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[docs] Named Vectors Examples (#1156)
Co-authored-by: Anush <anushshetty90@gmail.com>
This commit is contained in:
David Myriel
@@ -924,14 +924,16 @@ client.Query(context.Background(), &qdrant.QueryPoints{
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## Named Vectors
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Aside from storing multiple vectors of the same shape in a single point, Qdrant supports storing multiple different vectors in a single point.
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In Qdrant, you can store multiple vectors of different sizes in the same data [point](/documentation/concepts/points/). This is useful when you need to define your data with multiple embeddings to represent different features or modalities (e.g., image, text or video).
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Each of these vectors should have a unique configuration and should be addressed by a unique name.
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Also, each vector can be of a different type and be generated by a different embedding model.
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To store different vectors for each point, you need to create separate named vector spaces in the [collection](/documentation/concepts/collections/). You can define these vector spaces during collection creation and manage them independently.
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<aside role="status">
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Each vector should have a unique name. Vectors can represent different modalities and you can use different embedding models to generate them.
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</aside>
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To create a collection with named vectors, you need to specify a configuration for each vector:
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```http
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PUT /collections/{collection_name}
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{
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@@ -1082,8 +1084,260 @@ client.CreateCollection(context.Background(), &qdrant.CreateCollection{
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})
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```
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<!-- ToDo: Examples of insert and search -->
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To insert a point with named vectors:
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```http
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PUT /collections/{collection_name}/points
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{
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"points": [
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{
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"id": 1,
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"vector": {
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"image": [0.9, 0.1, 0.1, 0.2],
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"text": [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2]
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}
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}
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]
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}
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```
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```python
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client.upsert(
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collection_name="{collection_name}",
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points=[
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models.PointStruct(
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id=1,
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vector={
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"image": [0.9, 0.1, 0.1, 0.2],
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"text": [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2],
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},
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),
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],
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)
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```
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```typescript
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client.upsert("{collection_name}", {
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points: [
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{
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id: 1,
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vector: {
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image: [0.9, 0.1, 0.1, 0.2],
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text: [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2],
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},
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},
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],
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});
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```
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```rust
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use std::collections::HashMap;
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use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
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use qdrant_client::Payload;
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client
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.upsert_points(
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UpsertPointsBuilder::new(
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"{collection_name}",
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vec![
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PointStruct::new(
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1,
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HashMap::from([
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("image".to_string(), vec![0.9, 0.1, 0.1, 0.2]),
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(
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"text".to_string(),
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vec![0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2],
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),
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]),
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Payload::default(),
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),
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],
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)
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.wait(true),
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)
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.await?;
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```
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```java
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import java.util.List;
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import java.util.Map;
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import static io.qdrant.client.PointIdFactory.id;
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import static io.qdrant.client.VectorFactory.vector;
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import static io.qdrant.client.VectorsFactory.namedVectors;
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import io.qdrant.client.grpc.Points.PointStruct;
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client
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.upsertAsync(
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"{collection_name}",
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List.of(
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PointStruct.newBuilder()
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.setId(id(1))
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.setVectors(
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namedVectors(
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Map.of(
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"image",
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vector(List.of(0.9f, 0.1f, 0.1f, 0.2f)),
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"text",
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vector(List.of(0.4f, 0.7f, 0.1f, 0.8f, 0.1f, 0.1f, 0.9f, 0.2f)))))
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.build()))
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.get();
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```
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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var client = new QdrantClient("localhost", 6334);
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await client.UpsertAsync(
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collectionName: "{collection_name}",
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points: new List<PointStruct>
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{
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new()
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{
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Id = 1,
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Vectors = new Dictionary<string, float[]>
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{
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["image"] = [0.9f, 0.1f, 0.1f, 0.2f],
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["text"] = [0.4f, 0.7f, 0.1f, 0.8f, 0.1f, 0.1f, 0.9f, 0.2f]
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}
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}
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}
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);
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```
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```go
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import (
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"context"
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"github.com/qdrant/go-client/qdrant"
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)
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client, err := qdrant.NewClient(&qdrant.Config{
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Host: "localhost",
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Port: 6334,
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})
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client.Upsert(context.Background(), &qdrant.UpsertPoints{
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CollectionName: "{collection_name}",
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Points: []*qdrant.PointStruct{
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{
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Id: qdrant.NewIDNum(1),
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Vectors: qdrant.NewVectorsMap(map[string]*qdrant.Vector{
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"image": qdrant.NewVector(0.9, 0.1, 0.1, 0.2),
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"text": qdrant.NewVector(0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2),
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}),
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},
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},
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})
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```
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To search with named vectors (available in `query` API):
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```http
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POST /collections/{collection_name}/points/query
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{
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"query": [0.2, 0.1, 0.9, 0.7],
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"using": "image",
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"limit": 3
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}
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```
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```python
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from qdrant_client import QdrantClient
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client = QdrantClient(url="http://localhost:6333")
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client.query_points(
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collection_name="{collection_name}",
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query=[0.2, 0.1, 0.9, 0.7],
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using="image",
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limit=3,
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)
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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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client.query("{collection_name}", {
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query: [0.2, 0.1, 0.9, 0.7],
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using: "image",
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limit: 3,
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});
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```
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```rust
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use qdrant_client::qdrant::QueryPointsBuilder;
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use qdrant_client::Qdrant;
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let client = Qdrant::from_url("http://localhost:6334").build()?;
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client
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.query(
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QueryPointsBuilder::new("{collection_name}")
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.query(vec![0.2, 0.1, 0.9, 0.7])
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.limit(3)
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.using("image"),
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)
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.await?;
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```
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```java
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import java.util.List;
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.QdrantGrpcClient;
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import io.qdrant.client.grpc.Points.QueryPoints;
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import static io.qdrant.client.QueryFactory.nearest;
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QdrantClient client =
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new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
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client.queryAsync(QueryPoints.newBuilder()
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.setCollectionName("{collection_name}")
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.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
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.setUsing("image")
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.setLimit(3)
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.build()).get();
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```
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```csharp
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using Qdrant.Client;
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var client = new QdrantClient("localhost", 6334);
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await client.QueryAsync(
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collectionName: "{collection_name}",
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query: new float[] { 0.2f, 0.1f, 0.9f, 0.7f },
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usingVector: "image",
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limit: 3
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);
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```
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```go
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import (
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"context"
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"github.com/qdrant/go-client/qdrant"
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)
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client, err := qdrant.NewClient(&qdrant.Config{
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Host: "localhost",
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Port: 6334,
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})
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client.Query(context.Background(), &qdrant.QueryPoints{
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CollectionName: "{collection_name}",
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Query: qdrant.NewQuery(0.2, 0.1, 0.9, 0.7),
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Using: qdrant.PtrOf("image"),
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})
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```
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## Datatypes
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