# Qdrant Setup

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  <img class="date-icon" src="/icons/outline/date-blue.svg" alt="Calendar" />  Module 0 
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# Qdrant Setup

Before diving into multi-vector search, you need a running Qdrant instance. Whether you choose Qdrant Cloud for a managed solution or a local deployment, this lesson will get you up and running.

Multi-vector search requires specific collection configurations that differ from traditional single-vector setups. We'll cover the essentials to prepare your environment.

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## Qdrant Cloud Setup (Recommended)

Qdrant Cloud is the fastest way to get started with multi-vector search. It provides a fully managed, production-ready vector database with automatic backups, high availability, and secure TLS connections. Both Qdrant Cloud and the open-source version provide the same feature set - Cloud simply handles the infrastructure for you.

### Create Your Cluster

1. Sign up at [cloud.qdrant.io](https://cloud.qdrant.io/signup) using your email, Google, or GitHub account.

2. Navigate to **Clusters** -> **Create a Free Cluster**. The Free Tier provides sufficient resources for this course.

   ![Create cluster](/docs/gettingstarted/gui-quickstart/create-cluster.png)

3. Select a region closest to your location or application.

4. Once your cluster is ready, copy the API key from the cluster dashboard and store it securely. You can generate additional keys later from the **API Keys** section.

   ![Get API key](/docs/gettingstarted/gui-quickstart/api-key.png)

### Access the Web UI

Click **Cluster UI** in the top-right corner of your cluster page to open the dashboard.

![Access dashboard](/docs/gettingstarted/gui-quickstart/access-dashboard.png)

The Web UI provides several useful tools:
- **Console**: Test REST API calls directly in your browser
- **Collections**: Manage all your collections and their configurations
- **Tutorial**: Interactive walkthrough with sample data

### Save Your Credentials

Store your cluster URL and API key for use in upcoming lessons. Create an `.env` file in your working directory:

```env
QDRANT_URL=https://YOUR-CLUSTER.cloud.qdrant.io:6333
QDRANT_API_KEY=YOUR_API_KEY
```

Replace `YOUR-CLUSTER` with your actual cluster URL from the dashboard, and `YOUR_API_KEY` with the API key you copied earlier.

You'll use these credentials in the next lesson when we install and configure the Python client.

## Local Qdrant Installation

Qdrant's open-source version provides the same features as Qdrant Cloud but requires you to manage the infrastructure yourself. This option works well for development, testing, or when you need full control over your deployment.

### Docker Installation (Recommended)

The fastest way to run Qdrant locally is with Docker:

```bash
docker run -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
    qdrant/qdrant
```

This command:
- Exposes port `6333` for the REST API
- Exposes port `6334` for the gRPC API
- Mounts a local directory for persistent storage

Once running, you can access the Web UI at `http://localhost:6333/dashboard` to verify the installation.

### Alternative Installation Methods

For production deployments or other installation methods, see the [Qdrant Installation Guide](/documentation/operations/installation/index.md).

## Verifying Your Setup

Open the Qdrant Web UI:
- **Cloud users**: Click **Cluster UI** in the top-right corner of your cluster dashboard
- **Local users**: Navigate to `http://localhost:6333/dashboard`

If the Web UI loads and you can see the **Collections** tab, your setup is complete. In the next lesson, you'll install the Python dependencies to connect programmatically.

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Next, you'll install the Python dependencies needed to work with multi-vector embeddings.