RAGFlow：文档知识编译与多步检索

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to creat…

Cloud | Documentation | Roadmap | Discord 📕 Table of Contents 💡 What is RAGFlow? 🎮 Get Started 🔥 Latest Updates 🌟 Key Features 🔎 System Architecture 🏠 Local Deployment 📚 Documentation 📜 Roadmap 🏄 Community 🙌 Contributing 💡 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. 🎮 Get Started Try our cloud service at https://cloud.ragflow.io. For local deployment, see Local Deployment. 🔥 Latest Updates 2026-09-29 RAGFlow 1.0.0-rc1 released. 2026-09-10 Added website content ingestion through sitemaps. 2026-08-19 Introduced Knowledge Compilation to generate Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills at the document and dataset levels. 2026-08-19 Introduced Agentic RAG with Low, Medium, High, and Ultra thinking modes. 2026-07-02 Added Google BigQuery data source ingestion and incremental synchronization. See the full release notes for more updates. 🎉 Stay Tuned ⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟 🌟 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. 🧩 Knowledge Compilation Compile content at the document and dataset levels into structured knowledge artifacts. Use compilation templates to generate Wikis, Graphs, Trees, PageIndex, Mind Maps, Timelines, and Skills for different knowledge organization and reuse needs. Configure compilation models and processing rules, and view, update, and regenerate knowledge artifacts. 🧠 Agentic Retrieval Perform multi-step retrieval for complex questions: the model analyzes the question and, when needed, breaks it down, retrieves knowledge, and verifies evidence. Gather more complete context through multiple rounds of retrieval and reasoning to help generate well-grounded answers. Choose Low, Medium, High, or Ultra thinking modes to control retrieval and reasoning depth according to question complexity. 🌱 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 documents, PowerPoint presentations, Excel spreadsheets, TXT files, images, PDFs, 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 🏠 Local Deployment Local deployment supports both Docker deployment and launching from source. Docker is suitable for quick evaluation, integration testing, and production deployment, while launching from source is intended for RAGFlow development and debugging. Docker deployment does not require a Go compiler. Launching from source requires the Go version specified in go.mod; frontend development additionally requires Node.js and npm. 🐳 Docker Deployment 📝 Docker Deployment Prerequisites Recommended starting configuration: 4 CPU cores, 16 GB RAM, and 50 GB of available disk space. Actual requirements depend on the document engine, data volume, parsing tasks, and concurrency. Local models and other optional components may require additional resources. Docker ≥ 24.0.0, Docker Compose ≥ v2.26.1. Tip If you have not installed Docker on your local machine (Windows, macOS, or Linux), see Install Docker Engine. 🚀 Start up the server If using Elasticsearch, set vm.max_map_count on the Docker host to at least 262144. This step is usually unnecessary with Infinity: To check the value of vm.max_map_count: sysctl vm.max_map_count If you use Elasticsearch and the value is below 262144, reset it: # In this case, we set it to 262144: sudo sysctl -w vm.max_map_count=262144 This change will be reset after a system reboot. To ensure your change remains permanent, add or update the vm.max_map_count value in /etc/sysctl.conf accordingly: vm.max_map_count=262144 Clone the repository: git clone https://github.com/infiniflow/ragflow.git Check out the release tag and start the server with Docker Compose: Enter the Docker deployment directory. cd ragflow/docker Check out the v1.0.0-rc2 release tag. git checkout v1.0.0-rc2 Start the RAGFlow services and their dependencies. docker compose -f docker-compose.yml up -d With the default MySQL configuration, the image entrypoint runs database migrations before starting Admin, Syncer, Ingestor, API, and other services. In the RAGFlow open-source 1.0 release, DeepDoc uses CPU inference for layout analysis, OCR, and table recognition. Check the server status after startup: Use docker ps to check the dependency services. After the dependencies show healthy and the RAGFlow container remains running, use the HTTP endpoint to determine whether RAGFlow is ready. The RAGFlow container itself does not define a Compose healthcheck. curl -f http://localhost/api/v1/system/healthz After receiving an HTTP 200 response, open the Web UI. If you changed SVR_WEB_HTTP_PORT, replace the port in the health-check URL accordingly. If startup fails, inspect the container logs: docker logs --tail 50 ragflow-cpu In your web browser, enter the IP address of your server and log in to RAGFlow. With the default settings