infiniflow/ragflow

What it solves

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine designed to transform complex, unstructured data into production-ready AI systems. It addresses the challenge of extracting high-fidelity knowledge from documents with complicated formats, reducing hallucinations by providing grounded citations and traceable references.

How it works

The system uses a converged context engine and a Go-native service architecture. It employs "DeepDoc" for layout analysis, OCR, and table recognition to ensure high-quality data extraction. The workflow includes template-based chunking for explainable data splitting, multiple recall methods paired with fused re-ranking, and an "Agentic Retrieval" system that can break down complex questions and perform multi-step reasoning across different thinking modes (Low to Ultra).

Who it’s for

It is designed for developers and enterprises of any scale who need to build AI systems that rely on high-precision retrieval from large volumes of heterogeneous data sources (such as Word, Excel, PDFs, and web pages).

Highlights

• Knowledge Compilation: Converts documents and datasets into structured artifacts like Wikis, Graphs, Mind Maps, and Timelines.
• Agentic RAG: Supports multi-step retrieval and reasoning with adjustable thinking depth.
• Deep Document Understanding: High-fidelity extraction from unstructured data using layout analysis and OCR.
• Heterogeneous Data Support: Compatible with a wide range of formats including scanned copies, slides, and structured data.
• Grounded Citations: Visualizes text chunking and provides traceable references to minimize hallucinations.