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April M 2025-11-21 12:25:17 -08:00
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9
docs/docs/get-started/install.mdx
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@ -235,13 +235,14 @@ If the TUI detects OAuth credentials, it enforces the **Advanced Setup** path.
<PartialOnboarding /> <PartialOnboarding />
## Close the OpenRAG TUI ## Exit the OpenRAG TUI
To close the OpenRAG TUI, press <kbd>q</kbd>. To exit the OpenRAG TUI, press <kbd>q</kbd>.
The OpenRAG containers will continue to be served until the containers are stopped. The OpenRAG containers continue to run until they are stopped.
For more information, see [Manage OpenRAG containers with the TUI ](#tui-container-management). For more information, see [Manage OpenRAG containers with the TUI ](#tui-container-management).
To start the TUI again, run `uv run openrag`. To relaunch the TUI, run `uv run openrag`.
If you installed OpenRAG with `uvx`, run `uvx openrag`.
## Manage OpenRAG containers with the TUI {#tui-container-management} ## Manage OpenRAG containers with the TUI {#tui-container-management}

185
docs/docs/get-started/quickstart.mdx
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@ -19,10 +19,10 @@ For this quickstart, install OpenRAG with the automatic installer script and bas
1. Create a directory to store the OpenRAG configuration files, and then change to that directory: 1. Create a directory to store the OpenRAG configuration files, and then change to that directory:
```bash ```bash
mkdir openrag-workspace mkdir openrag-workspace
cd openrag-workspace cd openrag-workspace
``` ```
2. [Download the OpenRAG install script](https://docs.openr.ag/files/run_openrag_with_prereqs.sh), move it to your OpenRAG directory, and then run it: 2. [Download the OpenRAG install script](https://docs.openr.ag/files/run_openrag_with_prereqs.sh), move it to your OpenRAG directory, and then run it:
@ -37,26 +37,27 @@ For this quickstart, install OpenRAG with the automatic installer script and bas
3. Select **Basic Setup**. 3. Select **Basic Setup**.
4. Click **Generate Passwords** to automatically generate OpenRAG passwords. 4. Create passwords for your OpenRAG installation's OpenSearch and Langflow services. You can click **Generate Passwords** to automatically generate passwords.
5. Click **Start All Services**, and then wait while the startup process pulls and runs the necessary container images. The OpenSearch password is required. The Langflow admin password is optional.
This can take a few minutes. If you don't generate a Langflow admin password, Langflow runs in [autologin mode](https://docs.langflow.org/api-keys-and-authentication#langflow-auto-login) with no password required.
Proceed when you see the following messages in the TUI:
```bash Your passwords are saved in the `.env` file that is used to start OpenRAG.
Services started successfully You can find this file in your OpenRAG installation directory.
Command completed successfully
```
6. To open the OpenRAG application, navigate to the TUI main menu, and then click **Open App**. 5. Click **Save Configuration**, and then click **Start All Services**.
Wait a few minutes while the startup process pulls and runs the necessary container images.
Proceed when you see the following messages in the terminal user interface (TUI):
```bash
Services started successfully
Command completed successfully
```
6. To open the OpenRAG application, go to the TUI main menu, and then click **Open App**.
Alternatively, in your browser, navigate to `localhost:3000`. Alternatively, in your browser, navigate to `localhost:3000`.
:::tip
To quit OpenRAG, go to the TUI main menu, and then press <kbd>q</kbd>.
To restart OpenRAG, run `uvx openrag`.
:::
7. Select the **OpenAI** model provider, enter your OpenAI API key, and then click **Complete**. 7. Select the **OpenAI** model provider, enter your OpenAI API key, and then click **Complete**.
For this quickstart, you can use the default options for the model settings. For this quickstart, you can use the default options for the model settings.
@ -140,98 +141,98 @@ You can send and receive requests with the Langflow API using Python, TypeScript
2. Create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication), which is a user-specific token required to send requests to the Langflow server. 2. Create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication), which is a user-specific token required to send requests to the Langflow server.
This key doesn't grant access to OpenRAG. This key doesn't grant access to OpenRAG.
1. In the Langflow visual editor, click your user icon in the header, and then select **Settings**. 1. In the Langflow visual editor, click your user icon in the header, and then select **Settings**.
2. Click **Langflow API Keys**, and then click <Icon name="Plus" aria-hidden="true"/> **Add New**. 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**. 3. Name your key, and then click **Create API Key**.
