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How we index images for RAG

kapa.ai

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Re: How we index images for RAG

#12
Well I don't know if this one has been getting by others too but I have been doing this since 2 years ago and it works really well. Except the fact that for the documents I had to chunk containing these images I had to chase the authors(multiple of them) to update the relevant captions for their images. It is cost efficient than multi-modal. Lesser ingestion time altogether. Only part is that if the retrieval query is a question which can be answered only after looking at the image, then this architecture would need some little modification.

Re: How we index images for RAG

#13

why not a multimodal embedding model?

The article do mentions why they don't use multimodal retrieval. Also I think this approach is cheaper (compute wise) than multimodal retrieval. From the article:

  Multimodal retrieval does not suit this domain. CLIP-style embeddings wash out exactly the fine detail that matters in charts, tables, and annotated screenshots, and short technical queries ("how do I configure X") give too little signal to match against image vectors

Re: How we index images for RAG

#17

> we don't send images to the model at query time. We describe each image once, at indexing time, with a cheap vision model, store the descriptions as text, and retrieve them alongside ordinary text chunks This is what I've been doing in my Obsidian infodump for a while. If I know that an image is important, I generate a text description (Mermaid if possible, English if not) and paste it after the image in a block. T…

What does Mermaid text description of an image mean?

Descriptions of images that are charts or diagrams to start with?

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