diff --git a/docs/docs/get-started/quickstart.mdx b/docs/docs/get-started/quickstart.mdx
index 942433b5..80259617 100644
--- a/docs/docs/get-started/quickstart.mdx
+++ b/docs/docs/get-started/quickstart.mdx
@@ -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 **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 **Knowledge**.
+3. To confirm the agent is correct about the default knowledge, 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
-
-1. 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).
+4. To add documents to your knowledge base, click **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 **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 **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.
-4. In the **Language Model** component, under **Model**, select a different OpenAI model.
-5. Save your flow with Command+S.
-6. In OpenRAG, start a new conversation by clicking the 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 Command+S (Mac) or Ctrl+S (Windows).
+5. Return to the OpenRAG browser window, and start a new conversation by clicking 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).
-
- 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:
-
- ```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"
- }'
- ```
-
-
-2. To navigate to the OpenRAG OpenSearch Agent flow, click **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 **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 **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.
@@ -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!"
+ }'
```
@@ -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:
-
-
-Result
-
-```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
- }
- ]
- }
- ]
-}
-```
-
To further explore the API, see:
diff --git a/docs/static/img/opensearch-agent-flow.png b/docs/static/img/opensearch-agent-flow.png
index 7382fcd1..7027628d 100644
Binary files a/docs/static/img/opensearch-agent-flow.png and b/docs/static/img/opensearch-agent-flow.png differ