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docs: Query API snippets with Java (#1103)

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Anush
2024-08-23 22:43:34 +05:30
committed by GitHub
parent a8e022dd51
commit f444500ad2
8 changed files with 446 additions and 464 deletions
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qdrant-landing/content/documentation/concepts/explore.md
+94 -91
View File
@@ -112,32 +112,30 @@ client
```java
import java.util.List;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import static io.qdrant.client.PointIdFactory.id;
import static io.qdrant.client.VectorFactory.vector;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.RecommendPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.RecommendInput;
import io.qdrant.client.grpc.Points.RecommendStrategy;
import io.qdrant.client.grpc.Points.Filter;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import static io.qdrant.client.VectorInputFactory.vectorInput;
import static io.qdrant.client.QueryFactory.recommend;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.recommendAsync(
RecommendPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllPositive(List.of(id(100), id(200)))
.addAllPositiveVectors(List.of(vector(100.0f, 231.0f)))
.addAllNegative(List.of(id(718)))
.addAllPositiveVectors(List.of(vector(0.2f, 0.3f, 0.4f, 0.5f)))
.setQuery(recommend(RecommendInput.newBuilder()
.addAllPositive(List.of(vectorInput(100), vectorInput(200), vectorInput(100.0f, 231.0f)))
.addAllNegative(List.of(vectorInput(718), vectorInput(0.2f, 0.3f, 0.4f, 0.5f)))
.setStrategy(RecommendStrategy.AverageVector)
.build()))
.setFilter(Filter.newBuilder().addMust(matchKeyword("city", "London")))
.setLimit(3)
.build())
.get();
.build()).get();
```
```csharp
@@ -275,20 +273,21 @@ client
```java
import java.util.List;
import static io.qdrant.client.PointIdFactory.id;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.RecommendInput;
import io.qdrant.client.grpc.Points.RecommendPoints;
import static io.qdrant.client.VectorInputFactory.vectorInput;
import static io.qdrant.client.QueryFactory.recommend;
client
.recommendAsync(
RecommendPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllPositive(List.of(id(100), id(231)))
.addAllNegative(List.of(id(718)))
.setQuery(recommend(RecommendInput.newBuilder()
.addAllPositive(List.of(vectorInput(100), vectorInput(231)))
.addAllNegative(List.of(vectorInput(718)))
.build()))
.setUsing("image")
.setLimit(10)
.build())
.get();
.build()).get();
```
```csharp
@@ -383,17 +382,19 @@ client
```java
import java.util.List;
import static io.qdrant.client.PointIdFactory.id;
import io.qdrant.client.grpc.Points.LookupLocation;
import io.qdrant.client.grpc.Points.RecommendPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.RecommendInput;
client
.recommendAsync(
RecommendPoints.newBuilder()
import static io.qdrant.client.VectorInputFactory.vectorInput;
import static io.qdrant.client.QueryFactory.recommend;
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllPositive(List.of(id(100), id(231)))
.addAllNegative(List.of(id(718)))
.setQuery(recommend(RecommendInput.newBuilder()
.addAllPositive(List.of(vectorInput(100), vectorInput(231)))
.addAllNegative(List.of(vectorInput(718)))
.build()))
.setUsing("image")
.setLimit(10)
.setLookupFrom(
@@ -401,8 +402,7 @@ client
.setCollectionName("{external_collection_name}")
.setVectorName("{external_vector_name}")
.build())
.build())
.get();
.build()).get();
```
```csharp
@@ -578,35 +578,44 @@ client
```java
import java.util.List;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import static io.qdrant.client.PointIdFactory.id;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.RecommendPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.RecommendInput;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import static io.qdrant.client.VectorInputFactory.vectorInput;
import static io.qdrant.client.QueryFactory.recommend;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
Filter filter = Filter.newBuilder().addMust(matchKeyword("city", "London")).build();
List<RecommendPoints> recommendQueries =
List.of(
RecommendPoints.newBuilder()
.addAllPositive(List.of(id(100), id(231)))
.addAllNegative(List.of(id(718)))
