From bf77361081436af2552b8ecc8e423a003cba205c Mon Sep 17 00:00:00 2001
From: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
Date: Thu, 25 Sep 2025 10:44:19 -0400
Subject: [PATCH] initial-content

---
 docs/docs/core-components/agents.mdx | 22 ++++++++++++++++++++--
 docs/docs/get-started/quickstart.mdx |  4 +++-
 docs/sidebars.js                     |  2 +-
 3 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/docs/docs/core-components/agents.mdx b/docs/docs/core-components/agents.mdx
index 6eda9420..121ca3d5 100644
--- a/docs/docs/core-components/agents.mdx
+++ b/docs/docs/core-components/agents.mdx
@@ -24,7 +24,25 @@ In an agentic context, tools are functions that the agent can run to perform tas
 
 ## Use the OpenRAG Open Search Agent flow
 
-## Modify the OpenRAG Open Search Agent flow
+If you've chatted with your knowledge in OpenRAG, you've already experienced the OpenRAG Open Search Agent chat flow.
+To view the flow, click <Icon name="Settings" aria-hidden="true"/> **Settings**, and then click **Edit in Langflow**.
+This flow contains seven components:
+
+* The Agent component orchestrates the entire flow by deciding when to search the knowledge base, how to formulate search queries, and how to combine retrieved information with the user's question to generate a comprehensive response.
+The Agent behaves according to the prompt in the **Agent Instructions** field.
+* The Chat Input component is connected to the Agent component's Input port. This allows to flow to be triggered by an incoming prompt from a user or application.
+* The OpenSearch component is connected to the Agent component's Tools port. The agent may not use this database for every request; the agent only uses this connection if it decides the knowledge can help respond to the prompt.
+* The Language Model component is connected to the Agent component's Language Model port. The agent uses the connected LLM to reason through the request sent through Chat Input.
+* The Embedding Model component is connected to the Open Search component's Embedding port. This component converts text queries into vector representations that are compared with document embeddings stored in OpenSearch for semantic similarity matching. This gives your Agent's queries context.
+* The Text Input component is populated with the global variable `OPENRAG-QUERY-FILTER`. 
+This filter is the Knowledge filter, and filters which knowledge sources to search through.
+* The Agent component's Output port is connected to the Chat Output component, which returns the final response to the user or application.
 
 All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.
-If you want to try a different LLM, or modify the agent's internal prompt, here's how.
\ No newline at end of file
+To modify a flow, click <Icon name="Settings" aria-hidden="true"/> **Settings**, and click **Edit in Langflow**.
+Flows are edited in the same way as in the [Langflow visual editor](https://docs.langflow.org/concepts-overview).
+
+For an example of changing out the agent's LLM in OpenRAG, see the [Quickstart](/quickstart#change-components).
+
+To restore the flow to its initial state, in OpenRAG, click <Icon name="Settings" aria-hidden="true"/> **Settings**, and then click **Restore Flow**.
+OpenRAG warns you that this discards all custom settings. Click **Restore** to restore the flow.
\ No newline at end of file
diff --git a/docs/docs/get-started/quickstart.mdx b/docs/docs/get-started/quickstart.mdx
index 993d9739..fe039859 100644
--- a/docs/docs/get-started/quickstart.mdx
+++ b/docs/docs/get-started/quickstart.mdx
@@ -16,6 +16,8 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
 ## Find your way around 
 
 1. In OpenRAG, click <Icon name="MessageSquare" aria-hidden="true"/> **Chat**.
+   The chat is powered by the OpenRAG Open Search 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.
 3. To confirm the agent is correct, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
@@ -33,7 +35,7 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
 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
+## Swap out the language model to modify agent behavior {change-components}
 
 To modify the knowledge ingestion or Agent behavior, click <Icon name="Settings" aria-hidden="true"/> **Settings**.
 
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 21cfe52f..f76fdcda 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -54,7 +54,7 @@ const sidebars = {
         {
           type: "doc",
           id: "core-components/agents",
-          label: "Langflow Agent"
+          label: "Langflow Agents"
         },
       ],
     },