2 links tagged with all of: retrieval augmented generation + pixelrag
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PixelRAG converts web pages, PDFs, and images into screenshot tiles, embeds them using a fine-tuned Qwen3-VL model, and builds vector indexes for retrieval-augmented generation. It provides a CLI (pixelshot), pipeline commands for chunking, embedding, indexing, and serving (FAISS or Qdrant), plus a live API and a Claude plugin for visual search.
- PixelRAG ditches HTML parsing entirely, indexing screenshot tiles of pages/PDFs via a fine-tuned Qwen3-VL embedding model to preserve tables, charts, and layout
- Ships a pre-built index of 8.28 million Wikipedia articles, usable via a free live API or a ~217GB local download
- Includes a Claude Code plugin ("pixelbrowse") that lets Claude screenshot live pages and read diagrams/tables visually on request
- Supports both FAISS (simple/local) and Qdrant (disk-backed, quantized, multi-server) as swappable backends for scaling
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.