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10 KiB
Markdown
404 lines
10 KiB
Markdown
---
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title: Quickstart
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weight: 11
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aliases:
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- quick_start
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---
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# Quickstart
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In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query.
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<aside role="status">Before you start, please make sure Docker is installed and running on your system.</aside>
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## Download and run
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First, download the latest Qdrant image from Dockerhub:
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```bash
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docker pull qdrant/qdrant
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```
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Then, run the service:
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```bash
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docker run -p 6333:6333 -p 6334:6334 \
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-v $(pwd)/qdrant_storage:/qdrant/storage:z \
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qdrant/qdrant
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```
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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.
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Qdrant is now accessible:
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- REST API: [localhost:6333](http://localhost:6333)
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- Web UI: [localhost:6333/dashboard](http://localhost:6333/dashboard)
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- GRPC API: [localhost:6334](http://localhost:6334)
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## Initialize the client
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```python
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from qdrant_client import QdrantClient
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client = QdrantClient("localhost", port=6333)
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```
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```typescript
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import { QdrantClient } from "@qdrant/js-client-rest";
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const client = new QdrantClient({ host: "localhost", port: 6333 });
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```
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```rust
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use qdrant_client::client::QdrantClient;
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// The Rust client uses Qdrant's GRPC interface
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let client = QdrantClient::from_url("http://localhost:6334").build()?;
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```
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<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="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
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## Create a collection
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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.
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```python
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from qdrant_client.http.models import Distance, VectorParams
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client.create_collection(
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collection_name="test_collection",
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vectors_config=VectorParams(size=4, distance=Distance.DOT),
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)
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```
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```typescript
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await client.createCollection("test_collection", {
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vectors: { size: 4, distance: "Dot" },
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});
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```
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```rust
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use qdrant_client::qdrant::{vectors_config::Config, VectorParams, VectorsConfig};
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client
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.create_collection(&CreateCollection {
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collection_name: "test_collection".to_string(),
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vectors_config: Some(VectorsConfig {
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config: Some(Config::Params(VectorParams {
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size: 4,
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distance: Distance::Dot.into(),
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..Default::default()
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})),
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}),
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..Default::default()
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})
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.await?;
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```
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<aside role="status">TypeScript, Rust examples use async/await syntax, so should be used in an async block.</aside>
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## Add vectors
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Let's now add a few vectors with a payload. Payloads are other data you want to associate with the vector:
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```python
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from qdrant_client.http.models import PointStruct
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operation_info = client.upsert(
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collection_name="test_collection",
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wait=True,
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points=[
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PointStruct(id=1, vector=[0.05, 0.61, 0.76, 0.74], payload={"city": "Berlin"}),
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PointStruct(id=2, vector=[0.19, 0.81, 0.75, 0.11], payload={"city": "London"}),
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PointStruct(id=3, vector=[0.36, 0.55, 0.47, 0.94], payload={"city": "Moscow"}),
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PointStruct(id=4, vector=[0.18, 0.01, 0.85, 0.80], payload={"city": "New York"}),
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PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"city": "Beijing"}),
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PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44], payload={"city": "Mumbai"}),
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],
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)
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print(operation_info)
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```
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```typescript
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const operationInfo = await client.upsert("test_collection", {
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wait: true,
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points: [
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{ id: 1, vector: [0.05, 0.61, 0.76, 0.74], payload: { city: "Berlin" } },
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{ id: 2, vector: [0.19, 0.81, 0.75, 0.11], payload: { city: "London" } },
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{ id: 3, vector: [0.36, 0.55, 0.47, 0.94], payload: { city: "Moscow" } },
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{ id: 4, vector: [0.18, 0.01, 0.85, 0.80], payload: { city: "New York" } },
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{ id: 5, vector: [0.24, 0.18, 0.22, 0.44], payload: { city: "Beijing" } },
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{ id: 6, vector: [0.35, 0.08, 0.11, 0.44], payload: { city: "Mumbai" } },
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],
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});
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console.debug(operationInfo);
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```
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```rust
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use qdrant_client::qdrant::PointStruct;
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use serde_json::json;
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let points = vec![
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PointStruct::new(
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1,
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vec![0.05, 0.61, 0.76, 0.74],
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json!(
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{"city": "Berlin"}
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)
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.try_into()
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.unwrap(),
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),
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PointStruct::new(
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2,
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vec![0.19, 0.81, 0.75, 0.11],
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json!(
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{"city": "London"}
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)
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.try_into()
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.unwrap(),
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),
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// ..truncated
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];
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let operation_info = client
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.upsert_points_blocking("test_collection".to_string(), None, points, None)
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.await?;
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dbg!(operation_info);
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```
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**Response:**
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```python
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operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>
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```
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```typescript
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{ operation_id: 0, status: 'completed' }
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```
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```rust
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PointsOperationResponse {
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result: Some(UpdateResult {
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operation_id: 0,
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status: Completed,
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}),
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time: 0.006347708,
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}
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```
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## Run a query
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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]`?
