---
sidebar_position: 1
slug: /build_docker_image
---

# Build RAGFlow Docker image
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A guide explaining how to build a RAGFlow Docker image from its source code. By following this guide, you'll be able to create a local Docker image that can be used for development, debugging, or testing purposes.

## Target Audience

- Developers who have added new features or modified the existing code and require a Docker image to view and debug their changes.
- Developers seeking to build a RAGFlow Docker image for an ARM64 platform.
- Testers aiming to explore the latest features of RAGFlow in a Docker image.

## Prerequisites

- CPU ≥ 4 cores
- RAM ≥ 16 GB
- Disk ≥ 50 GB
- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1

## Build a Docker image

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  values={[
    {label: 'Build a Docker image without embedding models', value: 'without'},
    {label: 'Build a Docker image including embedding models', value: 'including'}
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This image is approximately 2 GB in size and relies on external LLM and embedding services.

:::danger IMPORTANT
- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
:::

```bash
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
uv run download_deps.py
docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
```


  </TabItem>
  <TabItem value="including">

This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.  

:::danger IMPORTANT
- While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
:::

```bash
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
uv run download_deps.py
docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
docker build -f Dockerfile -t infiniflow/ragflow:nightly .
```

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</Tabs>

## Launch a RAGFlow Service from Docker for MacOS

After building the infiniflow/ragflow:nightly-slim image, you are ready to launch a fully-functional RAGFlow service with all the required components, such as Elasticsearch, MySQL, MinIO, Redis, and more.

## Example: Apple M2 Pro (Sequoia)

1. Edit Docker Compose Configuration

Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.17.2-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.


2. Launch the Service

```bash
cd docker
$ docker compose -f docker-compose-macos.yml up -d
```

3. Access the RAGFlow Service

Once the setup is complete, open your web browser and navigate to http://127.0.0.1 or your server's \<IP_ADDRESS\>; (the default port is \<PORT\> = 80). You will be directed to the RAGFlow welcome page. Enjoy!🍻