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Mendon Kissling
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@ -10,7 +10,7 @@ OpenRAG can be installed in multiple ways:
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* [**Python wheel**](#install-python-wheel): Install the OpenRAG Python wheel and use the [OpenRAG Terminal User Interface (TUI)](/get-started/tui) to install, run, and configure your OpenRAG deployment without running Docker commands.
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* [**Docker Compose**](#install-and-run-docker): Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and dependencies.
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* [**Docker Compose**](/docker): Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and dependencies.
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## Prerequisites
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@ -79,46 +79,8 @@ For more information on virtual environments, see [uv](https://docs.astral.sh/uv
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Command completed successfully
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```
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7. To open the OpenRAG application, click **Open App**, press <kbd>6</kbd>, or navigate to `http://localhost:3000`.
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The application opens.
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8. Select your language model and embedding model provider, and complete the required fields.
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**Your provider can only be selected once, and you must use the same provider for your language model and embedding model.**
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The language model can be changed, but the embeddings model cannot be changed.
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To change your provider selection, you must restart OpenRAG and delete the `config.yml` file.
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<Tabs groupId="Embedding provider">
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<TabItem value="OpenAI" label="OpenAI" default>
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9. If you already entered a value for `OPENAI_API_KEY` in the TUI in Step 5, enable **Get API key from environment variable**.
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10. Under **Advanced settings**, select your **Embedding Model** and **Language Model**.
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11. To load 2 sample PDFs, enable **Sample dataset**.
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This is recommended, but not required.
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12. Click **Complete**.
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</TabItem>
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<TabItem value="IBM watsonx.ai" label="IBM watsonx.ai">
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9. Complete the fields for **watsonx.ai API Endpoint**, **IBM API key**, and **IBM Project ID**.
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These values are found in your IBM watsonx deployment.
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10. Under **Advanced settings**, select your **Embedding Model** and **Language Model**.
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11. To load 2 sample PDFs, enable **Sample dataset**.
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This is recommended, but not required.
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12. Click **Complete**.
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</TabItem>
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<TabItem value="Ollama" label="Ollama">
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9. Enter your Ollama server's base URL address.
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The default Ollama server address is `http://localhost:11434`.
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Since OpenRAG is running in a container, you may need to change `localhost` to access services outside of the container. For example, change `http://localhost:11434` to `http://host.docker.internal:11434` to connect to Ollama.
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OpenRAG automatically sends a test connection to your Ollama server to confirm connectivity.
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10. Select the **Embedding Model** and **Language Model** your Ollama server is running.
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OpenRAG automatically lists the available models from your Ollama server.
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11. To load 2 sample PDFs, enable **Sample dataset**.
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This is recommended, but not required.
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12. Click **Complete**.
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</TabItem>
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</Tabs>
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13. Continue with the [Quickstart](/quickstart).
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7. To open the OpenRAG application, click **Open App** or press <kbd>6</kbd>.
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8. Continue with the [Quickstart](/quickstart).
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### Advanced Setup {#advanced-setup}
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@ -138,80 +100,4 @@ The `LANGFLOW_PUBLIC_URL` controls where the Langflow web interface can be acces
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The `WEBHOOK_BASE_URL` controls where the endpoint for `/connectors/CONNECTOR_TYPE/webhook` will be available.
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This connection enables real-time document synchronization with external services.
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For example, for Google Drive file synchronization the webhook URL is `/connectors/google_drive/webhook`.
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## Docker {#install-and-run-docker}
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There are two different Docker Compose files.
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They deploy the same applications and containers, but to different environments.
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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.
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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 GPU support. Use this Docker compose file for environments where GPU drivers aren't available.
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To install OpenRAG with Docker Compose:
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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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cd openrag
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```
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2. Copy the example `.env` file that is included in the repository root.
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The example file includes all environment variables with comments to guide you in finding and setting their values.
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```bash
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cp .env.example .env
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```
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Alternatively, create a new `.env` file in the repository root.
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```
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touch .env
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```
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3. Set environment variables. The Docker Compose files are populated with values from your `.env`, so the following values are **required** to be set:
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```bash
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OPENSEARCH_PASSWORD=your_secure_password
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OPENAI_API_KEY=your_openai_api_key
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LANGFLOW_SUPERUSER=admin
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LANGFLOW_SUPERUSER_PASSWORD=your_langflow_password
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LANGFLOW_SECRET_KEY=your_secret_key
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```
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For more information on configuring OpenRAG with environment variables, see [Environment variables](/configure/configuration).
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For additional configuration values, including `config.yaml`, see [Configuration](/configure/configuration).
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4. Deploy OpenRAG with Docker Compose based on your deployment type.
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For GPU-enabled systems, run the following command:
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```bash
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docker compose up -d
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```
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For CPU-only systems, run the following command:
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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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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 users. |
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| Langflow | http://localhost:7860 | AI workflow engine and flow management. |
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| OpenSearch | http://localhost:9200 | Vector database for document storage. |
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| OpenSearch Dashboards | http://localhost:5601 | Database administration interface. |
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5. Verify installation by confirming all services are running.
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```bash
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docker compose ps
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```
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You can now access the application at:
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- **Frontend**: http://localhost:3000
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- **Backend API**: http://localhost:8000
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- **Langflow**: http://localhost:7860
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Continue with the Quickstart.
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For example, for Google Drive file synchronization the webhook URL is `/connectors/google_drive/webhook`.
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