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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.
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.
Get the PixelRAG source
Clone the repository and explore it locally.
git clone https://github.com/StarTrail-org/PixelRAG.gitcd PixelRAG# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
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 |
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.
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.
Talk to DEV.coRelated on DEV.co
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PixelRAG FAQ
Can I use PixelRAG without a GPU?
Is the public API (api.pixelrag.ai) free and private?
Can I build an index from my own PDFs and web pages?
How does PixelRAG compare to text-based RAG on my documents?
Custom software development, end to end
Evaluating PixelRAG is the easy part; running it is not. DEV.co offers software development services and AI software development services for rag frameworks teams that need a partner past the proof of concept.
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.