A multimodal Retrieval-Augmented Generation (RAG) pipeline can be built over PDFs using ColQwen2, which embeds PDF pages as images without requiring OCR or layout detection. These embeddings are stored in a Weaviate vector database, and text queries are embedded similarly with ColQwen2 to retrieve relevant pages, while Qwen2.5-VL generates answers based on both text and visual content.
Multimodal RAG over PDFs with ColQwen2 (without any OCR, layout detection, or chunking). In this tutorial, my colleague Tobias Christiani and I show you how to build a multimodal RAG pipeline over PDFs: • Embed screenshots of PDF pages as images with a multimodal late-interaction model, ColQwen2 • Store in a Weaviate vector database • Embed text query with ColQwen2 to retrieve PDFs from the database at query time • Generate an answer with VLM Qwen2.5-VL GitHub: https://lnkd.in/eBAgxJ-b
