Pixel-Native RAG: A Practical Guide to Visual Document Indexing
MarkTechPost 3 weeks ago 47 ● 2 sources
A tutorial describes building a pixel-native retrieval-augmented generation system that converts web pages and PDFs into image tiles, generates multimodal embeddings with vision models like SigLIP or CLIP, indexes them in FAISS, and retrieves relevant document sections via similarity search and hybrid ranking. The system uses 1024×1024 pixel tiles with 128-pixel overlap, combines dense embeddings with OCR-based BM25 scoring through reciprocal rank fusion, and optionally passes top-ranked evidence to vision-language models for answer generation. Users can evaluate retrieval quality via Recall@k metrics, train lightweight adapters with contrastive learning, and deploy the pipeline as a FastAPI service without relying on traditional HTML parsing or text extraction.
