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171 lines
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7.5 KiB
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---
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title: Deploy OpenRAG with self-managed services
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slug: /docker
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
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import PartialPrereqCommon from '@site/docs/_partial-prereq-common.mdx';
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import PartialPrereqNoScript from '@site/docs/_partial-prereq-no-script.mdx';
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import PartialPrereqWindows from '@site/docs/_partial-prereq-windows.mdx';
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import PartialPrereqPython from '@site/docs/_partial-prereq-python.mdx';
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import PartialInstallNextSteps from '@site/docs/_partial-install-next-steps.mdx';
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To manage your own OpenRAG services, deploy OpenRAG with Docker or Podman.
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Use this installation method if you don't want to [use the Terminal User Interface (TUI)](/tui), or you need to run OpenRAG in an environment where using the TUI is unfeasible.
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## Prerequisites
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<PartialPrereqWindows />
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<PartialPrereqCommon />
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<PartialPrereqPython />
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<PartialPrereqNoScript />
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## Prepare your deployment
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1. Clone the OpenRAG repository:
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```bash
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git clone https://github.com/langflow-ai/openrag.git
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```
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2. Change to the root of the cloned repository:
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```bash
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cd openrag
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```
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3. Install dependencies:
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```bash
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uv sync
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```
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4. Create a `.env` file at the root of the cloned repository.
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You can create an empty file or copy the repository's [`.env.example`](https://github.com/langflow-ai/openrag/blob/main/.env.example) file.
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The example file contains some of the [OpenRAG environment variables](/reference/configuration) to get you started with configuring your deployment.
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```bash
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cp .env.example .env
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```
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5. Edit the `.env` file to configure your deployment using [OpenRAG environment variables](/reference/configuration).
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The OpenRAG Docker Compose files pull values from your `.env` file to configure the OpenRAG containers.
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The following variables are required or recommended:
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* **`OPENSEARCH_PASSWORD` (Required)**: Sets the OpenSearch administrator password. It must adhere to the [OpenSearch password complexity requirements](https://docs.opensearch.org/latest/security/configuration/demo-configuration/#setting-up-a-custom-admin-password).
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* **`LANGFLOW_SUPERUSER`**: The username for the Langflow administrator user. Defaults to `admin` if not set.
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* **`LANGFLOW_SUPERUSER_PASSWORD` (Strongly recommended)**: Sets the Langflow administrator password, and determines the Langflow server's default authentication mode. If not set, the Langflow server starts without authentication enabled. For more information, see [Langflow settings](/reference/configuration#langflow-settings).
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* **`LANGFLOW_SECRET_KEY` (Strongly recommended)**: A secret encryption key for internal Langflow operations. It is recommended to [generate your own Langflow secret key](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key). If not set, Langflow generates a secret key automatically.
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* **Model provider credentials**: Provide credentials for your preferred model providers. If not set in the `.env` file, you must configure at least one provider during [application onboarding](#application-onboarding).
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* `OPENAI_API_KEY`
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* `ANTHROPIC_API_KEY`
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* `OLLAMA_ENDPOINT`
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* `WATSONX_API_KEY`
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* `WATSONX_ENDPOINT`
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* `WATSONX_PROJECT_ID`
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* **OAuth provider credentials**: To upload documents from external storage, such as Google Drive, set the required OAuth credentials for the connectors that you want to use. You can [manage OAuth credentials](/ingestion#oauth-ingestion) later, but it is recommended to configure them during initial set up so you don't have to rebuild the containers.
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* **Amazon**: Provide your AWS Access Key ID and AWS Secret Access Key with access to your S3 instance. For more information, see the AWS documentation on [Configuring access to AWS applications](https://docs.aws.amazon.com/singlesignon/latest/userguide/manage-your-applications.html).
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* **Google**: Provide your Google OAuth Client ID and Google OAuth Client Secret. You can generate these in the [Google Cloud Console](https://console.cloud.google.com/apis/credentials). For more information, see the [Google OAuth client documentation](https://developers.google.com/identity/protocols/oauth2).
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* **Microsoft**: For the Microsoft OAuth Client ID and Microsoft OAuth Client Secret, provide [Azure application registration credentials for SharePoint and OneDrive](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/app-registration?view=odsp-graph-online). For more information, see the [Microsoft Graph OAuth client documentation](https://learn.microsoft.com/en-us/onedrive/developer/rest-api/getting-started/graph-oauth).
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For more information and variables, see [Environment variables](/reference/configuration).
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6. Start `docling serve` on the host machine.
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OpenRAG Docker installations require that `docling serve` is running on port 5001 on the host machine.
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This enables [Mac MLX](https://opensource.apple.com/projects/mlx/) support for document processing.
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```bash
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uv run python scripts/docling_ctl.py start --port 5001
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```
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7. Confirm `docling serve` is running.
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```
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uv run python scripts/docling_ctl.py status
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```
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Make sure the response shows that `docling serve` is running, for example:
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```bash
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Status: running
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Endpoint: http://127.0.0.1:5001
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Docs: http://127.0.0.1:5001/docs
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PID: 27746
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```
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8. Deploy OpenRAG locally with the appropriate Docker Compose file for your environment.
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Both files deploy the same services.
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* [`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.
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* Docker:
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```bash
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docker compose build
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docker compose up -d
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```
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* Podman Compose:
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```bash
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podman compose build
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podman compose up -d
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```
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* [`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.
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* Docker:
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```bash
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docker compose -f docker-compose-cpu.yml up -d
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```
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* Podman Compose:
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```bash
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podman compose -f docker-compose-cpu.yml up -d
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```
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The OpenRAG Docker Compose file starts five containers:
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| Container Name | Default address | Purpose |
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| OpenRAG Backend | http://localhost:8000 | FastAPI server and core functionality. |
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| OpenRAG Frontend | http://localhost:3000 | React web interface for user interaction. |
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| Langflow | http://localhost:7860 | [AI workflow engine](/agents). |
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| OpenSearch | http://localhost:9200 | Datastore for [knowledge](/knowledge). |
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| OpenSearch Dashboards | http://localhost:5601 | OpenSearch database administration interface. |
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9. Wait while the containers start, and then confirm all containers are running:
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* Docker Compose:
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```bash
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docker compose ps
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```
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* Podman Compose:
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```bash
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podman compose ps
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```
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If all containers are running, you can access your OpenRAG services at their addresses.
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10. Access the OpenRAG frontend at `http://localhost:3000` to continue with [application onboarding](#application-onboarding).
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<PartialOnboarding />
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<PartialInstallNextSteps /> |