Hybrid and Multi-Stage Queries
Available as of v1.10.0
With the introduction of many named vectors per point, there are use-cases when the best search is obtained by combining multiple queries, or by performing the search in more than one stage.
Qdrant has a flexible and universal interface to make this possible, called Query API (API reference).
The main component for making the combinations of queries possible is the prefetch parameter, which enables making sub-requests.
Specifically, whenever a query has at least one prefetch, Qdrant will:
- Perform the prefetch query (or queries),
- Apply the main query over the results of its prefetch(es).
Additionally, prefetches can have prefetches themselves, so you can have nested prefetches.
Hybrid Search
One of the most common problems when you have different representations of the same data is to combine the queried points for each representation into a single result.
Fusing results from multiple queries
For example, in text search, it is often useful to combine dense and sparse vectors get the best of semantics, plus the best of matching specific words.
There are many ways to fuse the results. One versatile method is Reciprocal Rank Fusion (RRF), which considers the positions of each of points in the results, and boosts the ones that appear closer to the top in multiple queries.
Here is an example of RRF for a query containing two prefetches against different named vectors configured to respectively hold sparse and dense vectors.
POST /collections/{collection_name}/points/query
{
"prefetch": [
{
"query": {
"indices": [1, 42], // <┐
"values": [0.22, 0.8] // <┴─sparse vector
},
"using": "sparse",
"limit": 20,
},
{
"query": [0.01, 0.45, 0.67, ...], // <-- dense vector
"using": "dense",
"limit": 20,
}
],
"query": { "fusion": "rrf" }, // <--- reciprocal rank fusion
"limit": 10
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
prefetch=[
models.Prefetch(
query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]),
using="sparse",
limit=20,
),
models.Prefetch(
query=[0.01, 0.45, 0.67, ...], # <-- dense vector
using="dense",
limit=20,
),
],
query=models.FusionQuery(fusion=models.Fusion.RRF),
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
prefetch: [
{
query: {
values: [0.22, 0.8],
indices: [1, 42],
},
using: 'sparse',
limit: 20,
},
{
query: [0.01, 0.45, 0.67],
using: 'dense',
limit: 20,
},
],
query: {
fusion: 'rrf',
},
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{Fusion, PrefetchQueryBuilder, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice()))
.using("sparse")
.limit(20u64)
)
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest(vec![0.01, 0.45, 0.67]))
.using("dense")
.limit(20u64)
)
.query(Query::new_fusion(Fusion::Rrf))
).await?;
import static io.qdrant.client.QueryFactory.nearest;
import java.util.List;
import static io.qdrant.client.QueryFactory.fusion;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Fusion;
import io.qdrant.client.grpc.Points.PrefetchQuery;
import io.qdrant.client.grpc.Points.QueryPoints;
QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addPrefetch(PrefetchQuery.newBuilder()
.setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42)))
.setUsing("sparse")
.setLimit(20)
.build())
.addPrefetch(PrefetchQuery.newBuilder()
.setQuery(nearest(List.of(0.01f, 0.45f, 0.67f)))
.setUsing("dense")
.setLimit(20)
.build())
.setQuery(fusion(Fusion.RRF))
.build())
.get();
using Qdrant.Client;
using Qdrant.Client.Grpc;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch: new List < PrefetchQuery > {
new() {
Query = new(float, uint)[] {
(0.22f, 1), (0.8f, 42),
},
Using = "sparse",
Limit = 20
},
new() {
Query = new float[] {
0.01f, 0.45f, 0.67f
},
Using = "dense",
Limit = 20
}
},
query: Fusion.Rrf
);
Multi-stage queries
In many cases, the usage of a larger vector representation gives more accurate search results, but it is also more expensive to compute.
Splitting the search into two stages is a known technique:
- First, use a smaller and cheaper representation to get a large list of candidates.
- Then, re-score the candidates using the larger and more accurate representation.
There are a few ways to build search architectures around this idea:
- The quantized vectors as a first stage, and the full-precision vectors as a second stage.