List<QueryPoints> recommendQueries = List.of(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(recommend(
RecommendInput.newBuilder()
.addAllPositive(List.of(vectorInput(100), vectorInput(231)))
.addAllNegative(List.of(vectorInput(731)))
.build()))
.setFilter(filter)
.setLimit(3)
.build(),
RecommendPoints.newBuilder()
.addAllPositive(List.of(id(200), id(67)))
.addAllNegative(List.of(id(300)))
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(recommend(
RecommendInput.newBuilder()
.addAllPositive(List.of(vectorInput(200), vectorInput(67)))
.addAllNegative(List.of(vectorInput(300)))
.build()))
.setFilter(filter)
.setLimit(3)
.build());
client.recommendBatchAsync("{collection_name}", recommendQueries, null).get();
client.queryBatchAsync("{collection_name}", recommendQueries).get();
```
```csharp
@@ -799,42 +808,37 @@ client
```java
import java.util.List;
import static io.qdrant.client.PointIdFactory.id;
import static io.qdrant.client.VectorFactory.vector;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.ContextExamplePair;
import io.qdrant.client.grpc.Points.DiscoverPoints;
import io.qdrant.client.grpc.Points.TargetVector;
import io.qdrant.client.grpc.Points.VectorExample;
import io.qdrant.client.grpc.Points.ContextInput;
import io.qdrant.client.grpc.Points.ContextInputPair;
import io.qdrant.client.grpc.Points.DiscoverInput;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.VectorInputFactory.vectorInput;
import static io.qdrant.client.QueryFactory.discover;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.discoverAsync(
DiscoverPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setTarget(
TargetVector.newBuilder()
.setSingle(
VectorExample.newBuilder()
.setVector(vector(0.2f, 0.1f, 0.9f, 0.7f))
.build()))
.addAllContext(
List.of(
ContextExamplePair.newBuilder()
.setPositive(VectorExample.newBuilder().setId(id(100)))
.setNegative(VectorExample.newBuilder().setId(id(718)))
.setQuery(discover(DiscoverInput.newBuilder()
.setTarget(vectorInput(0.2f, 0.1f, 0.9f, 0.7f))
.setContext(ContextInput.newBuilder()
.addAllPairs(List.of(
ContextInputPair.newBuilder()
.setPositive(vectorInput(100))
.setNegative(vectorInput(718))
.build(),
ContextExamplePair.newBuilder()
.setPositive(VectorExample.newBuilder().setId(id(200)))
.setNegative(VectorExample.newBuilder().setId(id(300)))
ContextInputPair.newBuilder()
.setPositive(vectorInput(200))
.setNegative(vectorInput(300))
.build()))
.build())
.build()))
.setLimit(10)
.build())
.get();
.build()).get();
```
```csharp
@@ -984,34 +988,33 @@ client
```java
import java.util.List;
import static io.qdrant.client.PointIdFactory.id;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.ContextExamplePair;
import io.qdrant.client.grpc.Points.DiscoverPoints;
import io.qdrant.client.grpc.Points.VectorExample;
import io.qdrant.client.grpc.Points.ContextInput;
import io.qdrant.client.grpc.Points.ContextInputPair;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.VectorInputFactory.vectorInput;
import static io.qdrant.client.QueryFactory.context;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.discoverAsync(
DiscoverPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllContext(
List.of(
ContextExamplePair.newBuilder()
.setPositive(VectorExample.newBuilder().setId(id(100)))
.setNegative(VectorExample.newBuilder().setId(id(718)))
.setQuery(context(ContextInput.newBuilder()
.addAllPairs(List.of(
ContextInputPair.newBuilder()
.setPositive(vectorInput(100))
.setNegative(vectorInput(718))
.build(),
ContextExamplePair.newBuilder()
.setPositive(VectorExample.newBuilder().setId(id(200)))
.setNegative(VectorExample.newBuilder().setId(id(300)))
ContextInputPair.newBuilder()
.setPositive(vectorInput(200))
.setNegative(vectorInput(300))
.build()))
.build()))
.setLimit(10)
.build())
.get();
.build()).get();
```
```csharp
qdrant-landing/content/documentation/concepts/search.md
+71 -75
View File
@@ -296,26 +296,24 @@ client
import java.util.List;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import static io.qdrant.client.QueryFactory.nearest;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setFilter(Filter.newBuilder().addMust(matchKeyword("city", "London")).build())
.setParams(SearchParams.newBuilder().setExact(false).setHnswEf(128).build())
