From 3e618271c0f383ab2bf013cb9782460c83a9bedb Mon Sep 17 00:00:00 2001
From: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
Date: Tue, 28 Oct 2025 17:08:12 -0400
Subject: [PATCH] quickstart
---
docs/docs/_partial-onboarding.mdx | 4 +++-
docs/docs/get-started/quickstart.mdx | 22 +++++++++++++---------
2 files changed, 16 insertions(+), 10 deletions(-)
diff --git a/docs/docs/_partial-onboarding.mdx b/docs/docs/_partial-onboarding.mdx
index c956eb53..3f2de8fb 100644
--- a/docs/docs/_partial-onboarding.mdx
+++ b/docs/docs/_partial-onboarding.mdx
@@ -3,7 +3,9 @@ import TabItem from '@theme/TabItem';
## Application onboarding
-The first time you start OpenRAG, whether using the TUI or a `.env` file, you must complete application onboarding.
+The first time you start OpenRAG, whether using the TUI or a `.env` file, it's recommended that you complete application onboarding.
+
+To skip onboarding, click **Skip onboarding**.
Values from onboarding can be changed later in the OpenRAG **Settings** page.
diff --git a/docs/docs/get-started/quickstart.mdx b/docs/docs/get-started/quickstart.mdx
index 80259617..92ed71c8 100644
--- a/docs/docs/get-started/quickstart.mdx
+++ b/docs/docs/get-started/quickstart.mdx
@@ -7,7 +7,7 @@ import Icon from "@site/src/components/icon/icon";
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the OpenRAG API.
+Get started with OpenRAG by loading your knowledge, swapping out your language model, and then chatting with the Langflow API.
## Prerequisites
@@ -17,20 +17,20 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
1. In OpenRAG, click **Chat**.
The chat is powered by the OpenRAG OpenSearch Agent.
- For more information, see [Langflow Agents](/agents).
+ For more information, see [Langflow in OpenRAG](/agents).
2. Ask `What documents are available to you?`
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).
+ For more information, see [OpenSearch in OpenRAG](/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.
-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.
+ Click on a document to display the chunks derived from splitting the default documents into the OpenSearch vector database.
+4. To add documents to your knowledge base, click **Add Knowledge**.
+ * Select **File** to add a single file from your local machine.
+ * Select **Folder** to process an entire folder of documents from your local machine. The default directory is `/documents` in your OpenRAG directory.
* Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see [OAuth ingestion](/knowledge#oauth-ingestion).
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?`
+ 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.
6. Click **Function Call: search_documents (tool_call)**.
This log describes how the agent uses tools.
@@ -44,8 +44,12 @@ 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.
+To revert the flow to its initial state, click **Restore flow**.
2. OpenRAG warns you that you're entering Langflow. Click **Proceed**.
-The OpenRAG OpenSearch Agent flow appears in a new browser window.
+
+ If Langflow requests login information, enter the `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD` from the `.env` file in your OpenRAG directory.
+
+ The OpenRAG OpenSearch Agent flow appears in a new browser window.

3. Find the **Language Model** component, and then change the **Model Name** field to a different OpenAI model.