infiniflow / ragflow

💡 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.

什么是 RAGFlow？ RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，它将前沿的 RAG 技术与智能体（Agent）能力相结合，为大语言模型（LLM）构建了卓越的上下文层。它提供了一套可适配任何规模企业的精简 RAG 工作流。得益于融合的上下文引擎和预置的智能体模板，RAGFlow 使开发者能够以极高的效率和精度，将复杂数据转化为高保真、生产就绪的 AI 系统。

🎮 Get Started

Try our cloud service at https://cloud.ragflow.io.

快速开始 请访问我们的云服务：https://cloud.ragflow.io。

🔥 Latest Updates

2026-06-15: Support multiple chat channels such as Feishu, Discord, Telegram, Line, etc.
2026-04-24: Supports DeepSeek v4.
2026-03-24: RAGFlow Skill on OpenClaw — Provides an official skill for accessing RAGFlow datasets via OpenClaw.
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.
2025-03-19: Supports using a multi-modal model to make sense of images within PDF or DOCX files.

最新更新

2026-06-15：支持飞书、Discord、Telegram、Line 等多种聊天渠道。
2026-04-24：支持 DeepSeek v4。
2026-03-24：OpenClaw 上的 RAGFlow 技能——提供官方技能，可通过 OpenClaw 访问 RAGFlow 数据集。
2025-12-26：支持 AI 智能体的“记忆”功能。
2025-11-19：支持 Gemini 3 Pro。
2025-11-12：支持从 Confluence、S3、Notion、Discord、Google Drive 同步数据。
2025-10-23：支持使用 MinerU 和 Docling 作为文档解析方法。
2025-10-15：支持可编排的摄入流水线。
2025-08-08：支持 OpenAI 最新的 GPT-5 系列模型。
2025-08-01：支持智能体工作流和 MCP。
2025-05-23：为智能体添加了 Python/JavaScript 代码执行组件。
2025-03-19：支持使用多模态模型解析 PDF 或 DOCX 文件中的图像。

🌟 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.

核心功能

“高质量输入，高质量输出”：基于深度文档理解，从格式复杂的非结构化数据中提取知识。能在近乎无限的 Token 中找到“数据大海中的针”。
基于模板的分块：智能且可解释，提供多种模板选项供选择。
带溯源引用，减少幻觉：文本分块可视化，允许人工干预。通过快速查看关键参考资料和可追溯的引用，支持有据可查的回答。
兼容异构数据源：支持 Word、PPT、Excel、TXT、图像、扫描件、结构化数据、网页等多种格式。
自动化且轻松的 RAG 工作流：为个人和大型企业量身定制的精简 RAG 编排。支持配置 LLM 和 Embedding 模型。支持多路召回与融合重排序。提供直观的 API，可与业务无缝集成。

🎬 Self-Hosting

Prerequisites: CPU >= 4 cores, RAM >= 16 GB, Disk >= 50 GB, Docker >= 24.0.0 & Docker Compose >= v2.26.1, Python >= 3.13. gVisor is required only if you intend to use the code executor (sandbox) feature.

自托管 先决条件：CPU >= 4 核，内存 >= 16 GB，磁盘 >= 50 GB，Docker >= 24.0.0 及 Docker Compose >= v2.26.1，Python >= 3.13。仅当您打算使用代码执行器（沙箱）功能时，才需要 gVisor。