---
title: "Connect to a vector database"
description: "Find a vector database's endpoint and API keys in TIR, open the Qdrant dashboard, and connect from Python or HTTP."
content_type: "how-to"
product: "vector-database"
platform: "TIR"
url: "/tir/vector-database/how-to/connect-to-a-vector-database/"
topic: ["create-and-connect"]
---

# Connect to a vector database

Find the endpoint and API keys on the database's **Overview** tab, then connect from the Qdrant
dashboard, the Python client, or HTTP.

## Before you begin

You need a database in the **Running** state and a role with Read permission on
Vector Database. For the Python client, you also need Python 3.8 or later. An
[instance](/tir/tir-instance/) in the project can run it.

## Find the endpoint and the keys

1. In **Manage Vector Database**, select the database's row, then open **Overview**.
2. Copy the **Endpoint URL**. This is the hostname of your database.
3. Show **API Key** or **Read-only API Key** and copy it.

The **API Key** gives full read and write access. The **Read-only API Key** gives read-only access,
so give it to client applications that only query data. Store both keys somewhere safe, such as
environment variables, and keep them out of version control and client-side code.

Clients reach the database on port 6333 for REST, and on port 6334 for gRPC. Send the API key with
each request.

## Open the Qdrant dashboard

In the list, select the database's name, or select **Connect** in its row. Both open the Qdrant
dashboard once the database is **Running**. Enter your **API Key** and select **Apply**.

The dashboard has these sections:

- **Collections**: create, view and delete collections, browse points, see collection details and
  visualize vectors.
- **Console**: run Qdrant API commands, and browse the commands available.
- **Tutorial**: Qdrant's interactive guide to its API.
- **Datasets**: sample data to experiment with.

Use **Console** to try an API call before you add it to your application.

## Copy sample commands

In the list, select **Commands** in the database's row. The Python samples install the client,
connect, create and list collections, add vectors, search, and close the connection. Your endpoint is filled in; type your API key where the sample asks.

## Connect from Python

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

`<your-endpoint-url>` is the **Endpoint URL** and `<your-api-key>` is your API key.

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

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

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

To connect over gRPC, use port 6334 and turn the gRPC option on:

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

## Connect over HTTP

Create the same collection with cURL:

```bash
curl -X PUT \
  -H "api-key: <your-api-key>" \
  -H "Content-Type: application/json" \
  -d '{
    "vectors": { "size": 4, "distance": "Cosine" },
    "shard_number": 1,
    "replication_factor": 3
  }' \
  https://<your-endpoint-url>:6333/collections/my_collection
```

## What to do next

Add points and search them as in [Get started with Vector Database](/tir/vector-database/get-started/).
To feed the database from LangChain or LlamaIndex, see
[Use Qdrant with LangChain](/tir/vector-database/how-to/use-qdrant-with-langchain/) and
[Use Qdrant with LlamaIndex](/tir/vector-database/how-to/use-qdrant-with-llamaindex/). If a
connection fails, see [Troubleshooting Vector Database](/tir/vector-database/troubleshooting/vector-database/).