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Java library for the Qdrant vector search engine.
Java client library with handy utility methods and overloads for interfacing with Qdrant.
Important
Requires Java 8 or above.
To install the library, add the following lines to your build config file.
<dependency>
<groupId>io.qdrant</groupId>
<artifactId>client</artifactId>
<version>1.19.0</version>
</dependency>libraryDependencies += "io.qdrant" % "client" % "1.19.0"implementation 'io.qdrant:client:1.19.0'Note
Please make sure to include all necessary dependencies listed here in your project.
- Usage examples are available throughout the Qdrant documentation and API Reference.
- JavaDoc Reference
A client can be instantiated with
QdrantClient client =
new QdrantClient(QdrantGrpcClient.newBuilder("localhost").build());which creates a client that will connect to Qdrant on https://localhost:6334.
Internally, the high-level client uses a low-level gRPC client to interact with Qdrant. Additional constructor overloads provide more control over how the gRPC client is configured. The following example configures a client to use TLS, validating the certificate using the root CA to verify the server's identity instead of the system's default, and also configures API key authentication:
ManagedChannel channel = Grpc.newChannelBuilder(
"localhost:6334",
TlsChannelCredentials.newBuilder()
.trustManager(new File("ssl/ca.crt"))
.build())
.build();
QdrantClient client = new QdrantClient(
QdrantGrpcClient.newBuilder(channel)
.withApiKey("<apikey>")
.build());The client implements AutoCloseable, though a client will typically be created once and used for the lifetime of the application. When a client is constructed by passing a ManagedChannel, the client does not shut down the channel on close by default. The client can be configured to shut down the channel on closing with
ManagedChannel channel = Grpc.newChannelBuilder(
"localhost:6334",
TlsChannelCredentials.create())
.build();
QdrantClient client = new QdrantClient(
QdrantGrpcClient.newBuilder(channel, true)
.withApiKey("<apikey>")
.build());All client methods return ListenableFuture<T>.
Once a client has been created, create a new collection
client.createCollectionAsync("{collection_name}",
VectorParams.newBuilder()
.setDistance(Distance.Cosine)
.setSize(4)
.build())
.get();Insert vectors into a collection
// import static convenience methods
import static io.qdrant.client.PointIdFactory.id;
import static io.qdrant.client.ValueFactory.value;
import static io.qdrant.client.VectorsFactory.vectors;
List<PointStruct> points =
List.of(
PointStruct.newBuilder()
.setId(id(1))
.setVectors(vectors(0.32f, 0.52f, 0.21f, 0.52f))
.putAllPayload(
Map.of(
"color", value("red"),
"rand_number", value(32)))
.build(),
PointStruct.newBuilder()
.setId(id(2))
.setVectors(vectors(0.42f, 0.52f, 0.67f, 0.632f))
.putAllPayload(
Map.of(
"color", value("black"),
"rand_number", value(53),
"extra_field", value(true)))
.build());
UpdateResult updateResult = client.upsertAsync("{collection_name}", points).get();Search for similar vectors
List<ScoredPoint> points =
client
.searchAsync(
SearchPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.6235f, 0.123f, 0.532f, 0.123f))
.setLimit(5)
.build())
.get();Search for similar vectors with filtering condition
// import static convenience methods
import static io.qdrant.client.ConditionFactory.range;
List<ScoredPoint> points = client.searchAsync(SearchPoints.newBuilder()
.setCollectionName("{collection_name}")
.addAllVector(List.of(0.6235f, 0.123f, 0.532f, 0.123f))
.setFilter(Filter.newBuilder()
.addMust(range("rand_number", Range.newBuilder().setGte(3).build()))
.build())
.setLimit(5)
.build()
).get();