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docs: docs peer review
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README.md
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@ -47,7 +47,7 @@ To launch OpenRAG with the TUI, do the following:
The TUI opens and guides you through OpenRAG setup.
For the full TUI guide, see [TUI](https://docs.openr.ag/get-started/tui).
For the full TUI installation guide, see [TUI](https://docs.openr.ag/install).
## Docker installation

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docs/docs/_partial-onboarding.mdx
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@ -7,6 +7,8 @@ The first time you start OpenRAG, whether using the TUI or a `.env` file, you mu
Values from onboarding can be changed later in the OpenRAG **Settings** page.
Choose one LLM provider and complete only those steps:
<Tabs groupId="Provider">
<TabItem value="OpenAI" label="OpenAI" default>
1. Enable **Get API key from environment variable** to automatically enter your key from the TUI-generated `.env` file.

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@ -3,25 +3,24 @@ title: Install OpenRAG containers
slug: /get-started/docker
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
There are two different Docker Compose files.
They deploy the same applications and containers locally, but to different environments.
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.
- [`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 GPU support. Use this Docker compose file for environments where GPU drivers aren't available.
Both Docker deployments depend on `docling serve` to be running on port `5001` on the host machine. This enables [Mac MLX](https://opensource.apple.com/projects/mlx/) support for document processing. Installing OpenRAG with the TUI starts `docling serve` automatically, but for a Docker deployment you must manually start the `docling serve` process.
- [`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
- [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
- [uv](https://docs.astral.sh/uv/getting-started/installation/)
- [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/) installed
- [Docker Compose](https://docs.docker.com/compose/install/) installed. If you're using Podman, use [podman-compose](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or alias Docker compose commands to Podman commands.
- Install [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
- Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
- Install [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/)
- Install [Docker Compose](https://docs.docker.com/compose/install/). 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.
- Create an [OpenAI API key](https://platform.openai.com/api-keys). This key is **required** to start OpenRAG, but you can choose a different model provider during [Application Onboarding](#application-onboarding).
- 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.
- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment.
## Install OpenRAG with Docker Compose
@ -49,8 +48,7 @@ To install OpenRAG with Docker Compose, do the following:
touch .env
```
4. Set environment variables. The Docker Compose files will be populated with values from your `.env`.
The following values are **required** to be set:
4. The Docker Compose files are populated with the values from your .env. The following values must be set:
```bash
OPENSEARCH_PASSWORD=your_secure_password
@ -63,7 +61,8 @@ The following values are **required** to be set:
For more information on configuring OpenRAG with environment variables, see [Environment variables](/reference/configuration).
5. Start `docling serve` on the host machine.
Both Docker deployments depend on `docling serve` to be running on port `5001` on the host machine. This enables [Mac MLX](https://opensource.apple.com/projects/mlx/) support for document processing.
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
@ -74,7 +73,7 @@ The following values are **required** to be set:
uv run python scripts/docling_ctl.py status
```
Successful result:
Make sure the response shows that `docling serve` is running, for example:
```bash
Status: running
Endpoint: http://127.0.0.1:5001
@ -84,16 +83,21 @@ The following values are **required** to be set:
7. Deploy OpenRAG locally with Docker Compose based on your deployment type.
For GPU-enabled systems, run the following commands:
<Tabs groupId="Compose file">
<TabItem value="docker-compose.yml" label="docker-compose.yml" default>
```bash
docker compose build
docker compose up -d
```
For environments without GPU support, run:
```
</TabItem>
<TabItem value="docker-compose-cpu.yml" label="docker-compose-cpu.yml">
```bash
docker compose -f docker-compose-cpu.yml up -d
```
</TabItem>
</Tabs>
The OpenRAG Docker Compose file starts five containers:
| Container Name | Default Address | Purpose |
@ -110,7 +114,7 @@ The following values are **required** to be set:
docker compose ps
```
You can now access the application at:
You can now access OpenRAG at the following endpoints:
- **Frontend**: http://localhost:3000
- **Backend API**: http://localhost:8000
@ -129,7 +133,7 @@ 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](/get-started/tui#status).
These commands are also available in the TUI's [Status menu](/install#status).
### Upgrade containers

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@ -9,16 +9,24 @@ import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
[Install the OpenRAG Python wheel](#install-python-wheel), and then run the [OpenRAG Terminal User Interface(TUI)](#setup) to start your OpenRAG deployment with a guided setup process.
