landing_page/qdrant-landing/content/documentation/quick-start.md

title, weight, aliases

title	weight	aliases
Quickstart	11	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.

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

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("localhost", port=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()?;

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.http.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?;

TypeScript, Rust examples use async/await syntax, so should be used in an async block.

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.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)

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(), points, None)
    .await?;

dbg!(operation_info);

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,
}

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);

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,
}

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.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"))]
    ),
    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);

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,
}

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.