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.