Pixel-Native RAG: A Practical Guide to Visual Document Indexing
In this tutorial, we build a complete pixel-native retrieval-augmented generation pipeline from scratch and examine how document retrieval works without relying on conventional HTML parsing, text extraction, or fixed chunking strategies. We render web pages and PDF documents as images, divide them into overlapping tiles, generate multimodal embeddings with SigLIP, CLIP, or an optional Qwen3-VL backend, and store the resulting vectors in a FAISS index for efficient similarity search. We also str
Get smart on it
Pixel-native RAG is a system for searching documents by converting them into images, dividing them into overlapping tiles, and generating embeddings that enable similarity-based retrieval without relying on text extraction or HTML parsing. The approach matters because it preserves visual layout and formatting information that traditional text-based methods lose, particularly for complex documents like PDFs and scanned pages with tables and mathematical notation. The system combines image-based dense retrieval with OCR-based sparse scoring through reciprocal rank fusion, stores results in a FAISS index for efficient search, and can optionally use vision-language models to generate answers grounded in the retrieved visual evidence.
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