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setLimit(3)
.build())
.get();
.build()).get();
```
```csharp
@@ -425,20 +423,19 @@ import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setVectorName("image")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setUsing("image")
.setLimit(3)
.build())
.get();
.build()).get();
```
```csharp
@@ -539,19 +536,18 @@ import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.SparseIndices;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setVectorName("text")
.addAllVector(List.of(2.0f, 1.0f))
.setSparseIndices(SparseIndices.newBuilder().addAllData(List.of(1, 7)).build())
.setUsing("text")
.setQuery(nearest(List.of(2.0f, 1.0f), List.of(1, 7)))
.setLimit(3)
.build())
.get();
@@ -630,23 +626,22 @@ client
```
```java
import java.util.List;
import static io.qdrant.client.WithPayloadSelectorFactory.enable;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.WithVectorsSelectorFactory;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.WithPayloadSelectorFactory.enable;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(enable(true))
.setWithVectors(WithVectorsSelectorFactory.enable(true))
.setLimit(3)
@@ -726,20 +721,20 @@ client
```java
import java.util.List;
import static io.qdrant.client.WithPayloadSelectorFactory.include;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.WithPayloadSelectorFactory.include;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(include(List.of("city", "village", "town")))
.setLimit(3)
.build())
@@ -828,20 +823,20 @@ client
```java
import java.util.List;
import static io.qdrant.client.WithPayloadSelectorFactory.exclude;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.WithPayloadSelectorFactory.exclude;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(exclude(List.of("city")))
.setLimit(3)
.build())
@@ -1005,30 +1000,32 @@ client
```java
import java.util.List;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.ConditionFactory.matchKeyword;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
Filter filter = Filter.newBuilder().addMust(matchKeyword("city", "London")).build();
List<SearchPoints> searches =
List.of(
SearchPoints.newBuilder()
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
List<QueryPoints> searches = List.of(
QueryPoints.newBuilder()
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setFilter(filter)
.setLimit(3)
.build(),
SearchPoints.newBuilder()
.addAllVector(List.of(0.5f, 0.3f, 0.2f, 0.3f))
QueryPoints.newBuilder()
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setFilter(filter)
.setLimit(3)
.build());
client.searchBatchAsync("{collection_name}", searches, null).get();
client.queryBatchAsync("{collection_name}", searches).get();
```
```csharp
@@ -1148,21 +1145,21 @@ client
```java
import java.util.List;
import static io.qdrant.client.WithPayloadSelectorFactory.enable;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.WithVectorsSelectorFactory;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.WithPayloadSelectorFactory.enable;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(enable(true))
.setWithVectors(WithVectorsSelectorFactory.enable(true))
.setLimit(10)
@@ -1318,11 +1315,10 @@ import java.util.List;
import io.qdrant.client.grpc.Points.SearchPointGroups;
client
.searchGroupsAsync(
SearchPointGroups.newBuilder()
client.queryGroupsAsync(
QueryPointGroups.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(1.1f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setGroupBy("document_id")
.setLimit(4)
.setGroupSize(2)
@@ -1496,17 +1492,17 @@ client
```java
import java.util.List;
import static io.qdrant.client.WithPayloadSelectorFactory.include;
import static io.qdrant.client.WithVectorsSelectorFactory.enable;
import io.qdrant.client.grpc.Points.SearchPointGroups;
import io.qdrant.client.grpc.Points.QueryPointGroups;
import io.qdrant.client.grpc.Points.WithLookup;
client
.searchGroupsAsync(
SearchPointGroups.newBuilder()
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.WithVectorsSelectorFactory.enable;
import static io.qdrant.client.WithPayloadSelectorFactory.include;
client.queryGroupsAsync(
QueryPointGroups.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(1.0f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setGroupBy("document_id")
