---
title: "Quick Start Guide"
description: "Create a Qdrant vector database and run your first similarity search"
---

# Quick Start Guide

Create a Qdrant vector database, connect to it, and run your first similarity search.

---

## Prerequisites

*   **TIR Account:** An active TIR project with sufficient credits.
*   **Python 3.8+:** (Optional) Required if you plan to use the Python client. You may use [TIR Nodes](/docs/tir/Nodes/) for easy execution.

---

## Step 1: Navigate to Vector Database

1.  Log in to the [TIR Dashboard](https://tir.e2enetworks.com/).
2.  Create or select a **Project**.
3.  Click **Vector Database** in the sidebar.
4.  Click **CREATE DATABASE**.

---

## Step 2: Configure Your Database

### Choose a Plan

Select the plan that matches your workload. The plan determines the CPU, RAM, and GPU resources available per node.

| Setting | Description |
|---------|-------------|
| **Cluster Type** | **TIR Cluster** (managed) or **Private Cluster** (your own infrastructure) |
| **Plan** | CPU/RAM/GPU allocation per node |
| **Node Count** | 3 to 10 nodes (minimum 3 for fault tolerance) |
| **Disk Size** | 10 to 1,000 GB per node |
| **Disk Encryption** | AES-256 encryption at rest (irreversible once enabled) |

:::tip
Clusters with 3 or more nodes can tolerate one node failure. The higher the replication factor, the more resilient your database.
:::

### Configure Default Collection

| Setting | Description |
|---------|-------------|
| **Collection Name** | Name for your initial collection |
| **Vector Dimensions** | Size of your vectors (1 to 4,096) |
| **Distance Metric** | Cosine, Euclid, Dot, or Manhattan |
| **Shard Number** | Number of data partitions (set as a multiple of node count) |
| **Replication Factor** | Number of shard copies across nodes (minimum 3) |

The bottom panel displays the approximate vector capacity and pricing based on your configuration.

---

## Step 3: Launch

Review your cluster configuration and cost summary on the bottom right, then click **Launch**.

:::info
The cluster takes a few minutes to provision. Status changes from **Creating** to **Running**.
:::

---

## Step 4: Get Your Connection Details

Once the database is **Running**, click on it to open the **Overview** tab.

| Detail | Description |
|--------|-------------|
| **Endpoint URL** | Your cluster's hostname (copyable) |
| **API Key** | Full read/write access key (keep secure) |
| **Read-Only API Key** | Read-only access key for query-only clients |

| Port | Protocol |
|------|----------|
| `6333` | REST (HTTP) |
| `6334` | gRPC |

:::danger
Keep your API keys secure. Do not share them or commit them to version control.
:::

---

## Step 5: Connect and Create a Collection

### Using Python Client

```bash
pip install qdrant-client
```

```python
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams, PointStruct

client = QdrantClient(
    host="<your-endpoint-url>",
    port=6333,
    api_key="<your-api-key>"
)

# Create a collection
client.create_collection(
    collection_name="test_collection",
    vectors_config=VectorParams(size=4, distance=Distance.DOT),
    shard_number=6,
    replication_factor=2
)
```

:::info
For gRPC connections, use port `6334` and add `prefer_grpc=True` to the client constructor.
:::

### Using HTTP (cURL)

```bash
curl -X PUT \
  -H "api-key: <your-api-key>" \
  -H "Content-Type: application/json" \
  -d '{
    "vectors": { "size": 4, "distance": "Dot" },
    "shard_number": 6,
    "replication_factor": 2
  }' \
  https://<your-endpoint-url>:6333/collections/test_collection
```

---

## Step 6: Insert and Search Vectors

### Insert Points

```python
points = [
    PointStruct(id=1, vector=[0.05, 0.61, 0.76, 0.74], payload={"city": "Berlin"}),
    PointStruct(id=2, vector=[0.19, 0.81, 0.75, 0.11], payload={"city": "London"}),
    PointStruct(id=3, vector=[0.36, 0.55, 0.47, 0.94], payload={"city": "Moscow"}),
    PointStruct(id=4, vector=[0.18, 0.01, 0.85, 0.80], payload={"city": "New York"}),
    PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"city": "Beijing"}),
    PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44], payload={"city": "Mumbai"}),
]

client.upsert(collection_name="test_collection", points=points, wait=True)
```

### Search for Similar Vectors

```python
results = client.search(
    collection_name="test_collection",
    query_vector=[0.2, 0.1, 0.9, 0.7],
    limit=3
)

for point in results:
    print(f"ID: {point.id}, City: {point.payload['city']}, Score: {point.score}")
```

---

## Manage Your Database

### Start and Stop

1.  Click the **Actions** icon (arrow) for your database.
2.  Select **Stop** to pause or **Start** to resume.

:::info
Stopping pauses compute billing, but storage charges continue as long as the database exists.
:::

### Delete

1.  Click the **Actions** icon and select **Delete**.
2.  Confirm the deletion.

:::danger Permanent Action
Deleting a database permanently removes all data, collections, and configurations. Take a snapshot before deletion if you need to preserve data.
:::

---

## Next Steps

*   [Features](/docs/tir/VectorDatabase/Qdrant/Features) -- Dashboard, monitoring, scaling, snapshots, and integrations
*   [Pricing](/docs/tir/VectorDatabase/Qdrant/Pricing) -- Billing details and cost examples
*   [Qdrant Official Documentation](https://qdrant.tech/documentation/) -- Full Qdrant reference


---