If you prefer running Docker commands and manually editing `.env` files, see [Deploy with Docker](/get-started/docker).
The OpenRAG Terminal User Interface (TUI) allows you to set up, configure, and monitor your OpenRAG deployment directly from the terminal, on any operating system.
![OpenRAG TUI Interface](@site/static/img/OpenRAG_TUI_2025-09-10T13_04_11_757637.svg)
Instead of starting OpenRAG using Docker commands and manually editing values in the `.env` file, the TUI walks you through the setup. It prompts for variables where required, creates a `.env` file for you, and then starts OpenRAG.
Once OpenRAG is running, use the TUI to monitor your application, control your containers, and retrieve logs.
If you prefer running Docker commands and manually editing `.env` files, see [Install with Docker](/get-started/docker).
## Prerequisites
- [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
- [uv](https://docs.astral.sh/uv/getting-started/installation/)
- [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/) installed
- [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.
- Install [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
- Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
- Install [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/)
- Install [Docker Compose](https://docs.docker.com/compose/install/). 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.
- Create an [OpenAI API key](https://platform.openai.com/api-keys). This key is **required** to start OpenRAG, but you can choose a different model provider during [Application Onboarding](#application-onboarding).
- Optional: GPU support requires an NVIDIA GPU with [CUDA](https://docs.nvidia.com/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.
- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment.
## Install the OpenRAG Python wheel {#install-python-wheel}
@ -57,7 +65,7 @@ The OpenRAG wheel installs the Terminal User Interface (TUI) for configuring and
uv run openrag
```
5. Continue with [Setup OpenRAG with the TUI](#setup).
5. Continue with [Set up OpenRAG with the TUI](#setup).
## Set up OpenRAG with the TUI {#setup}
@ -65,19 +73,19 @@ The TUI creates a `.env` file in your OpenRAG directory root and starts OpenRAG.
If the TUI detects a `.env` file in the OpenRAG root directory, it sources any variables from the `.env` file.
If the TUI detects OAuth credentials, it enforces the **Advanced Setup** path.
**Basic Setup** generates all of the required values for OpenRAG except the OpenAI API key.
**Basic Setup** does not set up OAuth connections for ingestion from cloud providers.
For OAuth setup, use **Advanced Setup**.
**Basic Setup** and **Advanced Setup** enforce the same authentication settings for the Langflow server, but manage document access differently. For more information, see [Authentication and document access](/knowledge#auth).
<Tabs groupId="Setup method">
<TabItem value="Basic setup" label="Basic setup" default>
**Basic Setup** generates all of the required values for OpenRAG except the OpenAI API key.
**Basic Setup** does not set up OAuth connections for ingestion from cloud providers.
For OAuth setup, use **Advanced Setup**.
For information about the difference between basic (no auth) and OAuth in OpenRAG, see [Authentication and document access](/knowledge#auth).
1. To install OpenRAG with **Basic Setup**, click **Basic Setup** or press <kbd>1</kbd>.
2. Click **Generate Passwords** to generate passwords for OpenSearch and Langflow.
3. Paste your OpenAI API key in the OpenAI API key field.
4. Click **Save Configuration**.
Your passwords are saved in the `.env` file used to start OpenRAG.
5. To start OpenRAG, click **Start Container Services**.
Startup pulls container images and runs them, so it can take some time.
When startup is complete, the TUI displays the following:
@ -126,4 +134,60 @@ For OAuth setup, use **Advanced Setup**.
</TabItem>
</Tabs>
<PartialOnboarding />
<PartialOnboarding />
## Manage OpenRAG containers with the TUI
After installation, the TUI can deploy, manage, and upgrade your OpenRAG containers.
### Start container services
Click **Start Container Services** to start the OpenRAG containers.
The TUI automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for `CUDA`, `NVIDIA_SMI`, and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses.
The TUI then pulls the images and deploys the containers with the following command.
```bash
docker compose up -d
```
If images are missing, the TUI runs `docker compose pull`, then runs `docker compose up -d`.
### Start native services
A "native" service in OpenRAG refers to a service run natively on your machine, and not within a container.
The `docling serve` process is a native service in OpenRAG, because it's a document processing service that is run on your local machine, and controlled separately from the containers.
To start or stop `docling serve` or any other native services, in the TUI main menu, click **Start Native Services** or **Stop Native Services**.
