diff --git a/qdrant-landing/content/documentation/agentic-rag-langgraph.md b/qdrant-landing/content/documentation/agentic-rag-langgraph.md
index 51d589f0b..afa6603e7 100644
--- a/qdrant-landing/content/documentation/agentic-rag-langgraph.md
+++ b/qdrant-landing/content/documentation/agentic-rag-langgraph.md
@@ -2,7 +2,6 @@
title: Agentic RAG With LangGraph
weight: 13
partition: build
-social_preview_image: /documentation/examples/agentic-rag-langgraph/social_preview.png
---
# Agentic RAG With LangGraph and Qdrant
@@ -55,15 +54,11 @@ Before we dive into building our agent, let’s get everything set up.
We’ll use **Qdrant Cloud** as our vector store for document embeddings. Here’s how to set it up:
-1. **Create an Account**If you don’t already have one, head to Qdrant Cloud and sign up.
-2. **Set Up a Cluster**
- - Log in to your account and find the **Create New Cluster** button on the dashboard.
- - Follow the prompts to configure:
- - Select your **preferred region**.
- - Choose the **free tier** for testing.
-3. **Secure Your Details**Once your cluster is ready, note these details:
-- **Cluster URL** (e.g., https://xxx-xxx-xxx.aws.cloud.qdrant.io)
-- **API Key**
+| **Step** | **Description** |
+|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **1. Create an Account** | If you don’t already have one, head to Qdrant Cloud and sign up. |
+| **2. Set Up a Cluster** | Log in to your account and find the **Create New Cluster** button on the dashboard. Follow the prompts to configure:
- Select your **preferred region**.
- Choose the **free tier** for testing. |
+| **3. Secure Your Details** | Once your cluster is ready, note these details:
- **Cluster URL** (e.g., https://xxx-xxx-xxx.aws.cloud.qdrant.io)
- **API Key** |
Save these securely for future use!
@@ -97,13 +92,7 @@ brave_key = os.getenv("BRAVE_API_KEY")
---
-Let’s look at the imports now.
-
-### Imports
-
-Here are the imports required:
-
-### Document Processing: The First Building Block
+### Document Processing
Before we can create our agent, we need to process and store the documentation. We’ll be working with two datasets from Hugging Face: their general documentation and Transformers-specific documentation.