Verified project record

infiniflow/ragflow

RAGFlow provides a retrieval-augmented generation workflow focused on deep document understanding, processing complex unstructured formats to extract knowledge with traceable citations. It requires external LLM and embedding services to operate.

Project overview

Project type: AI Agent · RAG · Document Processing
Use cases: Knowledge Q&A
Deployment: Docker / Docker Compose
License: Apache-2.0

Best for

Developers and data teams building enterprise RAG systems who need to process complex, unstructured documents and require configurable LLMs.
Enterprise teams needing grounded answers with traceable citations to reduce LLM hallucinations.

Key capabilities

Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.

Limitations and risks

All Docker images are built for x86 platforms. Docker images for ARM64 are not currently offered.
The image relies on external LLM and embedding services to function, requiring access to external or self-hosted model APIs.

Getting started

Clone the repository, navigate to the docker directory, and check out a specific version tag. Set vm.max_map_count to at least 262144 before starting the server with docker compose.
Log in via web browser after server startup. Coding is optional, but configuring LLM API keys is required.

Evidence and sources

GitHub project description: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
README: RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
README: Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
README: Streamlined RAG orchestration catered to both personal and large businesses.
README: Quick view of the key references and traceable citations to support grounded answers.

Reviewed at: 2026-07-17T04:34:45Z