- Leverage Matryoshka Representation Learning (MRL) to generate candidate vectors with a shorter vector, and then refine them with a longer one.
- Use regular dense vectors to pre-fetch the candidates, and then re-score them with a multi-vector model like ColBERT.
To get the best of all worlds, Qdrant has a convenient interface to perform the queries in stages, such that the coarse results are fetched first, and then they are refined later with larger vectors.
Re-scoring examples
Fetch 1000 results using a shorter MRL byte vector, then re-score them using the full vector and get the top 10.
POST /collections/{collection_name}/points/query
{
"prefetch": {
"query": [1, 23, 45, 67], // <------------- small byte vector
"using": "mrl_byte"
"limit": 1000,
},
"query": [0.01, 0.299, 0.45, 0.67, ...], // <-- full vector
"using": "full",
"limit": 10
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
prefetch=models.Prefetch(
query=[1, 23, 45, 67], # <------------- small byte vector
using="mrl_byte",
limit=1000,
),
query=[0.01, 0.299, 0.45, 0.67, ...], # <-- full vector
using="full",
limit=10,
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
prefetch: {
query: [1, 23, 45, 67], // <------------- small byte vector
using: 'mrl_byte',
limit: 1000,
},
query: [0.01, 0.299, 0.45, 0.67, ...], // <-- full vector,
using: 'full',
limit: 10,
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest(vec![1.0, 23.0, 45.0, 67.0]))
.using("mlr_byte")
.limit(1000u64)
)
.query(Query::new_nearest(vec![0.01, 0.299, 0.45, 0.67]))
.using("full")
.limit(10u64)
).await?;
import static io.qdrant.client.QueryFactory.nearest;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.PrefetchQuery;
import io.qdrant.client.grpc.Points.QueryPoints;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addPrefetch(
PrefetchQuery.newBuilder()
.setQuery(nearest(1, 23, 45, 67)) // <------------- small byte vector
.setLimit(1000)
.setUsing("mrl_byte")
.build())
.setQuery(nearest(0.01f, 0.299f, 0.45f, 0.67f)) // <-- full vector
.setUsing("full")
.setLimit(10)
.build())
.get();
using Qdrant.Client;
using Qdrant.Client.Grpc;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch: new List<PrefetchQuery> {
new() {
Query = new float[] { 1,23, 45, 67 }, // <------------- small byte vector
Using = "mrl_byte",
Limit = 1000
}
},
query: new float[] { 0.01f, 0.299f, 0.45f, 0.67f }, // <-- full vector
usingVector: "full",
limit: 10
);
Fetch 100 results using the default vector, then re-score them using a multi-vector to get the top 10.
POST /collections/{collection_name}/points/query
{
"prefetch": {
"query": [0.01, 0.45, 0.67, ...], // <-- dense vector
"limit": 100,
},
"query": [ // <─┐
[0.1, 0.2, ...], // < │
[0.2, 0.1, ...], // < ├─ multi-vector
[0.8, 0.9, ...] // < │
], // <─┘
"using": "colbert",
"limit": 10
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
prefetch=models.Prefetch(
query=[0.01, 0.45, 0.67, ...], # <-- dense vector
limit=100,
),
query=[
[0.1, 0.2, ...], # <─┐
[0.2, 0.1, ...], # < ├─ multi-vector
[0.8, 0.9, ...], # < ┘
],
using="colbert",
limit=10,
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
prefetch: {
query: [1, 23, 45, 67], // <------------- small byte vector
limit: 100,
},
query: [
[0.1, 0.2], // <─┐
[0.2, 0.1], // < ├─ multi-vector
[0.8, 0.9], // < ┘
],
using: 'colbert',
limit: 10,
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest(vec![0.01, 0.45, 0.67]))
.limit(100u64)
)
.query(Query::new_nearest(vec![
vec![0.1, 0.2],
vec![0.2, 0.1],
vec![0.8, 0.9],
]))
.using("colbert")
.limit(10u64)
).await?;
import static io.qdrant.client.QueryFactory.nearest;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.PrefetchQuery;
import io.qdrant.client.grpc.Points.QueryPoints;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addPrefetch(
PrefetchQuery.newBuilder()
