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---
title: Local Quickstart
weight: 5
aliases:
- quick_start
- quick-start
- quickstart
---
# How to Get Started with Qdrant Locally
In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query.
<aside role="status">Before you start, please make sure Docker is installed and running on your system.</aside>
## 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 -p 6334:6334 \
-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:
- REST API: [localhost:6333](http://localhost:6333)
- Web UI: [localhost:6333/dashboard](http://localhost:6333/dashboard)
- GRPC API: [localhost:6334](http://localhost:6334)
## Initialize the client
```python
from qdrant_client import QdrantClient
client = QdrantClient(url="http://localhost:6333")
```
```typescript
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
```
```rust
use qdrant_client::client::QdrantClient;
// The Rust client uses Qdrant's GRPC interface
let client = QdrantClient::from_url("http://localhost:6334").build()?;
```
```java
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
// The Java client uses Qdrant's GRPC interface
QdrantClient client = new QdrantClient(
QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
```
```csharp
using Qdrant.Client;
// The C# client uses Qdrant's GRPC interface
var client = new QdrantClient("localhost", 6334);
```
<aside role="status">By default, Qdrant starts with no encryption or authentication . This means anyone with network access to your machine can access your Qdrant container instance. Please read <a href="/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
## 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.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" },
});
```
```rust
use qdrant_client::qdrant::{vectors_config::Config, VectorParams, VectorsConfig};
client
.create_collection(&CreateCollection {
collection_name: "test_collection".to_string(),
vectors_config: Some(VectorsConfig {
config: Some(Config::Params(VectorParams {
size: 4,
distance: Distance::Dot.into(),
..Default::default()
})),
}),
..Default::default()
})
.await?;
```
```java
import io.qdrant.client.grpc.Collections.Distance;
import io.qdrant.client.grpc.Collections.VectorParams;
client.createCollectionAsync("test_collection",
VectorParams.newBuilder().setDistance(Distance.Dot).setSize(4).build()).get();
```
```csharp
using Qdrant.Client.Grpc;
await client.CreateCollectionAsync(
collectionName: "test_collection",
vectorsConfig: new VectorParams { Size = 4, Distance = Distance.Dot }
);
```
<aside role="status">TypeScript, Rust examples use async/await syntax, so should be used in an async block.</aside>
<aside role="status">Java examples are enclosed within a try/catch block.</aside>
## 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.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);
```
```rust
use qdrant_client::qdrant::PointStruct;
use serde_json::json;
let points = vec![
PointStruct::new(
1,
vec![0.05, 0.61, 0.76, 0.74],
json!(
{"city": "Berlin"}
)
.try_into()
.unwrap(),
),
PointStruct::new(
2,
vec![0.19, 0.81, 0.75, 0.11],
json!(
{"city": "London"}
)
.try_into()
.unwrap(),
),
// ..truncated
];
let operation_info = client
.upsert_points_blocking("test_collection".to_string(), None, points, None)
.await?;
dbg!(operation_info);
```
```java
import java.util.List;
import java.util.Map;
import static io.qdrant.client.PointIdFactory.id;
import static io.qdrant.client.ValueFactory.value;
import static io.qdrant.client.VectorsFactory.vectors;
import io.qdrant.client.grpc.Points.PointStruct;
import io.qdrant.client.grpc.Points.UpdateResult;
UpdateResult operationInfo =
client
.upsertAsync(
"test_collection",
List.of(
PointStruct.newBuilder()
.setId(id(1))
.setVectors(vectors(0.05f, 0.61f, 0.76f, 0.74f))
.putAllPayload(Map.of("city", value("Berlin")))
.build(),
PointStruct.newBuilder()
.setId(id(2))
.setVectors(vectors(0.19f, 0.81f, 0.75f, 0.11f))
.putAllPayload(Map.of("city", value("London")))
.build(),
PointStruct.newBuilder()
.setId(id(3))
.setVectors(vectors(0.36f, 0.55f, 0.47f, 0.94f))
.putAllPayload(Map.of("city", value("Moscow")))
.build()))
// Truncated
.get();
System.out.println(operationInfo);
```
```csharp
using Qdrant.Client.Grpc;
var operationInfo = await client.UpsertAsync(
collectionName: "test_collection",
points: new List<PointStruct>
{
new()
{
Id = 1,
Vectors = new float[] { 0.05f, 0.61f, 0.76f, 0.74f },
Payload = { ["city"] = "Berlin" }
},
new()
{
Id = 2,
Vectors = new float[] { 0.19f, 0.81f, 0.75f, 0.11f },
Payload = { ["city"] = "London" }
},
new()
{
Id = 3,
Vectors = new float[] { 0.36f, 0.55f, 0.47f, 0.94f },
Payload = { ["city"] = "Moscow" }
},
// Truncated
}
);
Console.WriteLine(operationInfo);
```
**Response:**
```python
operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>
```
```typescript
{ operation_id: 0, status: 'completed' }
```
```rust
PointsOperationResponse {
result: Some(UpdateResult {
operation_id: 0,
status: Completed,
}),
time: 0.006347708,
}
```
```java
operation_id: 0
status: Completed
```
```csharp
{ "operationId": "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);
```
```rust
use qdrant_client::qdrant::SearchPoints;
let search_result = client
.search_points(&SearchPoints {
collection_name: "test_collection".to_string(),
vector: vec![0.2, 0.1, 0.9, 0.7],
limit: 3,
with_payload: Some(true.into()),
..Default::default()
})
.await?;
dbg!(search_result);
```
```java
import java.util.List;
import io.qdrant.client.grpc.Points.ScoredPoint;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.WithPayloadSelectorFactory.enable;
List<ScoredPoint> searchResult =
client
.searchAsync(
SearchPoints.newBuilder()
.setCollectionName("test_collection")
.setLimit(3)
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(enable(true))
.build())
.get();
System.out.println(searchResult);
```
```csharp
var searchResult = await client.SearchAsync(
collectionName: "test_collection",
vector: new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
limit: 3,
payloadSelector: true
);
Console.WriteLine(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: null,
vector: null,
},
{
id: 1,
version: 0,
score: 1.273,
payload: null,
vector: null,
},
{
id: 3,
version: 0,
score: 1.208,
payload: null,
vector: null,
},
];
```
```rust
SearchResponse {
result: [
ScoredPoint {
id: Some(PointId {
point_id_options: Some(Num(4)),
}),
payload: {},
score: 1.362,
version: 0,
vectors: None,
},
ScoredPoint {
id: Some(PointId {
point_id_options: Some(Num(1)),
}),
payload: {},
score: 1.273,
version: 0,
vectors: None,
},
ScoredPoint {
id: Some(PointId {
point_id_options: Some(Num(3)),
}),
payload: {},
score: 1.208,
version: 0,
vectors: None,
},
],
time: 0.003635125,
}
```
```java
[id {
num: 4
}
payload {
key: "city"
value {
string_value: "New York"
}
}
score: 1.362
version: 1
, id {
num: 1
}
payload {
key: "city"
value {
string_value: "Berlin"
}
}
score: 1.273
version: 1
, id {
num: 3
}
payload {
key: "city"
value {
string_value: "Moscow"
}
}
score: 1.208
version: 1
]
```
```csharp
[
{
"id": {
"num": "4"
},
"payload": {
"city": {
"stringValue": "New York"
}
},
"score": 1.362,
"version": "7"
},
{
"id": {
"num": "1"
},
"payload": {
"city": {
"stringValue": "Berlin"
}
},
"score": 1.273,
"version": "7"
},
{
"id": {
"num": "3"
},
"payload": {
"city": {
"stringValue": "Moscow"
}
},
"score": 1.208,
"version": "7"
}
]
```
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.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"))]
),
with_payload=True,
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" } }],
},
with_payload: true,
limit: 3,
});
console.debug(searchResult);
```
```rust
use qdrant_client::qdrant::{Condition, Filter, SearchPoints};
let search_result = client
.search_points(&SearchPoints {
collection_name: "test_collection".to_string(),
vector: vec![0.2, 0.1, 0.9, 0.7],
filter: Some(Filter::all([Condition::matches(
"city",
"London".to_string(),
)])),
limit: 2,
..Default::default()
})
.await?;
dbg!(search_result);
```
```java
import static io.qdrant.client.ConditionFactory.matchKeyword;
List<ScoredPoint> searchResult =
client
.searchAsync(
SearchPoints.newBuilder()
.setCollectionName("test_collection")
.setLimit(3)
.setFilter(Filter.newBuilder().addMust(matchKeyword("city", "London")))
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(enable(true))
.build())
.get();
System.out.println(searchResult);
```
```csharp
using static Qdrant.Client.Grpc.Conditions;
var searchResult = await client.SearchAsync(
collectionName: "test_collection",
vector: new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
filter: MatchKeyword("city", "London"),
limit: 3,
payloadSelector: true
);
Console.WriteLine(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,
},
];
```
```rust
SearchResponse {
result: [
ScoredPoint {
id: Some(
PointId {
point_id_options: Some(
Num(
2,
),
),
},
),
payload: {
"city": Value {
kind: Some(
StringValue(
"London",
),
),
},
},
score: 0.871,
version: 0,
vectors: None,
},
],
time: 0.004001083,
}
```
```java
[id {
num: 2
}
payload {
key: "city"
value {
string_value: "London"
}
}
score: 0.871
version: 1
]
```
```csharp
[
{
"id": {
"num": "2"
},
"payload": {
"city": {
"stringValue": "London"
}
},
"score": 0.871,
"version": "7"
}
]
```
<aside role="status">To make filtered search fast on real datasets, we highly recommend to create <a href="../concepts/indexing/#payload-index">payload indexes</a>!</aside>
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.
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