peer-review-install-page
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Mendon Kissling
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docs/docs/_partial-onboarding.mdx
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docs/docs/_partial-onboarding.mdx
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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### Application onboarding
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The first time you start OpenRAG, whether using the TUI or a `.env` file, you must complete application onboarding.
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Values input during onboarding can be changed later in the OpenRAG **Settings** page, except for the language model and embedding model _provider_.
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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 completely reinstall OpenRAG.
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<Tabs groupId="Provider">
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<TabItem value="OpenAI" label="OpenAI" default>
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1. Enable **Get API key from environment variable** to automatically enter your key from the TUI-generated `.env` file.
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2. Under **Advanced settings**, select your **Embedding Model** and **Language Model**.
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3. To load 2 sample PDFs, enable **Sample dataset**.
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This is recommended, but not required.
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4. Click **Complete**.
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5. Continue with the [Quickstart](/quickstart).
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</TabItem>
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<TabItem value="IBM watsonx.ai" label="IBM watsonx.ai">
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1. 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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2. Under **Advanced settings**, select your **Embedding Model** and **Language Model**.
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3. To load 2 sample PDFs, enable **Sample dataset**.
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This is recommended, but not required.
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4. Click **Complete**.
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5. Continue with the [Quickstart](/quickstart).
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</TabItem>
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<TabItem value="Ollama" label="Ollama">
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:::tip
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Ollama is not included with OpenRAG. To install Ollama, see the [Ollama documentation](https://docs.ollama.com/).
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:::
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1. 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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2. 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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3. To load 2 sample PDFs, enable **Sample dataset**.
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This is recommended, but not required.
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4. Click **Complete**.
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5. Continue with the [Quickstart](/quickstart).
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</TabItem>
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</Tabs>
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@ -1,5 +1,5 @@
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---
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title: Docker deployment
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title: Deploy with Docker
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slug: /get-started/docker
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---
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@ -40,7 +40,6 @@ To install OpenRAG with Docker Compose:
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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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@ -1,16 +1,15 @@
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---
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title: Install OpenRAG
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title: Install Python wheel
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slug: /install
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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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OpenRAG can be installed in multiple ways:
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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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* [**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**](get-started/docker): Clone the OpenRAG repository and deploy OpenRAG with Docker Compose, including all services and dependencies.
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If you prefer running Docker commands and manually editing `.env` files, see [Deploy with Docker](/docker).
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## Prerequisites
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@ -18,24 +17,23 @@ OpenRAG can be installed in multiple ways:
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- [uv](https://docs.astral.sh/uv/getting-started/installation/)
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- [Docker](https://docs.docker.com/get-docker/) or [Podman](https://podman.io/docs/installation) installed
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- [Docker Compose](https://docs.docker.com/compose/install/) installed. If using Podman, use [podman-compose](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or alias Docker compose commands to Podman commands.
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- For GPU support: (TBD)
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- [OpenAI API key](https://platform.openai.com/api-keys)
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- Optional: GPU support requires an NVIDIA GPU with CUDA support and compatible NVIDIA drivers installed on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment.
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## Python wheel {#install-python-wheel}
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## Install Python wheel {#install-python-wheel}
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The Python wheel is currently available internally, but will be available on PyPI at launch.
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The wheel installs the OpenRAG wheel, which includes the TUI for installing, running, and managing OpenRAG.
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For more information on virtual environments, see [uv](https://docs.astral.sh/uv/pip/environments).
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1. Create a new project with a virtual environment using [uv](https://docs.astral.sh/uv/pip/environments).
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The OpenRAG Python wheel is available on PyPI.
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The wheel includes the TUI for installing, running, and managing OpenRAG.
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1. Create a new project with a virtual environment using `uv`.
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For more information on virtual environments, see the [uv documentation](https://docs.astral.sh/uv/pip/environments).
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```bash
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uv init YOUR_PROJECT_NAME
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cd YOUR_PROJECT_NAME
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```
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2. Add the OpenRAG wheel to your project and install it in the virtual environment.
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Replace `PATH/TO/` and `VERSION` with your OpenRAG wheel location and version.
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```bash
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uv add PATH/TO/openrag-VERSION-py3-none-any.whl
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uv add openrag
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```
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3. Ensure all dependencies are installed and updated in your virtual environment.
