---
title: Install OpenRAG containers
slug: /docker
---
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import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
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OpenRAG has two Docker Compose files. Both files deploy the same applications and containers locally, but they are for different environments:
- [`docker-compose.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose.yml) is an OpenRAG deployment with GPU support for accelerated AI processing. This Docker Compose file requires an NVIDIA GPU with [CUDA](https://docs.nvidia.com/cuda/) support.
- [`docker-compose-cpu.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose-cpu.yml) is a CPU-only version of OpenRAG for systems without NVIDIA GPU support. Use this Docker Compose file for environments where GPU drivers aren't available.
## Prerequisites
- Install the following:
- [Python](https://www.python.org/downloads/release/python-3100/) version 3.13 or later.
- [uv](https://docs.astral.sh/uv/getting-started/installation/).
- [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/).
- [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/). To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands.
- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
Install WSL for OpenRAG
- Prepare model providers and credentials.
During [Application Onboarding](#application-onboarding), you must select language model and embedding model providers.
If your chosen provider offers both types, you can use the same provider for both selections.
If your provider offers only one type, such as Anthropic, you must select two providers.
Gather the credentials and connection details for your chosen model providers before starting onboarding:
- OpenAI: Create an [OpenAI API key](https://platform.openai.com/api-keys).
- Anthropic language models: Create an [Anthropic API key](https://www.anthropic.com/docs/api/reference).
- IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
- Ollama: Use the [Ollama documentation](https://docs.ollama.com/) to set up your Ollama instance locally, in the cloud, or on a remote server, and then get your Ollama server's base URL.
- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. This is required to use the GPU-accelerated Docker Compose file. If you choose not to use GPU support, you must use the CPU-only Docker Compose file instead.
## Install OpenRAG with Docker Compose
To install OpenRAG with Docker Compose, do the following:
1. Clone the OpenRAG repository.
```bash
git clone https://github.com/langflow-ai/openrag.git
cd openrag
```
2. Install dependencies.
```bash
uv sync
```
3. Copy the example `.env` file included in the repository root.
The example file includes all environment variables with comments to guide you in finding and setting their values.
```bash
cp .env.example .env
```
Alternatively, create a new `.env` file in the repository root.
```
touch .env
```
4. The Docker Compose files are populated with the values from your `.env` file.
The `OPENSEARCH_PASSWORD` value must be set.
`OPENSEARCH_PASSWORD` can be automatically generated when using the TUI, but for a Docker Compose installation, you can set it manually instead. To generate an OpenSearch admin password, see the [OpenSearch documentation](https://docs.opensearch.org/latest/security/configuration/demo-configuration/#setting-up-a-custom-admin-password).
The following values are optional:
```bash
OPENAI_API_KEY=your_openai_api_key
LANGFLOW_SECRET_KEY=your_secret_key
```
`OPENAI_API_KEY` is optional. You can provide it during [Application Onboarding](#application-onboarding) or choose a different model provider. If you want to set it in your `.env` file, you can find your OpenAI API key in your [OpenAI account](https://platform.openai.com/api-keys).
`LANGFLOW_SECRET_KEY` is optional. Langflow will auto-generate it if not set. For more information, see the [Langflow documentation](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key).
The following Langflow configuration values are optional but important to consider:
```bash
LANGFLOW_SUPERUSER=admin
LANGFLOW_SUPERUSER_PASSWORD=your_langflow_password
```
`LANGFLOW_SUPERUSER` defaults to `admin`. You can omit it or set it to a different username. `LANGFLOW_SUPERUSER_PASSWORD` is optional. If omitted, Langflow runs in [autologin mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) with no password required. If set, Langflow requires password authentication.
For more information on configuring OpenRAG with environment variables, see [Environment variables](/reference/configuration).
5. Start `docling serve` on the host machine.
OpenRAG Docker installations require that `docling serve` is running on port 5001 on the host machine.
This enables [Mac MLX](https://opensource.apple.com/projects/mlx/) support for document processing.
```bash
uv run python scripts/docling_ctl.py start --port 5001
```
6. Confirm `docling serve` is running.
```
uv run python scripts/docling_ctl.py status
```
Make sure the response shows that `docling serve` is running, for example:
```bash
Status: running
Endpoint: http://127.0.0.1:5001
Docs: http://127.0.0.1:5001/docs
PID: 27746
```
7. Deploy OpenRAG locally with Docker Compose based on your deployment type.
```bash
docker compose build
docker compose up -d
```
```bash
docker compose -f docker-compose-cpu.yml up -d
```
The OpenRAG Docker Compose file starts five containers:
| Container Name | Default Address | Purpose |
|---|---|---|
| OpenRAG Backend | http://localhost:8000 | FastAPI server and core functionality. |
| OpenRAG Frontend | http://localhost:3000 | React web interface for users. |
| Langflow | http://localhost:7860 | AI workflow engine and flow management. |
| OpenSearch | http://localhost:9200 | Vector database for document storage. |
| OpenSearch Dashboards | http://localhost:5601 | Database administration interface. |
8. Verify installation by confirming all services are running.
```bash
docker compose ps
```
You can now access OpenRAG at the following endpoints:
- **Frontend**: http://localhost:3000
- **Backend API**: http://localhost:8000
- **Langflow**: http://localhost:7860
9. Continue with [Application Onboarding](#application-onboarding).
To stop `docling serve` when you're done with your OpenRAG deployment, run:
```bash
uv run python scripts/docling_ctl.py stop
```
## Container management commands
Manage your OpenRAG containers with the following commands.
These commands are also available in the TUI's [Status menu](/install#status).
### Upgrade containers
Upgrade your containers to the latest version while preserving your data.
```bash
docker compose pull
docker compose up -d --force-recreate
```
### Rebuild containers (destructive)
Reset state by rebuilding all of your containers.
Your OpenSearch and Langflow databases will be lost.
Documents stored in the `./documents` directory will persist, since the directory is mounted as a volume in the OpenRAG backend container.
```bash
docker compose up --build --force-recreate --remove-orphans
```
### Remove all containers and data (destructive)
Completely remove your OpenRAG installation and delete all data.
This deletes all of your data, including OpenSearch data, uploaded documents, and authentication.
```bash
docker compose down --volumes --remove-orphans --rmi local
docker system prune -f
```