4. Copy the API key and store it securely. 4. Copy the API key and store it securely.
5. Exit the Langflow **Settings** page to return to the visual editor. 5. Exit the Langflow **Settings** page to return to the visual editor.
3. Click **Share**, and then select **API access** to get pregenerated code snippets that call the Langflow API and run the flow. 3. Click **Share**, and then select **API access** to get pregenerated code snippets that call the Langflow API and run the flow.
These code snippets construct API requests with your Langflow server URL (`LANGFLOW_SERVER_ADDRESS`), the flow to run (`FLOW_ID`), required headers (`LANGFLOW_API_KEY`, `Content-Type`), and a payload containing the required inputs to run the flow, including a default chat input message. These code snippets construct API requests with your Langflow server URL (`LANGFLOW_SERVER_ADDRESS`), the flow to run (`FLOW_ID`), required headers (`LANGFLOW_API_KEY`, `Content-Type`), and a payload containing the required inputs to run the flow, including a default chat input message.
In production, you might modify the inputs to suit your application logic. For example, you might replace the default chat input message with dynamic user input. In production, you might modify the inputs to suit your application logic. For example, you might replace the default chat input message with dynamic user input.
<Tabs> <Tabs>
<TabItem value="python" label="Python"> <TabItem value="python" label="Python">
```python ```python
import requests import requests
import os import os
import uuid import uuid
api_key = 'LANGFLOW_API_KEY' api_key = 'LANGFLOW_API_KEY'
url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow
# Request payload configuration # Request payload configuration
payload = { payload = {
"output_type": "chat", "output_type": "chat",
"input_type": "chat", "input_type": "chat",
"input_value": "hello world!" "input_value": "hello world!"
} }
payload["session_id"] = str(uuid.uuid4()) payload["session_id"] = str(uuid.uuid4())
headers = {"x-api-key": api_key} headers = {"x-api-key": api_key}
try: try:
# Send API request # Send API request
response = requests.request("POST", url, json=payload, headers=headers) response = requests.request("POST", url, json=payload, headers=headers)
response.raise_for_status() # Raise exception for bad status codes response.raise_for_status() # Raise exception for bad status codes
# Print response # Print response
print(response.text) print(response.text)
except requests.exceptions.RequestException as e: except requests.exceptions.RequestException as e:
print(f"Error making API request: {e}") print(f"Error making API request: {e}")
except ValueError as e: except ValueError as e:
print(f"Error parsing response: {e}") print(f"Error parsing response: {e}")
``` ```
</TabItem> </TabItem>
<TabItem value="typescript" label="TypeScript"> <TabItem value="typescript" label="TypeScript">
```typescript ```typescript
const crypto = require('crypto'); const crypto = require('crypto');
const apiKey = 'LANGFLOW_API_KEY'; const apiKey = 'LANGFLOW_API_KEY';
const payload = { const payload = {
"output_type": "chat", "output_type": "chat",
"input_type": "chat", "input_type": "chat",
"input_value": "hello world!" "input_value": "hello world!"
}; };
payload.session_id = crypto.randomUUID(); payload.session_id = crypto.randomUUID();
const options = { const options = {
method: 'POST', method: 'POST',
headers: { headers: {
'Content-Type': 'application/json', 'Content-Type': 'application/json',
"x-api-key": apiKey "x-api-key": apiKey
}, },
body: JSON.stringify(payload) body: JSON.stringify(payload)
}; };
fetch('http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID', options) fetch('http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID', options)
.then(response => response.json()) .then(response => response.json())
.then(response => console.warn(response)) .then(response => console.warn(response))
.catch(err => console.error(err)); .catch(err => console.error(err));
``` ```
</TabItem> </TabItem>
<TabItem value="curl" label="curl"> <TabItem value="curl" label="curl">
```bash ```bash
curl --request POST \ curl --request POST \
--url 'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false' \ --url 'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false' \
--header 'Content-Type: application/json' \ --header 'Content-Type: application/json' \
--header "x-api-key: LANGFLOW_API_KEY" \ --header "x-api-key: LANGFLOW_API_KEY" \
--data '{ --data '{
"output_type": "chat", "output_type": "chat",
"input_type": "chat", "input_type": "chat",
"input_value": "hello world!" "input_value": "hello world!"
}' }'
``` ```
</TabItem> </TabItem>
</Tabs> </Tabs>
4. Copy your preferred snippet (Python, TypeScript, or curl), and then run it: 4. Copy your preferred snippet (Python, TypeScript, or curl), and then run it: