ScriptsMar 31, 2026·2 min read

RAGFlow — Deep Document Understanding RAG Engine

Open-source RAG engine with deep document understanding. Parses complex PDFs, tables, images. Agent-powered Q&A with citations. Multi-model. 77K+ stars.

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Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

git clone https://github.com/infiniflow/ragflow.git
cd ragflow
docker compose -f docker/docker-compose.yml up -d

Open http://localhost and create an account. Upload documents and start asking questions.

Intro

RAGFlow is a leading open-source RAG engine that combines deep document understanding with agent capabilities. Unlike basic RAG that chunks text blindly, RAGFlow uses vision models to understand document layouts — tables, images, charts, multi-column text, and complex formatting. Provides accurate, cited answers with full source traceability. 77,000+ GitHub stars, Apache 2.0.

Best for: Enterprise document Q&A, knowledge bases with complex PDFs, legal/financial document analysis Works with: OpenAI, Anthropic, Ollama, Azure, any OpenAI-compatible API

Key Features

Deep Document Parsing

Vision-based layout analysis that understands:

  • Complex tables (merged cells, nested headers)
  • Embedded images and charts
  • Multi-column layouts
  • Headers, footers, page numbers
  • Mathematical formulas

Accurate Citations

Every answer includes exact source references — page number, paragraph, and highlighted text.

Agent Workflows

Built-in agent for multi-step reasoning, web search augmentation, and tool use.

Template-Based Chunking

Document-type-aware chunking strategies — different templates for papers, resumes, contracts, manuals.

Multi-Model Support

OpenAI, Anthropic, Google, Ollama, Azure, DeepSeek, Zhipu, and custom endpoints.

Knowledge Base Management

Web UI for uploading, organizing, and managing document collections across teams.

FAQ

Q: What is RAGFlow? A: An open-source RAG engine with deep document understanding. Parses complex PDFs with tables, images, and charts for accurate Q&A with citations. 77K+ GitHub stars.

Q: How is RAGFlow different from basic RAG? A: Basic RAG splits text into chunks by character count. RAGFlow uses vision models to understand document structure — tables remain as tables, images are captioned, and layouts are preserved.

🙏

Source & Thanks

Created by InfiniFlow. Licensed under Apache 2.0. infiniflow/ragflow — 77,000+ GitHub stars

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