RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine designed for deep document understanding and truthful question-answering. It targets businesses and individuals seeking to extract reliable insights from complex, multi-format data, offering a streamlined RAG workflow with grounded citations and reduced hallucinations.

How It Works

RAGFlow employs a deep document understanding approach to extract knowledge from unstructured data, including complex formats and even images within documents. It supports handling virtually unlimited token contexts and features template-based chunking for intelligent, explainable data segmentation. The system prioritizes grounded citations with visualizations for traceability and offers a configurable RAG orchestration with multiple recall and fused re-ranking strategies.

Quick Start & Requirements

• Install/Run: Docker Compose is the primary method.
• Prerequisites: CPU >= 4 cores, RAM >= 16 GB, Disk >= 50 GB, Docker >= 24.0.0, Docker Compose >= v2.26.1. Requires vm.max_map_count set to at least 262144.
• Setup: Cloning the repo and running docker compose -f docker-compose.yml up -d (CPU) or docker compose -f docker-compose-gpu.yml up -d (GPU).
• Docs: Documentation

Highlighted Details

• Supports multi-modal models for understanding images within PDFs/DOCX.
• Integrates with Internet search (Tavily) for deep research capabilities.
• Handles heterogeneous data sources including Word, slides, Excel, images, structured data, and web pages.
• Offers text-to-SQL statement generation via RAG.

Maintenance & Community

• Active development with frequent updates.
• Community channels: Discord, Twitter, GitHub Discussions.
• Roadmap available.

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Suitable for commercial use and integration with closed-source applications.

Limitations & Caveats

• Docker images are built for x86 platforms; ARM64 requires custom builds.
• Switching the document engine to Infinity is not officially supported on Linux/arm64.