Pixel-Native RAG system indexes visual documents using multimodal embeddings

This tutorial details the creation of a "Pixel-Native RAG" system for visual document indexing. The process involves rendering web pages and PDFs as images, segmenting them into tiles, and generating multimodal embeddings using models like SigLIP or Qwen3-VL. These embeddings are stored in a FAISS vector database for efficient similarity search, enhanced by OCR-based BM25 scoring and reciprocal rank fusion. The system can aggregate tile-level evidence into document-level results and optionally pass strong evidence to a vision-language model for grounded answer generation. AI

IMPACT Enables more robust document retrieval by leveraging visual information alongside text, potentially improving AI applications that process scanned documents or image-heavy content.

RANK_REASON The item describes a practical guide and implementation of a specific retrieval-augmented generation technique for visual documents.

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• BM25
• Faiss
• FastAPI
• Pixel-Native RAG
• Qwen2.5-VL-3B-Instruct
• Qwen3 VL
• retrieval-augmented generation
• SigLIP
• Transformer++
• vector database

• product
• infra

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