.setLimit(2)
.setGroupSize(2)
qdrant-landing/content/documentation/concepts/vectors.md
+7 -9
View File
@@ -377,20 +377,18 @@ import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.SparseIndices;
import io.qdrant.client.grpc.Points.Vectors;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setVectorName("text")
.addAllVector(List.of(0.1f, 0.2f, 0.3f, 0.4f))
.setSparseIndices(SparseIndices.newBuilder().addAllData(List.of(1, 3, 5, 7)).build())
.setUsing("text")
.setQuery(nearest(List.of(0.1f, 0.2f, 0.3f, 0.4f), List.of(1, 3, 5, 7)))
.setLimit(3)
.build())
.get();
qdrant-landing/content/documentation/guides/distributed_deployment.md
+7 -9
View File
@@ -1130,28 +1130,26 @@ client
```
```java
import java.util.List;
import static io.qdrant.client.ConditionFactory.matchKeyword;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.ReadConsistency;
import io.qdrant.client.grpc.Points.ReadConsistencyType;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.ConditionFactory.matchKeyword;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setFilter(Filter.newBuilder().addMust(matchKeyword("city", "London")).build())
.setQuery(nearest(.2f, 0.1f, 0.9f, 0.7f))
.setParams(SearchParams.newBuilder().setHnswEf(128).setExact(true).build())
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setLimit(3)
.setReadConsistency(
ReadConsistency.newBuilder().setType(ReadConsistencyType.Majority).build())
qdrant-landing/content/documentation/guides/multiple-partitions.md
+7 -5
View File
@@ -254,18 +254,20 @@ import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.Filter;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.QueryFactory.nearest;
import static io.qdrant.client.ConditionFactory.matchKeyword;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.setFilter(
Filter.newBuilder().addMust(matchKeyword("group_id", "user_1")).build())
.addAllVector(List.of(0.1f, 0.1f, 0.9f))
.setQuery(nearest(0.1f, 0.1f, 0.9f))
.setLimit(10)
.build())
.get();
qdrant-landing/content/documentation/guides/optimize.md
+13 -14
View File
@@ -219,21 +219,21 @@ client
```
```java
import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.QuantizationSearchParams;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setParams(
SearchParams.newBuilder()
.setQuantization(
@@ -580,21 +580,20 @@ client
```
```java
import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setParams(SearchParams.newBuilder().setHnswEf(128).setExact(false).build())
.setLimit(3)
.build())
qdrant-landing/content/documentation/guides/quantization.md
+21 -30
View File
@@ -22,14 +22,12 @@ This can be particularly beneficial in large-scale applications where minimizing
On the other hand, quantization introduces an approximation error, which can lead to a slight decrease in search quality.
The level of this tradeoff depends on the quantization method and its parameters, as well as the characteristics of the data.
## Scalar Quantization
*Available as of v1.1.0*
Scalar quantization, in the context of vector search engines, is a compression technique that compresses vectors by reducing the number of bits used to represent each vector component.
For instance, Qdrant uses 32-bit floating numbers to represent the original vector components. Scalar quantization allows you to reduce the number of bits used to 8.
In other words, Qdrant performs `float32 -> uint8` conversion for each vector component.
Effectively, this means that the amount of memory required to store a vector is reduced by a factor of 4.
@@ -45,7 +43,6 @@ In our experiments, we found that the error introduced by scalar quantization is
However, this value depends on the data and the quantization parameters.
Please refer to the [Quantization Tips](#quantization-tips) section for more information on how to optimize the quantization parameters for your use case.
## Binary Quantization
*Available as of v1.5.0*
@@ -76,7 +73,6 @@ The additional benefit of this method is that you can efficiently emulate Hammin
Specifically, if original vectors contain `{-1, 1}` as possible values, then the dot product of two vectors is equal to the Hamming distance by simply replacing `-1` with `0` and `1` with `1`.