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```python
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search_result = client.search(
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collection_name="test_collection", query_vector=[0.2, 0.1, 0.9, 0.7], limit=3
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)
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print(search_result)
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```
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```typescript
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let searchResult = await client.search("test_collection", {
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vector: [0.2, 0.1, 0.9, 0.7],
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limit: 3,
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});
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console.debug(searchResult);
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```
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```rust
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use qdrant_client::qdrant::SearchPoints;
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let search_result = client
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.search_points(&SearchPoints {
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collection_name: "test_collection".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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limit: 3,
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with_payload: Some(true.into()),
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..Default::default()
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})
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.await?;
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dbg!(search_result);
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```
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**Response:**
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```python
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ScoredPoint(id=4, version=0, score=1.362, payload={"city": "New York"}, vector=None),
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ScoredPoint(id=1, version=0, score=1.273, payload={"city": "Berlin"}, vector=None),
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ScoredPoint(id=3, version=0, score=1.208, payload={"city": "Moscow"}, vector=None)
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```
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```typescript
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[
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{
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id: 4,
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version: 0,
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score: 1.362,
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payload: null,
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vector: null,
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},
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{
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id: 1,
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version: 0,
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score: 1.273,
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payload: null,
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vector: null,
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},
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{
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id: 3,
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version: 0,
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score: 1.208,
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payload: null,
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vector: null,
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},
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];
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```
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```rust
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SearchResponse {
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result: [
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ScoredPoint {
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id: Some(PointId {
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point_id_options: Some(Num(4)),
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}),
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payload: {},
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score: 1.362,
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version: 0,
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vectors: None,
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},
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ScoredPoint {
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id: Some(PointId {
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point_id_options: Some(Num(1)),
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}),
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payload: {},
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score: 1.273,
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version: 0,
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vectors: None,
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},
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ScoredPoint {
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id: Some(PointId {
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point_id_options: Some(Num(3)),
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}),
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payload: {},
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score: 1.208,
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version: 0,
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vectors: None,
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},
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],
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time: 0.003635125,
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}
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```
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The results are returned in decreasing similarity order. Note that payload and vector data is missing in these results by default.
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See [payload and vector in the result](../concepts/search#payload-and-vector-in-the-result) on how to enable it.
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## Add a filter
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We can narrow down the results further by filtering by payload. Let's find the closest results that include "London".
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```python
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from qdrant_client.http.models import Filter, FieldCondition, MatchValue
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search_result = client.search(
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collection_name="test_collection",
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query_vector=[0.2, 0.1, 0.9, 0.7],
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query_filter=Filter(
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must=[FieldCondition(key="city", match=MatchValue(value="London"))]
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),
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with_payload=True,
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limit=3,
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)
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print(search_result)
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```
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```typescript
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searchResult = await client.search("test_collection", {
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vector: [0.2, 0.1, 0.9, 0.7],
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filter: {
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must: [{ key: "city", match: { value: "London" } }],
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},
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with_payload: true,
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limit: 3,
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});
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console.debug(searchResult);
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```
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```rust
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use qdrant_client::qdrant::{Condition, Filter, SearchPoints};
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let search_result = client
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.search_points(&SearchPoints {
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collection_name: "test_collection".to_string(),
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vector: vec![0.2, 0.1, 0.9, 0.7],
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filter: Some(Filter::all([Condition::matches(
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"city",
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"London".to_string(),
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)])),
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limit: 2,
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..Default::default()
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})
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.await?;
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dbg!(search_result);
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```
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**Response:**
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```python
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ScoredPoint(id=2, version=0, score=0.871, payload={"city": "London"}, vector=None)
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```
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```typescript
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[
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{
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id: 2,
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version: 0,
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score: 0.871,
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payload: { city: "London" },
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vector: null,
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},
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];
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```
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```rust
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SearchResponse {
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result: [
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ScoredPoint {
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id: Some(
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PointId {
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point_id_options: Some(
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Num(
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2,
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),
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),
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},
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),
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payload: {
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"city": Value {
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kind: Some(
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StringValue(
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"London",
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),
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),
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},
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},
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score: 0.871,
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version: 0,
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vectors: None,
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},
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],
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time: 0.004001083,
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}
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```
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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.
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## Next steps
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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.
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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/).
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**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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