To view the status, port, or PID of a native service, in the TUI main menu, click [Status](#status).
### Status
The **Status** menu displays information on your container deployment.
Here you can check container health, find your service ports, view logs, and upgrade your containers.
To view streaming logs, select the container you want to view, and press <kbd>l</kbd>.
To copy your logs, click **Copy to Clipboard**.
To **upgrade** your containers, click **Upgrade**.
**Upgrade** runs `docker compose pull` and then `docker compose up -d --force-recreate`.
The first command pulls the latest images of OpenRAG.
The second command recreates the containers with your data persisted.
To **reset** your containers, click **Reset**.
Reset gives you a completely fresh start.
Reset deletes all of your data, including OpenSearch data, uploaded documents, and authentication.
**Reset** runs two commands.
It first stops and removes all containers, volumes, and local images.
```
docker compose down --volumes --remove-orphans --rmi local
```
When the first command is complete, OpenRAG removes any additional Docker objects with `prune`.
```
docker system prune -f
```
## Diagnostics
The **Diagnostics** menu provides health monitoring for your container runtimes and monitoring of your OpenSearch security.

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docs/docs/get-started/quickstart.mdx
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@ -11,33 +11,30 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
## Prerequisites
- [Install and start OpenRAG](/install)
- Install and start OpenRAG with the [TUI](/install) or [Docker](/get-started/docker)
## Find your way around
## Load and chat with your own documents
1. In OpenRAG, click <Icon name="MessageSquare" aria-hidden="true"/> **Chat**.
The chat is powered by the OpenRAG OpenSearch Agent.
For more information, see [Langflow Agents](/agents).
2. Ask `What documents are available to you?`
The agent responds with a message summarizing the documents that OpenRAG loads by default, which are PDFs about evaluating data quality when using LLMs in health care.
The agent responds with a message summarizing the documents that OpenRAG loads by default.
Knowledge is stored in OpenSearch.
For more information, see [Knowledge](/knowledge).
3. To confirm the agent is correct, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
3. To confirm the agent is correct about the default knowledge, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database.
Click on a document to display the chunks derived from splitting the default documents into the vector database.
## Add your own knowledge
1. To add documents to your knowledge base, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**.
* Select **Add File** to add a single file from your local machine (mapped with the Docker volume mount).
* Select **Process Folder** to process an entire folder of documents from your local machine (mapped with the Docker volume mount).
4. To add documents to your knowledge base, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**.
* Select **Add File** to add a single file from your local machine.
* Select **Process Folder** to process an entire folder of documents from your local machine.
* Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see [OAuth ingestion](/knowledge#oauth-ingestion).
2. Return to the Chat window and ask a question about your loaded data.
5. Return to the Chat window and ask a question about your loaded data.
For example, with a manual about a PC tablet loaded, ask `How do I connect this device to WiFI?`
The agent responds with a message indicating it now has your knowledge as context for answering questions.
3. Click the <Icon name="Gear" aria-hidden="true"/> **Function Call: search_documents (tool_call)** that is printed in the Playground.
These events log the agent's request to the tool and the tool's response, so you have direct visibility into your agent's functionality.
If you aren't getting the results you need, you can further tune the knowledge ingestion and agent behavior in the next section.
6. Click <Icon name="Gear" aria-hidden="true"/> **Function Call: search_documents (tool_call)**.
This log describes how the agent uses tools.
This is helpful for troubleshooting when the agent isn't responding as expected.
## Swap out the language model to modify agent behavior {#change-components}
@ -48,58 +45,40 @@ In this example, you'll try a different LLM to demonstrate how the Agent's respo
1. To edit the Agent's behavior, click **Edit in Langflow**.
You can more quickly access the **Language Model** and **Agent Instructions** fields in this page, but for illustration purposes, navigate to the Langflow visual builder.
2. OpenRAG warns you that you're entering Langflow. Click **Proceed**.
3. The OpenRAG OpenSearch Agent flow appears.
The OpenRAG OpenSearch Agent flow appears in a new browser window.
![OpenRAG Open Search Agent Flow](/img/opensearch-agent-flow.png)
4. In the **Language Model** component, under **Model**, select a different OpenAI model.
5. Save your flow with <kbd>Command+S</kbd>.
6. In OpenRAG, start a new conversation by clicking the <Icon name="Plus" aria-hidden="true"/> in the **Conversations** tab.
