# 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.

## Install

Save as a script file and run:

## Quick Use

```bash
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.

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## 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

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## 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.

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### 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.

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## Source & Thanks

> Created by [InfiniFlow](https://github.com/infiniflow). Licensed under Apache 2.0.
> [infiniflow/ragflow](https://github.com/infiniflow/ragflow) — 77,000+ GitHub stars

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Source: https://tokrepo.com/en/workflows/7785d7a8-fc57-42ab-ba6b-4a970404fadc
Author: Script Depot