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docs: dense+sparse upload code snippets (#1317)

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Signed-off-by: Anush008 <anushshetty90@gmail.com>
This commit is contained in:
Anush
2024-11-25 19:51:59 +05:30
committed by GitHub
parent ddc3b06f0a
commit 991333e4ce
1 changed files with 136 additions and 97 deletions
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qdrant-landing/content/documentation/concepts/vectors.md
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@@ -324,8 +324,8 @@ await client.UpsertAsync(
points: new List < PointStruct > {
new() {
Id = 1,
Vectors = new Dictionary < string, Vector > {
["text"] = ([0.1 f, 0.2 f, 0.3 f, 0.4 f], [1, 3, 5, 7])
Vectors = new Dictionary <string, Vector> {
["text"] = ([0.1f, 0.2f, 0.3f, 0.4f], [1, 3, 5, 7])
}
}
}
@@ -925,7 +925,7 @@ client.Query(context.Background(), &qdrant.QueryPoints{
## Named Vectors
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).
In Qdrant, you can store multiple vectors of different sizes and [types](#vector-types) 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).
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.
@@ -938,48 +938,34 @@ To create a collection with named vectors, you need to specify a configuration f
```http
PUT /collections/{collection_name}
{
"vectors": {
"image": {
"size": 4,
"distance": "Dot"
},
"text": {
"size": 8,
"distance": "Cosine"
}
"vectors": {
"image": {
"size": 4,
"distance": "Dot"
},
"text": {
"size": 5,
"distance": "Cosine"
}
},
"sparse_vectors": {
"text-sparse": {}
}
}
```
```bash
curl -X PUT http://localhost:6333/collections/{collection_name} \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"image": {
"size": 4,
"distance": "Dot"
},
"text": {
"size": 8,
"distance": "Cosine"
}
}
}'
```
```python
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.create_collection(
collection_name="{collection_name}",
vectors_config={
"image": models.VectorParams(size=4, distance=models.Distance.DOT),
"text": models.VectorParams(size=8, distance=models.Distance.COSINE),
"text": models.VectorParams(size=5, distance=models.Distance.COSINE),
},
sparse_vectors_config={"text-sparse": models.SparseVectorParams()},
)
```
@@ -989,28 +975,38 @@ import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.createCollection("{collection_name}", {
vectors: {
image: { size: 4, distance: "Dot" },
text: { size: 8, distance: "Cosine" },
},
vectors: {
image: { size: 4, distance: "Dot" },
text: { size: 5, distance: "Cosine" },
},
sparse_vectors: {
text_sparse: {}
}
});
```
```rust
use qdrant_client::qdrant::{
CreateCollectionBuilder, Distance, VectorParamsBuilder, VectorsConfigBuilder,
CreateCollectionBuilder, Distance, SparseVectorParamsBuilder, SparseVectorsConfigBuilder,
VectorParamsBuilder, VectorsConfigBuilder,
};
use qdrant_client::Qdrant;
let client = Qdrant::from_url("http://localhost:6334").build()?;
let mut vector_config = VectorsConfigBuilder::default();
vector_config.add_named_vector_params("text", VectorParamsBuilder::new(4, Distance::Dot));
vector_config.add_named_vector_params("image", VectorParamsBuilder::new(8, Distance::Cosine));