7. Ask the same question as before to demonstrate how a different language model changes the results.
3. Find the **Language Model** component, and then change the **Model Name** field to a different OpenAI model.
4. Save your flow with <kbd>Command+S</kbd> (Mac) or <kbd>Ctrl+S</kbd> (Windows).
5. Return to the OpenRAG browser window, and start a new conversation by clicking <Icon name="Plus" aria-hidden="true"/> in the **Conversations** tab.
6. Ask the same question you asked before to see how the response differs between models.
## Integrate OpenRAG into your application
To integrate OpenRAG into your application, use the [Langflow API](https://docs.langflow.org/api-reference-api-examples).
Make requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results. Langflow provides code snippets to help you get started.
Langflow in OpenRAG includes pre-built flows that you can integrate into your applications using the [Langflow API](https://docs.langflow.org/api-reference-api-examples).
1. Create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication).
<details>
<summary>Create a Langflow API key</summary>
A Langflow API key is a user-specific token you can use with Langflow.
It is **only** used for sending requests to the Langflow server.
It does **not** access to OpenRAG.
To create a Langflow API key, do the following:
1. In Langflow, click your user icon, and then select **Settings**.
2. Click **Langflow API Keys**, and then click <Icon name="Plus" aria-hidden="true"/> **Add New**.
3. Name your key, and then click **Create API Key**.
4. Copy the API key and store it securely.
5. To use your Langflow API key in a request, set a `LANGFLOW_API_KEY` environment variable in your terminal, and then include an `x-api-key` header or query parameter with your request.
For example:
```bash
# Set variable
export LANGFLOW_API_KEY="sk..."
# Send request
curl --request POST \
--url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
--header "Content-Type: application/json" \
--header "x-api-key: $LANGFLOW_API_KEY" \
--data '{
"output_type": "chat",
"input_type": "chat",
"input_value": "Hello"
}'
```
</details>
2. To navigate to the OpenRAG OpenSearch Agent flow, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then click **Edit in Langflow** in the OpenRAG OpenSearch Agent flow.
3. Click **Share**, and then click **API access**.
The Langflow API accepts Python, TypeScript, or curl requests to run flows and get responses. You can use these flows as-is or modify them to better suit your needs.
The default code in the API access pane constructs a request with the Langflow server `url`, `headers`, and a `payload` of request data. The code snippets automatically include the `LANGFLOW_SERVER_ADDRESS` and `FLOW_ID` values for the flow. Replace these values if you're using the code for a different server or flow. The default Langflow server address is http://localhost:7860.
In this section, you'll run the OpenRAG OpenSearch Agent flow and get a response using the API.
1. To navigate to the OpenRAG OpenSearch Agent flow in Langflow, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then click **Edit in Langflow** in the OpenRAG OpenSearch Agent flow.
2. Create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication).
A Langflow API key is a user-specific token you can use with Langflow.
It is **only** used for sending requests to the Langflow server.
It does **not** access OpenRAG.
To create a Langflow API key, do the following:
1. Open Langflow, click your user icon, and then select **Settings**.
2. Click **Langflow API Keys**, and then click <Icon name="Plus" aria-hidden="true"/> **Add New**.
3. Name your key, and then click **Create API Key**.
4. Copy the API key and store it securely.
3. Langflow includes code snippets for the request to the Langflow API.
To retrieve the code snippet, click **Share**, and then click **API access**.
The default code in the API access pane constructs a request with the Langflow server `url`, `headers`, and a `payload` of request data. The code snippets automatically include the `LANGFLOW_SERVER_ADDRESS` and `FLOW_ID` values for the flow.
<Tabs>
<TabItem value="python" label="Python">
@ -171,12 +150,12 @@ Make requests with Python, TypeScript, or any HTTP client to run one of OpenRAG'
curl --request POST \
--url 'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false' \
--header 'Content-Type: application/json' \
--header "x-api-key: LANGFLOW_API_KEY" \
--header "x-api-key: LANGFLOW_API_KEY" \
--data '{
"output_type": "chat",
"input_type": "chat",
"input_value": "hello world!",
}'
"output_type": "chat",
"input_type": "chat",
"input_value": "hello world!"
}'
```
</TabItem>
@ -185,176 +164,6 @@ Make requests with Python, TypeScript, or any HTTP client to run one of OpenRAG'
4. Copy the snippet, paste it in a script file, and then run the script to send the request. If you are using the curl snippet, you can run the command directly in your terminal.
