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davidmyriel
2025-01-09 17:37:55 -05:00
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qdrant-landing/content/documentation/agentic-rag-langgraph.md
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title: Agentic RAG With LangGraph title: Agentic RAG With LangGraph
weight: 13 weight: 13
partition: build partition: build
social_preview_image: /documentation/examples/agentic-rag-langgraph/social_preview.png
--- ---
# Agentic RAG With LangGraph and Qdrant # 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: 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. | **Step** | **Description** |
2. **Set Up a Cluster** |------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
- Log in to your account and find the **Create New Cluster** button on the dashboard. | **1. Create an Account** | If you don’t already have one, head to Qdrant Cloud and sign up. |
- Follow the prompts to configure: | **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: <br> - Select your **preferred region**. <br> - Choose the **free tier** for testing. |
- Select your **preferred region**. | **3. Secure Your Details** | Once your cluster is ready, note these details: <br> - **Cluster URL** (e.g., https://xxx-xxx-xxx.aws.cloud.qdrant.io) <br> - **API Key** |
- 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! Save these securely for future use!
@@ -97,13 +92,7 @@ brave_key = os.getenv("BRAVE_API_KEY")
--- ---
Let’s look at the imports now. ### Document Processing
### Imports
Here are the imports required:
### Document Processing: The First Building Block
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. 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.