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```bash
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@ -47,95 +45,62 @@ For more information on virtual environments, see [uv](https://docs.astral.sh/uv
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uv run openrag
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```
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The OpenRAG TUI opens.
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## Setup OpenRAG with the TUI
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5. To install OpenRAG with Basic Setup, click **Basic Setup** or press <kbd>1</kbd>. Basic Setup does not set up OAuth connections for ingestion from Google Drive, OneDrive, or AWS. For OAuth setup, see [Advanced Setup](#advanced-setup).
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The TUI prompts you for the required startup values.
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Click **Generate Passwords** to autocomplete fields that contain **Auto-generated Secure Password**, or bring your own passwords.
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<details closed>
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<summary>Where do I find the required startup values?</summary>
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| Variable | Where to Find | Description |
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|----------|---------------|-------------|
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| `OPENSEARCH_PASSWORD` | Auto-generated secure password | The password for OpenSearch database access. Must be at least 8 characters and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character. |
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| `OPENAI_API_KEY` | [OpenAI Platform](https://platform.openai.com/api-keys) | API key from your OpenAI account. |
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| `LANGFLOW_SUPERUSER` | User generated | Username for Langflow admin access. For more, see [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-superuser). |
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| `LANGFLOW_SUPERUSER_PASSWORD` | Auto-generated secure password | Password for Langflow admin access. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-superuser). |
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| `LANGFLOW_SECRET_KEY` | Auto-generated secure key | Secret key for Langflow security. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-secret-key). |
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| `LANGFLOW_AUTO_LOGIN` | Auto-generated or manual | Auto-login configuration. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login). |
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| `LANGFLOW_NEW_USER_IS_ACTIVE` | Langflow | New user activation setting. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-new-user-is-active). |
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| `LANGFLOW_ENABLE_SUPERUSER_CLI` | Langflow server | Superuser CLI access setting. For more, see the [Langflow docs](https://docs.langflow.org/api-keys-and-authentication#langflow-enable-superuser-cli). |
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| `DOCUMENTS_PATH` | Set your local path | Path to your document storage directory. |
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</details>
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To complete credentials, click **Save Configuration**.
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**Basic Setup** completes or auto-generates most of the required values to start OpenRAG.
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**Basic Setup** does not set up OAuth connections for ingestion from Google Drive, OneDrive, or AWS.
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For OAuth setup, use **Advanced Setup**.
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6. To start OpenRAG with your credentials, click **Start Container Services**.
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Startup pulls container images and starts them, so it can take some time.
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The operation has completed when the **Close** button is available and the terminal displays:
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```bash
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Services started successfully
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Command completed successfully
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```
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If the TUI detects OAuth credentials, it enforces the **Advanced Setup** path.
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If the TUI detects a `.env` file in the OpenRAG root directory, it will source any variables from the `.env` file.
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<Tabs groupId="Setup method">
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<TabItem value="Basic setup" label="Basic setup" default>
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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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1. To install OpenRAG with **Basic Setup**, click **Basic Setup** or press <kbd>1</kbd>.
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2. Click **Generate Passwords** to generate passwords for OpenSearch and Langflow.
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3. Paste your OpenAI API key in the OpenAI API key field.
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4. Click **Save Configuration**.
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5. To start OpenRAG, click **Start Container Services**.
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Startup pulls container images and runs them, so it can take some time.
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When startup is complete, the TUI displays the following:
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```bash
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Services started successfully
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Command completed successfully
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```
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6. To open the OpenRAG application, click **Open App**.
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7. Continue with [Application Onboarding](#application-onboarding).
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</TabItem>
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<TabItem value="Advanced setup" label="Advanced setup">
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1. To install OpenRAG with **Advanced Setup**, click **Advanced Setup** or press <kbd>2</kbd>.
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2. Click **Generate Passwords** to generate passwords for OpenSearch and Langflow.
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3. Paste your OpenAI API key in the OpenAI API key field.
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4. Add your client and secret values for Google, Azure, or AWS OAuth.
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These values can be found in your OAuth provider.
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5. The OpenRAG TUI presents redirect URIs for your OAuth app.
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These are the URLs your OAuth provider will redirect back to after user sign-in.
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Register these redirect values with your OAuth provider as they are presented in the TUI.
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6. Click **Save Configuration**.
