Quickstart

Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API.

Prerequisites

Install and start OpenRAG
Create a Langflow API key
Create a Langflow API key
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 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:
  
  # 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" }'

Find your way around

In OpenRAG, click Chat. The chat is powered by the OpenRAG OpenSearch Agent. For more information, see Langflow Agents.
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. Knowledge is stored in OpenSearch. For more information, see Knowledge.
To confirm the agent is correct, click 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

To add documents to your knowledge base, click 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).
- Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see OAuth ingestion.
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.
Click the 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.

Swap out the language model to modify agent behavior

To modify the knowledge ingestion or Agent behavior, click Settings.

In this example, you'll try a different LLM to demonstrate how the Agent's response changes. You can only change the Language model, and not the Model provider that you started with in OpenRAG. If you're using Ollama, you can use any installed model.

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.
OpenRAG warns you that you're entering Langflow. Click Proceed.
The OpenRAG OpenSearch Agent flow appears.
In the Language Model component, under Model, select a different OpenAI model.
Save your flow with Command+S.
In OpenRAG, start a new conversation by clicking the in the Conversations tab.
Ask the same question as before to demonstrate how a different language model changes the results.

Integrate OpenRAG into your application

To integrate OpenRAG into your application, use the Langflow API. 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 with the Langflow API.

To navigate to the OpenRAG OpenSearch Agent flow, click Settings, and then click Edit in Langflow in the OpenRAG OpenSearch Agent flow.

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. Replace these values if you're using the code for a different server or flow. The default Langflow server address is http://localhost:7860.

Python
TypeScript
curl

import requests
import os
import uuid

api_key = 'LANGFLOW_API_KEY'
url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID"  # The complete API endpoint URL for this flow

# Request payload configuration
payload = {
    "output_type": "chat",
    "input_type": "chat",
    "input_value": "hello world!"
}
payload["session_id"] = str(uuid.uuid4())

headers = {"x-api-key": api_key}

try:
    # Send API request
    response = requests.request("POST", url, json=payload, headers=headers)
    response.raise_for_status()  # Raise exception for bad status codes

    # Print response
    print(response.text)

except requests.exceptions.RequestException as e:
    print(f"Error making API request: {e}")
except ValueError as e:
    print(f"Error parsing response: {e}")

const crypto = require('crypto');
const apiKey = 'LANGFLOW_API_KEY';
const payload = {
    "output_type": "chat",
    "input_type": "chat",
    "input_value": "hello world!"
};
payload.session_id = crypto.randomUUID();

const options = {
    method: 'POST',
    headers: {
        'Content-Type': 'application/json',
        "x-api-key": apiKey
    },
    body: JSON.stringify(payload)
};

fetch('http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID', options)
    .then(response => response.json())
    .then(response => console.warn(response))
    .catch(err => console.error(err));

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" \ 
     --data '{
		           "output_type": "chat",
		           "input_type": "chat",
		           "input_value": "hello world!",
		         }'

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:

Result

{
  "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
        }
      ]
    }
  ]
}

To further explore the API, see:

The Langflow Quickstart extends this example with extracting fields from the response.
Get started with the Langflow API

Prerequisites​

Find your way around​

Add your own knowledge​

Swap out the language model to modify agent behavior​

Integrate OpenRAG into your application​

Prerequisites

Find your way around

Add your own knowledge

Swap out the language model to modify agent behavior

Integrate OpenRAG into your application