landing_page/qdrant-landing/content/documentation/quickstart.md

title, short_description, description, weight, partition, aliases

title	short_description	description	weight	partition	aliases
Local Quickstart	Run Qdrant locally with Docker, connect a client SDK, and create your first collection for vector search.	Quickstart guide to running Qdrant locally with Docker, connecting an SDK, and building a first collection for semantic vector search.	110	develop	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.

Before you start, please make sure Docker is installed and running on your system.

Download and run

First, download the latest Qdrant image from Dockerhub:

docker pull qdrant/qdrant

Then, run the service:

docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant

On Windows, you may need to create a named Docker volume instead of mounting a local folder.

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
• Web UI: localhost:6333/dashboard
• GRPC API: localhost:6334

Initialize the client

from qdrant_client import QdrantClient

client = QdrantClient(url="http://localhost:6333")

import { QdrantClient } from "@qdrant/js-client-rest";

const client = new QdrantClient({ host: "localhost", port: 6333 });

use qdrant_client::Qdrant;

// The Rust client uses Qdrant's gRPC interface
let client = Qdrant::from_url("http://localhost:6334").build()?;

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());

using Qdrant.Client;

// The C# client uses Qdrant's gRPC interface
var client = new QdrantClient("localhost", 6334);

import "github.com/qdrant/go-client/qdrant"

// The Go client uses Qdrant's gRPC interface
client, err := qdrant.NewClient(&qdrant.Config{
	Host: "localhost",
	Port: 6334,
})

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 Security carefully for details on how to secure your instance.

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.

from qdrant_client.models import Distance, VectorParams

client.create_collection(
    collection_name="test_collection",
    vectors_config=VectorParams(size=4, distance=Distance.DOT),
)

await client.createCollection("test_collection", {
  vectors: { size: 4, distance: "Dot" },
});

use qdrant_client::qdrant::{CreateCollectionBuilder, VectorParamsBuilder};

client
    .create_collection(
        CreateCollectionBuilder::new("test_collection")
            .vectors_config(VectorParamsBuilder::new(4, Distance::Dot)),
    )
    .await?;

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();

using Qdrant.Client.Grpc;

await client.CreateCollectionAsync(collectionName: "test_collection", vectorsConfig: new VectorParams
{
    Size = 4, Distance = Distance.Dot
});

import (
	"context"

	"github.com/qdrant/go-client/qdrant"
)

client.CreateCollection(context.Background(), &qdrant.CreateCollection{
	CollectionName: "{collection_name}",
	VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
		Size:     4,
		Distance: qdrant.Distance_Cosine,
	}),
})

Add vectors

Let's now add a few vectors with a payload. Payloads are other data you want to associate with the vector:

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)

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);

use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};

let points = vec![
    PointStruct::new(1, vec![0.05, 0.61, 0.76, 0.74], [("city", "Berlin".into())]),
    PointStruct::new(2, vec![0.19, 0.81, 0.75, 0.11], [("city", "London".into())]),
    PointStruct::new(3, vec![0.36, 0.55, 0.47, 0.94], [("city", "Moscow".into())]),
    // ..truncated
];

let response = client
    .upsert_points(UpsertPointsBuilder::new("test_collection", points).wait(true))
    .await?;

dbg!(response);

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);

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);

import (
	"context"
	"fmt"

	"github.com/qdrant/go-client/qdrant"
)

operationInfo, err := client.Upsert(context.Background(), &qdrant.UpsertPoints{
	CollectionName: "test_collection",
	Points: []*qdrant.PointStruct{
		{
			Id:      qdrant.NewIDNum(1),
			Vectors: qdrant.NewVectors(0.05, 0.61, 0.76, 0.74),
			Payload: qdrant.NewValueMap(map[string]any{"city": "Berlin"}),
		},
		{
			Id:      qdrant.NewIDNum(2),
			Vectors: qdrant.NewVectors(0.19, 0.81, 0.75, 0.11),
			Payload: qdrant.NewValueMap(map[string]any{"city": "London"}),
		},
		{
			Id:      qdrant.NewIDNum(3),
			Vectors: qdrant.NewVectors(0.36, 0.55, 0.47, 0.94),
			Payload: qdrant.NewValueMap(map[string]any{"city": "Moscow"}),
		},
        // Truncated
	},
})
if err != nil {
	panic(err)
}
fmt.Println(operationInfo)

Response:

operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>

{ operation_id: 0, status: 'completed' }

PointsOperationResponse {
    result: Some(
        UpdateResult {
            operation_id: Some(
                0,
            ),
            status: Completed,
        },
    ),
    time: 0.00094027,
}

operation_id: 0
status: Completed

{ "operationId": "0", "status": "Completed" }

operation_id:0  status:Acknowledged

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]?