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7. To start OpenRAG, click **Start Container Services**.
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Startup pulls container images and runs them, so it can take some time.
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When startup is complete, the TUI displays the following:
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```bash
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Services started successfully
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Command completed successfully
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```
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8. To open the OpenRAG application, click **Open App**, press <kbd>6</kbd>, or navigate to `http://localhost:3000`.
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You will be presented with your provider's OAuth sign-in screen, and be redirected to the redirect URI after sign-in.
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Continue with Application Onboarding.
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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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Two additional variables are available for Advanced Setup:
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The `LANGFLOW_PUBLIC_URL` controls where the Langflow web interface can be accessed. This is where users interact with their flows in a browser.
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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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</TabItem>
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9. Continue with [Application Onboarding](#application-onboarding).
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</TabItem>
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</Tabs>
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13. Continue with the [Quickstart](/quickstart).
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### Advanced Setup {#advanced-setup}
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**Advanced Setup** includes the required values from **Basic Setup**, with additional settings for OAuth credentials.
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If the OpenRAG TUI detects OAuth credentials, it enforces the Advanced Setup path.
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1. Add your client and secret values for Google, Azure, or AWS OAuth.
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These values can be found in your OAuth provider.
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2. The OpenRAG TUI presents redirect URIs for your OAuth app.
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These are the URLs your OAuth provider will redirect back to after user sign-in.
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Register these redirect values with your OAuth provider as they are presented in the TUI.
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3. To open the OpenRAG application, click **Open App** or press <kbd>6</kbd>.
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You will be presented with your provider's OAuth sign-in screen, and be redirected to the redirect URI after sign-in.
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Two additional variables are available for Advanced Setup:
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The `LANGFLOW_PUBLIC_URL` controls where the Langflow web interface can be accessed. This is where users interact with their flows in a browser.
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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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<PartialOnboarding />
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@ -14,6 +14,8 @@ OpenRAG connects and amplifies three popular, proven open-source projects into o
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* [Docling](https://docling-project.github.io/docling/) - Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
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OpenRAG builds on Langflow's familiar interface while adding OpenSearch for vector storage and Docling for simplified document parsing, with opinionated flows that serve as ready-to-use recipes for ingestion, retrieval, and generation from popular sources like OneDrive, Google Drive, and AWS. And don't fear: every part of the stack is swappable. Write your own custom components in Python, try different language models, and customize your flows to build an agentic RAG system that solves problems.
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OpenRAG builds on Langflow's familiar interface while adding OpenSearch for vector storage and Docling for simplified document parsing, with opinionated flows that serve as ready-to-use recipes for ingestion, retrieval, and generation from popular sources like OneDrive, Google Drive, and AWS.
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Ready to get started? Install OpenRAG and then run the Quickstart to create a powerful RAG pipeline.
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What's more, every part of the stack is swappable. Write your own custom components in Python, try different language models, and customize your flows to build an agentic RAG system.
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Ready to get started? [Install OpenRAG](/install) and then run the [Quickstart](/quickstart) to create a powerful RAG pipeline.
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{
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type: "doc",
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id: "get-started/install",
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label: "Installation"
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label: "Install Python wheel"
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},
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{
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type: "doc",
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id: "get-started/docker",
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label: "Deploy with Docker"
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},
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{
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type: "doc",
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id: "get-started/quickstart",
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label: "Quickstart"
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},
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{
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type: "doc",
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id: "get-started/docker",
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label: "Docker Deployment"
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},
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{
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type: "doc",
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id: "get-started/tui",
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@ -28,3 +28,32 @@
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--ifm-color-primary-lightest: #4fddbf;
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--docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
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}
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/* Tabs Styling */
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.tabs-container {
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border: 1px solid var(--ifm-color-emphasis-300);
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border-radius: var(--ifm-global-radius);
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padding: 1rem;
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margin-bottom: 1rem;
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}
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.tabs {
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margin-bottom: 1rem;
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}
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.tabs__item {
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border: none;
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border-bottom: 1px solid var(--ifm-color-emphasis-200);
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margin-right: 0rem;
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padding-bottom: 0.5rem;
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border-radius: 0;
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}
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.tabs__item:hover {
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background-color: var(--ifm-hover-overlay);
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}
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.tabs__item--active {
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border-bottom-color: var(--ifm-tabs-color-active);
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}
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