#2525th of 32 in RAG, knowledge and searchFor data and documents

pixelrag

Visual RAG that renders pages and PDFs to screenshots and searches the images

Stars
10.3k
License
Apache-2.0
Last commit
Oct 2026
Last release
Jul 2026
Language
Python
Stars, last 21 weeks+10,322
26 May 2026: 1 stars2 Jun 2026: 43 stars9 Jun 2026: 47 stars16 Jun 2026: 267 stars23 Jun 2026: 4,667 stars30 Jun 2026: 5,654 stars7 Jul 2026: 6,122 stars14 Jul 2026: 6,589 stars21 Jul 2026: 6,981 stars28 Jul 2026: 7,457 stars4 Aug 2026: 9,089 stars11 Aug 2026: 9,397 stars18 Aug 2026: 9,559 stars25 Aug 2026: 9,694 stars1 Sept 2026: 9,811 stars8 Sept 2026: 9,879 stars15 Sept 2026: 9,949 stars22 Sept 2026: 10,059 stars29 Sept 2026: 10,101 stars6 Oct 2026: 10,196 stars13 Oct 2026: 10,264 stars20 Oct 2026: 10,323 stars

1 on 26 May 202610,323 on 20 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

PixelRAG renders web pages, PDFs and images to screenshot tiles with Playwright/CDP, embeds them with a LoRA-fine-tuned Qwen3-VL-Embedding-2B, and serves FAISS (or Qdrant) search through a FastAPI endpoint. Tables, charts and layout are kept because nothing is parsed to text. It also ships a pixelbrowse plugin for Claude Code and opencode, and a hosted index of 8.28M Wikipedia pages.

Who it is for: Engineers building document search where tables, charts and layout matter

Strengths

  • Retrieves over screenshots, so tables, charts and layout survive indexing
  • Hosted API over 8.28M Wikipedia pages, no setup or API key
  • Stages run standalone: pixelshot, chunk, embed, build-index, serve
  • Runs on Linux CUDA and Apple Silicon MPS; FAISS default, optional Qdrant backend

Weaknesses

  • Pre-built base Wikipedia index is about 217G to download
  • Docker image is not built in CI and runs Chrome without its sandbox
  • Training is a separate uv project with pinned torch 2.9.1+cu129
  • Bundled turbo headless_shell auto-installs on linux-x64 only

What it needs

  • GPU optional
  • Docker + Compose
  • Needs Chromium/Chrome (Playwright), FAISS, poppler (PDF), Qdrant (optional)
  • Models: Qwen3-VL-Embedding-2B, LoRA adapters on Hugging Face
  • port 30001

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