along with a template for quickly building multimodal RAG apps for slide decks.