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