vector_config.add_named_vector_params("text", VectorParamsBuilder::new(5, Distance::Dot));
vector_config.add_named_vector_params("image", VectorParamsBuilder::new(4, Distance::Cosine));
let mut sparse_vectors_config = SparseVectorsConfigBuilder::default();
sparse_vectors_config
.add_named_vector_params("text-sparse", SparseVectorParamsBuilder::default());
client
.create_collection(
CreateCollectionBuilder::new("{collection_name}").vectors_config(vector_config),
CreateCollectionBuilder::new("{collection_name}")
.vectors_config(vector_config)
.sparse_vectors_config(sparse_vectors_config),
)
.await?;
```
@@ -1020,19 +1016,35 @@ import java.util.Map;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Collections.CreateCollection;
import io.qdrant.client.grpc.Collections.Distance;
import io.qdrant.client.grpc.Collections.SparseVectorConfig;
import io.qdrant.client.grpc.Collections.SparseVectorParams;
import io.qdrant.client.grpc.Collections.VectorParams;
import io.qdrant.client.grpc.Collections.VectorParamsMap;
import io.qdrant.client.grpc.Collections.VectorsConfig;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.createCollectionAsync(
"{collection_name}",
Map.of(
"image", VectorParams.newBuilder().setSize(4).setDistance(Distance.Dot).build(),
"text",
VectorParams.newBuilder().setSize(8).setDistance(Distance.Cosine).build()))
CreateCollection.newBuilder()
.setCollectionName("{collection_name}")
.setVectorsConfig(VectorsConfig.newBuilder().setParamsMap(
VectorParamsMap.newBuilder().putAllMap(Map.of("image",
VectorParams.newBuilder()
.setSize(4)
.setDistance(Distance.Dot)
.build(),
"text",
VectorParams.newBuilder()
.setSize(5)
.setDistance(Distance.Cosine)
.build()))))
.setSparseVectorsConfig(SparseVectorConfig.newBuilder().putMap(
"text", SparseVectorParams.getDefaultInstance()))
.build())
.get();
```
@@ -1044,15 +1056,22 @@ var client = new QdrantClient("localhost", 6334);
await client.CreateCollectionAsync(
collectionName: "{collection_name}",
vectorsConfig: new VectorParamsMap {
Map = {
vectorsConfig: new VectorParamsMap
{
Map = {
["image"] = new VectorParams {
Size = 4, Distance = Distance.Dot
},
["text"] = new VectorParams {
Size = 8, Distance = Distance.Cosine
Size = 5, Distance = Distance.Cosine
},
}
},
sparseVectorsConfig: new SparseVectorConfig
{
Map = {
["text-sparse"] = new()
}
}
);
```
@@ -1078,24 +1097,33 @@ client.CreateCollection(context.Background(), &qdrant.CreateCollection{
Distance: qdrant.Distance_Dot,
},
"text": {
Size: 8,
Size: 5,
Distance: qdrant.Distance_Cosine,
},
}),
SparseVectorsConfig: qdrant.NewSparseVectorsConfig(
map[string]*qdrant.SparseVectorParams{
"text-sparse": {},
},
),
})
```
To insert a point with named vectors:
```http
PUT /collections/{collection_name}/points
PUT /collections/{collection_name}/points?wait=true
{
"points": [
{
"id": 1,
"vector": {
"image": [0.9, 0.1, 0.1, 0.2],
"text": [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2]
"text": [0.4, 0.7, 0.1, 0.8, 0.1],
"text-sparse": {
"indices": [1, 3, 5, 7],
"values": [0.1, 0.2, 0.3, 0.4]
}
}
}
]
@@ -1110,7 +1138,11 @@ client.upsert(
id=1,
vector={
"image": [0.9, 0.1, 0.1, 0.2],
"text": [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2],
