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Merve Noyan PRO

merve

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Post

3901

I have put together a notebook on Multimodal RAG, where we do not process the documents with hefty pipelines but natively use:
- vidore/colpali for retrieval 📖 it doesn't need indexing with image-text pairs but just images!
- Qwen/Qwen2-VL-2B-Instruct for generation 💬 directly feed images as is to a vision language model with no processing to text!
I used ColPali implementation of the new 🐭 Byaldi library by @bclavie 🤗
https://github.com/answerdotai/byaldi
Link to notebook: https://github.com/merveenoyan/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb

Post

2756

If you have documents that do not only have text and you're doing retrieval or RAG (using OCR and LLMs), give it up and give ColPali and vision language models a try 🤗

Why? Documents consist of multiple modalities: layout, table, text, chart, images. Document processing pipelines often consist of multiple models and they're immensely brittle and slow. 🥲

How? ColPali is a ColBERT-like document retrieval model built on PaliGemma, it operates over image patches directly, and indexing takes far less time with more accuracy. You can use it for retrieval, and if you want to do retrieval augmented generation, find the closest document, and do not process it, give it directly to a VLM like Qwen2-VL (as image input) and give your text query. 🤝

This is much faster + you do not lose out on any information + much easier to maintain too! 🥳

Multimodal RAG merve/multimodal-rag-66d97602e781122aae0a5139 💬
Document AI (made it way before, for folks who want structured input/output and can fine-tune a model) merve/awesome-document-ai-65ef1cdc2e97ef9cc85c898e 📖

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