search_result = client.query_points(
    collection_name="test_collection",
    query=[0.2, 0.1, 0.9, 0.7],
    with_payload=False,
    limit=3
).points

print(search_result)

let searchResult = await client.query(
    "test_collection", {
    query: [0.2, 0.1, 0.9, 0.7],
    limit: 3
});

console.debug(searchResult.points);

use qdrant_client::qdrant::QueryPointsBuilder;

let search_result = client
    .query(
        QueryPointsBuilder::new("test_collection")
            .query(vec![0.2, 0.1, 0.9, 0.7])
    )
    .await?;

dbg!(search_result);

import java.util.List;

import io.qdrant.client.grpc.Points.ScoredPoint;
import io.qdrant.client.grpc.Points.QueryPoints;

import static io.qdrant.client.QueryFactory.nearest;

List<ScoredPoint> searchResult =
    client.queryAsync(QueryPoints.newBuilder()
                .setCollectionName("test_collection")
                .setLimit(3)
                .setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
                .build()).get();
      
System.out.println(searchResult);

var searchResult = await client.QueryAsync(
    collectionName: "test_collection",
    query: new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
    limit: 3,
);

Console.WriteLine(searchResult);

import (
	"context"
	"fmt"

	"github.com/qdrant/go-client/qdrant"
)

searchResult, err := client.Query(context.Background(), &qdrant.QueryPoints{
	CollectionName: "test_collection",
	Query:          qdrant.NewQuery(0.2, 0.1, 0.9, 0.7),
})
if err != nil {
	panic(err)
}

fmt.Println(searchResult)

Response:

[
  {
    "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
  }
]

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 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".

from qdrant_client.models import Filter, FieldCondition, MatchValue

search_result = client.query_points(
    collection_name="test_collection",
    query=[0.2, 0.1, 0.9, 0.7],
    query_filter=Filter(
        must=[FieldCondition(key="city", match=MatchValue(value="London"))]
    ),
    with_payload=True,
    limit=3,
).points

print(search_result)

searchResult = await client.query("test_collection", {
    query: [0.2, 0.1, 0.9, 0.7],
    filter: {
        must: [{ key: "city", match: { value: "London" } }],
    },
    with_payload: true,
    limit: 3,
});

console.debug(searchResult);

use qdrant_client::qdrant::{Condition, Filter, QueryPointsBuilder};

let search_result = client
    .query(
        QueryPointsBuilder::new("test_collection")
            .query(vec![0.2, 0.1, 0.9, 0.7])
            .filter(Filter::must([Condition::matches(
                "city",
                "London".to_string(),
            )]))
            .with_payload(true),
    )
    .await?;

dbg!(search_result);

import static io.qdrant.client.ConditionFactory.matchKeyword;

List<ScoredPoint> searchResult =
    client.queryAsync(QueryPoints.newBuilder()
                .setCollectionName("test_collection")
                .setLimit(3)
                .setFilter(Filter.newBuilder().addMust(matchKeyword("city", "London")))
                .setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
                .setWithPayload(enable(true))
                .build()).get();

System.out.println(searchResult);

using static Qdrant.Client.Grpc.Conditions;

var searchResult = await client.QueryAsync(
    collectionName: "test_collection",
    query: new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
    filter: MatchKeyword("city", "London"),
    limit: 3,
    payloadSelector: true
);

Console.WriteLine(searchResult);

import (
	"context"
	"fmt"

	"github.com/qdrant/go-client/qdrant"
)

searchResult, err := client.Query(context.Background(), &qdrant.QueryPoints{
	CollectionName: "test_collection",
	Query:          qdrant.NewQuery(0.2, 0.1, 0.9, 0.7),
	Filter: &qdrant.Filter{
		Must: []*qdrant.Condition{
			qdrant.NewMatch("city", "London"),
		},
	},
	WithPayload: qdrant.NewWithPayload(true),
})
if err != nil {
	panic(err)
}

fmt.Println(searchResult)

Response:

[
    {
        "id": 2,
        "version": 0,
        "score": 0.871,
        "payload": {
            "city": "London"
        },
        "vector": null
    }
]

To make filtered search fast on real datasets, we highly recommend to create payload indexes!

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 is just as easy. Create an account 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 and create your own app with the help of our Examples.

When you're ready to deploy, run through the Production Checklist to make sure your instance is secure and optimized.

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, as it only takes a few moments to get setup.