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:

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

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:

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::client::QdrantClient;

// The Rust client uses Qdrant's GRPC interface
let client = QdrantClient::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);

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::{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?;

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 }
);

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;
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);

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);

Response:

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

{ operation_id: 0, status: 'completed' }

PointsOperationResponse {
    result: Some(UpdateResult {
        operation_id: 0,
        status: Completed,
    }),
    time: 0.006347708,
}

operation_id: 0
status: Completed

{ "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]?

search_result = client.search(
    collection_name="test_collection", query_vector=[0.2, 0.1, 0.9, 0.7], limit=3
)

print(search_result)

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

console.debug(searchResult);

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);

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);

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:

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)

[
  {
    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,
  },
];

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,
}

[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
]

[
  {
    "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 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.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)

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);

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);

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);

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:

ScoredPoint(id=2, version=0, score=0.871, payload={"city": "London"}, vector=None)

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

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,
}

[id {
  num: 2
}
payload {
  key: "city"
  value {
    string_value: "London"
  }
}
score: 0.871
version: 1
]

[
  {
    "id": {
      "num": "2"
    },
    "payload": {
      "city": {
        "stringValue": "London"
      }
    },
    "score": 0.871,
    "version": "7"
  }
]

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.

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.

Quickstart

Download and run

Initialize the client

Create a collection

Add vectors

Run a query

Add a filter

Next steps

Product

Community

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Latest Publications

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