.setQuery(nearest(0.01f, 0.45f, 0.67f)) // <-- dense vector
.setLimit(100)
.build())
.setQuery(
nearest(
new float[][] {
{0.1f, 0.2f}, // <─┐
{0.2f, 0.1f}, // < ├─ multi-vector
{0.8f, 0.9f} // < ┘
}))
.setUsing("colbert")
.setLimit(10)
.build())
.get();
using Qdrant.Client;
using Qdrant.Client.Grpc;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch: new List <PrefetchQuery> {
new() {
Query = new float[] { 0.01f, 0.45f, 0.67f }, // <-- dense vector****
Limit = 100
}
},
query: new float[][] {
[0.1f, 0.2f], // <─┐
[0.2f, 0.1f], // < ├─ multi-vector
[0.8f, 0.9f] // < ┘
},
usingVector: "colbert",
limit: 10
);
It is possible to combine all the above techniques in a single query:
POST /collections/{collection_name}/points/query
{
"prefetch": {
"prefetch": {
"query": [1, 23, 45, 67], // <------ small byte vector
"using": "mrl_byte"
"limit": 1000,
},
"query": [0.01, 0.45, 0.67, ...], // <-- full dense vector
"using": "full"
"limit": 100,
},
"query": [ // <─┐
[0.1, 0.2, ...], // < │
[0.2, 0.1, ...], // < ├─ multi-vector
[0.8, 0.9, ...] // < │
], // <─┘
"using": "colbert",
"limit": 10
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
prefetch=models.Prefetch(
prefetch=models.Prefetch(
query=[1, 23, 45, 67], # <------ small byte vector
using="mrl_byte",
limit=1000,
),
query=[0.01, 0.45, 0.67, ...], # <-- full dense vector
using="full",
limit=100,
),
query=[
[0.1, 0.2, ...], # <─┐
[0.2, 0.1, ...], # < ├─ multi-vector
[0.8, 0.9, ...], # < ┘
],
using="colbert",
limit=10,
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
prefetch: {
prefetch: {
query: [1, 23, 45, 67, ...], // <------------- small byte vector
using: 'mrl_byte',
limit: 1000,
},
query: [0.01, 0.45, 0.67, ...], // <-- full dense vector
using: 'full',
limit: 100,
},
query: [
[0.1, 0.2], // <─┐
[0.2, 0.1], // < ├─ multi-vector
[0.8, 0.9], // < ┘
],
using: 'colbert',
limit: 10,
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{PrefetchQueryBuilder, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(PrefetchQueryBuilder::default()
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest(vec![1.0, 23.0, 45.0, 67.0]))
.using("mlr_byte")
.limit(1000u64)
)
.query(Query::new_nearest(vec![0.01, 0.45, 0.67]))
.using("full")
.limit(100u64)
)
.query(Query::new_nearest(vec![
vec![0.1, 0.2],
vec![0.2, 0.1],
vec![0.8, 0.9],
]))
.using("colbert")
.limit(10u64)
).await?;
import static io.qdrant.client.QueryFactory.nearest;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.PrefetchQuery;
import io.qdrant.client.grpc.Points.QueryPoints;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addPrefetch(
PrefetchQuery.newBuilder()
.addPrefetch(
PrefetchQuery.newBuilder()
.setQuery(nearest(1, 23, 45, 67)) // <------------- small byte vector
.setUsing("mrl_byte")
.setLimit(1000)
.build())
.setQuery(nearest(0.01f, 0.45f, 0.67f)) // <-- dense vector
.setUsing("full")
.setLimit(100)
.build())
.setQuery(
nearest(
new float[][] {
{0.1f, 0.2f}, // <─┐
{0.2f, 0.1f}, // < ├─ multi-vector
{0.8f, 0.9f} // < ┘
}))
.setUsing("colbert")
.setLimit(10)
.build())
.get();
using Qdrant.Client;
using Qdrant.Client.Grpc;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch: new List <PrefetchQuery> {
new() {
Prefetch = {
new List <PrefetchQuery> {
new() {
Query = new float[] { 1, 23, 45, 67 }, // <------------- small byte vector
Using = "mrl_byte",
Limit = 1000
},
}
},
Query = new float[] {0.01f, 0.45f, 0.67f}, // <-- dense vector
Using = "full",
Limit = 100
}
},
query: new float[][] {
[0.1f, 0.2f], // <─┐
[0.2f, 0.1f], // < ├─ multi-vector
[0.8f, 0.9f] // < ┘
},
usingVector: "colbert",
limit: 10
);
Flexible interface
Other than the introduction of prefetch, the Query API has been designed to make querying simpler. Let’s look at a few bonus features:
Query by ID
Whenever you need to use a vector as an input, you can always use a point ID instead.
