1 link tagged with all of: retrieval augmented generation + pixelrag + vision language models + document indexing + ai
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PixelRAG skips HTML parsing by taking screenshots of pages and indexing image tiles with a vision-language model. It preserves tables, charts, and layout lost by text extractors, outperforming a top text-based RAG by 18.1% on Wikipedia and offering a live-page Claude Code plugin.
- PixelRAG indexes screenshots of pages instead of parsed HTML text, using Qwen3-VL-Embedding over image tiles with a FAISS index across 30M+ Wikipedia screenshots.
- It beats the best text-based RAG system by 18.1% on text QA because it preserves tables, charts, and layout that HTML parsers often drop (40%+ of content lost) or flatten, swinging accuracy by ~10 points.
- Ships as a Claude Code plugin that screenshots any URL, PDF, or local site and feeds pixels to a vision-language reader, with a single setup script and no extra backend.
- A separate described retrieval approach cuts corpus size 40x, reduces tokens per query 3x, and boosts vector-search relevance 2.3x through smarter chunking, dynamic indexing, and adaptive scoring.