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### What problem does this PR solve? update for v0.19.0 ### Type of change - [x] Documentation Update
92 lines
3.3 KiB
Plaintext
92 lines
3.3 KiB
Plaintext
---
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sidebar_position: 1
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slug: /build_docker_image
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---
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# Build RAGFlow Docker image
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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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.
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## Target Audience
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- Developers who have added new features or modified the existing code and require a Docker image to view and debug their changes.
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- Developers seeking to build a RAGFlow Docker image for an ARM64 platform.
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- Testers aiming to explore the latest features of RAGFlow in a Docker image.
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## Prerequisites
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- CPU ≥ 4 cores
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- RAM ≥ 16 GB
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- Disk ≥ 50 GB
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- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
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## Build a Docker image
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<Tabs
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defaultValue="without"
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values={[
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{label: 'Build a Docker image without embedding models', value: 'without'},
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{label: 'Build a Docker image including embedding models', value: 'including'}
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]}>
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<TabItem value="without">
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This image is approximately 2 GB in size and relies on external LLM and embedding services.
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:::danger IMPORTANT
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- 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.
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- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
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:::
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```bash
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git clone https://github.com/infiniflow/ragflow.git
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cd ragflow/
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uv run download_deps.py
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docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
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docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
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```
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</TabItem>
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<TabItem value="including">
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This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
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:::danger IMPORTANT
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- 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.
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- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
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:::
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```bash
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git clone https://github.com/infiniflow/ragflow.git
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cd ragflow/
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uv run download_deps.py
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docker build -f Dockerfile.deps -t infiniflow/ragflow_deps .
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docker build -f Dockerfile -t infiniflow/ragflow:nightly .
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```
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</TabItem>
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</Tabs>
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## Launch a RAGFlow Service from Docker for MacOS
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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.
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## Example: Apple M2 Pro (Sequoia)
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1. Edit Docker Compose Configuration
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Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.19.0-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
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2. Launch the Service
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```bash
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cd docker
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$ docker compose -f docker-compose-macos.yml up -d
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```
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3. Access the RAGFlow Service
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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!🍻 |