POST /collections/{collection_name}/points/query
{
"query": "43cf51e2-8777-4f52-bc74-c2cbde0c8b04" // <--- point id
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
query="43cf51e2-8777-4f52-bc74-c2cbde0c8b04", # <--- point id
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
query: '43cf51e2-8777-4f52-bc74-c2cbde0c8b04', // <--- point id
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{Condition, Filter, PointId, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client
.query(
QueryPointsBuilder::new("{collection_name}")
.query(Query::new_nearest(PointId::new("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")))
)
.await?;
import static io.qdrant.client.QueryFactory.nearest;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.QueryPoints;
import java.util.UUID;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(nearest(UUID.fromString("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")))
.build())
.get();
using Qdrant.Client;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
query: Guid.Parse("43cf51e2-8777-4f52-bc74-c2cbde0c8b04") // <--- point id
);
The above example will fetch the default vector from the point with this id, and use it as the query vector.
If the using parameter is also specified, Qdrant will use the vector with that name.
It is also possible to reference an ID from a different collection, by setting the lookup_from parameter.
POST /collections/{collection_name}/points/query
{
"query": "43cf51e2-8777-4f52-bc74-c2cbde0c8b04", // <--- point id
"using": "512d-vector"
"lookup_from": {
"collection": "another_collection", // <--- other collection name
"vector": "image-512" // <--- vector name in the other collection
}
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
query="43cf51e2-8777-4f52-bc74-c2cbde0c8b04", # <--- point id
using="512d-vector",
lookup_from=models.LookupFrom(
collection="another_collection", # <--- other collection name
vector="image-512", # <--- vector name in the other collection
)
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
query: '43cf51e2-8777-4f52-bc74-c2cbde0c8b04', // <--- point id
using: '512d-vector',
lookup_from: {
collection: 'another_collection', // <--- other collection name
vector: 'image-512', // <--- vector name in the other collection
}
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{LookupLocationBuilder, PointId, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client.query(
QueryPointsBuilder::new("{collection_name}")
.query(Query::new_nearest(PointId::new("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")))
.using("512d-vector")
.lookup_from(
LookupLocationBuilder::new("another_collection")
.vector_name("image-512")
)
).await?;
import static io.qdrant.client.QueryFactory.nearest;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.LookupLocation;
import io.qdrant.client.grpc.Points.QueryPoints;
import java.util.UUID;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(nearest(UUID.fromString("43cf51e2-8777-4f52-bc74-c2cbde0c8b04")))
.setUsing("512d-vector")
.setLookupFrom(
LookupLocation.newBuilder()
.setCollectionName("another_collection")
.setVectorName("image-512")
.build())
.build())
.get();
using Qdrant.Client;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
query: Guid.Parse("43cf51e2-8777-4f52-bc74-c2cbde0c8b04"), // <--- point id
usingVector: "512d-vector",
lookupFrom: new() {
CollectionName = "another_collection", // <--- other collection name
VectorName = "image-512" // <--- vector name in the other collection
}
);
In the case above, Qdrant will fetch the "image-512" vector from the specified point id in the
collection another_collection.
Re-ranking with payload values
The Query API can retrieve points not only by vector similarity but also by the content of the payload.
There are two ways to make use of the payload in the query:
- Apply filters to the payload fields, to only get the points that match the filter.
