Multimodal Retrieval-Augmented Generation (RAG) with Document Retrieval (ColPali) and Vision Language Models (VLMs)

🚨 WARNING: This notebook is resource-intensive and requires substantial computational power. If you’re running this in Colab, it will utilize an A100 GPU.

In this notebook, we demonstrate how to build a Multimodal Retrieval-Augmented Generation (RAG) system by combining the ColPali retriever for document retrieval with the Qwen2-VL Vision Language Model (VLM). Together, these models form a powerful RAG system capable of enhancing query responses with both text-based documents and visual data.

Instead of relying on a complex document processor pipeline that extracts data through OCR, we will leverage a Document Retrieval Model to efficiently retrieve the relevant documents based on a specific user query.

I also recommend checking out and starring the smol-vision repository, which inspired this notebook—especially this notebook. For an introduction to RAG, you can check this other cookbook!

Open-Source AI Cookbook

Multimodal Retrieval-Augmented Generation (RAG) with Document Retrieval (ColPali) and Vision Language Models (VLMs)