PixelRAG
PixelRAG is an open-source visual RAG system that renders documents into screenshot tiles and retrieves over images instead of flattening web pages, PDFs, and visual documents into text.
Overview
PixelRAG helps AI systems retrieve from web pages, PDFs, images, tables, charts, and layouts by preserving visual structure as screenshots instead of relying only on HTML parsing or text extraction.
Core Features & Capabilities
Ideal for AI engineers, RAG builders, search teams, AI agent developers, VLM researchers, data engineers, document AI teams, knowledge-base builders, open-source developers, enterprise AI teams, research labs, and developers working with visually complex pages, PDFs, dashboards, tables, diagrams, scientific papers, or web documents.
- Render web pages, PDFs, and documents into screenshot tiles instead of lossy plain-text chunks
- Search a hosted visual Wikipedia index using text, image, or hybrid multimodal queries
- Build local visual indexes with rendering, chunking, embedding, FAISS indexing, and serving pipelines
- Connect PixelRAG to agent frameworks through a plain HTTP API for visual retrieval workflows
- Preserve tables, charts, diagrams, layout, and visual context that standard text parsers often discard
Trending Use Cases
Why AI Builders Watch PixelRAG
Visit the PixelRAG website to try visual Wikipedia search or review the API documentation for text, image, and hybrid search. Developers can install PixelRAG from Python packages, use pixelshot to render pages or PDFs into tiles, query the hosted API, or build a local pipeline with rendering, embedding, FAISS indexing, and serving. For agent workflows, connect the search and tile endpoints as tools so the agent can search visually, fetch relevant screenshot tiles, and answer from what the page shows.
“PixelRAG treats documents as visual objects, preserving the tables, charts, diagrams, and layout signals that text-only RAG pipelines often lose.”
Getting Started with PixelRAG
By combining screenshot rendering, visual embeddings, hosted search APIs, local FAISS indexing, VLM-compatible retrieval, and agent integration, PixelRAG gives AI builders a practical way to search documents by visual structure instead of relying only on text extraction.
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PixelRAG
StarTrail-org is the open-source organization behind PixelRAG, a pixel-native visual retrieval system for screenshot-based RAG, visual search, document rendering, VLM workflows, local indexing, hosted APIs, and AI agent integrations.
PixelRAG is an open-source pixel-native retrieval system for AI agents, developers, and researchers who need RAG over visually complex documents. Instead of parsing web pages or PDFs into plain text, PixelRAG renders documents as screenshot tiles, embeds the visual content, retrieves relevant tiles, and lets vision-language models answer from what the page actually shows. The hosted PixelRAG API includes a pre-built visual Wikipedia index, while the open-source pipeline supports local rendering, chunking, embedding, FAISS index building, serving, and agent integration. PixelRAG is useful for search over tables, charts, diagrams, layouts, infographics, PDFs, and web pages where text extraction loses important context.
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