---
title: "Get started with Vector Database"
description: "Create a Qdrant vector database on TIR, connect to it from Python, and run your first similarity search."
content_type: "get-started"
product: "vector-database"
platform: "TIR"
url: "/tir/vector-database/get-started/"
---

# Get started with Vector Database

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

## Before you begin

You need a TIR project in Delhi or Chennai, enough credit to launch the nodes, and Python 3.8
or later for the client. An [instance](/tir/tir-instance/) in the project can run it.

## Create the database

1. In TIR, open your project. In the side menu, under **Storage**, select **Vector
   Database**.
2. Select **Create Database**. For a project's first database, the button reads **Launch
   Database**.
3. Keep the **Cluster Name** TIR fills in, or type your own.
4. In **Default Collection**, keep **Cosine** for **Distance**, and set **Vector Dimension** to the
   size of the vectors you will store. Keep **Shard Number** at 1 and **Replication Factor** at 3.
5. Under **Select Plan**, keep **E2E Cluster** and choose a plan. Keep **Node Count** at 3.
6. Select **Launch**.

The database appears in the list as **Deploying** and becomes **Running** after a few minutes. See
[Create a vector database](/tir/vector-database/how-to/create-a-vector-database/) for what each
choice means.

## Get the connection details

When the database is **Running**, select its row and open **Overview**. Copy the **Endpoint URL**,
then show and copy the **API Key**.

## Connect and run a search

Install the Python client:

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

The script connects, creates a second collection with the same settings as the default one (**Cosine**
distance, one shard, replication factor 3), and adds six points. It then searches for the three points nearest
to a query vector. Replace `<your-endpoint-url>` with the **Endpoint URL** you copied, leaving off the
`https://` at the start, and `<your-api-key>` with the **API Key**.

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

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

client.create_collection(
    collection_name="my_first_collection",
    vectors_config=VectorParams(size=4, distance=Distance.COSINE),
    shard_number=1,
    replication_factor=3,
)

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": "Chennai"}),
]
client.upsert(collection_name="my_first_collection", points=points, wait=True)

results = client.search(
    collection_name="my_first_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}")
```

The script prints three lines, one for each of the nearest points, with their scores. To use gRPC
instead of REST, connect on port 6334 with the gRPC option turned on:

```python
client = QdrantClient(
    host="<your-endpoint-url>", port=6334, https=True, api_key="<your-api-key>", prefer_grpc=True
)
```

## What to do next

- [Connect to a vector database](/tir/vector-database/how-to/connect-to-a-vector-database/) covers the
  keys, the Qdrant dashboard and the sample commands in the portal.
- [Take a snapshot](/tir/vector-database/how-to/take-a-snapshot/) before you scale or change a lot
  of data.
- [Stop or start the database](/tir/vector-database/how-to/stop-or-start-a-vector-database/) to pause
  its compute charge, and [delete it](/tir/vector-database/how-to/delete-a-vector-database/) to stop
  every charge.
- For the Qdrant documentation, see [qdrant.tech](https://qdrant.tech/documentation/).

For help with any message, see
[Troubleshooting Vector Database](/tir/vector-database/troubleshooting/vector-database/).