infiniflow/ragflow - Docker Image (original) (raw)

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📕 Table of Contents

⁠💡 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.

⁠🔥 Latest 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"
⁠🍱 Template-based chunking
⁠🌱 Grounded citations with reduced hallucinations
⁠🍔 Compatibility with heterogeneous data sources
⁠🛀 Automated and effortless RAG workflow

⁠🔎 System Architecture

⁠🎬 Self-Hosting

⁠📝 Prerequisites

Tip

If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).

⁠🚀 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 thevm.max_map_count value in /etc/sysctl.conf accordingly:

  2. Clone the repo:
$ git clone https://github.com/infiniflow/ragflow.git
  1. 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](https://ragflow.io/docs/dev/build\_docker\_image) to build a Docker image compatible with your system.

The command below downloads the v0.25.6 edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from v0.25.6, update the RAGFLOW_IMAGE variable accordingly in docker/.env before using docker compose to start the server.

   $ cd ragflow/docker

   # git checkout v0.25.6
   # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
   # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.

   # Use CPU for DeepDoc tasks:
   $ docker compose -f docker-compose.yml up -d

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

Note: Prior to v0.22.0, we provided both images with embedding models and slim images without embedding models. Details as follows:

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

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

  1. 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 abnormalerror because, at that moment, your RAGFlow may not be fully initialized. 2. 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. 3. 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.
The show is on!

⁠🔧 Configurations

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

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

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

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 .

Or if you are behind a proxy, you can pass proxy arguments:

docker build --platform linux/amd64 \
  --build-arg http_proxy=http://YOUR_PROXY:PORT \
  --build-arg https_proxy=http://YOUR_PROXY:PORT \
  -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
  1. Clone the source code and install Python dependencies:
git clone https://github.com/infiniflow/ragflow.git  
cd ragflow/  
uv sync --python 3.13 # install RAGFlow dependent python modules  
uv run python3 download_deps.py  
pre-commit install
  1. 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
  1. If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:
export HF_ENDPOINT=https://hf-mirror.com
  1. 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
  1. Launch backend service:
source .venv/bin/activate  
export PYTHONPATH=$(pwd)  
bash docker/launch_backend_service.sh
  1. Install frontend dependencies:
  2. Launch frontend service:
    The following output confirms a successful launch of the system:
  3. Stop RAGFlow front-end and back-end service after development is complete:
pkill -f "ragflow_server.py|task_executor.py"

⁠📚 Documentation

⁠📜 Roadmap

See the RAGFlow Roadmap 2026⁠

⁠🏄 Community

⁠🙌 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.