"text": [0.4, 0.7, 0.1, 0.8, 0.1],
"text-sparse": {
"indices": [1, 3, 5, 7],
"values": [0.1, 0.2, 0.3, 0.4],
},
},
),
],
@@ -1119,41 +1151,44 @@ client.upsert(
```typescript
client.upsert("{collection_name}", {
points: [
{
id: 1,
vector: {
image: [0.9, 0.1, 0.1, 0.2],
text: [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2],
},
},
],
points: [
{
id: 1,
vector: {
image: [0.9, 0.1, 0.1, 0.2],
text: [0.4, 0.7, 0.1, 0.8, 0.1],
text_sparse: {
indices: [1, 3, 5, 7],
values: [0.1, 0.2, 0.3, 0.4]
}
},
},
],
});
```
```rust
use std::collections::HashMap;
use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
use qdrant_client::qdrant::{
NamedVectors, PointStruct, UpsertPointsBuilder, Vector,
};
use qdrant_client::Payload;
client
.upsert_points(
UpsertPointsBuilder::new(
"{collection_name}",
vec![
PointStruct::new(
1,
HashMap::from([
("image".to_string(), vec![0.9, 0.1, 0.1, 0.2]),
(
"text".to_string(),
vec![0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2],
),
]),
Payload::default(),
),
],
vec![PointStruct::new(
1,
NamedVectors::default()
.add_vector("text", Vector::new_dense(vec![0.4, 0.7, 0.1, 0.8, 0.1]))
.add_vector("image", Vector::new_dense(vec![0.9, 0.1, 0.1, 0.2]))
.add_vector(
"text-sparse",
Vector::new_sparse(vec![1, 3, 5, 7], vec![0.1, 0.2, 0.3, 0.4]),
),
Payload::default(),
)],
)
.wait(true),
)
@@ -1182,7 +1217,9 @@ client
"image",
vector(List.of(0.9f, 0.1f, 0.1f, 0.2f)),
"text",
vector(List.of(0.4f, 0.7f, 0.1f, 0.8f, 0.1f, 0.1f, 0.9f, 0.2f)))))
vector(List.of(0.4f, 0.7f, 0.1f, 0.8f, 0.1f)),
"text-sparse",
vector(List.of(0.1f, 0.2f, 0.3f, 0.4f), List.of(1, 3, 5, 7)))))
.build()))
.get();
```
@@ -1191,22 +1228,25 @@ client
using Qdrant.Client;
using Qdrant.Client.Grpc;
var client = new QdrantClient("localhost", 6334);
await client.UpsertAsync(
collectionName: "{collection_name}",
points: new List<PointStruct>
{
new()
{
Id = 1,
Vectors = new Dictionary<string, float[]>
{
["image"] = [0.9f, 0.1f, 0.1f, 0.2f],
["text"] = [0.4f, 0.7f, 0.1f, 0.8f, 0.1f, 0.1f, 0.9f, 0.2f]
}
}
}
collectionName: "{collection_name}",
points: new List<PointStruct>
{
new()
{
Id = 1,
Vectors = new Dictionary<string, Vector>
{
["image"] = new() {
Data = {0.9f, 0.1f, 0.1f, 0.2f}
},
["text"] = new() {
Data = {0.4f, 0.7f, 0.1f, 0.8f, 0.1f}
},
["text-sparse"] = ([0.1f, 0.2f, 0.3f, 0.4f], [1, 3, 5, 7]),
}
}
}
);
```
@@ -1217,11 +1257,6 @@ import (
"github.com/qdrant/go-client/qdrant"
)
client, err := qdrant.NewClient(&qdrant.Config{
Host: "localhost",
Port: 6334,
})
client.Upsert(context.Background(), &qdrant.UpsertPoints{
CollectionName: "{collection_name}",
Points: []*qdrant.PointStruct{
@@ -1229,13 +1264,17 @@ client.Upsert(context.Background(), &qdrant.UpsertPoints{
Id: qdrant.NewIDNum(1),
Vectors: qdrant.NewVectorsMap(map[string]*qdrant.Vector{
"image": qdrant.NewVector(0.9, 0.1, 0.1, 0.2),
"text": qdrant.NewVector(0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2),
"text": qdrant.NewVector(0.4, 0.7, 0.1, 0.8, 0.1),
"text-sparse": qdrant.NewVectorSparse(
[]uint32{1, 3, 5, 7},
[]float32{0.1, 0.2, 0.3, 0.4}),
}),
},
},
})
```
You can also upload
To search with named vectors (available in `query` API):
```http