visual document search / pixel-native RAG
PixelRAG renders documents (web pages, PDFs, images) as screenshots and retrieves over the resulting visual index for RAG, enabling search by visual appearance rather than text extraction.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Use when you need mature OCR text extraction (100+ languages) from PDFs/images instead of a visual retrieval index.
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
Use when you want clean structured document data for LLMs through parsing rather than pixel-based search.
Get your documents ready for gen AI
Use for one-shot long-horizon parsing of documents into text/data instead of screenshot retrieval.
Unlimited OCR Works: Welcome the Era of One-shot Long-horizon Parsing.
Use when you need an ETL pipeline that converts complex documents to structured formats for LLMs, not a visual index.
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
Use for fast, open-source document parsing to LLM-ready structured data rather than visual retrieval.
A fast, helpful, and open-source document parser
Use for multilingual document layout parsing into structured data with a single vision-language model.
Multilingual Document Layout Parsing in a Single Vision-Language Model
Use for academic document image parsing via heterogeneous anchor prompting, not as a search engine.
The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.
Use for a lightweight, actively maintained OCR library for text and document images.
docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning. Ongoing development and maintenance by t2k.
Use for lightweight LMM-based document parsing to text, not visual retrieval.
A lightweight LMM-based Document Parsing Model
Use when you need text-based web scraping/search for LLM context rather than pixel-based visual retrieval.
The context API to search, scrape, and interact with the web at scale. 🔥
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Use only if you want an older, unmaintained OCR-free document understanding model for research; previous generation.
Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
These projects were analysed and named PixelRAG among their alternatives. The relationship is not symmetric — how PixelRAG rates them is a separate judgement, made when PixelRAG is analysed in its own right.
calls PixelRAG “choose when you want pixel-native document search instead of text-based parsing RAG.”
High accuracy RAG for answering questions from scientific documents with citations
calls PixelRAG “Choose for pixel-based document retrieval, removing the need for traditional parsing/embedding.”
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
calls PixelRAG “pixel-native web/document parsing for RAG, different indexing approach”
A lightweight LMM-based Document Parsing Model
calls PixelRAG “Choose it for pixel-native visual retrieval of rendered documents when text extraction and connector sync are not required.”
Open-source context retrieval layer for AI agents