If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.
The following is an example of a response from running the **Simple Agent** template flow:
<details>
<summary>Result</summary>
```json
{
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
"outputs": [
{
"inputs": {
"input_value": "hello world!"
},
"outputs": [
{
"results": {
"message": {
"text_key": "text",
"data": {
"timestamp": "2025-06-16 19:58:23 UTC",
"sender": "Machine",
"sender_name": "AI",
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
"text": "Hello world! 🌍 How can I assist you today?",
"files": [],
"error": false,
"edit": false,
"properties": {
"text_color": "",
"background_color": "",
"edited": false,
"source": {
"id": "Agent-ZOknz",
"display_name": "Agent",
"source": "gpt-4o-mini"
},
"icon": "bot",
"allow_markdown": false,
"positive_feedback": null,
"state": "complete",
"targets": []
},
"category": "message",
"content_blocks": [
{
"title": "Agent Steps",
"contents": [
{
"type": "text",
"duration": 2,
"header": {
"title": "Input",
"icon": "MessageSquare"
},
"text": "**Input**: hello world!"
},
{
"type": "text",
"duration": 226,
"header": {
"title": "Output",
"icon": "MessageSquare"
},
"text": "Hello world! 🌍 How can I assist you today?"
}
],
"allow_markdown": true,
"media_url": null
}
],
"id": "f3d85d9a-261c-4325-b004-95a1bf5de7ca",
"flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
"duration": null
},
"default_value": "",
"text": "Hello world! 🌍 How can I assist you today?",
"sender": "Machine",
"sender_name": "AI",
"files": [],
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
"timestamp": "2025-06-16T19:58:23+00:00",
"flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
"error": false,
"edit": false,
"properties": {
"text_color": "",
"background_color": "",
"edited": false,
"source": {
"id": "Agent-ZOknz",
"display_name": "Agent",
"source": "gpt-4o-mini"
},
"icon": "bot",
"allow_markdown": false,
"positive_feedback": null,
"state": "complete",
"targets": []
},
"category": "message",
"content_blocks": [
{
"title": "Agent Steps",
"contents": [
{
"type": "text",
"duration": 2,
"header": {
"title": "Input",
"icon": "MessageSquare"
},
"text": "**Input**: hello world!"
},
{
"type": "text",
"duration": 226,
"header": {
"title": "Output",
"icon": "MessageSquare"
},
"text": "Hello world! 🌍 How can I assist you today?"
}
],
"allow_markdown": true,
"media_url": null
}
],
"duration": null
}
},
"artifacts": {
"message": "Hello world! 🌍 How can I assist you today?",
"sender": "Machine",
"sender_name": "AI",
"files": [],
"type": "object"
},
"outputs": {
"message": {
"message": "Hello world! 🌍 How can I assist you today?",
"type": "text"
}
},
"logs": {
"message": []
},
"messages": [
{
"message": "Hello world! 🌍 How can I assist you today?",
"sender": "Machine",
"sender_name": "AI",
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
"stream_url": null,
"component_id": "ChatOutput-aF5lw",
"files": [],
"type": "text"
}
],
"timedelta": null,
"duration": null,
"component_display_name": "Chat Output",
"component_id": "ChatOutput-aF5lw",
"used_frozen_result": false
}
]
}
]
}
```
</details>
To further explore the API, see:

90
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@ -1,90 +0,0 @@
---
title: Terminal User Interface (TUI) commands
slug: /get-started/tui
---
The OpenRAG Terminal User Interface (TUI) allows you to set up, configure, and monitor your OpenRAG deployment directly from the terminal, on any operating system.
![OpenRAG TUI Interface](@site/static/img/OpenRAG_TUI_2025-09-10T13_04_11_757637.svg)
Instead of starting OpenRAG using Docker commands and manually editing values in the `.env` file, the TUI walks you through the setup. It prompts for variables where required, creates a `.env` file for you, and then starts OpenRAG.
Once OpenRAG is running, use the TUI to monitor your application, control your containers, and retrieve logs.
## Start the TUI
To start the TUI, run the following commands from the directory where you installed OpenRAG.
```bash
uv sync
uv run openrag
```
The TUI Welcome Screen offers basic and advanced setup options.
For more information on setup values during installation, see [Install OpenRAG](/install).
## Navigation
The TUI accepts mouse input or keyboard commands.
