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📕 Table of Contents

• 💡 What is RAGFlow?
• 🎮 Demo
• 📌 Latest Updates
• 🌟 Key Features
• 🔎 System Architecture
• 🎬 Get Started
• 🔧 Configurations
• 🔧 Build a docker image without embedding models
• 🔧 Build a docker image including embedding models
• 🔨 Launch service from source for development
• 📚 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.

🎮 Demo

Try our demo at https://demo.ragflow.io.

🔥 Latest Updates

• 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.
• 2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs.
• 2024-12-18 Upgrades Document Layout Analysis model in DeepDoc.
• 2024-08-22 Support text to SQL statements through RAG.

🎉 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.

🌱 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

🎬 Get Started

📝 Prerequisites

• CPU >= 4 cores
• RAM >= 16 GB
• Disk >= 50 GB
• Docker >= 24.0.0 & Docker Compose >= v2.26.1
• gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.

Tip

If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.

🚀 Start up the server

1. Ensure vm.max_map_count >= 262144:

   To check the value of vm.max_map_count:

   $ sysctl vm.max_map_count
   

   Reset vm.max_map_count to a value at least 262144 if it is not.

   # 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
   

2. Clone the repo:

   $ git clone https://github.com/infiniflow/ragflow.git
   

3. Start up the server using the pre-built Docker images:

Caution

All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64. If you are on an ARM64 platform, follow this guide to build a Docker image compatible with your system.

The command below downloads the v0.21.1-slim edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from v0.21.1-slim, update the RAGFLOW_IMAGE variable accordingly in docker/.env before using docker compose to start the server.

   $ cd ragflow/docker
   # Use CPU for embedding and DeepDoc tasks:
   $ docker compose -f docker-compose.yml up -d

   # To use GPU to accelerate embedding and DeepDoc tasks:
   # sed -i '1i DEVICE=gpu' .env
   # docker compose -f docker-compose.yml up -d

RAGFlow image tag	Image size (GB)	Has embedding models?	Stable?
v0.21.1	≈9	✔️	Stable release
v0.21.1-slim	≈2	❌	Stable release
nightly	≈2	❌	Unstable nightly build

Note: Starting with v0.22.0, we ship only the slim edition and no longer append the -slim suffix to the image tag.

4. Check the server status after having the server up and running:

   $ docker logs -f docker-ragflow-cpu-1
   

   The following output confirms a successful launch of the system:

   
         ____   ___    ______ ______ __
        / __ \ /   |  / ____// ____// /____  _      __
       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
   
    * Running on all addresses (0.0.0.0)
   

   If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a network anormal error because, at that moment, your RAGFlow may not be fully initialized.

5. In your web browser, enter the IP address of your server and log in to RAGFlow.

   With the default settings, you only need to enter http://IP_OF_YOUR_MACHINE (sans port number) as the default HTTP serving port 80 can be omitted when using the default configurations.

6. In service_conf.yaml.template, select the desired LLM factory in user_default_llm and update the API_KEY field with the corresponding API key.

   See llm_api_key_setup for more information.

   The show is on!

🔧 Configurations

When it comes to system configurations, you will need to manage the following files:

• .env: Keeps the fundamental setups for the system, such as SVR_HTTP_PORT, MYSQL_PASSWORD, and MINIO_PASSWORD.
• service_conf.yaml.template: Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment.
• docker-compose.yml: The system relies on docker-compose.yml to start up.

The ./docker/README file provides a detailed description of the environment settings and service configurations which can be used as ${ENV_VARS} in the service_conf.yaml.template file.

To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80 to <YOUR_SERVING_PORT>:80.

Updates to the above configurations require a reboot of all containers to take effect:

$ docker compose -f docker-compose.yml up -d

Switch doc engine from Elasticsearch to Infinity

RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to Infinity, follow these steps:

1. Stop all running containers:

   $ docker compose -f docker/docker-compose.yml down -v
   

Warning

-v will delete the docker container volumes, and the existing data will be cleared.

2. Set DOC_ENGINE in docker/.env to infinity.

3. Start the containers:

   $ docker compose -f docker-compose.yml up -d
   

Warning

Switching to Infinity on a Linux/arm64 machine is not yet officially supported.

🔧 Build a Docker image without embedding models

This image is approximately 2 GB in size and relies on external LLM and embedding services.

git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .

🔨 Launch service from source for development

1. Install uv and pre-commit, or skip this step if they are already installed:

   pipx install uv pre-commit
   

2. Clone the source code and install Python dependencies:

   git clone https://github.com/infiniflow/ragflow.git
   cd ragflow/
   uv sync --python 3.10 # install RAGFlow dependent python modules
   uv run download_deps.py
   pre-commit install
   

3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:

   docker compose -f docker/docker-compose-base.yml up -d
   

   Add the following line to /etc/hosts to resolve all hosts specified in docker/.env to 127.0.0.1:

   127.0.0.1       es01 infinity mysql minio redis sandbox-executor-manager
   

4. If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:

   export HF_ENDPOINT=https://hf-mirror.com
   

5. If your operating system does not have jemalloc, please install it as follows:

   # Ubuntu
   sudo apt-get install libjemalloc-dev
   # CentOS
   sudo yum install jemalloc
   # OpenSUSE
   sudo zypper install jemalloc
   # macOS
   sudo brew install jemalloc
   

6. Launch backend service:

   source .venv/bin/activate
   export PYTHONPATH=$(pwd)
   bash docker/launch_backend_service.sh
   

7. Install frontend dependencies:

   cd web
   npm install
   

8. Launch frontend service:

   npm run dev
   

   The following output confirms a successful launch of the system:

   

9. Stop RAGFlow front-end and back-end service after development is complete:

   pkill -f "ragflow_server.py|task_executor.py"
   

📚 Documentation

• Quickstart
• Configuration
• Release notes
• User guides
• Developer guides
• References
• FAQs

📜 Roadmap

See the RAGFlow Roadmap 2025

🏄 Community

• Discord
• Twitter
• GitHub Discussions

🙌 Contributing

RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our Contribution Guidelines first.