---
title: Quickstart
weight: 11
aliases:
- quick_start
---
# Quickstart
In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query.
## Download and run
First, download the latest Qdrant image from Dockerhub:
```bash
docker pull qdrant/qdrant
```
Then, run the service:
```bash
docker run -p 6333:6333 \
-v $(pwd)/qdrant_storage:/qdrant/storage:z \
qdrant/qdrant
```
Under the default configuration all data will be stored in the `./qdrant_storage` directory. This will also be the only directory that both the Container and the host machine can both see.
Qdrant is now accessible:
- API: [localhost:6333](http://localhost:6333)
- Web UI: [localhost:6333/dashboard](http://localhost:6333/dashboard)
## Initialize the client
```python
from qdrant_client import QdrantClient
client = QdrantClient("localhost", port=6333)
```
```typescript
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
```
## Create a collection
You will be storing all of your vector data in a Qdrant collection. Let's call it `test_collection`. This collection will be using a dot product distance metric to compare vectors.
```python
from qdrant_client.http.models import Distance, VectorParams
client.create_collection(
collection_name="test_collection",
vectors_config=VectorParams(size=4, distance=Distance.DOT),
)
```
```typescript
await client.createCollection("test_collection", {
vectors: { size: 4, distance: "Dot" },
});
```
## Add vectors
Let's now add a few vectors with a payload. Payloads are other data you want to associate with the vector:
```python
from qdrant_client.http.models import PointStruct
operation_info = client.upsert(
collection_name="test_collection",
wait=True,
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"}),
],
)
print(operation_info)
```
```typescript
const operationInfo = await client.upsert("test_collection", {
wait: true,
points: [
{ id: 1, vector: [0.05, 0.61, 0.76, 0.74], payload: { city: "Berlin" } },
{ id: 2, vector: [0.19, 0.81, 0.75, 0.11], payload: { city: "London" } },
{ id: 3, vector: [0.36, 0.55, 0.47, 0.94], payload: { city: "Moscow" } },
{ id: 4, vector: [0.18, 0.01, 0.85, 0.80], payload: { city: "New York" } },
{ id: 5, vector: [0.24, 0.18, 0.22, 0.44], payload: { city: "Beijing" } },
{ id: 6, vector: [0.35, 0.08, 0.11, 0.44], payload: { city: "Mumbai" } },
],
});
console.debug(operationInfo);
```
**Response:**
```python
operation_id=0 status=
```
```typescript
{ operation_id: 0, status: 'completed' }
```
## Run a query
Let's ask a basic question - Which of our stored vectors are most similar to the query vector `[0.2, 0.1, 0.9, 0.7]`?
```python
search_result = client.search(
collection_name="test_collection", query_vector=[0.2, 0.1, 0.9, 0.7], limit=3
)
print(search_result)
```
```typescript
let searchResult = await client.search("test_collection", {
vector: [0.2, 0.1, 0.9, 0.7],
limit: 3,
});
console.debug(searchResult);
```
**Response:**
```python
ScoredPoint(id=4, version=0, score=1.362, payload={"city": "New York"}, vector=None),
ScoredPoint(id=1, version=0, score=1.273, payload={"city": "Berlin"}, vector=None),
ScoredPoint(id=3, version=0, score=1.208, payload={"city": "Moscow"}, vector=None)
```
```typescript
[
{
id: 4,
version: 0,
score: 1.362,
payload: { city: "New York" },
vector: null,
},
{
id: 1,
version: 0,
score: 1.273,
payload: { city: "Berlin" },
vector: null,
},
{
id: 3,
version: 0,
score: 1.208,
payload: { city: "Moscow" },
vector: null,
},
];
```
The results are returned in decreasing similarity order. Note that payload and vector data is missing in these results by default.
See [payload and vector in the result](../concepts/search#payload-and-vector-in-the-result) on how to enable it.
## Add a filter
We can narrow down the results further by filtering by payload. Let's find the closest results that include "London".
```python
from qdrant_client.http.models import Filter, FieldCondition, MatchValue
search_result = client.search(
collection_name="test_collection",
query_vector=[0.2, 0.1, 0.9, 0.7],
query_filter=Filter(
must=[FieldCondition(key="city", match=MatchValue(value="London"))]
),
limit=3,
)
print(search_result)
```
```typescript
searchResult = await client.search("test_collection", {
vector: [0.2, 0.1, 0.9, 0.7],
filter: {
must: [{ key: "city", match: { value: "London" } }],
},
limit: 3,
});
console.debug(searchResult);
```
**Response:**
```python
ScoredPoint(id=2, version=0, score=0.871, payload={"city": "London"}, vector=None)
```
```typescript
[
{
id: 2,
version: 0,
score: 0.871,
payload: { city: "London" },
vector: null,
},
];
```
You have just conducted vector search. You loaded vectors into a database and queried the database with a vector of your own. Qdrant found the closest results and presented you with a similarity score.
## Next steps
Now you know how Qdrant works. Getting started with [Qdrant Cloud](../cloud/quickstart-cloud/) is just as easy. [Create an account](https://qdrant.to/cloud) and use our SaaS completely free. We will take care of infrastructure maintenance and software updates.
To move onto some more complex examples of vector search, read our [Tutorials](../tutorials/) and create your own app with the help of our [Examples](../examples/).
**Note:** There is another way of running Qdrant locally. If you are a Python developer, we recommend that you try Local Mode in [Qdrant Client](https://github.com/qdrant/qdrant-client), as it only takes a few moments to get setup.