RAG Frameworks · StarTrail-org

PixelRAG

PixelRAG is a Python framework that indexes documents as visual screenshots rather than text, enabling search over layout, tables, charts, and diagrams that traditional text parsing discards. It provides a pre-built index of 8.28M Wikipedia pages via a public API, plus tooling to build private indexes from PDFs, web pages, and local documents.

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Source: GitHub — github.com/StarTrail-org/PixelRAG

6.2k

GitHub stars

495

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	StarTrail-org/PixelRAG
Owner	StarTrail-org
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	6.2k
Forks	495
Open issues	31
Latest release	v0.3.0 (2026-06-23)
Last updated	2026-06-30
Source	https://github.com/StarTrail-org/PixelRAG

What PixelRAG is

PixelRAG renders documents to screenshot tiles, embeds them using a LoRA-fine-tuned Qwen3-VL-Embedding model, and stores vectors in FAISS indexes for retrieval. The pipeline is modular—pixelshot (Playwright/CDP-based rendering), chunk/embed/build-index stages, and a FastAPI server—with support for CUDA (Linux) and MPS (macOS). Pre-built Wikipedia index (~217GB) available via Hugging Face.

Quickstart

Get the PixelRAG source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/StarTrail-org/PixelRAG.git2cd PixelRAG3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Document retrieval where layout and visual structure matter

Tables, infographics, financial statements, technical diagrams, and charts that lose meaning in text parsing. PixelRAG preserves visual context that answers questions purely from how content looks.

Multi-modal RAG augmentation for Claude and vision models

The Claude Code plugin (pixelbrowse skill) replaces HTML parsing with screenshots, giving vision models access to rendered pages as humans see them—tables, sidebars, and layout intact.

Private or proprietary document search without text extraction

Index internal PDFs, reports, or web content locally; search visually without relying on OCR or brittle HTML parsing. Works offline on CPU (slow) or GPU-accelerated.

Implementation considerations

Embedding model (Qwen3-VL-Embedding-2B) must be downloaded from Hugging Face; requires valid tokenizer/model access and internet connectivity at index-build time.
Training pipeline is a separate uv project with pinned dependencies (torch==2.9.1+cu129, transformers==4.57.1); do not mix root environment—use `train/` directory.
FAISS index is memory-resident; 217GB Wikipedia index requires substantial RAM or disk-to-memory swapping. Scaling to larger collections may require partitioning or vector database migration.
Rendering via Playwright/CDP requires browser stack (Chromium) and may be slow on large document sets. Consider batch rendering or pre-computed tiles.
Config-driven pipeline (pixelrag.yaml) supports local files and HTTP sources; custom ingestion (databases, APIs) requires code extension.

When to avoid it — and what to weigh

You need very fast search latency on large indexes — Visual retrieval is computationally heavier than text BM25; pre-built Wikipedia index is 217GB. Latency on large datasets is not benchmarked in provided data.
Your documents are pure text with no visual structure — PixelRAG adds overhead for plain prose, emails, or logs where visual layout carries no meaning. Text-only RAG tools are simpler and lighter.
You need strict control over data residency or cannot download large indexes — Public API calls home to pixelrag.ai; pre-built Wikipedia index is 217GB. On-premises deployment requires manual index building and serving.
Your infrastructure lacks GPU or modern CPU resources — Embedding models (Qwen3-VL-2B) run on CPU but are slow; indexing ~3 min on Apple M-series, ~1 min on GPU. Training pipeline requires CUDA 12.9, cuDNN 9.20.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI license allowing commercial use, modification, and distribution under standard Apache terms (no trademark rights, liability disclaimer applies).

Apache-2.0 is a permissive open-source license that permits commercial use, but review the full license text to confirm compliance with your distribution model. Using the hosted public API (api.pixelrag.ai) is free and requires no license review. Redistribution or modification of the PixelRAG codebase itself must include license notice and the Apache-2.0 text. No commercial support or warranty implied.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

No security audit or threat model provided in source data. Rendering via Playwright/CDP exposes HTML parsing attack surface (e.g., malicious PDF, web page). Hosting images in FAISS index exposes visual content to anyone with index access—consider data sensitivity before indexing private documents. Public API (api.pixelrag.ai) processes queries over the internet; no privacy guarantees stated. Vision model (Qwen3-VL) loaded from Hugging Face—verify model integrity and upstream security. Standard OSS practices apply (pin dependencies, audit code before deploy).

Alternatives to consider

LangChain + text-based RAG (e.g., Chroma, Pinecone)

Lighter-weight, battle-tested, faster search, no GPU required. Best if documents are text-heavy; loses visual structure (tables, diagrams).

Multimodal RAG (e.g., ColPali, LLaVA + vector DB)

Similar visual-first approach but more modular; no pre-built indexes. Requires custom embedding and retrieval logic; gives more control.

Commercial document AI (e.g., Anthropic's Claude File API, LlamaIndex Document Intelligence)

Managed service, vendor-backed support, optimized for PDFs/documents. Costs money; less control over indexing and retrieval.

Software development agency

Build on PixelRAG with DEV.co software developers

Try the free public API, or download a pre-built Wikipedia index and run locally. No GPU required for basic search; GPU recommended for large indexes.

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PixelRAG FAQ

Can I use PixelRAG without a GPU?

Yes, but slowly. Embedding runs on CPU, taking ~3 min on Apple M-series, hours on slower CPUs. Use `device: auto` to select MPS (Mac) or CUDA (Linux). GPU is strongly recommended for large indexes.

Is the public API (api.pixelrag.ai) free and private?

The hosted Wikipedia index is free and no API key is required. However, queries are sent to their servers—not private. Self-host by downloading the FAISS index and running `pixelrag serve` locally.

Can I build an index from my own PDFs and web pages?

Yes. Use `pixelshot` to render documents to images, then `pixelrag index build` to create a FAISS index. Requires local GPU or CPU (slow). Serve locally with `pixelrag serve`.

How does PixelRAG compare to text-based RAG on my documents?

PixelRAG preserves layout, tables, and diagrams; text RAG discards them. Trade-off: visual retrieval is slower and more compute-intensive. No benchmark on your data is provided; test both.

Work with a software development agency

Adopting PixelRAG is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate rag frameworks software in production.

Index Your First Document in 5 Minutes

Try the free public API, or download a pre-built Wikipedia index and run locally. No GPU required for basic search; GPU recommended for large indexes.

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