- Order the results by the payload field.
Let’s see an example of when this might be useful:
POST /collections/{collection_name}/points/query
{
"prefetch": [
{
"query": [0.01, 0.45, 0.67, ...], // <-- dense vector
"filter": {
"must": {
"key": "color",
"match": {
"value": "red"
}
}
},
"limit": 10
},
{
"query": [0.01, 0.45, 0.67, ...], // <-- dense vector
"filter": {
"must": {
"key": "color",
"match": {
"value": "green"
}
}
},
"limit": 10
}
],
"query": { "order_by": "price" }
}
from qdrant_client import QdrantClient, models
client = QdrantClient(url="http://localhost:6333")
client.query_points(
collection_name="{collection_name}",
prefetch=[
models.Prefetch(
query=[0.01, 0.45, 0.67, ...], # <-- dense vector
filter=models.Filter(
must=models.FieldCondition(
key="color",
match=models.Match(value="red"),
),
),
limit=10,
),
models.Prefetch(
query=[0.01, 0.45, 0.67, ...], # <-- dense vector
filter=models.Filter(
must=models.FieldCondition(
key="color",
match=models.Match(value="green"),
),
),
limit=10,
),
],
query=models.OrderByQuery(order_by="price"),
)
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({ host: "localhost", port: 6333 });
client.query("{collection_name}", {
prefetch: [
{
query: [0.01, 0.45, 0.67], // <-- dense vector
filter: {
must: {
key: 'color',
match: {
value: 'red',
},
}
},
limit: 10,
},
{
query: [0.01, 0.45, 0.67], // <-- dense vector
filter: {
must: {
key: 'color',
match: {
value: 'green',
},
}
},
limit: 10,
},
],
query: {
order_by: 'price',
},
});
use qdrant_client::Qdrant;
use qdrant_client::qdrant::{Condition, Filter, PrefetchQueryBuilder, Query, QueryPointsBuilder};
let client = Qdrant::from_url("http://localhost:6334").build()?;
client.query(
QueryPointsBuilder::new("{collection_name}")
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest(vec![0.01, 0.45, 0.67]))
.filter(Filter::must([Condition::matches(
"color",
"red".to_string(),
)]))
.limit(10u64)
)
.add_prefetch(PrefetchQueryBuilder::default()
.query(Query::new_nearest(vec![0.01, 0.45, 0.67]))
.filter(Filter::must([Condition::matches(
"color",
"green".to_string(),
)]))
.limit(10u64)
)
.query(Query::new_order_by("price"))
).await?;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.QueryFactory.orderBy;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.PrefetchQuery;
import io.qdrant.client.grpc.Points.QueryPoints;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addPrefetch(
PrefetchQuery.newBuilder()
.setQuery(nearest(0.01f, 0.45f, 0.67f))
.setFilter(
Filter.newBuilder().addMust(matchKeyword("color", "red")).build())
.setLimit(10)
.build())
.addPrefetch(
PrefetchQuery.newBuilder()
.setQuery(nearest(0.01f, 0.45f, 0.67f))
.setFilter(
Filter.newBuilder().addMust(matchKeyword("color", "green")).build())
.setLimit(10)
.build())
.setQuery(orderBy("price"))
.build())
.get();
using Qdrant.Client;
using Qdrant.Client.Grpc;
using static Qdrant.Client.Grpc.Conditions;
var client = new QdrantClient("localhost", 6334);
await client.QueryAsync(
collectionName: "{collection_name}",
prefetch: new List <PrefetchQuery> {
new() {
Query = new float[] {
0.01f, 0.45f, 0.67f
},
Filter = MatchKeyword("color", "red"),
Limit = 10
},
new() {
Query = new float[] {
0.01f, 0.45f, 0.67f
},
Filter = MatchKeyword("color", "green"),
Limit = 10
}
},
query: (OrderBy) "price",
limit: 10
);
In this example, we first fetch 10 points with the color "red" and then 10 points with the color "green".
Then, we order the results by the price field.
This is how we can guarantee even sampling of both colors in the results and also get the cheapest ones first.