内容摘录
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<a href="https://ragflow.io/docs/dev/">Document</a> |
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<a href="https://demo.ragflow.io">Demo</a>
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<details open>
<summary><b>📕 Table of Contents</b></summary>
💡 What is RAGFlow?
🎮 Demo
📌 Latest Updates
🌟 Key Features
🔎 System Architecture
🎬 Get Started
🔧 Configurations
🔧 Build a Docker image
🔨 Launch service from source for development
📚 Documentation
📜 Roadmap
🏄 Community
🙌 Contributing
</details>
💡 What is RAGFlow?
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. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
🎮 Demo
Try our demo at https://demo.ragflow.io.
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🔥 Latest Updates
2025-12-26 Supports 'Memory' for AI agent.
2025-11-19 Supports Gemini 3 Pro.
2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive.
2025-10-23 Supports MinerU & Docling as document parsing methods.
2025-10-15 Supports orchestrable ingestion pipeline.
2025-08-08 Supports OpenAI's latest GPT-5 series models.
2025-08-01 Supports agentic workflow and MCP.
2025-05-23 Adds a Python/JavaScript code executor component to Agent.
2025-05-05 Supports cross-language query.
2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
🎉 Stay Tuned
⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new
releases! 🌟
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🌟 Key Features
🍭 **"Quality in, quality out"**
Deep document understanding-based knowledge extraction from unstructured data with complicated
formats.
Finds "needle in a data haystack" of literally unlimited tokens.
🍱 **Template-based chunking**
Intelligent and explainable.
Plenty of template options to choose from.
🌱 **Grounded citations with reduced hallucinations**
Visualization of text chunking to allow human intervention.
Quick view of the key references and traceable citations to support grounded answers.
🍔 **Compatibility with heterogeneous data sources**
Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
🛀 **Automated and effortless RAG workflow**
Streamlined RAG orchestration catered to both personal and large businesses.
Configurable LLMs as well as embedding models.
Multiple recall paired with fused re-ranking.
Intuitive APIs for seamless integration with business.
🔎 System Architecture
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