- <kbd>Arrow keys</kbd>: move between options
- <kbd>Tab</kbd>/<kbd>Shift+Tab</kbd>: switch fields and buttons
- <kbd>Enter</kbd>: select/confirm
- <kbd>Escape</kbd>: back
- <kbd>Q</kbd>: quit
- <kbd>Number keys (1-4)</kbd>: quick access to main screens
## Container management
The TUI can deploy, manage, and upgrade your OpenRAG containers.
### Start container services
Click **Start Container Services** to start the OpenRAG containers.
The TUI automatically detects your container runtime, and then checks if your machine has compatible GPU support by checking for `CUDA`, `NVIDIA_SMI`, and Docker/Podman runtime support. This check determines which Docker Compose file OpenRAG uses.
The TUI then pulls the images and deploys the containers with the following command.
```bash
docker compose up -d
```
If images are missing, the TUI runs `docker compose pull`, then runs `docker compose up -d`.
### Start native services
A "native" service in OpenRAG refers to a service run natively on your machine, and not within a container.
The `docling serve` process is a native service in OpenRAG, because it's a document processing service that is run on your local machine, and controlled separately from the containers.
To start or stop `docling serve` or any other native services, in the TUI main menu, click **Start Native Services** or **Stop Native Services**.
To view the status, port, or PID of a native service, in the TUI main menu, click [Status](#status).
### Status
The **Status** menu displays information on your container deployment.
Here you can check container health, find your service ports, view logs, and upgrade your containers.
To view streaming logs, select the container you want to view, and press <kbd>l</kbd>.
To copy your logs, click **Copy to Clipboard**.
To **upgrade** your containers, click **Upgrade**.
**Upgrade** runs `docker compose pull` and then `docker compose up -d --force-recreate`.
The first command pulls the latest images of OpenRAG.
The second command recreates the containers with your data persisted.
To **reset** your containers, click **Reset**.
Reset gives you a completely fresh start.
Reset deletes all of your data, including OpenSearch data, uploaded documents, and authentication.
**Reset** runs two commands.
It first stops and removes all containers, volumes, and local images.
```
docker compose down --volumes --remove-orphans --rmi local
```
When the first command is complete, OpenRAG removes any additional Docker objects with `prune`.
```
docker system prune -f
```
## Diagnostics
The **Diagnostics** menu provides health monitoring for your container runtimes and monitoring of your OpenSearch security.

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@ -3,18 +3,17 @@ title: What is OpenRAG?
slug: /
---
OpenRAG is an open-source package for building agentic RAG systems.
It supports integration with a wide range of orchestration tools, vector databases, and LLM providers.
OpenRAG is an open-source package for building agentic RAG systems that integrates with a wide range of orchestration tools, vector databases, and LLM providers.
OpenRAG connects and amplifies three popular, proven open-source projects into one powerful platform:
* [Langflow](https://docs.langflow.org) - Langflow is a powerful tool to build and deploy AI agents and MCP servers. It supports all major LLMs, vector databases and a growing library of AI tools.
* [Langflow](https://docs.langflow.org): Langflow is a popular tool for building and deploying AI agents and MCP servers. It supports all major LLMs, vector databases, and a growing library of AI tools.
* [OpenSearch](https://docs.opensearch.org/latest/) - OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.
* [OpenSearch](https://docs.opensearch.org/latest/): OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.
* [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.
* [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.
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.
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 Google Drive, OneDrive, and Sharepoint.
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.

4
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@ -7,9 +7,9 @@ import Icon from "@site/src/components/icon/icon";
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
OpenRAG recognizes [supported environment variables](#supported-environment-variables) from the following sources:
OpenRAG recognizes environment variables from the following sources:
* [Environment variables](#supported-environment-variables) - Values set in the `.env` file.
* [Environment variables](#configure-environment-variables) - Values set in the `.env` file.
* [Langflow runtime overrides](#langflow-runtime-overrides) - Langflow components may tweak environment variables at runtime.
* [Default or fallback values](#default-values-and-fallbacks) - These values are default or fallback values if OpenRAG doesn't find a value.

5
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@ -36,11 +36,6 @@ const sidebars = {
id: "get-started/quickstart",
label: "Quickstart"
},
{
type: "doc",
id: "get-started/tui",
label: "Terminal User Interface (TUI)"
},
{
type: "doc",
id: "core-components/agents",

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