<!-- hidden section -->
<details>
@@ -101,7 +97,6 @@ Specifically, if original vectors contain `{-1, 1}` as possible values, then the
As you can see, both functions are equal up to a constant factor, which makes similarity search equivalent.
Binary quantization makes it efficient to compare vectors using this representation.
## Product Quantization
*Available as of v1.2.0*
@@ -130,9 +125,9 @@ Here is a brief table of the pros and cons of each quantization method:
`*` - for compatible models
* **Binary Quantization** is the fastest method and the most memory-efficient, but it requires a centered distribution of vector components. It is recommended to use with tested models only.
* **Scalar Quantization** is the most universal method, as it provides a good balance between accuracy, speed, and compression. It is recommended as default quantization if binary quantization is not applicable.
* **Product Quantization** may provide a better compression ratio, but it has a significant loss of accuracy and is slower than scalar quantization. It is recommended if the memory footprint is the top priority and the search speed is not critical.
- **Binary Quantization** is the fastest method and the most memory-efficient, but it requires a centered distribution of vector components. It is recommended to use with tested models only.
- **Scalar Quantization** is the most universal method, as it provides a good balance between accuracy, speed, and compression. It is recommended as default quantization if binary quantization is not applicable.
- **Product Quantization** may provide a better compression ratio, but it has a significant loss of accuracy and is slower than scalar quantization. It is recommended if the memory footprint is the top priority and the search speed is not critical.
## Setting up Quantization in Qdrant
@@ -646,22 +641,21 @@ client
```
```java
import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.QuantizationSearchParams;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setParams(
SearchParams.newBuilder()
.setQuantization(
@@ -783,22 +777,21 @@ client
```
```java
import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.QuantizationSearchParams;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setParams(
SearchParams.newBuilder()
.setQuantization(
@@ -833,7 +826,6 @@ By adjusting the quantile, you find an optimal value that will provide the best
- **Enable rescore**: Having the original vectors available, Qdrant can re-evaluate top-k search results using the original vectors. On large collections, this can improve the search quality, with just minor performance impact.
#### Memory and speed tuning
In this section, we will discuss how to tune the memory and speed of the search process with quantization.
@@ -1042,22 +1034,21 @@ client
```
```java
import java.util.List;
import io.qdrant.client.QdrantClient;
import io.qdrant.client.QdrantGrpcClient;
import io.qdrant.client.grpc.Points.QuantizationSearchParams;
import io.qdrant.client.grpc.Points.QueryPoints;
import io.qdrant.client.grpc.Points.SearchParams;
import io.qdrant.client.grpc.Points.SearchPoints;
import static io.qdrant.client.QueryFactory.nearest;
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(
QueryPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setParams(
SearchParams.newBuilder()
.setQuantization(
qdrant-landing/content/documentation/quickstart.md
+8 -13
View File
@@ -332,20 +332,18 @@ dbg!(search_result);
import java.util.List;
import io.qdrant.client.grpc.Points.ScoredPoint;
import io.qdrant.client.grpc.Points.SearchPoints;
import io.qdrant.client.grpc.Points.QueryPoints;
import static io.qdrant.client.WithPayloadSelectorFactory.enable;
import static io.qdrant.client.QueryFactory.nearest;
List<ScoredPoint> searchResult =
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("test_collection")
.setLimit(3)
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(enable(true))
.build())
.get();
.build()).get();
System.out.println(searchResult);
```
@@ -444,16 +442,13 @@ dbg!(search_result);
import static io.qdrant.client.ConditionFactory.matchKeyword;
List<ScoredPoint> searchResult =
client
.searchAsync(
SearchPoints.newBuilder()
client.queryAsync(QueryPoints.newBuilder()
.setCollectionName("test_collection")
.setLimit(3)
.setFilter(Filter.newBuilder().addMust(matchKeyword("city", "London")))
.addAllVector(List.of(0.2f, 0.1f, 0.9f, 0.7f))
.setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
.setWithPayload(enable(true))
.build())
.get();
.build()).get();
System.out.println(searchResult);
```