Multimodal RAG

Much organizational knowledge is not clean prose. It lives in slide decks, scanned contracts, engineering drawings, financial tables and charts inside PDFs. A text-only pipeline typically runs OCR or a PDF parser, discards layout and chunks the output. Tables become jumbled rows, chart values vanish, and a diagram contributes nothing at all. Multimodal RAG keeps visual information retrievable. There are three broad approaches. The first converts everything to text: extract tables into Markdown or HTML, use a vision-language model to write descriptions of images and charts, and index those with ordinary text embeddings. It is simple and fits existing infrastructure, but anything the captioner misses is lost. The second embeds images and text in a shared vector space, as CLIP-style models do, so a text query can retrieve a photo or figure directly. This suits image-heavy collections such as product catalogs, but general image embeddings are weak at reading dense text inside documents. The third treats each page as a screenshot. ColPali, published in 2024, embeds page images with a vision-language model and scores them against queries using late interaction, comparing many query-token vectors with many image-patch vectors in the style of ColBERT. It skips OCR and layout parsing entirely, and it performed strongly on the ViDoRe document-retrieval benchmark introduced alongside it. Retrieval is only half the job. The generator must also see the content, so answers usually come from a vision-capable model given the page image or cropped figure, not just a caption. A common misconception is that multimodal RAG means abandoning text pipelines. In practice, hybrid systems that combine parsed text, table extraction and page-image retrieval are common, because each method catches failures of the others.